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The present study is an evaluation of published papers on machine learning as employed in mastitis research. The aim of this study was the quantitative evaluation of the scientific content and the bibliometric details of these papers. In total, 69 papers were found to combine machine learning in mastitis research and were considered in detail. There was a progressive yearly increase in published papers, which originated from 23 countries (mostly from China or the United States of America). Most original articles (n = 59) referred to work involving cattle, relevant to mastitis in individual animals. Most articles described work related to the development and diagnosis of the infection. Fewer articles described work on the antibiotic resistance of pathogens isolated from cases of mastitis and on the treatment of the infection. In most studies (98.5% of published papers), supervised machine learning models were employed. Most frequently, decision trees and support vector machines were employed in the studies described. 'Machine learning' and 'mastitis' were the most frequently used keywords. The papers were published in 39 journals, with most frequent publications in Computers and Electronics in Agriculture and Journal of Dairy Science. The median number of cited references in the papers was 39 (interquartile range: 31). There were 435 co-authors in the papers (mean: 6.2 per paper, median: 5, min.–max.: 1–93) and 356 individual authors. The median number of citations received by the papers was 4 (min.–max.: 0–70). Most papers (72.5%) were published in open-access mode. This study summarized the characteristics of papers on mastitis and artificial intelligence. Future studies could explore using these methodologies at farm level, and extending them to other animal species, while unsupervised learning techniques might also prove to be useful. [ABSTRACT FROM AUTHOR]

Herein, the challenge of accurately classifying the manufacturing origin of printing paper, including continent, country, and specific product, was addressed. One-dimensional convolutional neural network (1D CNN) models trained on infrared (IR) spectrum data acquired from printing paper samples were used for the task. The preprocessing of the IR spectra through a second-derivative transformation and the restriction of the spectral range to 1800 to 1200 cm-1 improved the classification performance of the model. The outcomes were highly promising. Models trained on second-derivative IR spectra in the 1800 to 1200-cm-1 range exhibited perfect classification for the manufacturing continent and country, with an impressive F1 score of 0.980 for product classification. Notably, the developed 1D CNN model outperformed traditional machine learning classifiers, such as support vector machines and feed-forward neural networks. In addition, the application of data point attribution enhanced the transparency of the decision-making process of the model, offering insights into the spectral patterns that affect classification. This study makes a considerable contribution to printing paper classification, with potential implications for accurate origin identification in various fields. [ABSTRACT FROM AUTHOR]

The evaluation and classification of chemical properties in different copypaper products could significantly help address document forgery. This study analyzes the feasibility of utilizing infrared spectroscopy in conjunction with machine learning algorithms for classifying copy-paper products. A dataset comprising 140 infrared spectra of copy-paper samples was collected. The classification models employed in this study include partial least squares-discriminant analysis, support vector machine, and K-nearest neighbors. The key findings indicate that a classification model based on the use of attenuated-total-reflection infrared spectroscopy demonstrated good performance, highlighting its potential as a valuable tool in accurately classifying paper products and ensuring assisting in solving criminal cases involving document forgery. [ABSTRACT FROM AUTHOR]

Handmade paper is a major carrier and restoration material of traditional Chinese ancient books, calligraphies, and paintings. In this study, we carried out a Raman spectroscopy analysis of 18 types of handmade paper samples. The main components of the handmade paper were cellulose and lignin, according to the wavenumber and Raman vibration assignment. We divided its Raman spectrum into eight subbands. Five machine learning models were employed: principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), k‐nearest neighbors (KNN), and random forest (RF). The Raman spectral data were normalized, and the fluorescence envelope was subtracted using the airPLS algorithm to obtain four types of data, raw, normalized, defluorescence, and fluorescence data. An RF variable importance analysis of data processing showed that data normalization eliminated the intensity differences of fluorescence signals caused by lignin, which contained important information of raw materials and papermaking technology, let alone the data defluorescence. The data processing also reduced the importance of the average variables in almost all spectral bands. Nevertheless, the data processing is worthwhile because it significantly improves the accuracy of machine learning, and the information loss does not affect the prediction. Using the machine learning models of PCA, PLS, and SVM combined with linear regression (LR), KNN, and RF, the classification and prediction of handmade paper samples were realized. For almost all processed data, including the fluorescence data, PCA‐LR had the highest classification and prediction accuracy (R2 = 1) in almost all spectral bands. PLS‐LR and SVM‐LR had the second‐highest accuracies (R2 = 0.4–0.9), whereas KNN and RF had the lowest accuracies (R2 = 0.1–0.4) for full band spectral data. Our results suggest that the abundant information contained in Raman spectroscopy combined with powerful machine learning models could inspire further studies on handmade paper and related cultural relics. [ABSTRACT FROM AUTHOR]

