Vegetation identification using remote sensing is essential for understanding terrestrial ecosystems and monitoring climate change. This technology by providing high-resolution images of the Earth's surface from satellites, aircraft, and drones has revolutionized the way scientists study and un-derstand vegetation patterns, providing information on the distribution, structure, and health of plant communities in different regions and biomes. However, identifying vegetation in multi and hyperspectral images remains a difficult, time-consuming, and costly process. Traditional methods for vegetation mapping involve the in-terpretation of spectral indices such as the Normalized Difference Vegetation Index (NDVI), alt-hough they often suffer from limitations such as manual interpretation and limited spatial resolution of satellite images. Furthermore, machine learning techniques, artificial neural networks, decision trees, support vector machines, and random forests have shown promising results in identifying and classifying vegetation cover from hyperspectral images. However, due to the need for per-son-nel with specialized expertise, their application on a large scale is still challenging. On the other hand, correlation methods between curves have emerged as powerful and simplified tool for comparing spectral signatures of hyperspectral image pixels and efficiently classifying vegetation in a study area. Pearson's correlation coefficient and spectral angle mapper are among the most common correlation methods used in vegetation mapping. These correlation methods, among many others available, offer an easily implemented and computationally efficient approach, making them particularly useful for applications in developing countries or regions with limited resources. In this article, a comparative study of correlation/distance metrics was conducted for the detection of vegetation pixels in hyperspectral images. The study allowed for the comparison of five distance and/or correlation metrics: direct correlation, cosine similarity, normalized Euclidean distance, Bray-Curtis’s distance, and Pearson correlation. The two metrics that yielded the best accuracy results in vegetation pixel detection were direct correlation and Pearson correlation. Based on the selected methods, a vegetation detection algorithm was implemented and validated on a hyper-spectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library was utilized for image processing, while the numpy and scipy libraries in the Python programming language were used for the mathematical calculation of correlations. Both the study's approach and the implemented algorithm aim to serve as a reference for conducting detection studies of various material types in hyperspectral images using open-access programming platforms.

Video behavior recognition often needs to focus on object motion processes. In this work, a self-organizing computational system oriented to behavioral clustering recognition is proposed, which achieves the extraction of motion change patterns by binary encoding and completes motion pattern summarization using a similarity comparison algorithm. And in the face of unknown behavioral video data, a self-organizing structure with layer-by-layer accuracy progression is used to achieve motion law summarization by using a multi-layer agent design approach. Finally, the real-time feasibility is verified in the prototype system using real scenes to provide a new feasible solution for unsupervised behavior recognition and space-time scenes.

The characteristics of the eddy mass transport are estimated depending on the values of the parameters of a large-scale flow that forms under the conditions of the shelf seas in the Arctic. For this, the results of numerical simulation of the Kara Sea with a horizontal resolution permitting the development of mesoscale eddies are used. The parameters resulting from numerical experiment are considered as a statistical sample and are analyzed using methods of sensitivity study and clustering of sample elements. Functional dependencies are obtained that are closest to the simulated distributions of quantities. These expressions make it possible, within the framework of large-scale models, to evaluate the characteristics of the cross-isobatic eddy mass transport in the diffusion approximation with a counter-gradient flux. Numerical experiments using the SibCIOM model showed that areas along the Fram branch of the Atlantic waters trajectory in the Arctic as well as the shelf of the East Siberian and Laptev seas with adjacent deep water areas are most sensitive to proposed parameterization of eddy exchanges. Accounting for counter-gradient eddy fluxes turned out to be less important.

This article presents an estimation of the hypertension risk based on a dataset on 1007 individuals. The application of a Tobit Model shows that “Hypertension” is positively associated to “Age”, “BMI-Body Mass Index”, and “Heart Rate”. The data show that the element that has the greatest impact in determining inflation risk is “BMI-Body Mass Index”. An analysis was then carried out using the fuzzy c-Means algorithm optimized with the use of the Silhouette coefficient. The result shows that the optimal number of clusters is 9. A comparison was then made between eight different machine-learning algorithms for predicting the value of the Hypertension Risk. The best performing algorithm is the Gradient Boosted Trees Regression according to the analyzed dataset. The results show that there are 37 individuals who have a predicted hypertension value greater than 0.75, 35 individuals who have a predicted hypertension value between 0.5 and 0.75, while 227 individuals have a hypertension value between 0.0 and 0.5 units.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion even early warning for physicians, however, they are mutually-exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. Long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the 3rd China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

Time series data are significant and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introduce hybrid algorithm named novel matrix profile (NMP) to solve the all-pairs similarity search problem for time series data. The proposed NMP inherits the features from two state-of-the art algorithms: similarity time-series automatic multivariate prediction (STAMP), and short text online microblogging protocol (STOMP). The proposed algorithm caches the output in an easy-to-access fashion for single- and multidimensional data. The proposed NMP algorithm can be used on large data sets and generates approximate solutions of high quality in a reasonable time. The proposed NMP can also handle several data mining tasks. It is implemented on a Python platform. To determine its effectiveness, it is compared with the state-of-the-art matrix profile algorithms i.e., STAMP and STOMP. The results confirm that the proposed NMP provides higher accuracy than the compared algorithms.

In this paper we formally define the hierarchical clustering network problem (HCNP) as the problem to find a good hierarchical partition of a network. This new problem focuses on the dynamic process of the clustering rather than on the final picture of the clustering process. To address it, we introduce a new hierarchical clustering algorithm in networks, based on a new shortest path betweenness measure. To calculate it, the communication between each pair of nodes is weighed by the importance of the nodes that establish this communication. The weights or importance associated to each pair of nodes are calculated as the Shapley value of a game, named as the linear modularity game. This new measure, (the node-game shortest path betweenness measure), is used to obtain a hierarchical partition of the network by eliminating the link with the highest value. To evaluate the performance of our algorithm, we introduce several criteria that allow us to compare different dendrograms of a network from two point of view: modularity and homogeneity. Finally, we propose a faster algorithm based on a simplification of the node-game shortest path betweenness measure, whose order is quadratic on sparse networks. This fast version is competitive from a computational point of view with other hierarchical fast algorithms, and, in general, it provides better results.

Chromophobe renal cell carcinoma (chRCC) and oncocytoma (RO) are renal tumor types originating from alpha intercalated cells of the collecting ducts of the kidney. Both tumor types have similar gross histological morphology and increased mitochondria, which leads to difficulties differentiating between these tumors, especially with core biopsy samples. This study aims to apply a machine learning approach to develop a molecular classifier based on transcriptomics data. Here we generated a meta-data set containing 62 chRCC and 45 RO gene expression arrays. Arrays were subjected to quality control steps, and genes were selected based on differential expression and ROC analysis. The final gene list was evaluated with UMAP based dimension reduction followed by density-based clustering with 95.5% accuracy. Molecular profiling by KEGG pathway analysis identified enrichment of fatty acid oxidation pathway in RO. We finally identified and validated the 30-gene signature, with an accuracy of 94.4% to distinguish chRCC from RO on UMAP analysis. Our results show that chRCC and RO have a distinct gene signature that can differentiate these tumors and complement histology for routine diagnosis of these two tumors.

This paper is devoted to the foundational problems of dendrogramic holographic theory (DH-theory). We use the ontic-epistemic (implicate-explicate order) methodology. The epistemic counterpart is based on representation of data by dendrograms constructed with hierarchic clustering algorithms. The ontic Universe is described as the p-adic tree; it is zero dimensional, totally disconnected, disordered, and bounded (in p-adic ultrametric). Interrelation classical-quantum loses its sharpness; generally simple dendrograms are ``more quantum’’ than complex one. We use the CHSH-inequality as a measure of quantum(-likeness). We demonstrate that it can be violated by classical experimental data represented by dendrograms. The seed of this violation is neither nonlocality nor rejection of realism. This is nonergodicity of dendrogramic time series. Generally, violation of ergodicity is one of the basic features of DH-theory. We also consider DH-theory for Minkovski geometry and monitor the dependence of CHSH-violation and nonergodicity on geometry as well as a Lorentz transformation of data.

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices and further compares the results through hierarchical clustering. This provides insight for the user into which data provides what type of information and in what situations a particular source is most useful.

SARS-CoV-2 is mutating and creating divergent variants across the world. An in-depth investigation of the amino acid substitution in the genomic signature of SARS-CoV-2 proteins is highly essential for understanding its host adaptation and infection biology. A total of 9587 SARS-CoV-2 structural protein sequences collected from 49 different countries are used to characterize protein-wise variants, substitution pattern (type and location), and major substitution changes. The majority of the substitutions are distinct, occurred mostly in a particular location, and leads to a change in amino acid's biochemical properties. In terms of mutational changes, Envelope (E) and Membrane (M) proteins are relatively stable than Nucleocapsid (N) and Spike (S) proteins. Several co-occurrence substitutions are observed, particularly in S and N proteins. Substitution specific to active sub-domains reveals that Heptapeptide Repeat, Fusion peptides, Transmembrane in S protein, and N-terminal and C-terminal domains in N protein are remarkably mutated, and also found few deleterious mutations in these domains.

