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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3232 条记录，以下是461-470 订阅

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Wind Energy Forecasting: A Comparative Analysis of machine learning Approaches 8

Wind Energy Forecasting: A Comparative Analysis of Machine L...

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8th international Scientific conference on Applying New Technology in Green Buildings, ATiGB 2023

作者： Pham, Thanh Son Nguyen, Duc Huy Phan, Nguyen Duy Minh FPT University Faculty of Computing FPT Greenwich Centre Danang Viet Nam FPT Software Quy Nhon Viet Nam The University of Danang University of Technology and Education Faculty of Mechanical Engineering Danang Viet Nam

ISBN: (纸本)9798350343977

This paper conducts a comprehensive comparative analysis of machine learning and deep learning techniques for wind energy forecasting. Wind energy prediction is a crucial aspect of renewable energy management. We study and assess the application of tree-based algorithms (LightGBM, CatBoost, and Random Forest) and deep learning approaches (feedforward networks with skip connection and FT-Transformers). In the numerical study, we evaluate forecasting accuracy, robustness, computational efficiency, and analyze the reasons why some methods are better than others. This research contributes to advancing wind energy forecasting methodologies in the renewable energy sector. Accurate predictive models play the most crucial role in optimizing wind energy integration into the grid, supporting a sustainable energy future. © 2023 IEEE.

关键词： Wind power

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Comparison and Analysis of the Effect of XGBoost Classification, BP Neural Network Classification and CatBoost Classification on Malware Attack Prediction

Comparison and Analysis of the Effect of XGBoost Classificat...

引用

2023 international conference on Intelligent Communication and Computer engineering, ICICCE 2023

作者： Wang, Shuo School of Safety Engineering Shenyang AerospaceUniversity Liaoning China

ISBN: (纸本)9798350395617

Malware attack refers to the hacker or attacker through various means, malicious software (such as viruses, trojans, spyware, etc.) implanted into the victim's computer or mobile device, in order to steal personal information, steal property, damage the system and other purposes. By comparing the effect of XGBoost classification, BP neural network classification and CatBoost classification on malware attack prediction, this paper summarizes and analyzes the advantages and disadvantages of each algorithm. The experimental results show that XGBoost classification model has the best accuracy, reaching 99.1%, while the accuracy of BP neural network classification model is 98.2%, and the accuracy of CatBoost classification model is only 82.8%. XGBoost and CatBoost are both machine learning algorithms based on gradient tree, while BP neural network is a neural network based algorithm. Comparing the three algorithms in principle, it can be found that XGBoost performs well on large-scale data, CatBoost performs well on class-unbalanced data, and BP neural networks require more training data to achieve better results. The research in this paper provides a certain research basis for the subsequent malware attack prediction, and also provides a reference for the selection of machine learning algorithms. © 2023 IEEE.

关键词： Forecasting

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Proceedings of the 2023 6th international conference on software engineering and Information Management, ICSIM 2023

Proceedings of the 2023 6th International Conference on Soft...

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6th international conference on software engineering and Information Management, ICSIM 2023

ISBN: (纸本)9781450398237

The proceedings contain 42 papers. The topics discussed include: deep learning techniques for stock market forecasting: recent trends and challenges;how to find social robots exactly?;an extractive text summarization based on reinforcement learning;evaluation of price prediction models for cryptocurrencies based on convolutional neural networks trained on candlestick charts;an explainable machine learning-based prediction model for in-hospital mortality in acute myocardial infarction patients with typical chest pain;a survey on current speech to text analysis to help programmers dictate code;knowledge and skill retention in introduction to programming course;the relationship between perceived organizational justice and innovative work behavior through knowledge sharing;methodology for the execution of programs based on different programming languages;the design of enterprise management information system based on cloud computing;and on the relationship between linter warning density and software maintainability: an empirical study of JavaScript projects.

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Enhancing IoT Network Defense: A Comparative Study of machine learning Algorithms for Attack Classification 33rd

Enhancing IoT Network Defense: A Comparative Study of Machin...

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33rd international conference on software and Data engineering

作者： McNair, Alkendria Precious-Esue, Divine Newson, Soundra Rahimi, Nick Univ Southern Mississippi Sch Comp Sci & Comp Engn Hattiesburg MS 39401 USA

