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In the ever-evolving world of cricket, the T20 format has captured the imaginations of fans worldwide, intensifying the anticipation for match outcomes with each passing delivery. This study explores the realm of predictive analytics, leveraging the power of machine learning to alleviate the suspense by forecasting T20 cricket match winners before the first ball is bowled. Drawing on a rich dataset encompassing factors such as past team performance and rankings, a diverse ensemble of predictive models, including logistic regression, support vector machine (SVM), random forest, decision tree, and XGBoost, is meticulously employed. Among these, the random forest Classifier emerges as the standout performer, boasting an impressive prediction accuracy rate of 84.06%. To assess the real-world applicability of our predictive framework, a post-case study is conducted, focusing on the high-stakes World Cup T20 matches of 2022, where England emerges as the triumphant team. The dataset underpinning this study is meticulously curated from ESPN CricInfo, ensuring the robustness of our analysis. Moreover, this paper extends its contribution by offering a comprehensive comparative analysis, scrutinizing performance metrics such as accuracy, precision, recall, and the F1-score across benchmark machine learning models for cricket match prediction. This in-depth evaluation not only validates the efficacy of our models but also sheds light on their superior execution time and statistical robustness, further bolstering their utility in the realm of cricket outcome forecasting. [ABSTRACT FROM AUTHOR]

The quality of sleep plays a crucial role in physical well-being, and individuals are becoming increasingly concerned about sleep quality and its associated health issues. Although various sleep monitoring devices exist, there remains a need for a highly accurate sleep state identification algorithm. To address this, we present a paper that utilizes machine learning techniques to identify human sleep states based on electroencephalogram (EEG) signals collected by an EEG instrument. We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns. [ABSTRACT FROM AUTHOR]

This paper presents a Hybrid Feature Selection Technique for Sentiment Classification. We have used a Genetic Algorithm and a combination of existing Feature Selection methods, namely: Information Gain (IG), CHI Square (CHI), and GINI Index (GINI). First, we have obtained features from three different selection approaches as mentioned above and then performed the UNION SET Operation to extract the reduced feature set. Then, Genetic Algorithm is applied to optimize the feature set further. This paper also presents an Ensemble Approach based on the error rate obtained different domain datasets. To test our proposed Hybrid Feature Selection and Ensemble Classification approach, we have considered four Support Vector Machine (SVM) classifier variants. We have used UCI ML Datasets of three domains namely: IMDB Movie Review, Amazon Product Review and Yelp Restaurant Reviews. The experimental results show that our proposed approach performed best in all three domain datasets. Further, we also presented T-Test for Statistical Significance between classifiers and comparison is also done based on Precision, Recall, F1-Score, AUC and model execution time. [ABSTRACT FROM AUTHOR]

Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target's sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Naïve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification. [ABSTRACT FROM AUTHOR]

Globally, heart disease is the primary cause of death. Early detection of this disease enables cardiologists to make more accurate judgments regarding the health of their patients. Due to machine learning's ability to identify patterns in data, its use in the medical industry has increased. Many heart disease prediction models have been developed by various researchers utilizing machine learning techniques (MLTs). The performance of MLTs on heart disease prediction may vary for different accuracy measures. Thus, the choice of the appropriate machine-learning technique for heart disease prediction is a challenging task. This paper proposes a multi-criteria decision-making (MCDM)-based method to evaluate the MLTs for heart disease prediction considering various performance measures taken into account altogether. The proposed approach uses the concept of a combined compromise solution (CoCoSo)- an MCDM method. For validation of the proposed approach, an experimental study was conducted to evaluate the performance of fifteen machine learning techniques for predicting heart disease over three heart disease datasets considering six performance measures taken into account altogether. Results show that the logistic regression and support vector machine are recommended as the most suitable MLTs for heart disease prediction modeling with respect to six performance measures considered simultaneously. [ABSTRACT FROM AUTHOR]

There is a widespread use of cluster-based routing in wireless sensor networks since it is the most energy-efficient. Idealizing cluster heads, on the other hand, is NP-hard and hence requires heuristic or metaheuristic approaches. However, while outperforming algorithms, metaheuristics computation time restricts its ability to respond to routing requests as rapidly as algorithms can today. A network's or an application's parameters can't be easily accommodated by routing methods. This paper offers the HMML, a combination model combining hybrid metaheuristics and machine-learning. Our HMML model makes use of an automated tuning metaheuristic (e.g. evolutionary algorithm) to fine-tune the heuristic technique for each specific configuration. For a variety of combinations, this is done. A network simulation is run using the modified heuristic algorithm in each configuration to arrive at a solution. As a result, a comprehensive dataset for a variety of conditions is produced (e.g., support vector machine). These characteristics include local (round-state), global (network-state), and application-specific aspects of the input feature vector. After training, the HMML model may be used to quickly cluster data. Machine learning's capacity to generalize helps us comprehend the metaheuristic algorithm's behavior in identifying optimal paths for previous configurations. Simulation studies show that HMML can adapt to varied applications while extending network life which increases upto 5% for total energy consumption. [ABSTRACT FROM AUTHOR]

