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[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]

It is critical to have an accurate prediction of surface roughness (Sa) in order to improve grinding productivity, reduce costs, and minimize the period of time required for trials and testing. Although many prediction methods have been developed, fewer studies have been conducted on the prediction of surface roughness in longitudinal ultrasonic vibration-assisted grinding (LUVAG). In this paper, a surface roughness prediction model algorithm based on ensemble learning of support vector machines (ELSVM) is proposed that can be used for surface roughness prediction of LUVAG alumina ceramics. This paper first details the development of the ELSVM surface roughness prediction model, which consists of four modules: the prepossessing module, the multi-algorithm regression module, the support vector machine algorithm (SVM) module, and the ensemble module. In addition, ELSVM was compared with four other machine learning methods based on experimental results for surface roughness prediction modeling. The error of ELSVM model was reduced by 6.3%, 7.9%, 8.9%, and 7.5%, respectively, compared to the individual prediction models such as I-AISPSO, I-AIS, SPSO, and KBaNN. Comparison results show that the ELSVM model has the lowest average ratio of Mean Absolute Error (MAE) for surface roughness prediction in LUVAG. • The artificial immune particle swarm optimization is improved. • The adaptive particle swarm optimization algorithm and the manual free algorithm are combined in parallel. • A surface roughness prediction model of support vector machine based on ensemble learning algorithm is proposed. • Longitudinal ultrasonic assisted grinding can reduce surface roughness. [ABSTRACT FROM AUTHOR]

• This paper presents an efficient algorithm for classification of Epileptic Seizure from normal, inter-ictal, and seizure EEG signals. • A new classifier named EMRVFLN is proposed which is an improved version of both RVFLN and ELM. • Efficient features are extracted after pre-processing from the EEG signal using VMD and HT. • Also, this paper presents digital implementation of the proposed EMRVFLN classifier in FPGA environment. • Two real time datasets i.e. Bonn university dataset and Neurology & Sleep Centre, Hauz Khas, New Delhi are used to validate the proposed method. In this paper, variational mode decomposition (VMD), Hilbert transform (HT), and proposed error-minimized random vector functional link network (EMRVFLN) are integrated to detect and classify epileptic seizure from electroencephalogram (EEG) signals. VMD is applied to decompose the EEG signal into Band-limited intrinsic mode functions (BLIMFs). The five efficacious instantaneous features are computed using HT to construct the feature vector. Proposed EMRVFLN classifier is used to classify the epileptic seizure. The performances of the proposed EMRVFLN are compared with recently developed classifiers such as least-square support vector machine (LSSVM) and extreme learning machine (ELM). The combination of VMD and HT with proposed EMRVFLN classifier outperforms other state-of-the-art methods with classification accuracy of 100% for two class classification problem and 99.74% for three class classification problem. The remarkable classification accuracy facilitates the digital implementation of the proposed EMRVFLN classifier which may aid to design an embedded system for real-time disease diagnosis. [ABSTRACT FROM AUTHOR]

The critical importance of user privacy in the context of Machine Learning is a hot research topic because it hinders data collection. Consequently, Federated Learning (FL) has emerged as a solution to the privacy problem. Instead of collecting users' data to train smart models, FL exchanges models with clients, which are trained and sent back to the server for aggregation and global model updating. However, FL still faces some hurdles, such as vulnerability to inference and poisoning attacks. For this reason, this paper proposes PolyFLAG_SVM: Polymorphic Federated Learning Aggregation of Gradients Support Vector Machines Framework. PolyFLAG_SVM is a novel, secure, communication-efficient framework that provides several variants of Support Vector Machines models that follow the Gradient Descent Update technique. The confidence in the security of the proposed model is the result of the polymorphism of the encryption keys used, which guarantees that a cracked or leaked key is useless, since it is not used twice within the FL cycle. Moreover, the proposed framework is communication efficient due to the small size of messages exchanged between servers and clients. The proposed model is explained in detail and evaluated appropriately in this paper. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive exploration of the development of an AI-driven Gen-Alpha education guidance indicator for Oman, employing cutting-edge machine learning techniques. The primary objective is to deliver a highly personalized support system and guidance mechanism that accompanies students throughout their academic journey. The research places a spotlight on the pivotal role of Artificial Intelligence (AI) within the realm of education. It skillfully demonstrates AI's robust capabilities in accurately forecasting student performance through the effective use of data mining and advanced machine learning techniques. An integral aspect of this research is the application of AI in aiding students to navigate the involved process of subject selection for their higher education pursuits. This multidimensional process encompasses exhaustive data collection, the detailed creation of a sophisticated machine learning model, and the application of a diverse array of state-of-the-art algorithms. Among these algorithms, K-Nearest Neighbors (KNN), Decision Trees, Random Forest, and Support Vector Machines (SVM) stand out prominently. Notably, the SVM algorithm emerges as the outright winner, delivering an extraordinary accuracy rate of 77%. This remarkable achievement underscores the model's unwavering robustness and its potential to redefine the educational landscape in Oman. In essence, this paper transcends the boundaries of conventional research and offers conclusive validation of AI's revolutionary potential in reshaping educational paradigms. By facilitating data-driven decision-making, the AI-driven Gen-Alpha education guidance indicator empowers students to embark on their educational journeys well-informed and confident, thereby playing a pivotal role in advancing Oman's education system. [ABSTRACT FROM AUTHOR]

