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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan vinoth kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

Hybrid Seagull and Whale Optimization Algorithm-Based Dynamic Clustering Protocol for Improving Network Longevity in Wireless Sensor Networks

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China Communications 2024年第10期21卷 113-131页

作者： P.vinoth kumar K.Venkatesh Department of Networking and Communications School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur 603203India

Energy efficiency is the prime concern in Wireless Sensor Networks(WSNs) as maximized energy consumption without essentially limits the energy stability and network lifetime. Clustering is the significant approach essential for minimizing unnecessary transmission energy consumption with sustained network lifetime. This clustering process is identified as the Non-deterministic Polynomial(NP)-hard optimization problems which has the maximized probability of being solved through metaheuristic *** adoption of hybrid metaheuristic algorithm concentrates on the identification of the optimal or nearoptimal solutions which aids in better energy stability during Cluster Head(CH) selection. In this paper,Hybrid Seagull and Whale Optimization Algorithmbased Dynamic Clustering Protocol(HSWOA-DCP)is proposed with the exploitation benefits of WOA and exploration merits of SEOA to optimal CH selection for maintaining energy stability with prolonged network lifetime. This HSWOA-DCP adopted the modified version of SEagull Optimization Algorithm(SEOA) to handle the problem of premature convergence and computational accuracy which is maximally possible during CH selection. The inclusion of SEOA into WOA improved the global searching capability during the selection of CH and prevents worst fitness nodes from being selected as CH, since the spiral attacking behavior of SEOA is similar to the bubble-net characteristics of WOA. This CH selection integrates the spiral attacking principles of SEOA and contraction surrounding mechanism of WOA for improving computation accuracy to prevent frequent election process. It also included the strategy of levy flight strategy into SEOA for potentially avoiding premature convergence to attain better trade-off between the rate of exploration and exploitation in a more effective manner. The simulation results of the proposed HSWOADCP confirmed better network survivability rate, network residual energy and network overall throughput on par wi

关键词： clustering energy stability network lifetime seagull optimization algorithm(SEOA) whale optimization algorithm(WOA) wireless sensor networks(WSNs)

Applying real time seismic monitoring technology for slope stability assessment—An Indian opencast coal mine perspective

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International Journal of Mining Science and Technology 2014年第1期24卷 75-80页

作者： vinoth S. Ajay kumar L. Department of Mining Engineering College of Engineering GuindyAnna University

This paper outlines the results obtained from real time microseismic monitoring of an opencast coal mine in South *** objective of the study is to investigate the stress changes within the rockmass along the slope due to underground mine development operation and their impact on the stability of the highwall *** installed microseismic systems recorded the seismic triggerings down toà2 moment *** general,most of the events recorded during the monitoring period are weak in seismic *** study adopts a simple and more reliable tool to characterize the seismically active zone for assessing the stability of the highwall in real *** impact of underground working on the slope is studied on the basis of the seismic event impact contours and seismic *** the monitoring period,it is observed that the intensity of the overall microseismic activity along the slope due to the mine development operations did not cause any adverse impact on the highwall stability.

关键词： Seismic monitoring Induced seismicity Seismic clusters Slope stability Opencast mining

Acoustical properties of a 3D printed honeycomb structure filled with nanofillers:Experimental analysis and optimization for emerging applications

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Defence Technology（防务技术） 2024年第5期35卷 248-258页

作者： Jeyanthi Subramanian vinoth kumar Selvaraj Rohan Singh Ilangovan S Naresh Kakur Ruban Whenish School of Mechanical Engineering Vellore Institute of TechnologyChennai600127Tamil NaduIndia Department of Mechanical Engineering SRM Institute of Science and TechnologyRamapuramChennai600089India Department of Chemical Engineering and Materials Science University of Southern CaliforniaLos Angeles90089USA Advanced Materials Research Centre Technology Innovation InstituteAbu Dhabi9639UAE Center for Biomaterials Cellular and Molecular TheranosticsVellore Institute of TechnologyVellore632014Tamil NaduIndia

The novelty of this research lies in the successful fabrication of a 3D-printed honeycomb structure filled with nanofillers for acoustic properties,utilizing an impedance tube setup in accordance with ASTM standard E *** Creality Ender-3,a 3D printer,was used for printing the honeycomb structures,and polylactic acid(PLA)material was employed for their *** organic,inorganic,and polymeric compounds within the composites were identified using fourier transformation infrared(FTIR)*** structure and homogeneity of the samples were examined using a field emission scanning electron microscope(FESEM).To determine the sound absorption coefficient of the 3D printed honeycomb structure,numerous samples were systematically developed using central composite design(CCD)and analysed using response surface methodology(RSM).The RSM mathematical model was established to predict the optimum values of each factor and noise reduction coefficient(NRC).The optimum values for an NRC of 0.377 were found to be 1.116 wt% carbon black,1.025 wt% aluminium powder,and 3.151 mm distance between parallel ***,the results demonstrate that a 3Dprinted honeycomb structure filled with nanofillers is an excellent material that can be utilized in various fields,including defence and aviation,where lightweight and acoustic properties are of great importance.

