Radiomics refers to the extraction and analysis of a wide range of medical imaging features in a non-invasive and cost-effective manner to comprehensively characterise tumours. In this study, machine learning models combining radiomics and clinical factors were developed to predict retroperitoneal lymph node metastasis in testicular germ cell tumours (TGCTs), with the aim of reducing unnecessary treatment in this group of young patients. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. After segmenting 273 retroperitoneal lymph nodes using dedicated radiomics software, we developed machine-learning prediction models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). For each classifier, we developed a radiomics-only, clinical-only, and combined radiomics-clinical prediction model. The models’ performances were evaluated using the area under the receiver operating characteristic curves (AUCs). The RF-based combined clinical and radiomic model showed the most robust performance in predicting LNM with an area under the curve (AUC) of 0.95 (±0.03; 95% CI), accuracy 87%, precision 89%, recall 86% and F1 score 87%, followed by the LGBM model with an area under the curve (AUC) of 0.93 (±0.05; 95% CI), accuracy 83%, precision 87%, recall 80% and F1 score 82%. Decision curve analysis demonstrated the clinical utility of our proposed RF-based combined clinical–radiomics model. Our study has identified reliable and predictive machine-learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine-learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment. Multi-centre validation is required to provide high-quality evidence for clinical application.

It is crucial to select the right machine learning classifier for image classification and face recog-nition. This study examines the effectiveness of four different face recognition classifiers - Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Neural Networks. An analysis of the Large Faces in the Wild (LFW) dataset was carried out using Principal Component Analysis (PCA). Classifiers are rigorously trained and evaluated based on the extracted features. Comparison of classifier performance is an insightful way to figure out their strengths and weaknesses. Having a visual representation of the classifier's performance gives a complete understanding of its capabilities. Through the selection of the most appropriate classifier, study results contribute to advancements in image classification, recognition, and biometric identification. The comparison study demonstrated that the Neural Network classifier was exceptionally accurate and proficient in recognizing faces from the LFW dataset when used in conjunction with PCA for feature extraction. According to the comparative analysis, the Neural Network classifier proved exceptionally accurate and proficient at identifying faces from the LFW dataset when combined with PCA for feature extraction.

Myofascial Pain Syndrome (MPS) is a prevalent chronic pain disorder characterized by myofascial trigger points (MTrPs). Current diagnosis relies on manual detection of MTrPs, which has low inter-rater reliability. This study aims to enhance diagnosis through machine learning (ML) and a combination of texture-features extracted from B-mode ultrasound (B-mode-US) images. Four texture-features were investigated: statistical-features, and their combination with Gabor, local binary (LBP), and SEGL, LBP + gray-level-co-occurrence-matrices + Edge for the classification of MTrPs on the B-mode-US images. B-mode-US images of trapezius muscles (n=90) were examined for MTrPs and healthy muscle. Three methods of LBP, SEGL, and Gabor were separately calculated for each B-mode-US image. Then, the statistical-features (e.g., entropy) were calculated over the B-mode-US images and those three methods. Seven Machine learning (ML) classifiers (e.g., neural network) with the calculated statistical-features were applied to discriminate MTrPs from healthy muscle. Additionally, traditional statistical analysis (i.e., ANOVA) was calculated between statistical-features within each method. The results indicated that the combination of statistical-features effectively discriminated between MTrPs and healthy muscles, based on traditional statistical analysis. However, ML classifiers struggled due to high variation and similar mean values among them. This discrepancy between them prompts further exploration of US texture-based automated ML for MTrPs diagnosis.

Quantum computing (QC) has opened the door to advancements in machine learning (ML) tasks that are currently implemented in the classical domain. Convolutional neural networks (CNNs) are classical ML architectures that exploit data locality and possess a simpler structure than fully-connected multi-layer perceptrons (MLPs) without compromising the accuracy of classification. However, the concept of preserving data locality is usually overlooked in the existing quantum counterparts of CNNs, particularly for extracting multi-features in multidimensional data. In this paper, we present a multidimensional quantum convolutional classifier (MQCC) that performs multidimensional and multi-feature quantum convolution with average and Euclidean pooling and thus adapting the CNN structure to a variational quantum algorithm (VQA). Average pooling is based on the quantum Haar transform (QHT) and Euclidean pooling is based on partial quantum measurement. The experimental work was conducted using multidimensional data to validate the correctness and demonstrate the scalability of the proposed method utilizing both noisy and noise-free quantum simulations. We evaluated the MQCC model with reference to reported work on state-of-the-art quantum simulators from IBM Quantum and Xanadu using a variety of standard ML datasets. The experimental results showed favorable characteristics of our proposed techniques compared to existing work with respect to a number of quantitative metrics such as the number of training parameters, cross-entropy loss, classification accuracy, circuit depth, and quantum gate count.

Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) and better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional neural network (DCNN)-based techniques overfit and are unable to extract global context information from more significant regions. While dilated convolution retains data resolution at the output layer and increases the receptive field without adding computation, stacking several dilated convolutions has the drawback of producing a grid effect. To handle gridding artifacts and extract both coarse and fine features from the images, this research suggests using a dilated parallel deep convolutional neural network (PDCNN) architecture that preserves a wide receptive field. To reduce complexity, initially, input images are resized and then grayscale transformed. Data augmentation has since been used to expand the number of datasets. Dilated PDCNN makes use of the lower computational overhead and contributes to the reduction of gridding artifacts. By contrasting various dilation rates, the global path uses a low dilation rate (2,1,1), while the local path uses a high dilation rate (4,2,1) for decremental even numbers to tackle gridding artifacts and extract both coarse and fine features from the two parallel paths. Using three different types of MRI datasets, the suggested dilated PDCNN with the average ensemble method performs better. The accuracy provided by the Multiclass Kaggle dataset-III, Figshare dataset-II, and Binary tumor identification dataset-I is 98.35%, 98.13%, and 98.67%, respectively. In comparison to state-of-the-art techniques, the suggested structure improves results by extracting both fine and coarse features, making it efficient.

Investigation of functional magnetic resonance imaging (fMRI) data with machine learning (ML) techniques, including also deep learning (DL) methods, have been widely used to study Autism Spectrum Disorder (ASD). This disorder is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for treatment development. This study compares traditional ML techniques with deep learning (DL) methods in the analysis of functional connectivity measures obtained from the time series of multicentric ABIDE dataset. Specifically, Support Vector Machines (SVM) classifiers, with both linear and Radial Basis Function (RBF) kernels, as well as eXtreme Gradient Boosting (XGBoost) classifiers, are compared against the TabNet classifier, which is a DL architecture customized for tabular data analysis and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. SVMs achieve performances, in terms of AUC, around 75%, compared to the best TabNet and MLP results, which are 65% and 71%, respectively. Additionally, this work investigates the brain regions that contribute most to the classification task, which are found to be those primarily responsible for sensory and spatial perception, as well as attention modulation, known to be altered in ASD.

Diabetes becomes a life threatening non-communicable disease in the world, according to International Diabetic Federation (IDF) in 2023, an estimated 537 million adults (20-79 years) are living with diabetes, which is equivalent to 9.3% of the global adult population. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is a persistent metabolic condition marked by increased levels of glucose in the bloodstream. It is a significant global health concern, affecting millions of individuals worldwide. It having the symptoms of drowsiness for the whole day if not properly examined or treated well. It is targeted to spread over the younger age community and the number is growing in the exponential fashion. Even day to day many advancements have come from the researcher to diagnose, to find solutions to prevent from newer entry. Authors have taken a dataset with 57 non diabetic and 20 diabetic patients with the total 28735 micro array gene to undergone pre-processing process and reduced up to 22960 gene data using Dimensionality Reduction (DR) such as Detrend Fluctuation Analysis (DFA), Chi square probability density function (Chi2PDF), Firefly algorithm Cuckoo search were used in this research. Meta heuristic algorithms like Particle swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Further seven classification techniques such as Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), Support Vector Machine – Radial Basis Function (SVM-RBF) are using to make a decision, predictive analysis and segregate the data according to the level of blood glucose as Diabetic Patient (DP) and Non-Diabetic Patient (NDP).

