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Artificial neural network with modified rider optimization for design and control of PV-integrated quasi Z-source cascaded multilevel inverter system

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International Journal of Modeling, simulation, and scientific Computing 2021年第1期12卷 87-111页

作者： V.C.Harish Kumar s.amala shanthi Department of EEE NICHE ThuckalayKanyakumariTamil NaduIndia Department of ECE NICHEThuckalayKanyakumariTamil NaduIndia

The modeling of the controller for quasi Z-source Cascaded Multilevel Inverter(qZsCMI)-dependent 3-phase grid-tie Photovoltaic(PV)power system is considered in this *** state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived ***,such processes are limited to known system models,which are uncertain in future ***,the controller for 3-phase qZs-CMI is modeled based on two phases,and the source PV voltage and output grid current are *** Phase I,optimized Proportional Integral(PI)controller is used for finding out the total PV voltage,and Phase II utilizes the optimized Proportional Resonant(PR)controller enabled with the Artificial Neural Network(ANN)for controlling the grid *** two phases,the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm(FE-ROA)is ***,in Phase II,ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA,and it guarantees the control of grid *** two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed *** main objective of phase I is to lessen the error among the reference PV voltage,and measured voltage,and phase II is to lessen the error among the reference and measured grid ***,the grid-tie current injection is achieved by the developed module,and system-level control offers independent Maximum Power Point Tracking(MPPT).Lastly,the performance of the proposed controller for qZs-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.

关键词： qZs-CMI PI controller PR controller artificial neural network fitness enabled-rider optimization algorithm

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Leveraging Blockchain with Optimal Deep Learning-Based Drug supply Chain Management for Pharmaceutical Industries

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Computers, Materials & Continua 2023年第11期77卷 2341-2357页

作者： shanthi Perumalsamy Venkatesh Kaliyamurthy Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathurTamil Nadu603203India

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients *** pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient ***(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake *** designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DsCM)*** this motivation,the study presents a new blockchain with optimal deep learning-enabled DsCM and recommendation scheme(BCODL-DsCMRs)for Pharmaceutical ***,Hyperledger fabric is used for DsC management,enabling effective tracking processes in the smart pharmaceutical *** addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and *** spotted hyena optimizer(sHO)algorithm is used to optimize the performance of the HDBN *** design of the HsO algorithm for tuning the HDBN model demonstrates the novelty of the *** presented model is tested on the UCI repository’s open-access drug reviews database.

关键词： Drug supply chain pharmaceutical industry deep learning blockchain hyper ledger fabric security drug recommendation

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An Optimized Novel Trust-Based security Mechanism Using Elephant Herd Optimization

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Computer systems science & Engineering 2023年第3期44卷 2489-2500页

作者： saranya Veerapaulraj M.Karthikeyan s.sasipriya A.s.shanthi Tamilnadu College of Engineering CoimbatoreIndia Krishna College of Engineering CoimbatoreIndia

Routing strategies and security issues are the greatest challenges in Wireless sensor Network(WsN).Cluster-based routing Low Energy adaptive Clustering Hierarchy(LEACH)decreases power consumption and increases net-work lifetime *** WsN is a challenging issue faced by *** systems are very helpful in detecting interfering nodes in *** have successfully applied Nature-inspired Metaheuristics Optimization Algorithms as a decision-making factor to derive an improved and effective solution for a real-time optimization *** metaheuristic Elephant Herding Optimizations(EHO)algorithm is formulated based on ele-phant herding in their *** considers two herding behaviors to solve and enhance optimization *** on Elephant Herd Optimization,a trust-based security method is built in this *** proposed routing selects routes to destination based on the trust values,thus,finding optimal secure routes for transmitting *** results have demonstrated the effectiveness of the proposed EHO based *** Average Packet Loss Rate of the proposed Trust Elephant Herd Optimization performs better by 35.42%,by 1.45%,and by 31.94%than LEACH,Elephant Herd Optimization,and Trust LEACH,respec-tively at Number of Nodes *** the proposed routing is efficient in selecting secure routes,the average packet loss rate is significantly reduced,improving the network’s *** is also observed that the lifetime of the network is enhanced with the proposed Trust Elephant Herd Optimization.

