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In recent years, some researchers have combined deep learning methods such as semantic segmentation with a visual SLAM to improve the performance of classical visual SLAM. However, the above method introduces the uncertainty of the neural network model. To solve the above problems, an improved feature selection method based on information entropy and feature semantic uncertainty is proposed in this paper. The former is used to obtain fewer and higher quality feature points, while the latter is used to correct the uncertainty of the network in feature selection. At the same time, in the initial stage of feature point selection, this paper first filters and eliminates the absolute dynamic object feature points in the a priori information provided by the feature point semantic label. Secondly, the potential static objects can be detected combined with the principle of epipolar geometric constraints. Finally, the semantic uncertainty of features is corrected according to the semantic context. Experiments on the KITTI odometer data set show that compared with SIVO, the translation error is reduced by 12.63% and the rotation error is reduced by 22.09%, indicating that our method has better tracking performance than the baseline method. [ABSTRACT FROM AUTHOR]

Sentiment analysis involves extricating and interpreting people's views, feelings, beliefs, etc., about diverse actualities such as services, goods, and topics. People intend to investigate the users' opinions on the online platform to achieve better performance. Regardless, the high-dimensional feature set in an online review study affects the interpretation of classification. Several studies have implemented different feature selection techniques; however, getting a high accuracy with a very minimal number of features is yet to be accomplished. This paper develops an effective hybrid approach based on an enhanced genetic algorithm (GA) and analysis of variance (ANOVA) to achieve this purpose. To beat the local minima convergence problem, this paper uses a unique two-phase crossover and impressive selection approach, gaining high exploration and fast convergence of the model. The use of ANOVA drastically reduces the feature size to minimize the computational burden of the model. Experiments are performed to estimate the algorithm performance using different conventional classifiers and algorithms like GA, Particle Swarm Optimization (PSO), Recursive Feature Elimination (RFE), Random Forest, ExtraTree, AdaBoost, GradientBoost, and XGBoost. The proposed novel approach gives impressive results using the Amazon Review dataset with an accuracy of 78.60 %, F1 score of 79.38 %, and an average precision of 0.87, and the Restaurant Customer Review dataset with an accuracy of 77.70 %, F1 score of 78.24 %, and average precision of 0.89 as compared to other existing algorithms. The result shows that the proposed model outperforms other algorithms with nearly 45 and 42% fewer features for the Amazon Review and Restaurant Customer Review datasets. [ABSTRACT FROM AUTHOR]

Predictive maintenance has revealed as one of the paradigms of Industry 4.0. This paper addresses a complete system for the acquisition, computing, monitoring and communication of ventilation equipment in underground tunnels based on TCP/IP protocol and accessible via WEB services. Not only does the proposed system collect different sensor data (temperatures, vibrations, pressures, tilt angles or rotational speed), it performs local data processing as well. This feature is the newest and most important of all those provided by the system design, and there is no equipment that offers a similar performance in current ventilation systems. This paper shows the design and implementation of the equipment (system architecture and processing), as well as the experimental results obtained. [ABSTRACT FROM AUTHOR]

The management of diabetes involves much compliance, disease awareness, and patient empowerment. Blood glucose levels in people with diabetes are abnormal, resulting in various health problems that affect their kidneys, hearts, and vision. Monitoring the patient's health has the benefit of using the Internet of Things (IoT) and diabetes patient monitoring systems. Because this system uses machine learning to classify data, its value goes beyond patient monitoring. So, this paper proposes a novel deep learning model with an effective feature selection mechanism for diabetes mellitus (DM) prediction in IoT-based healthcare environment. The proposed work starts by collecting the real-time IoT data of the patients from the Pima Indian Diabetes database. The data imbalance problem of the collected dataset is rectified by applying the synthetic minority oversampling technique. Afterward, the balanced dataset is preprocessed by applying missing value imputation and data standardization to improve the classification performance. Then, the hybrid algorithm called k-means clustering-based sailfish optimization is utilized, which performs clustering and optimization to select the essential features from the preprocessed dataset. Finally, the selected features are fed into the kaiming and switan included bidirectional long short-term memory for DM prediction. The proposed system achieves better results than the existing state-of-the-art techniques, which shows its effectiveness in predicting DM in real-time IoT datasets. [ABSTRACT FROM AUTHOR]

The salp swarm algorithm (SSA) is a well-known optimization algorithm that is increasingly being utilized to solve many sorts of optimization problems. However, SSA may converge to sub-optimal solutions when it is applied to discrete problems such as the feature selection (FS) problem. This paper presents the enhanced opposition-based learning salp swarm algorithm (EOSSA), which is an improved SSA algorithm for solving the FS problem in intrusion detection systems (IDS). EOSSA incorporates four improvements into the original SSA algorithm. Firstly, the opposition-based learning (OBL) method is used in the initialization step of SSA to boost its population diversity. Secondly, the Elite opposition-based learning (EOBL) is used in the improvement loop of SSA to improve its exploration ability. Third, a variable neighborhood search (VNS) method is used in the improvement loop of SSA to improve its exploration mechanism to improve the local search space. Lastly, the Sigmoid binary transform function is used to convert the continuous candidate solutions produced by SSA into discrete binary solutions. EOSSA was evaluated against eighteen popular optimization algorithms (e.g., improved salp swarm algorithm based on opposition-based learning (ISSA), SSA, particle swarm algorithm (PSO), cuckoo search (CS), bat algorithm (BA), and Harris Hawk Optimization (HHO)) using eleven popular intrusion detection datasets (CICIDS2017, CSE-CIC-IDS2018, CICDDOS2019, CIRA-CIC-DoH, Intrusion detection 2018, UNSW-NB15, NSL-KDD, Phishing Legitimate, Malmem2022, IoT, and LUFlow Network) to Detect IoT Botnet Attacks. Moreover, EOSSA was compared with four machine learning algorithms (Decision Tree (DT), logistic regression (LR), Naive Bayes (NB), and K-Nearest Neighborhood (KNN)). The overall simulation results suggested that the proposed method is superior to the other algorithms in terms of the accuracy and number of selected features. The statistical analysis of the simulation results using the Friedman and Wilcoxon signed-rank test confirms the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Optimization techniques play a crucial role in improving the performance of machine learning applications. However, traditional techniques may not be suitable for addressing complex problems that involve numerous parameters requiring adaptation. This paper presents a novel variant of the gradient-based optimizer (GBO) and investigates the effectiveness of integrating the harmony search (HS) algorithm to enhance the GBO algorithm. The HS algorithm offers several advantages, including fast convergence and fewer adjustable parameters. The proposed method, called GBOHS, is evaluated through three experiments. The first experiment focuses on solving global optimization problems, while the second experiment assesses its ability to select the most relevant features using sixteen benchmark feature selection datasets. The final experiment applies the GBOHS method to solve five real engineering problems. The results are compared against those of well-known optimization algorithms using various performance measures, including fitness function value and classification accuracy. The findings demonstrate that the proposed GBOHS method achieves high accuracy and consistently outperforms the compared methods across all the experiments, demonstrating its promising effectiveness. [ABSTRACT FROM AUTHOR]