关键词： 3D printing Honeycomb structure Acoustics Field emission scanning electron microscope Response surface methodology

Advanced Persistent Threat Detection and Mitigation Using Machine Learning Model

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Intelligent Automation & Soft Computing 2023年第6期36卷 3691-3707页

作者： U.Sakthivelu C.N.S.vinoth kumar Department of Networking and Communications College of Engineering and Technology(CET)SRM Institute of Science and TechnologyKattankulathurChennai603203India

The detection of cyber threats has recently been a crucial research domain as the internet and data drive people’s *** cyber-attacks lead to the compromise of data *** proposed system offers complete data protection from Advanced Persistent Threat(APT)attacks with attack detection and defence *** modified lateral movement detection algorithm detects the APT attacks,while the defence is achieved by the Dynamic Deception system that makes use of the belief update *** termination,every cyber-attack undergoes multiple stages,with the most prominent stage being Lateral Movement(LM).The LM uses a Remote Desktop protocol(RDP)technique to authenticate the unauthorised host leaving footprints on the network and host *** anomaly-based approach leveraging the RDP event logs on Windows is used for detecting the evidence of *** extracting various feature sets from the logs,the RDP sessions are classified using machine-learning techniques with high recall and *** is found that the AdaBoost classifier offers better accuracy,precision,F1 score and recall recording 99.9%,99.9%,0.99 and 0.98%.Further,a dynamic deception process is used as a defence mechanism to mitigateAPTattacks.A hybrid encryption communication,dynamic(Internet Protocol)IP address generation,timing selection and policy allocation are established based on mathematical models.A belief update algorithm controls the defender’s *** performance of the proposed system is compared with the state-of-the-art models.

关键词： Advanced persistent threats lateral movement detection dynamic deception remote desktop protocol Internet Protocol attack detection

A Hybrid Approach for COVID-19 Detection Using Biogeography-Based Optimization and Deep Learning

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Computers, Materials & Continua 2022年第2期70卷 3717-3732页

作者： K.Venkatachalam Siuly Siuly M.vinoth kumar Praveen Lalwani Manas kumar Mishra Enamul Kabir Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Institute for Sustainable Industries&Liveable Cities Victoria UniversityMelbourne14428Australia Department of Computer Science and Engineering Anna UniversityUniversity College of EngineeringDindigul624622India School of Sciences University of Southern QueenslandToowoombaDarling Heights4350Australia

The COVID-19 pandemic has created a major challenge for countries all over the world and has placed tremendous pressure on their public health care *** early diagnosis of COVID-19 may reduce the impact of the *** achieve this objective,modern computation methods,such as deep learning,may be *** this study,a computational model involving deep learning and biogeography-based optimization(BBO)for early detection and management of COVID-19 is ***,BBO is used for the layer selection process in the proposed convolutional neural network(CNN).The computational model accepts images,such as CT scans,X-rays,positron emission tomography,lung ultrasound,and magnetic resonance imaging,as *** the comparative analysis,the proposed deep learning model CNNis compared with other existingmodels,namely,VGG16,InceptionV3,ResNet50,and *** the fitness function formation,classification accuracy is considered to enhance the prediction capability of the proposed *** results demonstrate that the proposed model outperforms InceptionV3 and ResNet50.

关键词： Covid-19 biogeography-based optimization deep learning convolutional neural network computer vision

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

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Computer Systems Science & Engineering 2022年第2期40卷 581-592页

作者： K.Rajakumari M.vinoth kumar Garima Verma S.Balu Dilip kumar Sharma Sudhakar Sengan Department of Computer Science and Engineering School of EngineeringAvinashlingam Institute for Home Science and Higher Education for WomenCoimbatore641043Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India School of Computing DIT UniversityDehradun248009UttarakhandIndia Department of Computer Science and Engineering Paavai Engineering CollegePachal637018Tamil NaduIndia Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226M.P.India Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Cloud computing is an Information Technology deployment model established on *** scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing ***,task scheduling challenges such as optimal task scheduling performance solutions,are addressed in cloud ***,the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin *** recommended DWRR algorithm improves the task scheduling performance by considering resource competencies,task priorities,and ***,a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task *** the end,a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment.A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the ***,the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time,system throughput,and maximizing resource utilization.

关键词： Cloud Computing scheduling ant colony optimization fuzzy logic

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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Computer Systems Science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.vinoth kumar K.Sailaja kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

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Intelligent Automation & Soft Computing 2023年第1期35卷 983-995页

作者： Ravi Samikannu K.vinoth Narasimha Rao Dasari Senthil kumar Subburaj Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and TechnologyBotswana Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electrical Power Engineering College of EngineeringDefence UniversityEthiopia Department Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologySembakkamTamil NaduIndia

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the modern energy system with rapid *** renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental *** develop energy resources,electric power generation involved huge *** power and output voltages are plays important role in our work but it not considered in the existing *** considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy *** input information is collected from two input *** that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC *** layer 1 is transferred to hidden layer *** activation is employed for determining the output voltage with help of the *** last,the output layer offers the last value in GPIC-MDCNNC *** designed method was confirmed using one and multiple sources by stable and unpredictable input ***-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art *** control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

关键词： Multi-port converters renewable sources fuzzy PI controller gaussian activation function fuel cell