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共 606 条记录，以下是61-70 订阅

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Predicting Undergraduate Academic Success with machine learning Approaches 6th

Predicting Undergraduate Academic Success with Machine Learn...

引用

6th International conference on Soft computing and Data Mining (SCDM)

作者： Li, Yuan-Zheng Ng, Keng-Hoong Khor, Kok-Chin Lim, Yu-Hsuen Asia Pacific Univ Technol & Innovat Sch Comp Taman Teknol Malaysia Jalan Teknol 5 Kuala Lumpur 57000 Malaysia Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Sungai Long CampusJalan Sungai Long Kajang 43000 Selangor Malaysia

ISBN: (纸本)9783031669644;9783031669651

the opportunity to pursue tertiary education has increased in recent years, attributed to the initiatives and efforts made by governments, industry players, and educational institutions to make education more affordable and accessible. Hence, predicting undergraduate academic performance plays a crucial role in higher learning institutions' success. this is because the predicted outcomes could provide valuable insights and benefits such as early intervention and support for students at risk of academic struggles, graduating on time, personalized learning, etc. this study selected two machine learning algorithms, i.e., Light GBM and Random Forest, to predict undergraduate academic success in a higher-learning institution. the preliminary result indicates that Light GBM is the best performer, obtaining the highest accuracy of 79.3% after hyperparameter tuning. the result is marginally better than the recently published works that used identical or highly similar datasets.

关键词： Academic Success Random Forest Student Performance Student Dropout machine learning

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Pre-Trained Deep learning Models for Malware Image Based Classification and Detection 6

Pre-Trained Deep Learning Models for Malware Image Based Cla...

引用

6th International conference on Intelligent computing in Data Sciences, ICDS 2024

作者： El Ghabri, Nadia Belmekki, Elmostafa Bellafkih, Mostafa Rabat Morocco

ISBN: (纸本)9798350351200

In cybersecurity, there are difficulties in detecting malware: the majority of methods use known malware signatures, and it is necessary to spot malware that has never been identified before. Automatic detection and classification of malware is an important approach in the field of cybersecurity. this approach is based on convolutional neural networks (CNN), which have shown powerful performance compared with traditional techniques (static or dynamic analysis, machine learning techniques) in terms of consummation of time and resources. this article presents a critical comparative study of three Pretrained CNN models: Resnet50, VGG16, and XceptionNet, in order to de-Termine which offers the best accuracy for the malware image classification using Malimg dataset that contain the malware files represented as grayscale images. © 2024 IEEE.

关键词： Adversarial machine learning

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Combating Digital Deception: machine learning Approaches for Detecting Political Misinformation and Clickbait on Social Media 6

Combating Digital Deception: Machine Learning Approaches for...

引用

6th International conference on Advancements in computing, ICAC 2024

作者： Harindi, Dewmini Perera, Nirmal Liyanage, Chamath Dematagoda, Hirushini Fernando, Harinda Rathnayake, Samadhi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

this paper presents machine learning-based strategies for mitigating the problem of misinformation in social media, with a special focus on political misinformation detection and clickbait detection. the following research work attempts to address the pressing issue of the rapid spread of false or misleading information by making use of deep recurrent neural networks (RNN s) combined with semi-supervised learning methodologies. For political misinformation detection, a hybrid model integrating Long Short-Term Memory (LSTM) networks with fully connected layers was developed, achieving 94.22 % accuracy on the CIC Truth Seeker dataset. Clickbait detection was made using a sequential architecture that combines LSTM and Gated Recurrent Units (GRU) layers with Simple RNN layers to achieve an accuracy of 96% on test data. this study underlines the effectiveness of different machine learning techniques in battling misinformation that would contribute to online dignity in political discourse and ensure better public communication in a digital world. © 2024 IEEE.

关键词： Self-supervised learning

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Efficient machine learning Algorithms for DDoS Attack Detection 6

Efficient Machine Learning Algorithms for DDoS Attack Detect...

引用

6th International conference on computing and Informatics, ICCI 2024

作者： Al-Eryani, Amal M. Hossny, Eman Omara, Fatma A. Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt

ISBN: (纸本)9798350373875

Distributed Denial of Service (DDoS) attack is a widely spread attack that posing a major threat to organizations dependent on online services. DDoS attacks aim to disrupt services by overwhelming servers with fake traffic from multiple sources. Early and effective detection of DDoS attacks is crucial for mitigating their impact. Recently, the most widely used algorithms for detecting DDoS are based-on machine learning (ML) and Deep learning (DL). the work in this paper focuses on providing a comparative study between recently ML algorithms that were tested using the CICDoS2019 dataset. the objective of this comparison is determining the most effective ML algorithm for DDoS detection. Based on the comparative study results, it is found that the Gradient Boosting (GB) and the XGBoost algorithms are extraordinarily accurate and correctly predicted the type of network traffic with 99.99% and 99.98% accuracy respectively, in addition to, a low false alarm rate of approximately 0.004 for GB. © 2024 IEEE.

关键词： Denial-of-service attack

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Large Language Models to Enhance Malware Detection in Edge computing 6

Large Language Models to Enhance Malware Detection in Edge C...

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6th IEEE International conference on Trust, Privacy and Security in Intelligent Systems, and Applications, TPS-ISA 2024

作者： Rondanini, Christian Carminati, Barbara Ferrari, Elena Kundu, Ashish Jajoo, Akshay University of Insubria DiSTA Italy Cisco Research United States

ISBN: (纸本)9798350386745

the increasing sophistication of malware in recent years has underscored the need for more advanced detection approaches, particularly in edge computing environments. Traditional methods and machine learning models, have shown promise but face significant limitations in handling evolving malware types. To address these challenges, we explore the application of Large Language Models (LLMs) for malware detection in edge computing. LLMs offer enhanced pattern recognition, contextual understanding, and the ability to analyze complex data, making them well-suited for dynamic, resource-constrained edge environments. However, their use introduces challenges such as model size and computational demands. In this paper, we propose a vision architecture that leverages LLMs' strengths while addressing their limitations, providing a framework for continuous learning and adaptability. We discuss the architecture's potential impact on improving malware detection as well as future research directions in the field. © 2024 IEEE.

关键词： Adversarial machine learning

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A Comparative Study of Recent 2D Human Pose Estimation Methods 6

A Comparative Study of Recent 2D Human Pose Estimation Metho...

引用

6th International conference on computing and Informatics, ICCI 2024

作者： Elshami, Nada E. Salah, Ahmad Mohsen, Heba Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computers and Informatics Zagazig University Zagazig Egypt

ISBN: (纸本)9798350373875

Human Pose Estimation is a computer vision technique utilized in various fields such as healthcare, security, and sports to detect the pose of single or multi-person utilizing various machine learning and deep learning methodologies. these methodologies can be applied in a variety of ways, including top-down and bottom-up approaches. Furthermore, this technique can be used with images or videos, as well as in 2D or 3D domains. this paper delves into recent advancements in deep learning methods used for estimating the pose, covering commonly employed datasets, evaluation metrics, and the challenges encountered in this field. It aims to assist new researchers in getting familiar with this technique based on work published between 2019 and 2023, with a spotlight on 2D and omitting 3D. © 2024 IEEE.

关键词： Deep learning

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2024 IEEE 6th International conference on Power, Intelligent computing and Systems, ICPICS 2024

2024 IEEE 6th International Conference on Power, Intelligent...

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6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

ISBN: (纸本)9798350374315

the proceedings contain 299 papers. the topics discussed include: research on lightning fault analysis and lightning protection application of 500 kV transmission line;algorithm-based optimization of YOLOv5s model for robot trajectory detection study;boosting the feature extraction power of convolutional networks via mixed-weighting;deep learning based optimization of UAV orbital paths;fault prediction for distribution transformers based on stacking;electric field distribution and optimization design of XLPE cable joints under typical defect;research on auxiliary terminal system of power communication operation and maintenance based on augmented reality technology;an improved path planning algorithm for ship track planning based on grey wolf algorithm;and optimizing motion capture data in animation sequences using machine learning techniques.

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Analysis of Hierarchical Data Fusion Target Detection Architecture Based on Autonomous learning 6

Analysis of Hierarchical Data Fusion Target Detection Archit...

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6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Zhang, Jiatao Yang, Runze Xu, Chengjun Air Defense Academy of Artillery and Air Defense Forces Hefei China

ISBN: (纸本)9798350374315

this paper proposes an innovative algorithmic framework for drone target detection, aiming to improve image capture performance in complex environments. the algorithm integrates visible light and infrared images, employing a dual-channel architecture to independently extract multi-level characteristics from these two types of images. through a self-learning hierarchical fusion mechanism, it enhances the complementarity of image information. Based on bidirectional feature pyramid networks, it progressively strengthens the fusion of surface and deep features, thus comprehensively capturing multi-scale information about targets. Independent target prediction on feature mappings at different levels further enhances detector efficiency. Simulation and comparative experiments on multiple public datasets confirm the rational design and outstanding performance of the algorithm, demonstrating robust stability and generalization capabilities. © 2024 IEEE.

关键词： Adversarial machine learning

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Adaptive learning Tool to Enhance Educational Outcomes for Students with Inattentive Attention Deficit Hyperactivity Disorder (ADHD) 6

Adaptive Learning Tool to Enhance Educational Outcomes for S...

引用

6th International conference on Advancements in computing, ICAC 2024

作者： thawalampola, Oshada Jayasuriya, Danuja Kariyawasam, Supun Makawita, Madushi Wijendra, Dinuka Joseph, Jenny Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

this paper presents an adaptive learning tool to enhance educational outcomes for students with inattentive ADHD. the platform comprises four key components: content chunking and simplification with interactive content, an adaptive quizzing system, a personalized ADHD identifier and break recommendation system, and a tool to maximize learning potential. the content chunking component transforms traditional educational materials into ADHD-friendly formats using Natural Language Processing (NLP) technologies, making them more accessible and engaging. the adaptive quizzing system employs a modified spaced repetition algorithm to optimize memory retention and engagement through personalized quizzes. the personalized ADHD identifier uses a hybrid Convolutional Neural Network (CNN) model to analyze webcam data, tracking head movements and facial emotions to provide real-time break recommendations, enhancing focus and cognitive performance. the final component integrates machine learning and data mining to adaptively assign tasks and provide personalized feedback, fostering a supportive learning environment. Preliminary evaluations indicate significant improvements in academic performance and engagement among ADHD-diagnosed students. this research underscores the potential of adaptive learning technologies in creating inclusive educational environments addressing the unique challenges faced by students with ADHD. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on Path Planning for Mobile Robots Based on Improved Q-learning Algorithm 6

Research on Path Planning for Mobile Robots Based on Improve...

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6th IEEE International conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Ma, Yubo Pan, Xiuqin Li, Ruixiang Li, Jiayun Minzu University of China Beijing China

ISBN: (纸本)9798350374315

Path planning, as an important direction in robotics research, is the first condition to ensure that the robot accomplishes the task. this study presents an optimized version of the Q-learning algorithm for robot path planning, taking into account the limitations of the Q-learning algorithm's sluggish search speed and lengthy planning time. Firstly, the original four-neighborhood exploration is changed to eight-neighborhood exploration, and the robot's action space is dynamically adjusted according to the real-time relative locations of the objective point and the robot to reduce the exploration of invalid actions;Secondly, based on the attributes that call for more exploration in the initial phase and quicker convergence in the subsequent phase, a method with gradually increasing learning rate is proposed. Finally, the outcomes of the experiment indicate that the improved Q-learning algorithm is not only able to find the shortest path, but also has a faster convergence rate. © 2024 IEEE.

关键词： Adversarial machine learning

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