As one of the most common types of cancer among women, breast cancer is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. However, detecting breast cancer is challenging due to imbalanced classification, where the minority class (cancerous) is ominously smaller than the majority class (non-cancerous). In this paper, we explore the use of logistic regression (LR) and the adaptive synthetic resampling (ADASYN) technique to address imbalanced classification in breast cancer detection. To that end, we collected the Wisconsin Breast Cancer dataset, which contains 569 instances. The dataset is imbalanced, with 212 malignant (cancerous) cases and 357 benign (non-cancerous) cases. Then, we trained support vector machine, LR, K-nearest neighbor, gradient, and adaptive boosting on the imbalanced dataset. Finally, we trained these algorithms on resampled data with the ADASYN oversampling and we evaluated their performance using cross-validation score with 5-folds. The results of the experiment showed that using ADASYN with LR significantly improved the performance the LR model. The LR model achieves 99.46% accuracy on breast cancer diagnosis. Moreover, the confusion matrix shows that among the 188 samples, the model misclassified one cancerous instance. Thus, we concluded that the proposed model is effective for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Abstract - Student performance prediction has a pivotal role in educational institutions, providing valuable insights into students' academic outcomes and aiding educators in implementing personalized interventions. In the previous works, many variants of ML and AI techniques have been implemented with high accuracy. However, the lack of transparency due to the heterogeneous nature of features that lead to biases, forced inequalities or even challenges in interpreting the model implications have not been addressed. An approach that harnesses the power of hybrid intelligence, combining human expertise with artificial intelligence (AI), to enhance transparency and accountability in the decision-making process of student performance prediction is required. Co-evolutionary Hybrid Intelligence (CHI) approach, will not only discover interdisciplinary facets including past exam scores, attendance records, demographic information, and socio-economic factors but also interpret the influence of every component i.e., teacher, student, and learning environment to predict student performance. Two modules are discussed in this paper. The first module classifies the teachers into two classes of expertise by comparing the accuracy performance of six well-known machine learning techniques namely Logistic Regression (LR), MultiLayer Perceptron(MLP), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbor (kNN), and Random Forest (RF). Second, we propose a multiclass student performance prediction model (RLCHI) built upon a combination of reinforcement learning (RL) algorithms that will aid the model to perceive patterns and adjustments inherently and expert teachers' opinions(CHI) from the first model that will bring invaluably contextual understanding, nuanced judgment, and a deep knowledge of pedagogy to the equation. The performance of the RLCHI model is evaluated. The obtained results show that the proposed model scores better than models built only on the RL algorithm on the same dataset. The accuracy, precision, recall, and Fl score of the model achieved with hybrid intelligence is 79.5, 49.9, 52.3, 53.2 when compared to 71.9,48.30,46.6,46.1 achieved with only RL respectively. [ABSTRACT FROM AUTHOR]

Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, distinguishing toxic mushrooms from non-toxic ones is challenging, as their visual characteristics can be misleading. Deep learning, a subset of artificial intelligence, offers a promising approach to detecting and classifying mushrooms based on their toxicity. By training deep neural networks on datasets of labelled mushroom images, it becomes possible to create models capable of accurately differentiating between toxic and non-toxic mushrooms. Mushrooms are fungi that play significant roles in ecosystems, food, and medicine. However, manually distinguishing toxic mushrooms from non-toxic ones is a challenging and time-consuming task. This research work is to help the local farmers and exporters to identify the difference between the toxic and the edible mushroom which will eventually boost production and will directly have an impact on the economic growth in India. This paper compared different deep learning algorithms such as Support Vector Machine (SVM), Resnet50, YOLO V5 and Alexnet, which have been found to be the most useful in classifying the mushrooms effectively as toxic and non-toxic respectively. The patterns in the images of the mushrooms have been predicted through the above classifiers. The validation techniques show SVM to be the most effective with an accuracy of 83%. [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet of Things (IoT) has created a large attack surface for adversaries to launch destructive cyber attacks. Intrusion Detection Systems (IDS) can be used to identify and monitor anomalies and threats in the IoT system. In this paper, we compare the performance of various machine learning algorithms for building IDS, including decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), Ada Boost, and support vector machine (SVM), using the IoTID20 dataset. This dataset has three target classes, including a binary class for normal or abnormal behavior and classes for categories and sub-categories of the binary class. We select the most relevant features to minimize execution time and improve accuracy. Our results show that the decision tree algorithm generally achieved the highest accuracy at 99.80% with the lowest error rate compared to the other algorithms. [ABSTRACT FROM AUTHOR]

Next generation firewall is taking major part to secure network environment in the industry. This device will monitor all the traffic which is coming inside the network or going outside of the network. With all these security devices attackers can still perform various kind of attacks on the network. DDoS attack is one of the hardest attack to identify which will send packets to the network and which will look like normal traffic but it will act as a DDoS traffic. In this paper we used binary decision tree, XGBoost and support vector machine to identify DDoS attack traffic pattern from the different features in the packet header. Data will be fetched from the packet header and among them standard deviation of the packet bytes and packet flows are the features considered. We have applied this data on the trained dataset. Algorithm will predict whether the traffic coming inside the network is trusted or not. Out of the three algorithms Binary Decision tree algorithm is giving 99 percentage of accuracy and will predict the data as fast as possible. Here priority is to filter DDos attacks of any security level in the line speed of the NIDS or any other appliances. This method of DDoS attack detection will add extra layer of security in the next generation firewall which will make firewall more robust. [ABSTRACT FROM AUTHOR]

Spam is some unwanted material that may be put in the form of images. While many machine learning approaches are effective at detecting textual spam, this is not true for image spam. In this paper, a new framework for identifying image spams is proposed. The images are divided into two categories: undesirable material contained in the form of images which is referred to as a spam image, whereas anything else is referred to as a ham image. Our proposed methodology is based on applying different pre-trained deep learning models, including InceptionV3, Densely Connected Convolutional Networks 121(DenseNet121), Residual Networks (ResNet50), Visual Geometry Group (VGG16) and MobileNetV2, to filter out the unwanted spam images. Different standard test datasets such as Dredze Dataset, Image Spam Hunter (ISH) Dataset and Improved Dataset are utilized in this paper for performance testing. Furthermore, transfer learning and data augmentation are employed to address the issue of a shortage of labeled data. In our implementation, the fully connected (FC) layer in the aforementioned pre-trained models is replaced with a Support Vector Machine (SVM) classifier, resulting in an improved accuracy. The obtained results reveal that ResNet50 model yields the best performance achieving 99.87% accuracy, 99.88% area under the curve (AUC), 99.98% sensitivity, 99.79% precision, 98.99% F1 score and a computational testing time of in the order of one to two seconds for the ISH dataset. [ABSTRACT FROM AUTHOR]

Multi-view learning (MVL) is a rapidly evolving direction in the field of machine learning. Despite the positive results, most algorithms that combine multi-view learning with twin support vector machines (TSVM) focus on the traditional machine learning domain. No method has been accomplished for combining MVL, TSVM, and deep learning. In this paper, we propose two novel multi-view deep models to solve the multiclass classification problem, namely deep multi-view twin support vector machines (DMvTSVM) based on deep neural network (DNN) and auto-encoder (AE) network. They find two non-parallel hyperplanes such that each hyperplane is as close to its own class as possible while being as far away from the other class as possible. Meanwhile, we apply similarity regularization to the output of the Deep TSVM classifier for each view to learn consensus information between views, and use this to refine the joint weights of the deep model and TSVM. Finally, the novel models employ the o n e − v s − r e s t strategy to allow the DMvTSVM classifier to solve the multiclass classification problems. In the experiments, the proposed methods are compared with existing state-of-the-art algorithms to prove their effectiveness. • The first combination of twin support vector machines (TSVM) and deep neural networks. • Nesting of more advanced auto encoder networks. • Incorporating multi-view learning capabilities. • Mutual negotiation and simultaneous learning between deep neural networks and TSVM. • The experiments prove the effectiveness of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

