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Research on the visual system and its development is not only crucial for understanding how we see and interpret the environment from basic visual processing to complex perception but also aids in the development of technologies like virtual reality, augmented reality, and computer vision. In our previous paper, we have proposed a novel biological motion direction detection mechanism. Based on this mechanism, the authors of this paper proposed a learning artificial visual system (AVS) and use it to achieve motion direction detection. The learning AVS consists of two layers: the local visual feature detective neuron layer and the global feature detective neuron layer. For local motion direction detection, we use dendritic neurons to implement local motion direction-detective neurons and use them to extract local motion direction information. Global feature detective neurons are simply implemented by summing the local motion direction-detective neurons in each direction of possible motion. We trained the learning AVS by eliminating unnecessary branches or synapses and keeping only those that are strongly connected to the function of motion direction detection. Computer simulations demonstrate that AVS can learn the correct motion direction of objects with different shapes, size and positions. We compare the performance of AVS with support vector machine (SVM) and traditional convolutional neural networks (CNNs)—LeNet-5 and EfficientNet-B0 (EfN)—in motion direction detection and find that our learning AVS has better recognition accuracy, noise immunity and less demands on size of dataset and learning costs. [ABSTRACT FROM AUTHOR]

The importance of technology and managerial risk management in banks has increased due to the financial crisis. Banks are the most affected since there are so many of them with poor financial standing. Due to this problem, an unstable and inefficient financial system causes economic stagnation in both the banking sector and overall economy. Data envelopment analysis (DEA) has been used to examine decision-making units (DMUs) performance to enhance efficiency. Currently, with the rapid growth of big data, adding more DMUs will likely require a large amount of memory and CPU time on the computer system, which will be the biggest challenge. As a result, machine learning (ML) approaches have been used to analyze financial institution performance, but many of them have variances in predictions or model stability, making measuring bank efficiency extremely difficult. For this, ensemble learning is commonly used to evaluate the performance of financial institutions in this context. This paper presents a robust super learner ensemble technique for assessing bank efficiency, with four machine learning models serving as base learners. These models are the support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), and AdaBoost classifier (ADA) which represent the base learners and their results utilized to train the meta-learner. The super learner (SL) approach is an extension of the stacking technique, which generates an ensemble based on cross-validation. One important benefit of this cross-validation theory-based technique is that it can overcome the overfitting issue that plagues most other ensemble approaches. When SL and base learners were compared for their forecasting abilities using different statistical standards, the results showed that the SL is superior to the base learners, where different variable combinations were used. The SL had accuracy (ACC) of 0.8636–0.9545 and F1-score (F1) of 0.9143–0.9714, while the basic learners had ACC of 0.5909–0.8182 and F1 of 0.6897–0.9143. So, SL is highly recommended for improving the accuracy of financial data forecasts, even with limited financial data. [ABSTRACT FROM AUTHOR]

Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening. [ABSTRACT FROM AUTHOR]

Optical character recognition is a way of converting scanned images of printed or handwritten documents into machine-encoded text, making it easier to store, browse, retrieve, and process electronic data. In this research, a Printed Amharic Characters Recognition dataset is prepared to train and test a model. Images in the dataset only contain 231 basic Amharic characters that are normalized to 32 × 32 pixels. In this work, a hybrid model of the two super classifiers is developed: the convolutional neural network (CNN) and the support vector machine (SVM). In this novel hybrid CNN-SVM model, CNN works as an automatic feature extractor from the raw images, and then, the extracted feature vectors are given as input to SVM for classification and recognition. A 99.84% accuracy was achieved on the own-prepared dataset and 95.59% accuracy on the benchmark Amharic Optical Character Recognition Database in classifying the testing dataset images. The proposed hybrid CNN-SVM model gave better results than the CNN with a fully connected layer. Moreover, the proposed model outperforms previously existing works attempted by others to recognize printed Amharic characters on the same and different datasets. [ABSTRACT FROM AUTHOR]

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. [ABSTRACT FROM AUTHOR]

In educational institutions, it is now more important than ever to deliver high-quality academic instruction, and educational data mining is essential for resolving problems that arise from challenging unstructured data in this field. Using machine learning (ML) approaches, the performance of students and traits related to academia, a crucial indicator of higher education, is examined. In the proposed study, the educational dataset is subjected to feature ranking algorithms, including MRMR, ReliefF, Chi-Square, ANOVA, and Kruskal–Wallis, followed by important feature selection using Shapley. The dataset has 16 attributes of integer, categorical type, and after feature ranking approaches, the features with the most important information are chosen and ML techniques are applied to them. It takes two phases to complete the work. The results are obtained after the first phase, in which all features are taken into account for ML training. The second phase of ML training takes into account selective features that are derived using ranking approaches. ML models with only selective attributes are compared to models with all features in order to determine which is more precise. In comparison, the results of the ML models with selective attributes outperformed the models with all attributes. Overall, the ensemblers, i.e., bagged tree and AdaBoost, outperformed other ML techniques such as decision trees, neural networks, naive Bayes, K-nearest neighbor, and support vector machines presented in the proposed study. Bagged trees achieved an accuracy of 81.0 percent, while AdaBoost achieved an accuracy of 74.2 percent. [ABSTRACT FROM AUTHOR]

Categorical features appear in datasets from almost every practice area, including real estate datasets. One of the most critical handicaps of machine learning algorithms is that they are not designed to capture the qualitative nature of the categorical features, leading to sub-optimal predictions for the datasets with categorical observations. This study focuses on a new fuzzy regression functions framework, namely hierarchical fuzzy regression functions, that can handle categorical features properly for the regression task. The proposed framework is benchmarked with linear regression, support vector machines, deep neural networks, and adaptive neuro-fuzzy inference systems with real estate data having categorical features from six markets. It is observed that the proposed method produces better prediction performance for real estate price prediction than the benchmark methods in a wide variety of real estate markets. Since we provide all the required software codes to implement the proposed hierarchical fuzzy regression functions framework, our approach offers practitioners a readily applicable, high-performing tool for real estate price prediction and other regression problems involving categorical independent features. [ABSTRACT FROM AUTHOR]

During the takeoff phase, aircraft engines reach maximum speed and temperature to achieve the required thrust. Due to these harsh operating conditions, the performance of aircraft engines may decrease. This decrease in performance increases both fuel consumption and environmental damage. Reducing or eliminating the damages caused by aircraft is among the objectives of ICAO. In order to achieve this goal, aircraft engines are compulsorily tested, evaluated by experts and certified. The data obtained during the test process is recorded and stored in the engine emission databank (EEDB). During the takeoff phase, there is no system that can evaluate aircraft engines without dismantling and without expert knowledge. In this study, EEDB 2019 and 2021 takeoff phase data sets were used. Fuel flow T/O parameter is an important parameter used both in the calculation of aircraft emissions and in the evaluation of engine performance. Gaussian process regression (GPR), support vector machine (SVM) and multilayer perceptron (MLP) models were used to estimate the fuel flow T/O parameter. The results obtained were compared according to error performance criteria and the best model was selected. In MATLAB® environment, confidence intervals were plotted with the estimated fuel flow T/O value at 99% confidence level. This study demonstrates that the performance evaluation of aircraft engines during the takeoff phase can be performed without the need for expert knowledge. [ABSTRACT FROM AUTHOR]