With the exponential growth of internet usage and mobile devices, the volume of user behavior data has surged, offering significant opportunities and challenges for businesses. This paper explores user behavior analysis in the context of big data, highlighting its pivotal role in driving innovation and strategic decision-making. We begin by discussing the importance of user behavior data and reviewing technological advancements such as machine learning, natural language processing, and data mining that enable deep insights into user behavior. Through a case study on linguistic analysis in livestreaming e-commerce, we demonstrate how text-mining techniques can correlate linguistic characteristics with sales performance, providing actionable marketing insights. We further explore the broader business applications, including personalized recommendations, user portraits, product design, and strategic decision-making. Technical and ethical challenges, such as data integration, privacy concerns, and algorithmic bias, are also addressed. The paper concludes with a discussion on future trends, such as AI and ML advancements, edge computing, IoT proliferation, and ethical AI frameworks, poised to shape user behavior analysis. Our findings underscore the transformative potential of user behavior data in enhancing business operations and user experiences, providing recommendations for future research focused on real-time analytics, privacy-preserving techniques, and ethical implications of data-driven decisions.

In this paper, various machine learning techniques are discussed. These algorithms are used for many applications which include data classification, prediction, or pattern recognition. The primary goal of machine learning is to automate human assistance by training an algorithm on relevant data. This paper should also serve as a collection of various machine learning terminology for easy reference.

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML) built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future, uncertain causally-linked benefits. This multi-period, causal-linked process incorporating certainty and uncertainty replaces the single period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modelling and real world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. Causal coupling can also be applied at the macroeconomic level to gauge the effectiveness of policies that deliver various levels of cost and benefit coupling for individuals. With the growing interest in natural experiments in statistics and CML across many fields, such as healthcare, economics and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision making models that focus only on rationality, bounded to various degrees.

The importance of energy systems and its role in economics and politics is not hidden for anyone. This issue is not only important for the advanced industrialized countries, which are major energy consumers, but is also important for oil-rich countries. In addition to the nature of these fuels which contains polluting substances, the issue of their ending up has aggravated the growing concern. Biofuels can be used in different fields for energy production like electricity production, power production or for transportation. Various scenarios have been written about the estimated biofuels from different sources in the future energy system. The availability of biofuels for the electricity market, heating and liquid fuels is very important. Accordingly, the need for handling, modelling, decision making and future forecasting for biofuels can be one of the main challenges for scientists. Recently, machine learning and deep learning techniques have been popular in modeling, optimizing and handling the biodiesel production, consumption and its environmental impacts. The main aim of this study is to evaluate the ML and DL techniques developed for handling biofuels production, consumption and environmental impacts, both for modeling and optimization purposes. This will help for sustainable biofuel production for the future generations.

Automated Machine Learning (AutoML) is revolutionizing how businesses utilize data, making advanced analytics accessible to a broader range of organizations. By automating complex tasks like data preprocessing, model selection, and hyperparameter tuning, AutoML reduces the time and resources needed to develop and deploy machine learning models. This accelerates decision-making and enables quicker responses to market changes. AutoML empowers businesses to build accurate predictive models using sophisticated algorithms, optimizing model performance for reliable insights and better outcomes. A key advantage of AutoML is its accessibility; even organizations without a dedicated data science team can leverage machine learning, reducing technical barriers and democratizing innovation. As businesses grow, AutoML scales to handle larger datasets and more complex problems without extensive manual intervention. AutoML enhances efficiency, accuracy, and scalability, becoming a crucial driver of business innovation and success. The systematic review will examine the bibliometric literature on how AutoML can boost business, analyzing 74 academic and scientific documents from the Scopus database.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

Deep learning is an emerging area of machine learning (ML) research. It comprises multiple hidden layers of artificial neural networks. The deep learn- ing methodology applies nonlinear transformations and model abstractions of high level in large databases. The recent advancements in deep learning architec- tures within numerous fields have already provided significant contributions in artificial intelligence. This article presents a state of the art survey on the contri- butions and the novel applications of deep learning. The following review chron- ologically presents how and in what major applications deep learning algorithms have been utilized. Furthermore, the superior and beneficial of the deep learning methodology and its hierarchy in layers and nonlinear operations are presented and compared with the more conventional algorithms in the common applica- tions. The state of the art survey further provides a general overview on the novel concept and the ever-increasing advantages and popularity of deep learning.

Skin disease is a very common disease for humans. In the medical industry detecting skin disease and recognizing its type is a very challenging task. Due to the complexity of human skin texture and the visual closeness effect of the diseases, sometimes it is really difficult to detect the exact type. Therefore, it is necessary to detect and recognize the skin disease at its very first observation. In today's era, artificial intelligence (AI) is rapidly growing in medical fields. Different machine learning (ML) and deep learning(DL) algorithms are used for diagnostic purposes. These methods drastically improve the diagnosis process and also speed up the process. In this paper, a brief comparison between the machine learning process and the deep learning process was discussed. In both processes, three different and popular algorithms are used. For the machine Learning process Bagged Tree Ensemble, K-Nearest Neighbor (KNN), and Support Vector Machine(SVM) algorithms were used. For the deep learning process three pre-trained deep neural network models

Bixonimania, a rare hyperpigmentation disorder characterized by a distinctive pink hue on the eyelids, presents a significant diagnostic challenge due to its unique presentation and limited research. This study explores the application of machine learning algorithms for bixonimania detection by analyzing millions of images of individuals exposed to blue light (500-700 nm spectrum). Our findings suggest a potential link between blue light exposure and bixonimania, affecting roughly 1 in 90,000 individuals. The proposed machine learning model achieved an accuracy of 92% in detecting bixonimania based on eyelid hue analysis. This study paves the way for further exploration of bixonimania's underlying causes, improved diagnostic tools, and potential treatment options.

Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models has been contributing to risk reduction, policy suggestion, minimizing loss of human life and reducing the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods have highly contributed in the advancement of prediction systems providing better performance and cost effective solutions. Due to the vast benefits and potential of ML, its popularity has dramatically increased among hydrologists. Researchers through introducing the novel ML methods and hybridization of the existing ones have been aiming at discovering more accurate and efficient prediction models. The main contribution is to demonstrate the state of the art of ML models in flood prediction and give an insight over the most suitable models. The literature where ML models are benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed have been particularly investigated to provide an extensive overview on various ML algorithms usage in the field. The performance comparison of ML models presents an in-depth understanding about the different techniques within the framework of a comprehensive evaluation and discussion. As the result, the paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported the most effective strategy in improvement of the ML methods. This survey can be used as a guideline for the hydrologists as well as climate scientists to assist them choosing the proper ML method according to the prediction task conclusions.

The Transformer architecture, while adept at capturing context through self-attention, falls short in encapsulating complex syntactic structures effectively. Addressing this gap, we introduce the Linguistic Structure through Graphical Interpretation with BERT (LSGIB) approach in Machine Translation (MT) frameworks. Combining the strengths of Graph Attention Network (GAT) and BERT, LSGIB intricately captures syntactic dependencies as explicit knowledge from the source language. This enhances the source language representation and aids in more accurate target language generation. Our empirical analysis leverages gold-standard syntax-annotated sentences and employs a Quality Estimation (QE) model. This approach enables us to assess translation improvements in terms of syntactic accuracy, extending beyond traditional BLEU score metrics. The LSGIB model demonstrates superior translation quality across diverse MT tasks, maintaining robust BLEU scores. Our study delves into the optimal sentence lengths benefiting from LSGIB and identifies which syntactic dependencies are more precisely captured. We observe that GAT's ability to learn specific dependency relations directly influences the translation quality of sentences with those relations. Additionally, we discover that incorporating syntactic structure into BERT's intermediate and lower layers offers a novel approach to modeling linguistic structure in source sentences.

To support the decision making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications. Three artificial intelligence techniques were implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The results of the different algorithms were compared to each other using the metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.

