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| 1 | 查看详情显示文摘 | P.W.Cleary M.Prakash | | 0，，： | 1 |
| 2 | Secure Data Sharing with Confidentiality,Integrity and Access Control in Cloud Environment显示文摘Cloud storage is an incipient technology in today’s world.Lack of security in cloud environment is one of the primary challenges faced these days.This scenario poses new security issues and it forms the crux of the current work.The current study proposes Secure Interactional Proof System(SIPS)to address this challenge.This methodology has a few key essential components listed herewith to strengthen the security such as authentication,confidentiality,access control,integrity and the group of components such as AVK Scheme(Access List,Verifier and Key Generator).It is challenging for every user to prove their identity to the verifier who maintains the access list.Verification is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication process.Here,RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric encryption.Various methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS protocol.The proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality,authentication and access control.Stochastic Timed Petri(STPN)Net evaluation tool was used to verify and prove the formal analysis of SIPS methodology.This evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment. | V.Rajkumar M.Prakash V.Vennila | 2022 | Computer Systems Science & Engineering2022，40，2： | 0 |
| 3 | Enhanced Long Short Term Memory for Early Alzheimer's Disease Prediction显示文摘The most noteworthy neurodegenerative disorder nationwide is appar-ently the Alzheimer's disease(AD)which ha no proven viable treatment till date and despite the clinical trials showing the potential of preclinical therapy,a sen-sitive method for evaluating the AD has to be developed yet.Due to the correla-tions between ocular and brain tissue,the eye(retinal blood vessels)has been investigated for predicting the AD.Hence,en enhanced method named Enhanced Long Short Term Memory(E-LSTM)has been proposed in this work which aims atfinding the severity of AD from ocular biomarkers.Tofind the level of disease severity,the new layer named precise layer was introduced in E-LSTM which will help the doctors to provide the apt treatments for the patients rapidly.To avoid the problem of overfitting,a dropout has been added to LSTM.In the existing work,boundary detection of retinal layers was found to be inaccurate during the seg-mentation process of Optical Coherence Tomography(OCT)image and to over-come this issue;Particle Swarm Optimization(PSO)has been utilized.To the best of our understanding,this is thefirst paper to use Particle Swarm Optimization.When compared with the existing works,the proposed work is found to be per-forming better in terms of F1 Score,Precision,Recall,training loss,and segmen-tation accuracy and it is found that the prediction accuracy was increased to 10%higher than the existing systems. | M.Vinoth Kumar M.Prakash M.Naresh Kumar H.Abdul Shabeer | 2023 | Intelligent Automation & Soft Computing2023，，2： | 0 |
| 4 | Digital Mammogram Inferencing System Using Intuitionistic Fuzzy Theory显示文摘In the medical field,the detection of breast cancer may be a mysterious task.Physicians must deduce a conclusion from a significantly vague knowledge base.A mammogram can offer early diagnosis at a low cost if the breasts'satisfactory mammogram images are analyzed.A multi-decision Intuitionistic Fuzzy Evidential Reasoning(IFER)approach is introduced in this paper to deal with imprecise mammogram classification efficiently.The proposed IFER approach combines intuitionistic trapezoidal fuzzy numbers and inclusion measures to improve representation and reasoning accuracy.The results of the proposed technique are approved through simulation.The simulation is created utilizing MATLAB software.The screening results are classified and finally grouped into three categories:normal,malignant,and benign.Simulation results show that this IFER method performs classification with accuracy almost 95%compared to the already existing algorithms.The IFER mammography provides high accuracy in providing early diagnosis,and it is a convenient diagnostic tool for physicians. | Susmita Mishra M.Prakash | 2022 | Computer Systems Science & Engineering2022，41，6： | 0 |
| 5 | An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model显示文摘Cloud Computing provides various services to the customer in aflex-ible and reliable manner.Virtual Machines(VM)are created from physical resources of the data center for handling huge number of requests as a task.These tasks are executed in the VM at the data center which needs excess hosts for satis-fying the customer request.The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time.This process is carried out based on various algorithms which follow a pre-defined capacity of source VM leads to the capacity issue at the destination VM.The proposed VM migration technique performs the migration process based on the request of the requesting host machine.This technique can perform in three ways namely single VM migration,Multiple VM migration and Cluster VM migration.Common Deployment Manager(CDM)is used to support through negotiation that happens across the source host and destination host for providing the high quality service to their customer.The VM migration requests are handled with an exposure of the source host capabilities.The proposed analysis also uses the retired instructions with execution by the hypervisor to achieve high reliabil-ity.The objective of the proposed technique is to perform a VM migration process based on the prior knowledge of the resource availability in the target VM. | C.Saravanakumar R.Priscilla B.Prabha A.Kavitha M.Prakash C.Arun | 2022 | Computer Systems Science & Engineering2022，42，7： | 0 |
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