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Dimensionality Reduction (DR) is the process of reducing the numerous features or random variables under consideration to a limited number of features by obtaining a set of principal variables. These techniques cater great values in machine learning, which come in handy to simplify a classification or a regression dataset, thereby yielding a better-performing predictive model. Techniques used for DR include Feature Selection methods, Matrix Factorization, AutoEncoder methods, and Manifold Learning. Merits of DR include data compression, reduced space of storage, and removal of redundant features. This paper attempts to review various techniques used to carry out dimensionality reduction while providing an exhaustive comparative study over the merits and demerits of each of the techniques used under the empirical experiments performed by the authors whose work is being reviewed. [ABSTRACT FROM AUTHOR]

In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories: study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective: database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting. Key Points • Machine learning is new and rather complex for the radiology community. • Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting. • Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community. [ABSTRACT FROM AUTHOR]

As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method's efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy. [ABSTRACT FROM AUTHOR]

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability. [ABSTRACT FROM AUTHOR]

This study aims to demonstrate the feasibility of using a new wireless electroencephalography (EEG)–electromyography (EMG) wearable approach to generate characteristic EEG-EMG mixed patterns with mouth movements in order to detect distinct movement patterns for severe speech impairments. This paper describes a method for detecting mouth movement based on a new signal processing technology suitable for sensor integration and machine learning applications. This paper examines the relationship between the mouth motion and the brainwave in an effort to develop nonverbal interfacing for people who have lost the ability to communicate, such as people with paralysis. A set of experiments were conducted to assess the efficacy of the proposed method for feature selection. It was determined that the classification of mouth movements was meaningful. EEG-EMG signals were also collected during silent mouthing of phonemes. A few-shot neural network was trained to classify the phonemes from the EEG-EMG signals, yielding classification accuracy of 95%. This technique in data collection and processing bioelectrical signals for phoneme recognition proves a promising avenue for future communication aids. [ABSTRACT FROM AUTHOR]

• 252 chemicals were identified by SNTS in groundwater from four regions with diverse contamination histories. • A novel and robust systematic machine learning-based workflow for predicting chemical indicators was proposed. • The proposed workflow showed good predictive ability (Q2) of 0.897. • 51 chemical indicators for tracking groundwater contamination sources were suggested. In this study, the strong analytical power of gas chromatography coupled to a high resolution mass spectrometry (GC-HRMS) in suspect and non-target screening (SNTS) of organic micropollutants was combined with machine learning tools for proposing a novel and robust systematic environmental forensics workflow, focusing on groundwater contamination. Groundwater samples were collected from four different regions with diverse contamination histories (namely oil [OC], agricultural [AGR], industrial [IND], and landfill [LF]), and a total of 252 organic micropollutants were identified, including pharmaceuticals, personal care products, pesticides, polycyclic aromatic hydrocarbons, plasticizers, phenols, organophosphate flame retardants, transformation products, and others, with detection frequencies ranging from 3 % to 100 %. Amongst the SNTS identified compounds, a total of 51 chemical indicators (i.e., OC: 13, LF: 12, AGR: 19, IND: 7) which included level 1 and 2 SNTS identified chemicals were pinpointed across all sampling regions by integrating a bootstrapped feature selection method involving the bootfs algorithm and a partial least squares discriminant analysis (PLS-DA) model to determine potential prevalent contamination sources. The proposed workflow showed good predictive ability (Q2) of 0.897, and the suggested contamination sources were gasoline, diesel, and/or other light petroleum products for the OC region, anthropogenic activities for the LF region, agricultural and human activities for the AGR region, and industrial/human activities for the IND region. These results suggest that the proposed workflow can select a subset of the most diagnostic features in the chemical space that can best distinguish a specific contamination source class. [Display omitted] [ABSTRACT FROM AUTHOR]

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

Despite the complexity of thermodynamic models in predicting wax precipitation, they have not shown satisfactory results in the literature. This paper proposes the least-squares support vector machine optimized by the genetic algorithm to forecast wax precipitation based on the mole fractions, specific gravity, and temperature of crude oil samples. The genetic algorithm was used to prevent sticking in local optima in optimizing the parameters of the predictive model. A dataset of 88 experimental wax precipitation samples was used to evaluate the performance of the proposed method. The results revealed that the suggested algorithm could predict wax precipitation with an accuracy of 0.939 and 0.997 before and after sensitivity analysis, respectively. Furthermore, sensitivity analysis improved the Coefficient of Determination, Mean Squared Error, and Root Mean Squared Error from 0.939, 0.768, and 0.877 to 0.997, 0.041, and 0.202, respectively. The results were compared with the results of previous data-driven and mathematical models that showed the outperformance of the proposed method. The contribution of this paper can be summarized as demonstrating the ability of intelligent models to predict wax precipitation without a need for complex thermodynamic models. Also, it was shown that a proper feature selection could improve the model's performance. [ABSTRACT FROM AUTHOR]

The research of biomedical data is crucial for disease diagnosis, health management, and medicine development. However, biomedical data are usually characterized by high dimensionality and class imbalance, which increase computational cost and affect the classification performance of minority class, making accurate classification difficult. In this paper, we propose a biomedical data classification method based on feature selection and data resampling. First, use the minimal-redundancy maximal-relevance (mRMR) method to select biomedical data features, reduce the feature dimension, reduce the computational cost, and improve the generalization ability; then, a new SMOTE oversampling method (Spectral-SMOTE) is proposed, which solves the noise sensitivity problem of SMOTE by an improved spectral clustering method; finally, the marine predators algorithm is improved using piecewise linear chaotic maps and random opposition-based learning strategy to improve the algorithm's optimization seeking ability and convergence speed, and the key parameters of the spectral-SMOTE are optimized using the improved marine predators algorithm, which effectively improves the performance of the over-sampling approach. In this paper, five real biomedical datasets are selected to test and evaluate the proposed method using four classifiers, and three evaluation metrics are used to compare with seven data resampling methods. The experimental results show that the method effectively improves the classification performance of biomedical data. Statistical test results also show that the proposed PRMPA-Spectral-SMOTE method outperforms other data resampling methods. [ABSTRACT FROM AUTHOR]