Biofuels construct an essential pillar of energy systems. Biofuels are considered as a popular resource for electricity production, heating, household, and industrial usage, liquid fuels, and mobility around the world. Thus, the need for handling, modeling, decision-making, demand, and forecasting for biofuels are of utmost importance. Recently, machine learning (ML) and deep learning (DL) techniques have been accessible in modeling, optimizing, and handling biofuels production, consumption, and environmental impacts. The main aim of this study is to review and evaluate ML and DL techniques and their applications in handling biofuels production, consumption, and environmental impacts, both for modeling and optimization purposes. Hybrid and ensemble ML methods, as well as DL methods, have found to provide higher performance and accuracy in modeling the biofuels.

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions of Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, and clinical variables impact patient perceptions of service quality, influencing their healthcare choices. Through the analysis of mobility trends with machine learning, significant differences between regions were uncovered, highlighting the influence of regional context on perceived quality. The integration of SHAP (SHapley Additive exPlanations) values into our analysis provided deeper insights into the logistic regression model, elucidating the specific contribution of each variable to perceived healthcare quality. This incorporation of SHAP values underscores the study's commitment to employing advanced, explainable AI techniques to enhance the interpretability and fairness of healthcare service evaluations. The choice of logistic regression elucidated the impact of specific variables on quality perception, offering essential insights for optimizing healthcare resource distribution and underscoring the importance of data-driven strategies to foster more equitable, efficient, and patient-centred healthcare systems. Contributing to the understanding of perceived quality dynamics within the healthcare context, the research paves the way for further investigations into enhancing accessibility and service quality, leveraging machine learning as a tool for improving healthcare services efficiency in diverse regional settings.

Non-convex optimization is a critical tool in advancing machine learning, especially for complex models like deep neural networks and support vector machines. Despite challenges such as multiple local minima and saddle points, non-convex techniques offer various pathways to reduce computational costs. These include promoting sparsity through regularization, efficiently escaping saddle points, and employing subsampling and approximation strategies like stochastic gradient descent. Additionally, non-convex methods enable model pruning and compression, which reduce the size of models while maintaining performance. By focusing on good local minima instead of exact global minima, non-convex optimization ensures competitive accuracy with faster convergence and lower computational overhead. This paper examines the key methods and applications of non-convex optimization in machine learning, exploring how it can lower computation costs while enhancing model performance. Furthermore, it outlines future research directions and challenges, including scalability and generalization, that will shape the next phase of non-convex optimization in machine learning.

Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems and has enjoyed significant interest from the research community. This paper discusses the recent critical advancements in the PIML domain. Novel methods and applications of domain decomposition in physics-informed neural networks (PINN) in particular are highlighted. Additionally, we explore recent Works toward utilizing neural operator learning to intuit relationships in physics systems traditionally modeled by sets of complex governing equations and solved with expensive differentiation techniques. Finally, expansive applications of traditional physics-informed machine learning and potential limitations are discussed. In addition to summarizing recent work, we propose a novel taxonomic structure to catalog physics-informed machine learning based on how the physics-information is derived and injected into the machine learning process. The taxonomy assumes the explicit objectives of facilitating interdisciplinary collaboration in methodology, thereby promoting a wider characterization of what types of physics-problems are served by the physics-informed learning machines, and assisting in identifying apt targets for future work. To summarize, the major twofold goal of this work is to summarize recent advancements and introduce a taxonomic catalog for applications of physics-informed machine learning.

Abstract This study delved into the application of various machine learning (ML) models for the classification of lung cancer levels. Through meticulous monitoring of parameters such as minimum child weight and learning rate, efforts were made to mitigate overfitting while optimizing model performance. The Deep Neural Network (DNN) emerged as a standout performer, showcasing robust performance across training, validation, and testing stages. Ensemble methods like voting and bagging also demonstrated promising results. However, Support Vector Machine (SVM) models with the Sigmoid kernel faced challenges in achieving satisfactory performance. Overall, the investigation sheds light on the efficacy of different ML models in lung cancer level classification and underscores the importance of parameter tuning to address overfitting concerns.

A long time has passed since COVID-19 was discovered and widely disseminated. Both machine learning and deep learning have moved towards the research of diagnosing COVID-19. Compared to deep learning, traditional machine learning-based methods can also achieve good diagnostic results if they are improved based on innovative points. And deep learning remains a more popular research object. Based on the deep neural network in the field of deep learning, the convolutional neural network, recurrent neural network, long and short-term memory network, and Transformer model have been extended. These models improve the performance and processing power of neural networks by introducing new structures and algorithms. This paper will introduce the basic concepts of machine learning and deep learning, as well as the details of using the relevant methods of both to help diagnose COVID-19.

Portable Document Format (PDF) is one of the most widely used files types worldwide in data exchange, this has encourage hackers to utilize such files to spread any malicious content through PDF, utilizing different methods and techniques to accomplish that, on the other hand, security researches kept trying to improve detection methods to cope up to the rapidly increasing number of malwares daily, one of the commonly used detection technique nowadays is by utilizing artificial intelligence and Machine learning classificat; thision to help detecting PDF Malwares, in this paper, we utilize machine learning classifier Random Forest on a newly released PDF Malware dataset CIC-Evasive-PDFMal2022 to achieve the main goal of detecting malicious PDF documents, results showing a detection accuracy of around 99.5%

Machine Learning (ML) and Artificial Intelligence(AI) have revolutionized almost all fields that are linked to the acquisition of intelligent behavior in the real world. It is an attractive alternative for a researcher of artificial intelligence. Contrary to rule-based programming, ML is an algorithmic approach in which learning comes from existing data. The more data we have these computer systems look at, we say we’re ‘training’ the computer system, and as the computers begin to identify patterns in the data, identify abnormalities in the data from these abnormalities we improve the system architect according to the requirement. This article introduces the use of comprehensive concepts of machine learning, in general, particular, and their potential applications in communications. Furthermore, the current state and futuristic potentials of enabling universal communication with implications of machine learning methods have been explained. In this review paper, we offer a comprehensive talk on distinctive methods/techniques of information analytics, artificial intelligence (AI), and machine learning (ML) moved forward the contact aware communication system.

Artificial Intelligence (AI) is becoming increasingly important, especially in the medical field. While AI has been used in medicine for some time, its growth in the last decade has been remarkable. Specifically, Machine Learning (ML) and Deep Learning (DL) techniques in medicine have been increasingly adopted thanks to the growing abundance of health-related data, improved suitability of such techniques for managing large data-sets, and more computational power. The Italian scientific community has been instrumental in advancing this research area. This article aims to conduct a comprehensive investigation of the ML and DL methodologies and applications used in medicine by the Italian research community in the last five years.

This paper presents a novel method for automatic modulation classification (AMC) for cognitive radio (CR) networks based on a simple classifier that is trained with high-order cumulant. The proposed method focuses on the statistical behavior of modulated signals and includes analog modulation and digital schemes, which received less attention in the literature. The effectiveness of the proposed method is demonstrated through simulation results using high-quality generated signals under different signal-to-noise ratios (SNRs) and channel conditions. The classification performance achieved by the proposed method is superior to that of the more complex deep learning methods, making it well-suited for deployment in end units of CR networks, particularly in military and emergency service applications. The proposed method offers a cost-effective, high-quality solution for AMC that meets the stringent requirements of these critical applications.

Nowadays, there is extensive production and use of plastic materials for different industrial activities. These plastics, either from their primary production sources or through degradation processes of the plastics themselves, can contaminate the ecosystem with micro and nanoplastics. Once in the aquatic environment, these microplastics can be the basis for the adsorption of chemical pollutants, favoring that these chemical pollutants disperse more quickly in the environment and can affect living beings. Due to the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) to predict different microplastic/water partition coefficients (log Kd) were developed using two different approximations (based on the number of input variables). The best-selected machine learning models present, in general, correlation coefficients upper than 0.92 in the query phase, which indicate that these type of models could be used for a rapid estimation of the absorption of organic contaminants on microplastics.

Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

Cricket, the most popular sports in the world to chase by the fans. Many cricket fans are interested in analyzing the elements and activities that affect the outcomes of matches. Machine learning in sports analytics is a relatively new topic in computer science. The purpose of this survey is to anticipate the outcome of a PSL champion team and build a winning strategy. Machine learning (ML) has exhibited favorable outcomes in a variety of sectors for diverse prediction using classification, regression, and so on, and is proven to be accurate. The innovative frameworks based on ML have the ability to learn from previous experiences. The cricket pitch is the most important aspect, alongside home game an edge, coin toss, innings of play, day/night match, physical conditioning, and dynamic plans, among other things. The dataset from previously ESPN Cricifno was used for this purpose. Recent classification approaches including, Random Forest, Gradient Boost, Deep Neural Networks used to conduct a comparative analysis based on their results and performances, as well as team strategies.

