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Student Behavior modeling for an E-Learning System Offering Personalized Learning Experiences

Computer Systems Science & Engineering 2022年第3期40卷 1127-1144页

作者： k.Abhirami m.k.kavitha devi Department of Computer Science and Engineering Kings College of EngineeringPudukkottai613303India Department of Computer Science and Engineering Thiagarajar College of EngineeringMadurai625015India

With the advent of computing and communication technologies,it has become possible for a learner to expand his or her knowledge irrespective of the place and ***-based learning promotes active and independent *** scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning *** digital learning improves the quality of teaching and also promotes educational ***,the challenges in e-learning platforms include dissimilarities in learner’s ability and needs,lack of student motivation towards learning activities and provision for adaptive learning *** quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional *** is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning *** is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest *** this research work,a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective *** to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning *** adaptive e-learning system suits every category of learner,improves the learner’s performance and paves way for offering personalized learning experiences.

关键词： Learner behavior modeling e-learning intelligent learning system machine learning algorithm

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Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

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Computer Systems Science & Engineering 2023年第5期45卷 1743-1758页

作者： G.Sharmila m.k.kavitha devi Department of Computer Science&Engineering Syed Ammal Engineering CollegeRamanathapuram623502India Department of Computer Science&Engineering Thiagarajar College of EngineeringMadurai625015India

Over the past era,subgraph mining from a large collection of graph database is a crucial *** addition,scalability is another big problem due to insufficient *** are several security challenges associated with subgraph mining in today’s on-demand *** address this downside,our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs(BCCA-LSDG).The two-fold process is handled in the proposed BCCA-LSDG:graph indexing and authenticated query search(query processing).A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed *** resolve the issues and provide safe big data transmission,the proposed technique also combines blockchain with a consensus algorithm *** of the big data is ensured by dividing the BC network into distinct networks,each with a restricted number of allowed entities,data kept in the cloud gate server,and data analysis in the *** consensus algorithm is crucial for maintaining the speed,performance and security of the *** Dual Similarity based mapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature ***,the graph index refinement process is undertaken to improve the query *** query error,fuzzy logic is used to refine the index of the graph *** proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems,and the combination of blockchain and subgraph provides a secure communication platform,according to the findings.

关键词： Big data blockchain consensus algorithm trust management graph index

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Regression analysis of squeezing-induced hybrid nanofluid flow in Darcy-Forchheimer porous medium

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Applied mathematics and mechanics(English Edition) 2025年第1期46卷 193-208页

作者： k.mUHAmmAD m.SARFRAZ Department of Humanities and Sciences School of Electrical Engineering and Computer Science National University of Sciences and Technology Department of mathematics Quaid-i-Azam University

This article presents a mathematical model addressing a scenario involving a hybrid nanofluid flow between two infinite parallel plates. One plate remains stationary, while the other moves downward at a squeezing velocity. The space between these plates contains a Darcy-Forchheimer porous medium. A mixture of water-based fluid with gold(Au) and silicon dioxide(Si O2) nanoparticles is formulated. In contrast to the conventional Fourier's heat flux equation, this study employs the Cattaneo-Christov heat flux equation. A uniform magnetic field is applied perpendicular to the flow direction, invoking magnetohydrodynamic(mHD) effects. Further, the model accounts for Joule heating, which is the heat generated when an electric current passes through the fluid. The problem is solved via NDSolve in mATHEmATICA. Numerical and statistical analyses are conducted to provide insights into the behavior of the nanomaterials between the parallel plates with respect to the flow, energy transport, and skin *** findings of this study have potential applications in enhancing cooling systems and optimizing thermal management strategies. It is observed that the squeezing motion generates additional pressure gradients within the fluid, which enhances the flow rate but reduces the frictional drag. Consequently, the fluid is pushed more vigorously between the plates, increasing the flow velocity. As the fluid experiences higher flow rates due to the increased squeezing effect, it spends less time in the region between the plates. The thermal relaxation, however, abruptly changes the temperature, leading to a decrease in the temperature fluctuations.

