Pathological Brain Detection (PBD) is a crucial field aimed at identifying and diagnosing structural or functional abnormalities in the brain related to neurological, neurodegenerative, or psychiatric disorders. This detection process typically employs medical imaging technologies such as MRI, CT, or PET scans, along with neurological evaluations, blood tests, and other diagnostic tools. Early detection is essential for effective treatment, improving prognosis, and enhancing the quality of life for affected individuals. Each common computational method provides unique insights into brain pathologies. Despite significant advancements in technology and methodology, PBD faces challenges such as variability in brain anatomy, the complexity of disorders, data quality, standardization issues, interpretability of models, and ethical concerns. Addressing these challenges necessitates collaboration among researchers, clinicians, and policymakers to develop robust and ethical methods for improving the detection and diagnosis of brain disorders.

In recent years, video anomaly detection technology , which can intelligently analyze massive video and quickly find abnormal phenomena, has attracted extensive attention with the wide application of video surveillance technology . To address the complex and diverse problem of abnormal human behavior detection in surveillance videos, a surveillance video abnormal behavior detection and localization supervised method based on the deep network model and the traditional method is proposed. Specifically, we combined AGMM and YOLACT methods to obtain more accurate foreground information by fusing the foreground maps extracted by each technique. To further improve the accuracy , we use the PWC-Net technique to extract features of the foreground images and input them into an anomaly classification model for classification. The proposed method effectively detects and locates the abnormal behavior in the monitoring scene. In addition to the aforementioned methods, this paper also employs YOLOV5 and DeepSORT networks for object detection and tracking in the video, which allows us to track the detected objects for better understanding of the scene in the video. Experiments on the UCSD benchmark dataset and the comparison with state-of-the-art schemes prove the advantages of our method.

The proliferation of ransomware poses a significant threat to global cybersecurity. This study presents a comprehensive review of the methodologies employed in the detection and analysis of ransomware, emphasizing the dichotomy between static and dynamic analysis approaches. It introduces the historical context and the necessity for robust cybersecurity measures, followed by an outline of the methodological framework used to evaluate existing ransomware analysis techniques. The results detail the effectiveness and limitations of various analysis strategies, identifying key features and patterns that aid in the detection and classification of ransomware threats. The study concludes by summarizing the primary achievements, including the identification of gaps in current research and proposing future research directions aimed at enhancing ransomware detection and mitigation strategies. The synthesis provided in this survey offers a consolidated view of the state-of-the-art in ransomware threat analysis and serves as a resource for cybersecurity professionals and researchers.

The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirmed that the classification-based models far exceeds the unsupervised models in ASD. In this paper, we adopt two classification-based anomaly detection models: (1) Outlier classifier is to distinguish anomalous sounds or outliers from the normal; (2) ID classifier identifies anomalies using both the confidence of classification and the similarity of hidden embeddings. We conduct experiments in task 2 of DCASE 2020 challenge, and our ensemble method achieves an averaged area under the curve (AUC) of 95.82% and averaged partial AUC (pAUC) of 92.32%, which outperforms the state-of-the-art models.

In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.

We discuss two methods to detect the presence and location of a person in a small-scale room and compare the performances. The first method is Direct Intersection, which determines a coordinate point based on the intersection of spheroids defined by observed distances of high-intensity reverberations. The second method, Sonogram analysis, overlays all channel’s room impulse responses to generate an intensity map for the observed environment. We demonstrate that the former method has lower computation complexity and higher accuracy for small numbers of channels, while the latter performs more robustly.

Rodents are known to be reservoirs of Toxoplasma gondii and keep the parasite circulation in the environment. We conducted biomonitoring to assess the role of sylvatic rodents in maintaining T. gondii and to analyse the prevalence and seroprevalence of the parasite in seven wild rodent species. Rodents were collected in our study sites (woodland and open grasslands) located in northeastern Poland and dissected. We collected brain, spleen, blood and serum samples. We applied both molecular (PCR assay, nested-PCR assay) and serological (ELISA and agglutination tests) methods to indicate the best approach for application in the biomonitoring of T. gondii in small mammals. We screened samples from 95 individuals sing PCR assays and found no T. gondii DNA. The agglutination test showed no signal. We found antibodies against T. gondii in 6 sera samples out of 74 analysed (seroprevalence = 8.11% [4.0-17.1]). Our results confirm that rodents participate in the life cycle of T. gondii as reservoirs of this parasite in the sylvatic environment. However, biomonitoring should be performed with the ELISA tests to search for T. gondii antigens, rather than a molecular approach only.

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

Accurate localization of the fovea in fundus images is essential for diagnosing retinal diseases. Existing methods often require extensive data and complex processes to achieve high accuracy, posing challenges for practical implementation. In this paper, we propose an effective and efficient approach for fovea detection using simple image processing operations and a geometric approach based on the optical disc's position. A key contribution of this study is the successful determination of the temporal direction by leveraging readable asymmetries related to the optical disc and its surroundings. We discuss three methods based on asymmetry conditions, including blood vessel distribution, cup disc inclination, and optic disc location ratio, for detecting the temporal direction. This enables precise determination of the optimal foveal region of interest (ROI). Through this optimized fovea region, fovea detection is achieved using straightforward morphological and image processing operations. Extensive testing on popular datasets (DRIVE, DiaretDB1, and Messidor) demonstrates outstanding accuracy of 99.04% and a rapid execution time of 0.251 seconds per image. The utilization of asymmetrical conditions for temporal direction detection provides a significant advantage, offering high accuracy and efficiency while competing with existing methods.

The term new genomic techniques (NGTs), is an umbrella term to describe a variety of techniques that can alter the genetic material of an organism and that have emerged or have been developed since 2001, when the existing genetically modified organism (GMO) legislation was adopted. The analytical framework to detect GMOs in Europe is a established single harmonized procedure which is mandatory for authorization of GM food and feed, thus generating reliable, transparent and effective labelling scheme for GMO products. However, NGT products challenge the implementation and enforcement of the current regulatory system in the EU, relating in particular to the detection of NGT products that contain no foreign genetic material. Consequently, current detection methods will fail to meet minimum performance requirements. Although existing detection methods may be able to detect and quantify even small alterations in the genome, this does not necessarily confirm the distinction between products resulting from NGTs subject to the GMO legislation and other products. Therefore, this study provides a stepwise approach for the in silico prediction of PCR systems specificity by testing a bioinformatics pipeline for amplicon and primer set searches in current genomic databases. We have tested our methodology in two mutant genotypes produced by CRISPR/Cas9 in Arabidopsis thaliana. Overall, our results demonstrated that the single PCR system developed for the identification of a nucleotide insertion in the grf1-3 genotype has sufficient hits in the databases that would not allow discrimination of this mutanted event. Differently, the second mutated genotype grf8-61 which contains a -3bp deletion, found no matches in the sequence variant database. Our approach is the first step in decision making on PCR system and modification of them for empirical testings.

In order for robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities in order to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged on top of an object to securely grab it between the robotic gripper and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the application of deep learning methods in task generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. A number of the most promising approaches are evaluated and the most successful for grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is identified as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

The paper aims to study the workflow of the detection center of stealthy attacks on industrial installations that generate increase in energy consumption while avoiding triggering fault detection and damaging the installation. Such long-lasting attacks on industrial facilities make production more expensive and less competitive. We present the concept of the remote detection system of cyberattacks directed at maliciously changing the controlled variable in an industrial process air conditioning system. The monitored signals are gathered at the PLC-controlled installation and sent to the remote detection system, where the discrepancies of signals are analyzed based on the Control Performance Assessment indices. The results of performed tests prove the legitimacy of the adopted approach.

The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 77.64% to 93.51% (kappa 0.09–0.59) in the Southern New England study area and 59.57% to 79.57% (kappa 0.09–0.45) in the Rio Grande National Forest study area. In general, ORS products met or exceeded the overall accuracy and kappa of IDS and RTFD products. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data.

This article articulates the significance of conceptual analysis (CA) methods for the postgraduate philosophy scholar at the G-CAR institution. In order to clarify the significance, first the concepts were defined, their context examined and the problems posed by the concepts looked at. The activity of scholars who are bent on researching for concepts meaning, was highlighted and the methods scholars use in determining meaning were carefully examined. Three major methods discussed by philosophers, namely, constructive, reductive and detective were explained as well as their significance in helping philosophy scholars do qualitative research in meaning determination. Meaning was found to be a discovery of the observer’s relationship to the objective reality as depicted by concepts. The common factor in these three methods was the dominant role both the observer and the human subjects played in determining meaning – a constructive engagement. It was clarified too, that the ability to explicate concepts’ meaning is the tool kit or skill sets that all scholars need to navigate the semantic jungle in the 21^st Century.

Despite available global efforts and funding, Tuberculosis (TB) continues to affect a considerable number of patients worldwide. Policy makers and stakeholders set clear goals to reduce TB incidence and mortality, but the emergence of multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) complicate the reach of these goals. Drug-resistance TB needs to be diagnosed rapidly and accurately to effectively treat patients, prevent the transmission of MDR-TB, minimise mortality, reduce treatment costs and avoid unnecessary hospitalisations. In this narrative review, we provide a comprehensive overview of laboratory methods for detecting drug resistance in MTB, focusing on phenotypic, molecular and other drug susceptibility testing (DST) techniques. We found a large variety of methods used, with the BACTEC MGIT 960 being the most common phenotypic DST and the Xpert MTB/RIF being the most common molecular DST. We emphasise the importance of integrating phenotypic and molecular DST to address issues like resistance to new drugs, heteroresistance, mixed infections and low-level resistance mutations. Notably, most of the analysed studies adhered to the outdated definition of XDR-TB and did not consider the pre-XDR definition, thus posing challenges in aligning diagnostic methods with the current landscape of TB resistance.

