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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

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

Design,optimization,and characterization of an in-situ pore-forming system for controlled delivery of paliperidone

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Medicine in Novel Technology and Devices 2024年第1期21卷 71-79页

作者： priya Pate Bhumi Ladani Gayatri Patel Ramanbhai Patel College of Pharmacy Charotar University of Science and TechnologyCHARUSAT–CampusChanga388421AnandGujaratIndia

The purpose of the current investigation was to develop a pharmaceutical equivalent osmotic drug delivery formulation of Paliperidone(PLD)in the form of controlled porosity osmotic pump tablets(CPOT)in order to keep the drug's steady state concentration in the *** helps to achieve the greatest therapeutic benefit with the fewest side *** the purpose of identifying various formulation attributes,preliminary trials were *** design was used to study the influence of seven input factors namely drug to polymer ratio,polymer 1(HPMC K100 M)to polymer 2(HPMC K15M),drug to total osmogens,coating level,amount of pore former,concentration of ethyl cellulose and amount of plasticizer on dependent variable similarity factor(f2).Utilizing the Minitab 17,data analysis was *** similarity factor(f2)was computed using the osmotic tablet reference product INVEGA®.The findings demonstrate that each of the seven independent variables significantly affects the similarity *** optimized batch,both core and coated tablets showed acceptable pharmaco-technical *** release profile of the optimized batch tablets was found to be similar to that of reference product with good zero-order release *** release was observed through the channels formed by in-situ pores on tablet surface performed using *** the results it can be concluded that prepared CPOT of PLD was found pharmaceutical equivalent with commercial product which is cost effective and fully compliance with cGMP.

关键词： Controlled release pH-independent release similarity factor Pharmaceutical equivalent Reference product

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

Multi-scale habitat influences sprainting and group size of a freshwater-obligate smooth-coated otter(Lutrogale perspicillata) in Tungabhadra Otter Conservation Reserve, India

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Ecological Processes 2024年第1期13卷 159-175页

作者： Ankit Moun P.Ramesh Kumar M.Malathi priya T.Ramesh Riddhika Kalle sálim Ali Centre for Ornithology and Natural History Deputy Conservator of Forests Bellary Territorial Forest Division Deputy Conservator of Forests Working Plan and Survey and Research WingBellary Territorial Forest Division Centre for Functional Biodiversity University of Kwa Zulu-Natal

Background The impact of changing land-use patterns and associated anthropogenic threats on scale-dependent habitat use of semi-aquatic mustelids in scent-marking and social behaviour can provide important insights into the habitat ecology of smooth-coated otters(Lutrogale perspicillata).Methods We sampled 180 stream segments(100–400 m) as spatial replicates of 60 1-km2 sites to record indirect evidence(i.e. spraints and mass latrine sites) and group sizes of smooth-coated otters along the Tungabhadra Otter Conservation Reserve(TOCR) during the dry season. To quantify habitat, we recorded stream characteristics, riparian vegetation, and anthropogenic disturbances at the local scale, and hydro-environmental characteristics and land uses at the landscape scale. Using Markovian-chain detection and occupancy models, we assessed the multi-scale habitat use of otters in their selection of suitable areas for scent-marking based on repeated presence-absence data on spraint/latrine locations along the TOCR. We further used linear regression techniques to explore relationships between the number of individuals in smooth-coated otter group and hydro-environmental characteristics, spraint/latrine encounter rate, anthropogenic pressure, land cover, topography, and *** At the local scale, the probability of spraint deposition and group size decreased with anthropogenic disturbance while the probability of spraint detections decreased with grass cover. At the landscape scale, the probability of otter site use for spraint deposition and group sizes increased in southeast flowing streams. spraint deposition increased with the proportion of sugarcane fields, whereas in contrast, group size decreased with proportion of sugarcane *** Our findings highlight the first empirical evidence on multi-scale habitat use of a southern Indian population of smooth-coated otters in an inland freshwater ecosystem surrounded by the scrub jungle–agriculture matrix. We sug

关键词： Presence-absence modelling Inland river Freshwater ecosystem semi-aquatic mammal smooth-coated otter

A Novel Outlier Detection with Feature selection Enabled streaming Data Classification

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Intelligent Automation & soft Computing 2023年第2期35卷 2101-2116页

作者： R.Rajakumar s.sathiya Devi Anna University Chennai600025India University College of Engineering BIT CampusAnna UniversityTiruchirappalli620024India

Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor *** investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classiﬁcation problems have received consid-erable *** the same time,the detection of anomalies or outliers and feature selection(Fs)processes becomes *** study develops an outlier detec-tion with feature selection technique for streaming data classiﬁcation,named ODFsT-sDC ***,streaming data is pre-processed in two ways namely categorical encoding and null value *** addition,Local Correla-tion Integral(LOCI)is used which is signiﬁcant in the detection and removal of ***,red deer algorithm(RDA)based Fs approach is employed to derive an optimal subset of ***,kernel extreme learning machine(KELM)classiﬁer is used for streaming data classiﬁ*** design of LOCI based outlier detection and RDA based Fs shows the novelty of the *** order to assess the classiﬁcation outcomes of the ODFsT-sDC technique,a series of simulations were performed using three benchmark *** experimental results reported the promising outcomes of the ODFsT-sDC technique over the recent approaches.