关键词： convective boundary condition Darcy-Forchheimer medium hybrid nanofluid Joule heating magnetohydrodynamic (mHD) numerical solution squeezing flow regression analysis O361 O175.2

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Organic micro-pollutant removal in liquid-phase using carbonized silk cotton hull

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Journal of Environmental Sciences 2008年第9期20卷 1046-1054页

作者： m Sathishkumar A R Binupriya D kavitha R Selvakumar k k Sheema J G Choi Department of Food Science and Technology Institute of Agricultural Science and Technology Chonbuk National University Department of Chemical Engineering Yonsei University Department of Biotechnology Bharathiar University Department of Plant biology and Biotechnology Government Arts College National Agricultural Products Quality management Services

Phenolic compounds constitute one of the major pollutants in the modern world. Although many physical and chemical treatment technologies for their removal exist, most of them are economically not feasible. The present study was aimed at using silk cotton hull, a potent agricultural waste as an adsobent for removal of 2,4-dichlorophenol (2,4-DCP), which was used as a model phenolic compound. The process parameters were investigated and optimized conditions were determined. The equilibrium time was found to ...

关键词： silk cotton hull carbon adsorption 2 4-DCP temperature kinetic study

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A Novel Soft Clustering Approach for Gene Expression Data

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Computer Systems Science & Engineering 2022年第12期43卷 871-886页

作者： E.kavitha R.Tamilarasan Arunadevi Baladhandapani m.k.Jayanthi kannan A Constituent College of Anna University University College of EngineeringVillupuram605103India A Constituent College of Anna University University College of EngineeringPattukkottai614701India Department of Electronics and Communication Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Computer Science Engineering Faculty of Engineering and TechnologyJAIN(Deemed to be University)Bangalore562112India

Gene expression data represents a condition matrix where each rowrepresents the gene and the column shows the condition. micro array used todetect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messengerRNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with thevolume of data containing imprecision and outliers increases the challenges indealing with them. Clustering methods are hence essential to identify the patternspresent in massive gene data. many techniques involve hierarchical, partitioning,grid based, density based, model based and soft clustering approaches for dealingwith the gene expression data. Understanding the gene regulation and other usefulinformation from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate onproviding a soft clustering approach for analyzing the gene expression data. Thepopulation elements are grouped based on the fuzziness principle and a degree ofmembership is assigned to all the elements. An improved Fuzzy clustering byLocal Approximation of memberships (FLAmE) is proposed in this workwhich overcomes the limitations of the other approaches while dealing with thenon-linear relationships and provide better segregation of biological functions.

关键词： Reinforcement membership centroid threshold statistics bioinformatics gene expression data

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Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

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Computer Systems Science & Engineering 2023年第4期45卷 483-495页

作者： B.Chellapraba D.manohari k.Periyakaruppan m.S.kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on *** IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the *** was handled as a resource-rich solution to aforementioned *** high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user ***,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud *** presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud *** achieves the makespan by performing optimum TS procedures with various aspects of incoming *** designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim *** experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

关键词： metaheuristics task scheduling cloud computing internet of things makespan red fox optimizer

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Hybrid Convolutional Neural Network and Long Short-Term memory Approach for Facial Expression Recognition

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Intelligent Automation & Soft Computing 2023年第1期35卷 689-704页

作者： m.N.kavitha A.Rajivkannan Department of Computer Science&Engineering Kongu Engineering CollegePerunduraiErode638060Tamil NaduIndia Department of Computer Science&Engineering K.S.R.College of EngineeringTiruchengode637215Tamil NaduIndia

Facial Expression Recognition(FER)has been an importantfield of research for several *** of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class *** characteristics have not been completely explored in static *** studies used Convolution Neural Networks(CNNs)based on transfer learning and hyperparameter optimizations for static facial emotional *** Swarm Optimizations(PSOs)have also been used for tuning ***,these methods achieve about 92 percent in terms of *** existing algorithms have issues with FER accuracy and ***,the overall FER performance is degraded *** address this issue,this work proposes a combination of CNNs and Long Short-Term memories(LSTms)called the HCNN-LSTms(Hybrid CNNs and LSTms)approach for *** work is evaluated on the benchmark dataset,Facial Expression Recog Image Ver(FERC).Viola-Jones(VJ)algorithms recognize faces from preprocessed images followed by HCNN-LSTms feature extractions and FER ***,the success rate of Deep Learning Techniques(DLTs)has increased with hyperparameter tunings like epochs,batch sizes,initial learning rates,regularization parameters,shuffling types,and *** proposed work uses Improved Weight based Whale Optimization Algorithms(IWWOAs)to select near-optimal settings for these parameters using bestfitness *** experi-mentalfindings demonstrated that the proposed HCNN-LSTms system outper-forms the existing methods.

