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Recent Advances in computer science and Communications 2021年第5期14卷 1541-1550页

作者： Arora, Amandeep S. Raja, Linesh Bahl, Barkha Amity Institute of Information Technology Amity University Rajasthan Jaipur India Department of Computer Applications Manipal University Jaipur Rajasthan India Department of Computer Science Dehli Institute of Advanced Studies Gulu Gobind Singh Indraprastha University Dwarka New Delhi India

Aim: Cloud Security is a strong hindrance which discourages organisations to move toward cloud despite huge benefits. Distributed denial of service attacks operated via distributed systems compromise availability of cloud services which cause limited resources for authentic users and high expense for cloud service users and business owners. Objective: Techniques to identify distributed denial of service attacks with minimized false positives are highly required to ensure availability of cloud services to genuine users. Scarcity of solution which can detect denial of service attacks with minimum false positives and reduced detection delay has motivated us to compare classification algorithms for detection of distributed denial of service attacks with minimum false positive rate. Methods: Classification of incoming requests and outgoing responses using machine learning algorithms is a quite effective way of detection and prevention. We designed a performance tuned support vector machine algorithm with features of F-hold cross validation strategy. Results: F-hold crosses validation strategy, which can detect denial of service packets with 99.89% accuracy. Conclusion: This system ensures economic sustainability for business owners and limited resources mitigation for authenticated and valid cloud users. © 2021 Bentham science Publishers.

关键词： Denial-of-service attack

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Surrogate-based multilevel Monte Carlo methods for uncertainty quantification in the Grad-Shafranov free boundary problem

arXiv

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arXiv 2025年

作者： Elman, Howard C. Liang, Jiaxing Sánchez-Vizuet, Tonatiuh Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computational Applied Mathematics & Operations Research Rice University United States Department of Mathematics The University of Arizona United States

We explore a hybrid technique to quantify the variability in the numerical solutions to a free boundary problem associated with magnetic equilibrium in axisymmetric fusion reactors amidst parameter uncertainties. The method aims at reducing computational costs by integrating a surrogate model into a multilevel Monte Carlo method. The resulting surrogate-enhanced multilevel Monte Carlo methods reduce the cost of simulation by factors as large as 104 compared to standard Monte Carlo simulations involving direct numerical solutions of the associated Grad-Shafranov partial differential equation. Accuracy assessments also show that surrogate-based sampling closely aligns with the results of direct computation, confirming its effectiveness in capturing the behavior of plasma boundary and geometric *** Codes 65Z05, 65C05, 62P35, 35R35, 35R60 Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Unveiling E-Commerce User Behavior through Deep Learning Evolutionary Data Mining

Unveiling E-Commerce User Behavior through Deep Learning Evo...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), International Conference on

作者： C Jayanthi A Rukmani D Annalakshmi M Arumugam Thirunavukkarasu K S Sasikumar P Department of Computer Science Government Arts College (Autonomus) Karur Tamil Nadu India Department of Computer Technology-PG Kongu Engineering College Erode Tamil Nadu India Department of Computer Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of BBA Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

This study delves into the realm of E-commerce user behavior analysis using a novel approach that combines deep learning and evolutionary data mining techniques. With the explosive growth of online shopping and digital transactions, understanding user behavior has become paramount for businesses to enhance their services and increase customer satisfaction. In this paper, we propose a framework that leverages the power of deep learning algorithms to extract intricate patterns and insights from vast amounts of E-commerce data. Additionally, we integrate evolutionary data mining methods to refine the analysis process, allowing the model to evolve and adapt to changing user preferences over time. Through extensive experiments on real-world E-commerce datasets, we demonstrate the efficacy of our approach in uncovering hidden behavioral trends, predicting user actions, and optimizing personalized recommendations. Our findings underscore the significance of amalgamating deep learning and evolutionary data mining to gain a comprehensive understanding of E-commerce user behavior, thus empowering businesses to make informed decisions in an ever-evolving digital landscape.

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Diabetes Prediction using Enhanced and Optimized DRL-CNN Approaches

Diabetes Prediction using Enhanced and Optimized DRL-CNN App...

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Learning and Technology Conference (L&T)

作者： Usha Nandhini A K. Dharmarajan Bhisham Sharma Subrata Chowdhury Imed Ben Dhaou School of Computing VISTAS Chennai India Department of Information Technology VISTAS Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andra Pradesh India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia

This study develops an advanced automated prediction system using Machine Learning (ML) techniques to identify diabetes early. The research employs the WBSMOTE method for data preprocessing, addresses class imbalances with the WB-SMOTE approach, and introduces two novel classification methods, DRL-CNN and DRL-OCNN. Reliable datasets from sources like The Data World repository, PIMA Indian Diabetes (PIDD) dataset, and Real Time (RT) Diabetes Dataset are used, with an emphasis on data consistency and feature relevance. If we examine these datasets, it contains general attributes and no internal specific attributes related to diabetes is used. So, prediction results will be what type of diabetes we won’t get any specific characteristics and risk factors. Evaluation is based on Accuracy, Precision, and Recall criteria. The DRL-OCNN model, integrating SGD optimization and Delta Rule Learning, outperforms other ML models with a accuracy of 96%, precision of 0.93 and recall of 0.91, demonstrating its potential for accurate early diabetes identification and classification.