Maintaining the judiciary's power requires a high level of budgetary expenditure, but the specifics of this relationship have yet to be fully explored. Although several studies have analyzed the impact of spending in the judiciary through productivity and performance-related measures, none have employed machine learning techniques. This study examines the productivity of the judiciary based on spending and other variables using machine learning techniques, including clustering and neural networks. The final neural network model supports the results of Pearson's parametric correlation test, which found no significant correlation between expenditure and productivity. This study's findings demonstrate the importance of understanding that increased public budgetary expenditure alone is insufficient for improving the judiciary's efficiency. Instead, other administrative and technical measures are necessary to meet the demands of the Brazilian judiciary and improve service delivery rates. These findings offer important theoretical and managerial contributions to the field.

In higher education, the cultivation of research competency is pivotal for students’ critical thinking development and their subsequent transition into the professional workforce. While statistics plays a fundamental role in supporting the completion of a research project, it is often perceived as challenging, particularly by students in majors outside mathematics or statistics. The connection between students’ statistical proficiency and their research competency remains unexplored despite its significance. To address this gap, we utilized the supervised machine learning approach to predict students’ research competency, as represented by their performance in a research methods class, with predictors of students’ proficiency in statistical topics. Predictors relating to students’ learning behavior in a statistics course such as assignment completion and academic dishonesty are also included as auxiliary variables. Results indicate that the three primary categories of statistical skills—namely, understanding of statistical concepts, proficiency in selecting appropriate statistical methods, and statistics interpretation skills— can be used to predict students’ research competency, as demonstrated by their final course scores and letter grades. This study advocates for strategic emphasis on the identified influential topics to enhance efficiency in developing students’ research competency. Findings could inform instructors in adopting a strategic approach to teaching the statistical component of research for enhanced efficiency.

During a multi-detector computed tomography (MDCT) examination, it is crucial to efficiently organize, store, and transmit medical images in DICOM standard, which requires significant hardware resources and memory. Our project processed large amounts of DICOM images by classifying them based on cross-section views that may carry important information about a possible diagnosis. We ensured that images were retained and saved in PNG format to optimize hardware resources while preserving patient confidentiality. Furthermore, we have developed a graphical, user-friendly interface that allows physicians to visualize specific regions of interest in a patient's brain where changes may indicate disease. Our proposed method enables quick classification of medical images into predefined classes of confirmed diseases of brain parenchyma, contributing to swift decision-making for further diagnosis for more precisely evaluating and characterizing brain changes, and it can lead to the rapid application of adequate therapy, which may result in better outcomes.

The exponential growth of biomedical data in recent years urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling automatic feature extraction, selection and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology and disease genomics. We outline the challenges posed for machine learning, and in particular, deep learning in biomedicine and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Supervised machine learning (SML) is a powerful method for predicting a small number of well-defined output groups (e.g., potential buyers of a certain product) by taking as input a large number of known well-defined measurements (e.g., past purchases, income, ethnicity, gender, credit record, age, favorite color, favorite chewing gum). SML is predicated upon the existence of a training dataset in which the correspondence between the input and output is known to be true. SML has had enormous success in the world of commerce, and this success may have prompted a few scientists to employ it in the study of molecular and genome evolution. Here, we list the properties of SML that make it an unsuitable tool in certain evolutionary studies. In particular, we argue that SML cannot be used in an evolutionary exploratory context for the simple reason that training datasets that are known to be a priori true do not exist. As a case study, we use an SML study in which it was concluded that most human genomes evolve by positive selection through soft selective sweeps (Schrider and Kern 2017). We show that in the absence of legitimate training datasets, Schrider and Kern (2017) used (1) simulations that employ many manipulatable variables and (2) a system of cherry-picking data that would put to shame most modern evangelical exegeses of the Bible. These two factors, in addition to the lack of methodological detail and negative controls, lead us to conclude that all evolutionary inferences derived from so-called SML algorithms (e.g., S-HIC) should be taken with a huge shovel of salt.

A key component for the proper functioning, availability, and reliability of power grids is the power trans- former. Although these are very reliable assets, the early detection of incipient degradation mechanisms are very important to prevent failures that may shorten their lifetime. In this work a review and comparative analysis, of classical Machine Learning algorithms (such as single and ensemble classification algorithms) and two automatic machine learning classifiers, are presented for the fault diagnosis of power transformers. The goal is to determine whether fully automated ML approaches are worse or better than traditional ML frameworks that require a human in the loop (such as a data scientist) to identify transformer faults from oil-dissolved gases data. The methodology uses a DB compiled from published and proprietary transformer fault samples. Faults data is obtained from the literature, the Duval pentagon method, and user-expert knowledge. The parameters from, either single or ensemble classifiers, were optimized through standard machine learning procedures. The results showed that the best performing algorithm is a robust automatic machine learning classifiers model followed by classical algorithms such as neural networks and stacking ensembles. These results highlight the ability of a robust automatic machine learning model for, handling imbalanced power transformers faults datasets with high accuracy, using the minimum tuning effort by the electrical experts. By providing the most probable transformer fault condition will reduce the time required to find and solve the fault.

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Tremendous progress has been witnessed in artificial intelligence within the domain of neural network backed deep learning systems and its applications. As we approach the post Moore’s Law era, the limit of semiconductor fabrication technology along with a rapid increase in data generation rates have lead to an impending growing challenge of tackling newer and more modern machine learning problems. In parallel, quantum computing has exhibited rapid development in recent years. Due to the potential of a quantum speedup, quantum based learning applications have become an area of significant interest, in hopes that we can leverage quantum systems to solve classical problems. In this work, we propose a quantum deep learning architecture; we demonstrate our quantum neural network architecture on tasks ranging from binary and multi-class classification to generative modelling. Powered by a modified quantum differentiation function along with a hybrid quantum-classic design, our architecture encodes the data with a reduced number of qubits and generates a quantum circuit, loading it onto a quantum platform where the model learns the optimal states iteratively. We conduct intensive experiments on both the local computing environment and IBM-Q quantum platform. The evaluation results demonstrate that our architecture is able to outperform Tensorflow-Quantum by up to 12.51% and 11.71% for a comparable classic deep neural network on the task of classification trained with the same network settings. Furthermore, our GAN architecture runs the discriminator and the generator purely on quantum hardware and utilizes the swap test on qubits to calculate the values of loss functions. In comparing our quantum GAN, we note our architecture is able to achieve similar performance with 98.5% reduction on the parameter set when compared to classical GANs. With the same number of parameters, additionally, QuGAN outperforms other quantum based GANs in the literature for up to 125.0% in terms of similarity between generated distributions and original data sets.

Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

With the evolution of computing technology in many application like human robot interaction, human computer interaction and health-care system, 3D human body models and their dynamic motions has gained popularity. Human performance accompanies human body shapes and their relative motions. Research on human activity recognition is structured around how the complex movement of a human body is identified and analyzed. Vision based action recognition from video is such kind of tasks where actions are inferred by observing the complete set of action sequence performed by human. Many techniques have been revised over the recent decades in order to develop a robust as well as effective framework for action recognition. In this survey, we summarize recent advances in human action recognition, namely the machine learning approach, deep learning approach and evaluation of these approaches.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

In this paper, a seamless connection between sensor technology and machine learning (ML) has been presented which has wide applicability in rehabilitation and physical medicine. The intent is to identify an ML algorithm which has a better performance, easy to deploy, and has less training time and consumes less computational resources. In short, we propose an algorithm which maximizes the synergy between a sensor and ML by maximizing classification performance and easy to do implementation, which is robust in computational power and lower in resource consumption. The premise of the work is to show the important considerations for evaluating ML algorithms for Upper-limb activity recognition for Physical medicine and rehabilitation field.