With the rapid development of smart phones, tablets and their operative systems, many positioning enabled sensors have been built into these devices. Users can now accurately fix their location according to the function of GPS receivers. For indoor environments, as in the case we are studying, WiFi based positioning is preferred to GPS due to the attenuation or obstruction of signals. This paper deals with the automatic classification of customers in a Sports Shop Center on the basis of their movements around the shop's premises. To achieve this goal, we start by collecting (x,y) coordinates from customers while they visit the store. Consequently, any costumer's path through the shop is formed by a list of coordinates, obtained with a frequency of one measurement per minute. Then, a guess about the full trajectory is constructed and a number of parameters about these trajectories is calculated before performing an Unsupervised Learning Clustering Process. As a result, we can identify several types of customers, and the dynamics of their behavior inside the shop. This information is of great value to the company, to be used both in the long term and also in short periods of time, monitoring the current state of the shop at any moment, identifying different types of situation appearing during restricted periods, or predicting customer flow conditions

Considering the great growth of IoT networks and the need for highly reliable networks and also considering the manufacturing divides of IoT equipment which are highly limited by their memory, processing power and battery; we need a highly efficient routing for guaranteeing our network's high life span. So, this paper has suggested an efficient energy routing method based on the overlapping clustering method which is inspired from the Grey theory. The Overlap clustering method means that some Things collect data that must be sent to two or more Fog nodes for processing. In the suggested method the best node is selected as the cluster head based on factors such as remaining energy, distance, link expiration time, signal power for receiving data from things by the Fog nodes. In the next step the Fog node's data are sent in a hierarchical method using a symmetrical tree of processed data to the server. Thus, the main issue here is making using a proper routing method for data sending to the Cloud that doesn't just focus on energy, but also considers other factors such as delay and network life span. The simulation results show that the HR-IoT reduces the average end to end delay more than 17.2% and 23.1%, decreases the response time more than 20.1% and 25.78% and increase packet delivery rate more than 23.1% and 28.78% and lifetime more than 25.1% and 28.78% compared to EECRP and ERGID approaches.

MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization Time-of Flight) is a type of mass spectrometry (MS) that has been widely implemented for the rapid identification of microorganisms over the last decade. The accuracy and flexibility of this method has encouraged researchers to implement the analysis of protein spectra obtained by MALDI-TOF for the discrimination of close-related species and bacterial typing. In this study, a standardized methodology based on the detection of species-specific protein peaks from the spectra obtained with MALDI-TOF is described. The methodology was applied to a collection of Cryptococcus spp. (n=70) previously characterized by Amplified Fragment Length Polymorphism (AFLP) and sequencing of the ITS1-5.8S-ITS2 region. An expanded ad-hoc database was also built for their discrimination with MALDI-TOF. This approach did not allow the discrimination of the interspecies hybrids. However, the performance of peak analysis with the application of the PLS-DA and SVM algorithms in a two-step analysis allowed 96.95% and 96.55% correct discrimination of C. neoformans from the interspecies hybrids, respectively. Besides, PCA analysis prior to SVM provided 98.45% correct discrimination of the 3 analyzed species in a one-step analysis. The method is cost-efficient, rapid and user-friendly. The procedure can also be automatized for an optimized implementation in the laboratory routine.

Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous data is involved and continuously growing in the paradigm of big-data. To extract meaningful information adaptively from big educational data, some specific data mining techniques are needed. This paper presents a clustering approach to partition students into different groups or clusters based on their learning behavior. Furthermore, personalized e-learning system architecture is also presented which detects and responds teaching contents according to the students’ learning capabilities. The primary objective includes the discovery of optimal settings, in which learners can improve their learning capabilities. Moreover, the administration can find essential hidden patterns to bring the effective reforms in the existing system. The clustering methods K-Means, K-Medoids, Density-based Spatial Clustering of Applications with Noise, Agglomerative Hierarchical Cluster Tree and Clustering by Fast Search and Finding of Density Peaks via Heat Diffusion (CFSFDP-HD) are analyzed using educational data mining. It is observed that more robust results can be achieved by the replacement of existing methods with CFSFDP-HD. The data mining techniques are equally effective to analyze the big data to make education systems vigorous.

Software engineers from all over the world solve independently a lot of similar problems. In this condition the problem of code or even better architecture reusing becomes an issue of the day. In this paper two phase approach to determining the functional and structural likenesses of software projects is proposed. This approach combines two methods of artificial intelligence: natural language processing techniques with a novel method for comparing software projects based on ontological representation of their architecture automatically obtained from the projects source code. Additionally several similarity metrics are proposed to estimate similarity between projects.

Integration of multi-view datasets which are comprised of heterogeneous sources or different representations is challenging to understand the subtle and complex relationship in data. Such data integration methods attempt to combine efficiently the complementary information of multiple data types to construct a comprehensive view of underlying data. Nonnegative matrix factorization (NMF), an approach that can be used for signal compression and noise reduction, has aroused widespread attention in the last two decades. The Kullback–Leibler divergence (or relative entropy) information distance can be used to measure the loss function of NMF. In this article, we propose a fast and robust framework (RSNMF) based on symmetric nonnegative matrix factorization (SNMF) and similarity network fusion (SNF) for clustering human microbiome data including functional, metabolic and phylogenetic profiles. Many existing methods typically utilize all the information provided by each view to create a consensus representation, which often suffers a lot from noise in data and cannot provide a precise representation of the latent data structures. In contrast, RSNMF combines the strength of SNMF and the advantage of SNF to form a robust clustering indicator matrix thus can reduce the noise influence. We conduct experiments on one synthetic and two real dataset (microbiome data, text data) and the results show that the proposed RSNMF has better performance over the baseline and the state-of-art methods, which demonstrates the potential application of RSNMF for microbiome data analysis.

It is a significant task to assess the safety of the aging offshore jacket platform in order to extend the service life. This paper analyzes the multiple risk factors of an aging jacket platform in Bohai Bay, China and builds the safety evaluation index system, which includes three levels, namely, the target layer, the first-grade indicators layer and the second-grade indicators layer. The target layer consists of three first-grade indicators: ocean environments, structure status, and human and organizational factors. Each first-grade indicator is divided into three second-grade indicators. The weight of each indicator is calculated by analytic hierarchy process to weaken subjective effect. Grey clustering method is applied to estimate the failure risk of the platform in Bohai Bay qualitatively and quantitatively. The assessment standard is divided into five grades and the whitening function of each grey cluster is determined by the assessment scheme. The grey evaluation weight vector of each second-grade indicator is calculated by the table dispatching method. Through layer by layer calculation, the grey assessment value of the target layer is finally estimated by making the grey assessment weight vector single-value and the grey grade is determined according to the maximum principle. The evaluation results show quantitatively that the failure risk grade of the jacket platform in Bohai Bay is medium and the safety assessment method is reasonable and feasible.

Accurate cloud quantification is essential in climate change research. In this work, we construct an automated computer vision framework by synergistically incorporating deep neural networks and finite sector clustering to achieve robust whole sky image-based cloud classification, adaptive segmentation, and recognition under intricate illumination dynamics. A bespoke YOLOv8 architecture attains over 95% categorical precision across four archetypal cloud varieties curated from extensive annual observations (2020) at a Tibetan highland station. Tailor-made segmentation strategies adapted to distinct cloud configurations, allied with illumination-invariant image enhancement algorithms, effectively eliminate solar interference and substantially boost quantitative performance even in illumination-adverse analysis scenarios. Compared with the traditional threshold analysis method, the cloud quantification accuracy calculated within the framework of this paper is significantly improved. Collectively, the methodological innovations provide an advanced solution to markedly escalate cloud quantification precision levels imperative for climate change research, while offering a paradigm for cloud analytics transferable to various meteorological stations.

Finding the ideal statistical method for grouping phenological data is always an important step for breeders to draw correct conclusions from it possibly. In this paper, K-cluster analysis is presented as a perfect tool for grouping phenological data. The present research was performed based on the phenological data of 61 strawberries (Fragaria × ananassa) cultivars of different geo-graphical origins grown in Estonian conditions. Groups of strawberry cultivars were deter-mined according to flowering and ripening time: early, middle and late, based on the sum of ef-fective temperatures above +5°C. The result of the K-cluster analysis carried out in this way makes it possible to precisely plan the ripening time of berries of different strawberry cultivars. Using such analysis data, it is possible to combine with different early, mid or late strawberry cultivars to extend the picking period. Also, this technique can be used to study the effect of cli-matic changes occurring over the years on the phenology of strawberry cultivars grown in the region.