Molecularly imprinted polymer (MIP-QDs) with fluorescence quenching ability toward propazine was synthesized for propazine detection. b(Blue)-MIP-QDs were prepared using ZnCdS/ZnS QDs via reverse micro-emulsion, whereas r(red)-MIP-QDs were synthesized using CdSe/ZnS QDs. By utilizing graphene quantum dots (GQDs) as a stable fluorescence intensity reference, the wide-gamut fluorescence test paper was constructed on the basis of mixing b-MIP-QDs, r-MIP-QDs, and GQDs under the optimal ratio. When analyzing spiked propazine in fish and seawater samples using a test paper, satisfactory recoveries of 104.0 %–114.6 % and 92.0 %–96.4 % were obtained, with corresponding limits of detection of 5.0 μg/kg and 1.0 μg/L, respectively. The RGB extractor was utilized to extract the actual fluorescence color and construct a dataset consisting of R, G, and B values, as well as concentration data from 400 samples. The SVR model of Python 3.9.7 was used to obtain and analyze the concentration and feature data. After optimization, the constructed model achieved a correlation coefficient of 0.98 and an RMSE of only 1.81, indicating high prediction accuracy and excellent generalization ability that meet quenching prediction requirements. As an intelligent and rapid detection method, this model holds significant practical significance. [Display omitted] • A wide-gamut fluorescence visual test paper was fabricated. • The visual test paper demonstrates excellent linearity, accuracy, precision, and sensitivity for promazine detect. • An RGB model by support vector machine were constructed. • The model exhibits high accuracy and strong generalization ability. [ABSTRACT FROM AUTHOR]

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established. [ABSTRACT FROM AUTHOR]

[Display omitted] • The 2-D correlation methods do not require external perturbation variables. • They are pure mathematical methods that utilize the tensor product of spectral data. • The R2 values of KNN and RF for TDACM are close to 1, indicating nearly 100% improvement. The paper introduces two new methods, namely the cross correlation method (CCM) and two-dimensional correlation method (TDCM), for preprocessing Raman spectroscopy data for analyzing Chinese handmade paper samples. CCM expands the spectral dimension from 1 × N to 1 × 2 N - 1 by taking cross-correlation between two spectral data of the same category. TDCM includes two-dimensional synchronous correlation method (TDSCM) and two-dimensional asynchronous correlation method (TDACM), which expand the spectral dimension from 1 × N to N × N by taking tensor products between two spectral data and between one spectral data and the Hilbert transformation of the other spectral data of the same category, respectively. The experimental data were preprocessed using baseline removal, CCM, TDSCM, and TDACM methods. Four machine learning models were employed to evaluate the effects of these methods: principal component analysis (PCA) combined with linear regression (LR), support vector machine (SVM) combined with LR, k-Nearest Neighbors (KNN), and random forest (RF). The results show that the R-squared values for the PCA model were nearly 1 for all types of data, indicating high accuracy. However, for SVM-LR, KNN, and RF models, the R-squared values were sorted in the order of raw data, baseline removal data, CCM, TDSCM, and TDACM preprocessed data. The R-squared values of KNN and RF machine learning models for TDACM preprocessed data were approaching 1, indicating that the accuracy of machine learning was significantly improved by nearly 100%. This has led to a remarkable improvement in the accuracy of supervised models such as KNN and RF, bringing them closer to the level of unsupervised models such as PCA. [ABSTRACT FROM AUTHOR]

At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected. [ABSTRACT FROM AUTHOR]

Surface electromyography (EMG) has emerged as a promising clisnical decision support system, enabling the extraction of muscles' electrical activity through non-invasive devices placed on the body. This study focuses on the application of machine learning (ML) techniques to preprocess and analyze EMG signals for the detection of muscle abnormalities. Notably, state-of-the-art ML algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Random Forests (RF), and Naive Bayes (NB), have been harnessed by researchers in the biomedical sciences to achieve accurate surface EMG signal detection. Within this paper, we present a meticulously conducted systematic review, employing the PRISMA method to select relevant research papers. Various databases were thoroughly searched, and multiple pertinent studies were identified for detailed examination, weighing their respective merits and drawbacks. Our survey comprehensively elucidates the latest ML techniques used in surface EMG detection, offering valuable insights for researchers in this domain. Additionally, we outline potential future directions that can guide further advancements in this critical area of research. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) diagnostics requires specialized clinical expertise, posing accessibility and affordability barriers for many. To widen the availability of precise screening, this paper examines ensemble machine learning models that combine multiple algorithms for improved accuracy and generalizability. Specifically, this paper compares the performance of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and a Voting Classifier that integrates Logistic Regression, Decision Trees, and KNN. The comparison was conducted on separate datasets of pediatric and adult ASD questionnaire responses. The ensemble model significantly outperforms individual techniques, achieving higher accuracy for both pediatrics and adults with balanced sensitivity and specificity maintained across groups, indicating the viability of accessible community-available screening to alleviate diagnostic bottlenecks. Before scale-up, further model optimization for interpretability and testing on more diverse multi-site data are warranted. Overall, findings demonstrate the feasibility of mobile distributed pre-screening systems leveraging optimized ensembles to predict ASD with high precision across ages, opening possibilities for explainable AI to lower costs and widen access compared to in-person evaluation. [ABSTRACT FROM AUTHOR]

Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy. [ABSTRACT FROM AUTHOR]

The "Daijiworld News" forum, a well-known news website in coastal Karnataka, was the source of the comments for this paper's sentiment analysis study, which was done on about 15,000 reader comments. The comments were scraped using Beautiful Soup, a popular web scraping library and labelled as positive, negative, and neutral. Pre-processing of comments was made using techniques such as stop word removal, tokenization, stemming, lemmatization, and lowercase conversion. Logistic regression, support vector machine (SVM), naive Bayes, random forest, K-nearest neighbors (KNN), AdaBoost, gradient boosting and neural networks was used for classification. Performance metrics including accuracy, precision, recall, and F1 score were evaluated. Logistic regression achieved the highest precision (0.75), recall (0.74), accuracy (0.74), and F1 score (0.74), followed closely by the neural network classifier with a precision of 0.670, recall of 0.670, accuracy of 0.670, and F1 score of 0.669. The study demonstrates the effectiveness of logistic regression and neural networks in sentiment analysis of news forum comments, giving insightful information to grasp public opinion and improving user engagement. The findings contribute to the field of sentiment analysis, emphasising the significance of web scraping and pre-processing techniques in enhancing sentiment classification accuracy. The results serve as a reference for researchers and practitioners, assisting in the selection of appropriate classifiers for sentiment analysis in similar contexts. The study encourages further exploration of advanced techniques to enhance sentiment classification accuracy in regional news forums, paving the way for future research in sentiment analysis. [ABSTRACT FROM AUTHOR]