关键词： Facial expression recognition Gaussianfilter hyperparameter optimization improved weight-based whale optimization algorithm deep learning(DL)

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Clustering Gene Expression Data Through modified Agglomerative m-CURE Hierarchical Algorithm

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Computer Systems Science & Engineering 2022年第6期41卷 1027-1041页

作者： E.kavitha R.Tamilarasan N.Poonguzhali m.k.Jayanthi kannan A Constituent College of Anna University University College of EngineeringVillupuram605103India A Constituent College of Anna University University College of EngineeringPattukkottai614701India Department of Computer Science and Engineering Manakula Vinayagar Institute of TechnologyPuducherry605107India Department of Computer Science Engineering Faculty of Engineering and TechnologyJAIN(Deemed-To-Be University)Bangalore562112India

Gene expression refers to the process in which the gene information isused in the functional gene product synthesis. They basically encode the proteinswhich in turn dictate the functionality of the cell. The first step in gene expressionstudy involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise andimprecision. For a biological system to function, the essential cellular moleculesmust interact with its surrounding including RNA, DNA, metabolites and proteins. Clustering methods will help to expose the structures and the patterns inthe original data for taking further decisions. The traditional clustering techniquesinvolve hierarchical, model based, partitioning, density based, grid based and softclustering methods. Though many of these methods provide a reliable output inclustering, they fail to incorporate huge data of gene expressions. Also, thereare statistical issues along with choosing the right method and the choice of dissimilarity matrix when dealing with gene expression data. We propose to use amodified clustering algorithm using representatives (m-CURE) in this workwhich is more robust to outliers as compared to k-means clustering and also ableto find clusters with size variances.

关键词： Clustering gene identifiers representatives dimension reduction

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Cooperative Channel and Optimized Route Selection in Adhoc Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1547-1560页

作者： D.manohari m.S.kavitha k.Periyakaruppan B.Chellapraba Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119TamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035TamilnaduIndia Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107TamilnaduIndia Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is *** uses have expanded in lockstep with its *** to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is *** this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the conﬁdence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization *** that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated *** simulation work is validated in this section using the mATLAB ***,steps show how the node acts as a conﬁdent forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between *** cal-culate the conﬁdence of the node at the start of the network by combining the reliability report for theﬁrst hop and the reliability report for the secondary *** then refer to the same node as the conﬁdent node in order to operate as a *** a result,we witness an increase in the leftover energy in the *** percentage of data packets delivered has also increased.

关键词： Adhoc Network conﬁdent forwarder one-hop optimized route selection secondary report channel selection

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Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

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Intelligent Automation & Soft Computing 2023年第2期35卷 2353-2366页

Hyperspectral(HS)image classiﬁcation is a hot research area due to challenging issues such as existence of high dimensionality,restricted training data,*** recognition of features from the HS images is important for effective classiﬁcation ***,the recent advancements of deep learning(DL)models make it possible in several application *** addition,the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of *** this view,this article develops an automated red deer algorithm with deep learning enabled hyperspec-tral image(HSI)classiﬁcation(RDADL-HIC)*** proposed RDADL-HIC technique aims to effectively determine the HSI *** addition,the RDADL-HIC technique comprises a NASNetLarge model with Adagrad ***,RDA with gated recurrent unit(GRU)approach is used for the identiﬁcation and classiﬁcation of *** design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models *** experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different *** comparison study of the RDADL-HIC model demonstrated the enhanced per-formance over its recent state of art approaches.

关键词： Hyperspectral images image classiﬁcation deep learning adagrad optimizer nasnetlarge model red deer algorithm

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