Nonconventional luminescent materials (NLMs) that do not contain traditional aromatic chromophores are of great interest due to their unique chemical structures, optical properties and their potential applications in various areas such as cellular imaging and chemical sensing. However, most reported NLMs show weak or no emission in dilute solutions, severely limiting their applications. In this work, the dynamic hydrogen bonds were utilized to design NLMs with efficient emission in dilute solutions. To further validate the results, polymers P1 and P2 were successfully prepared and investigated. It was found that luminescence quantum efficiency of P1 and P2 at concentration of 0.1 mg/mL in water solution is 8.9 and 0.6%, respectively. The high efficiency can be attributed to the fact that polymer P1 has more intra- or intermolecular dynamic hydrogen bonds and other short interactions than P2 in dilute solutions, allowing P1 to achieve the through-space conjugation effect to increase the degree of system conjugation, restrict molecular motion, and decrease non-radiative transitions, which can effectively improve luminescence. In addition, polymer P2 exhibits the characteristics of clustering-triggered emission, excitation wavelength-dependent and concentration-dependent fluorescence properties, excellent photobleaching resistance, low cytotoxicity and selective recognition of Fe3+. This work is a preliminary study of the luminescence properties of NLMs in dilute solutions via modulating dynamic hydrogen bonds, which can be used as a semi-empirical method to design and construct novel NLMs in future.

Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetic described by the Smoluchowski theory. Molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, without having to conduct large scale experiments on living organisms.

The visualization of hyperspectral images in display devices, having RGB colour composition channels is quite difficult due to the high dimensionality of these images. Thus, principal component analysis has been used as a dimensionality reduction algorithm to reduce information loss, by creating uncorrelated features. To classify regions in the hyperspectral images, K-means clustering has been used to form clusters/regions. These two algorithms have been implemented on the three datasets imaged by AVIRIS and ROSIS sensors.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported an extremely high number of positive cases and deaths, while some reported too few COVID-19 related cases and mortality. In this paper, the factors that could affect the transmission of COVID-19 and its risk level in different counties have been determined and analyzed. Using Pearson Correlation, K-means clustering, and several classification models, the most critical ones were determined. Results showed that mean temperature, percent of people below poverty, percent of adults with obesity, air pressure, percentage of rural areas, and percent of uninsured people in each county were the most significant and effective attributes.

This paper presents an effective heart disease prediction model through detecting the anomalies, also known as outliers, in healthcare data using the unsupervised K-means clustering algorithm. Most existing approaches for detecting anomalies are based on constructing profiles of normal instances. However, such techniques require an adequate number of normal profiles to justify those models. Our proposed model first evaluates an \textit{optimal} value of K using Silhouette method. Next, it intends to locate anomalies that are far from a certain threshold distance with respect to their clusters. Finally, the five most popular classification techniques such as K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machines (SVM), Naive Bayes (NB), and Logistic Regression (LR) are applied to build the resultant prediction model. The effectiveness of the proposed methodology is justified using a benchmark dataset of heart disease.

In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al (2016), a new parameter-free spectral clustering approach is developed that automatically identifies parameters and does not require any user-input choices. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The parameter-free spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically-generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.

In Cameroon, alcoholic beverages remain the main consumed drink. In the Northern regions, indigenous sorghum beers are popular and widely consumed in an actively fermenting state by the people. In this study, some physicochemical parameters of the alcoholic sorghum beverages and correlations between them were evaluated during fermentation for 10 days. The indigenous white and red beers were produced at the laboratory scale assisted by experimented producers and some parameters (pH, total acidity, alcohol, sugars, density, total solids, temperature, and conductivity) were measured on the wort and fermented beverages. The pH decreases from 3.2 to 2.4 and 3.11 to 2.41; total acidity increases from 1.07 to 5.1 g/l and 0.5 to 4.6 g/l; alcohol enhances from 0 to 9.5% and 0 to 6.8% (v/v); total solids drop from 13.6 to 5°P and 12.2 to 3.3°P were observed respectively in the white and red sorghum beers. The multivariate analysis showed a good correlation between consumption of sugar, the decrease of total solids and density with the decrease in pH. It was also shown that, a perfect link exist between the production of alcohol and organic acids. The hierarchical analysis showed that indigenous red beer samples fermented for 1 and 2 days and those fermented for 4 to 10 days are related and could be separate in two distinct groups, whereas white turbid beer samples are separated in three different groups, those fermented for 1 to 4 days, those 5 to 6 days and those 7 to 10 days. The results obtained could serve as a guide to better understand the fermentation process of the indigenous alcoholic sorghum-based beverages.

Wireless Sensor Networks are critical for modern applications ranging from environmental monitoring to smart city infrastructures. This research proposes a novel optimization learning and deployment strategy for Wireless Sensor Networks (WSNs) by 3D WUSN model is uniformly distributed M sensor nodes across diverse terrains, with clusters representing 20% of sensors, showcasing its ability to balance power consumption and extend network longevity, grounded in fuzzy clustering based protocol using the Fuzzy C-Means (FCM) method, called FCM-3DWUSN. Comparative analysis against established models including LEACH-C, K-Means and LEACH emphasized FCM-3DWUSN's outperformed performance, with average power consumption such as 134.990, 341.790, 143.895, and 192.984; respectively. The experimental result shows that FCM protocol in the 3D WUSN model can reduce energy consumption and improve the network lifetime to gather data and send the information.

Historic sites are important components of every city's cultural history because they preserve rich historical knowledge and distinctive values passed down from previous generations to the present. Due to the progress of urbanization and modernization, many historic sites face pressure from damage and transformation. In this paper, a method for assessing cultural heritage damage was developed to measure the extent of spatial damage in historic sites. Using sample data obtained in Xiyang, Qixian, and Xiaoyi, all historic cities in Shanxi Province, Mainland China, and combined weights were estimated using the Delphi technique and the CRITIC weight method. Following this, the Spatial Damage Degree Model (SDDM) based on K-means cluster analysis and K-nearest neighbor (KNN) classification was developed. The findings show that the model efficiently solves the problem of assessing spatial damage levels in historic sites. Through multiple linear regression analysis, it was shown that the damage to historic sites was predominantly caused by three factors: natural erosion, construction damage, and planning and policy. SDDM was used to calculate the spatial damage levels of historic sites, allowing conservators to fully comprehend the features and concerns related to historic sites. As a result, more scientific and rational preservation approaches may be developed, improving the efficiency of historic site restoration and conservation and encouraging the sustainable development of urban and rural heritage.

The large quantity of information retrieved from communities, public data repositories, web pages, or data mining can be sparsed and poorly classified. This work shows how to employ unsupervised classification algorithms such as K-means proper to classify user reviews into their closest category, forming a balanced data set. Moreover, we found that the text vectorization technique significantly impacts the clustering formation, comparing TF-IDF and Word2Vec. The value for mapping a cluster with movie genre was 81.34% ± 20.48 of the cases when the TF-IDF was applied, whereas Word2Vec only yielded a 53.51% ± 24.1. In addition, we highlight the impact of the removal of stop-words. Thus, we detected that pre-compiled lists are not the best method to remove stop-words before clustering because there is much ambiguity, centroids are poorly separated, and only 57% of clusters could match a movie genre. Thus, our proposed approach achieved a 94% of accuracy. After analyzing the classifiers’ results, we appreciated a similar effect when divided by the stop-words method removal. Statistically significant changes were observed, especially in precision metric and Jaccard scores in both classifiers, using custom-generated stop lists rather than pre-compiled ones. Reclassifying sparse data is strongly recommended as using custom-generated stop lists.

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described by a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. To minimize the compactness is equivalent to maximize the dis-similarity of the generated cluster, i.e., concept, against the whole concept in each step. In this sense, the compactness plays the role of cluster quality. We also show that the average compactness of each feature with respect to objects and/or clusters in several clustering steps is useful as feature effectiveness criterion. Features having small average compactness are mutually covariate, and are able to detect geometrically thin structure embedded in the given multi-dimensional histogram-valued data. We obtain thorough understandings of the given data by the visualization using dendrograms and scatter diagrams with respect to the selected informative features. We illustrate the effectiveness of the proposed method by using an artificial data set and real histogram-valued data sets.