The global objective of this paper was to build an automated prediction system for surface damage. Practically, the damage initiates from the free surface because of the high-stress concentration that presents in valleys of the surface profile. Hence, the surface condition is a major factor in the fatigue strength of the metal. In this paper, the surface condition has been measured using an optical confocal measurement system (Alicona). Arithmetical mean height and Surface Flatness have been selected as input data source for the machine learning model. The machine learning model was built using the Support Vector Machine method. The role of this model is to select the best surface parameters to detect surface damage. The results show that the Surface Flatness parameter provides better prediction for surface damage than the Arithmetical mean height parameter. [ABSTRACT FROM AUTHOR]

Early detection of critical diseases is a pivotal aspect of modern healthcare, significantly impacting patient outcomes and healthcare costs. This research paper provides a comprehensive review and analysis of various machine learning algorithms employed in the realm of early disease detection. The study explores the strengths, limitations, and overall efficacy of prominent algorithms, including Logistic Regression, Support Vector Machines, Random Forests, Neural Networks, K-Nearest Neighbors, and Ensemble Learning. Each algorithm's suitability for early detection is assessed based on factors such as interpretability, scalability, and performance in handling diverse data types. Furthermore, the review discusses the specific applications of these algorithms in different medical contexts, highlighting their contributions to the early identification of critical diseases. By synthesizing the current state of research, this paper aims to provide valuable insights for researchers, and policymakers working towards advancing the field of early disease detection through machine learning. [ABSTRACT FROM AUTHOR]

Twin Support Vector Machine (TSVM) is a powerful machine learning method that is usually used to solve binary classification problems. But although the classification speed and performance of TSVM is better than that of primitive support vector machine, TSVM still faces the problem of difficult parameter selection; therefore, to overcome the problem of parameter selection of TSVM, this paper proposes a Chaotic Mapping Dung Beetle Optimization Algorithm-based Twin Support Vector Machine (CMDBO-TSVM) for automatic parameter selection. Due to the uncertainty of the random initialization population of the original Dung Beetle Optimization Algorithm, this paper additionally adds chaotic mapping initialization to improve the Dung Beetle Optimization Algorithm. Experiments on the dataset through this paper show that the classification accuracy of the CMDBO-TSVM has a better performance. [ABSTRACT FROM AUTHOR]

Stroke is a high morbidity and mortality disease that poses a serious threat to people's health. Early recognition of the various warning signs of stroke is necessary so that timely clinical intervention can help reduce the severity of stroke. Deep neural networks have powerful feature representation capabilities and can automatically learn discriminant features from large amounts of data. This paper uses a range of physiological characteristic parameters and collaborates with deep neural networks, such as the Wasserstein generative adversarial networks with gradient penalty and regression network, to construct a stroke prediction model. Firstly, to address the problem of imbalance between positive and negative samples in the stroke public data set, we performed positive sample data augmentation and utilized WGAN‐GP to generate stroke data with high fidelity and used it for the training of the prediction network model. Then, the relationship between observable physiological characteristic parameters and the predicted risk of suffering a stroke was modeled as a nonlinear mapping transformation, and a stroke prediction model based on a deep regression network was designed. Finally, the proposed method is compared with commonly used machine learning‐based classification algorithms such as decision tree, random forest, support vector machine, and artificial neural networks. The prediction results of the proposed method are optimal in the comprehensive measurement index F. Further ablation experiments also show that the designed prediction model has certain robustness and can effectively predict stroke diseases. [ABSTRACT FROM AUTHOR]

Heart disease remains a prevalent and critical health concern globally. This paper addresses the critical task of heart disease prediction through the utilization of advanced machine learning techniques. Our approach focuses on the enhancement of feature engineering by incorporating a novel integration of association and correlation analyses. A heart disease dataset from Kaggle was used for the experiments. Association analysis was applied to the categorical and binary features in the dataset. Correlation analysis was applied to the numerical features in the dataset. Based on the insights from association analysis and correlation analysis, a new dataset was created with combinations of features. Later, newly created features are integrated with the original dataset, and classification algorithms are applied. Five machine learning (ML) classifiers, namely decision tree, k-nearest neighbors (KNN), random forest, XG-Boost, and support vector machine (SVM), were applied to the final dataset and achieved a good accuracy rate for heart disease detection. By systematically exploring associations and relationships with categorical, binary, and numerical features, this paper unveils innovative insights that contribute to a more comprehensive understanding of the heart disease dataset. [ABSTRACT FROM AUTHOR]

