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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]

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]

One of the world's deadliest diseases is lung cancer. Based on a few features, machine learning techniques can help in the diagnosis of lung cancer. The performance of several classifiers: support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and K-nearest neighbor (KNN), was evaluated by the authors using the dataset available on Kaggle to create a systematic approach for the diagnosis of lung cancer disease based on readily observable signs and historical medical data without the requirement of CT scan images. The authors have proposed a novel approach for classification called Pearson correlation weighted KNN (PCWKNN), which is a modified version of KNN and uses Pearson correlation coefficient values to determine weights in a weighted KNN. The performance of the classifiers was evaluated using the hold-out validation method. SVM, LR, and RF were 96.77% accurate. NB obtained 95.16% accuracy. KNN achieved 91.93% accuracy. PCWKNN outperformed the employed classifiers and obtained an accuracy of 98.39%. Addressing the imperative for improved model generalization, the researchers utilized PCWKNN on an alternative, more extensive lung cancer dataset and subsequently broadened its application to diverse diseases, including the brain stroke dataset. The encouraging outcomes underscore PCWKNN's resilience and adaptability, suggesting its viability for real-world implementation. [ABSTRACT FROM AUTHOR]

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder affecting millions of people globally, with substantial health risks and economic burdens. This study aims to introduce an innovative hybrid approach combining deep learning and machine learning algorithms to improve the diagnosis of PD using handwriting dynamics indicative of Parkinson’s symptoms. The proposed approach integrates hybrid feature extraction using nine fine-tuned transfer learning models, i.e., InceptionV3, DenseNet201, EfficientNetB0, ResNet50, MobileNetV2, VGG16, Xception, NASNetMobile, and InceptionResNetV2. Initially, features from these models are used individually or in binary and ternary combinations. Given the limited sample size in PD datasets, some extracted features through fine-tuning may lack significance, and fully connected layers can lead to overfitting. To address this issue, Neighborhood Component Analysis (NCA) is employed to refine these features, retaining only the most informative ones. Finally, the selected features are classified using Support Vector Machines (SVM) maximizing the margin between classes and reducing the risk of overfitting. The proposed hybrid model achieves a state-of-the-art accuracy of 99.39% on the Parkinson Hand Drawings dataset. The combination of features extracted from DenseNet201, Xception, and NASNetMobile models, processed using NCA and SVM methods, has been identified as the most efficient model, balancing high accuracy with computational efficiency. Qualitative assessments further confirm the accuracy and reliability of the approach. [ABSTRACT FROM AUTHOR]

In this work, the application of three different Machine Learning algorithms, random forest (RF), support vector machine (SVM), and artificial neural network (ANN), to accurately classify ultra-low concentrations of Δ9-tetrahydrocannabinol in biological fluids such as saliva was successfully demonstrated. In doing so, experimental data consisting of the voltammetry signals of 0, 2, and 5 ng/mL of Δ9-tetrahydrocannabinol (THC) in synthetic and human saliva was employed. The dataset consists of person-to-person saliva variation and experimental variabilities in the procedure and artifacts. The results showed that RF was the most robust ML technique to classify different concentration levels of THC in the presence of a variety of variabilities in the experimental data, with an average training accuracy of 96% and an average testing accuracy of 87%. On the other hand, SVM and ANN models were susceptible to incoherencies in the experimental data. [ABSTRACT FROM AUTHOR]

Today, several tools and statistical techniques can be used to find profitable trading opportunities, particularly when it comes to predicting stock closing prices. Yet, a few studies have been done on daily low- and high-price predictions which are useful for estimating support and resistance prices and improving the timing of stock purchases and sales. In this paper, we suggest combining machine learning algorithms with seven statistical features to enhance the forecasts of the low and high prices for the upcoming 5 days. In the experiment, the performances of linear regression, k-nearest neighbor, support vector machine (SVM), and the enhanced bidirectional LSTM (Bi-LSTM) were compared. Our findings showed that the Bi-LSTM surpassed the competition with a low RMSE of 0.018, a decrease in the error of over 58% compared to SVM, and a reduction of 51% compared to the unimproved LSTM. The four implemented algorithms are accessible online through a web application that displays the trend for the upcoming 5 days together with projected lines for the best-selling price (the high price) and the best-purchasing price (the low price). [ABSTRACT FROM AUTHOR]

This document is a retraction note for an article titled "Fault classification and detection in wind turbine using Cuckoo-optimized support vector machine" published in the journal Neural Computing & Applications. The article has been retracted by the Editor-in-Chief and the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate references, and not being in scope of the journal or guest-edited issue. The authors of the article disagree with the retraction. Springer Nature, the publisher, remains neutral in regards to jurisdictional claims and institutional affiliations. [Extracted from the article]