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Rice science 2015年第6期22卷 300-308页

作者： s.j.s.rama devi Kuldeep sINGH B.UMAKANTH B.VIsHALAKsHI P.RENUKA K.VIjAY sUDHAKAR M.s.PRAsAD B.C.VIRAKTAMATH V.RAVINDRA BABU M.s.MADHAV Biotechnology Crop ImprovementIndian Institute of Rice ResearchIndian Council of Agriculture Research school of Agricultural Biotechnology Punjab Agricultural University Plant Pathology Indian Institute of Rice ResearchIndian Council of Agriculture Research

To develop and characterize introgression lines for leaf and neck blast resistance, 326 introgression lines were developed using various accessions of six different AA genome wild species in the genetic background of elite Indian varieties like PRl14 and Pusa 44 and were screened for blast resistance. stringent phenotyping coupled with genotyping using gene based markers led to the identification of four resistant introgression lines, which showed promising resistance and do not possess any of the tested genes. Furthermore, multi-location screening confirmed the field resistance of the four introgression lines to both leaf and neck blast. Molecular characterization of these introgression lines using genome-wide simple sequence repeat markers revealed the presence of small percentage of wild Oryza genome introgrssion. so these lines can be used for mapping and identification of novel leaf and neck blast resistance genes. Thus, these four introgression lines can be considered as new genetic resources for blast resistance.

关键词： rice blast resistance introgression line gene profiling wild species

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

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

Intelligent Energy Utilization Analysis Using IUA-sMD Model Based Optimization Technique for smart Metering Data

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journal of Harbin Institute of Technology(New series) 2024年第1期31卷 90-98页

作者： K.rama devi V.srinivasan G.Clara Barathi Priyadharshini j.Gokulapriya Department of Information Technology Panimalar Engineering CollegeChennaiTamil Nadu 600029India Department of Computer Applications Dayananda Sagar College of EngineeringBengaluru 560078KarnatakaIndia Department of Computer Appliations Karpagam Academy of Higher EducationCoimbatoreTamil Nadu 641021India Department of Computer science Rathinam College of Arts and ScienceCoimbatoreTamil Nadu 641021India

smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. smart metering solutions offer users the benefits of effectively interpreting their energy utilization and optimizing costs. Motivated by this,this paper presents an Intelligent Energy Utilization Analysis using smart Metering Data(IUA-sMD)model to determine energy consumption patterns. The proposed IUA-sMD model comprises three major processes:data Pre-processing,feature extraction,and classification,with parameter optimization. We employ the extreme learning machine(ELM)based classification approach within the IUA-sMD model to derive optimal energy utilization labels. Additionally,we apply the shell game optimization(sGO)algorithm to enhance the classification efficiency of the ELM by optimizing its parameters. The effectiveness of the IUA-sMD model is evaluated using an extensive dataset of smart metering data,and the results are analyzed in terms of accuracy and mean square error(MsE). The proposed model demonstrates superior performance,achieving a maximum accuracy of65.917% and a minimum MsE of0.096. These results highlight the potential of the IUA-sMD model for enabling efficient energy utilization through intelligent analysis of smart metering data.

关键词： electricity consumption predictive model data analytics smart metering machine learning

Video Recommendation system Using Machine-Learning Techniques

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journal of Harbin Institute of Technology(New series) 2024年第4期31卷 24-33页

作者： Meesala sravani Ch Vidyadhari s Anjali devi Department of Computer science and Engineering GMR Institute of TechnologyRajam 532127Andhra PradeshIndia Department of Information Technology Gokaraju Rangaraju Institute of Engineering and TechnologyKukatpallyHydarabad 500090TelanganaIndia Department of Computer science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522302Andhra PradeshIndia

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through *** this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different *** expectation depends on past evaluations,history,interest,IMDB rating,and so *** can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific *** required datasets for the video are taken from *** recommender framework is executed by utilizing Python Programming *** building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the *** that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information *** last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

关键词： video recommendation system KNN algorithms collaborative filtering content⁃based filtering classification algorithms artificial intelligence

Gas hydrate saturation from NGHP 02 LWD data in the Mahanadi Basin

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Energy Geoscience 2024年第2期5卷 279-289页

作者： Uma shankar Pradeep Kumar Yadav sneha devi Udham singh Yadav Department of Geophysics Institute of ScienceBHUVaranasi221005India Oil and Natural Gas Corporation Ltd. Gas Hydrate Research&Technology Centre(GHRTC)Panvel410221Navi MumbaiIndia

