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Feature selection with Deep Reinforcement Learning for Intrusion Detection system

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Computer systems science & Engineering 2023年第9期46卷 3339-3353页

作者： s.priya K.Pradeep Mohan Kumar Department of Computer science and Engineering SRM Institute of Science and TechnologyRamapuramChennai600089India Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathurChennai603203India

An intrusion detection system(IDs)becomes an important tool for ensuring security in the network.In recent times,machine learning(ML)and deep learning(DL)models can be applied for the identification of intrusions over the network effectively.To resolve the security issues,this paper presents a new Binary Butterfly Optimization algorithm based on Feature selection with DRL technique,called BBOFs-DRL for intrusion detection.The proposed BBOFsDRL model mainly accomplishes the recognition of intrusions in the network.To attain this,the BBOFs-DRL model initially designs the BBOFs algorithm based on the traditional butterfly optimization algorithm(BOA)to elect feature subsets.Besides,DRL model is employed for the proper identification and classification of intrusions that exist in the network.Furthermore,beetle antenna search(BAs)technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency.For ensuring the superior intrusion detection outcomes of the BBOFs-DRL model,a wide-ranging experimental analysis is performed against benchmark dataset.The simulation results reported the supremacy of the BBOFs-DRL model over its recent state of art approaches.

关键词： Intrusion detection security reinforcement learning machine learning feature selection beetle antenna search

Pre screening of Cervical Cancer Through Gradient Boosting Ensemble Learning Method

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Intelligent Automation & soft Computing 2023年第3期35卷 2673-2685页

作者： s.priya N.K.Karthikeyan D.Palanikkumar Coimbatore Institute of Technology CoimbatoreIndia Dr.NGP Institute of Technology CoimbatoreIndia

In recent years,cervical cancer is one of the most common diseases which occur in any woman regardless of any age.This is the deadliest disease since there were no symptoms shown till it is diagnosed to be the last stage.For women at a certain age,it is better to have a proper screening for cervical can-cer.In most underdeveloped nations,it is very difﬁcult to have frequent scanning for cervical cancer.Data Mining and machine learning methodologies help widely inﬁnding the important causes for cervical cancer.The proposed work describes a multi-class classiﬁcation approach is implemented for the dataset using support Vector Machine(sVM)and the perception learning method.It is known that most classiﬁcation algorithms are designed for solving binary classiﬁcation problems.From a heuristic approach,the problem is addressed as a multiclass classiﬁcation problem.A Gradient Boosting Machine(GBM)is also used in implementation in order to increase the classiﬁer accuracy.The proposed model is evaluated in terms of accuracy,sensitivity and found that this model works well in identifying the risk factors of cervical cancer.

关键词： Cervical cancer sVM GBM ensemble learning

Vision Based Real Time Monitoring system for Elderly Fall Event Detection Using Deep Learning

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Computer systems science & Engineering 2022年第7期42卷 87-103页

作者： G.Anitha s.Baghavathi priya Department of Information Technology Rajalakshmi Engineering CollegeChennai602105India Department of Computer science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Human fall detection plays a vital part in the design of sensor based alarming system,aid physical therapists not only to lessen after fall effect and also to save human life.Accurate and timely identiﬁcation can offer quick medical ser-vices to the injured people and prevent from serious consequences.several vision-based approaches have been developed by the placement of cameras in diverse everyday environments.At present times,deep learning(DL)models par-ticularly convolutional neural networks(CNNs)have gained much importance in the fall detection tasks.With this motivation,this paper presents a new vision based elderly fall event detection using deep learning(VEFED-DL)model.The proposed VEFED-DL model involves different stages of operations namely pre-processing,feature extraction,classiﬁcation,and parameter optimization.Primar-ily,the digital video camera is used to capture the RGB color images and the video is extracted into a set of frames.For improving the image quality and elim-inate noise,the frames are processed in three levels namely resizing,augmenta-tion,and min–max based normalization.Besides,MobileNet model is applied as a feature extractor to derive the spatial features that exist in the preprocessed frames.In addition,the extracted spatial features are then fed into the gated recur-rent unit(GRU)to extract the temporal dependencies of the human movements.Finally,a group teaching optimization algorithm(GTOA)with stacked autoenco-der(sAE)is used as a binary classiﬁcation model to determine the existence of fall or non-fall events.The GTOA is employed for the parameter optimization of the sAE model in such a way that the detection performance can be enhanced.In order to assess the fall detection performance of the presented VEFED-DL model,a set of simulations take place on the UR fall detection dataset and multi-ple cameras fall dataset.The experimental outcomes highlighted the superior per-formance of the presented method over the recent methods.

