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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

Computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose velmurugan subbiah parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

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Computers, Materials & Continua 2023年第1期74卷 2303-2316页

作者： K.Kalyani velmurugan subbiah parvathy Hikmat A.M.Abdeljaber T.Satyanarayana Murthy Srijana Acharya Gyanendra Prasad Joshi Sung Won Kim PG and Research Department of Computer Science Marudupandiyar College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613403India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmmanJordan Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelangana500075India Department of Convergence Science Kongju National UniversityGongju32588Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced *** successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial *** stockholders focused on the prediction of return rate and risk rate of financial ***,an automatic return rate bitcoin prediction model becomes essential for BC financial *** newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive *** study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial *** presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial ***,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification *** application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return *** experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many *** experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

关键词： Financial products blockchain return rate prediction model machine learning parameter optimization

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AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric Vehicles

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Computers, Materials & Continua 2022年第2期70卷 3333-3347页

作者： P.Thamizhazhagan M.Sujatha S.Umadevi K.Priyadarshini velmurugan subbiah parvathy Irina V.Pustokhina Denis A.Pustokhin Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationVijayawada522502India Centre for Nanoelectronics and VLSI Design SENSEVIT UniversityChennai CampusChennai600127India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential *** and Autonomous Electric Vehicle(CAEV)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and ***,Traffic Flow Prediction(TFP)is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning(DL)*** this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in *** presented model involves two major processes namely,feature engineering and *** feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature *** addition to the above,a Support Vector Data Description(SVDD)model is also used in the filtration of anomaly points and smoothen the raw ***,AIPAE model is applied to determine the predictive values of traffic *** order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct *** experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

关键词： Autonomous electric vehicle traffic flow predictive automation industry connected vehicles seep learning

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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Computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta velmurugan subbiah parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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Multi-label Emotion Classification of COVID–19 Tweets with Deep Learning and Topic Modelling

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Computer Systems Science & Engineering 2023年第6期45卷 3005-3021页

作者： K.Anuratha M.parvathy Department of Information Technology Sri Sai Ram Institute of TechnologyChennaiTamilnaduIndia Department of Computer Science and Engineering Sethu Institute of TechnologyMaduraiTamilnaduIndia

The COVID-19 pandemic has become one of the severe diseases in recent *** it majorly affects the common livelihood of people across the universe,it is essential for administrators and healthcare professionals to be aware of the views of the community so as to monitor the severity of the spread of the *** public opinions are been shared enormously in microblogging med-ia like twitter and is considered as one of the popular sources to collect public opinions in any topic like politics,sports,entertainment etc.,This work presents a combination of Intensity Based Emotion Classification Convolution Neural Net-work(IBEC-CNN)model and Non-negative Matrix Factorization(NMF)for detecting and analyzing the different topics discussed in the COVID-19 tweets as well the intensity of the emotional content of those *** topics were identified using NMF and the emotions are classified using pretrained IBEC-CNN,based on predefined intensity *** research aimed at identifying the emotions in the Indian tweets related to COVID-19 and producing a list of topics discussed by the users during the COVID-19 *** the Twitter Application Programming Interface(Twitter API),huge numbers of COVID-19 tweets are retrieved during January and July *** extracted tweets are ana-lyzed for emotions fear,joy,sadness and trust with proposed Intensity Based Emotion Classification Convolution Neural Network(IBEC-CNN)model which is *** classified tweets are given an intensity score varies from 1 to 3,with 1 being low intensity for the emotion,2 being the moderate and 3 being the high *** identify the topics in the tweets and the themes of those topics,Non-negative Matrix Factorization(NMF)has been *** of emotions of COVID-19 tweets has identified,that the count of positive tweets is more than that of count of negative tweets during the period considered and the negative tweets related to COVID-19 is less than 5%.Also,more than 75%neg