Energy efficiency of Data Center (DC) operations heavily relies on IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of IoT- enabled smart systems. Consequently, increased demand for computing power will bring about increased waste heat dissipation in data centers. In order to bring about a DC energy efficiency, it is imperative to explore the thermal characteristics analysis of an IT room (due to waste heat). This work encompasses the employment of an unsupervised machine learning modelling technique for uncovering weaknesses of the DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for energy efficiency improvement that will feed into DC recommendations. The methodology employed for this research includes statistical analysis of IT room thermal characteristics, and the identification of individual servers that frequently occur in the hotspot zones. A critical analysis has been conducted on available big dataset of ambient air temperature in the hot aisle of ENEA Portici CRESCO6 computing cluster. Clustering techniques have been used for hotspots localization as well as categorization of nodes based on surrounding air temperature ranges. The principles and approaches covered in this work are replicable for energy efficiency evaluation of any DC and thus, foster transferability. This work showcases applicability of best practices and guidelines in the context of a real commercial DC that transcends the set of existing metrics for DC energy efficiency assessment.

Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

Comorbidities can have a cumulative effect on hospital outcomes of care, such as the length of stay (LOS), and hospital mortality. This study examines patients hospitalized with Congestive Heart Failure (CHF), a life-threatening condition, which, when it coexists with a burdened disease profile, the risk for negative hospital outcomes increases. Since coexisting conditions co-interact, with a variable effect on outcomes, clinicians should be able to recognize these joint effects. In order to study CHF comorbidities, we used medical claims data from CMS. After extracting the most frequent cluster of CHF comorbidities, we: (i) Calculated, step-by-step, the conditional probabilities for each disease combination inside this cluster (ii) Estimated the cumulative effect of each comorbidity combination on the LOS and hospital mortality (iii) Constructed (a) Bayesian, scenario-based graphs and (b) Bayes-networks to visualize results. Results show that, for CHF patients, different comorbidity constructs have variable effect on the LOS and hospital mortality. Therefore, dynamic comorbidity risk assessment methods should be implemented for informed clinical decision making in any ongoing effort for quality of care improvements.

Assessment of drugs toxicity and associated biomarker genes is one of the most important tasks in the pre-clinical phase of drug development pipeline as well as in the toxicogenomic studies. There are few statistical methods for the assessment of doses of drugs (DDs) toxicity and their associated biomarker genes. However, these methods consume more time for computation of the model parameters using the EM (Expectation-Maximization) based iterative approaches. To overcome this problem, in this paper, an attempt is made to propose an alternative approach based on hierarchical clustering (HC) for the same purpose. There are several types of HC approaches whose performance depends on different similarity/distance measures. Therefore, we explored suitable combinations of distance measures and HC methods based on Japanese Toxicogenomics Project (TGP) datasets for better clustering/co-clustering between DDs and genes as well as to detect toxic DDs and their associated biomarker genes. We observed that Word’s HC method with each of Euclidean, Manhattan and Minkowski distance measures produces better clustering/co-clustering results. For an example, in case of glutathione metabolism pathway (GMP) dataset LOC100359539/Rrm2, Gpx6, RGD1562107, Gstm4, Gstm3, G6pd, Gsta5, Gclc, Mgst2, Gsr, Gpx2, Gclm, Gstp1, LOC100912604/Srm, Gstm4, Odc1, Gsr, Gss are the biomarker genes and Acetaminophen_Middle, Acetaminophen_High, Methapyrilene_High, Nitrofurazone_High, Nitrofurazone_Middle, Isoniazid_Middle, Isoniazid_High are their regulatory (associated) DDs explored by our proposed co-clustering algorithm based on the distance and HC method combination Euclidean: Word. Similarly, for the PPAR signaling pathway (PPAR-SP) dataset Cpt1a, Cyp8b1, Cyp4a3, Ehhadh, Plin5, Plin2, Fabp3, Me1, Fabp5, LOC100910385, Cpt2, Acaa1a, Cyp4a1, LOC100365047, Cpt1a, LOC100365047, Angptl4, Aqp7, Cpt1c, Cpt1b, Me1 are the biomarker genes and Aspirin_Low, Aspirin_Middle, Aspirin_High, Benzbromarone_Middle, Benzbromarone_High, Clofibrate_Middle, Clofibrate_High, WY14643_Low, WY14643_High, WY14643_Middle, Gemfibrozil_Middle, Gemfibrozil_High are their regulatory DDs. These results are validated by the available literature and functional annotation.

Currently, maritime traffic is increasing with economic growth in several regions worldwide. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features, such as islands, are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest distance between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance from rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.

Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load forecasting. The two first sections are dedicated to the industrial context and a review of individual electrical data analysis. We are interested in hierarchical time-series for bottom-up forecasting. The idea is to disaggregate the signal in such a way that the sum of disaggregated forecasts improves the direct prediction. The 3-steps strategy defines numerous super-consumers by curve clustering, builds a hierarchy of partitions and selects the best one minimizing a forecast criterion. Using a nonparametric model to handle forecasting, and wavelets to define various notions of similarity between load curves, this disaggregation strategy applied to French individual consumers leads to a gain of 16\% in forecast accuracy. We then explore the upscaling capacity of this strategy facing massive data and implement proposals using R, the free software environment for statistical computing. The proposed solutions to make the algorithm scalable combines data storage, parallel computing and double clustering step to define the super-consumers.

Containing hundreds of spectral bands (features), hyperspectral images (HSIs) have high ability in discrimination of land cover classes. Traditional HSIs data processing methods consider the same importance for all bands in the original feature space (OFS), while different spectral bands play different roles in identification of samples of different classes. In order to explore the relative importance of each feature, we learn a weighting matrix and obtain the relative weighted feature space (RWFS) as an enriched feature space for HSIs data analysis in this paper. To overcome the difficulty of limited labeled samples which is common case in HSIs data analysis, we extend our method to semisupervised framework. To transfer available knowledge to unlabeled samples, we employ graph based clustering where low rank representation (LRR) is used to define the similarity function for graph. After construction the RWFS, any arbitrary dimension reduction method and classification algorithm can be employed in RWFS. The experimental results on two well-known HSIs data set show that some dimension reduction algorithms have better performance in the new weighted feature space.

The energy-intensive sector of the cement industry needs to find technologically advanced methods to produce cement with as little energy as possible, without polluting the environment and without sacrificing the quality (fineness) of the cement. In addition, the stress on the machines changes the behavior of the data. By combining the k-means method, two (2) feature selection methods, nine (9) machine learning methods, and the differential evolution (DE) method, a dynamic system is created where the user determines the key performance indicators (KPIs) each time. The user has the potential to control the distribution of the data, the input of machine learning models, and the ability to switch the system to the suggested values without damage. The selection of the best-performing models is based on the normalized root mean squared error (NRMSE). The DE method optimizes the dynamic objective function. The optimal values of the manipulated variables are evaluated compared to the value of the objective function from the actual values of the variables. In all experiments, the recommendations are acceptable. The final results show an improvement in the operation of both the cement mill and kiln compared to the current operating condition.

The agricultural sector in Palestine has a significant role in its economy. However, the production of this sector is affected by different kinds of plant diseases, specifically leaf diseases. Automatic agricultural leaf disease detection is essential for the early diagnosis and controls the overall health of fields. Image segmentation techniques, clustering, and deep learning are often used to detect diseased leaves. This study proposes a novel hybrid approach based on image classification. The hybrid approach combines the k-means clustering algorithm with Convolutional Neural Network (CNN), where k-means is used to detect the leaf’s infected area, then CNN is used for specifying the disease. We used the PlantVillage dataset for experimental verification as it contains several crops with different kinds of challenging diseases. We also examined the selection of optimal k-value using the Silhouette coefficient, Elbow method, and Kneedle Algorithm. The Silhouette technique was analyzed using three distance metrics; Euclidean, Manhattan, and Cosine. Its scores for the three-distance metrics were low, near-zero, and failed to produce the optimum k value. Besides, the Elbow method was complicated to use in image segmentation in terms of executing and visualizing the k value in its graph plot. Based on verification results, the Kneedle Algorithm produced better results in the consistency of choosing the optimal k value and showed superiority over other approaches. Therefore, the processed images were segmented with the k-means clustering algorithm with a Kneedle algorithm-based k value. Finally, a Convolutional Neural Network (CNN) is trained to classify the type of disease based on analyzing and testing leaf images. The hybrid model achieved high accuracy of 93.79% in disease identification, confirming the proposed model’s robustness.

Software reliability is an important characteristic for ensuring the qualities of software products. Predicting the potential number of bugs from the beginning of a development project allows practitioners to make the appropriate decisions regarding testing activities. In the initial development phases, applying traditional software reliability growth models (SRGMs) with limited past data does not always provide reliable prediction result for decision making. To overcome this, herein we propose a new software reliability modeling method called deep cross-project software reliability growth model (DC-SRGM). DC-SRGM is a cross-project prediction method that uses features of previous projects’ data through project similarity. Specifically, the proposed method applies cluster-based project selection for training data source and modeling by a deep learning method. Experiments involving 15 real datasets from a company and 11 open source software datasets show that DC-SRGM can more precisely describe the reliability of ongoing development projects than existing traditional SRGMs and the LSTM model.