关键词： streaming data classiﬁcation outlier removal feature selection machine learning metaheuristics

Management of strawberry Grey Mold Disease Using Biocontrol Agents and Plant Extracts

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American Journal of Plant sciences 2024年第7期15卷 538-551页

作者： P. sakthi priya srushtideep Angidi Uday Kumar Thera s. V. Nandeesha Thangaswamy Rajesh Department of Plant Pathology College of Post Graduate Studies in Agricultural Sciences Central Agricultural University Imphal India Department of Plant Pathology North Dakota State University Fargo ND USA Department of Plant science and Landscape Architecture University of Maryland College Park MD USA

strawberry (Fragaria × ananassa Duch.) is a significant global soft fruit crop, prized for its nutrient content and pleasant flavor. However, diseases, particularly grey mold caused by Botrytis cinerea Pers. Fr. poses major constraints to strawberry production and productivity. Grey mold severely impacts fruit quality and quantity, diminishing market value. This study evaluated five B. cinerea isolates from various locations in the Ri-Bhoi district of Meghalaya. All isolates were pathogenic, with isolate sGM 2 identified as highly virulent. Host range studies showed the pathogen-producing symptoms in the fava bean pods, marigold, gerbera, and chrysanthemum flowers and in the fava bean, gerbera, and lettuce leaves. In vitro tests revealed that neem extract (15% w/v) achieved the highest mycelial growth inhibition at 76.66%, while black turmeric extract (5% w/v) had the lowest inhibition at 9.62%. Dual culture methods with bio-control agents indicated that Bacillus subtilis recorded the highest mean inhibition at 77.03%, while Pseudomonas fluorescens had the lowest at 20.36% against the two virulent isolates. Pot evaluations demonstrated that B. subtilis resulted in the lowest percent disease index at 20.59%, followed by neem extract at 23.31%, with the highest disease index in the control group at 42.51%. Additionally, B. subtilis significantly improved plant growth, yielding an average of 0.32 kg compared to 0.14 kg in the control. The promising results of B. subtilis and neem leaf extract from this study suggest their potential for eco-friendly managing grey mold in strawberries under field conditions.

关键词： strawberry Grey Mold BCA Plant Extracts Botrytis cinerea

Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless sensor Networks

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Intelligent Automation & soft Computing 2023年第3期35卷 3199-3212页

作者： K.Arutchelvan R.sathiya priya C.Bhuvaneswari Department of Computer and Information science Annamalai UniversityChidambaramTamilnaduIndia Department of Computer science Government Arts and Science CollegeThiruvennainallurTamil NaduIndia

Wireless sensor network(WsN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking *** comprises a massive set of nodes with restricted energy and processing *** dissipation is a major concern involved in the design of *** and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic *** order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for *** presented HBAC-AVOR model mainly aims to cluster the nodes in WsN effectually and organize the routes in an energy-efﬁcient *** presented HBAC-AVOR model follows a two stage *** the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing aﬁtness function involving many input ***,the AVOR approach was executed for determining the optimal routes to Bs and thereby lengthens the lifetime of WsN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR *** comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

关键词： Cluster based routing wireless sensor networks objective function lifetime metaheuristics

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG signals

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Computers, Materials & Continua 2023年第10期77卷 25-45页

作者： Premanand.s.sathiya Narayanan school of Electronics Engineering Vellore Institute of TechnologyChennai600127Indi

Machine Learning(ML)and Deep Learning(DL)technologies are revolutionizing the medical domain,especially with Electrocardiogram(ECG),by providing new tools and techniques for diagnosing,treating,and preventing ***,DL architectures are computationally more *** recent years,researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches,say for example,combining the convolutional layer blocks of Convolution Neural Networks(CNNs)into ML algorithms such as Extreme Gradient Boosting(XGBoost)and K-Nearest Neighbor(KNN)resulting in CNN-XGBoost and CNN-KNN,***,these approaches are homogenous in the sense that they use a fixed Activation Function(AFs)in the sequence of convolution and pooling layers,thereby limiting the ability to capture unique *** various AFs are readily available and each could capture unique features,we propose a Convolutionbased Heterogeneous Activation Facility(CHAF)which uses multiple AFs in the convolution layer blocks,one for each block,with a motivation of extracting features in a better manner to improve the *** proposed CHAF approach is validated on PTB and shown to outperform the homogeneous approaches such as CNN-KNN and *** PTB dataset,proposed CHAF-KNN has an accuracy of 99.55%and an F1 score of 99.68%in just 0.008 s,outperforming the state-of-the-art CNN-XGBoost which has an accuracy of 99.38%and an F1 score of 99.32%in 1.23 *** validate the generality of the proposed CHAF,experiments were repeated on MIT-BIH dataset,and the proposed CHAF-KNN is shown to outperform CNN-KNN and CNN-XGBoost.

关键词： Electrocardiogram convolution neural network machine learning activation function