Exascale computing, the next-generation computing environment, is expected to be applied to scientific and engineering applications. Accordingly, high-performance computing (HPC) technology is also being developed to improve the performance and high-speed parallelism of many-core processors. Previous researches on improving HPC performance have developed in the form of improving the overall system performance by analyzing the state of the system occurring in the range of the knowledge of expert. However, performance events occurring in a processor in a many-core environment have a large number of indicators, and it is difficult to analyze the correlation between them. In this paper, we propose an application-specific feature selection and clustering approach with HPC system profiling data. The proposed approach performs PCA-based feature selections for efficient performance analysis methods. In addition, the application-specific characteristics from profiling data can be analyzed by unsupervised learning. In our experiments, we evaluated highly parallel supercomputers with NAS parallel benchmark and were able to cluster applications efficiently. [ABSTRACT FROM AUTHOR]

High-dimensional imbalanced biomedical data has dual characteristics of high-dimensional and imbalanced distribution. It is important to improve classification accuracy by filtering out low-dimensional feature subsets that are highly correlated with the classification target and have minimal mutual redundancy. However, traditional feature selection algorithms tend to select the feature subset that is favorable to class with large sample size, resulting in poor classification performance for minority samples. In response to the above problems, the P-AdaBoost-PAUC algorithm is proposed to be applied to high-dimensional imbalanced biomedical data classification. The idea of P-AdaBoost-PAUC algorithm has two major contributions. The first is that an improved decision tree attribute optimization algorithm (DT-P) is proposed, which pays more attention to the correlation among attributes. The second is that an improved AdaBoost algorithm based on probabilistic AUC (AdaBoost-PAUC) is proposed, which comprehensively considers misclassification probability and AUC to pay more attention to minority samples. An ensemble algorithm for high-dimensional imbalanced biomedical data classification is formed, which is conducive to improve classification performance. Experimental results show that Recall, Specificity, F1, and AUC values of P-AdaBoost-PAUC ensemble algorithm have reached the highest values on datasets with different imbalance rate. Especially when the proportion of minority samples is only 12.6 % , Recall, Specificity, F1 and AUC values all reached above 0.95. And algorithm stability experiments show that P-AdaBoost-PAUC algorithm is more stable than other algorithms. Therefore, the P-AdaBoost-PAUC ensemble algorithm proposed in this paper improves classification performance of minority samples on high-dimensional imbalanced biomedical data to a certain extent. [ABSTRACT FROM AUTHOR]

One challenge of studying cloud workload traces is the lack of available users' identities. Therefore, clustering methods were used to address this challenge through extracting these identities from workload traces. For better extraction, it is beneficial to select attributes (columns in the traces) for clustering by using feature selection methods. However, the use of general selection methods requires details that are not available for workload traces (e.g. predefined number of clusters). Therefore, in this paper, we present an unsupervised feature selection method for cloud workload traces to identify good candidate attributes for clustering. This method uses Silhouette coefficients to rank attributes that are best for users' extraction through clustering. The performance of our SeQual method is evaluated in comparison with commonly used (supervised and unsupervised) feature selection methods with the help of clustering quality metrics (i.e. adjusted rand index, entropy and precision). The results show that the SeQual method can compete with the supervised methods and perform better than unsupervised ones, with an average accuracy between 90% and 99%. [ABSTRACT FROM AUTHOR]

The exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times. [ABSTRACT FROM AUTHOR]

Child Drawing Development Optimization is a recently developed metaheuristic algorithm that has been demonstrated to perform well on multiple benchmark tests. In this paper, a binary Child Drawing Development Optimization (BCDDO) is proposed for wrapper feature selection. The proposed BCDDO is utilized to choose a subset of important features to reach the highest classification accuracy. Harris Hawk optimization, salp swarm algorithm, gray wolf optimization, and whale optimization algorithm are utilized to evaluate the effectiveness and efficiency of the suggested feature selection method. In the field of feature selection to improve classification accuracy, the proposed method has gained a considerable classification accuracy advantage over previously mentioned methods. Four datasets are used in this research work; breast cancer, moderate COVID, big COVID, and Iris using XGBoost classifier and the classification accuracies were (98.83%, 98.75%, 99.36%, and 96%), respectively, for the four mentioned datasets. [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]

With the rapid development of Industry 4.0, the importance of cyber security for industrial control systems has become increasingly prominent. The complexity and diversity of industrial control systems result in data with high dimensionality and strong correlation, posing significant challenges in obtaining labeled data. However, current intrusion detection methods often demand large amounts of labeled data for effective training. To address this limitation, this paper proposes a semi-supervised anomaly detection framework, called SFSD, which leverages feature selection and deviation networks to detect anomalies in industrial control systems. Specifically, we introduce a feature selection algorithm (IG-PCA) that utilizes information gain and principal component analysis to reduce the dimensionality of features in industrial control data by eliminating redundant features. Then, we propose a semi-supervised learning method based on an improved deviation network, which utilizes an anomaly scoring network to learn end-to-end anomaly scores for the training data, thus assigning anomaly scores to each training data. Finally, using a limited amount of anomaly-labeled data, we design a specific deviation loss function to optimize the anomaly scoring network, enabling a significant score bias between positive and negative samples. Experimental results demonstrate that the proposed SFSD outperforms existing semi-supervised anomaly detection frameworks by improving the accuracy and detection rate by an average of 1–2%. Moreover, SFSD requires less training time compared to existing frameworks, resulting in a training time reduction of approximately 10% or more. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for robot navigation in high-dimensional environments that reduce the dimension of the state space using local and soft feature selection. The algorithm selects relevant features based on local correlations between states, avoiding duplicate inappropriate information and adjusting sensor values accordingly. By optimizing the value function approximation based on the local weighted features of states in the reinforcement learning process, the method shows improvements in the robot's motion flexibility, learning time, the distance traveled to reach its goal, and the minimization of collisions with obstacles. This approach was tested on an E-puck robot using the Webots robot simulator in different test environments. [ABSTRACT FROM AUTHOR]