Ubiquitous computing comprises scenarios where networks, devices within the network, and software components change frequently. Market demand and cost-effectiveness are forcing device manufacturers to introduce new-age devices. Also, the Internet of Things (IoT) is transitioning rapidly from the IoT to the Internet of Everything (IoE). Due to this enormous scale, effective management of these devices becomes vital to support trustworthy and high-quality applications. One of the key challenges of IoT device management is automatic device classification with the logically semantic type and using that as a parameter for device context management. This would enable smart security solutions. In this paper, a device classification approach is proposed for the context management of ubiquitous devices based on unsupervised machine learning. To classify unknown devices and to label them logically, a proactive device classification model is framed using a k-Means clustering algorithm. To group devices, it uses the information of network parameters such as Received Signal Strength Indicator (rssi), packet_size, number_of_nodes in the network, throughput, etc. Experimental analysis suggests that the well-formedness of clusters can be used to derive cluster labels as a logically semantic device type which would be a context for resource management and authorization of resources. This paper fulfills an identified need of proactive device classification for device management.

Hyper spectral imaging (HSI) is a technique that is used to obtain the spectrum for each pixel in the image. It helps in finding objects and identifying materials etc. Such an identification is very difficult using other imaging techniques. It allows the researchers to investigate the documents without any physical contact. Nowadays detection of unequal Ink mismatch based on HSI has shown vast improvement in distinguishing the inks. Detection of unequal Ink mismatch is an unbalanced clustering problem. This paper used K-means Clustering for ink mismatch detection. K-means Clustering find same subgroups in the data based on Euclidean distance. This paper demonstrates performance in unequal Ink mismatch based on HSI.

The paper presents the results of the research concerning an evaluation of information 1 technology of gene expression profiles processing stability with the use of gene expression profiles 2 with different levels of noise component. The information technology is presented as a structural 3 block-chart, which contains all stages of the studied data processing. The hybrid model of objective 4 clustering based on SOTA algorithm and the technology of gene regulatory networks reconstruction 5 have been studied to evaluate the stability to the level of the noise component. The results of the 6 simulation have shown that the hybrid model of objective clustering has high level of stability 7 to noise component and vice versa, the technology of gene regulatory networks reconstruction is 8 very sensitivity to level of noise component. The obtained results indicate the importance of gene 9 expression profiles preprocessing at early stage of gene regulatory network reconstruction in order to 10 remove background noise and non-informative genes in terms of used criteria

In this paper, the forecast horizon and solar variability influences on MHFM model based on multiscale decomposition, AR and NN models, are studied. This article follows the works published in [1] showing the performance of the MHFM using 3 multiscale decomposition methods and a forecast horizon equal to 1 hour. Several forecast horizon strategies and his influence on the MHFM performances are investigated. We show that the best strategy for a rRMSE variying from $4.43\%$ to $10.24\%$ is obtained for forecast horizons from $5$ minutes to $6$ hours. In a second part, the solar variability influence on the MHFM is studied. A classification based on a shows that the best performance of MHFM is obtained for clear sky days with a rRMSE of $2.91\%$ and worst for cloudy sky days with a rRMSE of $6.73\%$.

Underwater wireless sensor networks (UWSNs) represent a specialized category of WSNs with versatile applications including acoustic monitoring, oil and gas exploration, and military surveillance. UWSNs face formidable challenges such as limited energy resources, extended propagation delays, and harsh conditions. Existing clustering and multi-hop routing protocols often unevenly distribute nodes geographically, causing network fragmentation and disproportionately draining the battery life of nodes near the sink due to higher data transmission demands. In this paper, we introduce an Energy-efficient Artificial Fish Swarm-based Clustering Protocol (EAFSCP), inspired by the collective behavior of fish swarms. EAFSCP is a decentralized clustering algorithm designed for acoustic monitoring in UWSNs. Its decentralized nature makes it particularly well-suited for large-scale UWSNs, where centralized algorithms may not be feasible. Through comprehensive comparisons with existing cluster-based routing protocols, our findings indicate that EAFSCP consistently outperforms them across multiple key performance metrics, including network lifetime, energy consumption, packet delivery ratio, packet loss rate, and throughput. According to the results, EAFSCP represents an effective clustering algorithm that enhances network performance, prolongs network lifespan by reducing energy consumption, promotes scalability, and provides valuable guidance for emergency response efforts.

As a consequence of applying advanced maintenance practices, the theoretical probability of failures is relatively low. However, observation of low market intelligence and maintenance management has been reported. The experimental investigation is supported by findings from a survey targeting asset-intensive companies applying hydraulic power systems. Next, the study incorporates multidimensional data analysis using CA-AHC (Correspondance Analysis with Agglomerative Hierarchical Clustering) approach. The non-parametric machine learning models are used from generated feature subspace to extract features affecting maintenance performance indicators. The results support empirical evidence that equipment age increases the probability of failures. However, the novel findings show that number of maintenance personnel, equipment size measured by nominal working energy consumption, and activities dedicated to maintaining fluid cleanliness impact regression results of companies utilising hydraulic applications.

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules of regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which in turn affects vehicle idling time at intersections, fuel consumption, CO_2 emissions and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable and inexpensive way to identify the type of intersection control (e.g. traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicles crossing intersections is proposed. A modification of a well-known clustering algorithm for detecting stopping and decelerating events is presented. These detected events are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per junction arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the junction arms according to their traffic control type dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single junction arm are used (one-arm model) and another where features also from neighbouring junction arms of the same junction are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 94\% to 97\%.

This article identifies promising research areas on the PETROLEUM SCIENCE topic via bibliometric analysis of the 2018-2021 publications in the highly cited Journal of Petroleum Science and Engineering, which is included in the Journal Citation Reports Section: ENERGY & FUELS — Q2 Quartile, ENGINEERING, PETROLEUM — Q1 Quartile. Bibliometric metadata from Web of Science were used for 866 articles in 2018, 1,142 — in 2019, 1,138 — in 2020, and 1,832 in 2021. The clustering of articles was performed using the texts of the Title, Abstract, Keywords, and Keywords Plus fields. The demo version of the Lingo3G algorithm was used. For the two major clusters, the most promising research topics were determined by comparing the titles of the 350 most cited and 350 least cited articles for each year. The hypothesis that low-cited papers often have the same subject matter as high-cited papers of previous years is discussed.

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

Feature selection is a crucial step to overcome the curse of dimensionality problem in data mining. This work proposes Recursive k-means Silhouette Elimination (RkSE) as a new unsupervised feature selection algorithm to reduce dimensionality in univariate and multivariate time-series datasets. Where k-means clustering is applied recursively to select the cluster representative features, following a unique application of silhouette measure for each cluster and a user-defined threshold as the feature selection or elimination criteria. The proposed method is evaluated on a hydraulic test rig, multi sensor readings in two different fashions: (1) Reduce the dimensionality in a multivariate classification problem using various classifiers of different functionalities. (2) Classification of univariate data in a sliding window scenario, where RkSE is used as a window compression method, to reduce the window dimensionality by selecting the best time points in a sliding window. Moreover, the results are validated using 10-fold cross validation technique. As well as, compared to the results when the classification is pulled directly with no feature selection applied. Additionally, a new taxonomy for k-means based feature selection methods is proposed. The experimental results and observations in the two comprehensive experiments demonstrated in this work reveal the capabilities and accuracy of the proposed method.

In the current natural environment, due to the complexity of the background and the high similarity of the color between immature green tomato and plant, the occlusion of the key organs (flower and fruit) by the leaves and stems will lead to low recognition rate and poor generalization of the detection model. Therefore, an improved tomato organ detection method based on convolutional neural network has been proposed in this paper. Based on the original Faster R-CNN algorithm, Resnet-50 with residual blocks was used to replace the traditional vgg16 feature extraction network, and K-means clustering method was used to adjust more appropriate anchor size than manual setting to improve detection accuracy. A variety of data augmentation techniques were used to train the network. The test results showed that compared with the traditional Faster R-CNN model, the mean average precision (mAP) of the optimal model was improved from 85.2% to 90.7%, the memory requirement decreased from 546.9MB to 115.9 MB, and the average detection time was shortened to 0.073S/sheet. As the performance greatly improved, the training model can be transplanted to the embedded system, which lays a theoretical foundation for the development of precise targeting pesticide application system and automatic picking device.