In response to the rapidly evolving threat landscape in network security, this paper proposes an Evolutionary Machine Learning Algorithm designed for robust intrusion detection. We specifically address challenges such as adaptability to new threats and scalability across diverse network environments. Our approach is validated using two distinct datasets: BoT-IoT, reflecting a range of IoT-specific attacks, and UNSW-NB15, offering a broader context of network intrusion scenarios using GA based hybrid DT-SVM. This selection facilitates a comprehensive evaluation of the algorithm's effectiveness across varying attack vectors. Performance metrics including accuracy, recall, and false positive rates are meticulously chosen to demonstrate the algorithm's capability to accurately identify and adapt to both known and novel threats, thereby substantiating the algorithm's potential as a scalable and adaptable security solution. This study aims to advance the development of intrusion detection systems that are not only reactive but also preemptively adaptive to emerging cyber threats." During the feature selection step, a GA is used to discover and preserve the most relevant characteristics from the dataset by using evolutionary principles. Through the use of this technology based on genetic algorithms, the subset of features is optimised, enabling the subsequent classification model to focus on the most relevant components of network data. In order to accomplish this, DT-SVM classification and GA-driven feature selection are integrated in an effort to strike a balance between efficiency and accuracy. The system has been purposefully designed to efficiently handle data streams in real-time, ensuring that intrusions are promptly and precisely detected. The empirical results corroborate the study's assertion that the IDS outperforms traditional methodologies. [ABSTRACT FROM AUTHOR]

RNA modifications play an important role in actively controlling recently created formation in cellular regulation mechanisms, which link them to gene expression and protein. The RNA modifications have numerous alterations, presenting broad glimpses of RNA's operations and character. The modification process by the TET enzyme oxidation is the crucial change associated with cytosine hydroxymethylation. The effect of CR is an alteration in specific biochemical ways of the organism, such as gene expression and epigenetic alterations. Traditional laboratory systems that identify 5-hydroxymethylcytosine (5hmC) samples are expensive and time-consuming compared to other methods. To address this challenge, the paper proposed XGB5hmC, a machine learning algorithm based on a robust gradient boosting algorithm (XGBoost), with different residue based formulation methods to identify 5hmC samples. Their results were amalgamated, and six different frequency residue based encoding features were fused to form a hybrid vector in order to enhance model discrimination capabilities. In addition, the proposed model incorporates SHAP (Shapley Additive Explanations) based feature selection to demonstrate model interpretability by highlighting the high contributory features. Among the applied machine learning algorithms, the XGBoost ensemble model using the tenfold cross-validation test achieved improved results than existing state-of-the-art models. Our model reported an accuracy of 89.97%, sensitivity of 87.78%, specificity of 94.45%, F1-score of 0.8934%, and MCC of 0.8764%. This study highlights the potential to provide valuable insights for enhancing medical assessment and treatment protocols, representing a significant advancement in RNA modification analysis. [ABSTRACT FROM AUTHOR]

Student academic achievement is an important indicator for evaluating the quality of education, especially, the achievement prediction empowers educators in tailoring their instructional approaches, thereby fostering advancements in both student performance and the overall educational quality. However, extracting valuable insights from vast educational data to develop effective strategies for evaluating student performance remains a significant challenge for higher education institutions. Traditional machine learning (ML) algorithms often struggle to clearly delineate the interplay between the factors that influence academic success and the resulting grades. To address these challenges, this paper introduces the XGB-SHAP model, a novel approach for predicting student achievement that combines Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The model was applied to a dataset from a public university in Wuhan, encompassing the academic records of 87 students who were enrolled in a Japanese course between September 2021 and June 2023. The findings indicate the model excels in accuracy, achieving a Mean absolute error (MAE) of approximately 6 and an R-squared value near 0.82, surpassing three other ML models. The model further uncovers how different instructional modes influence the factors that contribute to student achievement. This insight supports the need for a customized approach to feature selection that aligns with the specific characteristics of each teaching mode. Furthermore, the model highlights the importance of incorporating self-directed learning skills into student-related indicators when predicting academic performance. [ABSTRACT FROM AUTHOR]

The exponential growth of IoT devices and their interdependency makes the technology more vulnerable to network attacks like Distributed Denial of Service (DDoS) that interrupt network resources. The prevalence of these attacks necessitates the development of robust and effective defense mechanisms. In recent years, many machine learning defense methodologies have been developed to address the ubiquitous growth of DDoS attacks on IoT, and the majority of them suffer from detection time delay issues. Thus, the paper presents an approach focusing on dimensionality reduction and feature selection techniques to minimize long-time detection without compromising accuracy. The proposed approach uses Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Factor Analysis and Recursive Feature Elimination with Cross Validation (RFECV) as the dimensionality reduction and feature selection techniques and Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), AdaBoost, and Logistic Regression (LR) as machine learning models to classify the malicious traffic. The approach provides a reliable DDoS detection model that effectively enhances the detection time delay with the combination of GNB with LDA and achieves 99.98% accuracy in 0.582 s of detection time. [ABSTRACT FROM AUTHOR]

Feature selection plays a critical role in reducing high-dimensional feature space in machine learning applications without affecting the accuracy of performance. The feature selection problem has been extensively studied in the literature. Nevertheless, few studies have been conducted on unsupervised text feature selection because of the absence of feature class labels and local optimization limitations. For that, this paper proposes three binary versions of the fire hawk optimizer based on different transfer functions for unsupervised text feature selection that can be used to select the most informative features for text clustering. The internal feature subset was evaluated using a mean absolute difference filter. The performance of the proposed methods is tested and compared with other state-of-the-art metaheuristic algorithms on several different benchmark text datasets from different sources using various evaluation metrics. The K-means clustering is applied to cluster documents based on the features selected by the methods. The results of the experiments indicate the effectiveness of the proposed method based on the S-shaped transfer function for improving the performance of document clustering compared with other feature selection methods. [ABSTRACT FROM AUTHOR]