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A Novel Method to Predict Chronic Kidney Disease using Optimized Deep Learning Algorithm

A Novel Method to Predict Chronic Kidney Disease using Optim...

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Learning and Technology Conference (L&T)

作者： I. Preethi K. Dharmarajan Bhisham Sharma Subrata Chowdhury Imed Ben Dhaou School of Computing Sciences VISTAS Chennai India Department of Information Technology VISTAS Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andhra Pradesh India Department of Computer Science Hekma School of Engineering Computing Design Dar Al-Hekma University Jeddah Saudi Arabia

Chronic Kidney Disease (CKD), a significant global health issue and has a major impact on a vast number of people. In the early stages, it is impossible to identify the disease’s symptoms. Very few people are aware of this illness and are able to foresee the symptoms earlier. The disorder is considered as potential risk factor. As a result, a potent deep learning framework is used in this paper to reduce the risk of acquiring these diseases as a way to prevent them. The machine learning algorithms used in early disease prediction, has been found to be computationally expensive, frequently overfit, and underperform in terms of accuracy since they must examine the large amount of clinical data until the model converges. Therefore, in this paper three novel work has been proposed an efficient novel hybrid feature selection strategy RFITLO algorithm is used to find the optimal features that gives the major contribution is classifying the CKD disease. Then two proposed classification algorithms namely Enhanced Multi-Layer Perceptron (HW-MLP) and Optimized Multi-Layer Perceptron (PKD-OMLP) are used in prediction model to capture the complex patterns and optimize the learning algorithm to predict the CKD at prior stage from the data gathered in Kaggle, Real Time and UCI Machine Learning Repository Dataset. In order to measure the classifications of disease, performance measures including accuracy, precision and recall are analyzed. The experimental findings show that the PKD-OMLP strategy produces better results than the proposed HW-MLP and other conventional approaches like Support Vector Machine (SVM), Linear Regression (LR) and Multi-Layer Perceptron (MLP). Among the preceding four models PKD-OMLP renders the best outcome as per its performance level producing a high accuracy of 94.89% on testing Real Time (RT) CKD dataset comparatively with other datasets such as Kaggle and UCI repository. Therefore, these proposed algorithms can support clinicians to enable secured an

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Decision Making on Optimal Selection of Advertising Agencies using Machine Learning 9

Decision Making on Optimal Selection of Advertising Agencies...

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9th International Conference on Information Management, ICIM 2023

作者： Joe Anand, M. Clement Martin, Nivetha Clementking, Arockiasamy Rani, S. Priyadharshini, Sujitha Siva, S. Department of Mathematics Karnataka Bengaluru560052 India Human Resource Development Karnataka Bengaluru560052 India Vels Institute of Science Technology & Advanced Studies Department of Information Technology Tamil Nadu Chennai600117 India Pvt. Ltd Karnataka Bengaluru560017 India Reva University Department of Computer Science & Applications Karnataka Bengaluru560064 India

ISBN: (纸本)9798350336382

The decision makers of every business firm take strenuous efforts to achieve the target of sales promotion using different marketing strategies. In recent times, advertising is becoming one of the most preferred and adopted marketing techniques to reach customers at larger magnitude. The business firms irrespective of their production sizes and scales associate with various advertising agencies to familiarize their products to the customers. The advertising strategic approach of marketing also gives rise to a decision-making problem on the optimal choice of advertising agencies as the wrong selection of the agencies harms the sales. This research paper intends to propose a decision making (DM) model integrating both multi-criteria decision methods and machine learning algorithms to make optimal selection of advertising agencies. The proposed DM model considers 25 alternatives of advertising agencies, six criteria, linguistic analytic hierarchy process (LAHP) and random forest algorithm for ranking the advertising agencies. The machine learning (ML) algorithm identifies the feasible alternatives with the help of criterion weights obtained using the method of LAHP. This method is more compatible as it minimizes the risks of decision making by grouping the alternatives into two groups of acceptance and rejection based on their likelihood. The computations are done using Python programming language especially in Google Colabs. © 2023 IEEE.