Optimizing energy consumption in heterogeneous GPU clusters is of paramount importance to enhance overall system efficiency and reduce operational costs. However, the diversity of GPU types in heterogeneous GPU clusters poses challenges for energy optimization. In this paper, we propose an utilization-prediction-aware energy optimization approach for heterogeneous GPU clusters. We utilize a feature correlation-based method to select feature vectors and improve the prediction accuracy of our GPU utilization prediction model. By constructing a model for energy consumption and combining it with the predicted results, our approach can avoid wasting too much energy on idle GPUs and reduce the energy consumption of the heterogeneous GPU cluster. To validate our approach, experiments are conducted on real Alibaba data. The results show that our model achieved a good performance in predicting the utilization for four types of GPUs, with RMSE, MAE and R 2 average values of 7.08, 3.81 and 0.93, respectively. Moreover, we calculate the energy consumption of the GPU cluster before and after the adjustment, and estimate the energy savings, which amount to an average of 35.55%. [ABSTRACT FROM AUTHOR]

Microarray data have a crucial role in identifying and classifying different types of cancer tissues. In cancer research, high dimensional of microarray datasets has always caused problems in the design of classifiers. Thus, microarray data before classification are preprocessed through feature selection (FS) techniques, whereby the features with lower information values are discarded away. Traditional FS as search involves selecting a subset of features from large number of features. On the other hand, other subsets of features also contain information, and the multimodal property of FS problem should be considered. In multimodal metaheuristic searches, instead of one solution, several semi-optimized solutions are found, where each solution is an independent view compared to others. In this paper, in the first stage, multimodal FS is performed using multimodal firefly algorithm (FA), where several feature subsets are obtained. Then, in the second stage, this subset of features is used for assigning weight to the classifiers and identifying the effective classifiers in the ensemble process based on Dempster–Shafer evidence theory. The mentioned weights are calculated so that an ensemble could be developed through weighted majority vote. Eventually, the proposed algorithm is evaluated on microarray data for cancer diagnosis. The proposed method efficiency is evaluated by applying on 11 microarray datasets. The obtained results indicated good performance of the multimodal FS method in relation to other methods compared. [ABSTRACT FROM AUTHOR]

This study aims to use a machine learning (ML)-based enhanced diagnosis and survival model to predict heart disease and survival in heart failure by combining the cuckoo search (CS), flower pollination algorithm (FPA), whale optimization algorithm (WOA), and Harris hawks optimization (HHO) algorithms, which are meta-heuristic feature selection algorithms. To achieve this, experiments are conducted on the Cleveland heart disease dataset and the heart failure dataset collected from the Faisalabad Institute of Cardiology published at UCI. CS, FPA, WOA, and HHO algorithms for feature selection are applied for different population sizes and are realized based on the best fitness values. For the original dataset of heart disease, the maximum prediction F-score of 88% is obtained using K-nearest neighbour (KNN) when compared to logistic regression (LR), support vector machine (SVM), Gaussian Naive Bayes (GNB), and random forest (RF). With the proposed approach, the heart disease prediction F-score of 99.72% is obtained using KNN for population sizes 60 with FPA by selecting eight features. For the original dataset of heart failure, the maximum prediction F-score of 70% is obtained using LR and RF compared to SVM, GNB, and KNN. With the proposed approach, the heart failure prediction F-score of 97.45% is obtained using KNN for population sizes 10 with HHO by selecting five features. Experimental findings show that the applied meta-heuristic algorithms with ML algorithms significantly improve prediction performances compared to performances obtained from the original datasets. The motivation of this paper is to select the most critical and informative feature subset through meta-heuristic algorithms to improve classification accuracy. [ABSTRACT FROM AUTHOR]

The social internet of things (SIoT) facilitates numerous networking services and novel applications for the IoT, making it more productive and powerful. The state-of-the-art machine learning architectures often face different challenges when processing big data such as increased memory volume, increased training time, and higher computational costs. To tackle these challenges, this paper presents a novel framework for big data classification. Initially, an adaptive filter removes unwanted data and noises. The dimensionality of the filter data is then reduced via the Hadoop map reducer. After that, the battle royale optimization (BRO) algorithm is applied for feature selection. The optimal feature selection process performed by the BRO algorithm improves the accuracy and performance of the proposed fuzzy optimal deep convolutional neural network (FO-DCNN). The FO-DCNN model is formed by integrating the fuzzy-based remora optimization (F-RO) algorithm and deep CNN architecture for robust SIoT data classification. When evaluated using the Twitter, Rotten tomato, skin disease, diabetes, and hepatitis datasets, the proposed model offers improvements in terms of classification accuracy, memory consumption, and computational time. [ABSTRACT FROM AUTHOR]

This paper develops a new architecture for bearing fault diagnosis based on independent vector analysis, feature selection, and extreme learning machines classifiers. The suggested method applied to vibration signals includes the following steps: First, the independent vector analysis is introduced to separate vibration signal components from each other. Second, statistical parameters are extracted from all the obtained sources. Then three binary optimisation algorithms such as binary bat algorithm, binary particle swarm optimisation and binary grey wolf optimisation are employed for feature selection one by one. Finally, three classifiers based on extreme learning, artificial neural networks and random forest are used to perform the classification step. The obtained results show that independent vector analysis followed by feature selection based on binary grey wolf optimisation and classification using an extreme learning machine provides an optimal input vector which contains only five features for each sample and a very small misdiagnosis rate equal to 0.76%. The obtained results also prove that the suggested methodology gives the best classification results and high visibility compared to the other studied approaches. [ABSTRACT FROM AUTHOR]