关键词： Wireless sensor network low energy adaptive clustering hierarchy trust systems elephant herding optimizations algorithm trust-based elephant herd optimization

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Fabrication of three-dimensional graphene anode for augmenting performance in microbial fuel cells

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Carbon Resources Conversion 2019年第2期2卷 134-140页

作者： Alka Pareek J.shanthi sravan s.Venkata Mohan Bioengineering and Environmental science Lab CEEFFCSIR-Indian Institute of Chemical Technology(CSIR-IICT)Hyderabad 500007India Academy of scientific and Innovative Research(AcsIR) India

In the present work,we have reported the synthesis of 3D graphene structures by simple chemical reduction method for application in microbial fuel cell(MFC)as an *** synthesis procedure includes synthesis of graphene oxide by modified Hummers method followed by simultaneous reduction of graphene oxide and the formation of *** 3D graphene structures are characterized using X-ray diffraction and Raman spectroscopy *** characterization is carried out to study the capacitive properties and impedance studies of electrodes in conductive electrolyte.3D graphene electrodes show remarkably high capacitive current,charge storage and lower charge transfer resistance as estimated through cyclic voltammetry and Nyquist *** order to evaluate the performance of 3D graphene electrodes in MFC,power density and polarization curves are recorded in three configurations by varying terminal electron acceptors(TEA)viz.,dissolved oxygen(MFC-DO),potassium ferricyanide and MFC without TEA(MFC-NDO).3D graphene electrodes exhibits maximum power density of 0.49 mW/m^(2) in potassium ferricyanide followed by MFC-DO(0.36 mW/m^(2))and MFC-NDO(0.34 mW/m^(2)).Present work highlights the viability of chemically synthesized 3D graphene electrodes as an efficient anode material in MFC.

关键词： Microbial fuel cell 3D graphene Wastewater treatment Chemical reduction Graphene electrodes

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Two-Dimensional Photonic Crystal Based sensor for Pressure sensing

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Photonic sensors 2014年第3期4卷 248-253页

作者： Krishnan VIJAYA shanthi savarimuthu ROBINsON PG scholar Communication Systems Mount Zion College of Engineering and Technology Pudukkottai-622507 TamilNadu India

In this paper, a two-dimensional photonic crystal （2DPC） based pressure sensor is proposed and designed, and the sensing characteristics such as the sensitivity and dynamic range are analyzed over the range of pressure from 0 GPa to 7 GPa. The sensor is based on 2DPC with the square array of silicon rods surrounded by air. The sensor consists of two photonic crystal quasi waveguides and L3 defect. The L3 defect is placed in between two waveguides and is formed by modifying the radius of three si rods. It is noticed that through simulation, the resonant wavelength of the sensor is shifted linearly towards the higher wavelength region while increasing the applied pressure level. The achieved sensitivity and dynamic range of the sensor is 2 nm/GPa and 7 Gpa, respectively.

关键词： Photonic crystal waveguide photonic band gap optical sensor FDTD method pressure sensor

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A hybrid constriction coefficientbased particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

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International Journal of Intelligent Computing and Cybernetics 2020年第2期13卷 129-165页

作者： sajad Ahmad Rather P.shanthi Bala Department of Computer science School of Engineering and TechnologyPondicherry UniversityPuducherryIndia

Purpose-In this paper,a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm(CPsOGsA)has been employed for training MLP to overcome sensitivity to initialization,premature convergence,and stagnation in local optima problems of ***/methodology/approach-In this study,the exploration of the search space is carried out by gravitational search algorithm(GsA)and optimization of candidate solutions,*** is performed by particle swarm optimization(PsO).For training the multi-layer perceptron(MLP),CPsOGsA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the ***,a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of ***-The experimental findings convey that CPsOGsA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed ***,it gives the best results for breast cancer,heart,sine function and sigmoid function datasets as compared to other participating ***,CPsOGsA also provides very competitive results for other ***/value-The CPsOGsA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of ***,CPsOGsA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation *** the research literature,a little work is available where CPsO and GsA have been utilized for training *** only related research paper was given by Mirjalili et al.,in *** have used standard PsO and GsA for training simple ***,the work employed only three datasets and used the MsE performance metric for evaluating the efficiency

关键词： Neural network Feedforward neural network(FNN) Gravitational search algorithm(GsA) Particle swarm optimization(PsO) Hybridization CPsOGsA Multi-layer perceptron(MLP)

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Xylanase-producing microflora in Eastern Ghats of Andhra Pradesh,India

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Journal of Forestry Research 2017年第2期28卷 291-298页

作者： G.Ramanjaneyulu A.Ramya B.s.shanthi Kumari Kanderi Dileep Kumar B.Rajasekhar Reddy Department of Microbiology Sri Krishnadevaraya UniversityAnantapuramu 515 003Andhra PradeshIndia