Voice is perceived as a form of biometrics which communicates valuable and rich information pertinent to an individual, such as his or her identity, gender, accent, age and emotion. Speaker identification denotes the task of identifying speakers based on their intrinsic voice characteristics. This study proposes a text-independent speaker identification system based on principal component analysis (PCA), fuzzy C-means (FCM) along with deepForest called PCAFCM-deepForest. The proposed approach is evaluated under neutral and adverse talking environments. Given this approach, we assessed our proposed model architecture on five benchmark corpora, namely private Arabic Emirati-accented speech dataset, public English dataset; Crowd-sourced emotional multimodal actors dataset (CREMA), public German database; Berlin database of emotional speech (EmoDB), public Chinese and English; emotional speech database (ESD), and public French dataset; public Canadian French emotional (CaFE) speech dataset. Our analysis shows that the performance of speaker identification has been immensely increased (greatly improved) when fuzzy logic and PCA are both applied to the extracted mel-frequency cepstral coefficients (MFCC). Speaker identification performance achieved by the proposed PCAFCM-deepForest is superior to that obtained by deepForest alone, FCM-deepForest as well as convolutional neural network (CNN). Besides, it surpasses the following conventional models: Random forest and support vector machine (SVM). Our findings demonstrate that the attained average speaker identification accuracy is equivalent to 98.20% using the Emirati database; an average performance which outperforms the existing frameworks. Moreover, PCAFCM-deepForest is fine-tuned using the grid search algorithm, and the achieved complexity is much less than that of CNN. [ABSTRACT FROM AUTHOR]

Accurate and early disease prediction enables patients to plan and improve their quality of life in the future. Early detection of neurodegenerative diseases, such as Parkinson's disease, is a high priority and a significant challenge in which physicians must act quickly to diagnose and predict the risk of disease severity. Machine learning (ML) models combined with feature selection (FS) techniques can assist physicians in quickly diagnosing a disease. FS technique optimally subsets features to improve model performance and reduce the number of tests required for a patient, thereby speeding up diagnosis. This paper proposes an e-diagnosis approach based on ML-FS algorithms to detect Parkinson's disease using data obtained from Parkinson's Progression Markers Initiative (PPMI) Online study. Also, it can be considered patient-oriented research as it uses self-reported online collected data. The results of six FS techniques pre-applied to classification algorithms named logistic regression, random forest, support vector machine, CatBoost, extreme learning machine, and XGBoost are shown in this study. Chi-square, mutual information, and analysis of variance (ANOVA) filter-based FS methods, while sequential feature selection, Boruta, and recursive feature elimination are considered wrapper methods. The outcomes show that random forest when trained on features selected by the recursive feature elimination technique help to build an efficient and effective approach for detecting Parkinson's disease. [ABSTRACT FROM AUTHOR]

Finding patches is a crucial step in a pavement performance survey. The study develops a machine learning and image processing algorithm-established automatic method for identifying asphalt pavement patches. The GrayLevel Co-Occurrence Matrix and image texture-based features derived from color channel statistics are used as input parameters to describe the condition of the pavement. For feature extraction, image processing methods like the projective integral of images, steerable filters, and an improved image thresholding method were used. Support Vector Machine is employed for categorizing the differentiating patched regions from non-patch ones. The suggested combination of image texture analytical methods has been trained using an IA data set created from 400 image samples. The feature set, which combines the characteristics of cracked objects with those generated from the projective integral, can produce the desired result. To make the model's execution simpler, a patch recognition program was created and implemented in MATLAB. As a result, the recently created method has the potential to be an instrument for traffic management organizations through the assessment of pavement performance. The results of the experiments indicate that the newly developed method may achieve excellent accuracy prediction results with a classifier performance rate of about 96%. Such a method could enable transportation authorities to assess the state of asphalt pavement. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is the second most common neurological disorder caused by damage to dopaminergic neurons. Therefore, it is important to develop systems for early and automatic diagnosis of PD. For this purpose, a study that will contribute to the development of systems for the automatic diagnosis of PD is presented. The Electroencephalography (EEG) signals were decomposed into sub-bands using adaptive decomposition methods, such as empirical mode decomposition, variational mode decomposition, and Vold-Kalman order filtering (VKF). Various features were extracted from the sub-band decomposed signals, and the significant ones were determined by Chi-squared test. These important features were applied as input to support vector machine (SVM), fitch neural network (FNN), k-nearest neighbours (KNN), and decision trees (DT), machine learning (ML) models and classification was performed. We analysed the performance of ML models by obtaining accuracy, sensitivity, specificity, positive predictive value, negative predictive values, F1-score, false-positive rate, kappa statistics, and area under the curve. The classification process was performed for two cases: PD ON-HC and PD OFF-HC groups. The most successful method in this study was the VKF method, which was applied for the first time in this field with the approach specified for both cases. In both instances, the SVM algorithm was employed as the ML model, with classifier performance criterion values close to 100%. The results obtained in this study seem to be successful compared to the results of recent research on the diagnosis of PD. [ABSTRACT FROM AUTHOR]

Insider threats have recently become one of the most urgent cybersecurity challenges facing numerous businesses, such as public infrastructure companies, major federal agencies, and state and local governments. Our purpose is to find the most accurate machine learning (ML) model to detect insider attacks. In the realm of machine learning, the most convenient classifier is usually selected after further evaluation trials of candidate models which can cause unseen data (test data set) to leak into models and create bias. Accordingly, overfitting occurs because of frequent training of models and tuning hyperparameters; the models perform well on the training set while failing to generalize effectively to unseen data. The validation data set and hyperparameter tuning are utilized in this study to prevent the issues mentioned above and to choose the best model from our candidate models. Furthermore, our approach guarantees that the selected model does not memorize data of the threats occurring in the local area network (LAN) through the usage of the NSL-KDD data set. The following results are gathered and analyzed: support vector machine (SVM), decision tree (DT), logistic regression (LR), adaptive boost (AdaBoost), gradient boosting (GB), random forests (RFs), and extremely randomized trees (ERTs). After analyzing the findings, we conclude that the AdaBoost model is the most accurate, with a DoS of 99%, a probe of 99%, access of 96%, and privilege of 97%, as well as an AUC of 0.992 for DoS, 0.986 for probe, 0.952 for access, and 0.954 for privilege. [ABSTRACT FROM AUTHOR]

Adversarial examples bring a considerable security threat to support vector machines (SVMs), especially those used in safety-critical applications. Thus, robustness verification is an essential issue for SVMs, which can provide provable robustness against various adversarial attacks. The evaluation results obtained through robustness verification can provide a security guarantee for the use of SVMs. The existing verification method does not often perform well in verifying SVMs with nonlinear kernels. To this end, we propose a method to improve the verification performance for SVMs with nonlinear kernels. We first formalize the adversarial robustness evaluation of SVMs as an optimization problem with a feedforward neural network representation. Then, the lower bound of the original problem is obtained by solving the Lagrangian dual problem. Finally, the adversarial robustness of SVMs is evaluated concerning the lower bound. We evaluate the adversarial robustness of SVMs with linear and nonlinear kernels on the MNIST and Fashion-MNIST datasets. The experimental results show that our method achieves a higher percentage of provable robustness on the test set compared to the state-of-the-art. [ABSTRACT FROM AUTHOR]

