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Secure Multi-Party Computation for Collaborative Data Analysis in Network Security 2

Secure Multi-Party Computation for Collaborative Data Analys...

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Tamilselvi, P. Lathika, V. Jayachitra, S. Arunkumar, S. Balasubramani, M. Kalaichelvi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Department of Cse Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Coimbatore Institute of Engineering and Technology Department of Ece Coimbatore India Vsb Engineering College Department of Computer Science and Engineering Karur India Sastra Deemed to Be University Srinivasa Ramanujan Centre Department of Cse Kumbakonam India

ISBN: (纸本)9798350306415

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. Advanced threat analysis may also benefit. © 2024 IEEE.

关键词： Collaborative Data Analysis Cryptographic Protocols Network Security Privacy Preservation Secure Multi-Party Computation (SMPC)

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Evaluating Generative Adversarial Networks Performance in Image Synthesis with Graphical Analysis of Loss Functions and Quality Metrics 2

Evaluating Generative Adversarial Networks Performance in Im...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Biju, J. Jeevitha, R. Richard, Titus Chowdhury, Rini Kumar, Prashant Radhika, K. Department of Information Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of English Vels Institute of Science Technology & Advance Studies Chennai India Department of Information Technology Project Circle BSNL Lalkuthi Kolkata India Department of Mathematics Kongu Engineering College Erode India

ISBN: (纸本)9798331504960

Generative Adversarial Networks (GANs) have revolutionized image synthesis by using two neural networks, a generator and a discriminator, to create realistic images from random noise. In this adversarial process, the generator attempts to fool the discriminator, which distinguishes between real and fake images. Advanced variants like conditional GANs and CycleGANs enable tasks like specified image generation and style transfer. Our paper presents improvements in image quality and training stability for GANs by introducing new training methods and loss function modifications to address issues like mode collapse. Evaluated on CelebA and CIFAR-10, our model outperforms previous GANs with Inception Scores of 9.69 and 10.79 and Frechet Inception Distances of 7.91 and -9.69, respectively. These results demonstrate better convergence, more stable training, and higher-quality image generation, establishing a new benchmark for GAN research and applications. © 2024 IEEE.

关键词： Generative adversarial networks

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Effect of preheating on machinability of titanium alloy

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Materials Today: Proceedings 2022年 64卷 799-803页

作者： Ananda Kumar, Eriki Pugazhenhi, R. Chandrasekaran, M. Anbuchezhiyan, G. Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai 600117 India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Chennai 601205 India

This works presents preheating method to improve the machinability of titanium compound. With the effect of warming work piece (pre-heating) using induction coil (heating) system, speed, feed and depth were investigated in the midst of end milling process on a vertical machining center (VC450-Spinner). Additionally, WC-Co PCD inserts were positioned 25 mm apart on the mill (R390-025B25-11M/R390-170408E-NLH13A) and the effects of preheating on tool life, smoothness of the surface, force of cut, vibration/chatter, irregularities, and chips/tool morphology were examined. The execution of uncoated WC-Co was greatly implemented (directionally around multiple times appeared differently compared to room temperature machining), the tool life estimations were essentially higher than those of PCD under room temperature machining. Preheating machining reduces vibration and chatter resulting in increased cutting force and extended tool life. In addition, chip-device contact length is extended and chip serrations are formed reducing equipment wear. As a result of the experimentation, it was found that the execution of uncoated WC-Co was greatly enhanced in the preheated method. Moreover, tool life estimations with PCD at room temperature are significantly improved. © 2022

关键词： Preheating

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Process of Data Cleansing and handling of Memory in Data Streams 15

Process of Data Cleansing and handling of Memory in Data Str...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, N. Kalpana, Y. Kumar, V. Department of Information Technology Vels Institute of Science Technology and Advanced Studies Tamil Nadu India Agricultural Engineering Department ACRI-TNAU Tamil Nadu India