Accurate pathogenic detection in wastewater is critical for safeguarding public health and the environment. However, the presence of free nucleic acids in wastewater samples poses significant challenges to molecular detection accuracy. This comprehensive review explores the current status and future potential of pretreatment methods to remove free nucleic acids from wastewater samples. The study contributes a comprehensive analysis of the mechanisms, strengths, and limitations of various pretreatment approaches, including physical, chemical, and enzymatic processes. The effect of various factors on the removal efficiency of these pretreatment methods is also discussed. This review enhances our comprehension of pretreatment techniques and their vital role in achieving precise pathogenic detection in complex wastewater matrices. Furthermore, it outlines future perspectives and developments for improving the speed and effectiveness of pathogenic detection, contributing significantly to disease surveillance, early warning systems, and environmental protection.

In Ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight~(LOS), non-line-of-sight~(NLOS), and multi-path (MP) conditions are important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS or MP). However, the major contributions in literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. Though, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the mentioned three classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental data-set. The data-set was collected in different conditions at different scenarios in indoor environments. Using the collected real measurement data, we compare three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results show that applying ML methods in UWB ranging systems are effective in identification of the above-mentioned three classes. In specific, the overall accuracy reaches up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it is 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we (will) provide the publicly accessible experimental research data discussed in this paper at PUB - Publications at Bielefeld University. The evaluations of the three classifiers are conducted using the open-source python machine learning library scikit-learn.

In this research, a model is presented to detect and monitor the rotor bar’s condition of large motors. This proposed model uses two diagnostic methods MCSA and ZCT, to extract the fault components. The input of the proposed model is only the motor current at two levels of 80% and 100% of the nominal motor load, which by using the two methods MCSA and ZCT and making changes in how to use them can be the disadvantages of other methods such as incorrect detection of rotor bars in Large motors with variable load, the harmonical stator voltage (or the presence of the drives) and asymmetric conditions. The extracted components are classified using neural network, instead of experimental and human resources constraints. Using this classification algorithm is high accuracy and the ability to implement in conventional processors. The proposed model has been trained andtested using currents of real large motor data and 96.87% accuracy using neural network to detect faults and monitor the online status of the rotor bars. On the other hand, the proposed model has been implemented in the industrial environment (FiroozkoohCement Factory) and has been able to correctly determine the rotor bars of 4 large suspicious motors announced by the factory technicians.

The i-4-1-Health project is a One Health project on the prevalence and spread of antimicrobial resistance in the human and veterinary domain in the Dutch-Belgian cross-border region. This paper describes the main components of the laboratory protocol that was developed to standardise the microbiological methods used for the detection of intestinal carriage of highly-resistant microorganisms.

Object detection is complex and involved diverse requirements for different applications. In critical application such as visual impaired navigation guide and real-time surveillance for instance, identifying a particular object of instance is required and this involved a complex approach for realization. Literatures were reviewed, starting with the application of deep learning techniques for object detection; their gaps were identified and addressed using real-time object detection models literature review. From the review gaps were also detected and addressed using literature review on occlusion detection techniques. Object of instance detection in clustered scene with similar object of interest was identified as an open gap not addressed in the existing literature; even through it is vital for many applications. This research recommended an Occlusion Based Object of Instance Detection (OBOID) technique which used the spatial information to identify instant object of interest in clustered scene with many similarly object of interest. Limitation of the recommended OBOID is that it requires only system where position and distance of object is necessary to inform other decision.

The video images captured at long range usually have low contrast floating objects of interest on a sea surface. A comparative experimental study of the statistical characteristics of reflections from floating objects and from the agitated sea surface showed the difference in the correlation and spectral characteristics of these reflections. The functioning of the recently proposed modified matched subspace detector (MMSD) is based on the separation of the observed data spectrum on two subspaces: relatively low and relatively high frequencies. In the literature the MMSD performance has been evaluated in generally and moreover using only a sea model (additive Gaussian background clutter). This paper extends the performance evaluating methodology for low contrast object detection and moreover using only the real sea dataset. This methodology assumes an object of low contrast if the mean and variance of the object and the surrounding background are the same. The paper assumes that the energy spectrum of the object and the sea are different. The paper investigates a scenario in which an artificially created model of a floating object with specified statistical parameters is placed on the surface of a real sea image. The paper compares the efficiency of the classical Matched Subspace Detector (MSD) and MMSD for detecting low-contrast objects on the sea surface. The article analyzes the dependence of the detection probability at a fixed false alarm probability on the difference between the statistical means and variances of a floating object and the surrounding sea.

Within the spectra of lanthanide atoms quite often overlapping spectral lines (blends) are observed. On the example of complicated blend situations within Pr I and La I it is shown that in such cases a combination of optogalvanic and laser-induced fluorescence detection is necessary to find all transitions contributing to the observed spectral signature.

Texture is essential information for image representation, capturing patterns, and structures. Consequently, texture plays a crucial role in the manufacturing industry and has been extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade the image quality and cause various issues. Therefore, there is a need for accurate and effective methods to detect texture defects. In this study, a simple autoencoder and Fourier transform were employed for texture defect detection. The proposed method combines Fourier transform analysis with the reconstructed template obtained from the simple autoencoder. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals. Moreover, analyzing the frequency domain enables effective defect detection because texture defects often exhibit characteristic changes in specific frequency ranges. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with those of existing approaches.

In emergency situations, every second counts for an ambulance navigating through traffic. Efficient use of traffic light systems can play a crucial role in minimizing response time. This paper introduces a novel automated Real-Time Ambulance in an Emergency Detector (RTAIAED). The proposed system uses special Lookout Stations (LS) suitably positioned at a certain distance from each involved traffic light (TL), to obtain timely and safe transitions to green lights as the Ambulance in an Emergency (AIAE) approaches. The RTAIAED is particularly pertinent on one-way roads, addressing the challenge of regulating the sequence of traffic-lights-signals so as to minimize the time needed to safely grant the green signal to the AIAE. The proposed solution leverages a part-based model made of elementary detectors for video analysis, specifically realized with a customized YOLOv8 model, and for audio analysis, thanks to an additional neural network based on Mel Frequency Cepstral Coefficients (MFCCs). This way, the RTAIAED ensures the accurate and robust identification of an AIAE heading towards a traffic light in time to ensure a green light thanks to the strategic positioning of the LS detectors. Extensive experiments demonstrate the robustness of the approach and its reliable application in real-world scenarios thanks to its predictions in real-time, showcasing the ability to detect AIAEs even in challenging conditions such as noisy environments, nighttime, or adverse weather conditions, provided a suitable-quality camera is appropriately positioned. The proposed system can also find application in traffic flow management.

Fall detection systems play a crucial role in addressing the significant health concern of elderly falls, a leading cause of health deterioration and mortality. As the aging population grows and life expectancy increases, the development of accessible tools becomes vital for predicting and preventing falls, offering a practical and widely applicable solution in contrast to costly and expertise-dependent assessment tools. In contrast, due to the formidable challenges encountered, a comprehensive investigation into the comparative performance of standard ML models within this field still needs to be explored. This paper proposes a standard pipeline for pre-processing, training, and evaluating ML models for fall detection on the SisFall dataset. We conducted extensive experiments to evaluate the performance of various ML models for fall detection. The results validate the efficiency of the deep model in identifying the time windows in which a fall occurred. Among the deep models, the architecture, including a combination of convolutional neural networks and fully connected layers, outperforms the others by macro-averaged Precision, macro-averaged Recall, and macro-averaged F1-Score of 87.03\%, 86.83\%, and 86.93\%, respectively.

Desktop fused filament fabrication (FFF) 3D printers have been growing in popularity among hobbyist and professional users as a prototyping and low-volume manufacturing tool. One issue these printers face is the inability to determine when a defect has occurred rendering the print unusable. Several techniques have been proposed to detect such defects but many of these approaches are tailored to one specific fault (e.g., filament runout/jam), use expensive hardware such as laser distance sensors, and/or use machine vision algorithms which are sensitive to ambient conditions, and hence can be unreliable. This paper proposes a versatile, reliable, and low-cost system, named MTouch, to detect millimeter-scale defects that tend to make prints unusable. At the core of MTouch is an actuated contact probe designed using a low-power solenoid, magnet, and hall effect sensor. This sensor is used to check for the presence, or absence, of the printed object at specific locations. The MTouch probe demonstrated 100% reliability, which was significantly higher than the 74% reliability achieved using a commercially available contact probe (the BLTouch). Additionally, an algorithm was developed to automatically detect common print failures such as layer shifting, bed separation, and filament runout using the MTouch probe. The algorithm was implemented on a Raspberry Pi mini-computer via an Octoprint plug-in. In head-to-head testing against a commercially available print defect detection system (The Spaghetti Detective), the MTouch was able to detect faults 44% faster on average while only increasing the print time by 8.49%. In addition, MTouch was able to detect faults The Spaghetti Detective was unable to identify such as layer shifting and filament runout/jam.

Accurate detection of players and teams in ice hockey games is crucial to the tracking of individual players on court and team tactical decisions, which is therefore becoming an important task for coaches and other analysts. However, hockey is a fluid sport due to complex dynamics and frequent substitutions by both teams, resulting in various body positions of players. Few traditional player detection models from other team sports take these characteristics into account, especially for the detection of teams without prior annotations. Here, we design a two-phase cascaded Convolutional Neural Network (CNN) model coupling between individual players position information and team uniform colors to hierarchically detect players in ice hockey games. Our model filters most of disturbing information, such as audience and sideline advertising bars, in Phase I, and refines the detection of targeted players in Phase II, which is efficient and accurate with a precision rate of 91.30% and a recall rate of 95.60% for individual players detection, and an average accuracy of 93.05% in team classification from a self-built dataset of collected images in the 2018 Winter Olympics. Meanwhile, we also present results based on the images and real-time detection from broadcasting videos of 2019-20 NHL regular games covering all 31 teams to show the robustness of our model.

