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E-voting system using cloud-based hybrid blockchain technology

Journal of Safety Science and resilience 2024年第1期5卷 102-109页

作者： Beulah Jayakumari S Lilly Sheeba maya Eapen Jani Anbarasi Vinayakumar ravi A.Suganya malathy Jawahar department of Information technology Tagore engineering college chennai India School of Computer Science and engineering Srm Institute of Science and technology ramapuram ChennaiIndia department of Information technology Jerusalem college of engineering ChennaiIndia School of Computer Science and engineering Vellore Institute of technology chennai India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Leather Process technology Division CSIr-Central Leather research Institute ChennaiIndia

With the invention of Internet-enabled devices,cloud and blockchain-based technologies,an online voting system can smoothly carry out election *** pandemic situations,citizens tend to develop panic about mass gatherings,which may influence the decrease in the number of *** urges a reliable,flexible,transparent,secure,and cost-effective voting *** proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system,and it is carried out in three phases:the registration phase,vote casting phase and vote counting phase.A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting *** smart contracts,third-party interventions are eliminated,and the transactions are secured in the blockchain ***,to provide accurate voting results,the practical Byzantine fault tolerance(PBFT)consensus mechanism is adopted to ensure that the vote has not been modified or ***,the overall performance of the proposed system is significantly better than that of the existing *** performance was analyzed based on authentication delay,vote alteration,response time,and latency.

关键词： E-voting Cloud Blockchain Smart contract Consensus mechanism

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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Computers, materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.rajasekar r.Jayalakshmi Surendran rajendran department of Software engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia department of Electronics and Communication engineering Sathyabama Institute of Science and TechnologyChennai600119India department of Computer Science and engineering Panimalar Engineering CollegeChennai600123India department of Computer Science and engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

rapid development in Information technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the road Side Unit(rSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based routing(FOA-EECPCr)technique in *** FOA-EECPCr technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCr technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCr *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCr approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Effect of Fluidic Diode on Performance of Unidirectional Impulse Turbine

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Journal of Thermal Science 2024年第3期33卷 807-814页

作者： DODDAmANI Hithaish ABDUS Samad mANABU Takao SHINYA Okuhara m m ASHrAFUL Alam department of Ocean engineering Indian Institute of Technology MadrasChennai600036India department of mechanical engineering National Institute of TechnologyMatsue CollegeShimane6908518Japan Center of Support Practical Education National Institute of TechnologyMatsue CollegeShimane6908518Japan department of Transportation engineering Osaka Sangyo UniversityDaito5748530Japan

A pair of unidirectional turbines(UT)can operate in oscillatory airflow without additional ***,this arrangement suffers from poor flow rectification.A fluidic diode(FD)offers variable hydrodynamic resistance based on the flow direction,and this can be coupled with UT to improve flow *** this work,a numerical investigation on the effect of FD with UT is presented using the commercial fluid dynamics software ANSYS Fluent 16.1 with k-ωSST turbulence closure *** domains of UT and FD are numerically validated individually with experimental ***,both are coupled to obtain the combined effect,and these results are compared with the analytical *** was observed that coupling FD with UT improved the unit's performance at the lower flow coefficient(rformance decreased as the flow coefficient *** to the diode's presence,fluid leaving the turbine experiences higher resistance at a higher flow coefficient,which decreases the overall performance of the combined unit.

关键词： wave energy oscillating water column unidirectional impulse turbine fluidic diode flow rectification

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Effect of cold-working on corrosion induced damage in lug joints

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Defence technology（防务技术） 2024年第6期36卷 175-182页

作者： ramanath m.N Chikmath L. murthy H. department of Aerospace engineering Indian Institute of Technology-MadrasChennai 600036India department of Aeronautical engineering KLS Gogte Institute of TechnologyBelagavi 590008India

Lug joints are preferred joineries for transferring heavy loads to parent components in aerospace *** experience corrosion due to environmental conditions,improper surface finishes and rubbing displacement between the pin and *** causes damage of different sizes and shapes near the *** degradation due to corrosion-induced damage is modelled as a through-pit at one of the identified critical locations through stress *** effect of this pit on fatigue crack initiation life is ***-hole is pre-stressed by cold-working and the benefits of inducing plastic wake on the intended performance of the lug joint during the damages due to corrosion are brought out and compared with non-cold-worked *** analysis is performed on this lug joint with *** results obtained highlight the benefits of cold-working and the methodology can be extended to damage growth and analyse the effect of surface treatments for better structural integrity of components of aerospace vehicles.

关键词： Lug joint Corrosion Crack initiation Cold-working Structural integrity

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Evaluation of red soil-bentonite mixtures for compacted clay liners

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Journal of rock mechanics and Geotechnical engineering 2024年第2期16卷 697-710页

作者： A.S.Devapriya T.Thyagaraj department of Civil engineering Indian Institute of Technology MadrasChennai600036India

Compacted clay liners are an integral part of the waste landfills,which are provided to contain the leachate within the landfills and protect the surrounding ***,locally available natural soils are used for the construction of compacted clay liners if they satisfy the design ***,not all soils in their natural state satisfy all the design criteria for the liner ***,there is a definite need to modify the locally available natural soils by blending with bentonite to meet the required design criteria for the *** view of this,the present study evaluates the suitability of an Indian red soil enhanced with bentonite as a liner *** achieve this,a series of experiments were carried out using locally available red soil and ***,the suitability of the red soil was evaluated as a liner *** experimental results showed that the red soil met all the selection criteria stipulated by the Environmental Protection Agencies(EPAs)for the liners except the hydraulic conductivity ***,the red soil was mixed with bentonite contents of 10%,20%and 30%,and the red soil-bentonite mixtures were evaluated for their suitability for liners in their compacted ***,as the liners in the arid and semi-arid regions are subjected to moisture variations due to seasonal moisture fluctuations and other factors,the red soil-bentonite mixtures were subjected to wetdry cycles,and their suitability was evaluated after wet-dry *** experimental results revealed that all the red soil-bentonite mixtures met the stipulated EPA criteria for the liners in the as-compacted ***,the red soil-bentonite mixtures with 20%and 30%bentonite contents only satisfied the hydraulic conductivity requirement even after wet-dry *** experimental findings were supplemented with the microstructural insights captured through digital camera images,scanning electron microscopy(SEm),and mercury intrusion porosimetry(m