Drug-induced pruritus triggers a desire to scratch, thereby diminishing one’s quality of life. Certain instances of this phenomenon follow complex mechanisms of action that diverge from histamine-mediated pathways, known contributors to pruritus. However, investigations into the relationship between drugs and pruritus are limited. In this study, data mining techniques were employed to comprehensively analyze the characteristics of drugs linked to pruritus, using the FDA Adverse Event Reporting System(FAERS) Data maintained in the United States. Reports linked to pruritus demonstrated noteworthy differences in gender, age, and weight when compared with non-pruritus cases. Among the leading candidates for drugs prompting pruritus were ophthalmic drugs, systemic antibacterials, contrast media, dermatological antifungals, and dermatological preparations. Principal component analysis showed that the second principal component served as an indicator for distinguishing between onsets at mucous membranes or the skin surface. Additionally, the third principal component functioned as an indicator for categorizing administration methods as either invasive or noninvasive. Furthermore, a hierarchical cluster analysis conducted on these obtained principal components revealed the potential for classifying drugs based on the site of pruritus onset and the method of drug administration. These findings contribute to the development of targeted prevention and treatment strategies for avoiding pruritus in clinical practice.

Life in the modern century is heavily reliant on an enormous amount of electricity consumption as technology has become the most integral part of daily life. In this context, smart grid systems play a pivotal role to maintain the uninterrupted power supply which needs to be monitored in a timely fashion to keep track of the electric consumers’ usage pattern. The smart meter is the one of smart applications of the smart grid that collects huge amounts of consumer load data on a daily basis which has become a focus for various researchers and analyzers to study load characterization. In this paper, an approach has been proposed to recognize the energy consumption patterns among diverse types of consumers ranging from residential to industrial levels. This approach is worth considering not only for load pattern recognition but also for involving customers in different events such as demand response or peak shaving. In such a way, this analytical mechanism certainly assists in reducing power wastage and saving costs. The proposed methodology is based on a two-fold clustering algorithm with the use of state-of-the-art technology, machine learning. The primary goal is to classify electric customers' data collected from smart meters. Then, analyzing the classified results with an aim to predict power consumption patterns for the customers in the future and making the right energy policy that will benefit both the grid operator and consumers as well.

In actuarial practice, the modeling of total losses tied to a certain policy is a non-trivial task. Traditional parametric models to predict total losses have limitations due to complex distributional features such as extreme skewness, zero inflation, multi-modality, etc., and the lack of explicit solutions for log-normal convolution. In the recent literature, the application of the Dirichlet process mixture for insurance loss has been proposed to eliminate the risk of model misspecification biases; however, the effect of covariates as well as missing covariates in the modeling framework is rarely studied. In this article, we propose novel connections among covariate-dependent Dirichlet process mixture, log-normal convolution, and missing covariate imputation. Assuming an individual loss is log-normally distributed, we develop a log skew-normal Dirichlet process to approximate the log-normal sum. As a generative approach, our framework models the joint of outcome and covariates, which allows to impute missing covariates under the assumption of missingness at random. The performance is assessed by applying our model to several insurance datasets, and the empirical results demonstrate the benefit of our model compared to the existing actuarial models such as the Tweedie-based generalized linear model, generalized additive model, or multivariate adaptive regression spline.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

Context: Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives: We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time. Methods: The key idea is to use on one side natural language processing solutions to detect problems at service level, extracting words that represent anomalies. Clustering and topic modeling techniques have been used to identify patterns and group them with respect to topics. On the other side time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Results: We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. We have observed a correspondence between anomalies in log files and monitoring data, e.g. an increase in memory usage or machine load. The results are extremely promising. Conclusion: Our model requires to integrate site administrators' expertise in order to consider all critical scenario in the data center and understand results properly.

Underwater Wireless Sensor Networks (UWSN) enables various oceanic applications which require effective packet transmission. In this case, sparse node distribution, dynamic network topology and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a Relay based Void Hole Prevention and Repair protocol (ReVOHPR) by multiple Autonomous Underwater Vehicles (AUV) for UWSN. ReVOHPR efficiently identifies and avoids void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as Ocean Depth (levels) based Equal Cluster Formation, Dynamic Sleep Scheduling, Virtual Graph based Routing, and Relay Assisted Void Hole Repair. For energy efficient cluster forming, Entropy based Eligibility Ranking (E2R) is presented which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented Dynamic Kernel Kalman Filter (DK2F) algorithm in which Sleep and Active modes based on the nodes current status. Inter Cluster Routing is performed by maximum matching nodes which selects by Dual criteria and also data transmitted to AUV. Finally, void holes are detected and repair by Bi-Criteria Mayfly Optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes, data packet loss and maximizes Quality of Service (QoS) and energy efficiency of the networks. This protocol is timely deal with node failures in packet transmission via multi-hop routing. Simulation is implemented by NS3 (AquaSim module) simulator that evaluates the performance in network simulation for following metrics as average energy consumption, delay, packet delivery rate and throughput.

With a reduction in the mortality rate of burn patients, patient length of stay (LOS) is increasingly adopted as an outcome measure. Some studies have attempted to identify factors that explain a burn patient's expected LOS. However, few have investigated the association between LOS and a patient's mental and socioeconomic status. There is anecdotal evidence for links between these factors and uncovering these will aid in better addressing the specific physical and emotional needs of burn patients, and facilitate the planning of scarce hospital resources. Here, we employ machine learning (clustering) and statistical models (regression) to investigate whether a segmentation by socioeconomic/mental status can improve the performance and interpretability of an upstream predictive model, relative to a unitary model derived for the full adult population of patients. Although we found no significant difference in the performance of the unitary model and segment-specific models, the interpretation of the segment-specific models reveals a reduced impact of burn severity in LOS prediction with increasing adverse socioeconomic and mental status. Furthermore, the models for the socioeconomic segments highlight an increased influence of living circumstances and source of injury on LOS. These findings suggest that, in addition to ensuring that the physical needs of patients are met, management of their mental status is crucial for delivering an effective care plan.

The Sharing Economy (SE) has dawn great attention from several stakeholders in society in the last five years. While business actors are interested in financial opportunities to meet consumer needs, new business models, the academia and governmental organizations are concerned with potential unintended effects on the society and environment. In the process of making a clearer comprehension of the SE phenomenon, researchers have identified that, despite its notable global growth, there still persists a lack of a more solid ground in understanding its origins and respective mechanisms under which it has been evolving over time as a category. In this research, we address the problematics of the origins and ascendency of the SE by examining the process by which the SE is arising as a new category, searching for conceptual clarification and pinpointing the legitimacy granted by key stakeholders. Our guiding research questions are: (1) how the SE was formed and evolved as a market category; and (2), as a market category, is the SE legitimate? Additionally, we attempt to identify the nature of the SE as a category. To answer these questions, we conducted an historical analysis of the expression SE and its equivalents. This paper deepens the discussion about the nature of the SE by providing evidence that (i) the SE has predominantly been formed by emergence processes, comprising social movement, similarity clustering and truce components. It is the combination of all these aforementioned processes that renders the SE a special case of market category formation, which, in turn, has been allowing communication, entrepreneurship, regulation and research about what really is the SE, and despite the evident lack of agreements regarding both the label and its content; (ii) there is a generalized legitimacy granted to the SE by a vast number of stakeholders, even though still lacking on the consolidation of socio-political legitimation, and (iii) the nature of the SE seems to fall in a metaphorical approach, particularly, the notion of radial categories.

Various type of soils have been identified based on their electrical and magnetic properties, especially with regards to peat soils. Peat soils are commonly considered as partly decomposed vegetation. In this study, electrical and magnetic properties have been used in K-means clustering to identify layers of peat soils. K-means clustering is a partitioning method that treats observations in the data. Data cores were obtained at every centimeter and examined for their electrical conductivity (σ) and magnetic susceptibility (χ_m) properties. A 291 cm core was obtained at Tegal Arum Village in South Kalimantan, Indonesia. The K-means clustering results indicate two different layers at 148 cm, and this is supported by loss on ignition (LOI) measurements. In the first layers, a 87.65% LOI was found associated with peat soils (above 248 cm). Whereas, in the second layers, there was a 26.11% LOI associated with mineral soils (below 248 cm). The results of this study using K-means clustering can be used to delineate soil layers.

Integrating photovoltaic (PV) systems plays a pivotal role in the global shift toward renewable energy, offering significant environmental benefits. In many instances, utility companies pay for both energy and peak demand. PV systems can effectively reduce energy consumption during their operational periods, while, their contribution during peak hours, particularly in the early morning and late evening, is often limited. To address this issue, battery storage systems are utilized for storing energy during off-peak hours and releasing it during peak times. However, finding the optimal size of PV-battery systems remains a challenge. This study proposes a novel statistical model to optimize PV-battery system sizing for enhanced peak demand reduction. The model aims to flatten 95% of peak demand days up to a certain demand threshold, ensuring consistent energy supply and financial benefit for utility companies. A straightforward and effective search methodology is employed to determine the optimal system sizes. Additionally, the model effectiveness is rigorously tested through a modified Monte Carlo simulation coupled with time series clustering to generate various scenarios to assess performance under different conditions. The results indicate that the optimal PV-battery system successfully flattens 95% of daily peak demand up to 2000 kW, yielding a financial benefit of $812,648 over 20 years.