COVID-19 represents an unprecedented threat to global public health and economies. Assays are urgently needed for rapid detection and diagnosis of SARS-CoV-2-infected patients in order to inform treatment and quarantine strategies. Establishing globally accepted easy-to-access diagnostic tests is extremely important to understanding the epidemiology of the present pandemic. While nucleic acid-based tests are considered to be more sensitive with respect to serological tests, but the present gold standard RT-PCR-based assays possess limitations such as low sample throughput, requirement for sophisticated reagents and instrumentation. To overcome these shortcomings, recent efforts of incorporating LAMP-based isothermal detection, and minimizing the number of reagents required are on rise. Novel CRISPR- and other nuclease-based techniques, when merge with isothermal and allied technologies, promises to provide sensitive and rapid detection of SARS-CoV-2 nucleic acids. Here we discuss and present compilation of state-of-the-art CRISPR based detection techniques for use in COVID diagnosis and epidemiology.

The paper aims at presenting the results obtained in the development of a system allowing the detection and monitoring of forest fires and the continuous comparison of their intensity when several events occur simultaneously, as usually happens in the European Mediterranean countries during the summer season. The system, called SFIDE (Satellite FIre DEtection), exploits a geostationary satellite sensor (SEVIRI on board of MSG satellite series). The algorithm was developed several years ago in the framework of a project (SIGRI) funded by the Italian Space Agency (ASI). This algorithm has been completely reviewed in order to enhance its efficiency by reducing false alarms rate preserving a high sensitivity. Due to the very low spatial resolution of SEVIRI images (4x4 km² at Mediterranean latitude) the sensitivity of the algorithm should be very high to detect even small fires. The improvement of the algorithm has been obtained by: introducing the sun elevation angle in the computation of the preliminary thresholds to identify potential thermal anomalies (hot spots), introducing a contextual analysis in the detection of clouds and in the detection of night-time fires. The results of the algorithm have been validated in the Sardinia region by using ground true data provided by the regional Corpo Forestale e di Vigilanza Ambientale (CFVA). A significant reduction of the commission error (less than 10%) has been obtained with respect to the previous version of the algorithm and also with respect to fire-detection algorithms based on low earth orbit satellites.

As one of the main equipment of coal transportation, the belt conveyor with detection system is an important direction for the development of intelligent mine. The occurrences of non-coal foreign objects making contact with belts are common phenomenon in complex production environments and improper human operations. In order to avoid major safety accidents caused by scratches, deviation and breakage of the belt, a foreign object detection method is proposed for belt conveyor in this work. Firstly, a foreign object image dataset is collected and established, and IAT image enhancement module and attention mechanism of CBAM are proposed to enhance image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed, and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine intelligent mining equipment laboratory, and the detections of different foreign objects are analyzed. Experimental results show that the accuracy, recall, and mAP50 of the proposed rotating frame foreign object detection method reaches 93.87%, 93.69%, and 93.72%, and the average inference time of foreign object detection is 25 ms.

Forest fires are one of the world's deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding attention mechanism in the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection effect of the model and the robustness of the model. In addition, random Mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model responsibility to improve the detection speed. Experimental results on homemade forest fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection.

Objective tools to track medication adherence are lacking. A tool to monitor pill intake that can be implemented in mHealth apps without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the circular Hough transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills need to be further improved, in particular for the detection of pills with non-oval shapes.

In today's digital landscape, cybersecurity has become a priority, with attacks becoming increasingly sophisticated. Traditional security approaches are no longer enough, requiring a more dynamic and advanced response. In this context, integrating Artificial Intelligence (AI) and Machine Learning (ML) appears key to addressing this growing threat. However, despite their high effectiveness, there is a developed problem: the integration of various data sources and technologies for comprehensive protection. This article presents an in-depth review of integrating Artificial Intelligence and Machine Learning in cybersecurity, focusing particularly on Open Extended Detection and Response (Open XDR) technology. The literature review in this article was meticulously conducted with specific criteria in mind. It focused on sourcing peer-reviewed journals, authoritative cybersecurity publications, and recent conferences that primarily concentrate on the advancements in AI and ML within the cybersecurity domain, particularly from 2018 to 2023. This timeframe was chosen to ensure the inclusion of the most current and relevant developments in Open XDR technology, Intrusion Detection Systems, Endpoint Detection and Response, and Security Information and Event Management systems. Additionally, the review gave special attention to studies and reports highlighting practical implementations and real-world applications of these technologies, thereby ensuring a comprehensive understanding of their impact and effectiveness in enhancing cybersecurity resilience. The methodology used is a detailed literature review, examining how various cybersecurity components interact and function. These components include Intrusion Detection Systems (IDS), which monitor networks for malicious activities; Endpoint Detection and Response (EDR), which focuses on detecting and investigating security incidents on endpoints; and Security Information and Event Management (SIEM), systems that provide real-time analysis of security alerts. The review also considers the role of Active Directory, a directory service for Windows domain networks, and the process of log forwarding, where log files are transmitted to a central server for analysis, in the context of AI and ML. The paper delves into the development of AI and ML, underscoring their roles in cybersecurity for advanced data processing, pattern recognition, and predicting threats. It explores both supervised (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data) in ML, and how these techniques bolster cybersecurity measures. The article highlights the significance of Open XDR as a critical innovation that integrates data from multiple sources for comprehensive security analysis. Further, the review discusses how the integration of AI and ML into various cybersecurity tools, such as IDS, EDR, and SIEM, augments capabilities in threat detection and response. It addresses the challenges and opportunities that AI and ML present in the cybersecurity domain, focusing on ethical issues, data privacy concerns, and the necessity for ongoing professional development in this rapidly advancing field. The paper concludes by affirming the effectiveness of merging AI and ML with these cybersecurity tools within the Open XDR framework.

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral feature and color feature at the same time, and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu ’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu ’er tea were obtained by this system and used for pixel classification. The experimental results show that the accuracy of Support Vector Machine (SVM) model based on combined features is 93%, which is 7% higher than that based on only spectral features. By applying median filtering algorithm and contour detection algorithm to the label matrix extracted from pixel-classified images, 8 impurities except hair were detected successfully. Moreover, taking advantage of the high resolution of visible light camera, small impurities can be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it is proved that the system can obtain multiple images to allow a more detailed and comprehensive understanding of the detected items, and has excellent ability to detect small impurities.

Hyperspectral anomaly detection is to recognize anomalies from complex scene in an unsupervised way. Currently, many spectral-spatial detection methods have been proposed with a cascaded manner. However, they often neglect complementary characteristics between spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral-spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets prove that the proposed SSIF produces superior detection result over other state-of-the-art detection techniques.

Biosensors are analytical devices that utilize biological interactions to detect and quantify clinical biomarkers, contaminants, allergens, and microorganisms. They combine different disciplines including analytical chemistry, molecular biology, and electrical engineering. Biosensors operate by coupling a bioreceptor, such as nucleic acid or proteins, with a transducer that converts the biological interaction into an electrical signal. Electrochemical and optical transduction are commonly used approaches due to their high detection capability and compatibility with miniaturization. Biosensors provide both high specificity and sensitivity and can be integrated into low-cost microfluidic platforms for rapid and point-of-care applications. These attributes make these devices valuable tools in analytical chemistry, particularly for early diagnostic applications. However, conventional biosensors face challenges related to the immobilization of biorecognition elements on the transducer surface, leading to issues like lost of sensitivity and selectivity. To address these problems, the introduction of nanomaterials, particularly magnetic nanoparticles (MNPs) and magnetic beads (MBs), has been implemented. MNPs combine their magnetic properties with other interesting characteristics such as small size, high surface-to-volume ratio, and excellent biocompatibility. They can be tailored for specific applications and have been extensively used in various fields, including biosensing and clinical diagnosis. Furthermore, MNPs simplify sample preparation by isolating target analytes through magnetic separation, thus improving sensitivity, and reducing analysis time and interference phenomena. The synthesis and modification of MNPs play a crucial role in adjusting their properties for different applications. This review presents an overview of the synthesis and surface modifications of magnetic nanoparticles, their role in the development of biosensors and bioassays, and their applications across various scientific areas.

Surface defect detection is a crucial step in the process of automotive wheel production. However, the task possesses challenges due to complex background and a wide range of defect types. In order to detect the defects on the wheel surface accurately and quickly, this paper proposes a YOLOv5-based algorithm for automotive wheel surface defect detection. The algorithm trains and tests the YOLOv5s model using the self-created automotive wheel surface defect dataset, which contains four kinds of defects: linear, dotted, sludge, pinhole. The extensive experimental results demonstrate that the deep learning network trained by our method can achieve an average accuracy of 71.7% and 57.14 FPS. Our findings prove that this detection algorithm performs better than other common target detection algorithms and meets the real-time requirements of industrial applications.

The device and method in accordance with the constant pressure regulation of micro droplet PCR in microfluidic chips are developed to explore the optimization way for the micro droplet movement, fragmentation, and bubble generation in microfluidic chips in existing digital PCR technology. In the developed device, an air source device is adopted to regulate the pressure in the chip, such that micro droplet generation and PCR amplification without bubbles can be achieved. In 3 min, the sample in 20ul will be distributed into nearly 50,000 water-in-oil droplets exhibiting a diameter of about 87μm, and the micro droplet will be subjected to a close arrangement in the chip without air bubbles. The device and chip are adopted to quantitatively detect the human gene . As indicated by the experimental results, a good linear relationship exists between detection signal and DNA concentration ranging from 101~ 105 copies /μL (R2=0.999). The micro droplet PCR devices based on constant pressure regulation chips exhibit a wide variety of advantages (e.g., achieving high pollution resistance, micro droplet fragmentation and integration avoidance, reducing human interference, and standardizing results). Thus, micro droplet PCR devices based on constant pressure regulation chips have promising applications in nucleic acid quantification.