The need for remote healthcare monitoring systems that utilize limited resources' biosensors is growing. These biosensors increase the amount of transmitted data across the Internet of Healthcare Things (IoHT) network. Therefore, it is necessary to decrease the transmitted data and make a decision at the edge gateway to save the energy of the biosensors and produce a quick response for the medical staff. This paper proposes an energy-efficient multisensor adaptive sampling and aggregation (EMASA) for patient monitoring in edge computing-based IoHT networks. In the edge-based IoHT network, EMASA operates on two levels: biosensors and the edge gateway. Each biosensor removes the redundant sensed data using the local emergency detection and sampling rate adaptation algorithms. In the edge gateway, it implements a machine learning-based Support Vector Machine (SVM) model to provide a suitable decision about the status of the monitored patient. We accomplished various examinations using real data from the patients' biosensors. According to the simulation results, EMASA reduced the size of transmitted data from 93.5% to 99% and saved 78.35% of energy when compared to a previous study. It keeps the whole score with a good representation at the Edge gateway and provides accurate and fast decisions based on the patient's condition. [ABSTRACT FROM AUTHOR]

As is the case for many National Statistics Institutes, the United States Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) has observed dwindling survey response rates, and the requests for more information at finer temporal and spatial scales have led to increased response burdens. Non-survey data are becoming increasingly abundant and accessible. Consequently, NASS is exploring the potential to complete some or all of a survey record using non-survey data, which would reduce respondent burden and potentially lead to increased response rates. In this paper, the focus is on a large set of records associated with potential farms, which are operations with undetermined farm status (farm/non-farm) and are referred to here as operations with unknown status (OUS). Although they usually have some agriculture, most OUS records are eventually classified as non-farms. Those OUS that are classified as farms tend to have higher proportions of producers from under-represented groups compared to other records. Determining the probability that an OUS record is a farm is an important step in the imputation process. The OUS records that responded to the 2017 U.S. Census of Agriculture were used to develop models to predict farm status using multiple data sources. Evaluated models include bootstrap random forest (RF), logistic regression (LR), neural network (NN), and support vector machine (SVM). Although the SVM had the best outcomes for three of the five metrics, the sensitivity for identifying farms was the lowest (13.8%). The NN model had a sensitivity of 80.5%, which was substantially higher than the other models, and its specificity of 45.3% was the lowest of all models. Because sensitivity was the primary metric of interest and the NN performed reasonably well on the other metrics, the NN was selected as the preferred model. [ABSTRACT FROM AUTHOR]

As is the case for many National Statistics Institutes, the United States Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) has observed dwindling survey response rates, and the requests for more information at finer temporal and spatial scales have led to increased response burdens. Non-survey data are becoming increasingly abundant and accessible. Consequently, NASS is exploring the potential to complete some or all of a survey record using non-survey data, which would reduce respondent burden and potentially lead to increased response rates. In this paper, the focus is on a large set of records associated with potential farms, which are operations with undetermined farm status (farm/non-farm) and are referred to here as operations with unknown status (OUS). Although they usually have some agriculture, most OUS records are eventually classified as non-farms. Those OUS that are classified as farms tend to have higher proportions of producers from under-represented groups compared to other records. Determining the probability that an OUS record is a farm is an important step in the imputation process. The OUS records that responded to the 2017 U.S. Census of Agriculture were used to develop models to predict farm status using multiple data sources. Evaluated models include bootstrap random forest (RF), logistic regression (LR), neural network (NN), and support vector machine (SVM). Although the SVM had the best outcomes for three of the five metrics, the sensitivity for identifying farms was the lowest (13.8%). The NN model had a sensitivity of 80.5%, which was substantially higher than the other models, and its specificity of 45.3% was the lowest of all models. Because sensitivity was the primary metric of interest and the NN performed reasonably well on the other metrics, the NN was selected as the preferred model. [ABSTRACT FROM AUTHOR]

