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Diversity of Sodium Transporter HKT1;5 in Genus Oryza

Rice science 2022年第1期29卷 31-46,I0002-I0017页

作者： Shalini PULIPATI Suji SOMASUNDARAM Nitika RANA kavitha kumaresan Mohamed SHAFI Peter CIVÁŇ Gothandapani SELLAMUTHU Deepa JAGANATHAN Prasanna Venkatesan RAMARAVI S.PUNITHA Kalaimani RAJU Shrikant S.MANTRI R.SOWDHAMINI Ajay PARIDA Gayatri VENKATARAMAN M.S.Swaminathan Research Foundation Chennai 600113India National Agri-Food Biotechnology Institute Punjab 140306India Krishi Vigyan Kendra Kanyakumari 629901India National Centre for Biological Sciences/Tata Institute of Fundamental Research Bangalore 560065India National Research Institute for Agriculture Food and Environment UniversityClermont-AuvergneClermont-Ferrand 63000France Forest Molecular Entomology Laboratory Faculty of Forestry and Wood SciencesCzech University of Life SciencesPraguePraha 16500Czech Republic Tamil Nadu Agricultural University Coimbatore 641003India International Business Machines India Chennai 600125India Institute of Life Sciences Bhubaneswar 751023India

Asian cultivated rice shows allelic variation in sodium transporter,OsHKT1;5,correlating with shoot sodium exclusion(salinity tolerance).These changes map to intra/extracellularly-oriented loops that occur between four transmembrane-P loop-transmembrane(MPM)motifs in OsHKT1;5.HKT1;5 sequences from more recently evolved Oryza species(O.sativa/O.officinalis complex species)contain two expansions that involve two intracellularly oriented loops/helical regions between MPM domains,potentially governing transport characteristics,while more ancestral HKT1;5 sequences have shorter intracellular loops.We compared homology models for homoeologous OcHKT 1;5-K and OcHKT1;5-L from halophytic O.coarctata to identify complementary amino acid residues in OcHKT1;5-L that potentially enhance affinity for Na+.Using haplotyping,we showed that Asian cultivated rice accessions only have a fraction of HKT1;5 diversity available in progenitor wild rice species(O.nivara and O.rufipogon).Progenitor HKT1;5 haplotypes can thus be used as novel potential donors for enhancing cultivated rice salinity tolerance.Within Asian rice accessions,10 non-synonymous HKT1;5 haplotypic groups occur.More HKT1;5 haplotypic diversities occur in cultivated indica gene pool compared to japonica.Predominant Haplotypes 2 and 10 occur in mutually exclusive japonica and indica groups,corresponding to haplotypes in O.sativa salt-sensitive and salt-tolerant landraces,respectively.This distinct haplotype partitioning may have originated in separate ancestral gene pools of indica and japonica,or from different haplotypes selected during domestication.Predominance of specific HKT1;5 haplotypes within the 3000 rice dataset may relate to eco-physiological fitness in specific geo-climatic and/or edaphic contexts.

关键词： HKT1 5 diversity single nucleotide polymorphism haplotype bacterial artificial chromosome salinity tolerance sodium transporter Oryza species

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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Computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet.Regrettably,this development has expanded the potential targets that hackers might exploit.Without adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or alteration.The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks.This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)units.The proposed model can identify various types of cyberattacks,including conventional and distinctive forms.Recurrent networks,a specific kind of feedforward neural networks,possess an intrinsic memory component.Recurrent Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended periods.Metrics such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual cyberattacks.RNNs are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection Model.This model utilises Recurrent Neural Networks,specifically exploiting LSTM techniques.The proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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An Efficient Stacked-LSTM Based User Clustering for 5G NOMA Systems

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Computers, Materials & Continua 2022年第9期72卷 6119-6140页

作者： S.Prabha kumaresan Chee Keong Tan Yin Hoe Ng Faculty of Engineering Multimedia UniversityCyberjaya63100Malaysia School of Information Technology Monash UniversitySubang Jaya47500Malaysia

Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.