Compressed Machine (ML) and Deep Learning(DL) techniques are the emerging areas in research, the use of compressed ML and DL algorithms is recommended on System on Chip(SoC) platforms as the latencies of ML or DL algorithms will be extremely high. This paper explores the need to use compressed ML/DL techniques by analyzing the various popular ML algorithms by implementing them on different platforms including SoCs, in particular Snapdragon 410c which will be used on the wearable biomedical device. This paper looks at fall prediction using wearables. The wearable device uses 410c as the platform and data is collected using IMU and heart rate sensors. A data set was created by collecting IMU and heart rate sensor values across different fall and non fall Activities of Daily Living(ADL) activities, over 70k data points was collected of which 70 percent was used to train the algorithm and 30 percent was used as test data. The same data set was used across varying algorithms and platforms and the performance as well as the latencies were analyzed.It was found that the latencies was found to be greater than 120 sec for even K Nearest Neighbours(KNN) when run on SoCs. This clearly indicates that we cannot use classical ML algorithms. When a public dataset SmartFall which had 92,780 data points was used, Support Vector Machine(SVM) algorithm did not converge even after several hours of computing. In case of Random Forest it took 2235 seconds for the algorithm to converge. As the size of the dataset increased obviously there was an increase in latency with complex ML algorithms are unable to converge, hence this research indicates the necessity of compressed ML algorithms. [ABSTRACT FROM AUTHOR]

Accuracy of a classifier is important for the success of any prediction model. The more accuracy a classifier possesses, the more robust the system is made on it. In this paper, a disease prediction model is developed in Python for the classification of diabetes in patients. In the research paper, study is performed to make a comparative analysis of the performance of machine learning classification algorithms. The classifier's performances are enhanced by of tuning the hyperparameters of classifiers and applied different dataset preprocessing methods. In this experimental analysis, four models have been created, and each model is based on a dataset, obtained by different preprocessing methods of PIMA dataset. For each model, K-Nearest Neighbors, Decision Tree, Random Forest, and Support vector machines classification algorithms, have been applied and classifier's hyperparameters are tuned to get better results from these models. A detail analysis has also performed to get the best prediction model, the best classifier and effective preprocessing methods for it. The prediction model use F1score as the main metric. The highest F1score and accuracy are 75.68 % and 88.61% respectively, which is achieved by Random Forest classifier for dataset model D3 obtained by removing the samples having missing or unknown values from PIMA dataset. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder affecting 60% of people over the age of 50 years. Patients with Parkinson's (PWP) face mobility challenges and speech difficulties, making physical visits for treatment and monitoring a hurdle. PD can be treated through early detection, thus enabling patients to lead a normal life. The rise of an aging population over the world emphasizes the need to detect PD early, remotely and accurately. This paper highlights the use of machine learning techniques in telemedicine to detect PD in its early stages. Research has been carried out on the MDVP audio data of 30 PWP and healthy people during training of 4 ML models. Comparison of results of classification by Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN) and Logistic Regression models, yield Random Forest classifier as the ideal Machine Learning (ML) technique for detection of PD. Random Forest classifier model has a detection accuracy of 91.83% and sensitivity of 0.95. Through the findings of this paper, we aim to promote the use of ML in telemedicine, thereby providing a new lease of life to patients suffering from Parkinson's disease. [ABSTRACT FROM AUTHOR]

In the current stage, software defect prediction is suffering the imbalanced data problem. Traditional methods are insensitive to defect-prone modules and tend to predict defect-prone modules as defect-free modules. To deal with this problem, sampling techniques are adopted to rebalance the defect-prone and defect-free data to train the predictive model in order to improve the performance. However, it is not clear on the combined effect of the sampling techniques and the machine learning classifiers on the performance of software defect prediction. The intent of the paper is to study the performance impact on defect prediction incurred by different combinations of sampling techniques and machine learning classifiers. Specifically, we investigate three types of sampling techniques as resampling, spread subsampling and SMOTE (Synthetic Minority Over-sampling Technique), and five types of machine learning classifiers as C4.5, naive Bayes, logistic regression, support vector machine and deep learning to study their combined effect on defect prediction. By using the Friedman test and Nemenyi test, we find that there isn't an optimal method among all the 12 combinations in defect prediction. However, support vector machine and deep learning have produced the best performance stably among all the investigated projects. With ANOVA analysis, we find that the sampling techniques have great impact on the outcomes of defect prediction because they produce different data distributions for model training. Nevertheless, the sampling proportion has significant impacts on TPR (True Positive Ratio) and FPR (False Positive Ratio) while it can merely influence the AUC (Area under Curve) and Balance of logistic regression. We explain the experimental results in the paper. [ABSTRACT FROM AUTHOR]

The aerosol particles present in the atmospheric region mainly affect the climate radiative forcing directly by scattering & absorbing the sunlight. Also, it indirectly influences the formation of clouds, precipitation and acts as a considerable uncertainty in assessing Earth's radiation budget. Determination of aerosol type is significant in characterizing the aerosol role in the atmospheric processes, feedback, and climate models. This paper proposes two aerosol classification models, one based on the source and another based on the composition, to classify the aerosols using aerosol optical properties. The source based aerosol classification method helps to identify the sources which cause pollution in a particular region. Based on the results, proper control measures can be taken to reduce pollution. The composition based aerosol classification helps to identify the nature of aerosol types, such as absorbing or non-absorbing. This classification helps to study the climate of the Kanpur region. The aerosol data is taken from AERONET (AErosol RObotic NETwork) for the period 2002–2018 for the Kanpur region. The composition based aerosol classification model uses Single Scattering Albedo (SSA), Angstrom Exponent (AE), and Fine Mode Fraction (FMF) parameters to categorize aerosols based on their composition. The source based aerosol classification model classifies the aerosols based on values of AE and Aerosol Optical Depth (AOD) and describes the source of the aerosol particles. Knowledge of aerosol sources and compositions helps execute policies or controls to reduce aerosol concentrations. Machine learning algorithms, Naïve Bayes, K Nearest Neighbor, Decision Tree, Support Vector Machine, and Random Forest are used to validate classification schemes. The performance analysis of machine learning algorithms is compared using ten different metrics, and the results are also compared with the existing aerosol classification models. The results of the classification show that the source based aerosols of the desert and arid background and the composition based aerosols of types, Mixture Absorbing, Coarse absorbing (Dust), and Black Carbon are dominant over the Kanpur region during the study period considered. The Number of non-absorbing (scattering) type aerosols are least in the study region considered during the study period at all the seasons. It is found that the Random Forest and Decision Tree models outperform the other machine learning models considered and the existing classification models in terms of accuracy (99.55 %) and other performance metrics considered. [ABSTRACT FROM AUTHOR]