There are many applications of anomaly detection in IoT domain. IoT technology consists of large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to Internet, they frequently meet with the challenges of illegitimate accesses in the form of intrusions, anomaly, fraud, etc. Identifying these illegitimate accesses in IoT domain can be an exciting research problem. In numerous applications fuzzy clustering and rough set theory have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower dimensional data cannot be applied efficiently. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques are proposed for anomaly detection. First of all, the nano topology is generated to find lower dimensional apace and then a couple of well-known fuzzy clustering techniques are employed on it for the efficient anomaly detection. The effectiveness of the proposed approaches is evaluated using time-complexity analysis, experimental studies with a synthetic dataset and a real-life dataset along with comparative studies with traditional fuzzy clustering approaches namely fuzzy c-means clustering (FCM) algorithm, Gustafson-Kessel (GK) Algorithm, Gath-Geva (GG) Algorithm, Mahalanobis Distance based Fuzzy C-Means algorithm (M-FCM), and Common Mahalanobis Distance based Fuzzy C-Means algorithm (CM-FCM). Experimentally, it has been found that the proposed approaches outperform the aforesaid algorithms in terms of detection rates, accuracy rates, false alarm rates and computational times.

Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the power of ensembles and deep learners. This work proposes merging both methods by combining a set of gated recurrent units, temporal convolutional neural networks, and long short-term memory networks trained with variants of the Adam optimization approach. We examine many Adam variants, each fundamentally based on the difference between present and past gradients, with step size adjusted for each parameter. We also combine Incorporating Multiple Clustering Centers and a bootstrap-aggregated decision trees ensemble, which is shown to further boost classification performance. In addition, we provide an ablation study for assessing the performance improvement that each module of our ensemble produces. Multiple experiments on a large set of datasets representing a wide variety of multilabel tasks demonstrate the robustness of our best ensemble, which is shown to outperform the state-of-the-art. The MATLAB code for generating the best ensembles in the experimental section will be made available at https://github.com/LorisNanni.

Chikungunya virus (CHIKV) and Zika virus (ZIKV) have recently emerged as global infections with consequential disability adjusted life years (DALYs) and economic burden. This study aimed to explore the spatiotemporal heterogeneity in the occurrence of CHIKV and ZIKV outbreaks throughout Barranquilla, Colombia during 2014 and 2016 and explored the potential for clustering. Incidence data were fitted using multiple Bayesian Poisson models based on a suite of explanatory variables as potential risk factors and multiple options for random effects. A best fit model was used to analyse the case incidence risk for both epidemics to identify any risk factors during their epidemics. Neighbourhoods in the northern region of Barranquilla were hotspots for the outbreaks of both CHIKV and ZIKV. Additional hotspots occurred in the south-western and central regions of the CHIKV and ZIKV outbreaks, respectively. Multivariate conditional autoregressive models strongly identified higher socioeconomic strata (SES) and residing in detached houses as risk factors for ZIKV case incidences. These novel findings challenge the belief that these infections are driven by social vulnerability and merits further study both in Barranquilla and throughout the tropical and subtropical regions of the world.

Chikungunya virus (CHIKV) and Zika virus (ZIKV) have recently emerged as global infections with consequential disability adjusted life years (DALYs) and economic burden. This study aimed to explore the spatiotemporal heterogeneity in the occurrence of CHIKV and ZIKV outbreaks throughout Barranquilla, Colombia during 2014 and 2016 and explored the potential for clustering. Incidence data were fitted using multiple Bayesian Poisson models based on a suite of explanatory variables as potential risk factors and multiple options for random effects. A best fit model was used to analyse the case incidence risk for both epidemics to identify any risk factors during their epidemics. Neighbourhoods in the northern region of Barranquilla were hotspots for the outbreaks of both CHIKV and ZIKV. Additional hotspots occurred in the south-western and central regions of the CHIKV and ZIKV outbreaks, respectively. Multivariate conditional autoregressive models strongly identified higher socioeconomic strata (SES) and residing in detached houses as risk factors for ZIKV case incidences. These novel findings challenge the belief that these infections are driven by social vulnerability and merits further study both in Barranquilla and throughout the tropical and subtropical regions of the world.

Grasping the spatial and temporal patterns of forest fires, along with the key factors that drive them and the ability to forecast such events accurately, is essential for the successful management of forests. In China's southern region, forest fires significantly endanger the ecological system, public safety, and economic well-being. Through the application of Geographic Information Systems (GIS) and LightGBM (Light Gradient Boosting Machine) model, this study investigates the determinants of fire incidents and formulates a predictive model for forest fire occurrences, alongside a zoning strategy, within the Central-South area. The results indicate: (i) Spatially, fire points exhibit significant clustering and autocorrelation characteristics; (ii) The Central-South Forest Fire Prediction Model shows exceptional accuracy, reliability, and predictive power, with high performance metrics across training and validation sets, including over 85% accuracy, precision, recall, and F1 scores, along with AUC values above 89%, underscoring its efficacy in forecasting forest fires and distinguishing between fire events; (iii) Throughout the year, forest fire risks in the Central-South region of China vary by region and season, with risk spikes from March to May in Guangdong, Guangxi, Hunan, Hubei, and Hainan. From June to August, localized risks are observed in Heyuan and Huangshi, while from September to November, an increase in risk is noted in Guangdong (Meizhou, Heyuan, Shaoguan), Guangxi (Nanning, Hezhou, Yulin), and Hunan (Binzhou, Yongzhou, Hengyang) due to cooler, drier conditions and leaf litter accumulation. From December to February, the risk continues in specific areas across Guangdong, Guangxi, Hunan, and Hubei.

: Glaucoma is the most common cause of blindness, which significantly reduces quality of life. Most glaucoma cases are primary glaucoma; nevertheless, many patients suffer from glaucoma caused by drugs, such as corticosteroids. A comprehensive review of the risks associated with corticosteroid-induced glaucoma is limited. Therefore, we used the Japanese Adverse Drug Event Reporting Database (JADER) published by the Pharmaceuticals and Medical Devices Agency (PMDA) to analyze the risk factors associated with glaucoma and the trends and characteristics of corticosteroid-induced glaucoma. We did not find sex or age differences associated with the onset of glaucoma. Hierarchical clustering and principal component analysis revealed that triamcinolone acetonide and betamethasone sodium phosphate, which are used around the eyes in Japan, are more likely to induce intraocular pressure (IOP) elevation compared with other corticosteroids. Increased IOP is a direct cause of glaucoma. Based on these findings, it may be necessary to limit or avoid the use of these corticosteroids.

The aim of this work is to explain the behaviour of the multiresistance percentage of Pseudomona aeruginosa in some countries of Europe through a multivariate statistical analysis and machine learning validation, using data from the European Antimicrobial Resistance Surveillance System, the World Health Organization and the World Bank. First, we will use a descriptive analysis and a principal components analysis. Then, we use a k-means clustering to determine the countries and regions that are most affected by the antibiotic resistance. Second, we expand the database by adding some socioeconomic, governance and antibiotic-consumption variables. We then run a data panel regression analysis to determine some functions that relates the multiresistance percentage with those new variables. Finally, we use machine learning techniques to validate a pooling panel data case, using XGBoost and random forest algorithms. The results of the data panel analysis indicate that the most important variables for the multiresistance percentage are corruption control and the rule of law. Similar results are found with the machine learning validation analysis, where the human development index is an additional important variable for the multiresistance percentage.

The aerospace industry develops prognosis and health management algorithms to ensure better safety on board. Particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, we developed a clustering algorithm using a deep neural network core. We encoded the time series into pictures to be feed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihood without prior knowledge. We compared it to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As state-of-the-art indexes were not producing relevant results, we built a new indicator to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

This article shows the numerical results and the analysis of households' degree of knowledge in an intermediary city such as Huelva (Andalusia, Spain) about the sustainable use of urban water. It analyzes the needs and values regarding water and the attitudes that households maintain regarding the acceptance of reclaimed water and the use of new technologies to achieve more efficient and sustainable consumption. These results are part of the stages of needfinding and synthesis of Design Thinking methodology, adopted as a framework to improve the efficiency and sustainability of urban water among households in this city. Different statistical analysis techniques of surveys sent to households and the use of clustering are the mathematical tools used to draw conclusions and recommendations that allow the design of a web-based prototype grounded on Product Service Systems methodology, as a tool to improve the engagement of households concerning water and align citizens with the sustainability of their city. Strategies of customization and technological facilitators will be the means to improve the hydrosocial contract among households in Huelva in future later stages of the project.