Starting from the current need for the safety of energy systems, in which power transformers play a key role, the study of the health of power transformers in service is a difficult and complex task, since the assessment consists of identifying indicators that can provide accurate data on the extent of degradation of transformer components and subcomponents, in order to establish a model for predicting the remaining life of transformers. Therefore, this paper proposes a model for assessing the remaining service life by diagnosing the condition of the transformer based on the health index (HI) obtained from a multi-parameter analysis. To determine the condition of power transformers, a number of methods are presented based on the combination of the combined Duval pentagon (PDC) method and ethylene concentration (C2H4) to determine the fault condition, the combination of the degree of polymerisation (DP) and moisture to determine the condition of the cellulose insulation and the use of the oil quality index (OQIN) to determine the condition of the oil. For each of the classification methods presented, applications based on machine learning (ML), in particular support vector machine (SVM), have been implemented for automatic classification using the Matlab development environment. The global algorithmic approach presented in this paper subscribes to the idea of event-based maintenance. Two case studies are also presented to validate SVM-based classification methods and algorithms. [ABSTRACT FROM AUTHOR]

Facial and object recognition are more and more applied in our life. Therefore, this field has become important to both academicians and practitioners. Face recognition systems are complex systems using features of the face to recognize. Current face recognition systems may be used to increase work efficiency in various methods, including smart homes, online banking, traffic, sports, robots, and others. With various applications like this, the number of facial recognition methods has been increasing in recent years. However, the performance of face recognition systems can be significantly affected by various factors such as lighting conditions, and different types of masks (sunglasses, scarves, hats, etc.). In this paper, a detailed comparison between face recognition techniques is exposed by listing the structure of each model, the advantages and disadvantages as well as performing experiments to demonstrate the robustness, accuracy, and complexity of each algorithm. To be detailed, let's give a performance comparison of three methods for measuring the efficacy of face recognition systems including a support vector machine (SVM), a visual geometry group with 16 layers (VGG-16), and a residual network with 50 layers (ResNet-50) in real-life settings. The efficiency of algorithms is evaluated in various environments such as normal light indoors, backlit indoors, low light indoors, natural light outdoors, and backlit outdoors. In addition, this paper also evaluates faces with hats and glasses to examine the accuracy of the methods. The experimental results indicate that the ResNet-50 has the highest accuracy to identify faces. The time to recognize is ranging from 1.1 s to 1.2 s in the normal environment. [ABSTRACT FROM AUTHOR]

One of the ideas behind vegetation monitoring is the ability to identify different vegetation units, such as species, communities, habitats, or vegetation types. Remote sensing data allow for obtaining such information remotely, which is especially valuable in areas that are difficult to explore (such as mountains or wetlands). At the same time, such techniques allow for limiting field research, which is particularly important in this context. Remote sensing has been utilized for vegetation inventories for many decades, using airborne and spaceborne platforms. Developing newer tools, algorithms and sensors is conducive to more new applications in the vegetation identification field. The Special Issue "Remote Sensing Applications in Vegetation Classification" is an overview of the applications of remote sensing data with different resolutions for the identification of vegetation at different levels of detail. In 14 research papers, the most frequent different types of crops were analysed. In three cases, the authors recognised different types of grasslands, whereas trees were the object of the studies in two papers. The most commonly used sensors were Copernicus Sentinel-1 and Sentinel-2; however, to a lesser extent, MODIS, airborne hyperspectral and multispectral data, as well as LiDAR products, were also utilised. There were articles that tested and compared different combinations of datasets, different terms of data acquisition, or different classifiers in order to achieve the highest classification accuracy. These accuracies were assessed quite satisfactorily in each publication; the overall accuracy (OA) for the best result varied from 72% to 98%. In all of the research papers, at least one of the two commonly used machine learning algorithms, random forest (RF) and support vector machines (SVM), was applied. Additionally, one paper presented software ARTMO's machine-learning classification algorithms toolbox, which allows for the testing of 13 different classifiers. The studies published in this Special Issue can be used by the vegetation research teams and practitioners to conduct deeper analysis via the utilization of the proposed solutions. [ABSTRACT FROM AUTHOR]

This study investigates the impact of air pollution on health outcomes in Middle Eastern countries, a region facing severe environmental challenges. As such, these are important in an effort to add up to policy-level as well as interventional changes that can be put in practice in the area of public health. Numeration analysis and association with health parameters was carried out by using Analytical tools such as, AIR Data, ARIMA,ANN, SVM and Exponential smoothing. Amongst the models, Support Vector Machine came again on top, with high accuracy yielding Mean Absolute Percentage Error of approximately 1%. Mortality of Air pollution in Qat from the case of Mortality of Air Pollution in Qatar is 959 while Auto regressive Integrated Moving average is 11.096, Exponential Smoothing 9.892 and Artificial Neural Networks are the source of inspiration for the development of this paper 4.61. The above perceptions indicate that there is need to adapt modeling strategies depending on the context and establish that it is possible to implement ML models in public health planning basket. This paper publishes the methodological frameworks for the purpose of modeling and analysis of the EHDs and serves as policy prescription for the policy makers to intending to reduce the effects of air borne pollution on health. [ABSTRACT FROM AUTHOR]

