ABSTRACT This paper presents a new data clustering technique aimed at enhancing the performance of the trainable path‐cost algorithm and reducing the computational complexity of data clustering models. The proposed method facilitates the discovery of natural groupings and behaviours, which is crucial for effective coordination in complex environments. It identifies natural groupings within a set of features and detects the best clusters with similar behaviour in the data, overcoming the limitations of traditional state‐of‐the‐art methods. The algorithm utilises a density peak clustering method to determine cluster centers and then extracts features from paths passing through these peak points (centers). These features are used to train the support vector machine (SVM) to predict the labels of other points. The proposed algorithm is enhanced using two key concepts: first, it employs Q‐Generalised Extreme Value (Q‐GEV) under power normalisation instead of traditional generalised extreme value distributions, thereby increasing modelling flexibility; second, it utilises the random vector functional link (RVFL) network rather than the SVM, which helps avoid overfitting and improves label prediction accuracy. The effectiveness of the proposed clustering algorithm is evaluated through various experiments, including those on UCI benchmark datasets and real‐world data, demonstrating significant improvements across multiple performance metrics, including F1 measure, Jaccard index, purity, and accuracy, highlighting its capability in accurately identifying paths between similar clusters. Its average F1 measure, Jaccard index, purity, and accuracy is measured 76.87%, 56.29%, 80.29%, and 79.64%, respectively.

Abstract To recognize the potential for colon polyps to develop into cancer over time, early diagnosis is crucial for preventative healthcare. Timely identification significantly improves the prognosis and treatment outcomes for colorectal cancer patients. Image segmentation is crucial in medical image analysis for accurate diagnosis and treatment planning. Therefore, in this study, we present an alternative multilevel thresholding polyp segmentation method (MPOA) to enhance the segmentation of polyp images. The proposed method is based on enhancing the planet optimization algorithm (POA) by integrating operators from the reptile search algorithm (RSA). The evaluation of the developed MPOA is tested with different polyp images and compared with other image segmentation approaches. The results highlight the superior capability of MPOA, as evidenced by various performance measures in effectively segmenting polyp images. Furthermore, metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and fitness values demonstrate that MPOA outperforms the basic version of POA and other methods. The evaluation outcomes underscore the significant impact of RSA in enhancing the performance of POA for the segmentation of polyp images.

Advances in language models have enabled significant strides in developing language technologies tailored for analyzing and processing Dialectical Arabic (DA), which exhibits unique linguistic features and variations compared to standard Arabic. This progress has sparked a surge of interest in various research tasks within the Arabic Natural Language Processing (ANLP) domain, encompassing areas such as sentiment analysis, dialect identification, normalization and classification, fake news detection, and part-of-speech tagging. The primary objective of this survey paper is to provide a comprehensive overview of the advancements made in dialectical ANLP from 2014 to 2024. A thorough analysis is undertaken, covering a corpus of approximately 200 research papers, to offer insights into the latest developments, resources, and applications concerning dialectical Arabic. By identifying and discussing the challenges and opportunities for future research, this study aspires to serve as a valuable reference for researchers, practitioners, and enthusiasts interested in the subject matter. Central to the investigation are the recent strides in natural language processing techniques that pertain to dialectical Arabic, namely DA sentiment analysis, DA identification, DA classification, DA normalization, DA part-of-speech tagging, and the role of DA in fake news detection, among other applications. Each research category is meticulously examined, providing a comprehensive understanding of their respective contributions, significance, encountered challenges, and the availability of pertinent datasets. This exhaustive survey paper encompasses existing studies within dialectical Arabic research categories. As a result, readers are presented with a detailed reference source in pursuing advancements and innovations within this field.

Human activity recognition (HAR) is a rapidly growing field of research that aims to automatically identify and classify human motions and activities from different tracking devices, such as cameras and sensors. One of the most widely used sensor modalities for HAR is the smartphone, which has various sensors, such as gyroscopes, accelerometers, and GPS, that can provide rich information about a person’s movements and actions. HAR applications are essential for the Internet of Things (IoT) and smart home industries. We used the recent advances in deep learning techniques to develop a new HAR model for wearable sensors. The proposed model, MLCNNwav, relies on residual convolutional neural networks and 1-D trainable discrete wavelet transform. The multilevel CNN is designed to capture global features, whereas the wavelet transformation enhances the representation and generalization by learning activity-related features. Several deep learning approaches are compared to assess the superiority of the developed model. Four public benchmarks HAR data sets were used for the evaluation. The outcomes confirmed that the developed MLCNNwav recorded high-accuracy rates on all data sets.