One of the main concerns of cloud-based services based on machine and deep learning algorithms is the privacy of users' data. This is particularly relevant when companies want to leverage such services because they have to outsource potentially sensible data to be processed. In this work, the problem of privacy-preserving anomaly detection on industrial vibrational data with machine learning is tackled. It consists in the detection of irregularities or deviations from expected patterns in the vibration signals generated by industrial machinery and equipment. Such anomalies can be indicative of potential equipment failures, maintenance needs, or process deviations, making their timely detection critical for ensuring the smooth operation and reliability of industrial systems. We combine this industrial need with the ability to guarantee data privacy by proposing encrypted vibrational anomaly detection (EVAD). EVAD allows the detection of anomalies on vibrational data in a privacy-preserving manner by integrating, for the first time in the literature, one-class support vector machines and homomorphic encryption, the latter being a particular kind of encryption that allows the computation of some operations directly on encrypted data. Experimental results show that, on two publicly available datasets for vibrational anomaly detection, EVAD is able to distinguish, in a privacy-preserving manner, between nominal and anomaly situations, in an effective and efficient way. To the best of our knowledge, EVAD represents the first privacy-preserving solution for the detection of anomalies in vibrational data present in the literature. [ABSTRACT FROM AUTHOR]

Text classification has critical applications, including healthcare, where it can assist patients in locating specialized clinics based on their symptom descriptions. This can enhance healthcare services by reducing misdiagnoses and efficiently guiding patients to appropriate specialists. This research focuses on building a model for multi-class text classification in Arabic healthcare. Two evaluation schemes were employed using health data from the Altibbi dataset. Various feature extraction techniques were employed, including term frequency-inverse document frequency (TF-IDF) and word-to-vector (Word2Vec). Classical machine learning models such as Logistic Regression (LR), Random Forest (RF), Multinomial Naïve Bayes (MNB), Stochastic Gradient Descent, and Support Vector Machine (SVM), along with ensemble classifications, were implemented. Evaluation measures were used to compare model performance, including precision, recall, and F1-score. Results indicated that TF-IDF generally outperformed Word2Vec regarding the F1-score across most classes. LR and RF consistently demonstrated strong performance, while MNB exhibited lower precision. Ensemble methods like stacking and bagging showed promise, with LR and RF performing well across multiple classes. The SVM model displayed lower precision in certain classes. Preliminary experiments with the transformer-based models for Arabic language AraBERT and MarBERT as feature extraction and classification models demonstrated impressive precision, recall, and F1 scores across various classes. The findings show the effectiveness of ensemble models using TF-IDF and Arabic transformer models in accurately classifying Arabic health texts. However, challenges related to standardization, semantic variations, data availability, and resources should be considered. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of six prominent machine learning models—random forest, decision trees, K-nearest neighbor, logistic regression, support vector machines, and Naïve Bayes—for intrusion detection systems in industrial Internet of Things environments. The evaluation encompasses the effects of data preprocessing techniques, including feature engineering, data normalization, recoding, and missing data mitigation. Furthermore, the research delves into dataset balancing, examining the effects of six different techniques on model performance. The investigations are conducted using the domain-specific WUSTL-IIOT-2021 dataset, which captures the unique characteristics of IIoT data. The study also investigates multi-class attack identification utilizing an innovative SMOTE-based multi-class balancing approach to tackle dataset imbalances. The results indicate that data preprocessing and intelligent dataset balancing produce consistent enhancements in the classification performance of the selected models across binary and multi-classification tasks. Random forest emerges as the standout algorithm, delivering consistently high performance with computational efficiency. [ABSTRACT FROM AUTHOR]

The catastrophic forgetting problem is one of the hotspots in the field of deep learning. At present, there is no doubt that storing samples of previous tasks in fixed-size memory is the best way to solve this problem. However, the number of samples stored in fixed-size memory is limited. With the increase in tasks, the number of samples stored in memory for a single task will decrease sharply. It is also difficult to balance the memory capacity and number of samples. To solve this problem, some methods use a fixed size of memory to store dimensionality reduction images. However, this will create new problems. 1) The quality of dimensionality reduction images is poor, and they are significantly different from original images. 2) How to choose the dimensionality reduction method of images. To address these problem, we put forward a new method. Firstly, we employ a simple and reliable scheme to solve the domain difference between dimensionality reduction images and original images. And we theoretically analyzed which image dimensionality reduction method is better. Secondly, to increase the generalization ability of our method and further mitigate the catastrophic forgetting phenomenon, we utilize a self-supervised image augmentation method and the output features similarity loss. Thirdly, we make use of the neural kernel mapping support vector machine theory to improve the interpretability of our method. Experimental results demonstrated that the top-1 average accuracy of our method is much higher than other methods when using the same size of memory. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent healthcare framework by incorporating modern computing technologies like machine learning and deep learning. The sole motivation of this paper is to predict diabetes status intelligently so that patients can be made aware of their diabetes status regularly. A novel ensemble machine learning-based diabetes prediction framework, namely "iDP", has been proposed in this paper. Six different machine learning approaches have been used for the iDP framework, namely random forest, decision tree, neural network, AdaBoost, support vector machine, and XGBoost. Training and testing of ensemble modeling have been performed based on these learning techniques. A comprehensive preliminary data analysis and screening of statistical analysis have been systematically demonstrated in this paper. The recursive feature elimination and fivefold cross-validation methods have been used to select the best model. All experiments have been performed using R-Studio 1.2.5 with the help of the Rattle library 5.4.0. The preliminary data analytic and Pearson correlation coefficient values have been computed to verify the linearity relationship among the data. Results have been reported in eight performance metrics, including accuracy, specificity, sensitivity, area under curve, area under convex hull, minimum error rate, minimum weighted loss, and precision. The proposed iDP framework improves the results on different aspects in order to compare them with individual learning approaches. Despite this, a comparative performance analysis of the iDP framework has also been performed with state-of-the-art prevailing models. As per experiment, the iDP framework gives better results in most cases, like accuracy: 95.26%, AUC: 91.15%, sensitivity: 96.81%, specificity: 97.72% and precision: 90.72%. [ABSTRACT FROM AUTHOR]

Makam is defined as melodies that are described with typical the agâz(beginning), seyir (the orientation style), and karar (ending) features in a certain perde düzen (tone/fret tunings). For this reason, determining the makam is the basic step in understanding the melodic progression, as in musical keys. Machine learning, an artificial intelligence discipline, offers stable solutions for estimating different situations based on known data. In this study, classifications for makam recognition in Turkish folk music were carried out using different machine learning methods on the original dataset. The success of the different methods used was compared, and also data augmentation was performed by using SMOTE, KMeansSMOTE, ADASYN, and SVMSMOTE oversampling techniques, which are widely used in the literature to increase the prediction success of the classifiers. For each of eight different machine learning techniques, namely light gradient boosting machine, eXtreme gradient boosting, naive Bayes, decision tree, support vector machine, K-nearest neighbor, random forest, and logistic regression, in addition to classification without data augmentation, a total of 40 classification processes were carried out using four different data augmentation techniques. The results of the classification processes are reported with four different parameters: accuracy, precision, recall, and F1-score. The highest accuracy value of 99.17% was obtained when the light gradient boosting machine classifier was used together with the SMOTE data augmentation technique. It was observed that the measurements obtained in the study were higher than the results obtained in similar studies conducted in the literature in recent years. [ABSTRACT FROM AUTHOR]