The integrated navigation system based on the Global Navigation Satellite System (GNSS) in conjunction with the strapdown inertial navigation system (SINS) and the Doppler Velocity Logger (DVL) is essential for accurate and long-distance navigation in maritime environments. However, the error of the integrated navigation system gradually diverges due to the inevitable velocity measurement error of DVL when GNSS outages occur. To ensure the high navigational accuracy and stability of SINS, it is necessary to dynamically adjust the damping state of SINS provided externally. In this paper, we have developed a novel method for damping state switching based on machine learning with SINS. We construct a model of the change in reference velocity error and use sliding window technology to obtain the reference velocity error for model training. Before training, the digital compass loop is designed to process and highlight the change in reference velocity change errors. In order to reduce the impact of the damping switching, a variable damping system is used to transform the traditional one-time switching of the damping coefficient into a gradual switching, effectively reducing the impact of a sudden change in the damping coefficient on the system. Simulation experiments and tests on ships show that the proposed method effectively reduces the overshoot error integrated underwater during state switching. This research is of great importance for the optimal design of integrated underwater navigation systems. [ABSTRACT FROM AUTHOR]

This paper proposes an advanced deep learning model that integrates the Diffusion-Transformer structure and parallel attention mechanism for the tasks of growth estimation and disease detection in jujube forests. Existing methods in forestry monitoring often fall short in meeting the practical needs of large-scale and highly complex forest areas due to limitations in data processing capabilities and feature extraction precision. In response to this challenge, this paper designs and conducts a series of benchmark tests and ablation experiments to systematically evaluate and verify the performance of the proposed model across key performance metrics such as precision, recall, accuracy, and F1-score. Experimental results demonstrate that compared to traditional machine learning models like Support Vector Machines and Random Forests, as well as common deep learning models such as AlexNet and ResNet, the model proposed in this paper achieves a precision of 95%, a recall of 92%, an accuracy of 93%, and an F1-score of 94% in the task of disease detection in jujube forests, showing similarly superior performance in growth estimation tasks as well. Furthermore, ablation experiments with different attention mechanisms and loss functions further validate the effectiveness of parallel attention and parallel loss function in enhancing the overall performance of the model. These research findings not only provide a new technical path for forestry disease monitoring and health assessment but also contribute rich theoretical and experimental foundations for related fields. [ABSTRACT FROM AUTHOR]

The accurate classification of human activities in crime scenes during forensics (criminalistics) is of utmost importance in classifying suspicious and unlawful activities, easing their acceptability and interpretability by judges during legal procedures in courts or by other non-experts in the field of forensics. This paper implements machine learning (ML) algorithms: support vector machine (SVM) and decision tree (DT), to demonstrate with a high accuracy, how data emanating from smartphones' sensors reveal and isolate relevant information about static and dynamic human activities in criminalistics. Smartphones' data from five different sensors (accelerometer, gravity, orientation, Gyroscope and light), related to ten recurrent crime scenes activities, grouped into three classes of events (normal, felony and none-felony events) are classified by the proposed algorithms, with novelty being the classification decisions based on the entire period of the events and not instantaneous decision makings. Three independent data-subsets were made, with permutations done between them and at each time, two sets used for training and the third set used for testing. Time- and frequency-domain features were initially used separately and then combined for the model training and testing. The best average training accuracies of 100% and 97.8% were obtained for the DT and SVM, respectively, and the testing accuracies of 89.1% were obtained for both algorithms. We therefore believe that these results will serve as a solid persuasive and convincing argument to judges and non-experts of the field of forensics to accept and easily interpret computer-aided classification of suspicious activities emanating from criminalistic studies. [ABSTRACT FROM AUTHOR]

Demand response is an effective solution for balancing supply and demand in modern energy supply systems. For utility or load aggregators, it is important to accurately target potential consumers to participate in demand response programs to recruit a massive number of users. This is especially important for the invitation-based demand response mode, which is currently often used in China. In this paper, a portrait-based method is proposed to effectively identify potential consumers for different demand response tasks based on historical loads. Eight indicators are proposed to quantify the energy consumption characteristics from different aspects, and an evaluation method is introduced. Then, a selection method based on the K-means clustering algorithm and support vector machine classifiers is proposed. The method is tested under two scenarios, including load shifting and monthly peak shaving. The results show that the proposed method can identify potential users effectively, and the accuracy of the trained classification model exceeds 99.9%. The proposed portrait-based identification method provides an effective way to describe users' energy consumption characteristics and select potential users effectively, which is very useful for helping the utility or virtual plant with load management. [ABSTRACT FROM AUTHOR]

In cognitive radio network (CRN), effective spectrum management provides better quality of service. The spectrum is limited but the significance of the spectrum is increasing at each network generation. Due to the ineffective spectrum allocation policies, several researches have indicated that a vast segment of the licensed radio is not viably used. A CRN is an intelligent spectrum utilization innovation that provides better spectrum interface. Spectrum sensing detects unused spectrum in the manner that protects interferences to the authorized users. In principle, the secondary user (SU) receives the primary user (PU) signal and reports it to the fusion center for decision or spectrum allocation. The SU cooperates in the detection of the presence or absence of the PU. This type of spectrum sensing is called cooperative spectrum sensing. However, the significance of this type of spectrum sensing is blurred by the security problems. Malicious users can deliberately report misleading information regarding the presence of the PU. Hence, in this paper, a support vector machine learning algorithm is proposed to statistically learn the behavior of the malicious users and it classifies the legitimate SU and malicious users. A particle swarm optimization algorithm is also integrated to learn the smallest possible distinguishable malicious users' energy report deviation from the legitimate SUs. The probability of detection and energy of detection have been applied to evaluate the contribution of the proposed method. Finally, the simulation results have confirmed that better spectrum management can be derived from the proposed statistical approach. [ABSTRACT FROM AUTHOR]