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1,139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-layer Perceptron (MLP) models is the best model to predict the cure class.

significant security threats to Ransomware attacks provide serious security hazards to personal and corporate data and information. The owners of computer-based resources suffer serious verification and privacy violations, monetary losses, and reputational damage due to a successful ransomware assault. As a result, it is reported critically, accurately, and swiftly identifying ransomware. Numerous methods have been proposed for ransomware, each with pros and cons. The main objective of this study is to discuss current trends and potential future debates on automated ransomware detection. The document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides a comprehensive study of existing methods for identifying, avoiding, minimizing, and recovering from ransomware. An analysis of studies between 2017 and 2022 is another advantage of the study. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection. It also highlights advancements in methods for combating ransomware attacks. In conclusion, this study highlights unanswered concerns and potential research challenges in ransomware detection.

The calculation of effective doses is crucial in many medical and radiation fields, in order to ensure safety and compliance with regulatory limits. Traditionally, Monte Carlo codes using detailed human body computational phantoms have been used for such calculations. Monte Carlo dose calculations can be time-consuming and require expertise in different processes when building the computational phantom and dose calculations. This study employs various machine learning (ML) algorithms to predict the organ doses and effective dose conversion coefficients (DCCs) for different anthropomorphic phantoms. A comprehensive data set comprising neutron energy bins, organ labels, masses, and densities is compiled from Monte Carlo studies, and is used to train and evaluate the supervised ML models. This study includes a broad range of phantoms, including those from the International Commission of Radiation Protection (ICRP-110, ICRP-116 phantom), the Visible-Human Project (VIP-man phantom), and the Medical Internal Radiation Dose Committee (MIRD-Phantom), with row data prepared using numerical data and organ categorical labeled data. Extreme gradient boosting (XGB), gradient boosting (GB), and the random forest-based Extra Trees regressor are employed to assess the performance of the ML models against published ICRP neutron DCC values using the mean square error, mean absolute error, and R² metrics. The results demonstrate that the ML predictions significantly vary in lower energy ranges and vary less in higher neutron energy ranges, while showing good agreement with ICRP values at mid-range energies. Moreover, the categorical data models align closely with the reference doses, suggesting the potential of ML in predicting effective doses for custom phantoms based on regional populations, such as the Saudi voxel-based model. This study paves the way for efficient dose prediction using ML, particularly in scenarios requiring rapid results without extensive computational resources or expertise. The findings also indicate potential improvements in data representation and the inclusion of larger data sets to refine model accuracy and prevent overfitting. Thus, ML methods can serve as valuable techniques for the continued development of personalized dosimetry.

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

The real estate market is exposed to many fluctuations in prices, because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem, that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, $k$-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.

Numerous new technologies have been implemented in image analysis methods that help researchers withdraw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Here is the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. Results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures As expected, machine learning methods applied to digital image analysis can overtake the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their great nutritional qualities and biological plasticity.

This paper presents a Multilayer Perceptron and Support Vector Machine algorithms approach to predict the number of COVID19 infections in different countries of America. It intends to serve as a tool for decision-making and tackling the pandemic that the world is currently facing. The models were trained and tested using open data from the European Union repository where a time series of confirmed contagious cases was modeled until May 25, 2020. The hyperparameters as number of neurons per layer were set up using a tabu list algorithm. The countries selected to carry out the study were Brazil, Chile, Colombia, Mexico, Peru and the United States. The metrics used are Pearson's correlation coefficient (CP), Mean Absolute Error (MAE), and Mean Percentage Error (MPE). For the testing stage we obtained the following results: Brazil, CP=0.65, MAE=2508 and MPE=17%; Chile, CP=0.64, MAE=504, MPE=16%; Colombia, CP=0.83, MAE=76, MPE=9%; Mexico, CP=0.77, MAE=231, MPE=9%; Peru, CP=0.76, MAE=686, MPE=18% and the United States of America, CP=0.93, MAE=799, MPE=4%. This resulted in powerful machine learning tools although it is necessary to use specific algorithms depending on the data and the stage of the country’s pandemic.

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, the authors include some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this is mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink,Google Scholar. The literature search was conducted upto January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd.

In this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.

This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These data sets include BI (bodily injury), PD (property damage) and COLL (collision) coverage, each documenting policy characteristics and claims across a four year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. All of the code will be made available on GitHub. Data was provided by a private insurance carrier to the CAS after anonymizing the data set. This data is available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public. Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.

The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.

Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, more specifically with higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. The recent advances in technology and algorithms gave rise to the computer-aided diagnosis (CAD) of PCa. With the availability of medical image datasets and emerging trends in state-of-the-art machine and deep learning techniques, there is a growth in recent related publications. Materials and Methods: In this study, we present a systematic review of PCa diagnosis with medical images using machine learning and deep learning techniques. We conducted a thorough review through relevant studies indexed in four databases (IEEE, PubMed, Springer and ScienceDirect) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. With well-defined search terms, a total of 608 articles were identified and 77 met the final inclusion criteria. Key elements in the included papers were presented and conclusions were drawn from them. Results: Findings showed that the United States has the most research in PCa diagnosis with machine learning, Magnetic Resonance Images are the most used datasets and transfer learning is the most used method of diagnosing PCa in recent times. In addition, some available PCa datasets and some key considerations for choice of loss function in the deep learning models were presented. The limitations and lessons learnt were discussed and some key recommendations were made. Conclusion: The discoveries and the conclusions of this work have been organized so as to enable researchers in the same domain to use this work and make crucial implementation decisions.

The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a reflection is developed on the dual attention network that applies the learning equations and coordinates subnetworks toward purposeful behaviors. Speculation is made on possible developments of functionalities that could provide additional skills and at the same time shed light on competencies typical of ``natural intelligence".

Advanced Persistent Threats (APTs) are sophisticated cyberattacks aimed at stealing sensitive information or causing damage over an extended period. Detecting APTs is crucial for maintaining cybersecurity, and machine learning (ML) has emerged as a powerful tool in this domain. This paper explores the role of ML algorithms in detecting APTs, comparing their accuracy and effectiveness. We evaluate various algorithms, discuss the challenges in achieving superior accuracy, and suggest strategies for improvement. Our findings highlight the potential and limitations of ML in APT detection, emphasizing the need for continuous advancements in this field.

This project focuses on using machine learning classification algorithms to determine whether two people are 6 feet apart or not. Two Raspberry Pis were used simulate smart phones. RSSI values of the Bluetooth beacons transmitted between the Raspberry Pis were collected and recorded to train the classifier. The Gaussian Support Vector Machine Classifer yielded the highest testing accuracy of 79.670 and the Decision Tree Classifier yielded the highest AUC of 0.80.

In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

Abstract: Online services have transformed into a boon for various industries and globalization as a whole. One of the ventures reliant upon the Web is finance. Pattern prediction and risk reduction in the stock market and other financial decisions are made possible by some technologies. These technologies promise to expand innovation, knowledge, and efficiency opportunities in the financial sector. Finance frequently uses machine learning technology to identify risks based on probabilistic statistics and historical data to support investment decisions. Additionally, it can be utilized for risk assessment and risk management planning. Internet technology, which enables real-time data analysis, streamlined transactions, and improved customer communication, has particularly benefited the financial sector. However, these advancements come with significant challenges, such as the need for robust online credit protection and security threats. This study looks at how advanced machine learning methods are used in the financial sector and focuses on how they have changed credit scoring, algorithmic trading, and predictive modeling.

Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data is made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist’s perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.

Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support have greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth. Numerous studies have elucidated the mechanistic details of circumnutation, but how this phenomenon is controlled remains unclear. Here, we use supervised machine learning algorithms to generate models that learn to discriminate between the presence/absence of a support in the environment. Results indicate that there is a difference in the pattern of circumnutation, depending on the presence of a support, that can be learned and classified rather accurately. We also identify distinctive kinematic features that contribute to the classification tasks. Overall, machine learning approaches appear to be powerful tools for understanding the movement of plants.

