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| 1 | Power Prediction of VLSI Circuits Using Machine Learning显示文摘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 times.This unit focuses on the assessment of test strength.Because 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 achieved.Nevertheless,test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip.Generally,effective power assessment is only possible in a limited sample of pre-selected experiments.Thus,a key objective is to find the experiments that might give the worst situations again for testing power.It offers a machine-based circuit power estimation(MLCPE)system for the selection of exams.Two distinct techniques of predicting are utilized.Firstly,to find testings with power dissipation,it forecasts the behavior of testing.Secondly,the changemovement and energy data are linked to the semiconductor design,identifying small problem areas.Several types of algorithms are utilized.In particular,the methods compared.The findings show great accuracy and efficiency in forecasting.That enables such methods suitable for selecting the worst scenario. | E.Poovannan S.Karthik | 2023 | Computers, Materials & Continua2023，，1： | 1 |
| 2 | Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR)in WSNs显示文摘Sensors are considered as important elements of electronic devices.In many applications and service,Wireless Sensor Networks(WSNs)are involved in significant data sharing that are delivered to the sink node in energy efficient man-ner using multi-hop communications.But,the major challenge in WSN is the nodes are having limited battery resources,it is important to monitor the consumption rate of energy is very much needed.However,reducing energy con-sumption can increase the network lifetime in effective manner.For that,clustering methods are widely used for optimizing the rate of energy consumption among the sensor nodes.In that concern,this paper involves in deriving a novel model called Improved Load-Balanced Clustering for Energy-Aware Routing(ILBC-EAR),which mainly concentrates on optimal energy utilization with load-balanced process among cluster heads and member nodes.For providing equal rate of energy consumption among nodes,the dimensions of framed clusters are measured.Moreover,the model develops a Finest Routing Scheme based on Load-Balanced Clustering to transmit the sensed information to the sink or base station.The evaluation results depict that the derived energy aware model attains higher rate of life time than other works and also achieves balanced energy rate among head node.Additionally,the model also provides higher throughput and minimal delay in delivering data packets. | D.Loganathan M.Balasubramani R.Sabitha S.Karthik | 2023 | Computer Systems Science & Engineering2023，44，1： | 0 |
| 3 | Blockchain Enabled Metaheuristic Cluster Based Routing Model for Wireless Networks显示文摘With recent advancements made in wireless communication techniques,wireless sensors have become an essential component in both data collection as well as tracking applications.Wireless Sensor Network(WSN)is an integral part of Internet of Things(IoT)and it encounters different kinds of security issues.Blockchain is designed as a game changer for highly secure and effective digital society.So,the current research paper focuses on the design of Metaheuristic-based Clustering with Routing Protocol for Blockchain-enabled WSN abbreviated as MCRP-BWSN.The proposed MCRP-BWSN technique aims at deriving a shared memory scheme using blockchain technology and determine the optimal paths to reach the destination in clustered WSN.In MCRP-BWSN technique,Chimp Optimization Algorithm(COA)-based clustering technique is designed to elect a proper set of Cluster Heads(CHs)and organize the selected clusters.In addition,Horse Optimization Algorithm(HOA)-based routing technique is also presented to optimally select the routes based onfitness function.Besides,HOA-based routing technique utilizes blockchain technology to avail the shared mem-ory among nodes in the network.Sensor nodes are treated as coins whereas the ownership handles the sensor nodes and Base Station(BS).In order to validate the enhanced performance of the proposed MCRP-BWSN technique,a wide range of simulations was conducted and the results were examined under different measures.Based on the performance exhibited in simulation outcomes,the pro-posed MCRP-BWSN technique has been established as a promising candidate over other existing techniques. | R.M.Bhavadharini S.Karthik | 2023 | Computer Systems Science & Engineering2023，44，2： | 0 |
| 4 | Towards Developing Privacy-Preserved Data Security Approach(PP-DSA)in Cloud Computing Environment显示文摘In the present scenario of rapid growth in cloud computing models,several companies and users started to share their data on cloud servers.However,when the model is not completely trusted,the data owners face several security-related problems,such as user privacy breaches,data disclosure,data corruption,and so on,during the process of data outsourcing.For addressing and handling the security-related issues on Cloud,several models were proposed.With that concern,this paper develops a Privacy-Preserved Data Security Approach(PP-DSA)to provide the data security and data integrity for the out-sourcing data in Cloud Environment.Privacy preservation is ensured in this work with the Efficient Authentication Technique(EAT)using the Group Signature method that is applied with Third-Party Auditor(TPA).The role of the auditor is to secure the data and guarantee shared data integrity.Additionally,the Cloud Service Provider(CSP)and Data User(DU)can also be the attackers that are to be handled with the EAT.Here,the major objective of the work is to enhance cloud security and thereby,increase Quality of Service(QoS).The results are evaluated based on the model effectiveness,security,and reliability and show that the proposed model provides better results than existing works. | S.Stewart Kirubakaran V.P.Arunachalam S.Karthik S.K annan | 2023 | Computer Systems Science & Engineering2023，44，3： | 0 |
| 5 | Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET显示文摘In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain.In tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and self-administration.Clustering in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and analytics.We present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations.Based on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each hub.Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications. | J.Shanthini P.Punitha S.Karthik | 2023 | Computer Systems Science & Engineering2023，44，3： | 0 |