Fall detection (FD) systems are crucial for identifying falls and ensuring timely assistance, thus reducing the risk of serious injuries. With the development of society and increasing attention to health issues, researchers have conducted extensive studies on falls to reduce the severe sequelae of falls. Integrating FD systems with the Internet of Things (IoT), particularly the Internet of Medical Things (IoMT), has significantly advanced healthcare and personal safety. This dynamic relationship between FD technology and IoT has opened up new vistas for monitoring and assisting individuals, particularly the elderly and those with health conditions that make them prone to falls. This article presents a review of wearable sensor-based FD techniques. We classify the detection methods into their categories from an algorithmic perspective: threshold-based, conventional machine learning-based, and deep learning-based methods. In addition, we identify and summarize the available data sets that can be used to evaluate the performance of the introduced methods. This review aims to provide researchers with a better comprehension of the FD problem, intending to foster further advancements in the field.

Abstract The technological revolution has made available a large amount of data with many irrelevant and noisy features that alter the analysis process and increase time processing. Therefore, feature selection (FS) approaches are used to select the smallest subset of relevant features. Feature selection is viewed as an optimization process for which meta-heuristics have been successfully applied. Thus, in this paper, a new feature selection approach is proposed based on an enhanced version of the Capuchin search algorithm (CapSA). In the developed FS approach, named ECapSA, three modifications have been introduced to avoid a lack of diversity, and premature convergence of the basic CapSA: (1) The inertia weight is adjusted using the logistic map, (2) sine cosine acceleration coefficients are added to improve convergence, and (3) a stochastic learning strategy is used to add more diversity to the movement of Capuchin and a levy random walk. To demonstrate the performance of ECapSA, different datasets are used, and it is compared with other well-known FS methods. The results provide evidence of the superiority of ECapSA among the tested datasets and competitive methods in terms of performance metrics.

Human activity recognition (HAR) applications have received much attention due to their necessary implementations in various domains, including Industry 5.0 applications such as smart homes, e-health, and various Internet of Things applications. Deep learning (DL) techniques have shown impressive performance in different classification tasks, including HAR. Accordingly, in this article, we develop a comprehensive HAR system based on a novel DL architecture called Multi-ResAtt (multilevel residual network with attention). This model incorporates initial blocks and residual modules aligned in parallel. Multi-ResAtt learns data representations on the inertial measurement units level. Multi-ResAtt integrates a recurrent neural network with attention to extract time-series features and perform activity recognition. We consider complex human activities collected from wearable sensors to evaluate the Multi-ResAtt using three public datasets, Opportunity; UniMiB-SHAR; and PAMAP2. Additionally, we compared the proposed Multi-ResAtt to several DL models and existing HAR systems, and it achieved significant performance.

The usage of cloud services is growing exponentially with the recent advancement of Internet of Things (IoT)-based applications. Advanced scheduling approaches are needed to successfully meet the application demands while harnessing cloud computing’s potential effectively to schedule the IoT services onto cloud resources optimally. This article proposes an alternative task scheduler approach for organizing IoT application tasks over the CCE. In particular, a novel hybrid swarm intelligence method, using a modified Manta ray foraging optimization (MRFO) and the salp swarm algorithm (SSA), is proposed to handle the problem of scheduling IoT tasks in cloud computing. This proposed method, called MRFOSSA, depends on using SSA to improve the local search ability of MRFO that typically enhances the rate of convergence towards the global solution. To validate the developed MRFOSSA, a set of experimental series is performed using different real-world and synthetic datasets with variant sizes. The performance of MRFOSSA is tested and compared with other metaheuristic techniques. Experiment results show the superiority of MRFOSSA over its competitors in terms of performance measures, such as makespan time and cloud throughput.