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, however, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of fishing nets detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces YOLOv5s’s backbone and the ordinary convolutional blocks in the feature fusion network with ShufflNetV2 and Ghost module.

Recent years have witnessed a rapid growth of code-reuse attacks in advance persistent threats and cyberspace crimes. Carefully crafted code-reuse exploits circumvent modern protection mechanisms and hijack the execution flow of a program to perform expected functionalities by chaining together existing codes. The sophistication and intrication of code-reuse exploits hinder the scrutinization and dissection on them. Although the previous literature has introduced some feasible approaches, effectiveness and reliability in practical applications remain severe challenges. To address this issue, we propose Horus, a data-driven framework for effective and reliable detection on code-reuse exploits. In order to raise the effectiveness against underlying noises, we comprehensively leverage the strengths of time-series and frequency-domain analysis, and propose a learning-based detector that synthesizes the contemporary twofold features. Then we employ a lightweight interpreter to speculatively and tentatively translate the suspicious bytes to open the black box and enhance the reliability and interpretability. Additionally, a functionality-preserving data augmentation is adopted to increase the diversity of limited training data and raise the generality for real-world deployment. Comparative experiments and ablation studies are conducted on a dataset composed of real-world instances to verify and prove the prevalence of Horus. The experimental results illustrate that Horus outperform existing methods on the identification of code-reuse exploits from data stream with an acceptable overhead. Horus does not rely on any dynamic executions and can be easily integrated into existing defense systems. Moreover, Horus is able to provide tentative interpretations about attack semantics irrespective of target program, which further improve system's effectiveness and reliability.

Maritime ship monitoring plays an important role in maritime transportation. Fast and accurate detection of maritime ship is the key to maritime ship monitoring. The main sources of marine ship images are optical images and synthetic aperture radar (SAR) images. Different from natural images, SAR images are independent to daylight and weather conditions. Traditional ship detection methods of SAR images mainly depend on the statistical distribution of sea clutter, which leads to poor robustness. As a deep learning detector, RetinaNet can break this obstacle, and the problem of imbalance on feature level and objective level can be further solved by combining with Libra R-CNN algorithm. In this paper, we modify the feature fusion part of Libra RetinaNet by adding a bottom-up path augmentation structure to better preserve the low-level feature information, and we expand the dataset through style transfer. We evaluate our method on the publicly available SAR dataset of ship detection with complex backgrounds. The experimental results show that the improved Libra RetinaNet can effectively detect multi-scale ships through expansion of the dataset, with an average accuracy of 97.38%.

Optical immunosensors are one of the most popular category of immunosensors with applications in many fields including diagnostics, environmental and food analysis. The latter field is of particular interest not only for the scientists but also for the regulatory authorities and the public since food is essential for life but can be also the source of many health problems. In this context, the current review aims to provide an overview of the different types of optical immunosensors focusing onto their application for the determination of pathogenic bacteria in food samples. In particular, after the description of main optical transduction techniques, their implementation for the immunochemical determination of bacteria will be discussed. Finally, a short commentary about the future trends in optical immunosensors for food safety applications will be provided.

In recent years, several industries have registered an impressive improvement in technological advances such as Internet of Things (IoT), e-commerce, vehicular networks, etc. These advances have sparked an increase in the volume of information that gets transmitted from different nodes of a computer network (CN). As a result, it is crucial to safeguard CNs against security threats and intrusions that can compromise the integrity of those systems. In this paper, we propose a machine mearning (ML) intrusion detection system (IDS) in conjunction with the Genetic Algorithm (GA) for feature selection. To assess the effectiveness of the proposed framework, we use the NSL-KDD dataset. Furthermore, we consider the following ML methods in the modelling process: decision tree (DT), support vector machine (SVM), random forest (RF), extra-trees (ET), extreme gradient boosting (XGB), and naïve Bayes (NB). The results demonstrated that using the GA algorithm has a positive impact on the performance of the selected classifiers. Moreover, the results obtained by the proposed ML methods were superior to existing methodologies.

Rosette caused by rose rosette virus (RRV) is the most devastating malady of rose in the United States. Because of the recent discovery of the virus and the completion of Koch’s postulates all assumptions about the disease are based on visual observations of material that may or may have not been infected by the virus. This study addresses several aspects of virus and disease epidemiology. Twenty rose genotypes were screened for mite and/or virus resistance. Phyllocoptes fructiphilus the only known vector of RRV, was able to establish, lay eggs and develop to nymphs and adults in all genotypes. ‘Stormy Weather’ shows resistance to the virus as assessed by both mite and cleft-grafting transmission experiments. The acquisition/latent and inoculation access periods were studied revealing long acquisition/latent periods but rapid inoculation time frames. The outputs of this study will assist in the better management of the vector and the disease. The resistant genotype identified could be used in areas with high disease pressure to minimize spread and for identification of the mechanisms behind resistance or as breeding material to incorporate virus resistance to new cultivars. The short inoculation access period indicates that chemical control for the vector may be a challenging undertaking.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection which detects the patient's exhaled VOCs combined with sensor arrays of Convolutional Neural Network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, due to low accuracy, high complexity computation and large number of parameters make the CNN algorithms difficult transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models such as EfficientNet, LTNet has fewer parameters (32K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than that of the five existing models’, and it also offers the shortest training (844.38s and 584.67s) and inference time (23s and 14s) in the same validation set. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Ultrasonic feedback energy is affected by the variety, planting and growth state of crops, it is difficult to fine application in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, jointing stage, booting stage, heading stage, filling stage and maturity stage. The relationship between the actual value of the height from the spray boom to the top of wheat (Habw) and the detected value of the height from the spray boom to the top of wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450 mm~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤5 cm during the full growth cycle. This study provides an ultrasonic detection model construction method, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

The application of deep learning in the detection of Synthetic Aperture Radar (SAR) targets has been primarily limited to large objects such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5,392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478x1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate 9 benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex background. Mix MSTAR is available at: https://github.com/TheGreatTreatsby/Mix-MSTAR-mmrotate.

In Thailand, parking time violation is a major problem, especially for mini-marts. At present the task of detecting parking time violation is mainly conducted manually using Closed-Circuit Television (CCTV). This method requires additional human labour to track incoming and outgoing vehicles. Therefore, low cost time violation tracking is needed. To the best of our knowledge, there has not been any research for parking violation detection and tracking conducted for parking time limits. This paper introduces a novel parking time violation detection algorithm using the Yolov8 and DeepSORT tracking algorithms to track vehicles in consecutive frames. The presented parking violation tracking algorithm can provide a guideline for research in parking time violation detection.

In engineering practice, various types of information data are affected by many factors during the collection process. For example, information data measurement errors are caused by equipment performance and the working environment. During the transmission of detection information, the signal distortion caused by energy loss and signal interference causes unpredictable detection errors in the collected information data. Through the study of fractional calculus theory, it was found that it is suitable for studying nonlinear, non-causal, and non-stationary signals, and has the dual functions of improving detection information and enhancing signal strength. Therefore, under the influence of many factors, we applied the fractional difference algorithm to the field of information data processing. Multi-sensor detection data fusion algorithm based on the fractional partial differential equation was adopted to establish its online detection data. A multi-sensor detection data fusion algorithm based on a fractional partial differential equation is established, which effectively fuses the information data detection errors caused by various influencing factors and greatly improves the detection accuracy of information data. The effectiveness of this method is proved through its application in an experiment.

This paper incorporates the definition of Intrusion Detection Systems and the methodologies utilised by these systems. As well as this, this research paper also encompasses a taxonomy and a survey of IDS and the specific strategies and principles. Finally, this paper also includes a discussion amongst other authors for instance what the authors differ and agree on, along with the previously related studies.

In the field of autonomous vehicles, lane detection and control plays an important role. In autonomous driving the vehicle has to follow the path to avoid the collision. A deep learning technique is used to detect the curved path in autonomous vehicles. In this paper a customized lane detection algorithm was implemented to detect the curvature of the lane. A ground truth labelling tool box for deep learning is used to detect the curved path in autonomous vehicle. By mapping point to point in each frame 80-90% computing efficiency and accuracy is achieved in detecting path.

Computer vision models of salient object detection attempt to mimic the ability of the human visual system to select relevant objects in images. To this end, the development of deep neural networks on high-end computers has recently made it possible to achieve high performance. However, it remains a challenge to develop deep neural network models of the same performance for devices with much more limited resources. In this work, we propose a new approach for a lightweight salient object detection neural network model, inspired by the cone and spatial opponent processes of the primary visual cortex (V1), that inextricably link color and shape in human color perception. Our proposed model, namely CoSOV1net, is trained from scratch, without using backbones from image classification or other tasks. Experiments, on the most widely used and challenging datasets for salient object detection, show that CoSOV1Net achieves competitive performance (i.e. Fβ=0.931 on the ECSSD dataset) with state-of-the-art salient object detection models, while having low number of parameters (1.14M), low FLOPS (1.4G) and high FPS (211.2) on GPU (nvidia Geforce RTX 3090 TI) compared to the state-of-the-art in the salient object detection or lightweight salient object detection task. Thus, CoSOV1net turns out to be a lightweight salient object detection that can be adapted to mobile environments and resource-constrained devices.