Machine learning-based IDSs have demonstrated promising outcomes in identifying and mitigating security threats within IoT networks. However, the efficacy of such systems is contingent on various hyperparameters, necessitating optimization to elevate their performance. This paper introduces a comprehensive empirical and quantitative exploration aimed at enhancing intrusion detection systems (IDSs). The study capitalizes on a genetic algorithm-based hyperparameter tuning mechanism and a pioneering hybrid feature selection approach to systematically investigate incremental performance improvements in IDS. Specifically, our work proposes a machine learning-based IDS approach tailored for detecting attacks in IoT environments. To achieve this, we introduce a hybrid feature selection method designed to identify the most salient features for the task. Additionally, we employed the genetic algorithm (GA) to fine-tune hyperparameters of multiple machine learning models, ensuring their accuracy in detecting attacks. We commence by evaluating the default hyperparameters of these models on the CICIDS2017 dataset, followed by rigorous testing of the same algorithms post-optimization through GA. Through a series of experiments, we scrutinize the impact of combining feature selection methods with hyperparameter tuning approaches. The outcomes unequivocally demonstrate the potential of hyperparameter optimization in enhancing the accuracy and efficiency of machine learning-based IDS systems for IoT networks. The empirical nature of our research method provides a meticulous analysis of the efficacy of the proposed techniques through systematic experimentation and quantitative evaluation. Consolidated in a unified manner, the results underscore the step-by-step enhancement of IDS performance, especially in terms of detection time, substantiating the efficacy of our approach in real-world scenarios. [ABSTRACT FROM AUTHOR]

Android malware has become an increasingly important threat to individuals, organizations, and society, posing significant risks to data security, privacy, and infrastructure. As malware evolves in sophistication and complexity, the detection and mitigation of these malicious software instances have become more challenging and time consuming since the required number of features to identify potential malware can be very high. To address this issue, we have developed an effective feature selection methodology for malware detection in Android. The critical concern in the field of malware detection is the complexity of algorithms and the use of features that are used to detect malware. The present paper delivers a methodology for pre-processing datasets to select the most optimal features that will allow detecting malware, while maintaining very high accuracy. The proposed methodology has been tested on two real world datasets and the results indicate that the number of features is significantly reduced from 489 to between 19 and 28 for the first dataset and from 9503 to between 9 and 27 for the second dataset, whilst the accuracy is maintained as if all features were used. [ABSTRACT FROM AUTHOR]

Ammonia emerges as a promising substitute for traditional fuels, offering a potential reduction in fossil fuel consumption and the associated emissions. Given its weak reactivity, an effective strategy to harness the potential of ammonia involves cofiring it with methane, to establish a self-sustaining combustion process. The understanding of heat release rate (HRR) in the cofiring process of ammonia and methane is crucial for burner design. This study introduces novel HRR indicators through a machine learning-based approach, focusing on species significantly contributing to HRR. Kinetic analysis is carried out utilizing a one-dimensional freely propagating flame model, applying a mechanism including 59 species and 356 reactions. Domain knowledge-based feature selection narrows down the search space, enhancing computational efficiency during HRR indicator identification. A random forest model is then employed to identify radical/radical combinations and their respective reaction orders, representing HRR optimally based on mean decrease impurity. The proposed HRR markers are [CH 4 ]0·76[OH]1.08, [HCO]0·65[NO]0.24, [CH 3 ]0.65[O]0.63, [NH 2 ][O]0.59, and [NH 3 ]0·93[OH]0.96. The root mean square error for each marker was extracted and compared with literature data, demonstrating higher reliability of the proposed indicators over those previously suggested. Additionally, the paper concludes with a discussion of the feasibility of measuring these markers from an experimental perspective. [Display omitted] • Thorough kinetic analysis of focusing on key HRR contributors. • Pioneering ML for crucial HRR indicator identification in ammonia-methane cofiring. • Domain-based feature selection streamlines HRR marker identification for efficiency. • Proposed 5 HRR markers that are superior in accuracy compared to existing markers. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE < 0.01, R2 > 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments. Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023. [ABSTRACT FROM AUTHOR]

Brain tumor is an extremely dangerous disease with a very high mortality rate worldwide. Detecting brain tumors accurately is crucial due to the varying appearance of tumor cells and the dimensional irregularities in their growth. This poses a significant challenge for detection algorithms. Currently, there are numerous algorithms utilized for this purpose, ranging from transform-based methods to those rooted in machine learning techniques. These algorithms aim to enhance the accuracy of detection despite the complexities involved in identifying brain tumor cells. The major limitation of these algorithms is the mapping of extracted features of a brain tumor in the classification algorithms. To employ a combination of transform methods to extract texture feature from brain tumor images. This paper employs a combination of transform methods based on sub band decomposition for texture feature extraction from MRI scans, hybrid feature optimization methods using firefly and glow-worm algorithms for selection of feature, employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods. The algorithm under consideration has been put into practice using MATLAB, utilizing datasets from BRATS (Brain Tumor Segmentation) for the years 2013, 2015, and 2018. These datasets serve as the foundation for testing and validating the algorithm’s performance across different time periods, providing a comprehensive assessment of its effectiveness in detecting brain tumors. The proposed algorithm achieves maximum detection accuracy, detection sensitivity and specificity up to 98%, 99% and 99.5% respectively. The experimental outcomes showcase the efficiency of the algorithm in detection of brain tumor. The proposed work mainly contributes in brain tumor detection in the following aspects: a) use of combination of transform methods for texture feature extraction from MRI scans b) hybrid feature selection methods using firefly and glow-worm optimization algorithms for selection of feature c) employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods. [ABSTRACT FROM AUTHOR]