| 6 | Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction显示文摘Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python. | S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar | 2022 | Computers, Materials & Continua2022，，7： | 0 |
| 7 | Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment显示文摘In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications. | M.Manikandan R.Subramanian M.S.Kavitha S.Karthik | 2022 | Computer Systems Science & Engineering2022，42，9： | 0 |
| 8 | A Quasi-Newton Neural Network Based Efficient Intrusion Detection System for Wireless Sensor Network显示文摘In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal function.Detecting this misbehaviour is a demanding issue.And so far the prevailing research methods show poor performance.AQN3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the performance.The proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)phases.In DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered path.Next,it is tested with the trained IDS.The IDS encompasses‘3’steps:pre-processing,matrix reduction,and classification.In pre-processing,the data is organized in a clear format.Then,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix values.Next,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal data.In an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing algorithms.The proposed work attains an enhanced outcome than the prevailing methods. | A.Gautami J.Shanthini S.Karthik | 2023 | Computer Systems Science & Engineering2023，45，4： | 0 |
| 9 | Efficient Energy-Aware Resource Management Model(EEARMM)Based Dynamic VM Migration显示文摘In cloud environment,an efficient resource management establishes the allocation of computational resources of cloud service providers to the requests of users for meeting the user’s demands.The proficient resource management and work allocation determines the accomplishment of the cloud infrastructure.However,it is very difficult to persuade the objectives of the Cloud Service Providers(CSPs)and end users in an impulsive cloud domain with random changes of workloads,huge resource availability and complicated service policies to handle them,With that note,this paper attempts to present an Efficient Energy-Aware Resource Management Model(EEARMM)that works in a decentralized manner.Moreover,the model involves in reducing the number of migrations by definite workload management for efficient resource utilization.That is,it makes an effort to reduce the amount of physical devices utilized for load balancing with certain resource and energy consumption management of every machine.The Estimation Model Algorithm(EMA)is given for determining the virtual machine migration.Further,VM-Selection Algorithm(SA)is also provided for choosing the appropriate VM to migrate for resource management.By the incorporation of these algorithms,overloading of VM instances can be avoided and energy efficiency can be improved considerably.The performance evaluation and comparative analysis,based on the dynamic workloads in different factors provides evidence to the efficiency,feasibility and scalability of the proposed model in cloud domain with high rate of resources and workload management. | V.Roopa K.Malarvizhi S.Karthik | 2022 | Computer Systems Science & Engineering2022，43，11： | 0 |
| 10 | Route Planning for Autonomous Transmission of Large Sport Utility Vehicle显示文摘The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans.Hence,there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs.An optimal prediction engine is required that suitably reads the background objects and make optimal decisions.In this paper,the author(s)develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles(SUVs)that uses three different modules involving(a)recognition model,(b)planning model and(c)prediction model.The study develops a direct realization method for an autonomous vehicle driving.The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving.The behavioral model includes recognition,planning and prediction modules that regulates the input trajectory processing of input video datasets.A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight.This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error(RMSE)to estimate its efficacy.Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning(E-CNN-RL)offers increased accuracy of 95.45%,reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks(CNN)and Ensemble Stacked CNN. | V.A.Vijayakumar J.Shanthini S.Karthik K.Srihari | 2023 | Computer Systems Science & Engineering2023，45，4： | 0 |
| 11 | Classification Model for IDS Using Auto Cryptographic Denoising Technique显示文摘Intrusion detection systems(IDS)are one of the most promising ways for securing data and networks;In recent decades,IDS has used a variety of categorization algorithms.These classifiers,on the other hand,do not work effectively unless they are combined with additional algorithms that can alter the classifier’s parameters or select the optimal sub-set of features for the problem.Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion.These algorithms,on the other hand,have a number of limitations,particularly when used to detect new types of threats.In this paper,the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared;These two IDS dataset is preprocessed,then Auto Cryptographic Denoising(ACD)adopted to remove noise in the feature of the IDS dataset;the classifier algorithms,K-Means and Neural network classifies the dataset with adam optimizer.IDS classifier is evaluated by measuring performance measures like f-measure,recall,precision,detection rate and accuracy.The neural network obtained the highest classifying accuracy as 91.12%with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset.Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area. | N.Karthikeyan P.Sivaprakash S.Karthik | 2023 | Computer Systems Science & Engineering2023，45，4： | 0 |