Foodborne illnesses can be infectious and dangerous and most of them are caused by bacteria. Some types of common food-related bacteria exist widely in nature and pose a serious threat to both humans and animals, and can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacteria detection are of paramount importance in food-safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical method have been widely used in food-safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical method for various bacteria detection (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)) considering different foods types, analytical performances and the reported limit of detection (LOD) are discussed. It is found that the bacteria type and food sample type contribute significantly to the analytical performance and LOD. Detection by LFIA has higher average LOD (24 CFU/ml) than detection by electrochemical method (12 CFU/ml) and PCR (6 CFU/ml). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. LFIA with gold and iron nanoparticles are prominent in the majority of articles of 26 CFU/ml and 12 CFU/ml respectively. Electrochemical method reveals that the average LOD is highest for cyclic voltammetry (CV) of 18 CFU/ml), followed by electrochemical impedance spectroscopy (EIS) of 12 CFU/ml and differential pulse voltammetry (DPV) of 8 CFU/ml. LOD usually decreases when replicate number increases until it remain unchanged. Exponential relations with (R²>0.95) between LOD of Listeria in milk by LFIA and electrochemical method with replicate number have been obtained. Finally, the review discusses the challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve the analytical performance for bacterial detection.

In this work, a ferrocene-containing gallic acid derivative modified carbon nanotubes paste electrode (Gal-Fc-CNT) obtained by simple mechanical mixing was studied for fast simultaneous voltammetric detection of doxorubicin (DOX), capecitabine (CPB) and cyclophosphamide (CPP) as cytostatic index by cumulative signal and selective detection of DOX. The electrochemical behaviors of individual and simultaneous DOX, CPB and CPP studied by cyclic voltammetry (CV) on Gal-Fc-CNT electrode at various pHs and potential ranges allowed the development of simple simultaneous detection method as cytostatic index at pH of 12 using square-wave voltammetry technique operated at modulation amplitude of 100 mV, step potential of 4 mV, frequency of 25 Hz and 100 mV·s−1 potential scan rate that allowed better performance than reported electrodes. Faster and selective detection of DOX with the lowest limit of detection of 75 ng⸱L-1 was achieved using square-wave voltammetry technique operated at modulation amplitude of 100 mV, step potential of 4 mV, frequency of 50 Hz and 200 mV·s−1 potential scan rate. The good results obtained for real tap water assessment indicated the utility of Gal-Fc-CNT paste electrode for practical applications (water samples, pharmaceutical formulations).

Semidiurnal tidal currents can exceed 5 ms−1 in Minas Passage, Bay of Fundy, where a tidal energy demonstration area has been designated to generate electricity using marine hydrokinetic turbines. The risk of harmful fish-turbine interaction cannot be dismissed for either migratory or local fish populations. Individuals belonging to several fish populations have been acoustically-tagged and monitored by using acoustic receivers moored within Minas Passage. Detection efficiency ρ is required as the first step to estimate probability of fish-turbine encounter. Moored Innovasea HR2 receivers and high residency (HR) tags were used to obtain detection efficiency ρ as a function of range and current speed, for near-seafloor signal paths within the tidal energy development area. Strong tidal currents moved moorings so HR tag signals, and their reflections from the sea surface, were used to measure ranges from tags to receivers. HR2 self-signals that reflected off the sea surface showed which moorings were displaced to lower and higher levels on the seafloor. Some of the range testing paths had anomalously low ρ which might be attributed to variable bathymetry blocking the line of sight signal path. Clear and blocked signal paths accord with mooring levels. Application of ρ is demonstrated for calculation of abundance, effective detection range, and detection-positive intervals. High residency signals were better detected than pulse position modulation (PPM) signals. Providing the presently obtained ρ applies to tagged fish that swim higher in the water column, there is a reasonable prospect that probability of fish-turbine encounter can be estimated by monitoring fish that carry HR tags.

Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

Botnets, a prominent threat to IoT security. ‘botnet’ this word is the composition of robot and network. A network of robots used to commit cybercrime. A bot means a compromised end-host or a device which is a member of a botnet. [2] Governments have become a popular target for malicious attacks. This is due to them holding mass confidential data on their network.

High costs for the repair of concrete structures can be prevented if damage at an early stage of degradation is detected and precautionary maintenance measures are applied. To this end, we use numerical wave propagation simulations to identify simulated damage in concrete using convolutional neural networks (CNN). Damage in concrete subjected to compression is modeled at the mesoscale using the discrete element method. Ultrasonic wave propagation simulation on the damaged concrete specimens are performed using the rotated staggered finite-difference grid method. The simulated ultrasonic signals are used to train a CNN based classifier capable of classifying three different damage stages (microcrack initiation, microcrack growth and microcrack coalescence leading to macrocracks). The performance of the classifier is improved by refining the dataset via an analysis of the averaged envelope of the signal. The classifier using the refined dataset has an overall accuracy of 90%.

Singing melody extraction is the task that identifies the melody pitch contour of singing voice from polyphonic music. Most of the traditional melody extraction algorithms are based on calculating salient pitch candidates or separating the melody source from the mixture. Recently, classification-based approach based on deep learning has drawn much attentions. In this paper, we present a classification-based singing melody extraction model using deep convolutional neural networks. The proposed model consists of a singing pitch extractor (SPE) and a singing voice activity detector (SVAD). The SPE is trained to predict a high-resolution pitch label of singing voice from a short segment of spectrogram. This allows the model to predict highly continuous curves. The melody contour is smoothed further by post-processing the output of the melody extractor. The SVAD is trained to determine if a long segment of mel-spectrogram contains a singing voice. This often produces voice false alarm errors around the boundary of singing segments. We reduced them by exploiting the output of the SPE. Finally, we evaluate the proposed melody extraction model on several public datasets. The results show that the proposed model is comparable to state-of-the-art algorithms.

In the rapidly evolving field of sentiment analysis, the introduction of Transformer-based architectures, particularly the BERT model, has markedly improved accuracy levels, setting new standards in the analysis of textual sentiment. These advancements have been instrumental in enhancing the model's ability to grasp the nuances and complexities inherent in human language, thereby providing deeper insights into the sentiment expressed in various texts. However, the impressive performance of such deep learning models comes at the cost of increased computational demands and a lack of transparency in their decision-making processes. These challenges have reignited interest in rule-based sentiment analysis methods, which utilize sentiment lexicons for a more straightforward and computationally economical approach to determining text sentiment. Despite being overshadowed by the rise of machine learning models in recent years, these lexicon-based methods possess distinct advantages, including ease of interpretation and lower resource requirements, making them particularly appealing for certain applications. This paper seeks to re-evaluate the relevance and effectiveness of two prominent lexicon-based sentiment analysis methods, SO-CAL and SentiStrength, which have been specifically adapted for the language, in light of the advancements represented by Transformer-based models like SentBERT. Through a comprehensive comparative analysis, we examine the performance of these methodologies in contrast to SentBERT across an extensive collection of 16 text corpora, spanning a variety of genres and contexts. Our findings reveal a nuanced landscape of performance, where SentBERT's advanced capabilities typically afford it a significant advantage in accurately capturing sentiment. Nevertheless, in a surprising turn, the SO-CAL method exhibits exceptional performance on a substantial portion of the datasets, underscoring the continuing value and potential of lexicon-based approaches in sentiment analysis. This study not only highlights the strengths and weaknesses of both deep learning and lexicon-based methods but also opens the door for future hybrid approaches that could leverage the best of both worlds to achieve even greater accuracy and efficiency in sentiment analysis tasks.

Deadlock is a real problem that can cause expensive, time-dependent hang-ups or failures in distributed computing systems. Some studies have been conducted with deadlock detection and resolution in generalized request computing models, most of these studies are based on the diffusing computation scheme, where forward and backward propagated probe and reply messages respectively are diffused between dependent processes. The replies carry the dependency information between processes for the initiator. Using this dependency information initiator can detect and resolve the deadlock cycle. It has been proven in the literature that a deadlock cycle length distribution is skewed with a large majority of cycles (90%) having length two. Based on this fact an improved algorithm is proposed for handling deadlock detection in distributed computing systems under the distributed request model where each process has several requests at a time. The improved algorithm depends mainly on message propagation and timeout policy. Probe and reply messages are propagated between the initiator and its successors only to detect deadlock cycles between them. Global timeout policy is used to detect potential deadlock cycles with large length distribution. The proposed algorithm can handle concurrent executions. Simulation experiments are performed to see the effectiveness of the improved algorithm. It is found that the improved algorithm compares favorably with other existing algorithms and it shows better results for several performance metrics especially in reducing deadlock latency and execution time.

Mobile Ad hoc Networks (MANETs) play an important role in the technological era by enabling wireless communication in dynamic and infrastructure-free contexts. Because of Manet’s unique qualities and decentralized nature, MANETs are subject to many attacks such as the Blackhole attack, grey hole attack, Byzantine attack, Wormhole attack, and Sybil attack. Sybil attacks pose a substantial risk to MANETs. A Sybil attack occurs when a malicious node impersonates many identities inside the network, resulting in the creation of multiple suspicious nodes that appear as independent entities. This study presents a thorough analysis of the literature on the detection of Sybil attacks on MANETs. To find gaps in the literature, current surveys are also looked at. Also taken into consideration are several contemporary systems built on distinct techniques. In terms of throughput, detection rate, low energy consumption, packet delivery ratio, and end-to-end latency, some recent methods-based approaches are also evaluated severely. To address the stated state-of-the-art issues in Sybil attack detection, this paper suggests ML-based strategies as the most suitable answer. Machine learning has huge potential for effective wireless network management. According to the researcher, this is the first comprehensive assessment of ML-based techniques for Sybil attack detection in wireless networks. Finally, we considered the unsolved issues in Sybil assault detection in wireless networks.