Support-vector machines (SVMs) have been successfully employed to diagnose neurological disorders like epilepsy and sleep disorders via electroencephalogram (EEG) signal classification. However, EEG signals are susceptible to noise and outliers while recording. Thus, classification of EEG signals require efficient methods that is robust to noise and outliers. SVMs suffer in the presence of noise and outliers as it treats each sample to be of equal importance. Fuzzy membership function has been successful in dealing with noise and outliers. Fuzzy support vector machine uses fuzzy membership to give the appropriate weight to each sample for reducing the effect of noise and outliers. To reduce the effect of noise and outliers, intuitionistic fuzzy twin support vector machines (IFTWSVM) uses both membership and non-membership weights to subsidise the effect of outliers. Moreover, IFTWSVM solves smaller size quadratic programming problems which makes it comparatively efficient than fuzzy support vector machines. However, IFTWSVM suffers as (i) it involves the computation of matrix inverses which makes it intractable as the size of the data increases. (ii) it solves different problems for linear and nonlinear cases, and nonlinear case with linear kernel does not reduce to the linear case. Thus, it can only solve the approximate formulation not the exact formulation. To overcome these issues, we propose improved intuitionistic fuzzy twin support vector machine (IIFTWSVM). The proposed IIFTWSVM introduces different Lagrangian functions to avoid the computation of matrix inverses. Also, in nonlinear case, kernel trick is applied directly and hence, the exact formulation is solved. We employed the proposed IIFTWSVM for the classification of EEG signals. The experimental results and statistical analysis show that the proposed IIFTWSVM model is better compared to the given baseline models. Also, we evaluate the performance of the models on KEEL datasets to check the robustness of the proposed IIFTWSVM model. [ABSTRACT FROM AUTHOR]

Twin support vector regression (TSVR) has been widely applied in regression problems. TSVR seeks a pair of ε -insensitive proximal planes by solving two support vector machine type problems. TSVR assumes that the matrices appearing in the dual formulation are positive definite. However, in real-world scenarios, such an assumption may not be fulfilled and, hence, leads to suboptimal performance. ε -Twin support vector regression (ε -TSVR) improved the TSVR by introducing the regularisation term to avoid the singularity issues. Most of the twin support vector machine models involve the computation of matrix inverses. Also, TSVR implements the empirical risk minimization principle. In this paper, we propose an improved twin support vector regression (ITSVR) for brain age estimation by introducing different Lagrangian functions for the primal problems of the TSVR. The proposed ITSVR implements the structural risk minimization principle and avoids the computation of the matrix inverses. To solve the optimization problem more efficiently, we used successive overrelaxation (SOR) technique. We evaluated the proposed ITSVR on cognitively healthy subjects, mild cognitive impairment subjects and Alzheimer's disease subjects. The experimental results demonstrate that the proposed ITSVR has superior performance compared to the baseline models for brain age estimation. [ABSTRACT FROM AUTHOR]

With the aging of the population, China has a large population base. The number of people suffering from mild cognitive impairment has gradually increased and gradually turned to senile dementia. The probability of mild cognitive impairment in China is about 5–7%, and about 15 million people are ill. To better distinguish mild cognitive impairment from Alzheimer's, this paper adopted the machine learning (ML) method to model. The evaluation table was established by considering the reaction time, education, background, memory, and other aspects of the patient. Machine learning has been applied in the fields of the Internet, finance, medicine, automation, biological science, etc. Through the machine learning support vector machine (SVM) and linear regression, the classification accuracy was compared. The results showed that the classification accuracy of SVM was 87.92% under the ML algorithm. It also showed that ML was more conducive to classification and recognition, which has played an important role in the identification of mild cognitive impairment in the current aging population. [ABSTRACT FROM AUTHOR]

Protein and carbohydrate content in the diet of the predator Hippodamia variegata Goeze (variegated ladybug) directly influences reproduction fitness by affecting foraging efficiency. The effect of three varied qualities of Aphis gossypii Glover (cotton aphid) prey on daily H. variegata feeding and daily egg production (DEP) were estimated using the support vector machine (SVM). The SVM can predict predator reproductive behavior as a function of the relationship between foraging and prey nutritional composition. We used the total number and weight of aphids consumed, volume of protein, lipid, carbohydrate, and glycogen, and total energy received by the ladybug after feeding on one prey item as input variables. Aphid quality varied as nitrogen (N) fertilization levels were 110, 160, and 210 ppm on the Cucumis sativus L. (cucumber) host plants. The model estimated female beetles consumed more aphids and nutrients on C. sativus plants with N levels of 160 ppm compared to lower (110 ppm) N levels and had higher reproductive transformation efficiencies. Transformation rates of aphid feeding to egg production in females exposed to the 160 ppm treatment were 65% greater, had lower nutrient and energy requirements, and achieved a 29.4% higher DEP than those exposed to high (210 ppm) N levels. The SVM predicted nutrient compositions of A. gossypii exposed to 160 ppm N were balanced such that H. variegata exhibited greater reproduction efficiency than the other N treatments for first 30 d from the start of reproduction. [ABSTRACT FROM AUTHOR]

Breast cancer is a significant global health concern, with millions of cases and deaths each year. Accurate diagnosis is critical for timely treatment and medication. Machine learning techniques have shown promising results in detecting breast cancer. Previous studies have primarily used single-modality data for breast cancer diagnosis. Hence, this work aims to mobilize the benefits of multimodal data over unimodality samples. This study proposes a custom deep learning-based model pipeline that works over this multimodal data. This work has been separated into three phases. Phase 1 and Phase 2 under the unimodal category examine gene expression data and histopathological images separately. The Cancer Genome Atlas makes these datasets available. In Phase 3, the proposed pipeline operates on both data types' samples for each patient in the multimodal category. This study investigates how data pre-processing (cleaning, transformation, reduction) and cascaded filtering affect model performance. Precision, recall, f1-score, and accuracy assessed the models, whereas L2 regularization, exponentially weighted moving average, and transfer learning minimized over-fitting. A custom deep neural network and support vector machine obtained 86% accuracy in Phase 1, whereas the VGG16 model reached 80.21% accuracy in Phase 2. In Phase 3, the curated multimodal dataset was applied to a custom deep learning pipeline (VGG16 backbone with hyper-tuned machine learning models as head classifiers) to achieve 94% accuracy, demonstrating the importance of multimodal data over unimodal in breast cancer subtype classification. These findings highlight the importance of multimodal data for breast cancer diagnosis and subtype prediction. [ABSTRACT FROM AUTHOR]

Mental task classification (MTC), based on the electroencephalography (EEG) signals is a demanding brain–computer interface (BCI). It is independent of all types of muscular activity. MTC-based BCI systems are capable to identify cognitive activity of human. The success of BCI system depends upon the efficient feature representation from raw EEG signals for classification of mental activities. This paper mainly presents on a novel feature representation (formation of most informative features) of the EEG signal for the both, binary as well as multi MTC, using a combination of some statistical, uncertainty and memory- based coefficient. In this work, the feature formation is carried out in the two stages. In the first stage, the signal is split into different oscillatory functions with the help of three well-known empirical mode decomposition (EMD) algorithms, and a new set of eight parameters (features) are calculated from the oscillatory function in the second stage of feature vector construction. Support vector machine (SVM) is used to classify the feature vectors obtained corresponding to the different mental tasks. This study consists the problem formulation of two variants of MTC; two-class and multi-class MTC. The suggested scheme outperforms the existing work for the both types of mental tasks classification. [ABSTRACT FROM AUTHOR]

