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检索条件"任意字段=2nd International Conference on Computer Science and Software Engineering, CSASE 2022"

共 8419 条记录，以下是61-70 订阅

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A Review of Design and Implementation of computer Based Census Management System 2

A Review of Design and Implementation of Computer Based Cens...

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2nd international conference on Multidisciplinary engineering and Applied science, ICMEAS 2023

作者： Bashir, Umar Mohammed Nyangwarimam, Ali Obadiah Thomas, Sadiq Odimba, Chinomso Roselyn Lawal, Shariff Aboaba, Adulfatah A. Elijah, Olakunle Nile University of Nigeria Dept. of Computer Engineering Abuja Nigeria Nile University of Nigeria Dept. of Industrial Chemistry Abuja Nigeria Airforce Institute of Technology Dept. of Mechanical Engineering Kaduna Nigeria University of Maiduguri Dept. of Computer Engineering Borno Nigeria

ISBN: (纸本)9798350358834

This Paper Presents a computer-based census management system which is a software application designed to collect, store, manage, and analyze census data electronically. It replaces traditional paper-based methods of data collection and processing, making the census process more efficient, accurate, and secure. The system typically consists of several components that work together to facilitate the census data management process. © 2023 IEEE.

关键词： Information management

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Detection and Categorization of Conflict Flows Within SDN Environments using Machine Learning Approach 2

Detection and Categorization of Conflict Flows Within SDN En...

引用

2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Kousalya, S. Sundararajan, Kanthimathi Vasudevan, I. Jaichitra Sheela, L. Jaba Thyagarajan, Sarunya Panimalar Engineering College M.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E. Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

software-Defined Networking (SDN) is known for its remarkable versatility and adaptability. SDN applications encompass a wide range of network management and control functionalities, including load balancing, access control, and routing. However, SDN can encounter issues stemming from conflicting traffic patterns that may degrade network performance in relation of efficiency and optimization. These conflicts arise when factors such as priority and action settings are adjusted. Furthermore, IP spoofing, commonly employed for anonymity and amplification, frequently leads to widespread distributed denial-of-service (DDoS) attacks. We've established a sufficient condition based on the duration and frequency of DDoS attacks. Notably, our sophisticated attack method generates legitimate traffic within the SIP network using reflection techniques. 'As a result, it has the ability to bypass security measures such as blacklists, IP blocking, rate limiting based on packet counts or session/transaction volumes, and automated systems designed to detect unnatural message generation. These security mechanisms are commonly employed in advanced security setups, but the system or technique in question can effectively avoid detection or restriction when interacting with digital environments.' © 2024 IEEE.

关键词： Network security

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Machine Learning Driven Load Balancing in software Defined Networks: Recent Progress and Emerging Challenges 2

Machine Learning Driven Load Balancing in Software Defined N...

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Sharma, Sonam Mahapatra, Rajendra Prasad Kapil, Manoj Seth, Dambarudhar Srm Institute of Science and Technology Department of Computer Science Engineering Delhi-NCR Campus Ghaziabad201204 India Swami Vivekananda Subharti University Faculty of Engineering and Technology NH-58 Delhi-Haridwar Bypass Road 250005 India

ISBN: (纸本)9798350390179

Load balancing has arisen as an important challenge in case of software Defined Network because of the dynamic patterns in the traffic and expanding demands of network. The centralized framework of SDN delivers unique opportunity. It enhances the performance of network and usage of resources by distributing the load of network dynamically across multiple devices and paths. This paper provides a concise overview of SDN and examines the influence of load balancing within this context. It includes a comprehensive study to analyse the algorithms of load balancing used by researchers for different scenarios and to identify the persistent challenges. Furthermore, a methodology has been proposed to highlight the impact of machine learning techniques to amplify the load balancing decision which can be useful to develop more intelligent and adaptive architectures of SDN. For future research, the study aims to deploy scalable and robust SDN solutions in next-generation network infrastructures. © 2024 IEEE.

关键词： Load Balancing Machine Learning Networking software Defined Network Traffic

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Deep Learning Models for software Defect Classification 2

Deep Learning Models for Software Defect Classification

引用

2nd IEEE international conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Singh, Kalpana Aggarwal, Alankrita Aneja, Rattan Deep Kumar, Rajender Soni, Punit Chandigarh University Mohali India Computer Science and Engineering Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350372847

With the increasing reliance on automatic software-based applications, it is critical to automate software defect classification and assure software dependability. This article proposes an expert-based automatic software flaw classification system. Defect report categorization is the process of categorizing defects into predefined groups. Many machine learning (ML) algorithms have recently been presented to categorize defect in various classes. This research proposes a classification model based on the Long Short Term Memory (LSTM) network and Convolution Neural Network (CNN) that uses these deep learning (DL) models to categorize defect reports by generating new word embedding from the defect reports. In terms of recall, accuracy and precision, the results are compared to pre-trained word embedding using Google's word2vec. According to the experimental results, LSTM beats the other models utilized in the study. On the redmine dataset, LSTM achieves a maximum accuracy of 70%. © 2024 IEEE.

关键词： Long short-term memory

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Improving software Defects Detection: An In-Depth Analysis of Machine Learning Methods and Static Analysis Tools for Greater Accuracy 2

Improving Software Defects Detection: An In-Depth Analysis o...

