Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

Cyberbullying(CB)is a distressing online behavior that disturbs mental health significantly.Earlier studies have employed statistical and Machine Learning(ML)techniques for CB detection.With this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social networks.The proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter optimization.In addition,GloVe approach is employed in the generation of word embedding.Besides,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for prediction.Moreover,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)algorithm.In order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining

Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures.

Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases.Magnetic resonance imaging(MRI)is a widely utilized tool for the classification and detection of prostate cancer.Since the manual screening process of prostate cancer is difficult,automated diagnostic methods become essential.This study develops a novel Deep Learning based Prostate Cancer Classification(DTL-PSCC)model using MRI images.The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors.In addition,the fuzzy k-nearest neighbour(FKNN)model is utilized for classification process where the class labels are allotted to the input MRI images.Moreover,the membership value of the FKNN model can be optimally tuned by the use of krill herd algorithm(KHA)which results in improved classification performance.In order to demonstrate the good classification outcome of the DTL-PSCC technique,a wide range of simulations take place on benchmark MRI datasets.The extensive comparative results ensured the betterment of the DTL-PSCC technique over the recent methods with the maximum accuracy of 85.09%.

Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially increased.To handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative data.So,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated information.This paper presents a Deep Learning based Attention Long Short Term Memory(DLALSTM)Model for Multi-document Biomedical Text Summarization.The proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further processing.Then,the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical documents.In order to tune the summarization performance of the DL-ALSTM model,chaotic glowworm swarm optimization(CGSO)algorithm is employed.Extensive experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed dataset.Comprehensive comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.

Detection of Lung Tumor Using ASPP-Unet with Whale Optimization Algorithm

The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.

Optimal Bidirectional LSTM for Modulation Signal Classification in Communication Systems

Modulation signal classification in communication systems can be considered a pattern recognition problem.Earlier works have focused on several feature extraction approaches such as fractal feature,signal constellation reconstruction,etc.The recent advent of deep learning(DL)models makes it possible to proficiently classify the modulation signals.In this view,this study designs a chaotic oppositional satin bowerbird optimization(COSBO)with bidirectional long term memory(BiLSTM)model for modulation signal classification in communication systems.The proposed COSBO-BiLSTM technique aims to classify the different kinds of digitally modulated signals.In addition,the fractal feature extraction process takes place by the use of Sevcik Fractal Dimension(SFD)approach.Moreover,the modulation signal classification process takes place using BiLSTM with fully convolutional network(BiLSTM-FCN).Furthermore,the optimal hyperparameter adjustment of the BiLSTM-FCN technique takes place by the use of COSBO algorithm.In order to ensure the enhanced classification performance of the COSBO-BiLSTM model,a wide range of simulations were carried out.The experimental results highlighted that the COSBO-BiLSTM technique has accomplished improved performance over the existing techniques.

Energy Aware Secure Cyber-Physical Systems with Clustered Wireless Sensor Networks

Recently,cyber physical system(CPS)has gained significant attention which mainly depends upon an effective collaboration with computation and physical components.The greatly interrelated and united characteristics of CPS resulting in the development of cyber physical energy systems(CPES).At the same time,the rising ubiquity of wireless sensor networks(WSN)in several application areas makes it a vital part of the design of CPES.Since security and energy efficiency are the major challenging issues in CPES,this study offers an energy aware secure cyber physical systems with clustered wireless sensor networks using metaheuristic algorithms(EASCPSMA).The presented EASCPS-MA technique intends to attain lower energy utilization via clustering and security using intrusion detection.The EASCPSMA technique encompasses two main stages namely improved fruit fly optimization algorithm(IFFOA)based clustering and optimal deep stacked autoencoder(OSAE)based intrusion detection.Besides,the optimal selection of stacked autoencoder(SAE)parameters takes place using root mean square propagation(RMSProp)model.The extensive performance validation of the EASCPS-MA technique takes place and the results are inspected under varying aspects.The simulation results reported the improved effectiveness of the EASCPS-MA technique over other recent approaches interms of several measures.