The purpose of this paper is to develop a theoretical predictive model for LNG shipping routes selection process. Strategic decisions about shipping costs could be improved if a deeper knowledge about products economic value is provided. Developments made on the extraction and industrial processes related to this fossil fuel are driving the natural gas sector towards a unique globalised market. Moreover, data analytics applications as well as machine learning are topics presented as perfect catalysers for achieving an unprecedented natural gas globalised market. Additionally, this paper aims at showing the state of the art of new techniques used in transportation engineering that might have synergies with other industries (eg. commodities cost reduction, energy supply…). Finally, this paper aims to provide foundation for further research and development using more sophisticated data and algorithms that will help to close the gap between theoretical and practical scope of this techniques.

Machine learning (ML) has emerged as a critical tool for making sense of the growing amount of genetic and genomic data available because of its ability to find complex patterns in high dimensional and heterogeneous data. While the complexity of ML models is what makes them powerful, it also makes them difficult to interpret. Fortunately, recent efforts to develop approaches that make the inner workings of ML models understandable to humans have improved our ability to make novel biological insights using ML. Here we discuss the importance of interpretable ML, different strategies for interpreting ML models, and examples of how these strategies have been applied. Finally, we identify challenges and promising future directions for interpretable ML in genetics and genomics.

Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7,389 participants and 53 variables. Initially, unsupervised K-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (Light-GBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised K-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery.

The proliferation of fake news on social media platforms poses significant challenges to information authenticity and public trust. In this study, the impact of various word embedding techniques on the performance of both machine learning and deep learning models for fake news detection is systematically evaluated. The comprehensive TruthSeeker dataset, which spans over a decade of labeled news articles and social media posts, is utilized. Three popular word embedding techniques are analyzed: TF-IDF, Word2Vec, and FastText. These embeddings were tested with multiple machine learning classifiers, including logistic regression, naive Bayes, K-nearest neighbors, support vector machines, multilayer perceptrons, decision trees, and random forests, as well as deep learning models, specifically convolutional neural networks (CNNs). Among machine learning models, the Support Vector Machine (SVM) with TF-IDF achieved the highest accuracy of 99.03%, precision of 98.92%, recall of 99.15%, and F1 score of 99.03%. The Multilayer Perceptron (MLP) also demonstrated strong performance with Word2Vec, reaching an accuracy of 95.24%, precision of 94.95%, recall of 95.50%, and F1 score of 95.07%. For deep learning models, CNNs combined with TF-IDF embeddings showed outstanding results, achieving an accuracy of 98.99%, precision of 98.56%, recall of 99.48%, and F1 score of 99.02%. These results highlight the critical role of word embeddings in enhancing model accuracy and reliability in misinformation detection.

In this paper, we study the usage of machine learning models for sales time series forecasting. The effect of machine learning generalization has been considered. A stacking approach for building regression ensemble of single models has been studied. The results show that using stacking technics, we can improve the performance of predictive models for sales time series forecasting.

Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins have become invaluable pharmacological probes, drug leads and therapeutics. Transcriptome sequencing of Conus venom glands followed by de novo assembly and homology-based toxin identification and annotation is currently the state-of-the-art for discovery of new conotoxins. However, homology-based search techniques, by definition, can only detect novel toxins that are homologous to previously reported conotoxins. To overcome these obstacles for discovery we have created ConusPipe, a machine learning tool that utilizes prominent chemical characters of conotoxins to predict whether a certain transcript in a Conus transcriptome, which has no otherwise detectable homologs in current reference databases, is a putative conotoxin. By using ConusPipe on RNASeq data of 10 species, we report 5,230 new putative conotoxin transcripts that have no homologues in current reference databases. 893 of these were identified by at least 3 out of 4 models used. These data significantly expand current publicly available conotoxin datasets and our approach provides a new computational avenue for the discovery of novel toxin families.

This research developed and evaluated a machine learning model for predicting thunderstorm frequency, in the Wa region of Ghana, with a focus on its impact for agricultural planning and management. Using a range of meteorological variables including Total Column Water(TCW), Total Column Rain Water(TCRW), and Convective Available Potential Energy (CAPE), the research aim to identify important predictors of thunderstorm occurrence and quantify their relative importance. A Random Forest algorithm was employed to create the predictive model, which was trained and tested on historical weather data. The model demonstrated good predictive capabilities, explaining approximate 72.6% of the variance in thunderstorm occurrences, with a mean absolute error of 2.34 storms and an index of agreement of 0.926. Key findings showed the importance of atmospheric moisture content, particularly TCW and TCRW , in predicting thunderstorm frequency. Atmospheric instability measures, such as CAPE, played a secondary but important role. The model showed strength in capturing overall trends in thunderstorm frequencies but exhibited some limitations in predicting extreme events. The research contributes to the field of meteorology by demonstrating the effectiveness of machine learning techniques in capturing complex atmospheric interactions leading to thunderstorm formation. It also provides a framework for linking thunderstorm predictions to potential agricultural impacts, enhancing the practical applicability of weather forecasting in the agricultural sector. The study lays the groundwork for more sophisticated, localized weather predictions systems that can greatly benefit agricultural planning and broader weather dependent activities in the region. Future research dimensions include exploring advanced feature engineering, integrating temporal and spatial analysis, and developing agricultural impacts models to further enhance the practical utility of thunderstorm predictions.

In this paper, we propose methods for brain tumor detection in MRI images based on ensemble learning. We build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. The proposed approach achieved accuracy scores of 0.98 , outperforming a benchmark VGG-16 model. Considerations to granular computing are given in the paper as well.

This review explores the critical difficulties confronting global agriculture in the face of changing climate patterns and limited resources, highlighting the importance of sustainable practices. With agriculture as the largest consumer of water worldwide, sustainable techniques are critical for long-term survival and ecological harmony. Even with advancements, rapid population increase and changing environment conditions demand immediate investment in cutting-edge technologies. Optimizing agricultural operations, increasing output, and reducing resource waste can be achieved through smart farming, which utilizes machine learning and Internet of Things tech-nologies. Particular difficulties like decreasing cultivator proportions and rising costs are empha-sized, with a focus on developing nations where agriculture plays a crucial role in the economy. Additionally, the anticipated rise in food demand highlights how urgent it is to find sustainable alternatives. But problems like inappropriate land and agricultural land fragmentation still exist, necessitating creative solutions. The paper explores how agriculture could be revolutionized by machine learning and IoT-based hydroponics, providing insights into how to improve resilience, productivity, and sustainability in the face of changing environmental and socioeconomic dy-namics. Through a thorough investigation, this paper advances the discussion on sustainable ag-riculture by demonstrating the important role that technology-driven strategies play in addressing concerns about environmental sustainability and global food security.

Recent SDN advances address traditional network management challenges through centralized control and plane separation. SDN prevents breaches using a centralized controller but introduces risks. The controller can be a single point of failure. Thus, an OpenFlow Controller's flow-based anomaly detection enhances SDN security. Our research explored two OpenFlow intrusion detection methods. The first employed machine learning, NSL-KDD dataset, and feature selection, yielding 82% accuracy with random forest. The second combined deep neural networks with GRU-LSTM, achieving 88% accuracy using ANOVA F-Test and feature elimination. Experiments highlighted deep learning as superior for OpenFlow intrusion detection.

Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying machine learning models. Application-wise, we show how machine learning surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate machine learning models may hold the key to making the simulation of complex biological models possible using a typical desktop computer.

Supplier selection is an important part of supply chain management (SCM) for any organisation to achieve their objectives. The problem has attracted great interest from academics and practitioners. The selection process starts with determining the most important criteria out of a wide range. Many academic researchers apply multi-criteria decision-making (MCDM) techniques for supplier selection. However, the complexity of such approaches may increase significantly, especially when considering a large number of suppliers and selection criteria. This paper proposes an integrated approach combining machine learning classification with the Analytic Hierarchy Process (AHP) to select and evaluate the most suitable supplier. A Decision Tree (DT) classifier is used to select the most important criteria, instead of applying AHP on the complete set of criteria. The applicability of the approach is demonstrated using data from Libyan companies. Results show that decision trees can successfully lead to a most important subset of selection criteria, which would lead to a less complex application of AHP.

