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检索条件"任意字段=3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024"

共 6842 条记录，以下是371-380 订阅

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Extreme learning machine with incremental hidden layers 3

Extreme learning machine with incremental hidden layers

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3rd international conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Zheng, Xuehui Xu, Gaofeng School of Information Technology and Intelligent Manufacturing Shanghai Xingjian College Shanghai China Shanghai Urban Construction Vocational College Shanghai China

ISBN: (纸本)9781510680449

The number of hidden nodes have strong influence on the accuracy of ELM (Extreme learning machine). More hidden nodes are needed as the increase of the size of training data set. Either ELM or Multi-hidden layer neural network make must be set the number of hidden layer in advance and then increase the number of nodes in every layer to achieve a smaller RMSE. Thus the computational complexity of the involved matrix becomes bigger and the learning efficiency will become worser. In this paper extreme learning machine with incremental hidden layers is proposed, in which the weights of hidden nodes can be assigned randomly to the current hidden layer nodes (small number, don't optimize and small complexity) and then the corresponding RMSE is obtained like ELM. MHL-ELM increases a hidden layer and repeats the method of "layer-wise pre-training"unless the RMSE is smaller than what we want. The complexity of MHL-ELM is ΣMl=1(N3l). Compared with some traditional algorithms like BP or OP-ELM, MHL-ELM can produce a better generalization performance, a smaller RMSE and a faster learning time on ten UCI, keel data sets and real data sets. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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Leveraging AI for Enhanced Botnet Detection - A Review of machine learning Approach for Cybersecurity 3

Leveraging AI for Enhanced Botnet Detection - A Review of Ma...

引用

3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Sonune, Harsha Kulkarni, Nilima Mksss's Cummins College of Engineering for Women Department of Information Technology Pune India Mitadt University Mitsoe Department of Computer Science and Engineering Maharashtra Pune India

ISBN: (纸本)9798350354348

Botnets represent a substantial threat to cybersecurity, enabling malicious actors to execute large-scale attacks that compromise network integrity and data confidentiality. Current detection methods often fall short in addressing the rapidly evolving strategies employed by botnets. This study has an aim of designing and developing a sophisticated botnet detection system leveraging artificial intelligence (AI) techniques. By integrating machine learning algorithms and advanced data analysis methods, the proposed system seeks to accurately identify and mitigate botnet activities in real-time. The study will evaluate the effectiveness of various AI models in detecting botnet traffic, provide a comparative analysis of their performance, and propose an optimal framework for implementation in cybersecurity infrastructures. © 2024 IEEE.

关键词： Bot (Internet)

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2024 3rd international Congress of Biomedical engineering and Bioengineering, CIIBBI 2024

2024 3rd International Congress of Biomedical Engineering an...

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3rd international Congress of Biomedical engineering and Bioengineering, CIIBBI 2024

ISBN: (纸本)9798331532352

The proceedings contain 49 papers. The topics discussed include: development of a prototype for the prevention of carpal tunnel syndrome based on EMG signals;development of a specialized methodology for custom upper limb prosthesis design in cyclists;estimation of blood pressure and glucose through photoplethysmography and artificial intelligence;APPLEE: automated preprocessing pipeline for low-electrode encephalography;comparative analysis of machine learning and deep learning techniques for hand gesture recognition using surface electromyography;advancements in fourth-generation endodontic apex locators for enhanced precision in root canal treatments;and system for data acquisition and measurement of muscle strength using EMG signals.

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machine learning Approaches for Efficient Traffic Flow in Smart Cities 3

Machine Learning Approaches for Efficient Traffic Flow in Sm...

引用

3rd Odisha international conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Kulkarni, Aniket Anitha, P. Valluri, John Yesudas Sunena Rose, M.V. Hemavathi, U. Hussein, Omar M. Software Qa Manager Drata Inc San DiegoCA United States Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu Srivilliputhur India Faculty of Management Studies Bengaluru India Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Avadi Chennai India Al-Farahidi University Medical Technical College Medical Instrumentation Engineering Baghdad Iraq

ISBN: (纸本)9798350354379

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction;this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible. © 2024 IEEE.

