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共 13501 条记录，以下是411-420 订阅

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Adaptive Multi-Prototype Representation learning 4

Adaptive Multi-Prototype Representation Learning

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4th international conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Sun, Wei Zhang, Shaojie University of Science and Technology of China Hefei China

ISBN: (纸本)9798350384062

Existing representation learning usually neglects two important issues, the intra-class representation diversity and underexploited label utilization, especially the negative feedback during training process. Fortunately, prototype learning, which summarizes the samples commonality, can potentially raise label utilizations and encourage intra-class diversity. In this paper, we investigate the intra-class diversity and effective updates in prototype learning. Specifically, we propose an Adaptive Multi-Prototype Representation learning, by incorporating the label awareness into both prototype formation and process to improve the representation quality. We separate the prototype updates from the representation optimization and exploit the label indexes to directly implement the prediction feedback. Experiments verify the effectiveness of our label-aware and joint multi-prototype updating strategies. © 2024 IEEE.

关键词： Machine learning

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the Role of AI and Machine learning in Cancer Diagnosis and Treatment 4

The Role of AI and Machine Learning in Cancer Diagnosis and ...

引用

4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gawande, Tejas Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artifical Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify cancer indicators which human experts commonly miss leading to better diagnostic accuracy. AI-powered Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan analysis allows doctors to perform quicker and more efficient medical diagnosis. the usage of ML models enables medical professionals to detect prognostic trace elements thus they can enhance treatment choices by analyzing tumor cell biological classification groups. AI actively participates in protein and gene targeting together with clinical trial planning to move personal cancer treatment forward. AI applications face multiple obstacles that stop their effective adoption in the field of oncology. the majority of present-day barriers to AI implementation in oncology stem from problems with data privacy together with algorithmic biases and model validation methods while the high complexity of systems and decreasing model interpretability specifically hinder usage. the success of AI models depends on extensive and diverse datasets although the actual availability of relevant data sets combined with standardization procedures continues to be difficult. Medical diagnostic applications where AI performs its decision-making work present important transparency issues that create doubts about its effectiveness. the analysis of data privacy through federated learning and XAI for interpretability and transfer learning for efficiency and deep learning for accuracy improvement are recent solutions being researched to resolve these problems. the primary aim of this research explores how AI and ML influence cancer diagnosis together with treatment methods while studying present challenges alongside suggested methods to boost their operational performance. the combination of better data governance toget

关键词： Federated learning

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A review of optical component defect detection based on deep learning 4

A review of optical component defect detection based on deep...

引用

4th international conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Shan, Ali Zheng, Jishi Lv, Feifan Kong, Linghua Yi, Dingrong Guo, Chenyu Wang, Yan School of Transportation Fujian University of Technology Fuzhou China School of Mechanical Engineering Fujian University of Technology Fuzhou China HuaQiao University Xiamen China

ISBN: (纸本)9781510686106

For the past few years, detecting object surface defects using deep learning has become an important tool in industry and a major research area for researchers involved. there is a wide variety of articles on object surface defect detection, this paper will help readers to better specialize in defect detection of optical components. this review focuses on the needs of optical component detection in industry, the acquisition of datasets for different kinds of special optical component parts, and focuses on the use of deep learning algorithms in the domain of optical component surface defect detection, introducing convolutional neural networks, attention mechanisms, self-encoders, and adversarial generative networks, and their applications in optical component defect detection, and listing the performance ratios of the pixel-level models for automated optical detection pixel-level model performance ratios. this review will give the reader a quick and basic understanding of optical component defect detection and the deep learning algorithms used today. © 2024 SPIE.

关键词： Convolutional neural networks

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

4th International Conference on Innovations in Computational...

引用

4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

ISBN: (纸本)9789819769940

the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: Dataset Balancing Techniques and Supervised learning algorithms for Predictive Analysis of Rice and Corn Yields;fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence;predictive Analysis for Early Detection of Breast Cancer through Artificial Intelligence algorithms;boosting Security: An Effective Approach to Intrusion Detection in Wireless Sensor Networks with AdaBoost Classifiers;Chat2Fluency: Enhancing Language learning through Conversational AI;REED-NET: Residual Enhanced Encoder-Decoder Network for Low-Dose CT Reconstruction;comparative Analysis of Large Language Models;Visualizing Insights to Empower HR Decision-Making: A Data-Driven Approach;handling Uncertainty in Parkinson’s Disease Voice Data Using Intuitionistic Fuzzy Entropy Measure;Exploring Shopping Opportunities and Elevating Customer Experiences through AI-Powered E-Commerce Strategies;ioT-Based Vehicle Class Detection for Smart Traffic Control;Domain Adaptation for NER Using mBERT;SQL Query Recommendation Based on Matrix Factorization;retenSure: Ensemble learning for Managing Employee Attrition;Subject–Verb Agreement Error Handling Using RNN Architectures;ad-Spend Analytics;an Efficient Real-Time Word-Level Recognition of Indian Sign Language;innovating Drug Design for Alzheimer’s Disease via Reinforcement learning for Enhanced Molecular Generation;an Efficient Real-Time Recognition of Static Kannada Sign Language;unveiling Diagnostic Clarity: A Machine learning Approach to Distinguish Borderline Personality Disorder and Bipolar Disorder for Enhanced Mental Health Diagnostics;DLSTM with Adam Waterwheel optimization for Groundwater Level Prediction in India;PhishGuard: Machine learning Model for Real-Time URL Detection;Improved Grasshopper optimization with Squeezenet (IGO-SNet)

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Network Traffic Anomaly Detection in CAN Bus Based on Ensemble learning 4

Network Traffic Anomaly Detection in CAN Bus Based on Ensemb...

引用

4th international conference on Machine learning and Intelligent Systems Engineering (MLISE)

作者： Wu, Yuxi Tao, Xiaodong Henan Univ Sch Comp & Informat Engn Kaifeng Peoples R China

ISBN: (纸本)9798350375084;9798350375077

As the transportation and information industries continue to advance, the increasing variety of application scenarios, devices with computing capabilities, and a growing number of open ports have heightened security risks for vehicle networks. To improve the accuracy of detecting abnormal traffic in vehicle networks, we propose a model based on ensemble learning with a Stacking model integration approach. this method includes a meta-classifier composed of decision trees, extremely randomized trees, and extreme gradient boosting. the final classification prediction results are obtained by linearly stacking input features and weights into a SoftMax meta-learner. Additionally, the research enhances the classification accuracy of network flow data through parameter optimization. Testing results on the real automotive hacker attack dataset, Car-Hacking, show that this method achieves an accuracy rate of up to 99.2% in detecting denial of service, gear spoofing, and RPM spoofing attack types, and up to 97.5% accuracy in Fuzzy attack types. the study indicates that this model has a low false positive rate, high detection accuracy, and high detection rate, significantly outperforming traditional detection methods based on other machine learning technologies.

关键词： in-vehicle network controller area network anomaly detection ensemble learning

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Harnessing Machine learning to Enhance Malware Classification: A Focus on Ensemble Techniques 4

Harnessing Machine Learning to Enhance Malware Classificatio...

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4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： AbdElminaam, Diaa Salama Mohamed, Tarek Aly, Mariam Samy Abdelmageed, Youssef Ehab Metwally, Moaz Mohamed Abdelaziz, Mohamed Rabeei Misr Int Univ Fac Comp Sci Cairo Egypt

ISBN: (纸本)9798350367782;9798350367775

In the ever-evolving domain of cybersecurity, malware detection and classification are critical for safeguarding digital infrastructure. this study explores the application of various machine learning algorithms, including K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and XGBoost, to classify malware using the Microsoft Malware Classification Challenge (BIG 2015) dataset. the dataset consists of diverse malware families, each exhibiting unique characteristics. A comprehensive preprocessing framework was applied, including data cleaning, feature extraction from binary and assembly files, and the creation of new features to improve model accuracy. Our experimental results indicate that Random Forest and XGBoost significantly outperform other models, achieving accuracies as high as 98%. Logistic Regression provided strong interpretability, making it suitable for applications where understanding model decisions is crucial. this study demonstrates the effectiveness of ensemble learning models in malware classification and highlights their potential for integration into real-world cybersecurity solutions, ensuring timely detection and prevention of cyber threats.

关键词： Malware Classification Random Forest XGBoost Logistic Regression KNN

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Transfer learning Approach for Detecting Keylogging Attack on the Internet of things 4

Transfer Learning Approach for Detecting Keylogging Attack o...

引用

4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Maz, Yahya Alhaj Anbar, Mohamad Manickam, Selvakumar Abualhaj, Mosleh M. Univ Sains Malaysia USM Natl Adv IPv6Ctr NAv6 George Town 11800 Malaysia Al Ahliyya Amman Univ Dept Networks & Cybersecur Amman Jordan

ISBN: (纸本)9798350354140;9798350354133

the Internet of things is an innovation that brings together an imagined space and the real world on one platform. With the rapid growth of IoT devices, the lack of standards has resulted in many unsecured devices connecting to networks, leading to an increase in cyberattacks on IoT, especially keylogging attacks. Intrusion detection systems (IDS) have traditionally relied on machine learning techniques, particularly deep learning, to detect attacks. However, these methods often require a large amount of labeled data to train, which is often unavailable for IoT networks. In this paper, we propose a transfer learning approach (TL) based on keylogging attacks where labeled data is sparse and unbalanced. We evaluated the proposed approach on the keylogger _detection dataset to show its effectiveness and compare it to current IDS approaches. the main findings of the experiments are as follows: the proposed approach has a high level of accuracy and a low level of false predictions (FPR). It performs better than traditional deep learning (DL) based IDS.

关键词： Convolutional Neural Network Deep learning Keylogging attack Recurrent Neural Network Transfer learning

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Improved Particle Swarm optimization MPPT for Standalone PV System Under Varying Environmental Conditions 4

Improved Particle Swarm Optimization MPPT for Standalone PV ...

引用

4th IEEE international conference in Power Engineering applications, ICPEA 2024

作者： Hashim, Norazlan Jamhari, Muhammad Khairul Azman Mohd Baharom, Rahimi Universiti Teknologi MARA School of Electrical Engineering College of Engineering Shah Alam40450 Malaysia

ISBN: (纸本)9798350344578

A photovoltaic (PV) system is highly sensitive to dynamic changes in environmental conditions. Improving the maximum power point tracking (MPPT) algorithm is one of the most cost-effective ways to enhance its performance. Currently, the most widely used MPPT algorithm is Particle Swarm optimization (PSO). However, as with many other artificial intelligence (AI) algorithms, PSO tends to become stagnant as it converges on the optimal solution and is therefore incapable of adapting to dynamic environmental changes. this paper presents an improved PSO MPPT called iPSO consisting of an intelligent mechanism to detect and adapt to dynamic environmental changes. In addition, to accelerate MPPT convergence, the inertia weight (ω) of iPSO is decreased exponentially with increasing iterations. Furthermore, a deterministic initialization method (DIM) is employed to improve the probability of locating the global maximum power point (MPP). the MATLAB/Simulink platform is utilised to evaluate the performance of the proposed iPSO under various environmental conditions. Its convergence speed and tracking efficiency are evaluated and compared to those of standard PSO and PSO with a reinitialization mechanism called PSO-reinit. Overall, the results revealed that iPSO is 2.2 s faster and 5.7% more efficient than its closest competitor, PSO-reinit. © 2024 IEEE.

关键词： Dynamics

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Forecasting Traffic Air Pollution Increases from Urban Transportation Using Machine learning in Metropolitan Cities-A Review 4

Forecasting Traffic Air Pollution Increases from Urban Trans...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Bhaskar, Hari Naga Vijaya Mayil, V. Valli KL University Dept. of Computer Science and Applications India

ISBN: (纸本)9798331523923

Rapid urban growth and industrial expansion in Indian cities have led to a 30% increase in vehicles over the past 5-10 years, causing a rise in air pollution and health risks. Existing methods to predict air quality face challenges like limited data, inaccurate models, slow predictions, and changing environmental conditions. this review explores how machine learning, including deep learning and hybrid models, can improve pollution forecasting. It also highlights issues such as unreliable sensors, high computing costs, and difficulty in understanding model results. Finally, the study suggests future directions for better air quality management using AI-driven predictions and policy improvements. © 2025 IEEE.

关键词： Air Pollution Forecasting Environmental Impact Machine learning Metropolitan Cities Public Health Urban Transportation

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Comparative Analysis of Multiclass Classification Machine learning Models for Cybersecurity Intrusion Detection 4th

Comparative Analysis of Multiclass Classification Machine Le...

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4th international conference on Digital Technologies and applications (ICDTA)

作者： Loughmari, Mohamed El Affar, Anass Sidi Mohamed Ben Abdellah Univ Polydisciplinary Fac Taza Engn Sci Lab LSI BP 1223 Taza Morocco

ISBN: (纸本)9783031686528;9783031686535

Recently, the use of the Internet and computer networks in general, has increased exponentially, leading to a high demand for cybersecurity to protect against all kinds of network attacks that are constantly evolving. Nowadays, Machine learning (ML) is implemented in various cybersecurity tools, Intrusion Detection System-based ML brings more capabilities to improve the detection of cyber attacks. this study aims to perform a comparative analysis of a multiclass classification problem for cybersecurity attack detection. the comparative analysis is performed using six different machine learning algorithms: Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, eXtreme Gradient Boosting, and Multi-Layer Perceptron, which are applied to the full NSL-KDD dataset, and three other subsets of datasets to confirm and verify the findings in terms of precision, accuracy, training time, and testing time. In all dataset subsets we worked on in addition to the NSL-KDD dataset, eXtreme Gradient Boosting significantly beat the other algorithms. From all the experimental results, it is concluded that XGBoost is a plausible choice for an intrusion detection system in terms of all the metrics compared to the other ML algorithms discussed.

关键词： Cybersecurity Machine learning Network Intrusion detection system NSL_KDD KDDcup UNSW-NB15

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