High-strength bolts play a crucial role in ultra-high-pressure equipment such as bridges and railway tracks. Effective monitoring of bolt conditions is of paramount importance for common fault repair and accident prevention. This paper aims to detect and classify bolt corrosion levels accurately. We design and implement a bolt corrosion classification system based on a Wireless Acoustic Emission Sensor Network (WASN). Initially, WASN nodes collect high-speed acoustic emission (AE) signals from bolts. Then, the ReliefF feature selection algorithm is applied to identify the optimal feature combination. Subsequently, the Extreme Learning Machine (ELM) model is utilized for bolt corrosion classification. Additionally, to achieve high prediction accuracy, an improved goose algorithm (GOOSE) is employed to ensure the most suitable parameter combination for the ELM model. Experimental measurements were conducted on five classes of bolt corrosion levels: 0%, 25%, 50%, 75%, and 100%. The classification accuracy obtained using the proposed method was at least 98.04%. Compared to state-of-the-art classification diagnostic models, our approach exhibits superior AE signal recognition performance and stronger generalization ability to adapt to variations in working conditions. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a significant dimensionality reduction technique in machine learning and data mining that is adept at managing high-dimensional data efficiently and enhancing model performance. Metaheuristic algorithms have become one of the most promising solutions in FS owing to their powerful search capabilities as well as their performance. In this paper, the novel improved binary walrus optimizer (WO) algorithm utilizing the golden sine strategy, elite opposition-based learning (EOBL), and population regeneration mechanism (BGEPWO) is proposed for FS. First, the population is initialized using an iterative chaotic map with infinite collapses (ICMIC) chaotic map to improve the diversity. Second, a safe signal is obtained by introducing an adaptive operator to enhance the stability of the WO and optimize the trade-off between exploration and exploitation of the algorithm. Third, BGEPWO innovatively designs a population regeneration mechanism to continuously eliminate hopeless individuals and generate new promising ones, which keeps the population moving toward the optimal solution and accelerates the convergence process. Fourth, EOBL is used to guide the escape behavior of the walrus to expand the search range. Finally, the golden sine strategy is utilized for perturbing the population in the late iteration to improve the algorithm's capacity to evade local optima. The BGEPWO algorithm underwent evaluation on 21 datasets of different sizes and was compared with the BWO algorithm and 10 other representative optimization algorithms. The experimental results demonstrate that BGEPWO outperforms these competing algorithms in terms of fitness value, number of selected features, and F1-score in most datasets. The proposed algorithm achieves higher accuracy, better feature reduction ability, and stronger convergence by increasing population diversity, continuously balancing exploration and exploitation processes and effectively escaping local optimal traps. [ABSTRACT FROM AUTHOR]

Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing computational complexity in huge feature spaces. The UFS problem has been addressed in several research efforts. Recent studies have witnessed a surge in innovative techniques like nature-inspired algorithms for clustering and UFS problems. However, very few studies consider the UFS problem as a multi-objective problem to find the optimal trade-off between the number of selected features and model accuracy. This paper proposes a multi-objective symbiotic organism search algorithm for unsupervised feature selection (SOSUFS) and a symbiotic organism search-based clustering (SOSC) algorithm to generate the optimal feature subset for more accurate clustering. The efficiency and robustness of the proposed algorithm are investigated on benchmark datasets. The SOSUFS method, combined with SOSC, demonstrated the highest f-measure, whereas the KHCluster method resulted in the lowest f-measure. SOSFS effectively reduced the number of features by more than half. The proposed symbiotic organisms search-based optimal unsupervised feature-selection (SOSUFS) method, along with search-based optimal clustering (SOSC), was identified as the top-performing clustering approach. Following this, the SOSUFS method demonstrated strong performance. In summary, this empirical study indicates that the proposed algorithm significantly surpasses state-of-the-art algorithms in both efficiency and effectiveness. Unsupervised learning in artificial intelligence involves machine-learning techniques that learn from data without human supervision. Unlike supervised learning, unsupervised machine-learning models work with unlabeled data to uncover patterns and insights independently, without explicit guidance or instruction. [ABSTRACT FROM AUTHOR]

Genetic and agronomic advances consistently lead to an annual increase in global barley yield. Since abiotic stresses (physical environmental factors that negatively affect plant growth) reduce barley yield, it is necessary to predict barley resistance. Artificial intelligence and machine learning (ML) models are new and powerful tools for predicting product resilience. Considering the research gap in the use of molecular markers in predicting abiotic stresses, this paper introduces a new approach called GenPhenML that combines molecular markers and phenotypic traits to predict the resistance of barley genotypes to drought and salinity stresses by ML models. GenPhenML uses feature selection algorithms to determine the most important molecular markers. It then identifies the best model that predicts atmospheric resistance with lower MAE, RMSE, and higher R2. The results showed that GenPhenML with a neural network model predicted the salinity stress resistance score with MAE, RMSE and R2 values of 0.1206, 0.0308 and 0.9995, respectively. Also, the NN model predicted drought stress scores with MAE, RMSE and R2 values of 0.0727, 0.0105 and 0.9999, respectively. The GenPhenML approach was also used to classify barley genotypes as resistant and stress-sensitive. The results showed that the accuracy, accuracy and F1 score of the proposed approach for salinity and drought stress classification were higher than 97%. [ABSTRACT FROM AUTHOR]

Purpose: As agricultural technology continues to develop, the scale of planting and production of date fruit is increasing, which brings higher yields. However, the increasing yields also put a lot of pressure on the classification step afterward. Image recognition based on deep learning algorithms can help to identify and classify the date fruit species, even in natural light. Method: In this paper, a deep fusion model based on whale optimization and an artificial neural network for Arabian date classification is proposed. The dataset used in this study includes five classes of date fruit images (Barhi, Khalas, Meneifi, Naboot Saif, Sullaj). The process of designing each model can be divided into three phases. The first phase is feature extraction. The second phase is feature selection. The third phase is the training and testing phase. Finally, the best-performing model was selected and compared with the currently established models (Alexnet, Squeezenet, Googlenet, Resnet50). Results: The experimental results show that, after trying different combinations of optimization algorithms and classifiers, the highest test accuracy achieved by DeepDate was 95.9%. It takes less time to achieve a balance between classification accuracy and time consumption. In addition, the performance of DeepDate is better than that of many deep transfer learning models such as Alexnet, Squeezenet, Googlenet, VGG-19, NasNet, and Inception-V3. Conclusion: The proposed DeepDate improves the accuracy and efficiency of classifying date fruits and achieves better results in classification metrics such as accuracy and F1. DeepDate provides a promising classification solution for date fruit classification with higher accuracy. To further advance the industry, it is recommended that stakeholders invest in technology transfer programs to bring advanced image recognition and AI tools to smaller producers, enhancing sustainability and productivity across the sector. Collaborations between agricultural technologists and growers could also foster more tailored solutions that address specific regional challenges in date fruit production. [ABSTRACT FROM AUTHOR]