ISBN: (纸本)9798331300579

Real-time Mining [1][2][3] and Streaming of Data have become more popular in the data field, with access to the fastest and the latest data. Real-time Data Mining seeks the development of a real-time framework for enhancing resource efficiency while minimizing environmental impact. Real-time analysis has a huge rate of change in data, and it has to be processed and updated frequently. Data mining is an interdisciplinary subject comprising machine learning, statistics, database technology, and artificial intelligence. Data mining primarily aims to decipher the past and anticipate the future through data exploration and analysis, known as Knowledge Discovery in a Database. Data mining attempts to store the data in the local data set hosted by local computers connected to the computer networks. In the real world, data has become abundant with the advent of data streams, invariably raising the question of data storage. Also, data will not be clean when received in the form of streams. Non-clean data cannot be stored in the database and will not be effective for data analysis. An organized repository of related information must be stored in the database;hence, data must be cleaned before storage. Data is cleaned as per the analysis required of the data. Once the data is cleaned, the question of the memory to store the data arises. Storing real-time data is a relatively trivial process, and there should not be any missing data from the streams. Data cleaning and memory management in real-time data is always challenging. This research proposes a novel methodology for data cleaning and management of memory to overcome these issues. An algorithm is executed using the scheduler at the specific interval separating the test and trial data. Trial data will be used for further analysis, and test data will be discarded at the specific interval. The test data will be a derivative of trial data. © Grenze Scientific Society, 2024.

关键词： Data consistency

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Improved Deepmaxoutmodel for Disease Prediction in Healthcare IoT Cloud System

Improved Deepmaxoutmodel for Disease Prediction in Healthcar...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Preetha P A. Packialatha Department of Computer Science Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

The Internet of Medical Things IoMT has grown rapidly due to the growing popularity of wearable technology and its applications in health monitoring systems. The IoMT significantly reduces the death rate by facilitating early sickness identification. Predicting cardiac disease is among the most significant issues with clinical dataset analysis. Finding the key components of heart disease prediction is the goal of the suggested investigation, which makes use of machine learning techniques. Despite several studies on the issue, the accuracy of the findings on the diagnosis of heart illness is low. The purpose of this paper is to provide an innovative framework for healthcare monitoring that includes the following phases. Data preprocessing, feature extraction, acquisition of data, and prediction are the primary stages. The process of acquiring data is at the IoTs layer. The medical data is considered in this case (benchmark datasets of different illnesses). The data pre-processing operations of data cleaning and data filtering at the cloud layer are applied to the data obtained from the IoT sensors (considering the dataset), where the data normalization process will take place. Subsequently, statistical features and raw features are extracted. The multi-layered Deepmaxout model will predict the diseases during in the prediction phase. Lastly, the evaluation of the suggested approach is compared to existing methods using several types of metrics.

关键词： Measurement Heart Filtering Data preprocessing Data acquisition Predictive models Feature extraction Sensors Monitoring Diseases

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Crow Search Optimization to Identify Adversary Nodes in Wireless Networks 2

Crow Search Optimization to Identify Adversary Nodes in Wire...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

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Investigation on Mechanical Properties of Al5052/BN Metal Matrix Composite

Investigation on Mechanical Properties of Al5052/BN Metal Ma...

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作者： Kumar, Patnala Padma Ruban, M. Department of Mechanical Engineering Vels Institute of Science Technology & Advanced Studies Chennai600117 India Department of Automobile Engineering Vels Institute of Science Technology & Advanced Studies Chennai600117 India

The paper aims to find the Mechanical properties of HNT and Boron Nitride, reinforced with Aluminium hybrid metal matrix composites. The aluminum matrix material are reinforced with different weight percentage of HNT(3%,5%,7%) and BN(2%,3%,4%).The hybrid matrix are fabricated through sir casting *** mechanical Properties of like hardness, tensile, impact and micro structure of the composites are examined. The hardness and tensile of the composite are increased with increase in weight percentage of boron nitride and HNT. Also the microstructure of the composites shows the clear distribution of the composites and XRD pattern shows the presence of reinforcement in the composites. The hardness and tensile strength increases when 7% of HNT and 4% of boron nitride are added towards the base materials. © 2022

关键词： Microstructure

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Empowered Artificial Intelligence Approach using Intuitionistic Fuzzy based Deep Neural Network for Chronic Diseases Prediction

Empowered Artificial Intelligence Approach using Intuitionis...