关键词： Compacted clay lines Hydraulic conductivity Wet-dry cycles microstructure

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Adaptive marine Predator Optimization Algorithm(AOmA)-Deep Supervised Learning Classification(DSLC)based IDS framework for mANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： m.Sahaya Sheela A.Gnana Soundari Aditya mudigonda C.kalpana k.Suresh k.Somasundaram Yousef Farhaoui department of Electronics and Communication engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India department of Computer Science and engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India department of Computer Science and engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India department of Computer Science and engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India department of Computer Science and engineering Sri Muthukumaran Institute of TechnologyChennai 600069India department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of mobile Ad-hoc Networks(mANET)is one of the difficult and challenging tasks *** mANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing mANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive marine Predator Optimization Algorithm(AOmA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOmA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security mobile Ad-hoc Network(mANET) min-max normalization Adaptive marine Predator Optimization Algorithm(AOmA) Deep Supervise Learning Classification(DSLC)

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A Novel Eccentric Intrusion Detection model Based on recurrent Neural Networks with Leveraging LSTm

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Computers, materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha krishnan muthunambu Senthil Prabakaran Balasubramanian Prabhu kavin kishore Senthil Siruvangur kavitha Chinnadurai Jehad Ali department of Computer Applications Karpagam College of EngineeringCoimbatore641032India department of Computer Science and engineering Karpagam College of EngineeringCoimbatore641032India department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India department of Computer Science and engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes recurrent Neural Networks(rNN)integrated with Long Short-Term memory(LSTm)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(rNNs)incorporating Long Short-Term memory(LSTm)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTm to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises recurrent Neural Networks,specifically exploiting LSTm *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTm) CICIDS2019 dataset innovative cyberattacks

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maximizing Solar Potential Using the Differential Grey Wolf Algorithm for PV System Optimization

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Energy engineering 2024年第8期121卷 2129-2142页

作者： Ezhilmathi Nagarathinam Buvana Devaraju karthiyayini Jayamoorthy Padmavathi radhakrishnan Santhana Lakshmi Chandramohan Vijayakumar Perumal karthikeyan Balakrishnan department of Electrical and Electronics engineering Pandian Saraswathiyadav Engineering CollegeSivagangaiTamil Nadu630561India department of Electrical and Electronics engineering Sri Balaji Chockalingam Engineering CollegeArniTamil Nadu632317India department of Information Science and engineering New Horizon College of EngineeringBangalore560103India department of Electrical and Electronics engineering Rajalakshmi Engineering CollegeChennaiTamil Nadu602105India department of Electrical and Electronics engineering Sona College of TechnologySalemTamil Nadu636005India School of Electronics engineering Vellore Institute of TechnologyChennaiTamil Nadu600127India department of Electrical and Electronics engineering Sri Krishna College of Engineering and TechnologyCoimbatoreTamil Nadu641008India

maximum Power Point Tracking(mPPT)is crucial for maximizing the energy output of photovoltaic(PV)systems by continuously adjusting the operating point of the panels to track the point of maximum power production under changing environmental *** work proposes the design of an mPPT system for solar PV installations using the Differential Grey Wolf Optimizer(DGWO).It dynamically adjusts the parameters of the mPPT controller,specifically the duty cycle of the SEPIC converter,to efficiently track the maximum Power Point(mPP).The proposed system aims to enhance the energy harvesting capability of solar PV systems by optimizing their performance under varying solar irradiance,temperature and shading *** results demonstrate the effectiveness of the DGWO-based mPPT system in maximizing the power output of solar PV installations compared to conventional mPPT *** research contributes to the development of advanced mPPT techniques for improving the efficiency and reliability of solar energy systems.

关键词： DGWO SEPIC converter mPPT PV module

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An Intelligent Big Data Security Framework Based on AEFS-kENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy muthubalaji Naresh kumar muniyaraj Sarvade Pedda Venkata Subba rao kavitha Thandapani Pasupuleti rama mohan Thangam Somasundaram Yousef Farhaoui department of Electrical and Electronics engineering CMR College of Engineering&TechnologyHyderabad 501401India department of Electronics and Communication engineering Vardhaman College of Engineering KacharamShamshabad 501218India department of Electronics and Communication engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India department of Electronics and Communication engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India department of Electrical and Electronics engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India department of Computer Science and engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDmS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a kernel based Extreme Neural Network(kENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-kernel based Extreme Neural Network(AEFS-kENN)big data security *** results reveal that the accuracy of proposed AEFS-kENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid machine Learning(mL) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) kernel Extreme Neural Network(kENN)

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Enhancing Human Action recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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Computers, materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran kutralakani Anguraju krishnan department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of engineering Al-Balqa Applied UniversitySalt19117Jordan department of mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India department of Computer Science and Software engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya department of Electronics and Communication engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India department of Electronics and Communication engineering Saveetha Engineering College(Autonomous)Chennai602105India department of Computer Science and engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity recognition(HAr)models have received significant *** developed HAr models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAr approaches are not suitable in a real-time ***,an updated HAr model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated recurrent Unit(GrU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAr *** EAHO-based HAr model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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