关键词： Traffic congestion

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The Rise of Intelligence: A Comprehensive Review on AI and AI Automation 3

The Rise of Intelligence: A Comprehensive Review on AI and A...

引用

3rd international conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Bhardwaj, Cheshtaa Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798331528201

This paper presents a comprehensive review of Artificial Intelligence (AI), machine learning (ML), and Deep learning (DL) technologies, focusing on their impact in driving AI automation across diverse sectors with a special emphasis on robotics. As AI continues to evolve, its integration into automation systems has revolutionized industries, enhancing efficiency, precision, and decision-making. The role of AI automation in robotics is critically examined, covering key architectural frameworks that enable autonomous operation and intelligent decision-making. Furthermore, the security aspects associated with AI in robotics, identifying attack surfaces and potential vulnerabilities has been analysed. This review highlights the transformative potential of AI automation while addressing the challenges and security concerns that accompany its deployment in modern systems, particularly in robotics. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimized Model Identification for Malware Detection in a File Using machine learning 3

Optimized Model Identification for Malware Detection in a Fi...

引用

3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Laya, N. Shruthi Shetty, J. Sarojadevi, H. Sowmya, P. Nitte Meenakshi Institute Of Technology Department Of Computer Science And Engineering Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Malware remains one of the most critical digital threats today due to the rapidly evolving landscape of online activities. Detecting malware has become a pivotal aspect of safeguarding computer system, especially as polymeric malware arises by utilizing dynamic modifications to confound traditional signature-based detection methods. To combat the surging volume of malware, machine learning (ML) has emerged as a key strategy. Hence, ML-based detection mechanisms are imperative. This overview underscores the diverse array of machine learning-driven detection techniques. The primary objective is to construct an optimal machine learning representation for malware detection within files. Three distinct machine learning algorithms are employed: random forest, logistic regression, and a basic neural network. From these, the superior model is determined based on rigorous evaluation metrics-accuracy, f1 score, and both normalized and non-normalized confusion matrices on both training and testing data. The selection process hinges on achieving the highest accuracy and f1 score. The random forest algorithm was depicted to be having highest train accuracy with 98.28% and test accuracy with 98.38% and f1 score was found to be 97.3% which was chosen as a final model for malware detection in a file. The chosen model subsequently serves as the foundation for malware detection. © 2024 IEEE.

关键词： Logistic regression

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A Framework for Epileptic Seizure Monitoring Based on IoT and machine learning Technologies 3

A Framework for Epileptic Seizure Monitoring Based on IoT an...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Alharbi, Alhasan Dhopeshwarkar, Mukta Ahmed, Zeyad A.T. Mahyoub, Ezzaldeen Tawfik, Mohammed Almadani, Ali Manour Dr. Babasaheb Ambedkar Marathwada University Department of Computer Science Aurangabad India Dr. Babasaheb Ambedkar Marathwada University Dept. of Computer Science Osmanabad India

ISBN: (纸本)9798350381931

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures that affects millions of people globally. The unpredictable nature of seizures in epilepsy necessitates vigilant monitoring of these patients to mitigate the risk of adverse events, including physical injury, that may occur during convulsive seizures. It is essential in handling cases of epilepsy that episodes be detected and patient data be stored for later examination. The Internet of Things (IoT) is a potentially revolutionary development in the medical industry since it makes it possible to collect data from individuals using biosensors. This information is then sent to a remote server or cloud storage where it can be analyzed by machine learning software. This article reviews epilepsy patient monitoring IoT platforms and systems that use machine learning and deep learning. It also discusses new research that has greatly used these technologies in monitoring and seizure detection. The most effective sensor devices for detecting and monitoring seizures are also highlighted, and information on the most prevalent kinds of epilepsy types is provided. © 2024 IEEE.