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： D. Karthik S. Arun Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Managing the increasing prevalence of chronic illnesses is a major worldwide health concern. It's important to understand the medical problem, its stages, types, and available treatments. The most important component in the early diagnosis of chronic diseases might enhance the diagnostic procedure more successfully. Medical professionals can diagnose a process with the help of numerous artificial intelligence tools. However, ambiguity, irrelevance, and inconsistency in the disease dataset have an impact on the classification accuracy. Conventional machine learning techniques are inadequate for managing scenarios of uncertainty that arise in datasets related to chronic illnesses. To anticipate chronic diseases, each characteristic in the suggested work is represented with a hesitation index. In order to compute the hesitation degree as a more informative procedure in determining the association among features, the belongingness and non-belongingness aspects of the intuitionistic fuzzy concept are induced in the prediction process. This work effectively manages the large chronic illness dataset by developing an intuitionistic fuzzy hesitation index-based similarity measure to weed out unimportant and strongly associated characteristics and enhance the classification model. Utilizing the scored feature information, the deep neural network modifies its hyper parameter to address the overfitting issues that frequently arise in DNNs. The performance of the empowered intuitionistic fuzzy-based deep neural network (EIF-DNN) is analyzed using five distinct chronic conditions, including hypertension, diabetes, hepatitis, cancer, and stroke. Comparing the proposed EIF-DNN against the current versions of multi-layered networks, the simulation results show that it produces promising results in predicting chronic diseases at an early stage.

关键词： Training Uncertainty Accuracy Artificial neural networks Stroke (medical condition) Prediction algorithms Indexes

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Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbreak: A Comprehensive study in Healthcare

Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbrea...

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MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： Pritam Ramesh Ahire K. Ulaga Priya Computer Science & Engineering Department Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

A comprehensive health monitoring & analyzing system that provides personalized recommendations, goal setting, and tracking based on relevant factors related to BMI & COVID-19. System aims to analyze attack obesity through prediction, prevention and ongoing monitoring through appropriate predictive analytical system. The system's Risk Assessment component categorizes individuals by their risk levels using factors like age, gender, medical history, and behavior related to pre & post covid-19 related to BMI, then relates appropriate connections in human body. The wellness solution includes features such as fitness tracking, data integration, personalized metrics, and predictive analytics to create a holistic approach for predictive analytics health management. The system healthcare monitoring and analyzing system is a groundbreaking solution that leverages predictive analytics to promote a healthy lifestyle, combat obesity, and enhance overall well-being. By considering a range of factors and employing cutting-edge technology, we empower individuals to take control of their health, achieve their fitness objectives, and mitigate health risks.

关键词： COVID-19 Obesity Pandemics Urban areas Sociology Medical services Mental health

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An Improved Deep Forest Classification and Intelligent Segmentation for skin Cancer Prediction Technique Inspired by Whale Optimization

An Improved Deep Forest Classification and Intelligent Segme...

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Advances in Modern Age Technologies for Health and engineering science (AMATHE), International Conference on

作者： Siva Prasad Reddy K.V S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Fast malignant melanoma tumor identification is vital for successful therapy. Melanoma has now been universally acknowledged to be the most lethal form of malignancy over all else since, even if not caught and addressed promptly, it possesses a substantially higher propensity to migrate towards remaining regions of physique. Definitive presentation of various disorders relies largely on quasi therapeutic machine vision / procuring of disorders scanned pics. These methods offer a tool for autonomous image enhancement that allows for a quick and precise appraisal of the abscess. Deficiency of vast samples constitutes one of primary hurdles for designing a trustworthy autonomous classification scheme. This research suggests integrating Deep—learning & Image—processing to develop an algorithm for artificially recognizing melanoma. This survey's phase involves amassing a repository of dermoscopy pics, pre—Processing, separation utilizing hybridized Fuzzy-C-Means, and weighted enhancement employing the whale-optimization-algo. Following the subdivision of the dermoscopic imagery, the chapped skin cellular membranes traits are captured utilizing extraction of features. For the layering of the retrieved attributes, Long -shortterm -memory {LsTm}, bidirectional LsTm && Deep-Forest predictors are deployed. After using freely searchable sample group, the reported classifier reliability was Ninety Seven Percent && the segmentation effectiveness reached ninety three percent.

关键词： Hair Training Object segmentation Melanoma Feature extraction Solids Skin

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