Emerging Trends in Social Networking Systems and Generation Gap with Neutrosophic Crisp Soft Mapping

This paper aims to introduce the novel concept of neutrosophic crisp soft set(NCSS),including various types of neutrosophic crisp soft sets(NCSSs)and their fundamental operations.We define NCS-mapping and its inverse NCS-mapping between two NCS-classes.We develop a robust mathematical modeling with the help of NCS-mapping to analyze the emerging trends in social networking systems(SNSs)for our various generations.We investigate the advantages,disadvantages,and natural aspects of SNSs for five generations.With the changing of the generations,it is analyzed that emerging trends and the benefits of SNSs are increasing day by day.The suggested modeling with NCS-mapping is applicable in solving various decision-making problems.

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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%).

New Antenna Array Beamforming Techniques Based on Hybrid Convolution/Genetic Algorithm for 5G and Beyond Communications

Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to 7G.Furthermore,it improves the array gain and directivity,increasing the detection range and angular resolution of radar systems.This study proposes two highly efficient SLL reduction techniques.These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,respectively.The convolution process determines the element’s excitations while the GA optimizes the element spacing.For M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,respectively.When the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher SLL.Tomitigate the increased HPBWof the odd and even excitations,the element spacing is optimized using the GA.Thereby,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL.Furthermore,for extreme SLL reduction,the DConv/GA is introduced.In this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors.It provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance.Antenna size affects the quality factor and the radiation loss of the antenna.Metamaterial antennas can overcome the limitation of bandwidth for small antennas.Machine learning(ML)model is recently applied to predict antenna parameters.ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna.The accuracy of the prediction depends mainly on the selected model.Ensemble models combine two or more base models to produce a better-enhanced model.In this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna.Two base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the ensemble.Dynamic Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base models.The proposed model is compared with three based models and the average ensemble model.The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

The design of an antenna requires a careful selection of its parameters to retain the desired performance.However,this task is time-consuming when the traditional approaches are employed,which represents a significant challenge.On the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended performance.In this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial antenna.The proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep network.This optimized network is used to retrieve the metamaterial bandwidth given a set of features.In addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

Theoretical-period characteristics of frame buildings designed under variable levels of seismicity and allowable-drift

Period equations provided by seismic codes are generally derived by calibration with recorded period data of buildings located in high seismicity regions.These period-equations do not account for either the design level of seismicity or the permissible limit of lateral drift.Buildings designed under high level of seismicity are expected to display higher stiffness and shorter periods than buildings designed under low-to-medium seismicity levels.In addition,buildings designed with high level of permissible lateral-drift are expected to display lower stiffness and longer periods than buildings designed with low level of permissible lateral-drift.In this study,the theoretical fundamental-periods of an ensemble of regular reinforced concrete(RC)and steel moment-resisting frame(MRF)buildings having 3-,6-,9-and 12-stories and designed with various levels of seismicity and permissible lateral-drift are evaluated.The obtained outcome indicates the sensitivity of the MRF theoretical-periods to the design seismicity and the permissible lateral-drift.The results also indicate the need for modifying the current period equations to realistically account for the variations in the design seismicity and allowable lateral-drift of MRF buildings.

Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction tasks.To prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were compared.The evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

Static and Dynamic Analysis Web Opening of Steel Beams

According to the purpose of the opening and the structural designer, the shape of the web opening is decided. It is too easy to choose the shape of openings from regular shape whether it is circular or rectangular shape. The presence of openings in the web of steel beams decreases stiffness of the beam and introducing a larger deflection than in the steel web opening with solid opening. A steel beam with web opening is analyzed in this paper. ABAQUS software is using for analyzing nonlinear static and dynamic opening of steel beam with different position and supporting conditions.

The Reaction of 2-Amino-4,5,6,7-tetrahydrobenzo[<i>b</i>]thiophenes with Benzoyl-Isothiocyanate: Synthesis of Annulated Thiophene Derivatives and Their Antitumor Evaluations

The reaction of the tetrahydrobenzo[b]thiophene derivatives 1a,b with benzoylisothiocyanate (2) afforded the thiourea derivatives 3a,b. Cyclization of the latter products gave the annulated products 4a,b. Compounds 3a,b reacted with either hydrazine hydrate (5a) or phenyl hydrazine (5b) afforded compounds 7a-d which underwent cyclization for compounds 7a,c afforded compound 9. On the other hand compounds 4a,b reacted with either hydrazine hydrate (5a) or phenyl hydrazine (5b) afforded compounds 6a-d a second pathway was applied to synthesize compound 6c which underwent cyclization afforded compound 9. Also compounds 4a,b reacted with either phenacyl bromide (10) afforded compounds 11a,b or ethylchloroacetae (12) compounds 13a,b were produced. The latter products reacted with each hydrazine hydrate (5a) and phenyl hydrazine (5b) afforded compounds 14a-d. Their antitumor activities were tested using three different cell lines.