The proper use of evolutionary operators is crucial to find optimal solutions in a search space. Moreover, the diversity of the population affects the performance of Evolutionary Algorithms (EAs). This article introduces an EA called BWEAD which balances the influence of the operators. The proposal also performs a statistical analysis of the population when the diversity is low and decides which solutions might be replaced. Then BWEAD is able to explore the search space and exploit the prominent regions. The BWEAD has been tested over the CEC2014 set of benchmark functions. The experiments provide competitive results showing an improvement of 30% in 30-dimensional and 50-dimensional functions in comparison with state-of-the-art algorithms, overcoming some addressed instances and providing evidence of its capabilities on complex optimization problems.

This paper proposes an alternative multilevel thresholding (MLT) image segmentation method by improving the behavior of the grasshopper optimization algorithm (GOA). This is achieved by using the operators of the sine-cosine algorithm (SCA) to work in a competitive manner with the operators of traditional GOA. This will lead to enhance the quality of the solutions during the updating process that will affect the convergence of the proposed GOASCA towards the global solution. In addition, the proposed GOASCA aims to minimize the difference between the fuzzy entropy and its opposite fuzzy entropy that is used as a fitness function to evaluate the quality of the solution. This objective function gives the GOASCA to explore the whole search space. To assess the quality of the obtained threshold values by GOASCA, a set of eight images are used which have different characteristics. Moreover, the results of GOASCA are compared with a set of well-known MLT image segmentation approaches, and these results have shown the high quality of GOASCA to segmented the image, as well as, shown that the current objective function provides results better than the traditional fuzzy entropy in terms of the performance measures of image segmentation.

Device-to-device (D2D) communication is a key enabling technology to facilely realizing the Internet of Things (IoT) due to its spectral and energy efficiencies features. Exploiting the physical-layer network coding (PNC) and energy harvesting (EH) technology, two-way relaying (TWR) D2D communication can achieve significant performance for IoT in terms of data rate and energy efficiency (EE). In this article, we investigate the EH-aided TWR D2D communication sharing the uplink (UL) spectrum of the traditional cellular networks. We assume that the D2D transmitters, receivers, and participating relays can collect renewable energy (RE) from natural resources. Also, the relays are considered to be powered by radio-frequency (RF) signals utilizing the power splitting (PS) protocol. Subject to the Quality of Service (QoS), power, subchannel assignment, EH, and maximum practical power constraints, two nonconvex mixed-integer nonlinear programming (MINLP) problems are formulated. The two problems provide a tradeoff on either maximizing the TWR D2D link (TDL) rate or its EE depending on the IoT application needs. Based on the particle swarm optimization (PSO) algorithm, we propose the rate and EE tradeoff EH-based algorithm (REET-EH) to deal with these problems. The proposed algorithm can optimally perform the resource allocation (RA), PS factors determination, power allocation (PA), and relay selection processes. The numerical results investigate the performance of the REET-EH algorithm and show its consistency over several parameters. Also, the results illustrate that our proposed algorithm improves the system performance compared with other state-of-the-art algorithms with regard to the D2D link rate and EE.

Moth-flame Optimisation Algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a huge interest due to its impressive characteristics mainly: no derivation information needed in the starting phase, few numbers of parameters, simple in implementation, scalable and flexible. Till now, different variants to solve various optimisation problems such as binary, real(continuous), constraint, single-objective, multi-objective, and multimodal MFO has been introduced. Many research papers have been presented and summarised. In this review, a general overview of MFO is presented at first. Then, different variants of MFO are described which are classified into three classes: modified, hybridised, and multi-objective. Furthermore, applications of MFO in Engineering, Computer Science, Wireless Sensor Networks, and other fields are discussed. Finally, many possible and future directions are provided.

This article introduces a new hybrid technique for predicting solar activity. The proposed hybrid technique consists of four phases. The first phase is feature selection, where the most relevant features (variables) are selected based on an artificial bee colony using a neighborhood rough set as the fitness function. The second phase employs the training support vector regression (SVR) using a part of the solar activity data set based on the selected features. Sequential minimal optimization is carried out to determine the optimal parameters of SVR. The third phase tests the regression model based on the second part of the data set. The fourth phase is the process of predicting solar activity. According to the prediction system, the maximum amplitude of cycle 25 is 80 ± 12, and it will occur in 2026. The solution quality is assessed by using three indices called average absolute percent relative error (AAPRE), root-mean-square error (RMSE), and coefficient of determination. These indices prove the high quality of the forecast sunspot number value and the actual ones. In addition, it can be concluded that the proposed system is significantly improved the predicting solar activity performance at acceptable assessment indices.