This paper addresses the problem of low accuracy of conventional channel estimation and signal detection in complex fields like mines; and the problem of replacing the deep receiver at the receiver side with a deep neural network requires a large amount of data for training and slow training time. Combining the receiver side with traditional communication knowledge, a channel estimation and signal detection method based on the joint ReEsNet and SDRNet under the mine is proposed. The detailed step is to use ReEsNet network for interpolation of conventional channel estimation. Then, on the basis of ReEsNet, the output data is channel equalized and pilots removed, and the previously processed data is input to SDRNet to perform signal recovery. In the complex mine environment, the MSE values are 2e-2 for LS, 6e-3 for MMSE, and 8.44e-5 for ReEsNet at the 1500th sample data for interpolation compared to the conventional channel estimation. the MSE of ReEsNet is the smallest and is closer to the real channel estimation. Compared with the proposed joint channel estimation and signal detection methods, fewer trainable parameters reduce 97% of the parameters compared to CCRNet and 84% of the parameters compared to CNNNet; their detection results, however, are not very different.

After long years of development, the technology of analyzing the working condition of power units based on vibration signals has had relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the limitations of the current development of key technologies. With the development of big data and artificial intelligence technology, the involvement of new technologies will be an important boost to the development of this field. To support the subsequent research, bibliometrics is used as a tool to sort out the development of the technology in this field at the macro level; at the micro level, the classical and key literature is studied to grasp the development status at the technical level and prepare for the selection of entry points to continue in-depth innovation afterwards.

Cybercrimes are becoming a bigger menace to both people and corporations. It poses a serious challenge to the modern digital world. According to a press release from 2019 Cisco and Cybersecurity Ventures, Cisco stopped seven trillion threats in 2018, or 20 billion threats every day, on behalf of its clients. According to Cybersecurity Ventures, the global cost of cybercrime will reach $6 trillion annually by 2021, which is significantly more than the annual damage caused by all natural disasters and more profitable than the global trade in all major illegal narcotics put together. Botnets are the most common and have a significant negative impact on any civilization among malware programmes. As a result, this study will explore various P2P botnet detection algorithms by outlining their essential characteristics, advantages and disadvantages, obstacles, and future research.

The anomaly detection task is very important in computer science. And there are a lot of anomaly detection methods. Different from some thresholding methods, some unsupervised methods could make us get more accurate and faster result, which is the object of the project. In this paper, I tried to use EWMA and some other methods in two datasets: Webank time consuming indicators dataset and AIOps Challenge dataset. The paper consists nine parts: background of the project, related work, description of algorithms, implementation details, experimental setup and data sets used, experimental results and discussion, future directions, reference and meeting notes.

pH value almost affects the function of cells and organisms in all aspects, so in biology, biochemical and many other research fields, it is necessary to apply simple, intuitive, sensitive, stable detection of pH and base characteristics inside and outside the cell. Therefore, many research groups have explored the design and application of pH probes based on surface enhanced Raman scattering（SERS）. In this review article, we discussed the basic theoretical background of explaining the working mechanism of pH SERS sensors, and also briefly described the significance of cell pH measurement, and simply classified and summarized the factors that affected the performance of pH SERS probes. Some applications of pH probes based on surface enhanced Raman scattering (SERS) in intracellular and extracellular pH imaging and the combination of other analytical detection techniques are described. And finally, the development prospect of this field is prospected.

The aim of this paper is to use deep learning tools to innovate pre-trained object detection models to improve the accuracy of non-destructive testing (NDT) of civil aviation maintenance. First, this thesis classifies object defects for NDT, such as cracks, undercut, etc. Nowadays, thesis surveys innovation deep-learning methods technology is used to improve the defect detection performance inferencing capability, increase the accuracy and efficiency of automatic identification which in enhanced the safety and reliability of aircraft fuselage in future, mark hidden cracks and solve the challenges that cannot be identified by manual inspection. Second, recent mainstream techniques the YOLOv4 neural network to the graphics card GPU core operator to speed up the recognition of defect images is being applied to the non-destructive inspection process of aircraft maintenance on A, C and D-Level, fully validating the deep learning model's powerful defect detection target capability. The attention-based YOLOv4 algorithm is improved by applying a one-stage attention mechanism to the YOLOv4, thereby improving the accuracy of the innovation model. Finally, thesis improved YOLOv4 based on an attention mechanism is proposed for object detection NDT via the deep learning method to effectively improve and shorten the inspection anomaly detection method for automatic detection sensor systems.

December 4, 2020 marks 90 years since the concept of neutrinos was proposed as a consequence of the observed discrepancies in several experiments on radioactive decays of various isotopes. There have been great many developments in our understanding of this elusive particle over the past nine decades, also leading to several Nobel Prizes awarded to work on neutrino physics. But there are many aspects of the neutrinos that are still not completely understood, including even its actual rest mass. The neutrino still remains an enigma and we have yet to learn a lot about its different properties. This article summarises the overall picture of the current understanding of the neutrino, right from its inception.

Monitoring is an essential component in ecosystem management and leveraging existing data sources for multiple species of interest can be one effective way to enhance information when making management decisions. Here we analyzed juvenile Chinook Salmon (Oncorhynchus tshawytscha) bycatch data that has been collected by the recently established Enhanced Delta Smelt Monitoring program (EDSM), a survey designed to estimate the abundance and distribution of the San Francisco Estuary’s (estuary) endangered Delta Smelt (Hypomesus transpacificus). Two key aspects of the EDSM program distinguish it from other fish surveys in the estuary: a stratified random sampling design and the spatial scale of its sampling effort. We integrated the EDSM dataset with other existing surveys in the estuary and used an occupancy model to assess detection probability differences across gear types. We saw no large-scale differences in size selectivity, and while detection probability varied among gear types, cumulative detection probability for EDSM was comparable to other surveys due to the program’s use of replicate tows. Based on our occupancy model and sampling effort in the estuary during spring of 2017 and 2018, we highlighted under-sampled regions that saw improvements in monitoring coverage due to EDSM. Our analysis also revealed that each sampling method has its own benefits and constraints. Although the use of random sites with replicates as conducted by EDSM can provide more statistically robust abundance estimates relative to traditional methods, the use of fixed stations and simple methods such as beach seine may provide a more cost-effective way of monitoring salmon occurrence in certain regions of the estuary. Stronger inference on salmon abundance and distribution can be made by leveraging the strengths of each survey’s method. Careful consideration of these trade-offs is crucial as the management agencies of the estuary continue to adapt and improve their monitoring programs.

The purpose of this paper is to provide a structural review of the progress made on detection and localization of leaks in pipelines by using approaches based on the Kalman filter. This is, to the best of our knowledge, the first review on the t opic. In particular, it is the first to try to draw the attention of the leak detection community to the important contributions that use the Kalman filter as the core of a computational pipeline monitoring system. Without being exhaustive, we try to gather the results from different research groups and present them in a unified fashion. For this reason, we propose a classification of the current approaches based on the Kalman filter. For each of the existing approaches within this classification, the basic concepts, fundamental results, and relations with the other approaches are discussed in detail. The review starts from a short summary of basic concepts about state observers. Then, a brief history of the use of the Kalman filter for diagnosing leaks is described by mentioning the most outstanding approaches. At last, we briefly discuss some emerging research problems, such as the leak detection in pipelines transporting heavy oils, and we discuss the main challenges and some open problems.

Nowadays, security is a top priority. In fact, biometrics uses cutting-edge technologies to identify terrorists and criminals. But the practice of distinguishing humans based on intrinsic physical or behavior traits goes back thousands of years. With the widespread use of computers in the late 20th century, new possibilities for digital biometrics emerged and new technologies were generously used. Among these, we remember high resolution security video cameras and drones. So, the aim of the present project is to study and explain the features of these technologies, especially the ones of the the Phantom 4 Pro+ aircraft and analyze its operating methods in order to identify human faces during live streaming of videos. For this purpose, it will be used Paul Viola and Michael Jones’ face detection algorithm, which includes Haar features and cascade classifiers to identify faces, eyes and ears of an individual.

The importance of detecting bacteria in various food products is ever-increasing, due to recent food trends that lend themselves to food contamination. Additionally, the detection of probiotics in food products is of increasing importance to consumers, who realize the benefits of probiotics on one’s diet. Existing technologies for detection of bacteria in food are accurate, but most are slow, increasingly costly and unsuitable for applications outside of research laboratories. Optic approaches have recently emerged as an alternative, allowing rapid detection of bacterial presence. This study employs a portable kinetics fluorometer, fabricated in-house, in conjunction with NADH sensitive fluorescence reporter for analysis of various food products. The presence of bacteria is detected in 5 minutes. Both pathogenic and probiotic bacteria were detected in food products, such as raw chicken and beef, spoiled lettuce and contaminated water, yogurt, and kombucha tea. The cellular activity of two probiotic pills was also verified. All samples displayed varying levels of bacterial activity. The study indicates the viability of biosensors being used as an alternate method to detect bacteria in food products – and the viability of a fluorescence-based biosensor to detect viable bacteria. The approach is suitable for both laboratory and field determinations.

We used an ultrasensitive, broadband optomechanical ultrasound sensor to study the acoustic signals produced by pressurized nitrogen escaping from a variety of small syringes. Harmonically related jet tones extending into the MHz region were observed for a certain range of flow (i.e., Reynolds number), in qualitative agreement with historical studies on gas jets emitted from pipes/orifices of much larger dimensions. For higher turbulent flow rates, we observed broadband ultrasonic emission in the ~ 0 – 5 MHz range, likely limited on the upper end by attenuation in air. These observations are made possible by the broadband, ultrasensitive response (for air-coupled ultrasound) of our optomechanical devices. Aside from their theoretical interest, our results could have practical implications for the non-contact monitoring and detection of early-stage leaks in pressured fluid systems.