关键词： Machine learning

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Analyzing Job Interview Performance Prediction using Graph Features and Convolutional Neural Networks

Analyzing Job Interview Performance Prediction using Graph F...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rath, Manoj Kumar Karthi, R. Roopalatha, N. Rao, Attru Hanumantha Thulasimani, T. Ali Khan, P.M.D. Kiit Deemed to Be University Bhubaneswar India E.G.S. Pillay Engineering College Department of Management Studies Nagapattinam India University Gitam School of Business Hyderabad India Aditya College of Engineering and Technology Department of Computer Science and Engineering Surampalem India Bannari Amman Institute of Technology Department of Mathematics Sathyamangalam India Mohan Babu University School of Computing Tirupati India

ISBN: (纸本)9798350375442

The psychological reactions of candidates to personnel selection are becoming more important since they significantly impact the outcome of the selection process. In this study, we created and evaluated the Multi-dimensional Job Interview Self-Efficacy Scale (MJISE). Five components of interviewees' self-efficacy form the basis of our proposed MJISE: self-promotion, dealing with difficult interviewers, interview-related anxiety, and content and practical interview *** proposed approach consists of three stages, which are preprocessing data, training the model, and extracting features. When it comes to text classification, preprocessing is king. In order to make text data useable by a classifier, it is necessary to extract a set of words from the text data and convert them into a feature set. Throughout the training process, it employed a GCNN. With a 91.30% average accuracy, our innovative approach surpasses CNN and ANN. © 2024 IEEE.

关键词： Convolutional neural networks

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9979-9984页

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing100081 China Yangtze Delta Region Academy Jiaxing314019 China Beijing Institute of Technology Advanced Technology Research Institute Jinan250307 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Birmingham City University School of Computing and Digital Technology BirminghamB55JU United Kingdom Khalifa University KU 6 G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs' activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. © 1967-2012 IEEE.

关键词： Channel estimation

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Toward a 2D Local Implementation of Quantum Low-Density Parity-Check Codes

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PRX Quantum 2025年第1期6卷 010306-010306页

作者： Noah Berthusen Dhruv Devulapalli Eddie Schoute Andrew M. Childs Michael J. Gullans Alexey V. Gorshkov Daniel Gottesman Joint Center for Quantum Information and Computer Science National Institute of Standards and Technology (NIST)–University of Maryland College Park Maryland 20742 USA Computer Computational and Statistical Sciences Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Computer Science and Institute for Advanced Computer Studies University of Maryland College Park Maryland 20742 USA Joint Quantum Institute National Institute of Standards and Technology (NIST)–University of Maryland College Park Maryland 20742 USA

Geometric locality is an important theoretical and practical factor for quantum low-density parity-check (qLDPC) codes that affects code performance and ease of physical realization. For device architectures restricted to two-dimensional (2D) local gates, naively implementing the high-rate codes suitable for low-overhead fault-tolerant quantum computing incurs prohibitive overhead. In this work, we present an error-correction protocol built on a bilayer architecture that aims to reduce operational overheads when restricted to 2D local gates by measuring some generators less frequently than others. We investigate the family of bivariate-bicycle qLDPC codes and show that they are well suited for a parallel syndrome-measurement scheme using fast routing with local operations and classical communication (LOCC). Through circuit-level simulations, we find that in some parameter regimes, bivariate-bicycle codes implemented with this protocol have logical error rates comparable to the surface code while using fewer physical qubits.

关键词： Quantum computers

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Performance Analysis Of Insulation Resistance Test And Cable Laying Installation In Library Block

Performance Analysis Of Insulation Resistance Test And Cable...

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Global Power, Energy and Communication Conference (GPECOM)

作者： B. Rubini R. Karthick Kalaiarasi G S. Saranya S. Pradeep Kumar M. Suganiya Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Electrical and Electronics Engineering C.K.College of Engineering and Technology Cuddalore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology & Research (Deemed to be University) Guntur India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350351088

ISBN: (纸本)9798350351095

The process of installing electrical cables physically in a specific location inside a library block is widely considered as an electrically equipped place. It involves planning the particular route, securing the cables and ensuring technical support and proper protection. In a library block, the process of installing electrical wires can provide power and electrical connectivity to various devices and systems. An insulation resistance tests were conducted a high voltage flow to the insulation and measures the resistance ratio to the actual current flow after cable installation. Testing of measurements given in table. Algorithms help to identify all kinds of weaknesses and faults in the insulation that could lead to any electrical leakages or short circuits. The insulation resistance test is primarily important to ensure the safety and reliability of electrical systems. It is particularly needed in an environment like a library. Libraries are filled with books made of papers that are easily flammable in nature. So, we should give top priority to electrical safety in libraries. Minimum constrained non-linear minimization solver is used to identify the iteration levels and verified with best function values and first order optimality values. This paper shows performance analysis of Insulation resistance test and Cable laying installation in a Library block.

关键词： Resistance Insulation Current measurement Wires Libraries Performance analysis Electrical resistance measurement

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