• A framework of fault prognosis strategy of external gear pump with insufficient availability of experimental data. • Generation of the high-fidelity CFD model to tackle the absence/ minimal availability of experimental data. • Generation of various working conditions of the pump to comprehend its behaviour. • Implementation of synthetic data generation methods to increase data availability and to generate degradation behaviour. • Noise sensitivity analysis of ML algorithms by adding white noise in the training and test dataset. Fault prognosis is an important area of research that aims to predict and diagnose faults in complex systems. The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equipment components in optimal condition in order to avoid downtime that may cause significant disruption. Due to this reason, in recent years, there has been an increasing interest in creating innovative methods for fault prognosis that can increase system reliability and reduce maintenance costs. Gear pumps are widely utilised in a variety of industrial applications, and their reliability and effectiveness are crucial for achieving optimal system performance. Gear pumps, on the other hand, are prone to malfunctions and failures, which can result in substantial downtime and maintenance costs. The challenge is to develop a fault prognosis approach that is reliable and accurate enough to detect and diagnose defects in a gear pump before they cause system failures. This paper proposes a novel computational strategy for the fault prognosis of an external gear pump using Machine Learning (ML) approaches. Due to the unavailability of sufficient experimental data in the vicinity of failure mechanisms, a novel approach to generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic (CFD) model of the gear pump in healthy and faulty working conditions is presented. However, considering the computational demand for rerunning the same CFD simulations, novel synthetic data generation techniques are implemented by perturbing the frequency content of the time series to recreate other working conditions and constructing degradation behaviour using linear and cubic interpolation methods for run-to-failure scenarios. The synthetically created datasets are used to train the underlying ML metamodel for fault prognosis. Two types of ML algorithms are employed for fault prognosis: Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms. A series of numerical examples are shown, allowing us to infer that the proposed modelling technique is feasible in an industrial setting and that employing the MLP algorithm delivers superior fault prognosis results when compared to SVM. Furthermore, datasets generated using the cubic interpolation method have lower prediction errors than datasets generated using the linear interpolation method due to the smoother degradation behaviour in the data. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TSVM) is a practical machine learning algorithm, whereas traditional TSVM can be limited for data with outliers or noises. To address this problem, we propose a novel TSVM with the symmetric LINEX loss function (SLTSVM) for robust classification. There are several advantages of our method: (1) The performance of the proposed SLTSVM for data with outliers or noise can be improved by using the symmetric LINEX loss function. (2) The introduction of regularization term can effectively improve the generalization ability of our model. (3) An efficient iterative algorithm is developed to solve the optimization problems of our SLTSVM. (4) The convergence and time complexity of the iterative algorithm are analyzed in detail. Furthermore, our model does not involve loss function parameter, which makes our method more competitive. Experimental results on synthetic, benchmark and image datasets with label noises and feature noises demonstrate that our proposed method slightly outperforms other state-of-the-art methods on most datasets. • This paper proposes SLTSVM by introducing symmetric LIENX loss function. • An interpretable method is proposed for selecting the loss parameter. • The convergence of the designed solution algorithm are further analyzed. • Experiments show that our method is efficient for data with outliers or noises. [ABSTRACT FROM AUTHOR]

[Display omitted] • Integrating deep learning with 2DCOS for wheat flour spectral classification. • The improved EfficientNet model achieved 100% accuracy in wheat flour identification. • Comparison of deep learning and machine learning methods in spectral analysis. • Transforms spectra into images, applicable beyond wheat flour. Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduces an innovative method for wheat flour recognition, combining deep learning (DL) with Two-dimensional correlation spectrum (2DCOS). In this investigation, 316 samples from four distinct types of wheat flour were collected using a near-infrared (NIR) spectrometer, and the raw spectra of each sample underwent preprocessing employing diverse methods. The discrete generalized 2DCOS algorithm was applied to generate 3792 2DCOS images from the preprocessed spectral data. We trained a deep learning model tailored for flour 2DCOS images – EfficientNet. Ultimately, this DL model achieved 100% accuracy in identifying wheat flour within the test set. The findings demonstrate the viability of directly transforming spectra into two-dimensional images for species recognition using 2DCOS and DL. Compared to the traditional stoichiometric method Partial Least Squares Discriminant Analysis (PLS_DA), machine learning methods Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), and deep learning methods one-dimensional convolutional neural network (1DCNN) and residual neural network (ResNet), the model proposed in this paper is better suited for wheat flour identification, boasting the highest accuracy. This study offers a fresh perspective on wheat flour type identification and successfully integrates the latest advancements in deep learning with 2DCOS for spectral type identification. Furthermore, this approach can be extended to the spectral identification of other products, presenting a novel avenue for future research in the field. [ABSTRACT FROM AUTHOR]

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system. • Machine learning techniques were employed to predict green purchase intention (GPI). • Green Self-identification & Environmental Consciousness were the crucial factors in influencing GPI. • Social and health consciousness had less of an impact on influencing GPI. • Perceived personal inconvenience was reported to have no significant impact on GPI. [ABSTRACT FROM AUTHOR]

Truck drivers are essential to modern supply chains. However, the companies that employ them are having trouble keeping them. At many trucking firms, annual turnover rates approach 100 percent or higher. This paper develops a method for predicting individual truck driver turnover events before they happen by applying supervised machine learning classifiers to a new source of operational truck driver data. Our paper uses Electronic Logging Device (ELD) data, which comprise newly federally-mandated, time-stamped work logs collected from approximately 1200 American long-haul truck drivers over 3 years. We train three supervised machine learning classifiers (logistic regression, random forests, and support vector machines) on this data and achieve 60 to 70 percent prediction accuracy and 50 to 60 percent recall across two 5-fold cross-validated experiments. We observe that the quantity and consistency of week-day driving assignments explain most of our models' predictive power. We offer these results as both a new technical tool, as well as novel managerial insights for improving global truck driver retention problems for the benefit of supply chains worldwide. [Display omitted] • Newly available operational data from long haul truck drivers is analyzed. • Three machine learning classifiers are trained on the operational data. • Predictive models achieve 60–70 percent accuracy, 50–60 percent recall. • Models show potential to enable interventions to retain truck drivers. [ABSTRACT FROM AUTHOR]

Identification through image processing has been used in various fields, including product examination and more recently infrastructure inspection, such as identifying cracks in structures. The conventional method, in which a human inspector uses a crack gauge or determines the size through a visual evaluation, results in a subjective evaluation of the extent of concrete life. Currently, an operator inspects the concrete structure by visually examining the concrete surface. However, this method presents numerous problems, including when the inspector has to work in dangerously high places. Therefore, we used machine learning to extract the features of cracks in image processing. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete appears monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in terms of detection. We also compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM) and other machine learning methods. In this paper, two issues are discussed: an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the classification results based on crack width. Crack width classification using machine learning is useful when sufficient image resolution is not available. The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false detection rate was also higher in the proposed method. We further attempted to classify these cracks in accordance with their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes on the basis of the different crack widths, and were able to classify these clacks. • We used machine learning to extract the features of cracks. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete is apparently monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in detection.. • We compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM). The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false-positive rate was also higher in the proposed method.. • We attempted to classify these cracks according to their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes based on the different crack widths. In this paper, the following two issues are discussed: the first is an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the second is the classification results based on crack width. Finally, we evaluated the degree of damage by classifying the cracks based on crack width using machine learning.. [ABSTRACT FROM AUTHOR]

• An improved singular spectrum decomposition is proposed. • High complexity components are secondarily decomposed to reduce its complexity. • An improved prediction model of optimization algorithm is proposed. • Intelligent weighting strategy is introduced to overcome traditional weighting problem. • A combined prediction model for carbon emission is proposed. Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. [ABSTRACT FROM AUTHOR]

Multi-view learning aims to make use of the advantages of different views to complement each other and fully mines the potential information in the data. However, the complexity of multi-view learning algorithm is much higher than that of single view learning algorithm. Based on the optimality conditions of two classical multi-view models: SVM-2K and multi-view twin support vector machine (MvTwSVM), this paper analyzes the corresponding relationship between dual variables and samples, and derives their safe screening rules for the first time, termed as SSR-SVM-2K and SSR-MvTwSVM. It can assign or delete four groups of different dual variables in advance before solving the optimization problem, so as to greatly reduce the scale of the optimization problem and improve the solution speed. More importantly, the safe screening criterion is "safe", that is, the solution of the reduced optimization problem is the same as that of the original problem before screening. In addition, we further give a sequence screening rule to speed up the parameter optimization process, and analyze its properties, including the similarities and differences of safe screening rules between multi-view SVMs and single-view SVMs, the computational complexity, and the relationship between the parameter interval and screening rate. Numerical experiments verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Spam in Short Message Service(SMS) is a serious issue that impacts mobile phone consumers all around the world. Many strategies have been applied using several deep learning and machine learning techniques to overcome these issues. The bagging approach is used in the study to combine four different algorithms, namely RVM, SVM, Naive Bayes, and KNN. Then the final prediction is calculated from the predictions obtained from each of these algorithms by using a majority-based voting approach. So, this paper offers research on the comparative analysis of various text classification algorithms for accurately detecting and classifying spam SMS messages. The dataset is first preprocessed and then vectorized using the TF-IDF method which gives more importance to the less frequent words rather than common words. The Relevance vector machine (RVM) implementation on the dataset, achieves the best performance on this dataset with an F1 score of 0.975175. According to the study's findings, the suggested RVM model may successfully categorize SMS spam messages and be applied in practical settings. [ABSTRACT FROM AUTHOR]

Data-driven models have been recognized as powerful tools to support Structural Health Monitoring (SHM). This paper contributes to the literature by exploring two data-driven approaches to detect damage through changes in a set of variables that assess the condition of the structure, and accommodates the challenge that may arise due to the influence of environmental and operational variabilities. This influence is reflected in the response of the structure and can reduce the probability of detecting damage in a structure or increase the probability of signaling false positives. This paper conducts a comparative study between a machine learning detection approach (supported by linear regression, random forest, support vector machine, and neural networks) and a cointegration approach, with the aim of detecting damage as early as possible. This study also contributes to the literature by evaluating the merits of the damage detection methods using real data collected from a small-scale structure. The structure is analyzed in a reference state and a perturbed state in which damage is emulated. The results show that both approaches are able to detect damage within the first 24 h, without ever signaling false positives. The cointegration based approach can notably detect damage after 10 h and 15 minutes, while the machine learning approach takes 20 h 30 m to detect damage. • Comparison of Machine Learning and Cointegration in SHM. • Consideration of damages and false alarms. • Usage of a reduced scale structure to reproduce the behavior of a bridge column. • Cointegration is the fastest to detect damage. [ABSTRACT FROM AUTHOR]

Despite the existence of evidence-based rehabilitation strategies that address biomechanical deficits, the persistence of recurrent ankle problems in 70% of patients with acute ankle sprains highlights the unresolved nature of this issue. Artificial intelligence (AI) emerges as a promising tool to identify definitive predictors for ankle sprains. This paper aims to summarize the use of AI in investigating the ankle biomechanics of healthy and subjects with ankle sprains. Articles published between 2010 and 2023 were searched from five electronic databases. 59 papers were included for analysis with regards to: i). types of motion tested (functional vs. purposeful ankle movement); ii) types of biomechanical parameters measured (kinetic vs kinematic); iii) types of sensor systems used (lab-based vs field-based); and, iv) AI techniques used. Most studies (83.1%) examined biomechanics during functional motion. Single kinematic parameter, specifically ankle range of motion, could obtain accuracy up to 100% in identifying injury status. Wearable sensor exhibited high reliability for use in both laboratory and on-field/clinical settings. AI algorithms primarily utilized electromyography and joint angle information as input data. Support vector machine was the most used supervised learning algorithm (18.64%), while artificial neural network demonstrated the highest accuracy in eight studies. The potential for remote patient monitoring is evident with the adoption of field-based devices. Nevertheless, AI-based sensors are underutilized in detecting ankle motions at risk of sprain. We identify three key challenges: sensor designs, the controllability of AI models, and the integration of AI-sensor models, providing valuable insights for future research. [Display omitted] • Application of sensors and artificial intelligence to analyse ankle biomechanics. • Lab sensors predict parameters, paving the way for post-modern rehabilitation. • Field devices show potential for ankle injury prediction. • Focus on streamlined smart sensors and effective data management in the future. [ABSTRACT FROM AUTHOR]

Cardiotocogram (CTG) is one of the monitoring tools to estimate the fetus health in womb. CTG mainly yields two results fetal health rate (FHR) and uterine contractions (UC). In total, there are 21 attributes in the measurement of FHR and UC on CTG. These attributes can help obstreticians to clasify whether the fetus health is normal, suspected, or pathological. This paper compares 7 algorithms to predict the fetus health: Artificial Neural Network (ANN), Long-short Term Memory (LSTM), XG Boost (XGB), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Light GBM (LGBM), and Random Forest (RF). By employing three scenarios, this paper reports the performance measurements among those algorithms. The results show that 5 out of 7 algorithms perform very well (89-99% accurate). Those five algorithms are XGB, SVM, KNN, LGBM, RF. Furthermore, only one from five algorithm that always performs well through three scenarios: LGBM. [ABSTRACT FROM AUTHOR]

Quite often a machine learning problem lends itself to be split in several well-defined subproblems, or tasks. The goal of Multi-Task Learning (MTL) is to leverage the joint learning of the problem from two different perspectives: on the one hand, a single, overall model, and on the other hand task-specific models. In this way, the found solution by MTL may be better than those of either the common or the task-specific models. Starting with the work of Evgeniou et al., support vector machines (SVMs) have lent themselves naturally to this approach. This paper proposes a convex formulation of MTL for the L1-, L2- and LS-SVM models that results in dual problems quite similar to the single-task ones, but with multi-task kernels; in turn, this makes possible to train the convex MTL models using standard solvers. As an alternative approach, the direct optimal combination of the already trained common and task-specific models can also be considered. In this paper, a procedure to compute the optimal combining parameter with respect to four different error functions is derived. As shown experimentally, the proposed convex MTL approach performs generally better than the alternative optimal convex combination, and both of them are better than the straight use of either common or task-specific models. [ABSTRACT FROM AUTHOR]

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classifi-cation between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. [ABSTRACT FROM AUTHOR]

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. [ABSTRACT FROM AUTHOR]

Diabetes is a condition associated with high levels of blood glucose and insulin deficiency that is becoming more prevalent among children and adolescents. It is the ninth leading cause of mortality globally at over 1 million deaths per year. Misdiagnosis of type 1 diabetes with type 2 are frequent and causes diabetic ketoacidosis, a critical complication. This paper seeks for the most accurate model between frequently used machine learning algorithms - Multilayer Perceptron, Support Vector Machine and Random Forest in predicting diabetes among patients with focus on blood content analysis, specifically using the Laboratory of Medical City Hospital (LMCH) Diabetes Dataset, which was retrieved from Mendeley. Using hyperparameter optimization and ANOVA F- Value feature scaling, results show that Random Forest produced the highest accuracy at 1.00, followed by Multilayer Perceptron at 0.987 and Support Vector Machine at 0.96. [ABSTRACT FROM AUTHOR]

Among the different contactless techniques for medical diagnosis, hyperspectral imaging has gained relevance due to the high accuracy in tissues classification. Several techniques have been proposed to elaborate these images, ranging from traditional machine learning methods to deep learning algorithms. This paper evaluates three popular machine learning methods, namely Support Vector Machine (SVM), Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) considering a dataset of hyperspectral skin cancer images. The study demonstrates that the proposed algorithms are suitable for medical hyperspectral data classification, particularly when considering a small dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical support vector machine based classifier for autocoding. The purpose of this method is to improve classification accuracy utilizing numerical features obtained from probability scores by support vector machine (SVM). The proposed method captures the tendency to be incorrectly predicated for each label from the probability scores obtained from SVM. Using the captured information, we train new support vector machines for each targeted label considering the feature of the targeted label to improve the classification accuracy. The numerical examples with governmental survey data show a better performance of the proposed method. [ABSTRACT FROM AUTHOR]

Researchers have extensively used machine learning techniques and data mining methods to build prediction models and classify data in various domains such as aviation, computer science, education, finance, marketing and particularly in medical field where those methods are applied as support systems for diagnosis and analysis in order to make better decisions. On this subject, our research paper attempts to assess the performance of Individual and Ensemble machine learning techniques based on the effectiveness and the efficiently, in terms of accuracy, specificity, sensitivity and precision to choose the most effective. The main object of our research paper is to define the best and effective machine learning approach for the Breast Cancer diagnosis and prediction. To achieve our objective, we applied individual based level machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Decision tree (C4.5), Simple Logistic and well known ensembles methods like Majority Voting and Random Forest with 10 cross field technique on the Breast Cancer Diagnosis Dataset obtained from UCI Repository. The experimental results show that the Majority Voting Ensemble technique based on 3 top classifiers SVM, K-NN, Simple Logistic gives the highest accuracy 98.1% with the lowest error rate 0.01% and outperformed all other individual classifiers. This study demonstrates that our proposal approach based on Majority Voting Ensemble technique was the best classification machine learning model with the highest level of accuracy for breast cancer prediction and diagnosis. All experiments are effectuated within a simulation environment and realized in Weka data mining tool. [ABSTRACT FROM AUTHOR]

This study designs an online sexual predator detection system using Social Behavior Biometric (SSB) features. Social biometric focuses on extracting the pattern a user exhibits while interacting and communicating through social networks. The paper addresses the online sexual predator problem by mining the vocabulary and emotional behavior, which could assist in identifying if the user is a benign or predator. The feature-set consists of vocabulary terms that appear differently in predator and victim content. In order to strengthen the detection model, the paper also focuses on distinguishing the two classes of users based on emotions reflected in their conversation. The experiments are performed on the PAN 2012 corpus. Two datasets are created with respect to vocabulary-based and emotion-based features. The results obtained on the test set have proved that by integrating the vocabulary and emotion-based attributes, the performance of the system is significantly enhanced. While comparing, the proposed approach has outperformed top existing methods by obtaining F 1 , F 2 , and F 0.5 values of 0.95, 0.94, and 0.96 respectively. Furthermore, we also recorded the best accuracy compared to state-of-the-art studies for our proposed SBB-based approach with 99.86%, 99.51%, and 99.88% for Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF) respectively. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [ABSTRACT FROM AUTHOR]

For the design of stainless steel bolted connections, current design codes firstly calculate the design resistance of each potential failure mode and then take the minimum of the design resistances calculated from all the potential failure modes as the final design failure load. However, this design framework has been found to be tedious and also leads to inaccurate failure load predictions. In this paper, a more accurate and unified predictive framework for stainless steel bolted connections made of different materials with various configurations and bolt hole patterns at both room and elevated temperatures is proposed based on machine learning. This paper starts with a collection of 301 experimental data of stainless steel bolted connections to establish a database. Then, regression models for failure load predictions were developed and trained by six machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine, Adaptive Boosting, Extreme Gradient Boosting and Cat Boosting, with the key hyperparameters for each machine learning algorithm tuned. This is followed by the evaluation of the model performance by means of a series of statistical indices, with the results indicating that the regression model trained by Support Vector Machine has the best model performance. On the basis of the collected experimental data, the regression model trained by Support Vector Machine and current design codes were assessed and compared, showing that the current design codes are rather inaccurate, while the machine-learning-assisted design method is a unified method and can provide accurate and consistent failure load predictions for stainless steel bolted connections made of different materials with various configurations and bolt hole patterns at both ambient and elevated temperatures. • A database of stainless steel bolted plate-to-plate connections was established. • Regression models were developed and trained by six machine learning algorithms. • The regression model with the best performance was compared with existing design codes. • The machine-learning-assisted design method resulted in much improved design accuracy and consistency. [ABSTRACT FROM AUTHOR]