Background: The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory symptoms and preserved pulmonary function(PPF). This paper evaluated the performance of several ML algorithms associated with the impulse oscillometry(IOS) analysis to aid in the diagnostic of respiratory changes in SAD. We also find out the best configuration for this task. Methods: IOS and spirometry were measured in 280 subjects, including a healthy control group (n = 78), a group with normal spirometry (n = 158) and a group with abnormal spirometry (n = 44). Various supervised machine learning (ML) algorithms and feature selection strategies were examined, such as Support Vector Machines (SVM), Random Forests (RF), Adaptive Boosting (ADABOOST), Navie Bayesian (BAYES), and K-Nearest Neighbors (KNN). Results: The first experiment of this study demonstrated that the best oscillometric parameter (BOP) was R5, with an AUC value of 0.642, when comparing a healthy control group(CG) with patients in the group without lung volume-defined SAD(PPFN). The AUC value of BOP in the control group was 0.769 compared with patients with spirometry defined SAD(PPFA) in the PPF population. In the second experiment, the ML technique was used. In CGvsPPFN, RF and ADABOOST had the best diagnostic results (AUC = 0.914, 0.915), with significantly higher accuracy compared to BOP (p < 0.01). In CGvsPPFA, RF and ADABOOST had the best diagnostic results (AUC = 0.951, 0.971) and significantly higher diagnostic accuracy (p < 0.01). In the third, fourth and fifth experiments, different feature selection techniques allowed us to find the best IOS parameters (R5, (R5-R20)/R5 and Fres). The results demonstrate that the performance of ADABOOST remained essentially unaltered following the application of the feature selector, whereas the diagnostic accuracy of the remaining four classifiers (RF, SVM, BAYES, and KNN) is marginally enhanced. Conclusions: IOS combined with ML algorithms provide a new method for diagnosing SAD in subjects with chronic respiratory symptoms and PPF. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients. [ABSTRACT FROM AUTHOR]

The rapid advancement of the Internet and digital payments has transformed the landscape of financial transactions, leading to both technological progress and an alarming rise in cybercrime. This study addresses the critical issue of financial fraud detection in the era of digital payments, focusing on enhancing operational risk frameworks to mitigate the increasing threats. The objective is to improve the predictive performance of fraud detection systems using machine learning techniques. The methodology involves a comprehensive data preprocessing and model creation process, including one‐hot encoding, feature selection, sampling, standardization, and tokenization. Six machine learning models are employed for fraud detection, and their hyperparameters are optimized. Evaluation metrics such as accuracy, precision, recall, and F1‐score are used to assess model performance. Results reveal that XGBoost and Random Forest outperform other models, achieving a balance between false positives and false negatives. The study meets the requirements for fraud detection systems, ensuring accuracy, scalability, adaptability, and explainability. This paper provides valuable insights into the efficacy of machine learning models for financial fraud detection and emphasizes the importance of striking a balance between false positives and false negatives. [ABSTRACT FROM AUTHOR]

AbstractMyocardial Infarction (MI) refers to damage to the heart tissue caused by an inadequate blood supply to the heart muscle due to a sudden blockage in the coronary arteries. This blockage is often a result of the accumulation of fat (cholesterol) forming plaques (atherosclerosis) in the arteries. Over time, these plaques can crack, leading to the formation of a clot (thrombus), which can block the artery and cause a heart attack. Risk factors for a heart attack include smoking, hypertension, diabetes, high cholesterol, metabolic syndrome, and genetic predisposition. Early diagnosis of MI is crucial. Thus, detecting and classifying MI is essential. This paper introduces a new hybrid approach for MI Classification using Spectrogram and Bayesian Optimization (MI-CSBO) for Electrocardiogram (ECG). First, ECG signals from the PTB Database (PTBDB) were converted from the time domain to the frequency domain using the spectrogram method. Then, a deep residual CNN was applied to the test and train datasets of ECG imaging data. The ECG dataset trained using the Deep Residual model was then acquired. Finally, the Bayesian approach, NCA feature selection, and various machine learning algorithms (k-NN, SVM, Tree, Bagged, Naïve Bayes, Ensemble) were used to derive performance measures. The MI-CSBO method achieved a 100% correct diagnosis rate, as detailed in the Experimental Results section. [ABSTRACT FROM AUTHOR]

The rising adoption of electric vehicles (EVs), driven by carbon neutrality goals, has prompted the need for accurate forecasting of EVs' charging behavior. However, this task presents several challenges due to the dynamic nature of EVs' usage patterns, including fluctuating demand and unpredictable charging durations. In response to these challenges and different from previous works, this paper presents a novel and holistic methodology for day-ahead forecasting of EVs' plugged-in status and power consumption in charging stations (CSs). The proposed framework encompasses data analysis, pre-processing, feature engineering, feature selection, the use and comparison of diverse machine learning forecasting algorithms, and validation. A real-world dataset from a CS in Boulder City is employed to evaluate the framework's effectiveness, and the results demonstrate its proficiency in predicting the EVs' plugged-in status, with XGBoost's classifier achieving remarkable accuracy with an F1-score of 0.97. Furthermore, an in-depth evaluation of six regression methods highlighted the supremacy of gradient boosting algorithms in forecasting the EVs' power consumption, with LightGBM emerging as the most effective method due to its optimal balance between prediction accuracy with a 4.22% normalized root-mean-squared error (NRMSE) and computational efficiency with 5 s of execution time. The proposed framework equips power system operators with strategic tools to anticipate and adapt to the evolving EV landscape. [ABSTRACT FROM AUTHOR]