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2nd international conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Bobade, Veer Puri, Chetan DMIHER Faculty of Engineering And Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India DMIHER Faculty of Engineering And Technology Department of Computer Science and Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, software Defect Prediction (SDP) uses machine learning approaches to anticipate and address any problems early in the development cycle. Numerous machine learning techniques, such as Support Vector Machines (SVM), Random Forest (RF), Logistic Regression, Naïve Bayes, Multilayer Perceptron (MLP), Decision Stump, J48, Lazy IBK, and ZeroR, are thoroughly examined in this work. With an emphasis on feature selection as a crucial step in increasing prediction accuracy and lower computing costs, these approaches are assessed for their capacity to detect software flaws precisely. This research study demonstrates how these models may improve fault identification and prevention procedures using historical records from sources such as the PROMISE repository. The study highlights the best practices for machine learning-based software quality assurance, lowering expenses while guaranteeing superior results. © 2025 IEEE.

关键词： Support vector machines

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Cotton Leaf Disease Classification and Pesticide Recommendation 2

Cotton Leaf Disease Classification and Pesticide Recommendat...

引用

2nd IEEE international conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Jamadar, Bhagesh Harikant, Bhavana Manjunath Chaithra, B.J. Mohit, J.S. Vasudevan, Vani Nitte Meenakshi Institute of Technology Computer Science Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350372892

The decline in cotton production due to various diseases poses significant challenges to agriculture. In response, this paper presents the development of a software application titled 'Cotton Leaf Disease Classification and Pesticide Recommendation.' The application aims to classify diseases affecting cotton leaves, recommend suitable pesticides, and suggest nearby shops for pesticide procurement. Focusing primarily on prevalent cotton leaf diseases such as cercospora, bacterial blight, curl virus, fusarium, and Ascochyta, the software employs image preprocessing techniques to process cotton leaf images. Convolutional neural network technologies are utilized for training the datasets to accurately classify diseases. Upon classification, the application suggests appropriate pesticides and nearby vendors, and sends the results to the user's email address. This software endeavors to mitigate the impact of cotton leaf diseases on agricultural productivity, particularly in regions like Karnataka, India. © 2024 IEEE.

关键词： Application programs

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Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems 2

Optimizing Railway Traffic Management Using Fuzzy Logic-Base...

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2nd international conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Ananthanagu, U. Vidyullatha, Pellakuri Pandiya, Dileep Kumar Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Zoominfo Technologies Principl Software Engineer Boston United States

ISBN: (纸本)9798350364699

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks. © 2024 IEEE.

关键词： Efficiency

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ML-Orchestrated Malware Detection Framework for Windows APIs in Critical Infrastructure Systems 2

ML-Orchestrated Malware Detection Framework for Windows APIs...

引用

2nd international conference on computer, Communication and Control, IC4 2024

作者： Vaghasiya, Shayalkumar Gupta, Rajesh Mohapatra, Santoshinee Tanwar, Sudeep Garg, Deepak Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat 382481 India Sr University School of Computer Science and Ai Telangana Warangal India

ISBN: (纸本)9798350387933

The security of critical infrastructure systems is crucial as they provide basic amenities and services necessary for functioning of a community, also they represent important part of country's economy. An attack on this critical infrastructure systems can have significant consequences. In this paper we discuss the This paper addresses the increasing security challenges in critical infrastructure systems due to cyber threats, particularly malware and propose a framework based on machine learning for robust malware classification. Our proposed framework includes a dataset of Windows API functions used by the software, preprocessing techniques, and the XGBoost model which achieves an accuracy of 86.98%. Using this model, we can detect and categorize five different types of malwares to provide security against malware attacks. The results show model's ability to successfully classify different kinds of malware. Realtime monitoring is another feature of this framework which is important for actively protecting critical infrastructure systems. © 2024 IEEE.

关键词： Machine learning

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Performance Analysis for Prediction of Trending Footwear Classification using DensNet compared with Logistic Regression model 2

Performance Analysis for Prediction of Trending Footwear Cla...

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2nd international conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Giridhar, E.V. Parthipan, V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Big Data and Network Security Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

The purpose of this work is to investigate the effectiveness of DensNet and Logistic Regression in terms of accurately predicting the classification of footwear *** this study, there are two groups: the Novel DensNet model and the Logistic Regression model. Each group is being iterated for a sample size of ten, which is being generated with the ClinCalc software. The alpha value is set at 0.05, and the confidence interval is set at 95%. We took into consideration the footwear prediction dataset that was available on *** and had a size of 15854. The statistical analysis reveals a significant difference between the DensNet algorithm and the Logistic Regression model, with a value of p=0.001 (independent sample t-test p © 2024 IEEE.

关键词： Logistic regression

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Development of an Adaptive Neuro-Fuzzy System to Navigate the AGV's 2

Development of an Adaptive Neuro-Fuzzy System to Navigate th...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Jayahariprabhu, M. Hindusthan College of Engineering and Technology Department of Computer Science and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350371055

The paper suggests an ANFIS, or adaptable neuro-fuzzy inference system, to fix AGV navigation issues. Two ANFIS controllers regulate the AGV's left and right angular velocities to move it to the target location, while the other two controllers optimize the heading adjustment to avoid obstacles. That's the whole system. All three sensor readings-front distance (FD), correct distance (RD), and left distance (LD)are sent to the two velocity controllers for low-level motion control. Using the angle difference (AD) between the AGV's heading and the angle to the target, two heading controllers can determine the best course of action. The viability of the suggested ANFIS technique has been investigated via simulation tests run under two distinct situations. Using the MATLAB software package, the simulated results demonstrate ANFIS's ability to navigate and design paths effectively in a workspace containing static barriers. © 2024 IEEE.

关键词： AD AGV ANFIS FD LD

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