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Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine learning. Each feature in a dataset has 2n possible subsets, making it challenging to select the optimum collection of features using typical methods. As a result, a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization (DTO-GW) algorithms has been developed in this research. Instability can result when the selection of features is subject to metaheuristics, which can lead to a wide range of results. Thus, we adopted hybrid optimization in our method of optimizing, which allowed us to better balance exploration and harvesting chores more equitably. We propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of attributes. In the proposed method, the number of features selected is minimized, while classification accuracy is increased. To test the proposed method's performance against eleven other state-of-theart approaches, eight datasets from the UCI repository were used, such as binary grey wolf search (bGWO), binary hybrid grey wolf, and particle swarm optimization (bGWO-PSO), bPSO, binary stochastic fractal search (bSFS), binary whale optimization algorithm (bWOA), binary modified grey wolf optimization (bMGWO), binary multiverse optimization (bMVO), binary bowerbird optimization (bSBO), binary hysteresis optimization (bHy), and binary hysteresis optimization (bHWO). The suggested method is superior and successful in handling the problem of feature selection, according to the results of the experiments. [ABSTRACT FROM AUTHOR]

Managing physical objects in the network's periphery is made possible by the Internet of Things (IoT), revolutionizing human life. Open attacks and unauthorized access are possible with these IoT devices, which exchange data to enable remote access. These attacks are often detected using intrusion detection methodologies, although these systems' effectiveness and accuracy are subpar. This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization. The employed metaheuristic optimizer is a new version of the whale optimization algorithm (WOA), which is guided by the dipper throated optimizer (DTO) to improve the exploration process of the traditionalWOA optimizer. The proposed voting classifier categorizes the network intrusions robustly and efficiently. To assess the proposed approach, a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization. The dataset records are balanced using the locality-sensitive hashing (LSH) and Synthetic Minority Oversampling Technique (SMOTE). The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach's effectiveness, stability, and significance. The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signal is a measure of the heart's electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%). [ABSTRACT FROM AUTHOR]

Mobile clouds are the most common medium for aggregating, storing, and analyzing data from the medical Internet of Things (MIoT). It is employed to monitor a patient's essential health signs for earlier disease diagnosis and prediction. Among the various disease, skin cancer was the wide variety of cancer, as well as enhances the endurance rate. In recent years, many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors, including malignant melanoma (MM) and other skin cancers. However, accurate cancer detection was not performed with minimum time consumption. In order to address these existing problems, a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification (MBDFS-CPRRDLC) technique is introduced for detecting cancer at an earlier stage. The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input, hidden, and output for feature selection and classification. The patient information is composed of IoT. The patient information was stored in mobile clouds server for performing predictive analytics. The collected data are sent to the recurrent deep learning classifier. In the first hidden layer, the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption. Followed by, the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data. This process is repeatedly performed until the error gets minimized. In this way, disease classification is accurately performed with higher accuracy. Experimental evaluation is carried out for factors namely Accuracy, precision, recall, F-measure, as well as cancer detection time, by the amount of patient data. The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches. [ABSTRACT FROM AUTHOR]

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance. The bandwidth restriction associated with small antennas can be solved using metamaterial antennas. Machine learning is gaining popularity as a way to improve solutions in a range of fields. Machine learning approaches are currently a big part of current research, and they're likely to be huge in the future. The model utilized determines the accuracy of the prediction in large part. The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna's bandwidth and gain. The basic models employed in the developed ensemble are Support Vector Regression (SVR), K-NearestRegression (KNR), Multi-Layer Perceptron (MLP), Decision Trees (DT), and Random Forest (RF). The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization (DTO) algorithm. To choose the best features from the dataset, the binary (bDTO) algorithm is exploited. The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically. In addition, two other ensembles are incorporated in the conducted experiments for comparison. These ensembles are average ensemble and K-nearest neighbors (KNN)-based ensemble. The comparison is performed in terms of eleven evaluation criteria. The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models. [ABSTRACT FROM AUTHOR]