With more than 50 million people worldwide at risk of heart disease, early diagnosis of cardiovascular disease is essential. The classification of electrocardiogram (ECG) recordings can diagnose Atrial Fibrillation (AF) and other types of arrhythmia. Short ECG segments recorded from wearable devices in an IoT-based system can further provide continuous abnormality detection. The performance of ECG classification is usually high in normal segments, but much lower in the target classes, i.e., AF and other arrhythmias, which could result from class imbalance and limited feature representation. Deep learning methods have been employed as feature extractors in previous studies. Among them, convolutional neural networks (CNN) can generate rich features in different scales. But CNN may omit precise temporal information such as the duration between R-waves in two QRS waves (RR interval) irregularity, which is insensitive to noise segments. Thus, aiming at improving the classification performance of AF and other classes in short ECG segments, we propose a hybrid feature fusion method integrating the above-mentioned features. The fused features are trained and tested in a support vector machine classifier. The F1-score results show that our method outperforms not only the same CNN method without feature fusion in all the four classes, which average F1-score reached 84.3% and classification time per single sample of 0.005 s, but also several state-of-the-art methods, especially in the target classes, which validates the effectiveness of the proposed method. We then further discuss the impact of length on the performance of the proposed method, providing insights into future applications. [ABSTRACT FROM AUTHOR]

In the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: "biological," "fibers," "film-coated surfaces," "MEMS devices and electrodes," "nanowires," "particles," "pattern surfaces," "porous sponge," "powder," and "tips," respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each class in the dataset. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is one of the most lethal kinds of cancer, so early detection is critical. Three datasets, namely CNN transfer learning with discrete wavelet transform (DWT), discrete cosine transform (DCT), and support vector machines (SVMs), were used to find CRC. In these instances, a quick and precise visual diagnosis of polyps is needed in the current scenario. The proposed process involves four distinct phases. First and foremost, convolutional neural networks (CNNs) are developed to test the efficacy of the model. Further, a transfer learning approach was incorporated using SVM and LSTM. Using the K-means technique, a visual explanation is finally presented. This system works with the balanced Hyper Kvasir and mixed datasets, which are made up of CVC Clinic DB, Kvasir2, and Hyper Kvasir. The system is called "ColoRectalCADx". The convolutional neural network (CNN) models are ResNet-50V2, DenseNet-201, VGG-16, and RDV-22. The system achieved the highest accuracy with CNN DesnseNet-201 in Hyper Kvasir (98.92% training, 98.91% testing, 93.62% SVM training, and 95.87% SVM tests). CNN DenseNet-201 also achieved the highest accuracy with the mixed dataset (98.91% training, 96.13% testing, 95.41% SVM training, and 94.86% SVM testing). The process involved three phases, namely individual CNN, combination of CNN with SVM, and combination of CNN, LSTM, and SVM. After three phases of the system, across both datasets, the CNN + SVM + LSTM combination was proven to be the most effective. Finally, the unsupervised K-means learning algorithm extracts the location of any cancerous polyps and upon classification using SVM classifier resulted with an accuracy of 80%. The K-means algorithm, which uses segmented images as input, accurately predicts the sites of tumours in colorectal cancer patients. [ABSTRACT FROM AUTHOR]

The selection of proper classifiers for a given data set is full of challenges. The critical problem of classifier selection is how to extract feature from data sets. This paper proposes a new method for feature extraction of a data set. Our method not only preserves the geometrical structure of a data set, but also characterizes the decision boundary of classification problems. Specifically speaking, the extracted feature can recover a data set that has the same Euclidean geometrical structure as the original data set. We present an efficient algorithm to compute the similarity between data set features. We theoretically analyze how the similarity between our features affects the performance of the support vector machine, a well-known classifier. The empirical results show that our method is effective in finding suitable classifiers. [ABSTRACT FROM AUTHOR]

Coconut water is the clear liquid found inside coconuts, famous for rehydrating after exercise or while suffering from a minor sickness. The essential issue tackled in this paper is how to estimate the appropriate stage of maturity of coconut water, which is a time-consuming task in the beverage industry since, as the coconut age increases, the coconut water flavor varies. Accordingly, to handle this issue, an adaptive model based on Fuzzy Neural Network and Sperm Whale Optimization, dubbed FNN–SWO, is developed to assess coconut water maturity. The Sperm Whale Optimization (SWO) algorithm is a meta-heuristic optimization algorithm. It is embedded in this model along with neural networks and fuzzy techniques (FNN system), which can be employed as an essential building block in the beverage industry. The proposed FNN–SWO model is trained and tested utilizing fuzzy rules with an adaptive network. In contrast, the SWO algorithm is adopted to determine the optimal weights for the fuzzy rules. Three subsets of data divided according to three levels of coconut water maturity-tender, mature, and very mature, are used to validate the combined FNN–SWO model. Depending on these three subsets of data, a comparison of the proposed FNN–SWO model has been conducted against a set of the most common conventional techniques. These techniques include Support Vector Machine, Naïve Bayes, FNN, Artificial Neural Network, as well as their embedding with other meta-heuristic optimization algorithms. For various key performance indicators, such as recall, F1-score, specificity, and accuracy, the proposed FNN–SWO model provides the best prediction outcomes compared to the current time-consuming techniques. The dominance of the proposed FNN–SWO model is evident from the final findings compared to its time-consuming peers for estimating coconut water maturity on time. As a result, the proposed FNN–SWO model is an effective heuristic for locating optimal solutions to classification problems. It can thereby be reassuringly applicable to other similar prediction problems. Additionally, it would benefit the scientific community interested in evaluating coconut water. [ABSTRACT FROM AUTHOR]

Accurate short-term electricity load forecasts are critical for the secure and economic operation of power systems. This paper presents a computationally efficient and powerful three-stage model to accurately forecast short-term electricity load. Variational mode decomposition (VMD) was used in the first stage to extract features from the historical load signal. The stacked kernel extreme learning machine (KELM)-based auto-encoders were utilized in the unsupervised feature learning-based second stage. In the third stage, the high-order learned features were used as the inputs for the KELM-based regression model. The proposed deep KELM architecture combines stacked KELM-based auto-encoders and a KELM-based regression model to forecast short-term electricity load effectively. In order to examine the performance improvement of the proposed forecasting model, several performance comparison tests were realized using publicly available electricity load and day-ahead forecast data from the Turkish transmission system operator (TSO). The proposed model results were compared with state-of-the-art deep extreme learning machine (ELM) architectures as well as the benchmark forecasting models based on original ELM, KELM, artificial neural network (ANN), support vector machine (SVM), and regression tree (RT). The comparison results indicated that the proposed model outperformed state-of-the-art architectures and was significantly more successful than the TSO model. [ABSTRACT FROM AUTHOR]