关键词： Computer vision elderly people fall detection deep learning metaheuristics object detection parameter optimization

Linear Precoder Design for Correlated Rician Fading Channel Under Imperfect CsI

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中国通信:英文版 2023年第9期20卷 160-170页

作者： s.B.M.priya school of Electrical and Electronics Engineering SASTRA Deemed UniversityThanjavur 613401India

In this paper,we have modeled a linear precoder for indoor multiuser multiple input multiple output(MU-MIMO)system with imperfect channel state information(CsI)at transmitter.The Rician channel is presumed to be mutually coupled and spatially,temporarily correlated.The imperfection with CsI is primarily due to the channel estimation error at receiver and feedback delay amidst the receiver and transmitter in CsI transmission.Along with,the insufficient spacing between the antenna at transmitter and receiver persuades mutual coupling(MC)among the array elements.In addition,the MIMO channel is presumed to be jointly correlated(Weichselberger correlation model).When we look back on the existing precoder design,it considered spatial correlation alone disregarding joint correlation of antenna array elements.With all above assumption,we have designed a linear precoder which minimizes mean squared error(MsE)subjected to total transmit power constraint for MUMIMO system.The simulation results proven that proposed precoder shows substantial enhancement in bit error rate(BER)performance in comparison with the existing technique.The mathematical analysis corroborates the simulation results.

关键词： channel estimation error feedback delay MMsE linear precoder mutual coupling spatial cor-relation Weichselberger correlation model

A Novel Radial Basis Function Neural Network Approach for ECG signal Classification

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Intelligent Automation & soft Computing 2023年第1期35卷 129-148页

作者： s.sathishkumar R.Devi priya Department of Information Technology Adhiyamaan College of EngineeringHosur621004India Department of Information Technology Kongu Engineering CollegeErode638060India

ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOsT(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.

关键词： Electrocardiogram signal gaborfilter discrete cosine based discrete orthogonal stock well transform genetic algorithm radial basis function neural network

An Intelligent Approach for Accurate Prediction of Chronic Diseases

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Computer systems science & Engineering 2023年第8期46卷 2571-2587页

作者： s.Kavi priya N.saranya Mepco schlenk Engineering College SivakasiTamilnaduIndia

Around the globe,chronic diseases pose a serious hazard to healthcare communities.The majority of the deaths are due to chronic diseases,and it causes burdens across the world.Through analyzing healthcare data and extracting patterns healthcare administrators,victims,and healthcare communities will get an advantage if the diseases are early predicted.The majority of the existing works focused on increasing the accuracy of the techniques but didn’t concentrate on other performance measures.Thus,the proposed work improves the early detection of chronic disease and safeguards the lives of the patients by increasing the specificity and sensitivity of the classifiers along with the accuracy.The proposed work used a hybrid optimization algorithm called the Hybrid Gravitational search algorithm and Particle swarm Optimization algorithm(HGsAPsO)to upgrade the detection of chronic diseases.Existing classifier parameters with their optimized parameters are compared and evaluated.Classifiers such as Artificial Neural Network(ANN),support Vector Machines(sVM),K-Nearest Neighbor(Knn),and Decision tree(DT)are used.Health care data are obtained from the UCI machine learning repository to evaluate the proposed work.The proposed work is assessed on 6 benchmark datasets and the performance metrics such as Accuracy,specificity,sensitivity,F-measure,Recall,and Precision are compared.The experimental results exhibit that the proposed work attains better accuracy on Artificial Neural Network-Hybrid Gravitational search algorithm and Particle swarm Optimization algorithm(ANN-HGsAPsO)classifier compared to other classifiers.ANN-HGsAPsO provides 93%accuracy for Chronic Kidney Disease(CKD),Cardio Vascular Disease(CVD)96%,Diabetes 82%,Hepatitis 94%,Wisconsin Breast Cancer(WBC)91%,and for Liver disease dataset 96%.

关键词： Chronic diseases optimization gravitational search algorithm particle swarm optimization machine learning algorithms

EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long short-Term Memory Network

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Intelligent Automation & soft Computing 2023年第5期36卷 1745-1766页

作者： s.Kavi priya K.Pon Karthika Mepco schlenk Engineering College(Autonomous) Sivakasi626005TamilnaduIndia

