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Implementation of machine learning Classification Techniques for Intrusion Detection System 4

Implementation of Machine Learning Classification Techniques...

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4th international conference on Intelligent engineering and Management, ICIEM 2023

作者： William, P. Inukollu, Venkata Narasimha Rao Ramasamy, Viswanathan Madan, Parul Shrivastava, Anurag Srivastava, Aparna Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Purdue University Fortwayne Fort WayneIN United States Koneru Lakshmaiah Education Foundation Department of CSE Guntur India Graphic Era Deemed to be University Depatment of CSE Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Lloyd Institute of Engineering and Technology Department of MCA Greater Noida India

ISBN: (纸本)9798350341126

In the region internet including service, flaws of software design that is present on the device on its own, protocol, and also in control. These vulnerabilities of the community can be very easily hacked by the community hackers and to the net put the potential intrusion attacks. With the time as there is an increase in cases of personal computers security attacks and also increase in the number, fundamental security attacks are most in the number of cases than before. No long these issues of community safety can be ignored. The protection of these types of assets is entailed by computer protection. Information's Availability, integrity, and confidentiality to the users are legal characteristics this may be represented. Properties of privacy and secrecy are deal with confidentiality. On the negative chose set of rules finds attacks and intrusion. This machine of intrusion detection is a machine sturdily gaining knowledge of capability and excellent adaptive capacity. Using it may stumble on numerous unknown kinds of intrusions of attacks and intrusions. © 2023 IEEE.

关键词： software design

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Status Quo and Problems of Requirements engineering for machine learning: Results from an international Survey 24th

Status Quo and Problems of Requirements Engineering for Mach...

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24th international conference on Product-Focused software Process Improvement (PROFES)

作者： Alves, Antonio Pedro Santos Kalinowski, Marcos Giray, Gorkem Mendez, Daniel Lavesson, Niklas Azevedo, Kelly Villamizar, Hugo Escovedo, Tatiana Lopes, Helio Biffl, Stefan Musil, Juergen Felderer, Michael Wagner, Stefan Baldassarre, Teresa Gorschek, Tony Pontifical Catholic Univ Rio Janeiro PUC Rio Rio De Janeiro Brazil BTH Karlskrona Sweden Vienna Univ Technol TU Wien Vienna Austria German Aerosp Ctr DLR Cologne Germany Univ Cologne Cologne Germany Univ Stuttgart Stuttgart Germany Univ Bari Bari Italy

ISBN: (纸本)9783031492655;9783031492662

Systems that use machine learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled systems is mainly dominated by isolated case studies with limited generalizability. We conducted an international survey to gather practitioner insights into the status quo and problems of RE in ML-enabled systems. We gathered 188 complete responses from 25 countries. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative analyses on the reported problems involving open and axial coding procedures. We found significant differences in RE practices within ML projects. For instance, (i) RE-related activities are mostly conducted by project leaders and data scientists, (ii) the prevalent requirements documentation format concerns interactive Notebooks, (iii) the main focus of non-functional requirements includes data quality, model reliability, and model explainability, and (iv) main challenges include managing customer expectations and aligning requirements with data. The qualitative analyses revealed that practitioners face problems related to lack of business domain understanding, unclear goals and requirements, low customer engagement, and communication issues. These results help to provide a better understanding of the adopted practices and of which problems exist in practical environments. We put forward the need to adapt further and disseminate RE-related practices for engineering ML-enabled systems.

关键词： Requirements engineering machine learning Survey

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An Efficient Convolutional Multi-Scale Vision Transformer for Image Classification 2

An Efficient Convolutional Multi-Scale Vision Transformer fo...

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2nd international conference on Image Processing, Computer Vision and machine learning, ICICML 2023

作者： Zhang, Ji Chen, Zhihao Ge, Yiyuan Yu, Mingxin Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Beijing Information Science and Technology University Computer School Beijing China Jilin University Department of Software Engineering Changchun China

ISBN: (纸本)9798350331417

This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it with the concept of multi-scale processing generally used in convolutional neural networks (CNNs). The work aims to address the limitations of conventional ViTs which typically operate at a single scale, hence overlooking the hierarchical structure in visual data. The multi-scale ViT enhances classification performance by processing image features at different scales, effectively capturing both low-level and high-level semantic information. Extensive experimental results demonstrate the superior performance of the proposed model over standard ViTs and other state-of-the-art image classification methods, signifying the effectiveness of the multi-scale approach. This research opens new avenues for incorporating scale-variance in transformer-based models for improved performance in vision tasks. © 2023 IEEE.

关键词： Classification Convolutional neural networks Multi-scale Vision Transformer

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Cognitive Effort in Interaction with software Systems for Self-regulation - An Eye-Tracking Study 20th

Cognitive Effort in Interaction with Software Systems for Se...