• Machine learning approaches to resolve identity issues. • Implementing TensorFlow to investigate policing dataset. • Predictive models using ML techniques to identify fraudulent or genuine IDs. This paper focuses on applications of various machine learning techniques on an anonymized policing dataset used in EU SPIRIT Horizon 2020 project to identify fraudulent identities and help Law Enforcement Agencies (LEAs) in their investigation in finding potential criminals and identity resolution. Lack of qualitative data and appropriate methodology to carry out research on criminal fraudulent identities is a common reason for fewer research in this area. Additionally, it is a very sensitive data to work with and minor inaccuracy in prediction of result causes massive impact in the society as genuine people could be questioned whereas criminals could be sent free. Both of these issues are addressed in this paper by application of 39 million records from policing dataset and working towards higher accuracy while building the model. Various machine learning approaches are applied to train the dataset to make predictions and the research focus on being able to predict the 5 suspected fraudulent identities out of 39 million records in the policing dataset. One of the applied machine learning techniques include TensorFlow along with Keras model which has seldomly been applied by researchers in detection of criminal data. To compare the results and test accuracy of TensorFlow model, other machine learning techniques such as Support Vector Machine, Naïve Bayes and K-nearest Neighbours are also applied to have a comparative study on the obtained outcomes from each model. The goal of this research is to find fraudulent IDs amongst all the anonymized IDs in the criminal dataset using TensorFlow and three other machine learning models and select the most optimal model out of them. Since the model is comparing two names so string-matching techniques such as Levenshtein edit distance, Hamming Distance, Jaro-Winkler and Soundex were applied to select an effective approach first before building the model and analysing the results. TensorFlow model demonstrated highest accuracy with relatively least execution time and the only model to successfully predict all the 5 suspects from the policing dataset. [ABSTRACT FROM AUTHOR]

There are multiple entities responsible for online training programs via the Taif Maharat platform at the university. There is a variation in objectives, content, trainers' efficiency, online interaction and participation patterns, training strategies, evaluation tools, and feedback techniques. In addition, there is a lower achievement of standards in dimensions such as planning and curriculum design, leadership, online learning and training, technology, assessment, student support, continuous improvement, and evaluation. Based on these justifications, the study proposed a Machine Learning (ML) approach, which predicts the student's performance to enhance the quality assurance of online training programs via the Maharat platform at Taif University based on the online learning training standards in the Kingdom of Saudi Arabia (KSA). The paper's primary objective is to forecast academic achievement by considering their participation in an online learning environment. The relevant feature was extracted using hybrid optimization, and following this classification process was carried out. For the predictions, the Support Vector Machine technique was applied. To identify and determine the degree of achievement of quality assurance of online training programs standards, we used a descriptive-analytical approach to analyze the sample opinions about quality assurance of online training via the Maharat platform at Taif University. [ABSTRACT FROM AUTHOR]

The alternation between cloudy and clear skies alters the photovoltaic production. This makes it necessary to anticipate these disturbances hours in advance for the correct operation of the electricity distribution plants and networks. In this paper, two short-term forecasting models (3 h) are developed to forecast the photovoltaic production in an integrated plant in the CIESOL building of the University of Almería. The methodology used is based on sky camera images and Artificial Intelligence techniques. Two models have been developed and compared applying artificial neural network (ANN) and support vector machine (SVM) techniques. The global irradiance predicted using sky camera images is used as an input variable in both models. In addition, the operational status of the plants has been included as an input parameter through the performance ratio. The results have shown that the errors made by ANN and SVM are very similar. For all sky conditions, the uncertainty of the production forecast differs by less than 2% from the uncertainty of the solar resource, which is the main source of error in the production models developed. [ABSTRACT FROM AUTHOR]

A standard assumption of the theory of machine learning is the data are generated from a fixed but unknown probability distribution. Although this assumption is based on the foundations of the theory of probability, however, for most learning problems we usually technically random shuffle the original datasets, such as random split into training and test datasets before the training model, to satisfy the assumption, and then we use the shuffled training dataset to train a machine learning model. However, honestly, for real-life learning applications, the data pairs are observed batch by batch under their own original order and it is not necessary to randomly shuffle the original order in advance. From a mathematical point of view, we test if the random shuffling will play a non-negligible influence on the generalization of learning machines. We reduce the problem of random shuffling into the problem of distribution-shift detection. This paper is devoted to testing the null hypothesis that random shuffling does not affect the generalization of learning machines and introduces a distributions-free martingales method against the null hypothesis. We report the five real-life benchmarks of experimental performance with the help of Support Vector Machines and a multi-layer perceptron model. The results show a bonafide fact that the distribution shift in itself of the data is an inescapable reality when we build machine learning algorithms as the original order. [ABSTRACT FROM AUTHOR]

• We propose a class of kernel interpolation-based methods, called kernel reconstruction learning, for solving machine learning problems. • We prove a reconstruction representer theorem, which indicates that conventional kernel methods can be viewed as special cases of kernel reconstruction learning. • We propose the kernel reconstruction vector machine, kernel reconstruction logistic regression, and kernel reconstruction density estimation methods, and show that they outperform popular kernel methods. This paper proposes a class of kernel interpolation-based methods, called kernel reconstruction learning, for solving machine learning problems. Kernel reconstruction learning uses kernel interpolators to reconstruct the unknown functions, which are needed to estimate in the problem, with estimated function values at selected knots. It can be applied to any learning problem that involves function estimation. We prove a reconstruction representer theorem, which indicates that conventional kernel methods, including kernel ridge regression, kernel support vector machine, and kernel logistic regression, can be viewed as special cases of kernel reconstruction learning. Furthermore, kernel reconstruction learning provides new algorithms for large datasets. The kernel reconstruction vector machine, kernel reconstruction logistic regression, and kernel reconstruction density estimation are discussed in detail. With appropriate implementations, they are shown to have higher prediction/estimation accuracy and/or less computational cost than popular kernel methods. [ABSTRACT FROM AUTHOR]