Automatic clustering based hybrid metaheuristic algorithms has attracted the center of interest of scientists and engineers which become a hot topic for different data analysis applications. For example, image clustering, bioinformatics, image segmentation, and natural language processing. Where the process of determining the number and position of centroids is an NP-hard problem. So, this paper presents an alternative automatic clustering algorithm based on the hybrid between the atom search optimization (ASO) and the sine-cosine algorithm (SCA). The main objective of the proposed clustering method, called ASOSCA, is to find automatically the optimal number of centroids and their positions in order to minimize the CS-index (which refers to Compact-separated index). To achieve this goal, the ASOSCA uses SCA as a local search operator to improve the quality of ASO. The performance of the proposed hybrid method is compared with other metaheuristic methods; in which all of them are tested on sixteen clustering datasets and using different cluster validity indexes as Dunn, Silihouette, Davies Bouldin, and Calinski Harabasz. The experimental results show that the ASOSCA depict high superiority in comparison with other types of hybrid metaheuristic in terms of clustering measures.

With the advent of social network services, Arabs’ opinions on the web have attracted many researchers in recent years toward detecting and classifying sentiments in Arabic tweets and reviews. However, the impact of word embeddings vectors (WEVs) initialization and dataset balance on Arabic sentiment classification using deep learning has not been thoroughly studied. In this article, a multi-channel embedding convolutional neural network (MCE-CNN) is proposed to improve Arabic sentiment classification by learning sentiment features from different text domains, word, and character n-grams levels. MCE-CNN encodes a combination of different pre-trained word embeddings into the embedding block at each embedding channel and trains these channels in parallel. Besides, a separate feature extraction module implemented in a CNN block is used to extract more relevant sentiment features. These channels and blocks help to start training on high-quality WEVs and fine-tuning them. The performance of MCE-CNN is evaluated on several standard balanced and imbalanced datasets to reflect real-world use cases. Experimental results show that MCE-CNN provides a high classification accuracy and benefits from the second embedding channel on both standard Arabic and dialectal Arabic text, which outperforms state-of-the-art methods.

Optimizing energy efficient Virtual Machine Consolidation (VMC) in a cloud computing environment, which is a non-linear multi-objective NP-hard problem, plays a vital role in decreasing energy consumption, and increasing Quality of Service (QoS). In this paper, VMC is formulated as a multi-objective optimization problem, which has three conflicting objectives, power consumption, Service Level Agreements Violation (SLAV) and Mean Time Before Host Shutdown (MTBHS). We propose a multi-objective optimization algorithm based on Multi-Objective Sine Cosine Algorithm (MOSCA) for the VMC. We evaluate the performance of our model by applying two multi-objective algorithms, namely, Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD) and Non-dominated Sorting Genetic Algorithm (NSGAII). Our research mainly focus on two tasks, i.e.,evaluating and comparing the multi-objective algorithms to find out the optimal solution and develop a MOSCA based algorithm to solve the proposed VMC model. The simulation results illustrated that the propose multi-objective model meets the optimal solutions amongst the three conflicting objectives, which significantly reduces the power consumption, SLAV and maximize the MTBHS. It got the best performance according to the Multi-objective Optimization Problem (MOP) indicators.

This paper presents an approach based on Artificial Bee Colony (ABC) to optimize the parameters of membership functions of Sugeno based Adaptive Neuro-Fuzzy Inference System (ANFIS).The optimization is achieved by Artificial Bee Colony (ABC) for the sake of achieving minimum Root Mean Square Error of ANFIS structure.The proposed ANFIS-ABC model is used to build a system for predicting the wind speed.To ensure the accuracy of the model, a different number of membership functions has been used.The experimental results indicates that the best accuracy achieved is 98% with ten membership functions and least value of RMSE which is 0.39.