During disaster response, clouds or darkness can prevent the use of optical images for detecting consequences of natural disasters, including landslides. In these situations, radar images can be used to detect changes more rapidly. However, Synthetic Aperture Radar (SAR) backscatter intensity images are underutilized for landslide detection. Unfortunately, there remains a lack of understanding about how to interpret landslide signatures in SAR imagery. In this study, we investigate how the morphometric features and material properties of landslides, and preexisting land cover, control their expression in SAR backscatter intensity change images. Trends in the spatial and temporal signatures of over 1000 landslides in 30 diverse case studies are investigated, using multi-temporal composites and dense time-series of Sentinel-1 C-band SAR backscatter intensity data. The results show that the orientation of landslide surfaces relative to the sensor, pre-existing land cover, and the roughness of the landslide surface, determine whether landslides will produce an increase or decrease in backscatter intensity values. In certain cases, we can identify morphometric features of landslides (e.g. scarps, transit zone, deposits, ponding) and material properties. Generally, we see that landslides appear most clearly with a strong increase in intensity when they occur in herbaceous vegetation or non-vegetated ground surfaces, due to an increase in surface roughness. While in forested or densely vegetated areas, landslides produce a more complex signature with both decreases due to radar shadow and vegetation removal, and an adjacent edge of increased intensity due to double bounce and direct return from vertical tree trunks and convex edges. In most cases, rough deposits produce an increase in intensity, while smooth deposits (e.g. from mudslides) exhibit specular reflection, and thus show decreased values. Landslides are less visible in cases with pre-event very rough ground, or mixed vegetation conditions. The conceptual model developed can aid interpretation of landslides in SAR imagery, and provide domain knowledge needed to train models for automatic landslide detection.

Surface defect detection is one of the most widely used research areas in the field of image processing and machine vision. Detection of surface defects is used in visual inspection systems and medical image analysis. In this manuscript, an innovative method for detecting surface defects based on energy changes in co-occurrence matrices is presented in several directions. The method presented in this manuscript includes two stages of learning and testing. In the learning phase, to extract texture features, the gray level co-occurrence matrix operator is applied on the healthy image of the desired level. Then the energy value of the output matrix is ​​calculated. In the following, changes in the amount of energy are considered as statistical characteristics that are a good representative of the image of a healthy surface. Finally, with its help, a suitable threshold for the health of the images is obtained. Then, in the test phase, with the help of the calculated quorum, the defective windows that have suffered from non-normality are distinguished from the healthy surface sections. In the results section, the efficiency of the mentioned method has been measured on medical images and stone and ceramic images, and its detection accuracy has been compared with some previous effective methods. The advantages of the presented method include high accuracy, low calculations and compatibility with all types of levels due to the use of the learning stage. The proposed approach can be used in medical applications to diagnose abnormalities such as diseases. All extracted features are statistical, so its detection speed is higher than deep neural networks.

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks like difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared to manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labelling a high variety of frames in different environments is too challenging, an artificial data generation pipeline has been developed. The data set consists of 20 000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enables the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples.

In recent years, weeds is responsible for most of the agricultural yield losses. To deal with this threat, farmers resort to spraying pesticides throughout the field. Such method not only requires huge quantities of herbicides but impact environment and humans health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide at the right place and at the right time (Precision Agriculture). Nowadays, Unmanned Aerial Vehicle (UAV) is becoming an interesting acquisition system for weeds localization and management due to its ability to obtain the images of the entire agricultural field with a very high spatial resolution and at low cost. Despite the important advances in UAV acquisition systems, automatic weeds detection remains a challenging problem because of its strong similarity with the crops. Recently Deep Learning approach has shown impressive results in different complex classification problem. However, this approach needs a certain amount of training data but, creating large agricultural datasets with pixel-level annotations by expert is an extremely time consuming task. In this paper, we propose a novel fully automatic learning method using Convolutional Neuronal Networks (CNNs) with unsupervised training dataset collection for weeds detection from UAV images. The proposed method consists in three main phases. First we automatically detect the crop lines and using them to identify the interline weeds. In the second phase, interline weeds are used to constitute the training dataset. Finally, we performed CNNs on this dataset to build a model able to detect the crop and weeds in the images. The results obtained are comparable to the traditional supervised training data labeling. The accuracy gaps are 1.5% in the spinach field and 6% in the bean field.

Human Face and facial parts are the most significant parts as it reveals a person’s true identity. It plays an important role in various biometric applications like crowd analysis, human tracking, photography, cosmetic surgery, etc. There are many techniques are available to detect a facial image. Among them, skin detection is the most popular one. The aim of this paper is to detect first the person's identity from facial image and finally check any spot present the the detected person. The first step is to detect the maximum skin region based on a combination method of RGB and HSV color space model. Next it is to verify the skin areas of human through machine learning approach. The Aggregated Channel Features (ACF) detector is used to identify the different facial parts like eye pairs, nose, and mouth. Bootstrap aggregation decision tree classifier is applied to classify the person’s identity based on Histogram Oriented Gradient (HOG) features value. The experimental results show that the proposed method gives the average 97% accuracy.

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

The CAN protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to various attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the normal behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs within the network. To tackle this issue, this paper presents an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. By analyzing different types of features separately using the LSTM-Autoencoder model, the system effectively detects anomalies. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system's high precision with a 99% detection rate in identifying anomalies.

This work analyzes a new indoor laboratory data set looking at early fire indicators in controlled and realistic experiments representing different incipient fire scenarios. The experiments were performed within the confines of an indoor laboratory setting using multiple distributed sensor nodes on different room positions. Each sensor node collected data of particulate matter (PM), volatile organic compounds (VOC), CO, CO2, H2, UV, air temperature and humidity in terms of a multivariate time series. These data hold immense value for researchers within the machine learning and data science communities who are keen on exploring innovative advanced statistical and machine learning techniques. They serve as a valuable resource for the development of early fire detection systems. The analysis of the collected data was carried out in dependence of the manhattan distance between the fire source and the sensor node. We found that especially larger particles (> 0.5 μm) and VOCs show a significant dependency with respect to the intensity as a function of the manhattan distance to the source. Moreover, we observed differences in the propagation behavior of VOCs, PM, and CO, which are particularly relevant in incipient fire scenarios due to the presence of strand popagation effects.

In the leading/trailing edge’s adaptive machining of the near-net-shaped blade, a small portion of the theoretical part is retained for securing aerodynamic performance by manual work. However, this procedure is time-consuming and depends on the human experience. In this paper, we defined retained theoretical leading/trailing edge as the reconstruction area. To accelerate the reconstruction process, an anchor-free neural network model based on Transformer was proposed, named LETR (Leading/trailing Edge Transformer). LETR extracts image features from an aspect of mixed frequency and channel domain. We also integrated LETR with the newest meta-Acon activation function. We tested our model on the self-made dataset LDEG2021 on a single GPU and got an mAP of 91.9\%, which surpassed our baseline model, Deformable DETR by 1.1\%. Furthermore, we modified LETR’s convolution layer and named the new model after GLETR (Ghost Leading/trailing Edge Transformer) as a lightweight model for real-time detection. It is proved that GLETR has fewer weight parameters and converges faster than LETR with an acceptable decrease in mAP (0.1\%) by test results.

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modeling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many other financial models can be modeled by implementing machine learning models. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, recall, precision, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of 97% and 96.84% respectively.

A super-porous hybrid platform can offer significantly increased number of reaction sites for the analytes and thus can offer advantages in the biosensor applications. In this work, a significantly improved sensitivity and selectivity of hydrogen peroxide (H2O2) cancer sensor is demonstrated by a super-porous hybrid CuO/Pt NP platform. The super-porous hybrid platform is fabricated by a physiochemical approach combining the physical vapor deposition of Pt NPs and electrochemical deposition of super-porous CuO structures by adopting a dynamic hydrogen bubble technique. Under an optimized condition, the hybrid CuO/Pt biosensor demonstrates a very high sensitivity of 2,205 µA/mM∙cm2 and a low limit of detection (LOD) of 140 nM with a wide range of H2O2 detection. This is meaningfully improved performance as compared to the CuO-based H2O2 sensors as well as to the previously reported metal oxide based H2O2 sensors. The hybrid CuO/Pt platform exhibits excellent selectivity against other interfering molecules such as glucose, fructose, dopamine, and ascorbic acid. Due to the synergetic effect of highly porous CuO structures and underlying Pt NPs, the CuO/Pt architecture offers extremely abundant active sites for the H2O2 reduction and electron transfer pathways.

The use of strobilurin fungicides in agriculture has increased steadily during the past 25 years, and although strobilurins have minimal water solubility, they regularly appear in surface waters, at times in concentrations approaching toxic levels for aquatic life. The present study examined concentrations of strobilurin fungicides in designated trout streams draining an agricultural watershed in southeastern Minnesota, USA, where fungicides may have contributed to a recent fish kill. Water samples (n = 131) were analyzed for the presence of five different strobilurin fungicides (azoxystrobin, fluoxastrobin, picoxystrobin, pyraclostrobin, trifloxystrobin). Samples were collected via grab and automated sampling during baseflow and stormflow events throughout an entire crop growing season from sites on each of three forks of the Whitewater River. Detection frequencies for the five strobilurins ranged from 44 to 82%. Fluoxastrobin and pyraclostrobin concentrations were above known toxic levels in 3% and 15% of total samples analyzed, respectively. The highest concentrations were detected in mid-summer (mid-June to mid-August) samples, coincident with likely strobilurin applications. Lower concentrations were present in water samples collected during the non-application periods in spring and fall, suggesting groundwater-stream interactions or steady leaching of fungicides from watershed soils or stream sediments. Further study is required to determine strobilurin concentrations in sediments, soils, and groundwater. Better tracking and guidance regarding strobilurin use is necessary to adequately protect aquatic life as fungicide use continues to increase.