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20th international conference on engineering Psychology and Cognitive Ergonomics (EPCE) Held as Part of the 25th international conference on Human-Computer Interaction (HCII)

作者： Drzyzga, Gilbert Harder, Thorleif Janneck, Monique TH Lubeck Inst Interact Syst Lubeck Germany

ISBN: (纸本)9783031353918;9783031353925

The importance of digital degree programs has grown increasingly in recent years, due in part to their ability to provide a personalized learning experience for students. However, degree programs in this format have higher dropout rates than traditional degree programs. In the process of a user-centered design approach, a dashboard for the online degree programs of a university network is developed to provide information and recommendations about the learning process based on descriptive analysis and machine learning (ML) methods. For this purpose, ML models are developed, trained and evaluated. The goal of the dashboard is to promote self-regulation among students and reduce dropout rates. It will be set up as a plug-in through the learning management system (LMS) Moodle exclusively for students. In order to understand which aspects are important for users in relation to the cognitive processes involved in interacting with the dashboard, an eye-tracking study was conducted using the thinking aloud technique. The goal of the study was to investigate which cognitive demands are set for the users when interacting with the prototype and how the automatically generated information is perceived. When integrating the LD into the LMS, care should be taken to ensure that all content is realized in an understandable and easy-to-follow manner, due to the fact that otherwise the effort required to focus on the content elements of the LD could become greater - and with it the cognitive requirements.

关键词： Higher Education learning-Management-System Learners Dashboard Self-regulation Motivation Structuring of Feedback Usability User Experience

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MDC-Net:Intelligent Malware Detection and Classification using Extreme learning machine 3

MDC-Net:Intelligent Malware Detection and Classification usi...

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3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Reddy, V.S.K. Nagaraju, I. Gayatri, Mantri Roopa Chandrika, R. Dileep, P. Revathy, P. Malla Reddy University Department of Ece Hyderabad India Telangana State Hyderabad India Malla Reddy College of Engineering and Technology Dept of Cse Telangana State Hyderabad India Narsimha Reddy Engineering College Dept of Cse Telangana State Hyderabad India

ISBN: (纸本)9781665462167

In the progress of digital era, there is a significant rise in the number of security flaws that are the result of assaults by malicious software, sometimes known as malware. Malware detection is still a popular area of study owing to the fact that a significant number of computer users, organizations, and governments have been impacted by the exponential development in malicious software assaults. These detection methods rely on malware signatures and behavioral patterns. Recent malwares make use of metamorphic, polymorphic, and other evasive strategies to rapidly modify their behavior and produce a huge number of new malwares. These tactics allow the malware to avoid detection. Recent years have seen an increase in the use of machine learning Algorithms (MLA) for the purpose of effective malware analysis. The majority of these newly discovered malwares are variations of previously discovered malware. However, the conventional models failed to classify the different types of malwares and resulted in poor classification performance. So, this work focused on implementation of Malware Detection and Classification (MDC-Net). Initially, dataset preprocessing is performed to remove the missing symbols, unknown data from MALIMG dataset. Then, convolutional neural network (CNN) model is used for extraction malware class dependent, and malware class specific features. Finally, Extreme learning machine (ELM) model trained with the CNN features. So, the ELM model can capable of classifying the various malware classes from every new data. The MDC-Net resulted in superior performance than existing approaches in terms various metrics. © 2023 IEEE.

关键词： machine learning

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Supervised machine learning Techniques for Power Consumption Usage Level Prediction 6

Supervised Machine Learning Techniques for Power Consumption...

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6th international conference on software engineering and Computer Science, CSECS 2023

作者： Tan, Yi-Fei Zhao, Ge-Zhi Cheeng, Tze-Hang Ooi, Chee-Pun Tan, Wooi-Haw Cheong, Soon-Nyean Multimedia University Faculty of Engineering Cyberjaya Malaysia University of Technology Faculty of of Engineering Chongqing Chongqing China Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia Multimedia University Faculty of Creative Multimedia Cyberjaya Malaysia

ISBN: (纸本)9798350306378

Electricity is an essential part of modern life and is crucial for many aspects of society and the economy. It is essential for industries to operate and create products, for transportation systems to function, and for communication and information technology to function. As the world continues to become more connected and technologically advanced, the demand for electricity is likely to increase, making it an even more critical resource in the future. Overusing electricity will lead to higher energy bills, which can put a strain on household or business budgets. In this paper, the authors developed a model to predict the level of electricity consumption usage. The model is developed in 2 stages. First stage is to categorize the electricity usage into several levels by using equally divided method. Second stage is to use supervised machine learning techniques to develop a prediction model for the level of electricity consumption. The experimental results indicated that using the Random Forest classifier leads to better performance in terms of accuracy, precision, recall, F1 score, and kappa score. Besides that, the results show that 2-level prediction performed better than 4-level prediction. © 2023 IEEE.