During the Indian National Gas Hydrate Program(NGHP)Expedition 02,Logging-while-drilling(LWD)logs were acquired at three sites(NGHP-02-11,NGHP-02-12,and NGHP-02-13)across the Mahanadi Basin in area *** applied rock physics theory to available sonic velocity logs to know the distribution of gas hydrate at site NGHP-02-11 and *** physics modeling using sonic velocity at well location shows that gas hydrate is distributed mainly within the depth intervals of 150-265 m and 100 -215 mbsf at site NGHP-02-11 and NGHP-02-13,respectively,with an average saturation of about 4%of the pore space and the maximum concentration of about 40%of the pore space at 250 m depth at site NGHP-02-11,and at site NGHP-02-13 an average saturation of about 2%of the pore space and the maximum concentration of about 20%of the pore space at 246 m depth,as gas hydrate is distributed mainly within 100-246 mbsf at this *** of gas hydrate estimated from the electrical resistivity method using density derived porosity and electrical resistivity logs from Archie's empirical formula shows high saturation compared to that from the sonic ***,estimates of hydrate saturation based on sonic P-wave velocity may differ significantly from that based on resistivity,because gas and hydrate have higher resistivity than conductive pore fluid and sonic P-wave velocity shows strong effect on gas hydrate as a small amount of gas reduces the velocity significantly while increasing velocity due to the presence of *** site NGHP-02-11,gas hydrate saturation is in the range of 15%e30%,in two zones between 150-180 and 245-265 *** NGHP-02-012 shows a gas hydrate saturation of 20%e30%in the zone between 100 and 207 *** NGHP-02-13 shows a gas hydrate saturation up to 30%in the zone between 215 and 246 *** observations from rock physics modeling and Archie’s approximation show the gas hydrate concentrations are relatively low(space)at the sites of the

关键词： Mahanadi Basin Gas hydrate sonic log Effective medium modeling Electrical resistivity log Archie’s analysis Gas hydrate saturation

Metabolic engineering and genome editing strategies for enhanced lipid production in microalgae

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BIOCELL 2024年第8期48卷 1181-1195页

作者： ANjANI devi CHINTAGUNTA sAMUDRALA PRAsHANT jEEVAN KUMAR NUNE sATYA sAMPATH KUMAR Department of Biotechnology Vignan’s Foundation ScienceTechnology and ResearchVadlamudiAndhra Pradesh522213India ICAR-Directorate of Floricultural Research Pune412307India

Depleting global petroleum reserves and skyrocketing prices coupled with succinct supply have been a grave concern,which needs alternative sources to conventional *** microalgae have been explored for enhanced lipid production,leading towards biodiesel *** microalgae have short life cycles,require less labor,and space,and are easy to scale ***,the primary source of lipids needed to produce biodiesel,is accumulated by most *** article focuses on different types of oleaginous microalgae,which can be used as a feedstock to produce *** biosynthesis in microalgae occurs through fatty acid synthesis and TAG synthesis ***-depth discussions are held regarding other efficient methods for enhancing fatty acid and TAG synthesis,regulating TAG biosynthesis bypass methods,blocking competing pathways,multigene approach,and genome *** most potential targets for gene transformation are hypothesized to be a malic enzyme and diacylglycerol acyltransferase while lowering phosphoenolpyruvate carboxylase activity is reported to be advantageous for lipid synthesis.

关键词： Oleaginous microalgae Biodiesel TAG synthesis Metabolic engineering Genome editing

A Case Report of Pulmonary Alveolar Proteinosis with Associated Opportunistic Infection of Pneumocystis jirovecii and Molluscum Contagiosum

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journal of Forensic science and Medicine 2024年第1期10卷 68-71页

作者： sapna Patel Deepika Gurumurthy smitha Rani sheela devi Department of Pathology JSSAHERMysoreKarnatakaIndia2Department of Forensic MedicineJSSAHERMysoreKarnatakaIndia

Pulmonary alveolar proteinosis(PAP)is an idiopathic rare diffuse pulmonary disease,first described in 1958 by Rosen et *** estimated prevalence is about 1 in 3.7-6.9×10^(6) with a male:female ratio of 1:1-2:*** of the patient’s age ranges between 20 and 50 *** on microscopy is characterized by the presence of massive insoluble,amorphous,phospholipid-rich protein deposits in the bronchial and alveolar *** patients with acquired PAP present with cough and exertional *** has been studied that there is increased risk of superinfection in PAP with opportunistic organisms like pneumocystis and vice *** diagnosis of Pneumocystis jirovecii pneumonia rests on the demonstration of the organism within the alveoli by special stains like Grocott Methenamine silver *** contagiosum(MC)is a common superficial skin infection caused by the *** is characterized by painless papules commonly seen in children and immunocompromised ***,we present a 34-year-old female who had complaints of severe difficulty in breathing and was brought dead to our *** external examination,she had multiple warts over chest,abdomen,and over *** examination was *** of kidney,lung,and skin biopsy of genital warts sent for histopathological examination revealed acute tubular necrosis,*** with PAP,and MC respectively.