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

The ability to detect and localize the human eye is critical for use in security applications and human identification and verification systems.This is because eye recognition algorithms have multiple challenges,such as multi-pose variations,ocular parts,and illumination.Moreover,the modern security applica-tions fail to detect facial expressions from eye images.In this paper,a Speeded-Up Roust Feature(SURF)Algorithm was utilized to localize the face images of the enrolled subjects.We highlighted on eye and pupil parts to be detected based on SURF,Hough Circle Transform(HCT),and Local Binary Pattern(LBP).Afterward,Deep Belief Neural Networks(DBNN)were used to classify the input features results from the SURF algorithm.We further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly detected.We apply Stochastic Gradient Descent(SGD)optimizer to address the overfitting problem,and the hyper-parameters arefine-tuned based on the applied DBNN.The accuracy of the proposed system is determined based on SURF,LBP,and DBNN classifier achieved 95.54%for the ORL dataset,94.07%for the BioID,and 96.20%for the CASIA-V5 dataset.The proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.

Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.

Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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.

Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices remotely.Due to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing attacks.That’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT networks.The imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network attacks.Therefore,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization algorithms.To prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE balancing.Experimental results showed that the proposed approach outperforms the other approaches and could boost the detection accuracy.In addition,a statistical analysis is performed to study the significance and stability of the proposed approach.The conducted experiments include seven different types of attack cases in the RPL-NIDS17 dataset.Based on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

Double Deep Q-Network Method for Energy Efficiency and Throughput in a UAV-Assisted Terrestrial Network

Increasing the coverage and capacity of cellular networks by deploying additional base stations is one of the fundamental objectives of fifth-generation(5G)networks.However,it leads to performance degradation and huge spectral consumption due to the massive densification of connected devices and simultaneous access demand.To meet these access conditions and improve Quality of Service,resource allocation(RA)should be carefully optimized.Traditionally,RA problems are nonconvex optimizations,which are performed using heuristic methods,such as genetic algorithm,particle swarm optimization,and simulated annealing.However,the application of these approaches remains computationally expensive and unattractive for dense cellular networks.Therefore,artificial intelligence algorithms are used to improve traditional RA mechanisms.Deep learning is a promising tool for addressing resource management problems in wireless communication.In this study,we investigate a double deep Q-network-based RA framework that maximizes energy efficiency(EE)and total network throughput in unmanned aerial vehicle(UAV)-assisted terrestrial networks.Specifically,the system is studied under the constraints of interference.However,the optimization problem is formulated as a mixed integer nonlinear program.Within this framework,we evaluated the effect of height and the number of UAVs on EE and throughput.Then,in accordance with the experimental results,we compare the proposed algorithm with several artificial intelligence methods.Simulation results indicate that the proposed approach can increase EE with a considerable throughput.

Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of data.Cloud computing and fog computing,two of the most common technologies used in IoT applications,have led to major security concerns.Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient.Several artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent years.Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features selected.On the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent decades.In this paper,we proposed a hybrid optimization algorithm for feature selection in IDS.The proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as GWDTO.The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance.On the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its superiority.In addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed approach.Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

Rainfall plays a significant role in managing the water level in the reser-voir.The unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the reservoir.Many individuals,especially those in the agricultural sector,rely on rain forecasts.Forecasting rainfall is challenging because of the changing nature of the weather.The area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting model.To estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),respectively.Moreover,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed approach.On the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.

Wind Power Prediction Based on Machine Learning and Deep Learning Models

Wind power is one of the sustainable ways to generate renewable energy.In recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar power.To achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base models.These regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)regressor.In addition,data cleaning and data preprocessing were performed to the data.The dataset used in this study includes 4 features and 50530 instances.To accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM network.Five evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection

Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations.