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Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

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Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara zivkovic miodrag zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia Informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and Informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and Informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

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Computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： Nebojsa Bacanin Khaled Alhazmi miodrag zivkovic K.Venkatachalam Timea Bezdan Jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at ***,to a very large search space,it is very difficult to find the proper values of connection weights and *** traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local *** commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient *** an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network *** thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural *** the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization *** results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local *** proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on *** mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

Protecting Data Mobility in Cloud Networks Using Metadata Security

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Computer Systems Science & Engineering 2022年第7期42卷 105-120页

作者： R.Punithavathi M.Kowsigan R.Shanthakumari miodrag zivkovic Nebojsa Bacanin Marko Sarac Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science and Engineering School of ComputingSRM Institute of TechnologyKattankulathur CampusChennai603203India Department of Information Technology Kongu Engineering CollegeErode638060India Singidunum University Belgrade11000Serbia

At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement *** mobility in cloud environments uses protection protocols and algorithms to secure sensitive user ***,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are *** to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data *** present,several security solu-tions support cloud *** of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security ***,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing *** our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud *** analyzing metadata,data are classiﬁed as secured and open data based on their *** data are sensitive user data,whereas open data are open to the *** the basis of data classiﬁcation,secured data are applied to the DMoS algorithm to achieve high security in *** pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated.

关键词： Data mobility data security cloud computing big data DMoS algorithm

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

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Computer Systems Science & Engineering 2022年第10期43卷 317-332页

作者： R.S.Latha B.Saravana Balaji Nebojsa Bacanin Ivana Strumberger miodrag zivkovic Milos Kabiljo Department of Computer Science and Engineering Kongu Engineering CollegeErode638060TamilnaduIndia Department of Information Technology Lebanese French UniversityErbilIraq Singidunum University Belgrade160622Serbia

Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage *** user’s access over the internet creates massive data processing over the *** data require an intelligent feature selection model by addressing huge varieties of *** feature selection techniques are only applicable to simple data *** techniques are needed in big data processing and machine learning for an efficient *** feature selection algorithms read the input features as they ***,the features are preprocessed and ***,an algorithm does not consider the *** feature selection,all features are misread as ***,a less optimal solution is *** our proposed research,we focus on the feature selection by using supervised learning techniques called grey wolf optimization(GWO)with decomposed random differential grouping(DrnDG-GWO).First,decomposition of features into subsets based on relatedness in variables is *** differential grouping is performed using a fitness value of two ***,every subset is regarded as a population in GWO *** combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this *** the features are optimized,we classify using advanced kNN process for accurate data *** result of DrnDGGWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed *** accuracy and time complexity of the proposed algorithm are 98%and 5 s,which are better than the existing techniques.

关键词： Feature selection data optimization supervised learning swarm intelligence decomposed random differential grouping grey wolf optimization

COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

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Computers, Materials & Continua 2022年第7期72卷 1685-1698页

作者： Nebojsa Budimirovic E.Prabhu Milos Antonijevic miodrag zivkovic Nebojsa Bacanin Ivana Strumberger K.Venkatachalam Singidunum University Belgrade11000Serbia Department of Electronics and Communication Engineering Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatore641112India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic

Computerized tomography(CT)scans and X-rays play an important role in the diagnosis of COVID-19 and *** the basis of the image analysis results of chest CT and X-rays,the severity of lung infection is monitored using a *** researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and *** overcome these issues,our proposed study implements four cascaded ***,for pre-processing,a mean filter is ***,texture feature extraction uses principal component analysis(PCA).Third,a modified whale optimization algorithm is used(MWOA)for a feature selection *** severity of lung infection is detected on the basis of age ***,image classification is done by using the proposed MWOAwith the salp swarm algorithm(MWOA-SSA).MWOA-SSA has an accuracy of 97%,whereas PCA and MWOA have accuracies of 81%and 86%.The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA(84.4%)and MWOA(95.2%).MWOA-SSA outperforms other algorithms with a specificity of 97.8%.This proposed method improves the effective classification of lung affected images from large datasets.

关键词： PCA WOA CT-image lung infection COVID-19

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

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Computer Systems Science & Engineering 2022年第5期41卷 751-765页

作者： G.R.Sreekanth S.Ahmed Najat Ahmed Marko Sarac Ivana Strumberger Nebojsa Bacanin miodrag zivkovic Department of Computer Science and Engineering Kongu Engineering CollegeErode638060India Department of Computer Engineering Lebanese French UniversityErbil44001Iraq Singidunum University Belgrade11000Serbia

Abstract:Fog computing provides quality of service for cloud *** the data computation intensifies,edge computing becomes ***,mobile fog computing is used for reducing traffic and the time for data computation in the *** previous studies,software-defined networking(SDN)and network functions virtualization(NFV)were used separately in edge *** industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance,reliability,and ***/NFV is still in *** traditional Internet of things(IoT)data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision *** paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog *** provides a generalization structure of the forwarding plane,which is separated from the control ***,NFV concentrates on virtualization by combining the forwarding model with virtual network functions(VNFs)as a single or chain of VNFs,which leads to interoperability and *** orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions:task creation,modification,operation,and *** proposed architecture is simulated on the EstiNet simulator,and total time delay,reliability,and satisfaction are used as evaluation *** simulation results are compared with the results of existing architectures,such as software-defined unified virtual monitoring function and ASTP,to analyze the performance of the proposed *** analysis results indicate that our proposed architecture achieves better performance in terms of total time delay(1800 s for 200 IoT devices),reliability(90%),and satisfaction(90%).

关键词： Mobile fog computing edge computing edge node IoT softwaredefined networking network functions virtualization orchestrator

Hybrid GLFIL Enhancement and Encoder Animal Migration Classification for Breast Cancer Detection

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Computer Systems Science & Engineering 2022年第5期41卷 735-749页

作者： S.Prakash M.Vinoth Kumar R.Saravana Ram miodrag zivkovic Nebojsa Bacanin Milos Antonijevic Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Anna UniversityUniversity College of Engineering DindigulDindigul624622India Department of Electronics and Communication Engineering Anna UniversityUniversity College of Engineering Dindigul624622India Singidunum University Belgrade11000Serbia

Breast cancer has become the second leading cause of death among women *** India,a woman is diagnosed with breast cancer every four *** has been no known basis behind it,and detection is extremely challenging among medical scientists and researchers due to unknown *** India,the ratio of women being identified with breast cancer in urban areas is 22:*** for this disease are micro calcification,lumps,and masses in mammogram *** sources are mostly used for early *** mammography is used for breast cancer *** this study,we introduce a new hybrid wavelet filter for accurate image *** main objective of enhancement is to produce quality images for detecting cancer sections in *** enhancement is the main step where the quality of the input image is improved to detect cancer *** this study,we use a combination of two filters,namely,Gabor and *** edges are detected using the Canny detector to smoothen the ***-quality enhanced image is obtained through the Gabor-Legendre filter(GLFIL)*** image is used by classification *** migration optimization with neural network is implemented for classifying the *** output is compared to existing filter ***,the accuracy achieved by the proposed technique is 98%,which is higher than existing algorithms.

关键词： Breast cancer Gabor filter Legendre filter GLFIL algorithm animal migration optimization neural networks

Ternary Au-Ga-Sb system：calculation of thermodynamic properties using general solution model

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Rare Metals 2016年第3期35卷 262-268页

作者： Lidija Gomidzelovic Dragana zivkovic Ljubisa Balanovic Dragan Manasijevic Mining and Metallurgy Institute 19210 Bor Serbia Technical Faculty University of Belgrade 19210 Bor Serbia

The calculated results of thermodynamic properties in ternary Au-Ga-Sb system by general solution model were presented in this *** calculations were carried out in nine cross sections from all three corners in the temperature range of 973–1,573 K,and integral molar excess Gibbs energies,partial molar Gibbs energies,activity coefficients and activities for all components are *** activity of gold and gallium shows negative deviation from Rault＇s law for all investigated sections,while antimony activity is close to the line of ideal conditions and even coincides with the line for high content of antimony in the *** obtained by calculation were compared with those found in *** is concluded that compared values show reasonable good agreement.

关键词： Alloy thermodynamics Calculation Au-Ga-Sb system General solution model

Relationship between mass discharge rate and granular temperature of silo flow with variance of outlets

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Particuology 2022年第4期20卷 76-82页

作者： Quan Chen Ran Li Wengzheng Xiu Vladimir zivkovic Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China School of Medical Instrument and Food Engineering University of Shanghai for Science and Technology Shanghai 200093China School of Engineering.Newcastle University NE17RUUnited Kingdom

A lower granular temperature zone near the orifice in silos was found by speckle visibility spectroscopy ***,the values of granular temperature in this area are inversely proportional to the mass discharge rate(MDR).This finding that links the macroscopic MDR with mesoscopic dynamics temperature of the particle system,is obtained by looking at the effect that different outlet sizes of silos on MDR and granular temperature *** from evidencing the relevance of a parameter that has been traditionally overlooked in silos,this relationship supposes a benchmark with which to explore the influence of small orifice sizes on *** these,we found that the frequency of intermittent clogging and their dependence on the size of the outlet.

关键词： Silos Granular flow Granular temperature Mass discharge rate