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Bio-Inspired Intelligent Routing in WsN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

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Computer Modeling in Engineering & sciences 2024年第10期141卷 127-150页

作者： V.G.saranya s.karthik Department of Electronics and Communication Engineering Faculty of Engineering&TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026India

Wireless sensor Networks(WsNs)are a collection of sensor nodes distributed in space and connected through wireless *** sensor nodes gather and store data about the real world around ***,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the *** research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and *** present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base *** performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy *** objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WsN in the *** proposed method outcomes proved to be better than traditional approaches such as Hybrid squirrel-Flying Fox Optimization Algorithm(HsFLBOA),Hybrid social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HsRODE-FFA),social spider Distance sensitive-Iterative Antlion Butterfly Cockroach Algorithm(sADss-IABCA),and Energy Efficient Clustering Hierarchy strategy-Improved social spider Algorithm Differential Evolution(EECHs-IssADE).

关键词： Enhanced ant colony optimization mayfly optimization algorithm wireless sensor networks cluster head base station(Bs)

Radiative heat transfer analysis of a concave porous fin under the local thermal non-equilibrium condition:application of the clique polynomial method and physics-informed neural networks

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Applied Mathematics and Mechanics(English Edition) 2024年第9期45卷 1613-1632页

作者： K.CHANDAN K.karthik K.V.NAGARAJA B.C.PRAsANNAKUMARA R.s.VARUN KUMAR T.MUHAMMAD Amrita school of Artificial Intelligence Amrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of studies in Mathematics Davangere UniversityDavangere 577002KarnatakaIndia Computational science Lab Amrita School of EngineeringAmrita Vishwa VidyapeethamBengaluru 560035KarnatakaIndia Department of Pure and Applied Mathematics School of Mathematical SciencesSunway UniversityPetaling Jaya 47500Malaysia Department of Mathematics College of ScienceKing Khalid UniversityAbha 61413Saudi Arabia

The heat transfer through a concave permeable fin is analyzed by the local thermal non-equilibrium(LTNE)*** governing dimensional temperature equations for the solid and fluid phases of the porous extended surface are modeled,and then are nondimensionalized by suitable dimensionless ***,the obtained nondimensional equations are solved by the clique polynomial method(CPM).The effects of several dimensionless parameters on the fin's thermal profiles are shown by graphical ***,the current study implements deep neural structures to solve physics-governed coupled equations,and the best-suited hyperparameters are attained by comparison with various network *** results of the CPM and physicsinformed neural network(PINN)exhibit good agreement,signifying that both methods effectively solve the thermal modeling problem.

关键词： heat transfer fin porous fin local thermal non-equilibrium(LTNE)model physics-informed neural network(PINN)

Context-Aware Augmented Reality Using Human-Computer Interaction Models

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Journal of Control and Decision 2024年第1期11卷 1-14页

作者： Ying sun Qiongqiong Guo shumei Zhao karthik Chandran G.Fathima Henan intelligent education and intelligent technology application engineering technology research center Zhengzhou Railway Vocational and technical collegeZhengzhouHenanPeople’s Republic of China Henan Provincial Research Center of Wisdom Education and Intelligent Technology Application Engineering Technology Zhengzhou Railway Vocational and technical collegeZhengzhouHenanPeople’s Republic of China Department of Mechatronics Engineering Jyothi Engineering CollegeKeralaIndia CsE Adhiyamaan College of Engineering HosurTamilnaduIndia

Augmented Reality is a technique that allows users to overlap digital information with their physical *** Augmented Reality(AR)displays have an exceptional characteristic from the Human–Computer Interaction(HCI)*** to its increasing popularity and application in diverse domains,increasing user-friendliness and AR usage are ***-aware is one approach since an AR application can adapt to the user,environment,needs and enhance ergonomic principles and *** paper proposes the Intelligent Contextaware Augmented Reality Model(ICAARM)for Human–Computer Interaction *** study explores and reduces interaction uncertainty by semantically modeling user-specific interaction with context,allowing personalised *** information is captured from an AR device to understand user interactions and *** depictions carry semantics to Augmented Reality applications about the user’s intention to interact with a specific device ***,this study describes personalised gesture interaction in VR/AR applications for immersive/intelligent environments.