This paper presents a real-time intrusion detection system (IDS) aimed at detecting the Internet of Things (IoT) attacks using multiclass classification models within the PySpark architecture. The research objective is to enhance detection accuracy while reducing the prediction time. Various machine learning algorithms are employed using the OneVsRest (OVR) technique. The proposed method utilizes the IoT-23 dataset, which consists of network traffic from smart home IoT devices, for model development. Data preprocessing techniques, such as data cleaning, transformation, scaling, and the synthetic minority oversampling technique (SMOTE), are applied to prepare the dataset. Additionally, feature selection methods are employed to identify the most relevant features for classification. The performance of the classifiers is evaluated using metrics such as accuracy, precision, recall, and F1 score. The results indicate that among the evaluated algorithms, extreme gradient boosting achieves a high accuracy of 98.89%, while random forest demonstrates the most efficient training and prediction times, with a prediction time of only 0.0311 s. The proposed method demonstrates high accuracy in real-time intrusion detection of IoT attacks, outperforming existing approaches. [ABSTRACT FROM AUTHOR]

The term "Internet of Things" (IoT) encompasses an entire group of gadgets that are capable of connecting to the Internet in order to gather and share data. The IoT paradigm is being pushed into computer networks by numerous highly advanced intrusions. Cloud computing greatly enhances the success of the IoT by enabling users to perform computing tasks using Internet-based services accessed through connected devices. This seamless integration of cloud technology and the IoT has become a powerful catalyst, revolutionizing the way we operate. The adoption of a distributed architecture, such as cloud computing, exposes the system to potential threats like Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks. To mitigate these risks, the concept of an intrusion detection system (IDS) has been introduced within the cloud environment. Various machine learning (ML) and deep learning (DL) algorithms have been proposed and implemented to effectively detect and respond to such malicious traffic in the cloud system. For dimension reduction during the training process of those algorithms, multiple independent and hybrid techniques have been proposed. This study presents an efficient ML-based real-time IDS framework with proposed hybrid feature selection techniques. Additionally, in this study, a concise comparative analysis has been conducted using five well-known public datasets. The findings presented in this paper reveal that our proposed IDS achieved a maximum accuracy of 99.98% in identifying malicious traffic. [ABSTRACT FROM AUTHOR]

Ensuring a secure online environment hinges on the timely detection of network attacks. Nevertheless, existing detection methods often grapple with the delicate balance between speed and accuracy. In this paper, we introduce a novel intrusion detection algorithm that marries quantum particle swarm optimization with an extreme learning machine (QPSO-ELM). Firstly, we present a feature selection algorithm grounded in partitioned gains to distill vital features from data samples, thereby diminishing feature count to amplify both model training speed and accuracy. Subsequently, we unveil an intrusion detection scheme underpinned by QPSO-ELM, capable of achieving exceptional levels of training and detection speed, all while maintaining high accuracy. Finally, we fine-tune the trained model using the proposed hidden layer node selection algorithm, reducing the detection model size without compromising detection accuracy, thus further elevating its speed. The experiment results indicate that compared to the current baseline, our proposed intrusion detection scheme achieves the best results in terms of accuracy, precision, recall, and detection latency. Furthermore, the ablation experiment results demonstrate the effectiveness of our proposed method in improving both detection speed and detection accuracy. [ABSTRACT FROM AUTHOR]

Machine Learning and Data Mining algorithms are used extensively to enhance the performance of Intrusion Detection Systems. The number of training instances and the dimensionality of data are crucial factors affecting the performance of the model built during the training of any supervised learning algorithms. A sufficient proportion of instances having relevant features from all classes of attacks and normal traffic are considered most desirable while building the classification model that classifies the network traffic into attack and normal. This paper proposes a methodology to improve the accuracy of the model by giving importance to the relevant features that can contribute to model building. The feature selection using correlation‐based and information gain‐based techniques during training and testing contributes much to the detection of stealthier attacks and minority attacks. Then the features of the less detected attacks are identified as the second phase of the filter that is used to improve the performance. The relevant features of stealthy attacks are identified based on the correlation of corresponding features of the attack and normal data as the attacks are made stealthy mostly by making it resemble the normal traffic. Finally, the attacks that are rarely found in the training data are oversampled to improve their detection. CICIDS 2017 data set is employed as it comprises stealthier attacks generated using modern tools. NSL KDD data set is also used for evaluation to compare the proposed work with existing literature as it is used in most of the available literature. The results show superior performance with an accuracy of 99.8%, false positive rate of 0.2%, and a detection rate and 99.8%. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices are leading to advancements in innovation, efficiency, and sustainability across various industries. However, as the number of connected IoT devices increases, the risk of intrusion becomes a major concern in IoT security. To prevent intrusions, it is crucial to implement intrusion detection systems (IDSs) that can detect and prevent such attacks. IDSs are a critical component of cybersecurity infrastructure. They are designed to detect and respond to malicious activities within a network or system. Traditional IDS methods rely on predefined signatures or rules to identify known threats, but these techniques may struggle to detect novel or sophisticated attacks. The implementation of IDSs with machine learning (ML) and deep learning (DL) techniques has been proposed to improve IDSs' ability to detect attacks. This will enhance overall cybersecurity posture and resilience. However, ML and DL techniques face several issues that may impact the models' performance and effectiveness, such as overfitting and the effects of unimportant features on finding meaningful patterns. To ensure better performance and reliability of machine learning models in IDSs when dealing with new and unseen threats, the models need to be optimized. This can be done by addressing overfitting and implementing feature selection. In this paper, we propose a scheme to optimize IoT intrusion detection by using class balancing and feature selection for preprocessing. We evaluated the experiment on the UNSW-NB15 dataset and the NSL-KD dataset by implementing two different ensemble models: one using a support vector machine (SVM) with bagging and another using long short-term memory (LSTM) with stacking. The results of the performance and the confusion matrix show that the LSTM stacking with analysis of variance (ANOVA) feature selection model is a superior model for classifying network attacks. It has remarkable accuracies of 96.92 % and 99.77 % and overfitting values of 0.33 % and 0.04 % on the two datasets, respectively. The model's ROC is also shaped with a sharp bend, with AUC values of 0.9665 and 0.9971 for the UNSW-NB15 dataset and the NSL-KD dataset, respectively. [ABSTRACT FROM AUTHOR]

Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions. [ABSTRACT FROM AUTHOR]

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested. [ABSTRACT FROM AUTHOR]

Information and communication technology has evolved dramatically, and now the majority of people are using internet and sharing their opinion more openly, which has led to the creation, collection and circulation of hate speech over multiple platforms. The anonymity and movability given by these social media platforms allow people to hide themselves behind a screen and spread the hate effortlessly. Online hate speech (OHS) recognition can play a vital role in stopping such activities and can thus restore the position of public platforms as the open marketplace of ideas. To study hate speech detection in social media, we surveyed the related available datasets on the web-based platform. We further analyzed approximately 200 research papers indexed in the different journals from 2010 to 2022. The papers were divided into various sections and approaches used in OHS detection, i.e., feature selection, traditional machine learning (ML) and deep learning (DL). Based on the selected 111 papers, we found that 44 articles used traditional ML and 35 used DL-based approaches. We concluded that most authors used SVM, Naive Bayes, Decision Tree in ML and CNN, LSTM in the DL approach. This survey contributes by providing a systematic approach to help researchers identify a new research direction in online hate speech. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection & preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends & suggestion for future works. In addition, this paper offers a holistic view of machine learning's role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area. [ABSTRACT FROM AUTHOR]

Increasing the integration of distributed generation (DG) into distribution networks provides many technological benefits, including improving system security, performance, and reliability. The intermittent nature of renewable DGs poses certain difficulties for this integration. Moreover, the large integration of DGs will lead to islanding conditions in the power system. In the islanded operation, the microgrid keeps power injection into the network. The islanding event can occur intentionally or unintentionally; the former is controllable and required for maintaining the main utility, whereas the latter is uncontrollable, caused by regular faults. However, islanding detection is important for ensuring the system's reliability and operation. Hence, a comprehensive review is made in this paper to examine different methods for islanding detection in the power system. Unlike the previous review papers, the proposed review paper focused on different types of islanding detection methods, including active, reactive, hybrid active–passive methods, deep learning and machine learning techniques. All of these methods have their advantage and disadvantages. Moreover, the complexities in power systems increased with the increasing penetration of DGs. Thus, the rapid islanding detection technique is necessary for improving the system's performance. This review has provided some recent literature for signal processing, which includes the recent feature selection and advance finding for islanding occurrences. From the comparative analysis, it is found that the Non-detection zone (NDZ) is more in the passive method is higher than in the active and hybrid active–passive methods. At the same time, the remote islanding detection methods are NDZ free, but it has computational complexities when compared with existing methods. Moreover, the DL based methods have higher computational time due to large training and testing data. It is found that hybrid methods are more feasible for providing accurate results in islanding detection. In addition to that, a feasible and economical solution in terms of recent research trends is provided. [ABSTRACT FROM AUTHOR]

With the promotion and development of oversized data technology, many data analysis platforms based on super large data storage and computing frameworks have emerged in the industry. While the platforms with oversized economic data analysis combined with machine learning models are still relatively lacking. And oversized data also brings a new problem, that is the security of economic development. It is an important and difficult task to analyse and detect risks from oversized economic data. Based on machine learning, data analysis, economic market and other multidisciplinary fields, this paper proposes a machine learning method, which is a genetic algorithm (GA) based feature selection method: FSGA. This method abstracts every possible feature selection result into an individual in GA, generates a population through genetic operation, and measures the merits of the individual through fitness. In addition, this paper has conducted multitudinous simulation experiments on the GA‐based FSGA method and the traditional LSTM data analysis method respectively. The accuracy rate and other indicators are obtained by comparing the training. The experimental results show that the GA‐based FSGA machine learning method has higher prediction accuracy when analysing oversized economic data. And it is practical to accelerate the development of digital economy. [ABSTRACT FROM AUTHOR]

The genetic algorithm with aggressive mutations GAAM, is a specialised algorithm for feature selection. This algorithm is dedicated to the selection of a small number of features and allows the user to specify the maximum number of features desired. A major obstacle to the use of this algorithm is its high computational cost, which increases significantly with the number of dimensions to be retained. To solve this problem, we introduce a surrogate model based on machine learning, which reduces the number of evaluations of the fitness function by an average of 48% on the datasets tested, using the standard parameters specified in the original paper. Additionally, we experimentally demonstrate that eliminating the crossover step in the original algorithm does not result in any visible changes in the algorithm’s results. We also demonstrate that the original algorithm uses an artificially complex mutation method that could be replaced by a simpler method without loss of efficiency. The sum of the improvements resulted in an average reduction of 53% in the number of evaluations of the fitness functions. Finally, we have shown that these outcomes apply to parameters beyond those utilized in the initial article, while still achieving a comparable decrease in the count of evaluation function calls. Tests were conducted on 9 datasets of varying dimensions, using two different classifiers. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and deep learning (DL) are used in numerous fields, particularly to develop effective intrusion detection systems (IDS). Existing wireless network IDS, which rely on a single ML algorithm and have limitations. These include a high rate of false positives, difficulties in recognizing distinct attack patterns, and a high acquisition cost for annotated training datasets. However, hostile threats are always evolving, networks need a smart security solution. In comparison to other ML approaches, DL algorithms are more successful in intrusion detection. This paper presents a DL based ensemble model that combines Multi-verse through Chaotic Atom Search Optimization (MCA) for preprocessing, which eliminates unsolicited/recurrent information in the dataset. The process of optimized feature selection uses Principal Component Analysis (PCA), Chaotic Manta-ray Foraging Optimizations (CMFO), and a grounded grouping method to partition the optimized feature dataset into k-diverse clusters. The recommended model then stacks Support Vector Machine (SVM) as the ensemble model’s meta-learner classifier, pre-training the hybrid DL prototypes using the optimized feature dataset cluster. The CNN-LSTM and CNN-GRU models, which integrate Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), are the hybrid DL prototype’s key components. The suggested model’s performance has been enhanced and compared to six ML techniques: NB, SVM, J48, RF, MLP, and kNN models, utilizing measures such as accuracy, precision, recall, and F-measure. The public can access the Aegean Wi-Fi Intrusion Dataset (AWID) which is used for evaluating the recommended model and is outperformed the contemporary models in the literature. [ABSTRACT FROM AUTHOR]

Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network's measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests. [ABSTRACT FROM AUTHOR]

It is vital for the development of attention deficit hyperactivity disorders (ADHDs) diagnostic tools that help healthcare practitioners to have automated recognition of ADHDs. Recently, the detection of ADHD using EEG data has attracted a lot of interest due to its apparent rapidity in diagnosis and treatment. This is because the disease seems to be caused by hyperactivity and inattention. This paper presents the method for diagnosing ADHDs based on EEG data, with the nonlinear characteristics being retrieved via the Synchrosqueezing Transform (ST). The ST filter banks are used to break down the 16 channels of EEG signal information into the ideal number of time-frequency subbands. At each stage of the decomposition process, the distinctive feature vectors are assessed using CNN as well as the proposed nonlinear fractal dimensions approach. Using ResNet34, it was discovered that a modified CNN classifier that used a 10-fold cross-validation approach was an efficient classifier for differentiating between normal people and those with ADHD. The suggested method was able to successfully categorize ADHD and normal participants with the best accuracy, as shown by several performance indicators. The statistical study demonstrated that the CNN and the proposed nonlinear feature estimation techniques produce possible characteristics that are acceptable for the automated identification of ADHD. These features may be categorized with good accuracy, sensitivity and specificity. The technique that has been presented is capable of correctly differentiating between participants who have ADHD and those who do not have ADHD with an ultimate precision of 99.5%. [ABSTRACT FROM AUTHOR]

This paper (i) explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties, and (ii) further explores an inverse design approach to molecular design consisting of using machine learning methods to approximate the atomic composition of molecules, using QM9 data. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from physical‐chemical properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well‐defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner, core region that concentrates clustered inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inliner nature. The spatial structure exhibits a relationship with molecular weight. Despite the structural differences between the two datasets, the predictability of variables of interest for inverse molecular design is high. This is exemplified by models estimating the number of atoms of the molecule from both the original properties and from lower dimensional embedding spaces. In the generative approach the input is given by a set of desired properties of the molecule and the output is an approximation of the atomic composition in terms of its constituent chemical elements. This could serve as the starting region for further search in the huge space determined by the set of possible chemical compounds. The quantum mechanic's dataset QM9 is used in the study, composed of 133,885 small organic molecules and 19 electronic properties. Different multi‐target regression approaches were considered for predicting the atomic composition from the properties, including feature engineering techniques in an auto‐machine learning framework. High‐quality models were found that predict the atomic composition of the molecules from their electronic properties, as well as from a subset of only 52.6% size. Feature selection worked better than feature generation. The results validate the generative approach to inverse molecular design. [ABSTRACT FROM AUTHOR]

Cold spray is an additive manufacturing and coating process in which powder particles are accelerated to supersonic speeds without melting them and then deposit on a surface to form a layer of a coating. Process parameters and materials affect the characteristics of manufactured parts and therefore must be chosen with care. Machine learning (ML) techniques have been specifically applied in additive manufacturing for tasks such as predicting and characterizing porosity. Machine learning algorithms can learn how a variation in the input spray parameters affects annotated output data, such as experimentally measured part properties. In this work, a dataset was developed from experiments reported in published academic papers, to train ML algorithms for the porosity prediction of cold spray manufactured parts. Data cleaning steps, such as null value replacement and categorical feature handling, were applied to prepare the dataset for the training of different ML models. The dataset was split into training and testing portions, and floating feature selection and hyperparameter optimization were performed using parts of the training set. A final evaluation of all trained models, using the test portion of the dataset, showed that a prediction accuracy with an average deviation of 0-2% porosity of the predicted values compared to the true values can be achieved. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has garnered considerable attention from academic and industrial circles as a pivotal technology in recent years. The escalation of security risks is observed to be associated with the growing interest in IoT applications. Intrusion detection systems (IDS) have been devised as viable instruments for identifying and averting malicious actions in this context. Several techniques described in academic papers are thought to be very accurate, but they cannot be used in the real world because the datasets used to build and test the models do not accurately reflect and simulate the IoT network. Existing methods, on the other hand, deal with these issues, but they are not good enough for commercial use because of their lack of precision, low detection rate, receiver operating characteristic (ROC), and false acceptance rate (FAR). The effectiveness of these solutions is predominantly dependent on individual learners and is consequently influenced by the inherent limitations of each learning algorithm. This study introduces a new approach for detecting intrusion attacks in an IoT network, which involves the use of an ensemble learning technique based on gray wolf optimizer (GWO). The novelty of this study lies in the proposed voting gray wolf optimizer (GWO) ensemble model, which incorporates two crucial components: a traffic analyzer and a classification phase engine. The model employs a voting technique to combine the probability averages of the base learners. Secondly, the combination of feature selection and feature extraction techniques is to reduce dimensionality. Thirdly, the utilization of GWO is employed to optimize the parameters of ensemble models. Similarly, the approach employs the most authentic intrusion detection datasets that are accessible and amalgamates multiple learners to generate ensemble learners. The hybridization of information gain (IG) and principal component analysis (PCA) was employed to reduce dimensionality. The study utilized a novel GWO ensemble learning approach that incorporated a decision tree, random forest, K-nearest neighbor, and multilayer perceptron for classification. To evaluate the efficacy of the proposed model, two authentic datasets, namely, BoT-IoT and UNSW-NB15, were scrutinized. The GWO-optimized ensemble model demonstrates superior accuracy when compared to other machine learning-based and deep learning models. Specifically, the model achieves an accuracy rate of 99.98%, a DR of 99.97%, a precision rate of 99.94%, an ROC rate of 99.99%, and an FAR rate of 1.30 on the BoT-IoT dataset. According to the experimental results, the proposed ensemble model optimized by GWO achieved an accuracy of 100%, a DR of 99.9%, a precision of 99.59%, an ROC of 99.40%, and an FAR of 1.5 when tested on the UNSW-NB15 dataset. [ABSTRACT FROM AUTHOR]