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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

An Optimized Transfer Learning Model Based Kidney stone Classification

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Computer systems science & Engineering 2023年第2期44卷 1387-1395页

作者： s.devi Mahalakshmi Department of Computer science and Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia

The kidney is an important organ of humans to purify the *** healthy function of the kidney is always essential to balance the salt,potassium and pH levels in the ***,the failure of kidneys happens easily to human beings due to their lifestyle,eating habits and diabetes *** pre-diction of kidney stones is compulsory for timely *** processing-based diagnosis approaches provide a greater success rate than other detection *** this work,proposed a kidney stone classiﬁcation method based on optimized Transfer Learning(TL).The Deep Convolutional Neural Network(DCNN)models of DenseNet169,MobileNetv2 and GoogleNet applied for clas-siﬁ*** combined classiﬁcation results are processed by ensemble learning to increase classiﬁcation *** hyperparameters of the DCNN model are adjusted by the metaheuristic algorithm of Gorilla Troops Optimizer(GTO).The proposed TL model outperforms in terms of all the parameters compared to other DCNN models.

关键词： DCNN GTO kidney stone transfer learning

AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias

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Computers, Materials & Continua 2024年第2期78卷 2137-2158页

作者： s.Jayanthi s.Prasanna devi Department of Computer science and Engineering SRM Institute of Science and Technology-Vadapalani CampusChennaiTamil NaduIndia

In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)***,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation *** glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of *** approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model *** validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our *** the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for *** second pipeline is dedicated to feature extraction and classification,utilizing deep learning ***,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class *** ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model *** our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.

关键词： Automated machine learning neural networks deep learning arrhythmias

Design of Hybrid True Random Number Generator for Cryptographic Applications

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Computer systems science & Engineering 2022年第11期43卷 423-437页

作者： s.Nithya devi s.sasipriya Electronics and Communication Engineering Dr.N.G.P.Institute of TechnologyCoimbatore641110India Electronics and Communication Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India

In real-time applications,unpredictable random numbers play a major role in providing cryptographic and encryption *** of the existing random number generators are embedded with the complex nature of an amplifier,ring oscillators,or ***,this research focused more on implementing a Hybrid Nature of a New Random Number *** key objective of the proposed methodology relies on the utilization of True random number *** randomness is *** additions of programmable delay lines will reduce the processing time and maintain the quality of *** performance comparisons are carried out with power,delay,and lookup *** proposed architecture was executed and verified using *** Hybrid TRNG is evaluated under simulation and the obtained results outperform the results of the conventional random generators based on slices,area and Lookup *** experimental observations show that the proposed Hybrid True Random Number Generator(HTRNG)offers high operating speed and low power consumption.

关键词： True random number generators lookup table random number generator digital circuit seed

Modified Differential Box Counting in Breast Masses for Bioinformatics Applications

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Computers, Materials & Continua 2022年第2期70卷 3049-3066页

作者： s.sathiya devi s.Vidivelli University College of Engineering-BIT Campus TrichyIndia Anna University ChennaiTamilnaduIndia

Breast cancer is one of the common invasive cancers and stands at second position for death after lung *** present research work is useful in image processing for characterizing shape and gray-scale *** proposed Modified Differential Box Counting(MDBC)extract Fractal features such as Fractal Dimension(FD),Lacunarity,and succolarity for shape *** traditional DBC method,the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different *** problem is overcome by the proposedMDBCmethod that uses box over counting and under counting that covers the whole image with required *** MDBC method,the suitable box size selection and Under Counting shifting rule computation handles over counting *** advantage of the model is that the proposed MDBC work with recently developed methods showed that our method outperforms automatic detection and *** extracted features are fed to K-Nearest Neighbour(KNN)and support Vector Machine(sVM)categorizes the mammograms into normal,benign,and *** method is tested on mini MIAs datasets yields good results with improved accuracy of 93%,whereas the existing FD,GLCM,Texture and shape feature method has 91%accuracy.

关键词： Breast cancer computer-aided diagnosis K-nearest neighbour mammograms modified differential box counting support vector machine

An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in streaming Data

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China Communications 2024年第5期21卷 249-260页

作者： R.Rajakumar s.sathiya devi srinivasa Ramanujan Centre SASTRA Deemed UniversityKumbakonamTamil Nadu 612001India University College of Engineering BIT CampusAnna UniversityTiruchirappalli 620024India

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,*** recent developments of deep learning(DL)models find helpful in the detection and classification of *** article designs an oversampling with an optimal deep learning-based streaming data classification(Os-ODLsDC)*** aim of the OsODLsDC model is to recognize and classify the presence of anomalies in the streaming *** proposed Os-ODLsDC model initially undergoes preprocessing *** streaming data is unbalanced,support vector machine(sVM)-synthetic Minority Over-sampling Technique(sVM-sMOTE)is applied for oversampling ***,the Os-ODLsDC model employs bidirectional long short-term memory(Bi LsTM)for AD and ***,the root means square propagation(RMsProp)optimizer is applied for optimal hyperparameter tuning of the Bi LsTM *** ensuring the promising performance of the Os-ODLsDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDs 2018,KDD-Cup 1999,and NsL-KDD datasets.