Recently, various techniques have been implemented to investigate the cavity beneath the pavement using ground-penetrating radar (GPR) data. These techniques usually focus on the determination of the height of the cavity and the depth at which it is located, which play a vital role in the agencies' decision-making on the appropriate rehabilitation method for the pavement. Image processing, a relatively advanced technique that utilizes the image generated from the GPR data, has been used for this purpose. However, it is still time-consuming and yields low accuracy. As a solution, this study proposes a machine learning (ML)-based method to estimate cavity properties, such as depth and height, from GPR images with high precision in a significantly reduced time. In this study, a deep convolutional generative adversarial networks-based hyperbolic fitting algorithm, called GAN–cavity, was developed based on the Pix2Pix model to identify multi-shape signature segmentation from the GPR data. The GAN–cavity was trained and tested using 1920 and 480 images, respectively, and the model performed well with an average F1–score of 83.1 per cent. Consequently, an extreme gradient boosting (XGBoost) algorithm was used to predict the cavity depth and height based on the GAN–cavity results. Moreover, the developed model (XGB–cavity classifier) can classify the cavity's height into three classes: small, medium, and large. The XGBoost model XGB–cavity regressor used 341 and 87 cavities for training and testing, respectively, yielding regression scores (R2) and root mean squared error (RMSE) of 0.88 and 3.28 cm in cavity depth prediction, respectively. By applying the support vector machine synthetic minority oversampling technique (SVM–SMOTE) algorithm to overcome the data imbalance, the XGB–cavity classifier model obtained a high accuracy of 0.89 with precision–avg, recall–avg, and F1–score–avg of 0.83, 0.83, and 0.83, respectively. Based on the results, the proposed method (GAN–XGB–cavity) of combining the GAN–cavity and XGB–cavity can successfully identify and fit multi-shape segmentation for depth estimation and height classification of cavities. [ABSTRACT FROM AUTHOR]

Weirs are hydraulic structures mostly used to measure the flow discharge and control the flow level in artificial or natural open channels. The ratio of the actual discharge to the theoretical discharge (discharge coefficient—Cd) must be known, in order to calculate the discharge of the channel having the weir. In this study, 91 experimental measurements are taken on seven trapezoidal broad-crested weirs with different upstream and downstream slopes. Experimentally measured flow properties are used to validate numerical models based on the computational fluid dynamics (CFD) methods. Two new weir geometries, not experimentally measured, are added in the numerical modeling, and 270 Cd values are calculated for nine weir geometries using numerical modeling. Theoretical Cd values are estimated using the artificial neural network (ANN), support vector machine (SVM), and M5Tree methods. In the models, the Froude number in the upstream region and dimensionless parameters of the flow are used as inputs. The performance of these methods has been examined to estimate the Cd values for eight cases. The performances of the methods are evaluated by the coefficient of determination (R2), root-mean-square error, mean absolute percentage error, and Nash–Sutcliffe model efficiency coefficient. The study results show that the Froude number significantly increases the performance of the models in estimating Cd values, and the ANN method is more successful in determining Cd than other methods. [ABSTRACT FROM AUTHOR]

In a smart grid, the main goals are to provide grid stability, improve power system performance and security, and reduce operations, system maintenance, and planning costs. The prediction stability of smart grid (SG) systems is essential in terms of power loss minimization and the importance of adequate energy policies. SG systems must accurately predict the energy demand and ensure the right amount of energy is available at the right time. If the prediction is inaccurate, it can lead to costly energy production or usage errors and create considerable inefficiencies in the power grid. Due to this, this manuscript offers five different cascade methods to detect the stability of SG systems. Detecting the stability of SG systems enables the grid to respond quickly to changes in demand and supply, improves system reliability, reduces power outages, and increases the overall efficiency of the grid. The present work proposed five different cascade methods with pre-processing, training and testing division, and the classification stages of the classification procedure for estimating SG stability. In the first pre-processing stage, the SG dataset is pre-proceeded with the feature selection (Relief, Fast Correlation-Based Filter (FCBF), and supervised attribute filter). The resampling (the bootstrapping), the Fuzzy C-Means Clustering-Based Feature Weighting (FCMFW), the resampling then feature selection (supervised attribute filter), and the feature selection (supervised attribute filter), then FCMFW. In the second stage, the training and testing division stage, the SG dataset was separated into three test and training data methods before the classification algorithm: The 5 Fold Cross Validation (FVC), 10 FVC, and hold-out (50–50%). In the third stage, the classification stage, five different classification algorithms, including Naive Kernel Bayes, Linear Support Vector Machine (SVM), Weighted K-Nearest Neighbors, Begged Trees, and Narrow Neural Network classifying algorithms, are used to classify the SG dataset. The simulation results of this study demonstrated that the suggested cascade ML system had achieved significant accuracy in predicting SG stability. The best cascade method is the feature selection (supervised attribute filter) + FCMFW + 10 FCV and then performing the bagged trees algorithm; thus, the new approach affords an accuracy of 99.9%. Furthermore, due to the rapid growth of ML techniques, sensors, and smart meters technologies, with Machine to Machine communication via the internet of things (IoT), the real-time identification process is made practical with higher accuracy. For this reason, our future research will focus on an IoT-based SG system, an E-stability determination system. Thanks to the proposed cascade method, the SG dataset can be classified easily, quickly, and reliably. E-stability determination systems can help to fast detect, predict, and respond, which is an important application of IoT on the grid systems. [ABSTRACT FROM AUTHOR]

With the increased use of social network sites, such as Twitter, attackers exploit these platforms to spread counterfeit content. Such content can be fake advertisements or illegal content. Classifying such content is a challenging task, especially in Arabic. The Arabic language has a complex structure and makes classification tasks more difficult. This paper presents an approach to classifying Arabic tweets using classical machine learning (non-deep machine learning) and deep learning techniques. Tweets corpus were collected through Twitter API and labelled manually to get a reliable dataset. For an efficient classifier, feature extraction is applied to the corpus dataset. Then, two learning techniques are used for each feature extraction technique on the created dataset using N-gram models (uni-gram, bi-gram, and char-gram). The applied classical machine learning algorithms are support vector machines, neural networks, logistics regression, and naïve Bayes. Global vector (GloVe) and fastText learning models are utilised for the deep learning approaches. The Precision, Recall, and F1-score are the suggested performance measures calculated in this paper. Afterwards, the dataset is increased using the synthetic minority oversampling technique class to create a balanced dataset. After applying the classical machine learning models, the experimental results show that the neural network algorithm outperforms the other algorithms. Moreover, the GloVe outperforms the fastText model for the deep learning approach. [ABSTRACT FROM AUTHOR]

The operation of large utility companies such as Consolidated Edison Company of New York, Inc. (Con Edison) typically rely on large quantities of regulation documents from external institutions which inform the company of upcoming or ongoing policy changes or new requirements the company might need to comply with if deemed applicable. As a concrete example, if a recent regulatory publication mentions that the timeframe for the Company to respond to a reported system emergency in its service territory changes from within X time to within Y time—then the affected operating groups will be notified, and internal Company operating procedures may need to be reviewed and updated accordingly to comply with the new regulatory requirement. Each such regulation document needs to be reviewed manually by an expert to determine if the document is relevant to the company and, if so, which department it is relevant to. In order to help enterprises improve the efficiency of their operation, we propose an automatic document classification pipeline that determines whether a document is important for the company or not, and if deemed important it forwards those documents to the departments within the company for further review. Binary classification task of determining the importance of a document is done via ensembling the Naive Bayes (NB), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) together for the final prediction, whereas the multi-label classification problem of identifying the relevant departments for a document is executed by the transformer-based DocBERT model. We apply our pipeline to a large corpus of tens of thousands of text data provided by Con Edison and achieve an accuracy score over 80 % . Compared with existing solutions for document classification which rely on a single classifier, our paper i) ensemble multiple classifiers for better accuracy results and escaping from the problem of overfitting, ii) utilize pretrained transformer-based DocBERT model to achieve ideal performance for multi-label classification task and iii) introduce a bi-level structure to improve the performance of the whole pipeline where the binary classification module works as a rough filter before finally distributing the text to corresponding departments through the multi-label classification module. [ABSTRACT FROM AUTHOR]