The contemporary real-world datasets often suffer from the problem of class imbalance as well as high dimensionality. For combating class imbalance, data resampling is a commonly used approach whereas for tackling high dimensionality feature selection is used. The aforesaid problems have been studied extensively as independent problems in the literature but the possible synergy between them is still not clear. This paper studies the effects of addressing both the issues in conjunction by using a combination of resampling and feature selection techniques on binary-class imbalance classification. In particular, the primary goal of this study is to prioritize the sequence or pipeline of using these techniques and to analyze the performance of the two opposite pipelines that apply feature selection before or after resampling techniques i.e., F + S or S + F. For this, a comprehensive empirical study is carried out by conducting a total of 34,560 tests on 30 publicly available datasets using a combination of 12 resampling techniques for class imbalance and 12 feature selection methods, evaluating the performance on 4 different classifiers. Through the experiments we conclude that there is no specific pipeline that proves better than the other and both the pipelines should be considered for obtaining the best classification results on high dimensional imbalanced data. Additionally, while using Decision Tree (DT) or Random Forest (RF) as base learner the predominance of S + F over F + S is observed whereas in case of Support Vector Machine (SVM) and Logistic Regression (LR), F + S outperforms S + F in most cases. According to the mean ranking obtained from Friedman test the best combination of resampling and feature selection techniques for DT, SVM, LR and RF are SMOTE + RFE (Synthetic Minority Oversampling Technique and Recursive Feature Elimination), Least Absolute Shrinkage and Selection Operator (LASSO) + SMOTE, SMOTE + Embedded feature selection using RF and SMOTE + RFE respectively. [ABSTRACT FROM AUTHOR]

Accident severity prediction is a hot topic of research aimed at ensuring road safety as well as taking precautionary measures for anticipated future road crashes. In the past decades, both classical statistical methods and machine learning algorithms have been used to predict traffic crash severity. However, most of these models suffer from several drawbacks including low accuracy, and lack of interpretability for people. To address these issues, this paper proposed a hybrid of Balanced Bagging Classification (BBC) and Light Gradient Boosting Machine (LGBM) to improve the accuracy of crash severity prediction and eliminate the issues of bias and variance. To the best of the author's knowledge, this is one of the pioneer studies which explores the application of BBC-LGBM to predict traffic crash severity. On the accident dataset of Great Britain (UK) from 2013 to 2019, the proposed model has demonstrated better performance when compared with other models such as Gaussian Naïve Bayes (GNB), Support vector machines (SVM), and Random Forest (RF). More specifically, the proposed model managed to achieve better performance among all metrics for the testing dataset (accuracy = 77.7%, precision = 75%, recall = 73%, F1-Score = 68%). Moreover, permutation importance is used to interpret the results and analyze the importance of each factor influencing crash severity. The accuracy-enhanced model is significant to several stakeholders including drivers for early alarm and government departments, insurance companies, and even hospitals for the services concerned about human lives and property damage in road crashes. [ABSTRACT FROM AUTHOR]

Quantitative investment can manage enterprise assets better to obtain higher revenues. This paper analyzed quantitative investment prediction using machine learning algorithms. First, the support vector machine (SVM) algorithm was introduced, and stock changes were predicted by the SVM algorithm. Then, the feature factors in stock data were extracted by maximum information coefficient (MIC) as the input of the SVM algorithm. Finally, the performance and backtest results of the SVM algorithm was analyzed. It was found that the SVM algorithm had a good performance, and its F1-score was 0.9884, which was better than C4.5 and random forest algorithms. In terms of backtesting, the portfolio built based on the prediction results of the SVM algorithm obtained a higher annualized return rate when the number of stocks was small; when the number of stocks was 10, the portfolio built based on the SVM algorithm had an annualized return rate of 83.67%, a smaller maximum retracement, and a higher Sharpe ratio than the other algorithms, which balanced the risk and return well. The results demonstrate the reliability of the SVM algorithm in predicting quantitative investment, which is beneficial to achieving the optimization of enterprise asset management. [ABSTRACT FROM AUTHOR]

Software defect prediction is used to assist developers in finding potential defects and allocating their testing efforts as the scale of software grows. Traditional software defect prediction methods primarily concentrate on creating static code metrics that are fed into machine learning classifiers to predict defects in the code. To achieve the desired classifier performance, appropriate design decisions are required for deep neural network (DNN) and convolutional neural network (CNN) models. This is especially important when predicting software module fault proneness. When correctly identified, this could help to reduce testing costs by concentrating efforts on the modules that have been identified as fault prone. This paper proposes a CONVSDP and DNNSDP (cognitive and neural network) approach for predicting software defects. Python Programming Language with Keras and TensorFlow was used as the framework. From three NASA system datasets (CM1, KC3, and PC1) selected from PROMISE repository, a comparative analysis with machine learning algorithms (such as Random Forest (RF), Decision Trees (DT), Nave Bayes (NF), and Support Vector Machine (SVM) in terms of F-Measure (known as F1-score), Recall, Precision, Accuracy, Receiver Operating Characteristics (ROC) and Area Under Curve (AUC) has been presented. We extract four dataset attributes from the original datasets and use them to estimate the development effort, development time, and number of errors. The number of operands, operators, branch count, and executable LOCs are among these attributes. Furthermore, a new parameter called cognitive weight (Wc) of Basic Control Structure (BCS) is proposed to make the proposed cognitive technique more effective, and a cognitive data set of 8 features for NASA system datasets (CM1, KC3, and PC1) selected from the PROMISE repository to predict software defects is created. The experimental results showed that the CONVSDP and DNNSDP models was comparable to existing classifiers in both original datasets and cognitive data sets, and that it outperformed them in most of the experiments. [ABSTRACT FROM AUTHOR]