The present research project entitled "Development of a method for predicting the optimal maturity level of avocado using machine learning" aims to establish an accurate and efficient approach to assess the maturity of avocados using machine learning methodologies. This research specifically focuses on identifying the relevant physical and chemical characteristics of avocados, creating a dataset containing categorized images of maturity levels, and creating machine learning models capable of accurately predicting fruit maturity. The studies reveal that the application of machine learning, in particular convolutional neural networks and multisensor models, has the potential to transform the prediction of avocado maturity and thus improve product quality and customer satisfaction. The findings indicate that the proposed techniques achieve an accuracy rate of over 90%, demonstrating their viability for integration into mobile applications that can benefit growers and suppliers in their decision-making processes.

The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

Major depressive disorder (MDD) and generalized anxiety disorder (GAD) exert significant burdens on individuals and society, underscoring the importance of accurate predictions using advanced machine learning (ML) algorithms. Leveraging electronic health records (EHRs) and survey data, these algorithms offer potential in forecasting such mental health conditions. Yet, the precision of these predictions can be compromised by biases or inaccuracies inherent in subjective survey responses. In this research, we assess the reliability of four prominent ML algorithms—Random Forest, XGBoost, Logistic Regression, and Naïve Bayesian—in predicting MDD and GAD. Our dataset encompasses a rich array of information, from biomedical metrics and demographic details to self-reported survey insights. A focal point of our investigation is the algorithms' performance under scenarios with varying degrees of subjective response inaccuracies, such as memory recall biases or subjective interpretation. Our findings reveal that while all algorithms exhibit commendable accuracy with pristine survey data, their performance diverges when faced with erroneous or biased responses. Notably, XGBoost remains stable and excels in identifying true positive cases, even in the presence of such noise. These observations underscore the criticality of algorithmic resilience in mental health prediction, especially when relying on subjective data. The robustness of certain algorithms to noisy inputs positions them as more reliable choices for predicting mental health conditions based on self-reported data, emphasizing the need for careful algorithm selection in such contexts.

This article presents a review of current advances and future prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.

Tracking and early identification of suspected cases are essential to control and prevent potential COVID-19 outbreaks. One of the most popular techniques used to track this disease is the use of Infrared cameras to identify individuals with elevated body temperatures. However, they are limited by their inability to be implemented in open public settings such as public parks or even outdoor recreational centers. This limits their ability to effectively track possible COVID-19 patients as open public recreational places such as parks, concert venues and other public venues are hotspots for the spreading of the virus. Other technological solutions such as thermal scanners require an individual to perform the actual testing as they are not individual standalone technologies. This method of testing can potentially cause the transmission of the virus between the tester and the individual getting tested. As can be seen, an alternative solution is essential to solving this issue. In this study, we aim to present the system, design and potential scope of a non-invasive system that can diagnose and identify potential COVID-19 patients using thermal and optical images of the individual using drone technology. The proposed system (COVIDRONE) combines multi-modal machine intelligence, computer vision and real-time monitoring to enable scalable monitoring. The system will also involve the use of machine learning algorithms for better and more accurate diagnosis. We envisage that development of such technologies may help in developing technological solutions to combat infectious disease threats in the future pandemics.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

Energy demand forecasting is a fundamental aspect of modern energy management. It impacts resource planning, economic stability, environmental sustainability, and energy security. This importance is making it critical for countries worldwide, particularly in cases like Türkiye, where the energy dependency ratio is notably high. The goal of this study is to propose ensemble machine learning methods such as boosting, bagging, blending, and stacking with hyperparameter tuning and k-fold cross-validation, and investigate the application of these methods for predicting Türkiye's energy demand. This study utilizes population, GDP per capita, imports and exports as input parameters based on historical data from 1979 to 2021 in Türkiye. Eleven combinations of all predictor variables were analyzed, and the best one was selected. It was observed that a very high correlation exists among population, GDP, imports, exports, and energy demand. In the first phase, the preliminary performance was investigated of 19 different machine learning algorithms using 5-fold cross-validation and measured their performance using five different metrics: MSE, RMSE, MAE, R-squared, and MAPE. Secondly, ensemble models were constructed by utilizing individual machine learning algorithms, and the performance of these ensemble models was compared with both each other and the best-performing individual machine learning algorithm. The analysis of the results revealed that placing Ridge as the meta-learner and using ET, RF and Ridge as the base learners in the stacking ensemble model produced superior results compared to the other models across performance metrics. It is anticipated that the findings of this research can be applied globally and prove valuable for energy policy planning in any country. The results obtained not only highlight the accuracy and effectiveness of our predictive model but also underscore the broader implications of this study within the framework of the United Nations' Sustainable Development Goals (SDGs).

In the field of orthodontics, providing patients with accurate treatment time estimates is of utmost importance. As orthodontic practices continue to evolve and embrace new advancements, incorporating machine learning (ML) methods becomes increasingly valuable in improving orthodontic diagnosis and treatment planning. This study aimed to develop a novel ML model capable of predicting the orthodontic treatment duration based on essential pre-treatment variables. Patients who completed comprehensive orthodontic treatment at the Indiana University School of Dentistry were included in this retrospective study. Fifty-seven pre-treatment variables were collected and used to train and test 9 different ML models. The performance of each model was assessed using descriptive statistics, intraclass correlation coefficients, and one-way analysis of variance tests. Random Forest, Lasso, and Elastic Net were found to be the most accurate, with a mean absolute error of 7.27 months in predicting treatment duration. Extraction decision, COVID, intermaxillary relationship, lower incisor position, and additional appliances were identified as important predictors of treatment duration. Overall, this study demonstrates the potential of ML in predicting orthodontic treatment duration using pre-treatment variables.

While the use of crime data has been widely advocated in the literature, its availability is often limited to large urban cities and isolated databases tend not to allow for spatial comparisons. This paper presents an efficient machine learning framework capable of predicting spatial crime occurrences, without using past crime as a predictor, and at a relatively high resolution: the U.S. Census Block Group level. The proposed framework is based on an in-depth multidisciplinary literature review allowing the selection of 188 best-fit crime predictors from socio-economic, demographic, spatial, and environmental data. Such data are published periodically for the entire United States. The selection of the appropriate predictive model was made through a comparative study of different machine learning families of algorithms, including generalized linear models, deep learning, and ensemble learning. The gradient boosting model was found to yield the most accurate predictions for violent crimes, property crimes, motor vehicle thefts, vandalism, and the total count of crimes. Extensive experiments on real-world datasets of crimes reported in 11 U.S. cities demonstrated that the proposed framework achieves an accuracy of 73 and 77% when predicting property crimes and violent crimes, respectively.

Physical-law based models are widely utilized in the aerospace industry. One such use is to provide flight dynamics models for use in flight simulators. For human-in-the-loop use, such simulators must run in real-time. Due to the complex physics of rotorcraft flight, to meet this real-time requirement, simplifications to the underlying physics sometimes have to be applied to the model, leading to model response errors in the predictions compared to the real vehicle. This study investigated whether a machine-learning technique could be employed to provide rotorcraft dynamic response predictions, with the ultimate aim of this model taking over when the physics-based model's accuracy degrades. In the current work, a machine-learning technique was employed to train a model to predict the dynamic response of a rotorcraft. Machine learning was facilitated using a Gaussian Process (GP) non-linear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large data sets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the GP model predictions.

Biofilms are congregations of bacteria on a surface, and they grow into obstacles for the functionalities of any device or machinery involves anything biological. Biofilms are developed through a biochemical system known as ‘Quorum Sensing’ that accounts for the chemical signaling that direct either biofilm formation or inhibition. Computational models that relate chemical and structural features of compounds to their performance properties have been used to aide in the discovery of active small molecules for many decades. These quantitative structure-activity relationship (QSAR) models are also important for predicting the activity of molecules that can have a range of effectiveness in biological systems. This study uses QSAR methodologies combined with and different machine learning algorithms to predict and assess the performance of several different compounds acting in Quorum Sensing. Through computational probing of the quorum sensing molecular interaction, new design rules can be elucidated for countering biofilms.