For semi-supervised learning, a few labeled data and a large number of unlabeled data are used to construct a reasonable classifier. In recent years, many semi-supervised learning methods have been proposed and achieved good performance, especially for the graph-based approaches that can exploit the geometric information embedded in the data. Motivated by the success of generalized elastic net Lp-norm nonparallel support vector machine (GLpNPSVM) and the graph-based regularization term, in this paper, a novel Laplacian generalized elastic net Lp-norm nonparallel support vector machine for semi-supervised learning (Lap-GLpNPSVM) is proposed. A Lp-norm graph regularization term is introduced to improve the performance by the adjustability of the value of p. Experimental results on two synthetic datasets, fifteen UCI datasets, and Handwritten Numeral datasets demonstrate that proposed methods outperform other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Assessing the quality of road surfaces for maintenance has typically been an expensive exercise for local government agencies. This paper evaluates the feasibility of using accelerometer and speed data collected from on-board diagnostic (OBD-II) devices and labelled using the PASER system of road classification to classify road quality and identify poor stretches. An ordinal logistic model and a support vector machine (SVM) classifier were first trained on features created from raw data. The SVM and an artificial neural network (ANN) were then trained separately on the raw data parameters followed by a convolutional neural network (CNN) model. All models have comparable results in terms of overall classification accuracy on validation datasets (around 67%) but the ANN and CNN models were superior in their ability to correctly identify 'Poor' and 'Good' stretches. The CNN model had the best performance among all models evaluated. The approach presented offers a low-cost method to map poor roads and identify stretches that require more attention as an alternative to more expensive traditional methods. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) brain–computer interface (BCI) systems have broad application prospects in rehabilitation and other fields. However, to achieve accurate and practical MI-BCI applications, there are still several critical issues, such as channel selection, electroencephalogram (EEG) feature extraction and EEG classification, needed to be better resolved. In this paper, these issues are studied for lower limb MI which is more difficult and less studied than upper limb MI. First, a novel iterative EEG source localization method is proposed for channel selection. Channels FC1, FC2, C1, C2 and Cz, instead of the commonly used traditional channel set (TCS) C3, C4 and Cz, are selected as the optimal channel set (OCS). Then, a multi-domain feature (MDF) extraction algorithm is presented to fuse single-domain features into multi-domain features. Finally, a particle swarm optimization based support vector machine (SVM) method is utilized to classify the EEG data collected by the lower limb MI experiment designed by us. The results show that the classification accuracy is 88.43%, 3.35–5.41% higher than those of using traditional SVM to classify single-domain features on the TCS, which proves that the combination of OCS and MDF can not only reduce the amount of data processing, but also retain more feature information to improve the accuracy of EEG classification. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic has been globally impacting the health and prosperity of people. A persistent increase in the number of positive cases has boost the stress among governments across the globe. There is a need of approach which gives more accurate predictions of outbreak. This paper presents a novel approach called diffusion prediction model for prediction of number of coronavirus cases in four countries: India, France, China and Nepal. Diffusion prediction model works on the diffusion process of the human contact. Model considers two forms of spread: when the spread takes time after infecting one person and when the spread is immediate after infecting one person. It makes the proposed model different over other state-of-the art models. It is giving more accurate results than other state-of-the art models. The proposed diffusion prediction model forecasts the number of new cases expected to occur in next 4 weeks. The model has predicted the number of confirmed cases, recovered cases, deaths and active cases. The model can facilitate government to be well prepared for any abrupt rise in this pandemic. The performance is evaluated in terms of accuracy and error rate and compared with the prediction results of support vector machine, logistic regression model and convolution neural network. The results prove the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

With each passing day, the number of smart vehicles is increasing manifold, hence, automatic/automated parking lot detection is gaining a lot of importance among Smart City applications. A robust approach is desired to identify parking spaces effectively and efficiently. This work presents a deep learning classifier based on convolutional neural network (CNN) and extreme learning machine (ELM), i.e., CNN-ELM to classify the parking space as vacant or occupied. CNN is well-known for efficient image classification, but its training time is highly influenced by backpropagation of errors in the fully connected layer. Thus, ELM is plugged into CNN to replace the fully connected layer and perform classification whereas, feature extraction is performed using CNN. The performance of CNN-ELM is validated on the publicly available PKLot dataset, which contains approximately 700,000 images categorized into sunny, overcast, and rainy weather conditions. The experimental results indicate that CNN-ELM approach outperforms other hybrid CNN models using different classifiers such as support vector machine, Xgboost, and Extra Trees in terms of sensitivity, specificity, and accuracy. The comparison of results with other state-of-the-art approaches based on accuracy and Area under the curve (AUC) score further justifies the effectiveness of the proposed approach in real-time parking space detection. [ABSTRACT FROM AUTHOR]

As a semi-supervised learning method, Laplacian support vector machine (LapSVM) is popular. Unfortunately, the model generated by LapSVM has a poor sparsity. A sparse decision model has always been fascinating because it could implement data reduction and improve performance. To generate a sparse model of LapSVM, we propose an ℓ 1 -norm Laplacian support vector machine ( ℓ 1 -norm LapSVM), which replaces the ℓ 2 -norm with the ℓ 1 -norm in LapSVM. The ℓ 1 -norm LapSVM has two techniques that can induce sparsity: the ℓ 1 -norm regularization and the hinge loss function. We discuss two situations for the ℓ 1 -norm LapSVM, linear and nonlinear ones. In the linear ℓ 1 -norm LapSVM, the sparse decision model implies that features with nonzero coefficients are contributive. In other words, the linear ℓ 1 -norm LapSVM can perform feature selection to achieve the goal of data reduction. Moreover, the nonlinear (kernel) ℓ 1 -norm LapSVM can also implement data reduction in terms of sample selection. In addition, the optimization problem of the ℓ 1 -norm LapSVM is a convex quadratic programming one. That is, the ℓ 1 -norm LapSVM has a unique and global solution. Experimental results on semi-supervised classification tasks have shown a comparable performance of our ℓ 1 -norm LapSVM. [ABSTRACT FROM AUTHOR]