Crop type and field boundary mapping enable cost-efficient crop management on the field scale and serve as the basis for yield forecasts. Our study uses a data set with crop types and corresponding field borders from the federal state of Bavaria, Germany, as documented by farmers from 2016 to 2018. The study classified corn, winter wheat, barley, sugar beet, potato, and rapeseed as the main crops grown in Upper Bavaria. Corresponding Sentinel-2 data sets include the normalised difference vegetation index (NDVI) and raw band data from 2016 to 2018 for each selected field. The influences of clouds, raw bands, and NDVI on crop type classification are analysed, and the classification algorithms, i.e., support vector machine (SVM) and random forest (RF), are compared. Field boundary detection and extraction are based on non-iterative clustering and a newly developed procedure based on Canny edge detection. The results emphasise the application of Sentinel’s raw bands (B1–B12) and RF, which outperforms SVM with an accuracy of up to 94%. Furthermore, we forecast data for an unknown year, which slightly reduces the classification accuracy. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

In recent years, distributed generation technology has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. In order to give full play to the advantages of Distributed Generation (DG) and meet the challenges after power grid access, Active Distribution Network (ADN) is considered as the future development direction of traditional distribution network because of its ability of active management. Nowadays, multi-scenario analysis is widely used in the research of optimal allocation of distributed power supply in active distribution network. Aiming at the problems that may arise when using multi-scenario analysis to plan DG with uncertainties in large-scale scenarios, a scenario reduction method based on improved clustering algorithm is proposed. The validity of the scene reduction method is tested, and the feasibility of the method is verified. At present, there are few studies on the optimal allocation of DG in ADN under fault state. In this paper, comprehensive safety indicators are introduced. Considering the timing characteristics of DG and the influence of active management mode, a bi-level programming model is established, which aims at minimizing the investment of annual life cycle and the removal of active power. The bi-level model is a complex mixed integer non-linear programming model. A hybrid algorithm combining cuckoo search algorithm and primal dual interior point method is used to solve the model. Finally, through the simulation of the IEEE-33 node system, the superiority of the scenario reduction method and the comprehensive security index used in this paper to optimize the configuration of DG in ADN is verified.

With increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Since its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high power (3.5kW) and high speed (12m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging to optimize the signal duration. Our results also suggest that large gaps (> 0.3mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2mm weld seam.

Applications for Wireless Sensor Networks (WSNs) range from monitoring the environment to automating factories. However, sustained and effective functioning is made more difficult by Sensor Nodes (SNs) limited energy supplies in which optimization is the main issue. So with the aim of increasing the lifespan by decreasing the energy consumption of WSN, Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol with Deep Learning (DL) algorithm is analyzed in this paper. LEACH is a hierarchical mechanism that elects Cluster Heads (CHs) and regularly rotates their positions in order to allocate energy use effectively by using the same amount of energy. However, Deep Learning (DL) method is used to further improve energy optimization. In many applications, the types of Deep Learning methods like Artificial Neural Networks (ANNs) have shown to be very useful. Using this method, WSNs may make more efficient decisions that reduce energy consumption. Data aggregation, duty cycling, and transmission protocols may all be optimized by Deep Learning model's ability to recognize patterns and forecast network behavior. This results in lower energy consumption, a longer lifespan for the network, and better overall performance.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Keywords: Requirements prioritization, Next release plan, Software product planning, Decision support, MoSCoW, AHP, K-Menas, GMM, BIRCH, PAM, Hierarchical, Clustering, Clusters Evaluation

Examining the similarity of event environments or surroundings, more precisely settings, provides additional insight in analyzing event sequences as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences we consider spatial and temporal scales to define the bounds of the setting and incorporates dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, these similarity measures have many potential real-world applications, and offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

The Upper Hunter Valley is a major coal mining area in New South Wales (NSW), Australia. Due to the ongoing increase of mining activities, PM10 (air-borne particle with an aerodynamic diameter less than 10 micrometres) pollution has become a major air quality concern in local communities. The early part of this study identified two air quality subregions in the valley. This paper aims to provide a holistic summarisation of the relationships between elevated PM10 pollution in two subregions and the local- and synoptic-scale meteorological conditions for spring and summer, when PM10 pollution is relatively high. A catalogue of twelve synoptic types and a set of six local meteorological patterns have been quantitatively derived and linked to each other using the self-organising map (SOM) technique. The complex meteorology-air pollution relationships have been visualised/interpretated on the SOM planes for two representative locations. It was found that the influence of local meteorological patterns differed significantly for mean PM10 levels vs the occurrence of elevated pollution events and between air quality subregions. In contrast, synoptic types showed generally similar relationships with mean vs elevated PM10 pollution in the valley. Two local meteorological patterns, the hot-dry-northwesterly wind conditions and the hot-dry-calm conditions, were found most PM10 pollution conducive in the valley when combined with a set of synoptic counterparts. These synoptic types are featured with the influence of eastward migrating continental high pressure system and westerly troughs, or a ridge extending northwest towards coastal northern NSW or southern Queensland from the Tasman Sea. The method and results can be used in air quality research for other locations of NSW, or similar regions elsewhere.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to currently not provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed `quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Recently, the Integrated Air Defense System (IADS) has become vital for the defense system as the military defense system is vital for national security. Placing Integrated Air Defense System batteries among locations to protect locations assets is a crucial problem because optimal solutions are needed for interceptor missiles to intercept attacker missiles for maximizing protection of assets across locations or places. In this research, the procedures of using network algorithms along with developing several network algorithms are going to be demonstrated to develop a model for sequential development of seven network securing strategies of placing Surface to Air Missile (SAM) batteries to maximize the protection of assets across locations (based on given asset values) by generating optimal solutions through computation to destroy maximum attacker missiles by using minimum interceptor missiles with given intercept probability. This network securing strategies can be implemented not only for Integrated Air Defense System (IADS) planning but also Counter Air (CA) planning as Integrated Air Defense System (IADS) is conducted with defensive counter air supported by attack operations in offensive counter air.

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capabilities in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possibility and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering was minimally discussed in literature neither. This paper first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model into generating new game levels within two well-known game settings; as well as validating the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments were also utilized for evaluating the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and spatial results, several potential drawbacks and future enhancement of the established model were outlined, with the aim of maximizing the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

Mobile phones have become an integral part of our lives in the last two decades, leaving a digital trace of our activities and communication. This study aims to develop a data processing framework to evaluate human mobility and socioeconomic status based on call detail records. The methodology proposed first calculates radius of gyration and entropy for each user, then estimates the socioeconomic status by the price and age of the subscribers' phones. Finally, an unsupervised machine learning algorithm was used to group the cells into clusters based on their mobility and socioeconomic metrics. The research showed differences between Buda and Pest during a large scale social event using mobile phone ages and prices. Additionally, the clustering results revealed homogenous groups of cells around Budapest, with similar mobility and socioeconomic metrics. The main conclusion is that mobile network data combined with mobile phone properties offer a useful tool for characterising urban mobility and socioeconomic status.

The safe and reliable operation of autonomous vehicles in complex and dynamic environments is heavily dependent upon exteroceptive and proprioceptive sensors. A LiDAR system generates accurate 3D point clouds, which are crucial for the detection, classification, and tracking of multiple targets. LiDAR data, however, presents significant challenges due to its density, noise, and varying sampling rates. In this study, various clustering and MTT techniques for LiDAR point clouds will be identified and classified within the context of autonomous driving to assess the state-of-the-art methods’ key challenges and their performances. We have categorized clustering and MTT methods used in AV applications, identified research gaps and challenges and analyzed existing algorithms and their challenges in detail. Researchers and practitioners in the field of autonomous driving will find this review to be a valuable resource.

A Wireless Body Area Sensor Network (WBASN) is combination of numerous sense nodes, positioned onto/close or inside a person body. Wireless Body Area Sensor Networks (WBASN) is a developing automation trend that exploits wireless sensor nodes to put instantaneous wearable well-being of ill person to improve individual’s existence. The sensor nodes might be used outwardly to observe abundant health parameters (like heart activity, blood pressure and cholesterol) of an ill person at a vital site within hospital. Hence the goal of WBASN is much crucial, enhancing the lifetime of nodes is compulsory to sustain many issues such as utility and efficiency. It is essential to evaluate time that when the first node will die it we want to refresh or change the battery reason is that loss of crucial information is not tolerable. The lifetime is termed as the time interval when a first node dies out due to battery exhaustion. In our proposed protocol life time of a network is the main concern as well other protocol related issues such as throughput, path loss, and residual energy. Bio-sensors are used for deployment on human body. Poisson distribution and equilibrium model techniques have been used for attaining the required results. Multi-hop network topology and random network node deployment used to achieve minimum energy consumption and longer network lifetime.