Dementia is a chronic and degenerative condition, which has become a major health concern among the elderly. With ever-continuing cases of dementia, it has become a very challenging task in the 21st century to provide care for patients with dementia. This paper proposes a framework for the prediction of dementia using the data collected from the OASIS (Open Access Series of Imaging Studies) project which was made available by the Washington University Alzheimer's Disease Research Centre. Different techniques have been implemented for data imputation, pre-processing and data transformation to create suitable data for training the model. Machine learning approaches like Adaboost (AB), Decision Tree (DT), Extra Tree (ET), Gradient Boost (GB), K-Nearest Neighbour (KNN), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and SVM (Support Vector Machine) has been used for a combination of features. These techniques have been applied to the full set of features and features selected from Least Absolute Shrinkage and Selection Operator (LASSO) techniques. A comparison between the accuracy, precision, and other metrics based on the results of the classification algorithms has been provided. The experimental results show that the highest accuracy of 96.77% was obtained by Support Vector Machine (SVM) with full features. The proposed methodology is promising and if developed and deployed can be helpful for the rapid assessment of Alzheimer's Disease (AD). [ABSTRACT FROM AUTHOR]

The peak shear strength of clayey soil-geomembrane interfaces is a vital parameter for the design of relevant engineering infrastructure. However, due to the large number of influence factors and the complex action mechanism, accurate prediction of the peak shear strength for clayey soil-geomembrane interfaces is always a challenge. In this paper, a machine learning model was established by combining Mind Evolutionary Algorithm (MEA) and the ensemble algorithm of Adaptive Boosting Algorithm (ADA)-Back Propagation Artificial Neural Network (BPANN) to predict the peak shear strength of clayey soil-geomembrane interfaces based on the results of 623 laboratory interface direct shear experiments. By comparing with the conventional machine learning algorithms, including Particle Swarm Optimisation Algorithm (PSO) and Genetic Algorithm (GA) tuned ADA-BPANN, MEA tuned Support Vector Machine (SVM) and Random Forest (RF), the superior performance of MEA tuned ADA-BPANN has been validated, with higher predicting precision, shorter training time, and the avoidance of local optimum and overfitting. By adopting the proposed novel model, sensitivity analysis was carried out, which indicates that normal pressure has the largest influence on the peak shear strength, followed by geomembrane roughness. Furthermore, an analytical equation was proposed to assess the peak shear strength that allows the usage of machine learning skills for the practitioners with limited machine learning knowledge. The present research highlights the potential of the MEA tuned ADA-BPANN model as a useful tool to assist in preciously estimating the peak shear strength of clayey soil-geomembrane interfaces, which can provide benefits for the design of relevant engineering applications. • The ensemble artificial intelligence algorithms in predicting the peak shear strength of interfaces is compared. • An analytical equation for estimating the peak shear strength is proposed. • Physical experiments are conducted to validate the effectiveness of the proposed analytical equation. [ABSTRACT FROM AUTHOR]

Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved. [ABSTRACT FROM AUTHOR]

The 5G cellular system will enable heterogeneous networks that incorporate 5G, 4G, Wi-Fi, and other telecommunication connectivity. This will result in huge changes in existing infrastructure and architecture network. Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine Type Communication (mMTC) are some of the 5G's basic application cases. The variety of heterogeneous networks makes it challenging to manage, network traffic, inter-cell, and intra-cell interfaces. Due to diverse interfaces and data transmission rates, network traffic in heterogeneous networks can be difficult to manage and control. Advanced techniques including Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) are used in the 5G Network to overcome such issues. In this paper, a Support Vector Machine (SVM) learning-based resource allocation approach for heterogeneous networks is designed. A machine learning method is proposed to enhance the performance of 5G NR mobile networks. This method is used to upgrade the throughput of the overall networks. The SVM learning function detects by splitting network data of channel state information (CSI). The considered channel state data of two variables as labels with X1 and X2 and trained using SVM-LINEAR, SVM-RBF, and SVM-SIGMOID. The proposed SVM classifier by splitting data for training 80% and testing 20%. A Static Error rate is set as a threshold limit at 10% (0.10) of BLER (Block Error Rate). If the error rate passed the threshold, then update Carrier Aggregation with aggregated component else set carrier aggregation with ordinary carrier component values to Zero. The proposed approach further compares with the round robin (RR), best channel quality indicator (BCQI), fractional frequency reuse (FFR), MU-MIMO, and beamforming resource scheduling techniques. The innovative results indicated that the proposed approach enhanced spectral efficiency, average cell throughput, edge throughput peak throughput, and fairness index. [ABSTRACT FROM AUTHOR]

Handwritten digit recognition has recently gained importance, attracting many researchers due to its use in various machine learning and computer vision applications. As technology and science progressing, there is a need for a system to recognize the handwritten script in several real-time applications to reduce human effort. There a lot of work has been done on the recognition and generation of handwritten digits of high-resource languages such as English. However, insufficient work has been done on Dzongkha digits recognition, as Dzongkha digits are low-resource and more complex than English patterns. This paper aims to perform handwritten character recognition of Dzongkha digit using several machine learning techniques. The unavailability of the Dzongkha handwritten digit dataset is the prime motivation behind this work. To facilitates the recognition of Dzongkha handwritten digit, we have collected the data of Dzongkha handwritten digit from indigenous and non-indigenous people of Bhutan and provided the dataset for further research. Moreover, we have used several machine algorithms, including a support vector machine, K-nearest neighbor, and decision tree. Among these algorithms, the support vector machine classification algorithm has achieved a remarkable result with an accuracy of 98.29%. [ABSTRACT FROM AUTHOR]

The latest advancements in wireless communication promotes the researchers to concentrate more in the expansion of Mobile Ad hoc Networks (MANETs), where nodes communicate each other to provide the demanded real time infotainment services. However, due to wireless connectivity and decentralized architecture, constructing secure routing is still a challenging issue in MANETs. Therefore, in this paper, A Machine Learning and Trust Based AODV Routing Protocol (ML-AODV) to mitigate Flooding and Blackhole Attacks in MANET is proposed. Primarily, cooperative intermediate nodes in the network are selected through trust estimation at each node to avoid (flooding attack) unnecessary transmission of huge number of routing packets to nonexistent destinations. Towards this, the node distinct metrics like Hop Count (HC), Residual Energy (RE), and Link Expiration Time (LET) are considered for trust estimation. The nodes with highest trust value are chosen as trusted relay forwarders to find the optimal paths with reduced packet drop rate (Blackhole attack). Further, finds best optimal path to eliminate the energy disparity and delay for packet transmission using machine learning based Artificial Neural Network (ANN) with Support Vector Machine (SVM) classifier. Here, SVM is used to identify the intruder in the selected route to improve efficiency and intrusion detection accuracy. The performance of the proposed ML-AODV is carried out using NS-2 and compared over existing routing schemes under attack. Based on the simulation results, the proposed ML based AODV exhibits improved throughput to 4% and reliability to 44% over existing approaches. In addition, delay, routing overhead, packet loss ratio are reduced to 12%, 15%, and 10% respectively. [ABSTRACT FROM AUTHOR]