关键词： Twitter topic detection emotion classification COVID-19 corona virus non-negative matrix factorization(NMF) convolutional neural network(CNN) sentiment classification healthcare

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Fabrication of Large Area High Density, Ultra-Low Reflection Silicon Nanowire Arrays for Efficient Solar Cell Applications

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Nano Research 2011年第11期4卷 1136-1143页

作者： Subramani Thiyagu B. parvathy Devi Zingway Pei Graduate Institute of Optoelectronic Engineeringi Department of Electdcal Engineeringi Chung Hsing University Taichung 40227Taiwan China

High density vertically aligned and high aspect ratio silicon nanowire （SiNW） arrays have been fabricated on a Si substrate using a template and a catalytic etching process. The template was formed from polystyrene （PS） nanospheres with diameter 30-50 nm and density 10^10/cm^2, produced by nanophase separation of PS-containing block-copolymers. The length of the SiNWs was controlled by varying the etching time with an etching rate of 12.5 nm/s. The SiNWs have a biomimetic structure with a high aspect ratio （-100）, high density, and exhibit ultra-low reflectance. An ultra-low reflectance of approximately 0.1% was achieved for SiNWs longer than 750 nm. Well-aligned SiNW/poly（3,4-ethylenedioxy-thiophene）：poly（styrenesulfonate）（PEDOT：PSS） heterojunction solar cells were fabricated. The n-type silicon nanowire surfaces adhered to PEDOT：PSS to form a core-sheath heterojunction structure through a simple and efficient solution process. The large surface area of the SiNWs ensured efficient collection of photogenerated carriers. Compared to planar cells without the nanowire structure, the SiNW/PEDOT：PSS heterojunction solar cell exhibited an increase in short-circuit current density from 2.35 mA/cm^2 to 21.1 mA/cm^2 and improvement in power conversion efficiency from 0.4% to 5.7%.

关键词： Ultra-low reflection silicon nanowire polystyrene nanosphere heterojunction solar cell

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Pedestrian risk analysis at uncontrolled midblock and unsignalised intersections

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Journal of Traffic and Transportation Engineering(English Edition) 2018年第2期5卷 137-147页

作者： Kodavanti Venkata Raghavendra Ravishankar parvathy Maheswari Nair Department of Civil Engineering National Institute of Technology Warangal Highways-Civil and Infrastructure AECOM India (GDC) Private Ltd.

Transportation is defined as port to port transfer of person or goods by a medium which can be a vehicle or a person. Pedestrians being the most neglected mode of transportation in terms of safety and facility, face difficult situations while crossing near intersections and midblock crossings. It becomes more of a risk when the place of crossing is *** if behaviour of pedestrians while crossing is analysed in such conditions, it might be possible to create suitable solution to lessen the risk and ensure safety. In most of the cities, accepting suitable gaps between vehicles in uncontrolled midblock and intersection crossings pose threat to pedestrians＇ safety. The present study examines the safety of pedestrian crossing behaviour at midblock and unsignalised intersection *** time, speed, stages of crossing, number of interruptions while crossing, and the type of vehicles for which pedestrians accept the gap were extracted from the video. The tendency to show rolling gap behavior was observed and examined for different age and gender groups to analyse the risk involved in such type of crossings. The risks analysed from the study in correlation with the pedestrian demand in such uncontrolled crossings will help in design of safer pedestrian facilities. It was observed that the size of the vehicle has a significant influence on gap acceptance and crossing behaviour of pedestrians. Male pedestrians take more risks than female pedestrians in crossing unsignalized intersections. Middle aged pedestrian category poses 60.1% more chances of interrupted crossing than the other elder and young age categories of pedestrians. Male pedestrian category and the middle aged pedestrian category are more tended to accept the smallest gap between the vehicles showing a risky nature of crossing.

