Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

In recent years,the usage of social networking sites has considerably increased in the Arab world.It has empowered individuals to express their opinions,especially in politics.Furthermore,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales.This is attributed to business owners’understanding of social media’s importance for business development.However,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns.Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

An Optimized Novel Trust-Based Security Mechanism Using Elephant Herd Optimization

Routing strategies and security issues are the greatest challenges in Wireless Sensor Network(WSN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime considerably.Securing WSN is a challenging issue faced by researchers.Trust systems are very helpful in detecting interfering nodes in WSN.Researchers have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization problem.The metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their clans.EHO considers two herding behaviors to solve and enhance optimization problem.Based on Elephant Herd Optimization,a trust-based security method is built in this work.The proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting data.Experimental results have demonstrated the effectiveness of the proposed EHO based routing.The Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes 3000.As the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s performance.It is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.

Improving time series forecasting using elephant herd optimization with feature selection methods

The time series data is chaotic,non seasonal,non stationary and random in nature.It becomes quite challenging to discover the hidden patterns of time series data.In this paper the time series data is predicted with the help of a machine learning algorithm i.e.Elephant Herd Optimization(EHO).Three different types of time series data are used to testify the superiority of the proposed method namely stock market data,currency exchange data and absenteeism at work.The data are first subjected to feature selection methods namely ANOVA and Friedman test.The feature selection methods provide relevant set of features which is fed to the neural network trained with the method.The proposed method is also compared with other methods such as local linear radial basis functional neural network and particle swarm optimization.The results prove supremacy of EHO over other methods.

Hybrid Energy Storage to Control and Optimize Electric Propulsion Systems

Today,ship development has concentrated on electrifying ships in commercial and military applications to improve efficiency,support highpower missile systems and reduce emissions.However,the electric propulsion of the shipboard system experiences torque fluctuation,thrust,and power due to the rotation of the propeller shaft and the motion of waves.In order tomeet these challenges,a new solution is needed.This paper explores hybrid energy management systems using the battery and ultracapacitor to control and optimize the electric propulsion system.The battery type and ultracapacitor are ZEBRA and MAXWELL,respectively.The 3-,4-and 5-blade propellers are considered to produce power and move rapidly.The loss factor has been reduced,and the sea states have been found through the Elephant Herding Optimization algorithm.The efficiency of the proposed system is greatly enhanced through torque,thrust and power.The model predictive controller control strategy is activated to reduce load torque and drive system Root Average Square(RMS)error.The implementations are conducted under the MATLAB platform.The values for torque,current,power,and error are measured and plotted.Finally,the performance of the proposed methodology is compared with other available algorithms such as BAT and Dragonfly(DF).The simulation results show that the results of the proposed method are superior to those of various techniques and algorithms such as BAT and Dragonfly.

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing processes.Sensors play a vital role in emerging technologies of the 20th century.Localization of sensors in needed locations is a very serious problem.The environment is home to every living being in the world.The growth of industries after the industrial revolution increased pollution across the environment.Owing to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are needed.An interesting and challenging task is choosing the place to fit the sensors.Many meta-heuristic techniques have been introduced in node localization.Swarm intelligent algorithms have proven their efficiency in many studies on localization problems.In this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor network.First,our work aims at selecting industrial areas in the sensed location.We use random forest regression methodology to select the polluted area.Then,the elephant herding algorithm is used in sensor node localization.These two algorithms are combined to produce the best standard result in localizing the sensor nodes.To check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with algorithms.The results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer perceptron.Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology.Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5.

Heterogeneous Ensemble Feature Selection Model(HEFSM)for Big Data Analytics

Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.

Method for Fault Diagnosis and Speed Control of PMSM

In the field of fault tolerance estimation,the increasing attention in electrical motors is the fault detection and diagnosis.The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis.It has now turned out to be essential to diagnose faults at their very inception;as unscheduled machine downtime can upset deadlines and cause heavy financial burden.In this paper,fault diagnosis and speed control of permanent magnet synchronous motor(PMSM)is proposed.Elman Neural Network(ENN)is used to diagnose the fault of permanent magnet synchronous motor.Both the fault location and fault severity are considered.In this,eccentricity fault may occur in the motor.To control the speed of the permanent magnet synchronous motor,Dolphin Swarm Optimization(DSO)algorithm is used.The proposed work is simulated by using MATLAB in terms of amplitude,speed and torque.The comparison graph of speed vs.torque obtained by the proposed method gives better result compared to the other existing techniques.The proposed work is also compared with Particle Swarm Optimization(PSO)and Elephant Herding Optimization(EHO)algorithm.The proposed usage of Elman Neural Network to detect the fault and the usage of Dolphin Swarm Optimization algorithm to control the speed of the permanent magnet synchronous motor gives better outcome.

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

RSs(Radar Systems)identify and trace targets and are commonly employed in applications like air traffic control and remote sensing.They are necessary for monitoring precise target trajectories.Estimations of RSs are non-linear as the parameters TDEs(time delay Estimations)and Doppler shifts are computed on receipt of echoes where EKFs(Extended Kalman Filters)and UKFs(Unscented Kalman Filters)have not been examined for computations.RSs,certain times result in poor accuracies and SNRs(low signal to noise ratios)especially,while encountering complicated environments.This work proposes IUKFs(Iterated UKFs)to track onlinefilter performances while using optimization techniques to enhance outcomes.The use of cost functions can assist state corrections while lowering costs.A new parameter is optimized using MCEHOs(Mutation Chaotic Elephant Herding Optimizations)by linearly approximating system non-linearity where OIUKFs(Optimized Iterative UKFs)predict a target's unknown parameters.To obtain optimal solutions theoretically,OIUKFs take less iteration,resulting in shorter execution times.The proposed OIUKFs provide numerical approximations which are derivative-free implementations.Simulation evaluation results with estimators show better performances in terms of reduced NMSEs(Normalized Mean Square Errors),RMSEs(Root Mean Squared Errors),SNRs,variances,and better accuracies than current approaches.

Control of BLDC motor using MRPID controller with modified landsman converter

Purpose-The principal intention behind the activity is to regulate the speed,current and commutation of the brushless DC(BLDC)motor.Thereby,the authors can control the torque.Design/methodology/approach-In order to regulate the current and speed of the motor,the Multiresolution PID(MRPID)controller is proposed.The altered Landsman converter is utilized in this proposed suppression circuit,and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor.The adaptive neuro-fuzzy inference system-elephant herding optimization(ANFISEHO)calculation mirrors the conduct of the procreant framework in families.Findings-Brushless DC motor’s dynamic properties are created,noticed and examined by MATLAB/Simulink model.The performance will be compared with existing genetic algorithms.Originality/value-The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.