Intrusion Detection is very important in computer networks because the widespread of internet makes the computers more prone to several cyber-attacks. With this inspiration, a new paradigm called Intrusion Detection System (IDS) has emerged and attained a huge research interest. However, the major challenge in IDS is the presence of redundant and duplicate information that causes a serious computational problem in network traffic classifications. To solve this problem, in this paper, we propose a novel IDS model based on statistical processing techniques and machine learning algorithms. The machine learning algorithms incudes Fuzzy C-means and Support Vector Machine while the statistical processing techniques includes correlation and Joint Entropy. The main purpose of FCM is to cluster the train data and SVM is to classify the traffic connections. Next, the main purpose of correlation is to discover and remove the duplicate connections from every cluster while the Joint entropy is applied for the discovery and removal of duplicate features from every connection. For experimental validation, totally three standard datasets namely KDD Cup 99, NSL-KDD and Kyoto2006+ are considered and the performance is measured through Detection Rate, Precision, F-Score, and accuracy. A five-fold cross validation is done on every dataset by changing the traffic and the obtained average performance is compared with existing methods. [ABSTRACT FROM AUTHOR]

Hundreds of people dying from heart disease almost every day that is how terrific a delayed diagnosis can be. Living in an advanced era full of intelligent systems, the increasing number of deaths can be reduced. This research paper focuses on the development of a cardiovascular disease prediction system particularly a heart disease, by developing machine learning classifiers, for instance, Support Vector Machine (SVM), Decision Tree, and XGBoost Classifiers. We also scaled the features to standardize unconstrained features in data, available in a fixed range for better optimization of models. For efficiency, the classification of features was also done in two categories, Independent features, and dependent features. Furthermore, the performance measures helped with best practices for model assessment & classifier performance. Eventually, after tuning hyper-parameters, the results exhibit high accuracy for XGBoost among other trained classifiers. After a comparative analysis, the best-suited algorithm can be utilized for heart disease detection, in the medical field, and regarding the economy, as costly treatments are taken into consideration. This indicates that a non-expert can also attempt for diagnosis without fretting over expensive treatments. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a condition associated with impairments in communication, social, and repetitive behaviour; the degree of impairment varies between individuals with ASD. Since ASD has a substantial impact on the individual, caregivers, and family members due to the social and economic costs involved, early ASD screening becomes fundamental to enable faster access to healthcare resources. One of the important studied groups in ASD research is toddlers – detecting autistic traits at an early stage can help physicians develop treatment plans. This paper aims to improve the detection rate of ASD screening for toddlers using a data driven approach by identifying the impactful feature set related to ASD, and then processing these features using classification algorithms to accurately screen for ASD. To achieve the aim, a data driven framework consisting of feature selection and classification algorithms is proposed, and then implemented on a real dataset related to the ASD screening of toddlers. Empirical evaluations on the ASD screening dataset using different classification methods reveal that when support vector machine (SVM) or Naïve Bayes are integrated with the proposed framework good predictive models are constructed for toddler ASD screening. These predictive models can be adopted by different medical staff and caregivers to replace scoring functions of conventional screening methods. [ABSTRACT FROM AUTHOR]

This paper refers to a new resilient cyber-physical machine learning-based system that enables the generation of high-resolution tomographic images. The research object was a model of a tank filled with tap water. Using electrical impedance tomography (EIT) with 16 electrodes, the possibility of identifying inclusions inside the reservoir was investigated. A two-stage hybrid approach was proposed. In the first stage, three independent models were trained for the Elastic Net, Artificial Neural Networks (ANN) and Support Vector Machine (SVM) methods. In the second stage, a k-Nearest Neighbors (kNN) classification model was trained, that optimizes tomographic reconstructions by selecting the best method for each pixel, taking into account the specificity of a given measurement vector. Research has shown that applying the new concept results in a higher reconstruction quality than other methods used singly. It should be emphasized that our research is not intended to develop a new homogenous machine learning method. Instead, the goal is to invent an innovative, original, and flexible way to simultaneously use multiple machine learning methods for image optimization in industrial electrical impedance tomography. [ABSTRACT FROM AUTHOR]

On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users' related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results. [ABSTRACT FROM AUTHOR]

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model. [ABSTRACT FROM AUTHOR]

With the increase in the amount of data available today, the responsibility of keeping that data safe has also taken a more severe form. To prevent confidential data from getting in the hands of an attacker, some measures need to be taken. Here comes the need for an effective system, which can classify the traffic as an attack or normal. Intrusion Detection Systems can do this work with perfection. Many machine learning algorithms are used to develop efficient IDS. These IDS provide remarkable results. However, ensemble-based IDS using voting have been seen to outperform individual approaches (Support Vector Machine and ExtraTree). Since the Voting methodology is able to work around both, theoretically similar and different classifiers and produce a single classifier based on the majority characteristics, it proved to be better than the other ensemble based techniques. In this paper, an ensemble IDS implementation is presented based on the voting ensemble method, using the two algorithms, Support Vector Machine (SVC) and ExtraTree. The experiment is performed on the KDDCup99 Dataset. The evaluation of the performance of the proposed method is based on the comparison with an unoptimized implementation of the same. The results based on performing the experiment in Python fetched an accuracy of 99.90%. [ABSTRACT FROM AUTHOR]

As the size and complexity of projects grows, estimates are increasingly used, especially in the agile community. Software development cannot begin without first conducting thorough planning and estimation. Estimating how much work a project will take is a common first step in the software development life cycle. By employing ensemble techniques, we integrate multiple learning algorithms to build a more accurate predictive model. The core elements of our proposed stacked ensemble strategy include Decision Tree, Principal Components Regression, Random Forest, NeuralNet, GLMNET, XGBoost, Earth, and Support Vector Machine. Moreover, we augment the model's performance by incorporating a blend of these foundational algorithms with other ensemble regression methods. Extensive testing in the suggested research work with a number of Super Learners demonstrates that Regression is the best technique for judging effort. The evaluation of the different estimators involved the use of various metrics, including Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Percentage of Close Approximations within 25% of the True Values (PRED (25)), R-Squared Coefficients, Precision, Recall, and F1-Score. The proposed method yields more trustworthy predicted performance than either single-model approaches or stacked ensembles. Effort estimation serves as the foundation for the rest of the project management process. [ABSTRACT FROM AUTHOR]

Power system load forecasting is a method that uses historical load data to predict electricity load data for a future time period. Aiming at the problems of general prediction accuracy and slow prediction speed in using typical machine learning methods, an improved fuzzy support vector regression machine method is proposed for power load forecasting. In this method, the boundary vector extraction technique is employed in the design of the membership function for fuzzy support vectors to differentiate the importance of different samples in the regression process. This method utilizes a membership function based on boundary vectors to assign differential weights to different sample points that used to differentiate the importance of different types of samples in the regression analysis process in order to improve the accuracy of electricity load prediction. The key parameters of the fuzzy support vector regression model are optimized, further enhancing the precision of the forecasting results. Simulation experiments are conducted using real power load data sets, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and speed in predicting power load data compared to other prediction models. This method can be widely applied in real power production and scheduling processes. [ABSTRACT FROM AUTHOR]

Background: In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA). Objective: This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals. Methods: The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. Results: The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. Conclusions: By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification. [ABSTRACT FROM AUTHOR]

Solid waste management has become a challenge for developing countries mainly because of surging economic activities, rapid urbanisation and rise in community living standards. Many researchers have identified its related problems and have recommended solutions while others have established models to forecast the amount of solid waste generated over a period. However, an efficient and effective management of solid waste requires adequate categorisation of solid waste generation areas to aid in the provision of area-specific or targeted solutions for each categorised area. In this study, we used primary data on some important socio-demographic variables (household size, house type, predominant religion of household, age and educational level of household head, residency type household waste disposal method, frequency of waste collection etc) and the amount of solid waste generated from 2102 households in Greater Accra Region, Ghana. We assessed the classification performances of a traditional statistical classifiers and some selected machine learning algorithms in classifying the surveyed areas in Greater Accra into low, medium, and high solid waste generation areas. The Support Vector Machine with the Cubic Kernel was found to be the best performing classifier with a Specificity of 86%, Sensitivity, Precision and Accuracy of 73% and Area under the curve (AUC) of 0.90. The Support Vector Machine with the Cubic Kernel is therefore recommended as a suitable algorithm for the categorisation of solid waste generation areas. Stakeholders responsible for solid waste management could leverage on the evidence from this study to categorise their waste generation areas and to proffer targeted community-based interventions. [ABSTRACT FROM AUTHOR]

Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702. [ABSTRACT FROM AUTHOR]

A machine learning model intends to produce a secure model with low bias and variance. Finding the optimal machine learning model for a dataset is a challenging task. A suitable machine learning model is yet to be specified for the Arthritis Profile Data dataset. Autoimmune disease is widely spread all over the world. Some autoimmune arthritis diseases are Rheumatoid Arthritis, Psoriatic Arthritis, Juvenile Arthritis, etc. These diseases come under both categories autoimmune and inflammatory diseases. The proposed work is designed to suggest the best machine learning model with the highest observed accuracy for the Arthritis Profile Data. Many authors do not compare newly created datasets with previously used datasets. This can lead to inaccurate results due to the lack of reliable comparison. Additionally, it can prevent researchers from detecting potential bias in the data. Comparing datasets can help to identify and address any potential issues and improve the accuracy of the results. It is important to review existing datasets before beginning a new project to ensure the accuracy of the results. This article is the first study on the topic that analysis the accuracy behavior of each machine learning model concerning the Arthritis Profile Data and various benchmark disease datasets with different hold-out and k-fold cross-validation methods. The study concludes with a glimpse of whether dataset and feature size affect model prediction accuracy and proffers a machine learning model for the Arthritis Profile Data. The proposed research explores base learning classification algorithms and ensemble methods such as Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting from machine learning. Our empirical evidence clearly states XGBoost ensemble technique shows the highest accuracy for the Arthritis Profile Data. [ABSTRACT FROM AUTHOR]

Support Vector Machine (SVM) is a machine learning with excellent classification performance, which has been widely used in various fields such as data mining, text classification, face recognition and etc. However, when data volume scales to a certain level, the computational time becomes too long and the efficiency becomes low. To address this issue, we propose a parallel balanced SVM algorithm based on Spark, named PB-SVM, which is optimized on the basis of the traditional Cascade SVM algorithm. PB-SVM contains three parts, i.e., Clustering Equal Division, Balancing Shuffle and Iteration Termination, which solves the problems of data skew of Cascade SVM and the large difference between local support vector and global support vector. We implement PB-SVM in AliCloud Spark distributed cluster with five kinds of public datasets. Our experimental results show that in the two-classification test on the dataset covtype, compared with MLlib-SVM and Cascade SVM on Spark, PB-SVM improves efficiency by 38.9% and 75.4%, and the accuracy is improved by 7.16% and 8.38%. Moreover, in the multi-classification test, compared with Cascade SVM on Spark on the dataset covtype, PB-SVM improves efficiency and accuracy by 94.8% and 18.26% respectively. [ABSTRACT FROM AUTHOR]

The significance of data analytics and machine learning has increased in managing vast quantities of healthcare data effectively. According to recent statistics, Alzheimer's disease is the most prevalent cause of dementia worldwide. Moreover, Alzheimer's arises in some people with increasing mild cognitive impairment (MCI). Early detection and treatment of Alzheimer's disease and its prodromal stage, also known as MCI, is vital to prevent its progression. For selecting the most relevant features, the four feature selection methods, including Mutual Information (MI), Recursive Feature Elimination (RFE), T -test, and Genetic algorithm (GA), are used. Also, three classification methods include Adaboost, random forest, and support vector machine (SVM) with the linear kernel; Moreover, a resnet network is applied to categorize the patients as cognitive normal, MCI, which includes both progressive (pMCI) and stable (sMCI) cases, and Alzheimer's disease using positron emission tomography (PET) scan images. Among these machine learning methods, combining a t -test and a genetic algorithm for selecting the most relevant features and applying a support vector machine with 8-fold cross-validation have produced the best results on high-dimensional images of Alzheimer's disease neuroimaging initiative (ADNI). The proposed method differentiates between sMCI and pMCI patients with a 95.45% accuracy rate and 95.23% F1-score, outperforms the performance of recent studies, as well as AD and CN with 97.36% accuracy rate and 100% recall, making it acceptable for supporting clinical applications. [ABSTRACT FROM AUTHOR]

In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases. [ABSTRACT FROM AUTHOR]

The spectrum scarcity problem in today's wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels (R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models. [ABSTRACT FROM AUTHOR]

Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc), sensitivity (Sn), and specificity (Sp), which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&7-class classification, respectively. [ABSTRACT FROM AUTHOR]