关键词： Non-orthogonal multiple access(NOMA) deep neural network(DNN) long short term memory(LSTM) temporal channel user clustering

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A Sensitive Wavebands Identification System for Smart Farming

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Computer Systems Science & Engineering 2022年第10期43卷 245-257页

作者： M.kavitha M.Sujaritha Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641025India

Sensing the content of macronutrients in the agricultural soil is an essential task in precision agriculture.It helps the farmers in the optimal use of fertilizers.It reduces the cost of food production and also the negative environmentalimpacts on atmosphere and water bodies due to indiscriminate dosageof fertilizers.The traditional chemical-based laboratory soil analysis methodsdo not serve the purpose as they are hardly suitable for site specific soil management.Moreover,the spectral range used in the chemical-based laboratory soil analysismay be of 350-2500 nm,which leads to redundancy and confusion.Developing sensors based on the discovery of spectral wavebands that respondto soil macronutrient concentrations,on the other hand,is an innovative and successfultechnology since the results are dependable and timely.The goal of thisarticle is to use a supervised neuro-fuzzy based dimensionality reduction approachin the sensor development process to determine sensitive wavebands of soilmacronutrients.Accordingly,the spectral signatures of the soil are collected inan outdoor environment and mapped with its macronutrient concentrations.In thisspectral analysis,the spectral reflectance of 424 wavelengths has been obtainedand these wavelengths are evaluated through combined and individual modesas well.Appropriate wavelengths are selected in each case by minimizing the fuzzy reflectance assessment index.The effectiveness of these selected wavelengthsin each mode is validated by modeling the relation between the reduced reflectancespace and each macronutrient concentration using Partial Least Squares Multi Variable Regression(PLS-MVR)method.Set of optimal wavebands areidentified and the results are compared with the existing systems.

关键词： Sensitive waveband determination macronutrients feuro-fuzzy based dimensionality reduction partial least squares multi variable regression reflectance

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Development of Algorithm for Person Re-Identification Using Extended Openface Method

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Computer Systems Science & Engineering 2023年第1期44卷 545-561页

作者： S.Michael Dinesh A.R.kavitha Anna University Chennai600025India Department of Computer Science&Engineering SRM Institute of Science&TechnologyChennai600026India

Deep learning has risen in popularity as a face recognition technology in recent ***,a deep convolutional neural network(DCNN)developed by Google,recognizes faces with 128 bytes per face.It also claims to have achieved 99.96%on the reputed Labelled Faces in the Wild(LFW)dataset.How-ever,the accuracy and validation rate of Facenet drops down eventually,there is a gradual decrease in the resolution of the images.This research paper aims at developing a new facial recognition system that can produce a higher accuracy rate and validation rate on low-resolution face images.The proposed system Extended Openface performs facial recognition by using three different features i)facial landmark ii)head pose iii)eye gaze.It extracts facial landmark detection using Scattered Gated Expert Network Constrained Local Model(SGEN-CLM).It also detects the head pose and eye gaze using Enhanced Constrained Local Neur-alfield(ECLNF).Extended openface employs a simple Support Vector Machine(SVM)for training and testing the face images.The system’s performance is assessed on low-resolution datasets like LFW,Indian Movie Face Database(IMFDB).The results demonstrated that Extended Openface has a better accuracy rate(12%)and validation rate(22%)than Facenet on low-resolution images.