ISBN: (纸本)9783031752001;9783031752018

As the Internet of Things (IoT) continues to expand rapidly, securing these interconnected devices and networks from cyber threats has become a critical challenge. This research investigates the application of machine learning techniques for accurately classifying IoT network traffic data to discriminate between benign activities and various types of cyber-attacks targeting IoT systems. We propose a program that employs multiple machine learning algorithms, including Decision Tree, Logistic Regression, Naive Bayes, and Random Forest, trained on a comprehensive IoT network traffic dataset the CICIoTDataset2023. Through extensive experiments, we evaluate the performance of these classification models in detecting different IoT attack categories such as web-based attacks, spoofing, denial-of-service, Mirai, reconnaissance, distributed denial-of-service, and brute force attacks. Our results demonstrate the efficacy of machine learning approaches, with the Random Forest algorithm emerging as the top performer, achieving an overall accuracy of 98.41%. We also address challenges like class imbalance through hybrid sampling techniques and implement strategies like regularization and hyperparameter tuning to mitigate overfitting and enhance model generalization. Additionally, we conduct a performance analysis of the classification models on different IoT attack categories to gain insights into their specific strengths and weaknesses. By leveraging machine learning for accurate IoT attack classification, this research contributes to developing robust security solutions that can proactively identify and mitigate cyber threats, enabling a more secure IoT ecosystem. The findings pave the way for safeguarding interconnected devices, protecting user privacy, and fostering confidence in the widespread adoption of IoT technologies.

关键词： Internet of Things (IoT) machine learning Data Classification Decision Tree Logistic Regression Naive Bayes Overfitting Random Forest

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Proceedings - 2023 IEEE 9th international conference on Space Mission Challenges for Information Technology, SMC-IT 2023

Proceedings - 2023 IEEE 9th International Conference on Spac...

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9th IEEE international conference on Space Mission Challenges for Information Technology, SMC-IT 2023

ISBN: (纸本)9798350341379

The proceedings contain 15 papers. The topics discussed include: a data pre-processing module for improved-accuracy machine-learning-based micro-single-event-latchup detection;availability vs. lifetime trade-space in spacecraft computers;early design exploration of space system scenarios using assume-guarantee contracts;web-based 3d visualization for flight mission network cybersecurity analysis;ablation study of how run time assurance impacts the training and performance of reinforcement learning agents;how to scrub a launch: spaceport cybersecurity;a research agenda for space flight software security;intelligent requirement-to-test-case traceability system via natural language processing and machine learning;a hybrid space architecture for robust and resilient satellite services;and coverage-guided state space exploration of autonomous cyber-physical systems.

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

3rd International Conference on Artificial-Business Analytic...

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3rd international conference on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

ISBN: (纸本)9789819725076

The proceedings contain 60 papers. The special focus in this conference is on Artificial-Business Analytics, Quantum and machine learning: Trends, Perspectives, and Prospects. The topics include: Particle Swarm Optimization for Efficient Data Dissemination in VANETs;a Reactive Approach for High-Accuracy and Data-Driven Customer Behaviour Analysis and Prediction;a Brief Survey on Fabric Defect Detection;CNN Based Real Time Detection of Words from Lip Movements and Automated into Text;application of Artificial Intelligence for the Diagnosis of Dementia (Alzheimer): A Systematic Evaluation;shortest Job First with Gateway-Based Resource Management Strategy for Fog Enabled Cloud Computing;software Vulnerability Analysis Based on Statistical Characteristics;hate Speech Detection on Twitter: A Comparative Evaluation of Different machine learning Techniques;detection of Heart Disease Using machine learning;SVM-Based Framework for Breast Cancer Detection;hand Gesture Recognition System Using machine learning;deep learning Method for Plant Disease Recognition and Prediction;Lung Cancer Detection by Using CNN Architecture Models;deep Fake Analyser: A Review Based on Detecting the Deepfakes;predicting the Outcomes of La Liga Matches;Artifact Detection and Removal in EEG: A Review of Methods and Contemporary Usage;analyzing the Employee Attrition Rate: A Comparative Study of Various machine learning Approaches to Foresee Employee Attrition;satellite Image Classification Using Deep learning for Big Earth Data;tackling Misinformation Through Tweets: A Comparative Study of Various machine learning Approaches;feature-Based Anomaly Detection in Static Social Networks;framework for Optimising Supply Chain Analysis Using machine learning;evaluation of Load Forecasting in Intelligent Grid Systems Through machine learning Techniques.

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Exploring the Influence of Text Features on User Interface Design Aesthetics: A Computational Approach

Exploring the Influence of Text Features on User Interface D...

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2023 IEEE international conference on Industrial engineering and engineering Management, IEEM 2023

作者： Zhou, Jintang Ben, Xiang Zhang, Yin Wei, Zhiyong Kan, Yajing School of Mechanical Engineering Southeast University Nanjing China

ISBN: (纸本)9798350323153

In professional software interfaces, text content usually plays a crucial role as a fundamental component. This study investigates the relationship between the text feature in interfaces and the interface aesthetics. We analyzed the impact of font size, letter spacing, line height, luminance contrast, font weight, and text area proportion on the interface aesthetics. The Text Layout Aesthetic Model and the Text Luminance Aesthetic Model were developed to capture the relationships between the first five text features and the interface aesthetics. We proposed a computational framework that integrates these two models with text areas, employing machine learning techniques to depict the connection between the text feature and the interface aesthetics. As the result, this model demonstrates a good fitting and a generalization performance. This work contributes to an improved accuracy in the assessment of the text aesthetics for professional software interfaces. © 2023 IEEE.