关键词： Pedestrian risk analysis Risk factors Rolling gap Safety margin Stages of crossing

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Nasal airflow of patient with septal deviation and allergy rhinitis

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 120-130页

作者： Zi Fen Lim parvathy Rajendran Muhamad Yusri Musa Chih Fang Lee School of Aerospace Engineering Universiti Sains Malaysia11800 Pulau PinangMalaysia Faculty of Engineering&Computing First City University College47800 SelangorMalaysia Advanced Medical and Dental Institute Universiti Sains Malaysia11800 Pulau PinangMalaysia

A numerical simulation of a patient’s nasal airflow was developed via computational fluid ***,computerized tomography scans of a patient with septal deviation and allergic rhinitis were *** three-dimensional(3D)nasal model was designed using InVesalius 3.0,which was then imported to(computer aided 3D interactive application)CATIA V5 for modification,and finally to analysis system(ANSYS)flow oriented logistics upgrade for enterprise networks(FLUENT)to obtain the numerical *** velocity contours of the cross-sectional area were analyzed on four main surfaces:the vestibule,nasal valve,middle turbinate,and *** pressure and velocity characteristics were assessed at both laminar and turbulent mass flow rates for both the standardized and the patient’s model nasal *** developed model of the patient is approximately half the size of the standardized model;hence,its velocity was approximately two times more than that of the standardized model.

关键词： Three-dimensional nasal airflow model Septal deviation Allergy rhinitis Computational fluid dynamics

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Control of Nozzle Flow Using Rectangular Ribs at Sonic and Supersonic Mach Numbers

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Fluid Dynamics & Materials Processing 2024年第8期20卷 1847-1866页

作者： Vigneshvaran Sethuraman parvathy Rajendran Sher Afghan Khan Abdul Aabid Muneer Baig School of Aerospace Engineering Universiti Sains MalaysiaEngineering CampusNibong TebalPulau Pinang14300Malaysia Department of Mechanical and Aerospace Engineering Faculty of EngineeringInternational Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Engineering Management College of EngineeringPrince Sultan UniversityP.O.Box 66833Riyadh11586Saudi Arabia

This study deals with base pressure management in a duct for various values of the Mach number(M),namely,Mach number corresponding to sonic and four supersonic *** addition to the Mach number,the nozzle pressure ratio(NPR),the area ratio,the rib dimension,and the duct length are influential *** following specific values are examined at M=1,1.36,1.64,and 2,and NPRs between 1.5 and *** base pressure is determined by positioning ribs of varying heights at predetermined intervals throughout the length of the square *** the level of expansion is varied,it is seen that the base pressure initially drops for overexpanded flows and increases for under-expanded *** ribs are present,the flow field in the duct and pressure inside the duct fluctuate as the base pressure ***-expanded flows can achieve a base pressure value that is suitably high without experiencing excessive changes in the duct flow in terms of static pressure if a rib height around 10%of the duct height close to the nozzle exit is *** rib passive control does not negatively affect the duct’s flow field.

关键词： Base pressure internal flows ribs suddenly expanded flow wall pressure

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Acute dapsone poisoning in a 3-year-old child: Case report with review of literature

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World Journal of Clinical Cases 2015年第10期3卷 911-914页

作者： Menon Narayanankutty Sunilkumar Thekkuttuparambil Ananthanarayanan Ajith Vadakut Krishnan parvathy Department of Paediatrics Amala Institute of Medical Sciences Department of Biochemistry Amala Institute of Medical Sciences

Dapsone(DDS-diamino diphenyl sulphone) is a sulfone antibiotic being used for a variety of clinical conditions. Poisoning in children by DDS is rarely reported. Poisoning in acute cases will be frequently unrecognized due to relative lack of severe signs and symptoms. Methemoglobinemia is the major life-threatening situation associated with poisoning of DDS. Hence, any delay for medical attention can lead to increased rate of mortality. In this case, we describe acute DDS poisoning in a 3-year-old child and the successful management using intravenous methylene blue.

关键词： Dapsone Methemoglobinemia Ascorbic acid Methylene blue Hemolysis

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