In today's digital world, Android phones play a vital part in a variety of facets of both professionals and individuals' personal and professional lives. Android phones are great for getting things done faster and more organized. The proportionate increase in the number of malicious applications has also been seen to be expanding. Since the play store offers millions of apps, detection of malware apps is challenging task. In this paper, a methodology is introduced for detecting malware in Android applications through the utilization of global image shape transform (GIST) features extracted from grayscale images of the applications. The dataset comprises samples of both malware and benign apps collected from the virus share website. After converting the apps into grayscale images, GIST features are extracted to capture their global spatial layout. Various machine learning (ML) algorithms, such as logistic regression (LR), k-nearest neighbour (KNN), AdaBoost, decision tree (DT), Naïve Bayes (NB), random forest (RF), support vector machine (SVM), extra tree classifier (ETC), and gradient boosting (GB), are employed to classify the applications according to their GIST features. Furthermore, a feed forward neural network (FFNN) is utilized as a deep learning (DL) technique to further improve the accuracy of classification. The performance of each algorithm is evaluated using metrics such as accuracy, precision and recall. The results demonstrated that the FFNN achieves superior accuracy compared to traditional ML classifiers, indicating its effectiveness in detecting malware in Android apps. [ABSTRACT FROM AUTHOR]

With the goal of "carbon peak and carbon neutrality," the need for environmentally friendly gases to replace SF6, a high greenhouse effect gas, is urgent. C4F7N, as an environmentally friendly gas with the greatest potential to replace SF6 as an arc extinguishing medium in circuit breakers, can be mixed with CO2 to greatly improve the shortcomings of its high liquefaction temperature, and the calculation of the physical parameters of the mixed gas plasma is a prerequisite for the computational simulation of the arc process in the opening of circuit breakers. Because solving the physical parameters is expensive, based on the system of differential equations, this paper adopts several machine learning algorithms by mining the relationship between the data using the known physical parameter data to predict the results of the physical parameters to be solved under certain conditions, which greatly reduces the cost of computation. The machine learning algorithms used in this paper are K-nearest-neighbor regression, decision tree, random forest, support vector machine, and gradient boosting regression, of which for the support vector machine, hyperparameters find it difficult to determine the problem of optimization using the gray wolf algorithm. The prediction results of several algorithms show that they are more accurate and that the problem can be solved better by using the method of machine learning. Finally, the comparison results show that the support vector machine exhibits better performance in most cases and that the gray wolf algorithm can make the results of the support vector machine more accurate. [ABSTRACT FROM AUTHOR]

An aggressive driving state impacts drivers' decisions, which could potentially lead to accidents. Real-time recognition of driving state is particularly important for improving road safety. However, the majority of modeling in existing studies relies on a single algorithm, which may lead to unreliable predictions. This paper proposes a stacking ensemble aggressive driving state recognition model using electroencephalography (EEG), which is able to combine different heterogeneous classification algorithms. Five types of classification algorithms and their variants are tested and compared to identify suitable base classifiers. All of these classifiers are optimized by Bayesian optimizer before the comparison. Three stacking ensemble recognition models using different meta-classifiers (i.e., logistic regression, random forest, and AdaBoost) and an equal-weight voting ensemble recognition model are established. The aforementioned recognition models are evaluated by using a dataset collected from a car-following simulated driving experiment. Fast Fourier transformation (FFT) and wavelet packet transformation (WPT) are adopted to extract features from raw EEG data. The results suggest that the stacking ensemble recognition models outperform the best single (i.e., support vector machine) model; the random Forest stacking recognition model achieves the best performance and the accuracy is increased from 81.21% to 84.23% using FFT features and from 86.45% to 87.38% using WPT features. [ABSTRACT FROM AUTHOR]

Borehole data are essential for conducting precise urban geological surveys and large-scale geological investigations. Traditionally, explicit modelling and implicit modelling have been the primary methods for visualizing borehole data and constructing 3D geological models. However, explicit modelling requires substantial manual labour, while implicit modelling faces problems related to uncertainty analysis. Recently, machine learning approaches have emerged as effective solutions for addressing these issues in 3D geological modelling. Nevertheless, the use of machine learning methods for constructing 3D geological models is often limited by insufficient training data. In this paper, we propose the semi-supervised deep learning using pseudo-labels (SDLP) algorithm to overcome the issue of insufficient training data. Specifically, we construct the pseudo-labels in the training dataset using the triangular irregular network (TIN) method. A 3D geological model is constructed using borehole data obtained from a real building engineering project in Shenyang, Liaoning Province, NE China. Then, we compare the results of the 3D geological model constructed based on SDLP with those constructed by a support vector machine (SVM) method and an implicit Hermite radial basis function (HRBF) modelling method. Compared to the 3D geological models constructed using the HRBF algorithm and the SVM algorithm, the 3D geological model constructed based on the SDLP algorithm better conforms to the sedimentation patterns of the region. The findings demonstrate that our proposed method effectively resolves the issues of insufficient training data when using machine learning methods and the inability to perform uncertainty analysis when using the implicit method. In conclusion, the semi-supervised deep learning method with pseudo-labelling proposed in this paper provides a solution for 3D geological modelling in engineering project areas with borehole data. [ABSTRACT FROM AUTHOR]