Form deviation generated during the milling profile process poses challenges to the precision and functionality of industrial fixtures and product manufacturing across various sectors. The inspection of contour profile quality often relies on commonly employed contact methods for measuring form deviation, but these methods frequently face limitations that can impact the reliability and overall accuracy of the inspection process. This paper introduces a novel approach, the novel intelligent inspection method (NIIM), developed to accurately inspect and categorize contour profiles in machined parts manufactured through the milling process by computer numerical control (CNC) machines. The NIIM integrates a calibration piece, a vision system (RAM−StarliteTM), and machine learning techniques to analyze the line profile and classify the quality of contour profile deformation generated during CNC milling. The calibration piece is specifically designed to identify form deviations in the contour profile during the milling process. The RAM−StarliteTM vision system captures contour profile images corresponding to curves, lines, and slopes. An algorithm generates a profile signature, extracting Fourier descriptor features from the contour profile to analyze form deviations when compared to an image reference. A feed-forward neural network is employed to classify contour profiles based on quality properties. Experimental evaluations involving 60 machined calibration pieces, resulting in 356 images for training and testing, demonstrate the accuracy and computational efficiency of the proposed NIIM for profile line tolerance inspection. Results demonstrate that the NIIM offers 96.99% accuracy, low computational requirements, 100% inspection capability, quality classification.

This article uses machine learning analysis of wearable sensor data to explore the topic of driver differentiation. The study focuses on using detailed analyzes of data collected from wearable sensors to identify patterns that differentiate skilled drivers from inexperienced ones. On a predetermined driving route, participants experienced a variety of driving conditions, including parking, navigating cities, driving on highways and driving through residential neighborhoods. The gathered data was analyzed using machine learning techniques, yielding a classification accuracy of 94% overall. The results highlight important differences in sensor data between inexperienced and experienced drivers, providing insight into possible uses for improving safety protocols, driver education, and personalized feedback systems. The article fosters improvements in traffic safety and driver education by offering insightful information on the link between driver behavior analysis and machine learning.

Sentiment analysis of public opinion expressed in social networks has been developed into various applications, especially in English. Hybrid approaches are potential models for reducing sentiment errors on increasingly complex training data. This paper aims to test some hybrid deep learning models' reliability in some domains' Vietnamese language. Our research questions are to determine whether it is possible to produce hybrid models that outperform the Vietnamese language. Hybrid deep sentiment-analysis learning models are built and tested on reviews and feedback of the Vietnamese language. The hybrid models outperformed the accuracy of Vietnamese sentiment analysis on Vietnamese datasets. It contributes to the growing body of research on Vietnamese NLP, providing insights and directions for future studies in this area.

With the rise of Blockchain technology, the cryptocurrency market has been gaining significant interest. In particular, the number of cryptocurrency traders and the market capitalization have grown tremendously. However, predicting cryptocurrency price is very challenging and difficult due to the high price volatility. In this paper, we propose a classification machine learning approach in order to predict the direction of the market (i.e., if the market is going up or down). We identify key features such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) to feed the machine learning model. We illustrate our approach through the analysis of Bitcoin close price. We evaluate the proposed approach via different simulations. Particularly, we provide a backtesting strategy. The evaluation results show that the proposed machine learning approach provides buy and sell signals with more than 86% accuracy.

This research investigates the potential benefits of integrating machine learning algorithms and IoT sensors in modern agriculture. The focus is on optimizing crop production and reducing waste through informed decisions about planting, watering, and harvesting crops. The paper discusses the current state of machine learning and IoT in agriculture, highlighting key challenges and opportunities. It also presents experimental results that demonstrate the impact of changing labels on the accuracy of data analysis algorithms. The findings recommend that by analyzing wide-ranging data collected from farms, including real-time data from IoT sensors, farmers can make more informed verdicts about factors that affect crop growth. Eventually, the integration of these technologies can transform modern agriculture by increasing crop yields while minimizing waste. In our studies, we achieve a classification accuracy of 99.59% using the Bayes Net algorithm and 99. 46% using Naïve Bayes Classifier, and Hoeffding Tree algorithms. Our results indicate that we achieved high accuracy results in our experiments in order to increase crop growth.

Advances in computing technology provide an opportunity to grow and develop an effective crop protection system. This study is a new way of diagnosing plant diseases using a deep and convolutional neural network. Deep learning is the strengthened method of machine learning. It uses a neural network that imitates like a human brain and gains certain information. Although powerful, its training process necessitates millions of tagged data points. The study's Methodology consists of three key stages: data acquisition, pre-processing, and Model Building.

In this paper we use quantum machine learning to detect and classify ocular diseases across age related macular degradation, cataract, diabetic, glaucoma, hypertension, and patological myopia categories versus a control group. We analyze fundus imagery from 1000 patients. Early findings indicate there may be benefit in terms of accuracy and loss function minimization of 2.07% and 1.979x respectively compared to a similar method implemented using traditional computers.

Graph neural networks (GNNs) build on the success of deep learning models by extending them for use in graph spaces. Transfer learning has proven extremely successful for traditional deep learning problems: resulting in faster training and improved performance. Despite the increasing interest in GNNs and their use cases, there is little research on their transferability. This research demonstrates that transfer learning is effective with GNNs, and describes how source tasks and the choice of GNN impact the ability to learn generalisable knowledge. We perform experiments using real-world and synthetic data within the contexts of node classification and graph classification. To this end, we also provide a general methodology for transfer learning experimentation and present a novel algorithm for generating synthetic graph classification tasks. We compare the performance of GCN, GraphSAGE and GIN across both the synthetic and real-world datasets. Our results demonstrate empirically that GNNs with inductive operations yield statistically significantly improved transfer. Further we show that similarity in community structure between source and target tasks support statistically significant improvements in transfer over and above the use of only the node attributes.

Artificial intelligence (AI) technologies, particularly the subfield of machine learning (ML), has been expected to bring significant benefits to all sectors of business and public services. The manufacturing industry is considered one of the domains most likely to benefit from AI tech-nologies. During the recent years, there has been a growing research and development effort on machine learning–based solutions for manufacturing industries, as shown by the growing number of research publications. However, the business, environmental or societal benefits of applying ML in manufacturing industry have not been reviewed and evaluated. This paper re-views the reported benefits of ML in the manufacturing industry by analysis of scientific publi-cations. The focus of the review is on research papers that were published from 2017 to 2021. The review pays particular attention to the industry sector, manufacturing operations, machine learning methods used, maturity of results and the benefits pursued and achieved. The benefits are divided into business benefits, environmental sustainability, and societal sustainability. The analysis shows that the maturity of ML-based solutions is still low, despite the intensive research. Consequently, future research projects should place more emphasis on the maturity of the solu-tions, which would lead to their broader adoption into operational use, thus benefiting industry and society at large. This calls for closer collaboration between the research community and in-dustry.

Biofilm is a complex community of microorganisms that are attached to surfaces and encased in a self-produced extracellular matrix. Machine learning (ML) techniques have been applied to various aspects of biofilm research, such as predicting biofilm formation, identifying key genes, and designing new therapeutic strategies. In this study, we conducted a bibliometric analysis of machine learning in biofilm research to provide a comprehensive overview of the current state of the field. We searched the Web of Science database for articles published included "machine learning biofilm". A total of 126 articles were identified and analysed. Our results showed that the number of publications on machine learning in biofilm has been increasing rapidly over the past decade, indicating a growing interest in the application of ML techniques to biofilm research. The analysis also revealed that the most common research topics in this area were related to biofilm formation, prediction, and control. Furthermore, the most frequently used ML techniques in biofilm research were artificial neural networks and support vector machines. Overall, our study provides valuable insights into the current trends and future directions of machine learning in biofilm research. It also highlights the importance of interdisciplinary collaboration between biofilm researchers and ML experts to drive innovation in this field.

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.

Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. After testing 156 classifiers from 8 most popular classifier families, we found that some classifiers achieve highly satisfactory accuracy rates. Furthermore, we have rank-ordered the performances and investigated performance variations amongst and within the 8 classifier families. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data. Its findings both confirm and contrast existing classifier performance literature. Given the typically highly correlated nature of financial data, we investigated the impact of correlation on classifier performance. The techniques used in this paper should be of interest for financial institutions seeking a CDS Proxy method, and can serve for proxy construction for other financial variables. Some directions for future research are indicated.