关键词： Internet of things

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Attribute Extraction of Geographic Entities Based on the LERT Pre-trained Model 24

Attribute Extraction of Geographic Entities Based on the LER...

引用

3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Li, Zeyu North China Institute of Computing Technology Beijing China

ISBN: (纸本)9798400707308

Geographic information systems(GIS) are now changing from two-dimensional expression to three-dimensional expression, and the geographic data gradually transforms into a more intuitive and more applied method of expression. In order to support the data expression in GIS, attributes extraction from unstructured Chinese geographic texts is needed. Manual extraction or pattern-matching methods have been insufficient to get geographic attributes, therefore deep learning methods for attribute extraction are required to meet the needs of the supplementary for entity properties. This paper took LERT for pre-training in the word-embedding layer and the BiLSTM module combined with attention mechanism for the feature extraction layer, finally obtained the attribute-value pairs as the output. The experimental results show that the accuracy, recall rate and F1 value of the proposed model on the test set improved compared with the existing model, proving the effectiveness of the proposed model. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Optimizing House Price Prediction: Comparative Analysis of machine learning Techniques 3

Optimizing House Price Prediction: Comparative Analysis of M...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Sul, Astha Jagtap, Vaishnavi Jesalpura, Parantap Nema, Anushka Rajkumar, R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350369083

This research presents a comparative analysis of machine learning models for house price prediction across four distinct metropolitan areas: Pune, Bangalore, Mumbai, and Delhi. Leveraging historical data encompassing multidimensional attributes such as location, size, and amenities, we systematically assess the performance of diverse models, including Linear Regression, Decision Regression Tree, Random Forest, K-Nearest Neighbors (KNN), Decision Tree Classifier, and Gaussian Naïve Bayes. Through rigorous examination, we establish the Decision Regression Tree model as the optimal choice, demonstrating the highest average accuracy score of 96.27%. This model's capacity to capture intricate non-linear relationships within the dataset contributes to enhanced predictive efficacy, while its interpretability facilitates stakeholders' comprehension of underlying price determinants. By shedding light on model performance variations across different urban landscapes, this research furnishes valuable insights for informed decision-making in house price prediction within diverse real estate markets. © 2024 IEEE.

关键词： Prediction models

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Secure federated learning applied to medical imaging with fully homomorphic encryption 3

Secure federated learning applied to medical imaging with fu...

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3rd IEEE international conference on AI in Cybersecurity, ICAIC 2024

作者： Lessage, Xavier Collier, Leandro Van Ouytsel, Charles-Henry Bertrand Legay, Axel Mahmoudi, Said Massonet, Philippe Cetic Applied Research Centre Charleroi Belgium UCLouvain Faculty of Engineering Louvain-la-Neuve Belgium Umons Faculty of Engineering Mons Belgium

ISBN: (纸本)9798350381856

This study explores the convergence of Federated learning (FL) and Fully Homomorphic Encryption (FHE) through an innovative approach applied to a confidential dataset composed of mammograms from Belgian medical records. Our goal is to clarify the feasibility and challenges associated with integrating FHE into the context of Federated learning, with a particular focus on evaluating the memory constraints inherent in FHE when using sensitive medical data. The results highlight notable limitations in terms of memory usage, underscoring the need for ongoing research to optimize FHE in real-world applications. Despite these challenges, our research demonstrates that FHE maintains comparable performance in terms of Receiver Operating Characteristic (ROC) curves, affirming the robustness of our approach in secure machine learning applications, especially in sectors where data confidentiality, such as medical data management, is imperative. The conclusions not only shed light on the technical limitations of FHE but also emphasize its potential for practical applications. By combining Federated learning with FHE, our model preserves data confidentiality while ensuring the security of exchanges between participants and the central server © 2024 IEEE.

关键词： Medical imaging

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