Feature selection plays a very significant role for the success of pattern recognition and data mining. Based on the maximal relevance and minimal redundancy (mRMR) method, combined with feature subset, this paper proposes an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on feature subset. In ImRMR, the Pearson correlation coefficient and mutual information are first used to measure the relevance of a single feature to the sample category, and a factor is introduced to adjust the weights of the two measurement criteria. And an equal grouping method is exploited to generate candidate feature subsets according to the ranking features. Then, the relevance and redundancy of candidate feature subsets are calculated and the ordered sequence of these feature subsets is gained by incremental search method. Finally, the final optimal feature subset is obtained from these feature subsets by combining the sequence forward search method and the classification learning algorithm. Experiments are conducted on seven datasets. The results show that ImRMR can effectively remove irrelevant and redundant features, which can not only reduce the dimension of sample features and time of model training and prediction, but also improve the classification performance. [ABSTRACT FROM AUTHOR]

Researchers typically increase training data to improve neural net predictive capabilities, but this method is infeasible when data or compute resources are limited. This paper extends previous research that used long short-term memory–fully convolutional networks to identify aircraft engine types from publicly available automatic dependent surveillance-broadcast (ADS-B) data. This research designs two experiments that vary the amount of training data samples and input features to determine the impact on the predictive power of the ADS-B classification model. The first experiment varies the number of training data observations from a limited feature set and results in 83.9% accuracy (within 10% of previous efforts with only 25% of the data). The findings show that feature selection and data quality lead to higher classification accuracy than data quantity. The second experiment accepted all ADS-B feature combinations and determined that airspeed, barometric pressure, and vertical speed had the most impact on aircraft engine type prediction. [ABSTRACT FROM AUTHOR]

Present heterogeneous architectures interconnect nodes including multiple multi-core microprocessors and accelerators that allow different strategies to accelerate the applications and optimize their energy consumption according to the specific power-performance trade-offs. In this paper, a multilevel parallel procedure is proposed to take advantage of all nodes of a heterogeneous CPU–GPU cluster. Two more alternatives have been implemented, and experimentally compared and analyzed from both running time and energy consumption. Although the paper considers an evolutionary master–worker algorithm for feature selection in EEG classification, the conclusions from the experimental analysis here provided can be frequently applied, as many other useful bioinformatics and data mining applications show the same master–worker profile than the classification problem here considered. Our parallel approach allows to reduce the time by a factor of up to 83, with only about a 4.9% of energy consumed by the sequential procedure, in a cluster with 36 CPU cores and 43 GPU compute units. [ABSTRACT FROM AUTHOR]

Criminal activities generate an estimated $2 trillion in laundered money per year, highlighting the need for financial institutions to detect and report suspicious activity to protect their reputation. However, rule-based models commonly used for this purpose generate a high number of false positives, draining compliance team time, and increasing investigation costs. However, the application of machine learning in conjunction with rule-based models presents noteworthy implications, encompassing the potential reduction in false positives and the concomitant risk of machine learning inadvertently suppressing true positive alerts. This paper proposes a framework called automatic suppression based on XGBoost for anti-money laundering (ASXAML) to enhance detection by reducing false positives. ASXAML leverages recursive feature elimination with cross-validation for optimal feature selection. Subsequently, Optuna is employed to fine-tune hyperparameters for the XGBoost model. Results indicate that ASXAML achieves an optimal balance between reducing false positives and avoiding missed money laundering events, with an 86% F-beta score and only 11% money laundering customers were incorrectly closed out of 1926 in the test data. [ABSTRACT FROM AUTHOR]

The accurate identification of bird image is of great significance to protecting the ecological environment and bird species diversity. To address the issue of low recognition accuracy arising from the similarities in features among different bird species and the susceptibility of shallow edge features to loss, this paper proposes a fine-grained bird image classification model that incorporates hierarchical feature fusion and counterfactual feature selection. The model is based on vision transformer and builds a hierarchical feature fusion module and a counterfactual feature enhancement module. The hierarchical feature fusion module superimposes shallow features rich in fine-grained information into deep features to improve the problem of lack of edge detail information in key features. The counterfactual feature enhancement module selects distinguishing features through counterfactual intervention to reduce classification errors caused by highly similar features of different species of birds. The experimental results show that the method can achieve 91.9 % and 91.4 % accuracy on two available datasets, CUB-200-2011 and NABirds, respectively, which is higher than the current mainstream fine-grained bird recognition algorithms and shows excellent classification performance. [ABSTRACT FROM AUTHOR]

Aspect terms are opinion targets for people to express and understand opinions in reviews. Aspect terms extraction is an essential subtask in aspect-level sentiment analysis. To extract aspect terms from a sentence, existing methods mainly focus on context features generated by pre-trained models. However, these models either neglect the crucial implicit linguistic features, e.g., post-of-tag, head, and head dependency, or fail to explore sufficient valuable features for aspect term extraction, which lead to the deficiency in aspect term extraction task. To address the challenges, in this paper, we propose a novel and effective framework for aspect term extraction by integrating both contextual and linguistic features with the artificial bee colony-based feature selection method. Firstly, a novel variant of artificial bee colony is designed to identify the most valuable linguistic features to reduce the high sparsity and dimensionality of the raw dataset. Next, the selected features and context embeddings are integrated to improve the performance of aspect extraction. Finally, extensive experiments are conducted on real-world datasets, and the results exhibit that our proposed framework can outperform the competitive baselines. Compared with the latest baselines, the proposed framework achieves the comparatively higher F1 scores of 80.7%, 84.7%, 72.2%, and 74.8% on the four groups of datasets. Furthermore, the ablation study shows that the proposed method with the designed feature selection module significantly outperforms the method with the original artificial bee colony, having 4.15%, 4.4%, 4.4%, and 3.2% improvements in F1 score on all the four datasets, respectively. [ABSTRACT FROM AUTHOR]

Advancements in high-speed computer technology play an ever-increasing role in analyzing various types and massive size data. However, handling big high-dimensional data sets is a challenge in terms of computational storage and capacity. Through feature selection methods, data dimensions can be reduced by eliminating the dummy variables, allowing for more extensive analysis. In this paper, data are classified based on the ratio of response into two types: balanced (almost the same ratio for each class) and partially balanced (consists of majority and minority, with virtually the same ratio for minority classes). Performance comparisons of various feature selection methods for balanced and partially balanced data are provided. This approach will help in selecting sampling strategy and feature selection methods that perform well while utilizing appropriate resources for high-dimensional data. [ABSTRACT FROM AUTHOR]

In the past decade, social media networks have received much attention among ordinary people, agencies, and research scholars. Twitter is one of the fastest-growing social media tools. By means of the Twitter application on smartphones, users are able to immediately report events happening around them on a real-time basis. The information disseminated by millions of active users every day generates a new version of a dynamic database that contains information about various topics. Twitter data can be utilized as a major traffic data source along with conventional sensors. In this aspect, this paper presents a novel firefly algorithm-based feature selection with a deep learning model for traffic flow analysis (FFAFS-DLTFA) using Twitter data. The goal of FFAFS-DLTFA is to determine the class labels for tweets as relevant to traffic events. The proposed FFAFS-DLTFA encompasses several processes, such as preprocessing, feature extraction, feature selection, and classification. Primarily, tweets are preprocessed in several ways, such as tokenization, removal of stop words, and stemming. At the same time, three types of embedding vectors, unigram, bigram, and POS features, are used. In addition, the firefly algorithm (FFA) is applied for the optimal selection of feature subsets. Finally, a deep neural network (DNN) model is applied for the identification of tweets into three classes, namely, positive, neutral, and negative. The performance validation of FFAFS-DLTFA takes place using the benchmark Kaggle repository, and the results are inspected under different aspects. The experimental values demonstrate the better performance of FFAFS-DLTFA on the other techniques with the maximum accuracy of 98.83%. [ABSTRACT FROM AUTHOR]

Multi-label learning deals with data in which an instance may belong to multiple class labels simultaneously. Due to the increasing applications of multi-label data, especially in text and image classification problems, multi-label feature selection has attracted much attention in recent years. The main problem of the existing multi-label feature selection algorithms is that they are not able to consider all possible subsets of feature space in evaluating a candidate feature, because of computational concerns. Therefore they ignore much of the useful higher-order information, hidden in larger subsets. This paper proposes a new approach to address the higher-order relevance and redundancy analysis. The proposed method generates an adjacency matrix using pairwise features relevance and redundancy then uses the geometric series of the matrix as a part of the evaluation function. In this way it is able to consider all possible subsets of feature space in evaluating a single candidate feature. Experimental results on five benchmarks from different domains clearly demonstrate the superiority of the proposed method against seven state-of-the-art multi-label feature selection algorithms. The codes are available at https://github.com/Sadegh28/Inf-MLFS. [ABSTRACT FROM AUTHOR]

The use of current data management technologies for the Internet of Things (IoT) is not straightforward due to interface heterogeneity, highly complex and potentially unprotected conditions, and the huge scale of the network. A vast number of devices and sensors around the world are connected to wireless networks, making the data transmitted to and from them vulnerable to attack. In addition, data sent through such devices can be unrelated, duplicated or inaccurate, making it impossible to execute the necessary tasks. Therefore, the transmitted data must be screened and chosen to fit the issue being handled in order to maintain the best possible degree of protection and thereby keep devices free from any data that could pose a threat. Reducing the size of the data often contributes to an increase in the level of communication between different networks, which in turn leads to a boost in the overall performance of the wireless network infrastructure. The process of obtaining only the relevant features from all of the data in a dataset for use in a particular task is known as feature selection (FS). In this paper, a new FS wrapper model is proposed that uses a shuffled shepherd optimization (SSO) algorithm as a search mechanism for the problem space and a K-nearest neighbor classifier to solve IoT problems with FS. Shuffled shepherd optimization was applied in a basic form as well as in two hybrid models with simulated annealing (SA) named SSO-SA1 and SSO-SA2. In order to test the performance of the three proposed models, they were applied to nine well-known IoT datasets. All the obtained results indicated that the two hybrids SSO-SA1 and SSO-SA2 enhanced the search capability of the original SSO algorithm. In comparison experiments, the SSO-SA2 outperformed three FS wrapper approaches, namely, EPC, MOPSO and MOPSO-Lévy. The SSO-SA2 surpassed the other methods in six out of the nine IoT benchmark datasets with an accuracy rate of 0.987. As for number of selected features, the SSO-SA2 surpassed the other methods in five out of the nine datasets with a selection size rate of 39.11 features. Furthermore, the wrapper-based SSO-SA2 surpassed four filter methods, namely, ReliefF, correlation, information gain and symmetrical. [ABSTRACT FROM AUTHOR]

Several organizations are implementing large-scale Internet of Things (IoT)-based ecosystems, such as Industrial IoT (IIoT). Such systems are vulnerable to new threats and intrusions because of the nature of their networks. It is necessary to secure such systems by developing feature selection integrated with robust machine learning models. In this paper, a two-phase IIoT traffic prediction model is built for detecting normal and anomalous IIoT behavior. The system is divided into two phases. In the first phase, multi-objective non-dominated sorting with whale optimization approach (MNSWOA) and ideal point method (IPM) is implemented for feature selection. The key quality attributes are retrieved for effective classification. In the second phase, the feature selection results are sent to the random forest (RF) classifier for traffic prediction. Experimental results on IIoT datasets, namely CTU-13, AWID and Gas–Water show that the proposed technique MNSWOA–IPM–RF obtains enhanced performance in terms of accuracy, true positive rate, true negative rate and area under the ROC curve. Also, comparisons indicate that the proposed MNSWOA–IPM–RF model has superior results than benchmark multi-objective optimization techniques. [ABSTRACT FROM AUTHOR]

Feature selection by removing redundant and noisy features is one of the crucial steps in the classification problem. This paper presents a novel chaotic-based divide-and-conquer (CDC) algorithm to select optimal features from an available feature set (the UCI Arrhythmia Dataset). We then employed it for a quick and automatic heart function examination which is essential for monitoring the heart functionality of risky patients. The method begins with chaos numbers to select several features as cluster-heads. We used chaos sequences to escape from the dependency on initial values and getting stuck in local optima. Then, it assigns each feature to a group of cluster-heads and finally selects a representative from each group. The proposed method resulted in performance rates of 88.21%, 89.41%, 87.64%, and 86.54% in terms of accuracy, sensitivity, specificity, and F-measure, respectively. Since this method removes the redundant or improper features of the dataset without any data loss, it approximately needs 0.6 seconds to diagnose and classify cardiac arrhythmias. It is highly time-effective compared to the current state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify user's psychological states. In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information. [ABSTRACT FROM AUTHOR]

The selection of text features is a fundamental task and plays an important role in digital document analysis. Conventional methods in text feature extraction necessitate indigenous features. Obtaining an efficient feature is an extensive process, but a new and real-time representation of features in text data is a challenging task. Deep learning is making inroads in digital document mining. A significant distinction between deep learning and traditional methods is that deep learning learns features in a digital document in an automatic manner. In this paper, logistic regression and deep dependency parsing (LR-DDP) methods are proposed. The logistic regression token generation model generates robust tokens by means of Napierian grammar. With the robust generated tokens, a deep transition-based dependency parsing using duplex long short-term memory is designed. Experimental results demonstrate that our dependency parser achieves comparable performance in terms of digital document parsing accuracy, parsing time and overhead when compared to existing methods. Hence, these methods are found to be computationally efficient and accurate. [ABSTRACT FROM AUTHOR]

The naive Bayes classifier is a widely used text classification method that applies statistical theory to text classification. Due to the particularity of the text, related feature items may generate new semantic information, which may be lost when the traditional vector space model represents text. This paper mainly studies the construction and improvement of distributed naive Bayes automatic classification system. The application of Hadoop cloud computing in web page classification is one of the focuses of this article. Firstly, the text classification system and Bayesian classification model are analyzed and discussed, including the representation and extraction of text information, text classification methods and Bayesian text classification methods. Then, in view of the shortcomings of the above-mentioned naive Bayesian text classification method, when training text, we use the mutual information method to check the correlation between the feature sets generated after feature selection, and then combine the features with higher correlation degree appropriately. Through a series of tests, the experimental data show that the improved text classification system can achieve better classification results. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a major chronic autoimmune disorder which affects multiple organs and joints of human body. Disease varies in its behavior and concern such that an early prediction is a complex process with regard to time so the diagnosis is not an easy task for the physicians. The common existing methodologies employed to analyze the severity of RA are the clinical, laboratory and physical examinations. The advancement of data mining has been employed for the RA diagnosis through learning from history of datasets. To improve the efficiency and reliability of the approach, this paper presents a hybrid optimization strategy called REACT, which is based on the combination of the features of Iterative Dichotomiser 3 and Particle Swarm Optimization for feature selection and classification of RA. The effectiveness of the proposed diagnosis strategy is validated through its prediction accuracy, specificity, sensitivity, positive predictive value and negative predictive value with existing approaches. [ABSTRACT FROM AUTHOR]

Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms Search (SOS) is one of the most successful meta-heuristic algorithms inspired by organisms' interaction in nature called mutualism, commensalism, and parasitism. In this paper, three SOS-based binary approaches are offered to solve the feature selection problem. In the first and second approaches, several S-shaped transfer functions and several Chaotic Tent Function-based V-shaped transfer functions called BSOSST and BSOSVT are used to make the binary SOS (BSOS). In the third approach, an advanced BSOS based on changing SOS and the chaotic Tent function operators called EBCSOS is provided. The EBCSOS algorithm uses the chaotic Tent function and the Gaussian mutation to increase usefulness and exploration. Moreover, two new operators, i.e., BMPT and BCPT, are suggested to make the commensalism and mutualism stage binary based on a chaotic function to solve the feature selection problem. Finally, the proposed BSOSST and BSOSVT methods and the advanced version of EBCSOS were implemented on 25 datasets than the basic algorithm's binary meta-heuristic algorithms. Various experiments demonstrated that the proposed EBCSOS algorithm outperformed other methods in terms of several features and accuracy. To further confirm the proposed EBCSOS algorithm, the problem of detecting spam E-mails was applied, with the results of this experiment indicating that the proposed EBCSOS algorithm significantly improved the accuracy and speed of all categories in detecting spam E-mails. [ABSTRACT FROM AUTHOR]

Malware detection from the smartphone has become a challenging issue for academicians and researchers. In this research paper, we applied five distinct machine learning algorithms and three different ensemble methods to develop a model for detecting malware from an Android-based smartphone. In this study, we proposed a framework that helps in selecting the right sets of the feature with an aim to improve the performance of the malware detection models. The proposed malware detection framework is then validated by considering two distinct performance parameters, i.e., accuracy and F-measure as a benchmark to detect malware from real-world apps. We performed an empirical study on thirty different categories of Android apps. The experimental data set consists of 1,94,659 benign apps and 67,538 malware apps that are collected from different promised repositories. Empirical results reveal that the models developed by using the proposed feature selection framework are able to detect more malware-infected apps when compared to all extracted feature sets. Moreover, the malware detection model build by using nonlinear ensemble decision tree forest (NDTF) approach is achieved a detection rate of 98.8%. In addition to that, the proposed malware detection framework is more effective in detecting malware-infected apps as compared to different anti-virus scanners and different frameworks or approaches developed in the literature. [ABSTRACT FROM AUTHOR]

Class imbalance has become a big problem that leads to inaccurate traffic classification. Accurate traffic classification of traffic flows helps us in security monitoring, IP management, intrusion detection, etc. To address the traffic classification problem, in literature, machine learning (ML) approaches are widely used. Therefore, in this paper, we also proposed an ML-based hybrid feature selection algorithm named WMI_AUC that make use of two metrics: weighted mutual information (WMI) metric and area under ROC curve (AUC). These metrics select effective features from a traffic flow. However, in order to select robust features from the selected features, we proposed robust features selection algorithm. The proposed approach increases the accuracy of ML classifiers and helps in detecting malicious traffic. We evaluate our work using 11 well-known ML classifiers on the different network environment traces datasets. Experimental results showed that our algorithms achieve more than 95% flow accuracy results. [ABSTRACT FROM AUTHOR]