In this manuscript, imbalanced and small sample space (IB-SSS) dataset problems for pedestrian gender classification using fusion of selected deep and traditional features (PGC-FSDTF) are considered. In this regard, data preparation is first done through data augmentation and preprocessing steps to handle imbalanced classification problem and environmental effects, respectively. The proposed approach follows different types of feature extraction schemes, for instance, pyramid histogram of oriented gradients, hue saturation value histogram, deep visual features of DenseNet201 and InceptionResNetV2-based convolutional neural network architectures. The parallel fusion method computes the maximum and average values-based features from the learned features of both deep networks. Features are selected through features selection methods such as entropy and principal component analysis (PCA). The subsets of features are serially fused and provided to multiple classifiers to perform gender classification on IB-SSS datasets. Resultantly, the proposed PGC-FSDTF method shows better results in terms of different accuracies (overall, mean, and balanced), and area under curve on selected datasets. Further, improved results are achieved on applied datasets using PCA-based selected features and medium Gaussian support vector machine (M-SVM) classifier. These results on different datasets confirm that the selected feature combination provides a way to handle IB-SSS issues for PGC effectively. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common neurological diseases, affecting approximately 50 million people. This illness can be diagnosed by electroencephalogram (EEG), whose analysis depends on human interpretation, which may lead to divergent results and be imprecise and error-prone. Moreover, one estimates more than 80% of the epilepsy examinations return no anomalies at all, wasting the effort of analysis. In this context, machine learning (ML) methods are used to aid in epilepsy detection. An essential task for classifier building is feature extraction, for which there are many measures that can be computed. However, part of these features can be irrelevant or hinder the performance of ML methods. This paper proposes an automatic way to classify EEG segments by using a reduced set of features. The proposed method combines multispectral analysis, feature selection, and classifier building approaches. As our main contribution, a methodology to minimize the number of features for classifier building is proposed. A total of 285 measures are extracted. Afterward, two attribute selection approaches are used: Pearson's correlation coefficient filter and wrapper based on the genetic algorithm. Then, five well-known ML methods (k-nearest-neighbor, support vector machine, naive Bayes, artificial neural network, and random forest) were used to build 281 different classifiers. As a result, the proposed classifiers reached an accuracy between 87.2 and 90.99% and considerably reduced the number of features from 285 to 30, keeping competitive scores. Additionally, statistical hypothesis tests prove that our proposed approach is as efficient as using the complete feature dataset for classifier building. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) are one of the most advanced techniques for classifying images in a range of applications. One of the most prevalent cancers that cause death in women is breast cancer. For survival rates to increase, early detection and treatment of breast cancer is essential. Deep learning (DL) can help radiologists diagnose and classify breast cancer lesions. This paper proposes a computer-aided system based on DL techniques for automatically classify breast cancer tumors in histopathological images. There are nine DCNN architectures used in this work. Four schemes are performed in the proposed framework to find the best approach. The first scheme consists of pre-trained DCNNs based on the transfer learning concept. The second scheme performs feature extraction of the DCNN architectures and uses a support vector machine (SVM) classifier for evaluation. The third one performs feature integration to show how the integrated deep features may enhance the SVM classifiers' accuracy. Finally, in the fourth scheme, the Chi-square (χ2) feature selection method is applied to reduce the large feature size in the feature integration step. The results of the proposed system present a promising performance for breast cancer classification with an accuracy of 99.24%. The system performance shows that the proposed tool is suitable to assist radiologists in diagnosing breast cancer tumors. [ABSTRACT FROM AUTHOR]

One of the most important problems for farmers who produce large amounts of apples is the classification of the apples according to their types in a short time without handling them. Support vector machines (SVM) and deep residual networks (ResNet-50) are machine learning methods that are able to solve general classification situations. In this study, the classification of apple varieties according to their genus is made using machine learning algorithms. A database is created by capturing 120 images from six different apple species. Bag of visual words (BoVW) treat image features as words representing a sparse vector of occurrences over the vocabulary. BoVW features are classified using SVM. On the other hand, ResNet-50 is a convolutional neural network that is 50 layers deep with embedded feature extraction layers. The pre-trained ResNet-50 architecture is retrained for apple classification using transfer learning. In the experiments, our dataset is divided into three cases: Case 1: 40% train, 60% test; Case 2: 60% train, 40% test; and Case 3: 80% train, 20% test. As a result, the linear, Gaussian, and polynomial kernel functions used in the BoVW + SVM algorithm achieved 88%, 92%, and 96% accuracy in Case 3, respectively. In the ResNet-50 classification, the root-mean-square propagation (rmsprop), adaptive moment estimation (adam), and stochastic gradient descent with momentum (sgdm) training algorithms achieved 86%, 89%, and 90% accuracy, respectively, in the set of Case 3. [ABSTRACT FROM AUTHOR]

A computerized coconut tree detection system can help dendrologists and laypersons in identifying coconut trees based on three morphological parameters including height, inclination, and orientation. These three parameters help to determine the health and the nature of growth of coconut trees which influences the design and use of robots for harvesting coconuts. Deep learning is a powerful tool used for feature extraction as it is better in extracting deeper details (features) in an image. In this research work, a new Modified Inception Net based Hyper Tuning Support Vector Machine classification method named MIN-SVM is proposed for coconut tree classification based on three morphological parameters including height, inclination and orientation. The features from the pre-processed coconut tree images were extracted using four distinct Convolutional Neural Network models including Visual Geometry Group, Inception Net, ResNet, and MIN-SVM. These extracted features were then classified using a Machine Learning model named Support Vector Machine (SVM). The MIN-SVM have achieved a remarkable accuracy of 95.35 percent as contrasted to Visual Geometry Group (91.90%), Inception Net (81.66%), and ResNet (71.95%). The features extracted from Modified Inception Net fitted good with SVM classifier. Experimental results show that MIN-SVM can be powerful computerized automated system to identify coconut trees based on height, inclination, and orientation. [ABSTRACT FROM AUTHOR]

Academic performance, a globally understood metric, is utilized worldwide across disparate teaching and learning environments and is regarded as a quantifiable indicator of learning gain. The ability to reliably estimate student's academic performance is important and can assist academic staff to improve the provision of support. However, it is recognized that academic performance estimation is non-trivial and affected by multiple factors, including a student's engagement with learning activities and their social, geographic, and demographic characteristics. This paper investigates the opportunity to develop reliable models for predicting student performance using Artificial Intelligence. Specifically, we propose two-step academic performance prediction using feature weighted support vector machine and artificial neural network (ANN) learning. A feature weighted SVM, where the importance of different features to the outcome is calculated using information gain ratios, is employed to perform coarse-grained binary classification (pass, P 1 , or fail, P 0 ). Subsequently, detailed score levels are divided from D to A+, and ANN learning is employed for fine-grained, multi-class training of the P 1 and P 0 classes separately. The experiments and our subsequent ablation study, which are conducted on the student datasets from two Portuguese secondary schools, have proved the effectiveness of this hybridized method. [ABSTRACT FROM AUTHOR]

Discovering a hearing disorder at an earlier intervention is critical for reducing the effects of hearing loss and the approaches to increase the remaining hearing ability can be implemented to achieve the successful development of human communication. Recently, the explosive dataset features have increased the complexity for audiologists to decide the proper treatment for the patient. In most cases, data with irrelevant features and improper classifier parameters causes a crucial influence on the audiometry system in terms of accuracy. This is due to the dependent processes of these two, where the classification accuracy performance could be worsened if both processes are conducted independently. Although the filter algorithm is capable of eliminating irrelevant features, it still lacks the ability to consider feature reliance and results in a poor selection of significant features. Improper kernel parameter settings may also contribute to poor accuracy performance. In this paper, an ensemble filters feature selection based on Information Gain (IG), Gain Ratio (GR), Chi-squared (CS), and Relief-F (RF) with harmonize optimization of Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented to mitigate these problems. Ensemble filters are utilized so that the initial top dominant features relevant for classification can be considered. Then, PSO and SVM are optimized simultaneously to achieve the optimal solution. The results on a standard Audiology dataset show that the proposed method produces 96.50% accuracy with optimal solution compared to classical SVM, which signifies the proposed method is effective in handling high dimensional data for hearing disorder prediction. [ABSTRACT FROM AUTHOR]