关键词： Bronchoalveolar lavage molluscum contagiosum Pneumocystis jirovecii pulmonary alveolar proteinosis

An Integrated Bipolar Picture Fuzzy Decision Driven system to scrutinize Food Waste Treatment Technology through Assorted Factor Analysis

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Computer Modeling in Engineering & sciences 2024年第9期140卷 2665-2687页

作者： Navaneethakrishnan suganthi Keerthana devi samayan Narayanamoorthy Thirumalai Nallasivan Parthasarathy Chakkarapani sumathi Thilagasree Dragan Pamucar Vladimir simic Hasan Dinçer serhat Yüksel Department of Mathematics Bharathiar UniversityCoimbatore641046India Department of Mathematics Sri Ramakrishna Mission Vidyalaya College of Arts and ScienceCoimbatore641020India Department of Operations Research and statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Department of Industrial Engineering and ManagementYuan Ze UniversityTaoyuan City320315TaiwanChina Faculty of Transport and Traffic Engineering University of BelgradeVojvode Stepe 305Belgrade11000Serbia Department of Computer science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea school of Business Istanbul Medipol UniversityIstanbul34083Turkey Department of Economics and Management Khazar UniversityBakuAzerbaijan

Food Waste(FW)is a pressing environmental concern that affects every country *** one-third of the food that is produced ends up as waste,contributing to the carbon ***,the FW must be properly treated to reduce environmental *** study evaluates a few available Food Waste Treatment(FWT)technologies,such as anaerobic digestion,composting,landfill,and incineration,which are widely used.A Bipolar Picture Fuzzy set(BPFs)is proposed to deal with the ambiguity and uncertainty that arise when converting a real-world problem to a mathematical model.A novel Criteria Importance Through Intercriteria Correlation-stable Preference Ordering Towards Ideal solution(CRITIC-sPOTIs)approach is developed to objectively analyze FWT selection based on thirteen criteria covering the industry’s technical,environmental,and entrepreneurial *** CRITIC method is used for the objective analysis of the importance of each criterion in FWT *** sPOTIs method is adopted to rank the alternative hassle-free,following the *** proposed model offers a rank reversal-free model,i.e.,the rank of the alternatives remains unaffected even after the addition or removal of an *** addition,comparative and sensitivity analyses are performed to ensure the reliability and robustness of the proposed model and to validate the proposed result.

关键词： Fuzzy food waste treatment selection bipolar picture fuzzy set and decision-making

stature Estimation Using Hand and Foot Metrics among the Meena Tribal Population of the North-western Indian Region

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journal of Forensic science and Medicine 2024年第2期10卷 111-119页

作者： suraj Kataria shivani Dechalwal Rutwik shedge Naorem Kiranmala devi Mohinder P.sachdeva sonal jain Department of Anthropology University of DelhiNew DelhiIndia school of Forensic sciences National Forensic Sciences UniversityTripura CampusAgartalaTripuraIndia

Background:studies on stature estimation models for North West Indian populations are *** present study attempts to develop stature estimation models for the Indian population using hand and foot *** and Methods:Hand and foot measurements of 280 individuals(140 males and 140 females)were recorded for the estimation of stature in the present *** stature and nine hand and foot anthropometric measurements were obtained using standard methods and relevant *** bilateral and sex differences,independent and paired t-tests were used to statistically analyze the ***:The findings showed statistically significant sex differences(Pstature,foot,and hand ***,there were no significant bilateral *** Pearson correlation coefficient assessed the association between stature and hand/foot *** the studied parameters in both sexes demonstrated a statistically significant(Pstrong positive correlation with *** and females had the strongest correlation coefficients for right-foot length(r=0.70)and right-hand length(r=0.64),*** sex’s regression equations were calculated *** predictions were accurate to within 4.02-6.64 ***:stature estimated from hand and foot measurements can help in personal identification by creating an individual’s biological *** differences in feet and hands demonstrate the significance of developing ethnic group-specific models to determine *** to the study’s findings,the stature of a person belonging to the Indian subcontinent can be estimated using hands and feet for anthropological and forensic purposes.

关键词： Forensic anthropology forensic anthropometry human identification osteobiography stature estimation