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images.Hyperspectral remote sensing contains acquisition of digital images from several narrow,contiguous spectral bands throughout the visible,Thermal Infrared(TIR),Near Infrared(NIR),and Mid-Infrared(MIR)regions of the electromagnetic spectrum.In order to the application of agricultural regions,remote sensing approaches are studied and executed to their benefit of continuous and quantitativemonitoring.Particularly,hyperspectral images(HSI)are considered the precise for agriculture as they can offer chemical and physical data on vegetation.With this motivation,this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification(HOADTL-CC)model onHyperspectralRemote Sensing Images.The presentedHOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images.To accomplish this,the presentedHOADTL-CC model involves the design ofHOAwith capsule network(CapsNet)model for generating a set of useful feature vectors.Besides,Elman neural network(ENN)model is applied to allot proper class labels into the input HSI.Finally,glowworm swarm optimization(GSO)algorithm is exploited to fine tune the ENNparameters involved in this article.The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects.Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.

Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

Recent advancements of the intelligent transportation system(ITS)provide an effective way of improving the overall efficiency of the energy management strategy(EMSs)for autonomous vehicles(AVs).The use of AVs possesses many advantages such as congestion control,accident prevention,and etc.However,energy management and traffic flow prediction(TFP)still remains a challenging problem in AVs.The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs.In this view,this paper presents novel sustainable energy management with traffic flow prediction strategy(SEM-TPS)for AVs.The SEM-TPS technique applies type II fuzzy logic system(T2FLS)energy management scheme to accomplish the desired engine torque based on distinct parameters.In addition,the membership functions of the T2FLS scheme are chosen optimally using the barnacles mating optimizer(BMO).For accurate TFP,the bidirectional gated recurrent neural network(Bi-GRNN)model is used in AVs.A comprehensive experimental validation process is performed and the results are inspected with respect to several evaluation metrics.The experimental outcomes highlighted the supreme performance of the SEM-TPS technique over the recent state of art approaches.

Echo Location Based Bat Algorithm for Energy Efficient WSN Routing

Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes popular.WSN has marked its importance in both practical and research domains.Energy is the most significant resource,the important challenge in WSN is to extend its lifetime.The energy reduction is a key to extend the network’s lifetime.Clustering of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving WSN.In this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm(ECHO-BAT).ECHO-BAT works in two stages.First Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using BAT algorithm.The second stage aims to find the nodes if any,with high residual energy within each cluster.CHs will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s lifetime.The performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering Algorithm(PEZCA)and Chaotic Firefly Algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution time.Simulation results show that ECHO-BAT outperforms the other methods in all the considered measures.The overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.

Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification

Atherosclerosis diagnosis is an inarticulate and complicated cognitive process.Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions.Since the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research communities.This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification(MDL-BADDC)model.The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter tuning.Besides,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection technique.Moreover,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis disease.Furthermore,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter values.In order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical datasets.The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,etc.At the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor nodes.WSN comprises a massive number of SNs and records the position of the nodes,which becomes a tedious process.Besides,the SNs might be subjected to node mobility and the position alters with time.So,a precise node localization(NL)manner is required for determining the location of the SNs.In this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for WSN.The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes.The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season.In addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown SNs.For demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment.Technological revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart devices.One of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through internet.IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data.With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data.This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT.The proposed Machine Learning based Block Chain Technology malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and blockchain.Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight.Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes.Furthermore,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy.ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.

Blockchain Driven Metaheuristic Route Planning in Secure Vehicular Adhoc Networks

Nowadays,vehicular ad hoc networks(VANET)turn out to be a core portion of intelligent transportation systems(ITSs),that mainly focus on achieving continual Internet connectivity amongst vehicles on the road.The VANET was utilized to enhance driving safety and build an ITS in modern cities.Driving safety is a main portion of VANET,the privacy and security of these messages should be protected.In this aspect,this article presents a blockchain with sunflower optimization enabled route planning scheme(BCSFO-RPS)for secure VANET.The presented BCSFO-RPSmodel focuses on the identification of routes in such a way that vehicular communication is secure.In addition,the BCSFO-RPS model employs SFO algorithm with a fitness function for effectual identification of routes.Besides,the proposed BCSFO-RPS model derives an intrusion detection system(IDS)encompassing two processes namely feature selection and classification.To detect intrusions,correlation based feature selection(CFS)and kernel extreme machine learning(KELM)classifier is applied.The performance of the BCSFO-RPS model is tested using a series of experiments and the results reported the enhancements of the BCSFO-RPS model over other approaches with maximum accuracy of 98.70%.