As an urgent international challenge, the sudden change in climate due to global warming needs to be addressed in the near future. This can be achieved through a reduction in fossil fuel utilization and through carbon sequestration, which reduces the concentration of CO2 in the atmosphere. In this study, a self-sustainable impact sensor is proposed through implementing a triboelectric nanogenerator with a CaCO3 contact layer fabricated via a CO2 absorption method. The triboelectric polarity of CaCO3 with the location between the polyimide and the paper and the effects of varying the crystal structure are investigated first. The impact sensing characteristics are then confirmed at various input frequencies and under applied forces. Further, the high mechanical strength and strong adherence of CaCO3 on the surface of the device are demonstrated through enhanced durability compared to the unmodified device. For the intended application, the as-fabricated sensor is used to detect the turning state of the paper Ddakji in a slap match game using a supervised learning algorithm based on a support vector machine presenting a high classification accuracy of 95.8%. The robust CaCO3-based triboelectric device can provide an eco-friendly advantage due to its self-powered characteristics for impact sensing and carbon sequestration. [ABSTRACT FROM AUTHOR]

Reservoir productivity prediction is a key component of oil and gas field development, and the rapid and accurate evaluation of reservoir productivity plays an important role in evaluating oil field development potential and improving oil field development efficiency. Fracture-vuggy reservoirs are characterized by strong heterogeneity, complex distribution, and irregular development, causing great difficulties in the efficient prediction of fracture-vuggy reservoirs' productivity. Therefore, a PSO-BP fracture-vuggy reservoir productivity prediction model optimized by feature optimization was proposed in this paper. The Chatterjee correlation coefficient was used to select the appropriate combination of seismic attributes as the input of the prediction model, and we applied the PSO-BP model to predict oil wells' production in a typical fracture-vuggy reservoir area of Tahe Oilfield, China, with the selected seismic attributes and compared the accuracy with that provided by the BP neural network, linear support vector machine, and multiple linear regression. The prediction results using the four models based on the test set showed that compared with the other three models, the MSE of the PSO-BP model increased by 23% to 62%, the RMSE increased by 12 to 38 percent, the MAE increased by 18 to 44 percent, the SSE increased by 23 to 62 percent, and the R-square value increased by 2 to 13 percent. This comparison proves that the PSO-BP neural network model proposed in this paper is suitable for the productivity prediction of fracture-vuggy reservoirs and has better performance, which is of guiding significance for the development and production of fracture-vuggy reservoirs. [ABSTRACT FROM AUTHOR]

In the healthcare sector, predictive analytics plays a vital role, presenting a challenging task but offering potential benefits in making informed decisions about patient health and treatment based on big data. This research paper delves into the realm of predictive analytics in healthcare, employing four distinct machine learning algorithms. The experiment involves the utilization of a dataset comprising patients' medical records, upon which the four algorithms are applied. A comprehensive analysis is conducted using a diverse range of algorithms, including logistic regression, decision trees, random forests and support vector machines. These algorithms' effectiveness is assessed using important measures like precision, recall, precision, accuracy and F1-score. By comparing the different machine learning techniques employed in the present study, the analysis aims to determine the most suitable algorithm for predicting diabetes. [ABSTRACT FROM AUTHOR]

Education is essential and increasingly crucial for the development of almost all countries worldwide. As educational data has become increasingly available, scholars have shown a growing interest in exploring the correlation between students’ academic achievements and other factors that may impact their performance using machine learning algorithms. This research paper introduces a novel hybrid classifier that aims to predict the academic performance of students by using a combination of different single algorithms. The proposed hybrid classifier (PHC) is compared to six available classification algorithms (random forest (RF), C4.5, classification and regression trees (CART), support vector machines (SVM), naive Bayes (NB) and K-nearest neighbors (KNN)) using recall, precision, F1-score, and accuracy evaluation measures. Our experimental results reveal that the PHC classifier consistently outperforms the individual classifiers across multiple evaluation metrics. Specifically, the PHC classifier achieved an accuracy rate of 92.40%, surpassing the RF, C4.5, and CART classifiers, which were the next best performers. In terms of precision and F1 score, the PHC also demonstrated superior performance, indicating its robustness in correctly identifying positive instances and providing balanced accuracy. While the C4.5 classifier performed comparably to the PHC classifier concerning the recall metric, the hybrid model’s overall performance highlights its effectiveness in leveraging the complementary strengths of the included classifiers. The suggested hybrid model has the potential to enhance students’ academic performance and success more effectively and efficiently. It could benefit students, educators, and academic institutions. Additionally, it provides practical insights for educators and institutions striving to improve student achievement using predictive analysis. [ABSTRACT FROM AUTHOR]

In this modern era, people are working hard to meet their physical needs and non-effective their ability to spend time for themselves which leads to physical stress and mental disorder. Many reports state that stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. According to the World Health Organization (WHO), stroke is the greatest cause of death and disability globally. Early recognition of the various warning signs of a stroke can help reduce the severity of the stroke. In this research work, with the aid of machine learning (ML), several algorithms has been used and evaluated for the long term risk prediction of stroke occurrence. We have collected datasets to analyze data and mining using 8 algorithms of machine learning to predict whether the patient suffers from stroke or not. This paper used a dataset retrieved from kaggle repository, which consists of 12 attributes (Features). This work is implemented using K-Nearest Neighbors (KNN), Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (TD), Neural Network (NN) and eXtreme Gradient Boosting (XGB) algorithms. Results showed eXtreme Gradient Boosting (XGB) gave the best result with an accuracy of up to 95.14%. [ABSTRACT FROM AUTHOR]