As a supervised learning algorithm, Support Vector Machine (SVM) is very popularly used for classification. However, the traditional SVM is error-prone because of easy to fall into local optimal solution. To overcome the problem, a new SVM algorithm based on Relief algorithm and particle swarm optimization-genetic algorithm (Relief-PGS) is proposed for feature selection and data classification, where the penalty factor and kernel function of SVM and the extracted feature of Relief algorithm are encoded as the particles of particle swarm optimization-genetic algorithm (PSO-GA) and optimized by iteratively searching for optimal subset of features. To evaluate the quality of features, Relief algorithm is used to screen the feature set to reduce the irrelevant features and effectively select the feature subset from multiple attributes. The advantage of Relief-PGS algorithm is that it can optimize both feature subset selection and SVM parameters including the penalty factor and the kernel parameter simultaneously. Numerical experimental results indicated that the classification accuracy and efficiency of Relief-PGS are superior to those of other algorithms including traditional SVM, PSO-GA-SVM, Relief-SVM, ACO-SVM, etc. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is a common heart disease that causes the blockage of coronary arteries. To reduce fatality, an accurate diagnosis of this disease is very important. Angiography is one of the most trustworthy and conventional methods for CAD diagnosis however, it is risky, expensive, and time-consuming. Therefore in this study, we proposed a differential evolution-based support vector machine (SVM) for early and accurate detection of CAD. To improve the accuracy, different data preprocessing techniques such as one-hot encoding and normalization are also used with differential evolution for feature selection before performing classification. The proposed approach is benchmarked with the Z-Alizadeh Sani and Cleveland datasets against four state-of-the-art machine learning algorithms, and a highly cited genetic algorithm-based SVM (N2GC-nuSVM). The experimental results show that our proposed differential evolution-based SVM outperforms all the compared algorithms. The proposed method provides accuracies of 95±1% and 86.22% for predicting CAD on the benchmark datasets. [ABSTRACT FROM AUTHOR]

This study is devoted to the development of an Adaptive-Neuro-Fuzzy-Inference-System (ANFIS) model for the prediction of ultimate load of rectangular concrete-filled steel tubular structural members. The learning process of the model is performed by conducting a combination of backpropagation gradient descent and least-squares techniques. The performance of the model is examined by several quality metrics such as coefficient of determination (R2), Root-Mean-Squared-Error (RMSE), Mean-Absolute-Error (MAE), Index of Agreement (IA) and Slope of linear regression. Monte Carlo random sampling technique is employed to propagate input variations to the output response. Moreover, the performance of ANFIS is also compared with other machine learning models including Artificial Neural Network (ANN), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Ensemble. Results show that the ANFIS model yields higher prediction performance than other machine learning models, for both training and testing data points and regarding all quality metrics. For instance, using training data points, the ANFIS model exhibits a RMSE of 0.0283 compared to 0.0342, 0.0588, 0.0291, and 0.0464 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding gain values are+17.3%,+51.9%,+2.8%, and+39.0%, respectively). On the other hand, using testing data points, the ANFIS model exhibits a RMSE of 0.0276 compared to 0.0393, 0.0987, 0.0403, and 0.0460 using ANN, Ensemble, GPR, and SVM, respectively (the corresponding values of gain are+29.8%,+72.1%,+31.5%, and+40.0%, respectively). The same observation can be made for other quality metrics. It can be concluded that the ANFIS model outperforms other models for both training and testing datasets. The ANFIS model is also compared with existing works in the past, showing its improvement in prediction results. Finally, sensitivity analysis is performed to determine the degree of effect of the input parameters on the ultimate load. [ABSTRACT FROM AUTHOR]

Plant diseases can cause significant losses to agricultural productivity; therefore, their early prediction is much needed. So far, many machine learning-based plant disease prediction models have been recommended, but these models face a problem of noisy class label dataset that degrades the performance. Noisy class label dataset results from the improper assignment of positive class labels into negative class data samples or vice versa. Hence, a precise and noise-free plant disease model is required for a better prediction. The current study proposes noise reduction-based hybridized classifiers for plant disease prediction. One tomato and four soybean disease datasets have been selected to conduct the proposed research. The Adaptive Sampling-based Class Label Noise Reduction (AS-CLNR) method has been used along with the Support Vector Machine (SVM) approach for noise reduction. The noise-minimized datasets have been fed into the Extreme Learning Machine (ELM), Decision Tree (DT), and Random Forest (RF) classifiers whose parameters are optimized using Genetic Algorithm (GA) for developing plant disease prediction models. The performances of all these models viz. Hybrid SVM-GA-ELM, Hybrid SVM-GA-DT, and Hybrid SVM-GA-RF have been evaluated using Accuracy, Area under ROC Curve, and F1-Score metrics. Further, these classifiers have been ranked using the statistical Friedman Test in which the Hybrid SVM-GA-RF classifier performed the best. Lastly, the Nemenyi test has also been performed to find out if significant differences exist between various classifiers or not. It was found that 33.33% of the total pairs of hybrid classifiers show a remarkably different performance from one another. [ABSTRACT FROM AUTHOR]