关键词： Augmented Reality Context-aware Human–Computer Interaction

Isolated traumatic orbital floor fracture:An analysis of the National Trauma Data Bank

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World Journal of Otorhinolaryngology-Head and Neck surgery 2024年第3期10卷 187-192页

作者： Emma De Ravin D Katherine Xu Christian Fritz Harman s.Parhar karthik Rajasekaran Department of Otorhinolaryngology Head and Neck surgery University of PennsylvaniaPhiladelphiaPennsylvaniaUSA Perelman school of Medicine at the University of Pennsylvania PhiladelphiaPennsylvaniaUSA Leonard Davis Institute of Health Economics University of PennsylvaniaPhiladelphiaPennsylvaniaUSA

Objectives:To date,no study provides a comprehensive analysis of traumatic orbital floor fractures across the United *** aimed to characterize patient demographics,injury-related variables,and operative management in this ***:The National Trauma Data Bank was queried for open or closed orbital floor fractures from 2008 to *** data were ***:Overall,148,592 orbital floor fractures were identified,with 142,577(95.9%)closed-and 6158(4.1%)open-type fractures.A total of 106,243(71.5%)patients were male and the median patient age was 41 *** majority of patients(79.2%)had abbreviated injury scale scores of≤2,indicating minor/moderate *** mechanism of injury(MOI)differed by gender,with the most frequent being unarmed fights in men(34.3%)and falls in women(14.0%).There were 29,600 patients(19.9%)with isolated orbital floor(I-OF)*** MOI most strongly associated with operative intervention of with I-OF fractures were penetrating injuries caused by a firearm(odds ratio[OR]:2.91;95%confidence interval[CI]:1.62–5.20)and cuttings/piercings(OR:2.17;95%CI:1.29–3.65).Conclusion:This large epidemiological study reveals that orbital floor fractures tend to present with minor or moderate injuries and are more likely to require operative intervention in setting of firearm or cut/pierce injuries.

关键词： epidemiology National Trauma Data Bank orbital fracture risk factors trauma

A study on the Protective Effect of Cynodon dactylon Leaves Extract in Diabetic Rats

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Biomedical and Environmental sciences 2011年第2期24卷 190-199页

作者： karthik D Ravikumar s Department of Biotechnology PRIST University

Objective To investigate the antidiabetic, antioxidant and hypolipidemic efficacy of Cynodon dactylon in diabetic rats. Methods The experimental rats were randomly divided into three groups： Group I： control; Group II： Alloxan diabetic, untreated; and Group III： Alloxan diabetic treated with ethanolic extract of C. dactylon leaves （450 mg/kg bw）. Experimental diabetes was induced by alloxan in a single dose of 150 mg/kg bw. Results A significant diminution of fasting blood sugar level was observed and also significant increase in HDL and decrease （P0.05） in cholesterol, triglyceride, LDL and VLDL were observed after 15 days of treatment. The investigation also revealed, the activities of AsT, ALT, ALP, AP, LDH, and CPK （P0.05） were decreased in the extract‐supplemented group. The significant decrease in protein content and sOD, CAT, GPx, and GsH （P0.05） activity and increase in LPO in plasma were found to be ameliorated after treatment. Conclusion Our result supports the fact that administration of extract of C. dactylon leave is able to reduce hyperglycemia and hyperlipidemia risk and also reduced the oxidative stress in diabetic rats.

关键词： Cynodon dactylon Ethanolic extract GC‐Ms Alloxan diabetes Antidiabetic Antioxidant Lipid profile Enzyme profile

Energy Prediction in IoT systems Using Machine Learning Models

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Computers, Materials & Continua 2023年第4期75卷 443-459页

作者： s.Balaji s.karthik Department of ECE SRM Institute of Science and TechnologyVadapalani CampusNo1 Jawaharlal Nehru RoadVadapalani600026TNIndia

The Internet of Things (IoT) technology has been developed fordirecting and maintaining the atmosphere in smart buildings in real time. Inorder to optimise the power generation sector and schedule routine maintenance,it is crucial to predict future energy demand. Electricity demandforecasting is difficult because of the complexity of the available demandpatterns. Establishing a perfect prediction of energy consumption at thebuilding’s level is vital and significant to efficiently managing the consumedenergy by utilising a strong predictive model. Low forecast accuracy is justone of the reasons why energy consumption and prediction models havefailed to advance. Therefore, the purpose of this study is to create an IoTbasedenergy prediction (IoT-EP) model that can reliably estimate the energyconsumption of smart buildings. A real-world test case on power predictionsis conducted on a local electricity grid to test the practicality of the *** proposed (IoT-EP) model selects the significant features as input neurons,the predictable data is selected as output nodes, and a multi-layer perceptronis constructed along with the features of the Convolution Neural Network(CNN) algorithm. The analysis of the proposed IoT-EP model has higheraccuracy of 90%, correlation of 89%, and variance of 16% in less training timeof 29.2 s, and with a higher prediction speed of 396 (observation/sec). Whencompared to existing models, the results showed that the proposed (IoT-EP)model outperforms with a satisfactory level of accuracy in predicting energyconsumption in smart buildings.

关键词： Machine learning wireless networks internet of things energy prediction

Power Prediction of VLsI Circuits Using Machine Learning

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Computers, Materials & Continua 2023年第1期74卷 2161-2177页

作者： E.Poovannan s.karthik Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennai600026TNIndia

The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit.A big database is needed to undertake important analytical work like statistical method,heat research,and IR-drop research that results in extended running *** unit focuses on the assessment of test *** of the enormous number of successful designs for currentmodels and the unnecessary time required for every test,maximum energy ratings with all tests cannot be ***,test safety is important for producing trustworthy findings to avoid loss of output and harm to the ***,effective power assessment is only possible in a limited sample of pre-selected ***,a key objective is to find the experiments that might give the worst situations again for testing *** offers a machine-based circuit power estimation(MLCPE)system for the selection of *** distinct techniques of predicting are ***,to find testings with power dissipation,it forecasts the behavior of ***,the changemovement and energy data are linked to the semiconductor design,identifying small problem *** types of algorithms are *** particular,the methods *** findings show great accuracy and efficiency in *** enables such methods suitable for selecting the worst scenario.

关键词： Power estimation Machine learning circuit simulation VLsI implementation

Resource scheduling approach in cloud Testing as a service using deep reinforcement learning algorithms

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CAAI Transactions on Intelligence Technology 2021年第2期6卷 147-154页

作者： Priyadarsini karthik karthik sekhar Department of Computer science and Engineering Vels Institute of Science Technology and Advanced Studies(VISTAS)ChennaiIndia Department of Electronics and Communication Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalani CampusChennaiIndia

Many organizations around the world use cloud computing Testing as service(Taas)for their *** computing is principally based on the idea of on-demand delivery of computation,storage,applications,and additional *** depends on delivering user services through Internet *** addition,it uses a pay-as-you-go business design to deliver user *** offers some essential characteristics including on-demand service,resource pooling,rapid elasticity,virtualization,and measured *** are various types of virtualization,such as full virtualization,para-virtualization,emulation,〇s virtualization,and application *** scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and *** of the cloud environment,uncertainty,and perhaps heterogeneity,resource allocation cannot be addressed with prevailing *** situation remains a significant concern for the majority of cloud providers,as they face challenges in selecting the correct resource scheduling algorithm for a particular *** authors use the emergent artificial intelligence algorithms deep RM2,deep reinforcement learning,and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.

关键词： service cloud prevailing

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

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Intelligent Automation & soft Computing 2023年第7期37卷 727-743页

作者： s.Balaji s.karthik Department of ECE SRM Institute of Science and TechnologyVadapalani CampusVadapalani600026TamilnaduIndia

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural *** the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of *** use of soft computing for energy prediction is an essential part of the solution to these kinds of *** study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things *** IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy *** order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing *** technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and *** addition to this,the IHHO algorithm is used as a technique for making adjustments to the *** experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present *** to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LsTM,and CNN-LsTM models,which had MAPE values of 83.22,44.57,and 34.62 *** findings provided evidence of the IHHOD

关键词： Energy consumption forecasting models deep learning fusion models IoT environment gated recurrent unit artificial intelligence