The text clustering technique is an appropriate method used to partition a huge amount of text documents into groups. The documents size affects the text clustering by decreasing its performance. Subsequently, text documents contain sparse and uninformative features, which reduce the performance of the underlying text clustering algorithm and increase the computational time. Feature selection is a fundamental unsupervised learning technique used to select a new subset of informative text features to improve the performance of the text clustering and reduce the computational time. This paper proposes a hybrid of particle swarm optimization algorithm with genetic operators for the feature selection problem. The k-means clustering is used to evaluate the effectiveness of the obtained features subsets. The experiments were conducted using eight common text datasets with variant characteristics. The results show that the proposed algorithm hybrid algorithm (H-FSPSOTC) improved the performance of the clustering algorithm by generating a new subset of more informative features. The proposed algorithm is compared with the other comparative algorithms published in the literature. Finally, the feature selection technique encourages the clustering algorithm to obtain accurate clusters. [ABSTRACT FROM AUTHOR]

It is of great significance and practical application value to extract land-cover type accurately. However, the input data usually used in classification such as reflectance data or vegetation index are very simple and quantitative remote sensing products are rarely used. In this paper, a multi-spectral land-cover classification method based on deep learning is proposed. Using the excellent detail capture ability of contourlet transform to obtain the potential information to supplement the spectral feature space, combined with deep learning for feature selection and feature extraction, a spectral–texture classification model is constructed. The multi-spectral sensing remote data and field measurement data in Dadukou District of Chongqing, northern Negev, and Changping region of Beijing were used for evaluation. Experiment results show the proposed method can outperform principal component analysis, linear discriminant analysis and neural network, and effectively improve the classification accuracy of multi-spectral images; this method provides a new perspective for land-use classification. [ABSTRACT FROM AUTHOR]

Nowadays, deep learning is one of the popular research areas of the computer sciences, and many deep networks have been proposed to solve artificial intelligence and machine learning problems. Residual networks (ResNet) for instance ResNet18, ResNet50 and ResNet101 are widely used deep network in the literature. In this paper, a novel ResNet-based signal recognition method is presented. In this study, ResNet18, ResNet50 and ResNet101 are utilized as feature extractor and each network extracts 1000 features. The extracted features are concatenated, and 3000 features are obtained. In the feature selection phase, 1000 most discriminative features are selected using ReliefF, and these selected features are used as input for the third-degree polynomial (cubic) activation-based support vector machine. The proposed method achieved 99.96% and 99.61% classification accuracy rates for gender and activity recognitions, respectively. These results clearly demonstrate that the proposed pre-trained ensemble ResNet-based method achieved high success rate for sensors signals. [ABSTRACT FROM AUTHOR]

In the field of computational biology, prediction of high-resolution protein structure is regarded as a major challenge. Physical and chemical properties of the protein structure determine its quality and differentiate native structures from predicted structures. Various machine learning classification models are studied with six physical and chemical properties to classify the root mean square deviation of the protein structure. This work proposes an improved version of a meta-heuristic technique named grey wolf optimization (IGWO), which is an extension of traditional grey wolf optimization (GWO) for the feature selection. The proposed novel IGWO ascertains optimal subset of features, and further, four machine learning classifiers have been used for efficient prediction of protein structure. Artificial neural network classifier predicts the protein structure with a maximum approximate accuracy of 91%. The experimental result reveals that the proposed meta-heuristic technique is stable enough to maximize the accuracy and minimize the number of optimal features. In this paper, the result of the proposed technique has been compared with other related evolutionary techniques and the proposed optimizer outperforms all other techniques. The dataset used in the study is available at http://bit.ly/RMSDClassification-DS. [ABSTRACT FROM AUTHOR]

Incomplete data imputation plays an important role in big data analysis and smart computing. Existing algorithms are of low efficiency and effectiveness in imputing incomplete high-dimensional data. The paper proposes an incomplete high-dimensional data imputation algorithm based on feature selection and cluster analysis (IHDIFC), which works in three steps. First, a hierarchical clustering-based feature subset selection algorithm is designed to reduce the dimensions of the data set. Second, a parallel $$k$$ -means algorithm based on partial distance is derived to cluster the selected data subset efficiently. Finally, the data objects in the same cluster with the target are utilized to estimate its missing feature values. Extensive experiments are carried out to compare IHDIFC to two representative missing data imputation algorithms, namely FIMUS and DMI. The results demonstrate that the proposed algorithm achieves better imputation accuracy and takes significantly less time than other algorithms for imputing high-dimensional data. [ABSTRACT FROM AUTHOR]

As known, the artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honey bee swarm that has been proven its efficacy and successfully applied to a large number of practical problems. Aiming at the trade-off between convergence speed and precocity of ABC algorithm with elite-guided search equations (ABC_elite), an enhanced version, namely EABC_elite, is proposed in this paper, and the improvements are twofold. As the global best (gbest) solution is introduced to the search equation and acceleration of the convergence in the bee phase of EABC_elite, the former in the ordinary solution is embodied to the search equation yet balance the gbest's ability. The enhancement to the global search by making the information of gbest and ordinary solutions be adequately used while keeping the exploration–exploitation balance well maintained, the usual solution is introduced to the search equation to avoid the precocity problem in the onlooker bee phase of EABC_elite as the latter one. Experimental analysis and evaluations of EABC_elite against several state-of-the-art variants of the ABC algorithm demonstrate that the EABC_elite is significantly better than the compared algorithms in the feature selection problem. Also, the proposed EABC_elite algorithm is modified to combine the K-means initialization strategy and chaotic parameters strategy to further enhance the global search of EABC_elite for data clustering. Experimental results show that the derived EABC_elite clustering algorithm "Two-step EABC_elite," TEABC_elite for short, delivered better and promising results than previous works for data clustering. [ABSTRACT FROM AUTHOR]