Coronary artery diseases are one of the high-risk diseases, which occur due to the insufficient blood supply to the heart. The different types of plaques formed inside the artery leads to the blockage of the blood stream. Understanding the type of plaques along with the detection and classification of plaques supports in reducing the mortality of patients. The objective of this study is to present a novel clustering method of plaque segmentation followed by wavelet transform based feature extraction. The extracted features of all different kinds of calcified and sub calcified plaques are applied to first train and test three machine learning classifiers including support vector machine, random forest and decision tree classifiers. The bootstrap ensemble classifier then decides the best classification result through a voting method of three classifiers. A training dataset including 64 normal CTA images and 73 abnormal CTA images is used, while a testing dataset consists of 111 normal CTA images and 103 abnormal CTA images. The evaluation metrics shows better classification rate and accuracy of 97.7%. The Sensitivity and Specificity rates are 97.8% and 97.5%, respectively. As a result, our study results demonstrate the feasibility and advantages of developing and applying this new image processing and machine learning scheme to assist coronary artery plaque detection and classification. [ABSTRACT FROM AUTHOR]

Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation (Q LOO 2 = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation. [ABSTRACT FROM AUTHOR]

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups. [ABSTRACT FROM AUTHOR]

Authorship Attribution is a process to determine and/or identify the author of a given text document. The relevance of this research area comes to the fore when two or more writers claim to be the prospective authors of an unidentified or anonymous text document or are unwilling to accept any authorship. This research work aims to utilize various Machine Learning techniques in order to solve the problem of author identification. In the proposed approach, a number of textual features such as Token n-grams, Stylometric features, bag-of-words and TF-IDF have been extracted. Experimentation has been performed on three datasets viz. Spooky Author Identification dataset, Reuter_50_50 dataset and Manual dataset with 3 different train-test split ratios viz. 80-20, 70-30 and 66.67-33.33. Models have been built and tested with supervised learning algorithms such as Naive Bayes, Support Vector Machine, K-Nearest Neighbor, Decision Tree and Random Forest. The proposed system yields promising results. For the Spooky dataset, the best accuracy score obtained is 84.14% with bag-of-words using Naïve Bayes classifier. The best accuracy score of 86.2% is computed for the Reuter_50_50 dataset with 2100 most frequent words when the classifier used is Support Vector Machine. For the Manual dataset, the best score of 96.67% is obtained using the Naïve Bayes Classification Model with both 5-fold and 10-fold cross validation when both syntactic features and 600 most frequent unigrams are used in combination. [ABSTRACT FROM AUTHOR]

There are various diseases associated with the human liver, some of which are hard to detect using just the information exchanged between a patient and a doctor. Motivated by the vast potential of AI in medicine, in this study, we attempted to find a model which can predict the occurrence of liver disease in a given patient with the highest accuracy, based on different input factors. A dataset was chosen to train and test this model; Indian Liver Patient Dataset obtained from UCI ML Repository. We implemented different machine learning and deep learning algorithms (Multi-Layer Perceptron, Stochastic Gradient Descent, Restricted Boltzmann Machine with Logistic Regression, Support Vector Machines, and Random Forest) and filtered out the DL-based MLP (Multi-Layer Perceptron) model as the one providing the highest Accuracy, which was compared for each model along with the Precision, Recall and f1 scores. This research aims to impart insight additional to the current state-of-the-art discoveries by focusing on a comparative analysis of some of the best ML/DL techniques which haven't been scrutinized altogether yet. [ABSTRACT FROM AUTHOR]

At present, the mankind of the entire world is under serious threat due to the unexpected COVID-19 pandemic. The advent of this pandemic exposes many drawbacks in the medical and healthcare system. As per the guidelines of WHO, the spread of the virus must be controlled through proper measures that help cease the virus. Tracing infected subjects (people/patients) is exceedingly difficult across the globe. The testing process in many countries is hampered by the unavailability of COVID-19 Test kits. Therefore, a testing process needs a robust mechanism to identify the infected subject to reduce the infection rate. To address this issue, a Symptom-based COVID-19 Test Recommendation System using Machine Learning methods is proposed and tested on real data set. It is found that the results of the system are promising and accurate up to 99%. The proposed piece of work undergoes four steps. First, it creates synthesized data set by using inputs of the Superintendent of Physical Health Centre (Rajam). Second, the synthesized data set is balanced by using Random under-sampling (RUS) followed by Synthetic minority oversampling (SMOTE). Third, different machine learning techniques such as K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes, Random Forest (RF), Stochastic Gradient Descent (SGD), and Support vector machine (SVM) are applied on both the Synthesized and balanced data sets to classify subjects into different classes based on age, comorbidity-chronic disease- and other symptoms (cold, cough, fever, and breathlessness). Finally, the COVID-19 Test Recommended System is created and integrated with the best classification model. From the experimental results, it is observed that the training and testing accuracy of all the classification models is more than 99% consequently, the COVID-19 Testing recommended system also gives 100% accuracy in predicting the category of a subject based on input symptoms. [ABSTRACT FROM AUTHOR]