关键词： Aesthetics computational model text

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An Identification of software Defect Using Cost Based Support Vector machine and Robust Similarity Aware Clustering Model 1

An Identification of Software Defect Using Cost Based Suppor...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Patchala, Shreya Mundher Adnan, Myasar Vontela, Tejaswini Chanti, Yerrolla Indira, R. Enquero Global-A Genpact Company Bangalore India The Islamic University Najaf Iraq Hyderabad India School of Computer Science & Artificial Intelligence Sr University Warangal India Panimalar Engineering College Department of Ece Chennai India

ISBN: (纸本)9798350316469

software fault prediction is the prominent research area that aims at the successful delivery of the software to the clients. The success of the software relies on the successful development of the software without any of the faulty modules. The presence of the large number of the software faults results in ineffective product and the faults may be due to any of the hominoid mistake. The software faults are detected in the stress, computational faults, faults in syntax, capacity, load balance, and so on. It is the need of the hour to detect the faults effectively and precisely, but predicting the software faults remained a challenge. This research proposed a software fault prediction and classification approach based on the combination of the spiral model, Cost based Support Vector machine (CSVM) and Robust Similarity Aware Clustering (RSC) process. The proposed machine learning algorithm efficiently identify the fault occurred on the software modules. RSC algorithm helps to cluster the resultant classes based on the similarity measure. This proposed technique helps to achieve less computing time and also reduces the failure ratio for testing the software modules. The experimental analysis of the proposed CSVM method is done on publicly available dataset such as NASA-CM1 data. The analysis demonstrated that the proposed algorithm is better than various existing approaches with respect to Accuracy (0.991), Precision (0.944), Recall (0.962), and F-measure (0.945) and such proved that it is capable of predicting faults within limited resource budget. © 2023 IEEE.

关键词： Cost based Support Vector machine machine learning Robust Similarity Aware Clustering software fault prediction Spiral model

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Big Data Framework to Detect and Mitigate Distributed Denial of Service(DDoS) Attacks in software-Defined Networks (SDN)

Big Data Framework to Detect and Mitigate Distributed Denial...

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2023 international conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Hamedani, Amirreza Fazely Aziz, Muzzamil Wieder, Philipp Yahyapour, Ramin Göettingen University Department of Computer Science Göttingen Germany EScience Group Göttingen Germany

ISBN: (纸本)9798350322972

The software-defined network is in recent years come into the sight of so many network designers as a successor to the traditional network. This type of network is vulnerable to DDoS attacks, targeting a different part of the SDN network architecture by continuously injecting fake flows. It imposes substantial processing on the controller, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to legitimate *** paper proposes a scalable and reliable real-time DDoS attack detection and mitigation framework using machine learning incorporated with a big data pipeline infrastructure for the SDN environment. The framework supports the detection of DDoS attacks with the ability of processing and analyzing the huge amount of network traffic in near real-time. The framework employs the power of big data solutions, such as Apache Kafka, and Apache Spark in order to realize a scalable pipeline for big data *** main objective of this research is to offer a vital supporter which can assist network security systems in addressing the DDoS threats in the SDN environment. For this purpose, machine learning algorithms are also utilized to provide a viable opportunity for classifying network data and detecting DDoS attacks in this regard. © 2023 IEEE.

关键词： machine learning

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Reading Technology for Blind People Using OCR and Neura machine learning Method 6

Reading Technology for Blind People Using OCR and Neura Mach...

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6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Preethi, C. Haripriya, K. Hammed, S. Shahul Pavalarajan, S. Priyadharshini, S. Varshambigai Achithra, B.R. Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350394702

This study focuses on the creation of a Neura Smart Book Reader that can help people who have visual impairments, especially those who are blind or have low vision and do not know Braille. The project uses IoT technology, which includes hardware, infrastructure, and services within the Internet of Things framework. The project primarily uses the energy-efficient Raspberry Pi device, which has machine-learning capabilities, making it adaptable. The Raspberry Pi computer is small and portable and operates on minimal power, showcasing advanced computing and machine learning algorithms. The system has a camera for capturing book pages, and the software performs Optical Character Recognition (OCR), combined with machine learning-driven character recognition. This improves the system's proficiency with each use. Once the image is recognized, the Neura Smart Book Reader converts it into audible text, allowing users to listen without tactile interaction. This proves how machine learning can improve accessibility. The device also offers online text-to-voice conversion via WiFi and 4G services and uses machine learning-based algorithms for enhanced linguistic articulation. This ensures a seamless reading experience for users across digital and hardcopy books. This amalgamation of IoT, Raspberry Pi, and machine learning technologies highlights the latest accessible reading solutions for visually impaired individuals,proving how machine learning can improve accessibility technology. © 2023 IEEE.

关键词： machine learning

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