This exploration of paper presents an investigation of the Forest Region Inclusion Dataset that gives data on the backwoods inclusion of different nations overall from 1990 to 2020. The dataset contains country-wise information on population, population density, population development rate, total population rate, and forest region inclusion. We examined this dataset to decide the patterns in woodland region inclusion across various nations and mainlands, as well as the connection among populace and backwoods region inclusion. Our discoveries show that while certain nations have essentially expanded their forest region inclusion, others have encountered a decline. Besides, we found that population density and development rate are adversely related with forest area coverage. Authors have implemented four machine learning algorithms that are Linear Regression, Decision Tree, Random Forest and Support Vector Machine on the dataset. [ABSTRACT FROM AUTHOR]

Coordinated cyber-physical attacks (CCPAs) are dangerously stealthy and have considerable destructive effects against power grids. The problem of stealthy CCPA (SCCPA) localization, specifically identifying disconnected lines in attack, is a nonlinear multi-classification problem. To the best of our knowledge, only one paper has studied the problem; nevertheless, the total number of classifications is not appropriate. In the paper, we propose several methods to solve the problem of SCCPA localization. Firstly, considering the practical constraints and abiding by one of our previous studies, we elaborately determine the total number of classifications and design an approach for generating training and testing datasets. Secondly, we develop two algorithms to solve multiple classifications via the support vector machine (SVM) and random forest (RF), respectively. Similarly, we also present a one-dimensional convolutional neural network (1D-CNN) architecture. Finally, extensive simulations are carried out for IEEE 14-bus, 30-bus, and 118-bus power system, respectively, and we verify the effectiveness of our approaches in solving the problem of SCCPA localization. [ABSTRACT FROM AUTHOR]

Automatic deception detection is an important task with several applications in both direct physical human communication, as well as in computer-mediated one. The objective of this paper is to study the nature of deceptive language. The primary goal of this study is to investigate deception in Romanian written communication. We created a number of artificial intelligence models (based on Support Vector Machine, Random Forest, and Artificial Neural Network) to detect dishonesty in a topic-specific corpus. To assess the efficiency of the Linguistic Inquiry and Word Count (LIWC) categories in Romanian, we conducted a comparison between multiple text representations based on LIWC, TF-IDF, and LSA. The results show that in the case of datasets with a common subject such as the one we used regarding friendship, text categorization is more successful using general text representations such as TF-IDF or LSA. The proposed approach achieves an accuracy of the classification of 91.3%, outperforming the similar approaches presented in the literature. These findings have implications in fields like linguistics and opinion mining, where research on this subject in languages other than English is necessary. [ABSTRACT FROM AUTHOR]

This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms – Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models' effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features. [ABSTRACT FROM AUTHOR]

In shallow water, Sentinel-2 multispectral imagery has only four visible bands and limited quantization levels, which easily leads to the occurrence of the same spectral profile but different depth (SSPBDD) phenomenon, resulting in a one-to-many relationship between water depth and spectral profile. Investigating the impact of this relationship on water depth inversion models is the main objective of this paper. The Stumpf model and three machine learning models (Random Forest, Support Vector Machine, and Mixture Density Network) are employed, and the performance of these models is analysed based on the spatial distribution of the training dataset and the input information composition of these models. The results show that the root mean square errors (RMSEs) of the depth inversion of Random Forest and Support Vector Machine are significantly affected by the spatial distribution of the training dataset, while minimal effects are observed for the Stumpf model and the Mixture Density Network model. The SSPBDD phenomenon is widespread in Sentinel-2 images at all depths, particularly between 5 m and 15 m, with most of the depth maximum difference of the SSPBDD pixels ranging from 0 to 5 m. The SSPBDDs phenomenon can significantly reduce the inversion accuracy of any model. The number and the depth maximum difference of the SSPBDDs pixels are the main influencing factors. However, by increasing the visible spectral information and the spatial neighbourhood information in the input layer of machine learning models, the inversion accuracy and stability of the models can be improved to a certain extent. Among the models, the Mixture Density Network achieves the best inversion accuracy and stability. [ABSTRACT FROM AUTHOR]

A high-precision, complex, three-dimensional (3D) geological model can directly express the attributes of stratum thickness, geological structure, lithology and spatial form, which can provide a reliable basis for the development and utilization of underground space and planning decisions. However, it is difficult to perform accurate modelling due to the lack of basic data. As such, this paper proposes coupling a machine learning algorithm (K-nearest neighbour (KNN)) with the kriging algorithm to construct the topological relationship between the Delaunay triangle and the Thiessen polygon in order to perform the simulation and prediction of virtual drilling. Based on KNN, support vector machine (SVM) and neural network algorithms as well as the virtual borehole encryption data, data standardization processing and analysis are carried out. Through model verification, algorithm optimization is realized, and the optimal modelling method is explored. The results show that the fine KNN algorithm improved by Bayesian optimization can effectively improve the modelling accuracy through 0.1-m encryption, standardization processing and 5-fold cross-validation. Stratum modelling combined with the fine KNN and kriging algorithms can obtain a more accurate modelling without adding virtual boreholes. The improved levels of upper and lower hybrid modelling with an appropriate number of profile boreholes can also effectively optimize model accuracy. Both modelling accuracy and efficiency can be significantly improved by using Delaunay triangles and Thiessen polygons with virtual boreholes. Stratum modelling can effectively express the geological pinch-out in areas with adequate degrees of stratification, and hybrid modelling performs well in irregular geological bodies such as karsts and lenses. [ABSTRACT FROM AUTHOR]

In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve. [ABSTRACT FROM AUTHOR]