关键词： Forecasting

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Hardware - software Co-Design Approach in Customizable Programmable Logic Based Neuromorphic System Design 30

Hardware - Software Co-Design Approach in Customizable Progr...

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30th IEEE international conference on Electronics, Circuits and Systems, ICECS 2023

作者： Yüksel, Osman Erkmen, Burcu Yildiz Technical University Electronics & Communications Engineering Dept Istanbul Turkey

ISBN: (纸本)9798350326499

Neuromorphic Computing offers energy efficient AI inference for Edge Computing as well as morphologically adaptive and complex machine learning systems inspired by the nature. Hardware-software codesign approach presents the optimum solution for performing neuromorphic computations. Hardware software Codesign is an embedded systems approach that target sharing computational costs between the software and hardware with using system-on-chip (SoC) that contains general purpose microprocessors, FPGAs, ASIC cores, memory block periperals and interconnection buses in one *** this study, we determine the basic requirements of neuromorphic hardware systems and the problems in the hardware design. We use the hardware-software codesign approach to solve those problems. By comparing the HDL models of spiking neurons with a programmable spiking neuron designed with hardware-software codesign approach in terms of speed, timing and hardware cost, we demonstrate the superiority of the hardware-software co-design approach in neuromorphic computing over pure hardware or pure software solutions. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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software Defect Prediction using ANN Algorithm 7

Software Defect Prediction using ANN Algorithm

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Shankar, B. Maruthi Sivakumar, S.A. Dhabliya, Dharmesh Sundari, P. Abinaya Asmitha, M. Shree, S.M. Gobiga Sri Krishna College of Engineering and Technology Electronics and Communication Engineering Coimbatore Kuniyamuthur India Dr.N.G.P. Institute of Technology Electronics and Communication Engineering Coimbatore India Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350341485

The software industry is rapidly evolving due to increasing demand and technology. software created by humans can contain a number of errors which are known as defects. The primary responsibility of a tester is to identify these errors. Because defects can manifest at any stage during software development, it can be difficult to begin testing at an early stage. When the manager misinterprets customer needs, it can result in a defect during the system design phase. Quality assurance is essential for software, and as the demand for software applications increases, the complexity of software designs also rises, leading to a greater number of bugs. This can lead to manual detection of the errors, which takes more time to complete. The only way to improve software quality is to fix the defects. By implementing a time-saving method of software defect prediction through an enhanced machine learning and Artificial Neural Network techniques, this paper increase the quality of the software. © 2023 IEEE.

关键词： Defects

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Deep learning based defect detection for printed circuit boards 3

Deep learning based defect detection for printed circuit boa...

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3rd international conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Liu, Xuehua Wang, Jian Zhang, Runxu Ruan, Weixiang Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9781510667563

Printed circuit boards (PCB) are manufactured and transported and stored in such a way that many factors can lead to different types of defects. Currently, manual defect detection and machine vision-based defect detection methods have problems such as slow detection speed, high false detection rate and fewer types of defects that can be detected. In this paper, a modeling method for PCB defect detection model based on deep learning is proposed. First, to address the problem of difficult feature extraction due to the small size of PCB defects, the original image is first generated at super-resolution by generative adversarial networks, and then the original backbone network of FasterRCNN is replaced by Restnet101 for feature extraction, with a deeper network ensuring better feature extraction results. Experiments show that the model can effectively detect six types of PCB defects: missing holes, pseudo-copper, short circuit, stray, mouse bite and open circuit. © 2023 SPIE.

关键词： Printed circuit boards

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An Analysis of machine learning Methods for Electricity Price Forecasting 1

An Analysis of Machine Learning Methods for Electricity Pric...

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1st international conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Sahoo, Satyabrata Swain, Sarat Chandra Dash, Ritesh School of Electrical Engineering Kiit Deemed to Be University Bhubaneswar India School of Eee Reva University Bengaluru India

ISBN: (纸本)9798350315769

Short-term electricity price forecasting plays an essential role in shaping the decision-making process of market participants in the electricity industry, as it enables them to make informed decisions about their energy trading activities. In recent years, there has been a growing interest in developing accurate and reliable price forecasting techniques, which can help to reduce the uncertainty associated with electricity price movements. In this study, we present a comparative analysis of various short-term electricity price forecasting techniques based on machine learning methods. The study is based on a comprehensive data set of hourly electricity prices from a major energy market, spanning a period of five *** statistical measures such as root mean squared error (RMSE), mean absolute error (MAE), R-squared value are used to evaluate the performance of the different machine learning models for forecasting. The results highlights the accuracy of different machine learning models during training and testing. The Various regression analysis methods were employed to forecast the price of the ISO New England electricity market using MATLAB software. © 2023 IEEE.

关键词： Power markets

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