Globally,depression is perceived as the most recurrent and risky disor-der among young people and adults under the age of 60.Depression has a strong inﬂuence on the usage of words which can be observed in the form of written texts or stories posted on social media.With the help of Natural Language Proces-sing(NLP)and Machine Learning(ML)techniques,the depressive signs expressed by people can be identiﬁed at the earliest stage from their social Media posts.The proposed work aims to introduce an efﬁcacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long short-Term Memory network(LsTM)model.The feature extraction process combines a novel feature selection method called Elite Term score(ETs)and Word2Vec to extract the syntactic and semantic information respectively.First,the ETs method leverages the document level,class level,and corpus level prob-abilities for computing the weightage/score of the terms.Then,the ideal and per-tinent set of features with a high ETs score is selected,and the Word2vec model is trained to generate the intense feature vector representation for the set of selected terms.Finally,the resultant word vector obtained is called EliteVec,which is fed to the hybrid LsTM model based on Honey Badger optimizer with population reduction technique(PHB)which predicts whether the input textual content is depressive or not.The PHB algorithm is integrated to explore and exploit the opti-mal hyperparameters for strengthening the performance of the LsTM network.The comprehensive experiments are carried out with two different Twitter depres-sion corpus based on accuracy and Root Mean square Error(RMsE)metrics.The results demonstrated that the proposed EliteVec+LsTM+PHB model outperforms the state-of-art models with 98.1%accuracy and 0.0559 RMsE.

关键词： Depression detection dimensionality reduction feature extraction feature selection hybrid LsTM network population reduction honey badger optimization social media twitter

Mathematical Modeling of Moored ship Motion in Arbitrary Harbor utilizing the Porous Breakwater

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China Ocean Engineering 2023年第5期37卷 738-752页

作者： Prachi priya Prashant Kumar Gulshan Rajni Government Model sanskriti senior secondary school Ganaur Sonipat 131101India Department of Applied sciences(Mathematics) National Institute of TechnologyDelhi 110036India JindalGobal Business school O.P.Jindal Global UniversitySonepat 131001India

The motion of the moored ship in the harbor is a classical hydrodynamics problem that still faces many challenges in naval operations,such as cargo transfer and ship pairings between a big transport ship and some small ships.A mathematical model is presented based on the Laplace equation utilizing the porous breakwater to investigate the moored ship motion in a partially absorbing/reflecting harbor.The motion of the moored ship is described with the hydrodynamic forces along the rotational motion(roll,pitch,and yaw)and translational motion(surge,sway,and heave).The efficiency of the numerical method is verified by comparing it with the analytical study of Yu and Chwang(1994)for the porous breakwater,and the moored ship motion is compared with the theoretical and experimental data obtained by Yoo(1998)and Takagi et al.(1993).Further,the current numerical scheme is implemented on the realistic Visakhapatnam Fishing port,India,in order to analyze the hydrodynamic forces on moored ship motion under resonance conditions.The model incorporates some essential strategies such as adding a porous breakwater and utilizing the wave absorber to reduce the port’s resonance.It has been observed that these tactics have a significant impact on the resonance inside the port for safe maritime navigation.Therefore,the current numerical model provides an efficient tool to reduce the resonance within the arbitrarily shaped ports for secure anchoring.

关键词： boundary element method Laplace equation porous breakwater partially reflecting/absorbing harbor wall moored ship motion

Reinforcement Learning to Improve Qos and Minimizing Delay in IoT

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Intelligent Automation & soft Computing 2023年第5期36卷 1603-1612页

作者： Mahendrakumar subramaniam V.Vedanarayanan Azath Mubarakali s.sathiya priya Department of Electronics and Communications Engineering Velalar College of Engineering and TechnologyErodeTamil NaduIndia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennaiTamil NaduIndia College of Computer science King Khalid UniversityAbhaSaudi Arabia Department of Electronics and Communications

Machine Learning concepts have raised executions in all knowledge domains,including the Internet of Thing(IoT)and several business domains.Quality of service(Qos)has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors,information and usage.sen-sor data gathering is an efﬁcient solution to collect information from spatially dis-seminated IoT nodes.Reinforcement Learning Mechanism to improve the Qos(RLMQ)and use a Mobile sink(Ms)to minimize the delay in the wireless IoT s proposed in this paper.Here,we use machine learning concepts like Rein-forcement Learning(RL)to improve the Qos and energy efﬁciency in the Wire-less sensor Network(WsN).The Ms collects the data from the Cluster Head(CH),and the RL incentive values select CH.The incentives value is computed by the Qos parameters such as minimum energy utilization,minimum bandwidth utilization,minimum hop count,and minimum time delay.The Ms is used to col-lect the data from CH,thus minimizing the network delay.The sleep and awake scheduling is used for minimizing the CH dead in the WsN.This work is simu-lated,and the results show that the RLMQ scheme performs better than the base-line protocol.Results prove that RLMQ increased the residual energy,throughput and minimized the network delay in the WsN.

关键词： Wireless sensor network quality of service parameters Internet of things reinforcement learning mobile sink