关键词： Constrained local model enhanced constrained local neuralfield enhanced hog scattered gated expert network support vector machine

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Deep Learning Enabled Financial Crisis Prediction Model for Small-Medium Sized Industries

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Intelligent Automation & Soft Computing 2023年第1期35卷 521-536页

作者： kavitha Muthukumaran K.Hariharanath SSN School of Management KalavakkamChennai603110India

Recently,data science techniques utilize artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to take an influence and grow their businesses.For SMEs,owing to the inexistence of consistent data and other features,evaluating credit risks is difficult and costly.On the other hand,it becomes necessary to design efficient models for predicting business failures orfinancial crises of SMEs.Various data classification approaches forfinancial crisis prediction(FCP)have been presented for predicting thefinancial status of the organization by the use of past data.A major process involved in the design of FCP is the choice of required features for enhanced classifier out-comes.With this motivation,this paper focuses on the design of an optimal deep learning-basedfinancial crisis prediction(ODL-FCP)model for SMEs.The proposed ODL-FCP technique incorporates two phases:Archimedes optimization algorithm based feature selection(AOA-FS)algorithm and optimal deep convo-lution neural network with long short term memory(CNN-LSTM)based data classification.The ODL-FCP technique involves a sailfish optimization(SFO)algorithm for the hyperparameter optimization of the CNN-LSTM method.The performance validation of the ODL-FCP technique takes place using a benchmarkfinancial dataset and the outcomes are inspected in terms of various metrics.The experimental results highlighted that the proposed ODL-FCP technique has out-performed the other techniques.

关键词： Small medium-sized enterprises deep learning FCP financial sector prediction metaheuristics sailfish optimization

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Feature Selection with Optimal Variational Auto Encoder for Financial Crisis Prediction

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Computer Systems Science & Engineering 2023年第4期45卷 887-901页

作者： kavitha Muthukumaran K.Hariharanath Vani Haridasan SSN School of Management KalavakkamChennai603110India

Financial crisis prediction(FCP)received significant attention in the financial sector for decision-making.Proper forecasting of the number of firms possible to fail is important to determine the growth index and strength of a nation’s economy.Conventionally,numerous approaches have been developed in the design of accurate FCP processes.At the same time,classifier efficacy and predictive accuracy are inadequate for real-time applications.In addition,several established techniques carry out well to any of the specific datasets but are not adjustable to distinct datasets.Thus,there is a necessity for developing an effectual prediction technique for optimum classifier performance and adjustable to various datasets.This paper presents a novel multi-vs.optimization(MVO)based feature selection(FS)with an optimal variational auto encoder(OVAE)model for FCP.The proposed multi-vs.optimization based feature selection with optimal variational auto encoder(MVOFS-OVAE)model mainly aims to accomplish forecasting the financial crisis.For achieving this,the proposed MVOFS-OVAE model primarily pre-processes the financial data using min-max normalization.In addition,the MVOFS-OVAE model designs a feature subset selection process using the MVOFS approach.Followed by,the variational auto encoder(VAE)model is applied for the categorization of financial data into financial crisis or non-financial crisis.Finally,the differential evolution(DE)algorithm is utilized for the parameter tuning of the VAE model.A series of simulations on the benchmark dataset reported the betterment of the MVOFS-OVAE approach over the recent state of art approaches.

关键词： Financial crisis prediction forecasting feature selection data classification machine learning

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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 decades.Extraction of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class variances.Facial characteristics have not been completely explored in static pictures.Previous studies used Convolution Neural Networks(CNNs)based on transfer learning and hyperparameter optimizations for static facial emotional recognitions.Particle Swarm Optimizations(PSOs)have also been used for tuning hyperparameters.However,these methods achieve about 92 percent in terms of accuracy.The existing algorithms have issues with FER accuracy and precision.Hence,the overall FER performance is degraded significantly.To 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 FERs.The 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 classifications.Further,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 momentum.This proposed work uses Improved Weight based Whale Optimization Algorithms(IWWOAs)to select near-optimal settings for these parameters using bestfitness values.The 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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Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

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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 time.Web-based learning promotes active and independent learning.Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process.This digital learning improves the quality of teaching and also promotes educational equity.However,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 environment.The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy.It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course.It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level.In 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 attainment.Catering to the demands of e-learner,an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm.An 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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