关键词： anomaly detection deep learning hyperparameter optimization oversampling sMOTE streaming data

screening of stimulatory effects of dietary risk factors on mouse intestinal cell kinetics

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World Journal of Gastroenterology 2005年第2期11卷 242-248页

作者： Pooja shivshankar shyamala C.s.devi Department of Biochemistry and Molecular Biology University of MadrasGuindy CampusChennai- 600 025India

AIM: Although epidemiological and experimental studies validate influence of genetic, environmental and dietary factors in the causation of various types of cancers including colon, results from all these sources are inconclusive. Hypothesizing that high fat diet and obesity are among the major predisposing factors in the incidence of colon cancer, we evaluated the role of diet constituted with food material derived from a tropical plant, Tamarindus indica Linn (TI). METHODs: A two part randomized double-blind study was conducted employing inbred swiss albino mice from a single generation for the whole investigation. One day-old neonates (n=12) were subcutaneously administered with monosodium glutamate (MsG) to induce obesity (OB). At weaning these animals were maintained on modified AIN-76 diet supplemented with 10% TI and 10% fat bolus (w/w,TIFB) for 8 wk. subsequently, in the second part of study, four groups of animals belonging to the same generation, age and gender (n=12 per group), were maintained on: AIN-76 control diet (CD); AIN-76 mixed with 10% TI extract (TI); and, mixed with 10% TI and 10% FB (TIFB) for 8 wk, to determine intestinal crypt cell proliferation, functionally-specific enzyme activities, fermentation profile, and energy preferences. REsULTs: We observed a significant increase in the crypt cell production rate in distal colonic segment of experimental animals when compared with the controls. This segment also contained significantly low butyrate levels compared to control and TIFB groups. All the experimental groups showed a gross decrease in the enzyme activities viz., succinate dehydrogenase, acid-galactosidase and dipeptidyl amino peptidase IV demonstrating pathological stress caused by the test regimens, and an altered metabolic flux in the cellular environment. CONCLUsION: We have demonstrated a cumulative response to the three dietary factors, one of which (TI) is reported, herein, for the first time to modulate kinetics of large intesti

关键词： Large intestinal mucosa Kinetics Tamarindus indica Linn Diet

Ensemble Learning Based Collaborative Filtering with Instance selectionand Enhanced Clustering

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Computers, Materials & Continua 2022年第5期71卷 2419-2434页

作者： G.Parthasarathy s.sathiya devi Anna University Chennai600025India University College of Engineering BIT CampusAnna UniversityTiruchirappalli620024India

Recommender system is a tool to suggest items to the users from the extensive history of the user’s ***,it is an emerging research area concerning academics and industries,where it suffers from sparsity,scalability,and cold start *** paper addresses sparsity,and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie *** this paper,an effective movie recommendation system is proposed by Classification and Regression Tree(CART)algorithm,enhanced Balanced Iterative Reducing and Clustering using Hierarchies(BIRCH)algorithm and truncation *** this research paper,a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process,where the proposed algorithm is named as enhanced *** proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad *** this paper,the proposed model is tested on Movielens dataset,and the performance is evaluated by means of Mean Absolute Error(MAE),precision,recall and *** experimental results showed the superiority of proposed model in movie recommendation compared to the existing *** proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M ***,the proposed model obtained 0.83 of precision,0.86 of recall and 0.86 of f-measure on Movielens 100k dataset,which are effective compared to the existing models in movie recommendation.

关键词： Clustering ensemble learning feature selection gradient boost tree instance selection truncation parameter

Carbon stock and rate of carbon sequestration in Dipterocarpus forests of Manipur,Northeast India

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Journal of Forestry Research 2015年第2期26卷 315-322页

作者： L.supriya devi P.s.Yadava Department of Life sciences Manipur University

We examined the carbon stock and rate of carbon sequestration in a tropical deciduous forest dominated by Dipterocarpus tuberculatus in Manipur,North East *** of aboveground biomass was determined by harvest method and multiplied with density of tree *** aboveground biomass was between18.27–21.922 t ha-1and the carbon stock ranged from9.13 to 10.96 t C ha-1across forest *** biomass and carbon stock increased with the increase in tree *** rate of carbon sequestration varied from1.4722 to 4.64136 t ha-1year-1among the dominant tree species in forest stands in tropical deciduous forest *** rate of carbon sequestration depends on species composition,the density of large trees in different girth classes,and anthropogenic disturbances in the present forest *** work is required to identify tree species having the highest potential to sequester CO2 from the atmosphere,which could lead to recommendations for tree plantations in a degraded ecosystem.

关键词： Carbon budget Carbon sequestration Dipterocarpus Biomass Net production