| 12 | Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks显示文摘In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,etc.Hence,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to attacks.The recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and threats.In traditional models,the solution has been made for defending the networks,only to specific attacks.However,in real-time applications,the kind of attack that is launched by the adversary is not known.Additionally,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be considered.With that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various attacks.Moreover,for efficient model design,the work includes the part of feature evaluation of some general attacks of WSNs.The IDRM also includes determination of optimal secure paths and Node security for secure routing operations.The performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN communications.It is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works. | R.Sabitha C.Gokul Prasad S.Karthik | 2023 | Computer Systems Science & Engineering2023，45，6： | 0 |
| 13 | Deep Learning Based Energy Consumption Prediction on Internet of Things Environment显示文摘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 gas.For 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 energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This 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 settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In 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 step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.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 approaches.According 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 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts. | S.Balaji S.Karthik | 2023 | Intelligent Automation & Soft Computing2023，，7： | 0 |
| 14 | Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals显示文摘Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states.At the same time,latest develop-ments of artificial intelligence(AI)techniques have the ability to manage and ana-lyzing massive amounts of biomedical datasets results in clinical decisions and real time applications.They can be employed for medical imaging;however,the 1D biomedical signal recognition process is still needing to be improved.Electrocardiogram(ECG)is one of the widely used 1-dimensional biomedical sig-nals,which is used to diagnose cardiovascular diseases.Computer assisted diag-nostic modelsfind it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals.To resolve these issues,this study designs automated deep learning based 1D biomedical ECG sig-nal recognition for cardiovascular disease diagnosis(DLECG-CVD)model.The DLECG-CVD model involves different stages of operations such as pre-proces-sing,feature extraction,hyperparameter tuning,and classification.At the initial stage,data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing.In addition,deep belief network(DBN)model is applied to derive a set of feature vectors.Besides,improved swallow swarm optimization(ISSO)algorithm is used for the hyper-parameter tuning of the DBN model.Lastly,extreme gradient boosting(XGBoost)classifier is employed to allocate proper class labels to the test ECG signals.In order to verify the improved diagnostic performance of the DLECG-CVD model,a set of simulations is carried out on the benchmark PTB-XL dataset.A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy,sensitivity,specificity,kappa,Mathew correlation coefficient,and Hamming loss. | S.Karthik M.Santhosh M.S.Kavitha A.Christopher Paul | 2022 | Computer Systems Science & Engineering2022，42，7： | 0 |
| 15 | Energy Prediction in IoT Systems Using Machine Learning Models显示文摘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 approach.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. | S.Balaji S.Karthik | 2023 | Computers, Materials & Continua2023，，4： | 0 |
| 16 | Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems显示文摘Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around.Hence,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield.In present decade,the application of deep learning models in many fields of research has created greater impact.The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil quality.With that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil quality.Firstly,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)model.Secondly,soil nutrient data has been given as second input to the DNNR model.By utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated.For training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the dataset.The results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy.The results show that the proposed model achieves 96.7%of accuracy rate compared with existing models. | P.Sumathi V.V.Karthikeyan M.S.Kavitha S.Karthik | 2023 | Computer Systems Science & Engineering2023，45，5： | 0 |
| 17 | Hash Function Based Keyword Searchable Encryption Framework in Cloud Server Using MD5 and MECC显示文摘Cloud Computing expands its usability to various fields that utilize data and store it in a common space that is required for computing and the purpose of analysis as like the IoT devices.These devices utilize the cloud for storing and retrieving data since the devices are not capable of storing processing data on its own.Cloud Computing provides various services to the users like the IaaS,PaaS and SaaS.The major drawback that is faced by cloud computing include the Utilization of Cloud services for the storage of data that could be accessed by all the users related to cloud.The use of Public Key Encryptions with keyword search(PEKS)provides security against the untrustworthy third-party search capability on publicly encryption keys without revealing the data’s contents.But the Security concerns of PEKs arise when Inside Keywords Guessing attacks(IKGA),is identified in the system due to the untrusted server presume the keyword in trapdoor.This issue could be solved by using various algorithms like the Certificateless Hashed Public Key Authenticated Encryption with Keyword Search(CL-HPAEKS)which utilizes the Modified Elliptic Curve Cryptography(MECC)along with the Mutation Centred flower pollinations algorithm(CM-FPA)that is used in enhancing the performance of the algorithm using the Optimization in keys.The additional use of Message Digests 5(MD5)hash function in the system enhances the security Level that is associated with the system.The system that is proposed achieves the security level performance of 96 percent and the effort consumed by the algorithm is less compared to the other encryption techniques. | R.Lakshmana Kumar R.Subramanian S.Karthik | 2021 | Journal of Information Hiding and Privacy Protection2021，3，4： | 0 |
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