Hyperspectral (HS) imaging plays a pivotal role in various fields, including medical diagnostics, where precise human vein detection is crucial. Hyperspectral image data are very large and can cause computational complexities. Dimensionality reduction techniques are often employed to streamline HS image data processing. This paper investigates the effectiveness of three dimensionality reduction techniques, namely: Principal Component Analysis (PCA), Folded PCA (FPCA), and Ward’s Linkage Strategy using Mutual Information (WaLuMI) for vein detection using HS images. A HS image dataset, encompassing left and right-hand images captured from 100 subjects with varying skin tones was created and annotated using anatomical data to represent vein and non-vein areas within the images. To generate experimental results, the HS image dataset was divided into train and test datasets. Optimum performing parameters for each of the dimensionality reduction techniques in conjunction with The Support Vector Machine binary classification were determined using the Training dataset. The performance of the three dimensionality reduction based vein detection methods was then assessed and compared using the test image dataset. Results show that the FPCA-based method outperforms the other two methods in terms of accuracy. For visualization purposes, the classification prediction image for each technique is post-processed using morphological operators, and results show the significant potential of HS imaging in vein detection.

With increasing concerns over environmental impact, and overall health of both the environment and its people, a need to quantify contaminants is of the utmost importance. Chemosensors with low detection limits and relative ease of application can address this challenge. Nitrite ions are known to be detrimental to both the environment and human health. A new colorimetric chemodosimeter has been prepared from a homolytic photochemical cleavage of a reaction between pyrrole and pyridine. The product, 4(pyrrol1-yl)pyridine, yields a limit of detection of 0.330 (±0.09) ppm for the detection of nitrite in aqueous solution, employing a colorimetric change from yellow to pink. It is also highly selective for nitrite when various competitive anions such as SO32- , NO3-, PO43-, SO4-2, Cl-, F-, I-, Br-, and CN- are present in great excess. The molecule’s especially high sensitivity to nitrite is apparently the result of a complex supramolecular mechanism, characterized by both dynamic light scattering of the aggregate and the Tyndall effect. Consequently, this new sensor provides a simple, low-cost way to rapidly detect nitrite anions in aqueous solution.

Our previous research provides evidence that vergence eye movements may significantly influence cognitive processing and could serve as a reliable measure of cognitive issues. The rise of consumer-grade eye tracking technology, which uses sophisticated imaging techniques in the visible light spectrum to determine gaze position, is noteworthy. In our study, we explored the feasibility of using webcam-based eye tracking to monitor the vergence eye movements of patients with Mild Cognitive Impairment (MCI) during a visual oddball paradigm. We simultaneously recorded eye positions using a remote infrared-based pupil eye tracker. Both tracking methods demonstrated more pronounced cognitive vergence responses for targets as opposed to distractors. In summary, the use of a consumer-grade webcam to record cognitive vergence shows potential. This method could potentially lay the groundwork for future research aimed at creating an affordable screening tool for mental health care.

This study introduces an original comprehensive system centered on identifying specific terms that indicate a user's position, particularly the discrete values representing latitude and longitude. This system not only detects these terms but also retrieves the corresponding numerical data for accurate and efficient determination of locations. The importance of this study can be applied various fields, notably aiding offline operations of military personnel, who often lack internet access. In such scenarios, precise awareness of location is vital for strategic manoeuvres, rescue operations, and navigating unfamiliar landscapes. The system allows these personnel by allowing them to extract exact location coordinates from spoken terms, thereby enhancing their awareness even in challenging surroundings. Apart from its military utility, the project holds broader significance. Teams responding to emergencies, personnel involved in disaster management, and exploratory missions can all gain from this technology during disruptions in communication infrastructure. Furthermore, travelers, adventurers, and outdoor enthusiasts can utilize this system to accurately determine their positions in remote areas without relying on online maps. We used offline speech recognition techniques to precisely transcribe spoken terms, achieving an accuracy of over 91.3% and a word error rate of 4.2%. For sound recognition, the OpenAI Whisper model was used, and a conversion process from SpeechRecognition to AudioSegmentation was implemented, followed by transforming the audio into .wav format, we have also developed the interface of the app to use it efficiently using Streamlit. This was done to ensure seamless compatibility with the Whisper model and uninterrupted audio input. By training the system to identify specific linguistic linked to location, it achieves robust detection and extraction of relevant terms. This approach eliminates the necessity for constant internet connectivity, rendering it exceptionally useful in remote, offline, and resource-limited situations.

Cloud detection in remote sensing images is a crucial preprocessing step that efficiently identifies and extracts cloud-covered areas within the images, ensuring the precision and reliability of subsequent analyses and applications. Given the diversity of clouds and the intricacies of the surface, distinguishing the boundaries between thin clouds and the underlying surface is a major challenge in cloud detection. To address these challenges, an advanced cloud detection method, CloudformerV3, is presented in this paper. The proposed method employs a multi-scale adapter to incorporate dark and bright channel prior information into the model's backbone, enhancing the model's ability to capture prior information and multi-scale details from remote sensing images. Additionally, multi-level large window attention is utilized, enabling high-resolution feature maps and low-resolution feature maps to mutually focus and subsequently merge during the resolution recovery phase. This facilitates the establishment of connections between different levels of feature maps and offers comprehensive contextual information for the model's decoder. Experimental results on the GF1_WHU dataset demonstrate that the method introduced in this paper exhibits superior detection accuracy when compared to state-of-the-art cloud detection models. Furthermore, enhanced detection performance is achieved along cloud edges and with respect to thin clouds, showcasing the efficacy of the proposed method.

Previous camera self-calibration methods have exhibited certain notable shortcomings. On one hand, they either exclusively emphasized scene cues or solely focused on vehicle-related cues, resulting in a lack of adaptability to diverse scenarios and a limited number of effective features. Furthermore, these methods either solely utilized geometric features within traffic scenes or exclusively extracted semantic information, failing to comprehensively consider both aspects. This limited the comprehensive feature extraction from scenes, ultimately leading to a decrease in calibration accuracy. Additionally, conventional vanishing point-based self-calibration methods often required the design of additional edge-background models and manual parameter tuning, thereby increasing operational complexity and the potential for errors. Given these observed limitations, and in order to address these challenges, we propose an innovative roadside camera self-calibration model based on the Transformer architecture. This model possesses a unique capability to simultaneously learn scene features and vehicle features within traffic scenarios while considering both geometric and semantic information. Through this approach, our model can overcome the constraints of prior methods, enhancing calibration accuracy and robustness while reducing operational complexity and the potential for errors. Our method outperforms existing approaches on both real-world dataset scenarios and publicly available datasets, demonstrating the effectiveness of our approach.

Deployments of real-world object-detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, a conditioning method is investigated. The method aims to guide the training loop of thermal object detection systems by leveraging an auxiliary branch to predict the weather, while directly or indirectly conditioning the baseline detection system. Leveraging such an approach to train detection networks does not necessarily improve the performance of native architectures, however, it can be observed that conditioned networks manage to extract a signal from thermal images that guides the network to detect objects that baseline models miss. As the extracted signal appears to be quite noisy and very challenging to regress accurately, further work is needed to identify an ideal optimization vector.

As formally is known, nanozymes are the nanomaterials or nanoscale materials which reveal enzyme-like properties. These nanomaterilas can be utilzied instead of the natural enzymes for simulation of enzymatic reactions in harsh environmental conditions. Among different nanoscale enzyme-like materials, the majority of them reveal peroxidase-like activity, due to the high catalytic efficiency and stability of peroxidase-like nanozymes, they had been widely utilized for sensing and detection via constructing the colorimetric and fluorescence-based analytical and bioanalytical sensors. There are several high impact nanozymatic sensors which reported in the literature. Although the review articles published in this field, however, the current reviews focus on the introducing nanozyme field not description on the best articles published in this filed. Hence, this article aimed to present a brief introduction on the nanozyme-based chemistry with emphasizing on the historical overview of recent nanozymatic sensors. In this regard the best nanozymatic sensors which published over 2016-2023 were briefly described and overviewed.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which - upon its removal from the graph - yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ-ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.}

Research in brain stroke prediction is very crucial as it can lead to the development of early detection techniques and interventions that can enhance the prognosis for stroke victims. Early detection and intervention can help to minimise the damage caused by a stroke, reduce the risk of long-term complications, and enhance the general quality of life for people who have survived a stroke. Additionally, research in stroke prediction can help to identify risk factors and improve understanding of the underlying causes of stroke, which can lead to the development of better prevention strategies. Research on brain stroke prediction is ongoing and has led to the development of various models and tools for predicting the risk of stroke and detecting it early. However, the implementation and use of these tools in clinical practise vary depending on several factors, such as the availability of resources, the specific healthcare system, and the level of awareness and acceptance of these tools among healthcare providers and patients. In general, risk prediction models may be used to quickly identify individuals at high risk of stroke and target them for preventive interventions, such as lifestyle changes, medication management, and screenings. Early detection tools can be used to quickly identify stroke symptoms and initiate appropriate treatment, which can improve outcomes for stroke patients. However, it is important to note that research and development of these models and tools are ongoing, and their use in clinical practise is constantly evaluated and updated. It may take time for these tools to be widely adopted in clinical practise and to see their real-world impact. This research paper focuses on predicting brain stroke occurrence using a range of machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gaussian Naive Bayes (GNB), Bernoulli Naive Bayes (BNB), and a Voting Classifier. The main emphasis of this research was to compare the effectiveness of oversampling techniques, as the data was significantly imbalanced. We have used evaluation metrics in this research to assess the accuracy, precision, recall, and other key performance indicators of a model's predictions. Area under the Receiver Operating Characteristics Curve (AUC), minority class accuracy, and majority class accuracy were used to assess the approaches.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

Computer vision is a rapidly developing field that focuses on highly sophisticated picture analysis, manipulation, and comprehension. Its objective is to analyze what is happening in front of a camera and utilize that understanding to control a computer or robotic system or to present users with fresh visuals that are more enlightening or appealing than the original camera images. Computer vision technologies make it feasible for new user interfaces, augmented reality gaming, biometrics, automobile, photography, movie creation, Web search, and many more applications. This essay seeks to explain how computer vision can be utilized to play blackjack successfully.