Due to the prevalence of IoV technology, big data has increasingly been promoted as a revolutionary development in a variety of applications. Indeed, the received big data from IoV is valuable particularly for those involved in analyzing driver's behaviors. For instance, in the fleet management domain, fleet administrators are interested in fine-grained information about fleet usage, which is influenced by different driver usage patterns. In the vehicle insurance market, usage-based insurance or pay-as-you-drive schemes aim to adapt the insurance premium to individual driver behavior or even to provide various value-added services to policy holders. These applications can be expected to improve and to make safer the driving style of various individuals. Nowadays, big data analysis is becoming indispensable for automatic discovering of intelligence that is involved in the frequently occurring patterns and hidden rules. It is essential and necessary to study how to utilize these large-scale data. Regarding driving behaviors analysis, this paper presents a preliminary study based on feature selection and statistical approach. Feature selection is one of the important and frequently used techniques in data preprocessing for big data mining. Feature selection, as a dimensionality reduction technique, focuses on choosing a small subset of the significant features from the original data by removing irrelevant or redundant features. According to selection process, the most significant feature is vehicle speed for the collected vehicular data. Afterward, the statistical approach calculates skewness and dispersion in speed distribution as the statistical features for driving behaviors analysis. Finally, the established classification rules not only provide data-driven services and big data analytics but also offer training data samples for supervised machine learning algorithms. To validate the feasibility of the proposed method, over 150 drivers and more than 200,000 trips are verified in the simulation. As expected, experimental results are well matched with our observations. [ABSTRACT FROM AUTHOR]

Resource provisioning in cloud servers depends on future resource utilization of different jobs. As resource utilization trends vary dynamically, effective resource provisioning requires prediction of future resource utilization. The problem becomes more complicated as performance metrics related to one resource may depend on utilization of other resources also. In this paper, different multivariate frameworks are proposed for improving the future resource metric prediction in cloud. Different techniques for identifying the set of resource metrics relevant for the prediction of desired resource metric are analyzed. The proposed multivariate feature selection and prediction frameworks are validated for CPU utilization prediction in Google cluster trace. Joint analysis based on the prediction performance of the multivariate framework as well as its stability is used for selecting the most suitable feature selection framework. The results of the joint analysis indicate that features selected using the Granger causality technique perform best for multivariate resource usage prediction. [ABSTRACT FROM AUTHOR]

The fields of data science and data mining are enduring high-dimensionality issues because of a high volume of data. Conventional machine learning techniques give disgruntled responses to high-dimensional datasets. Feature selection is used to get the appropriate information from the dataset to reduce the dimensionality of the data. The recently proposed Salp Swarm Algorithm (SSA) is a population-based meta-heuristic optimization technique inspired by the Sea Salps Swarming technique. SSA failed to converge initial random solutions to the global optimum owing to its complete dependency on the number of iterations for the process of exploration and exploitation. The proposed improved SSA (iSSA) aims to enhance the ability of Salps to explore divergent areas by randomly updating its location. Randomizing the Salps location via Levy flight enriches the exploitation potential of SSA resulting in it converging the model toward the global optima. The performance of the proposed iSSA is investigated using six different high-dimensional microarray datasets. While comparing the ability to converge, it is understood that the proposed model outperforms SSA providing 0.1033% more confidence in the selected features. The results of the simulation revealed that the iSSA can provide better competitive and significant results compared to SSA. [ABSTRACT FROM AUTHOR]

Fog computing as a complementary paradigm to cloud computing is a heuristic shift in service delivery that promises a leap in efficiency and flexibility for cloud-based Internet of Things applications. The performance characteristics of cloud/fog computing attract significant attention from researchers lately. One of the critical challenges in this field is controlling and reducing the massive amount of energy consumption in the cloudlets while still maintaining the Service Level Agreement's performance requirements. Many virtual machine (VM) allocation and consolidation strategies are investigated to address the challenges mentioned earlier. However, many of the solutions save energy at the cost of performance degradation. This paper proposes a novel multi-step VM allocation algorithm called enhanced performance-to-power ratio for workflow applications "E-PRWA" in cloud/fog environment. The proposed heuristic algorithm strives to achieve a trade-off between node performance and power consumption. Operating machine hosts at the highest performance-to-power ratio can save a tremendous amount of energy without degrading system performance. The proposed model consists of four stages: (a) detecting overutilized or underutilized nodes based on the preferred utilization (PU); (b) VM selection for migration from the overutilized nodes to underutilized nodes; (c) switching off selected underutilized nodes; (d) deploying the migration VMs based on the modified best-fit decreasing algorithm with PPR, latency overhead, and computational cost consideration. Extensive simulation results illustrate that compared with three baseline energy-efficient VM allocation and selection algorithms, E-PRWA can achieve an average of up to 65.41% of energy-saving with fewer migration number in fog computing. [ABSTRACT FROM AUTHOR]

The Chimp Optimization Algorithm (ChOA) is a cutting-edge swarm intelligence algorithm that models the social status ties and hunting behavior of chimps to solve complex optimization problems. Although ChOA is known for its simplicity and efficiency, it may encounter challenges such as convergence speed and local optima. This study presents a comprehensive and competitive multi-strategy-enhanced chimp optimization algorithm (SLDChOA), which comprehensively enhances the optimization performance of the algorithm through four strategies. Firstly, a low-difference Sobol sequence strategy is used to initialize the chimp population to increase the diversity of the initial population. Secondly, different location update strategies are adopted according to different iteration stages. The early iteration stage employs the Lévy flight-based location update strategy to help chimps explore the space more abundantly and improve the global search ability of the algorithm. In contrast, the proposed probability-based elitist operation strategy is used in the late iteration stage to help the chimps obtain higher-quality optimal solutions and improve the convergence accuracy and speed of the algorithm. Finally, the dimension learning-based hunting search strategy is introduced to facilitate information sharing among chimps and enable the algorithm to jump out of the local optimum effectively. To demonstrate its comprehensive performance, SLDChOA is compared with 17 state-of-the-art algorithms on 23 traditional benchmark functions, CEC 2014 and CEC 2019 test sets (totaling 63 test functions). Moreover, its efficacy and excellence are further demonstrated in 4 well-known engineering optimization issues and 2 feature selection problems of multimodal Parkinson's speech datasets. A series of simulations demonstrate that SLDChOA has good comprehensive merit-seeking ability and is extremely competitive. [ABSTRACT FROM AUTHOR]