We screened soil samples collected from underneath shrubs and/or large trees at different locations in the Eastern Ghats of Andhra Pradesh for xylanase-producing microorganisms. Xylose-utilizing bacteria were numerically dominant in soils of most locations whereas xylose-and xylan-utilizing actinobacteria were minor components. Xylan-utilizing fungi constituted a major share of total microbial populations in soil samples collected at half of the sites, whereas xylan-utilizing bacteria were predominant at other sampling locations. some of the isolates of fungi exhibited xylanase activity with a range of400–4000 U/ml, indicating great potential for their uses in paper, pulping and bioethanol industries for producing value-added products.

关键词： utilizing producing xylan microbial fungi populations Eastern utilize India letters

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Intelligent Deep Learning Based Disease Diagnosis Using Biomedical Tongue Images

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Computers, Materials & Continua 2022年第3期70卷 5667-5681页

作者： V.Thanikachalam s.shanthi K.Kalirajan sayed Abdel-Khalek Mohamed Omri Lotfi M.Ladhar sri sivasubramaniya Nadar College of Engineering Chennai603110India Department of Information Technology Department of Electronics and Communication EngineeringCARE College of EngineeringTiruchirappalli620009India Department of Electronics and Communication Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Mathematics and statistics College of ScienceTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Mathematics Department Faculty of ScienceSohag UniversitySohagEgypt Deanship of scientific Research King Abdulaziz UniversityJeddahSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

The rapid development of biomedical imaging modalities led to its wide application in disease ***-based diagnostic procedures are proficient and non-invasive in nature to carry out secondary diagnostic processes ***,physicians examine the characteristics of tongue prior to *** this scenario,to get rid of qualitative aspects,tongue images can be quantitatively inspected for which a new disease diagnosis model is *** model can reduce the physical harm made to the *** tongue image analytical methodologies have been proposed ***,there is a need exists to design an intelligent Deep Learning(DL)based disease diagnosis *** this motivation,the current research article designs an Intelligent DL-basedDisease Diagnosis method using Biomedical Tongue Images called IDLDD-BTI *** proposed IDLDD-BTI model incorporates Fuzzy-based Adaptive Median Filtering(FADM)technique for noise removal ***,squeezeNet model is employed as a feature extractor in which the hyperparameters of squeezeNet are tuned using Oppositional Glowworm swarm Optimization(OGsO)*** last,Weighted Extreme Learning Machine(WELM)classifier is applied to allocate proper class labels for input tongue color *** design of OGsO algorithm for squeezeNet model shows the novelty of the *** assess the enhanced diagnostic performance of the presented IDLDD-BTI technique,a series of simulations was conducted on benchmark dataset and the results were examined in terms of several *** resultant experimental values highlighted the supremacy of IDLDD-BTI model over other state-of-the-art methods.

关键词： Biomedical images image processing tongue color image deep learning squeezenet disease diagnosis

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Attribute driven inverse materials design using deep learning Bayesian framework

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npj Computational Materials 2019年第1期5卷 22-35页

作者： Piyush M.Tagade shashishekar P.Adiga shanthi Pandian Min sik Park Krishnan s.Hariharan subramanya Mayya Kolake Next Gen Research Samsung Advanced Institute of TechnologySamsung R&D InstituteBangalore 560037India Autonomous Material Development Lab Samsung Electronics130 Samsung-roSuwonGyeonggi-do 443-803Republic of Korea

Much of computational materials science has focused on fast and accurate forward predictions of materials properties,for example,given a molecular structure predict its electronic *** is achieved with first principles calculations and more recently through machine learning approaches,since the former is computation-intensive and not practical for high-throughput *** for the right material for any given application,though follows an inverse path—the desired properties are given and the task is to find the right *** we present a deep learning inverse prediction framework,structure Learning for Attributedriven Materials Design Using Novel Conditional sampling(sLAMDUNCs),for efficient and accurate prediction of molecules exhibiting target *** apply this framework to the computational design of organic molecules for three applications,organic semiconductors for thin-film transistors,small organic acceptors for solar cells and electrolyte additives with high redox *** method is general enough to be extended to inorganic compounds and represents an important step in deep learning based completely automated materials discovery.

关键词： inverse learning enough

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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Computer systems science & Engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.s.sujith Kumar G.shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer science and Engineering Sejong UniversitySeoul05006Korea Department of software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water strider Algorithm based Node Localization(CWsANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(Bs),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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