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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

共 20256 条记录，以下是691-700 订阅

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Exploring Nutraceuticals in Diabetes Mellitus Management through Machine learning Techniques - A Review 3

Exploring Nutraceuticals in Diabetes Mellitus Management thr...

引用

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

作者： Manivannan, E. Richard, J. Dineshkumar, P. Munirathinam, T. Velusamy, A. Mohanraj, B. Department of Pharmacology Salem India Sona College of Technology Department of It Salem India Kpr Institute of Engineering and Technology Department of Cse Coimbatore India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528201

A chronic metabolic disorder called diabetes mellitus is characterized by elevated blood glucose levels. It results from deficiencies in insulin production, action, or both. Diabetes has traditionally been managed with insulin, oral hypoglycemic drugs, and lifestyle changes. However, growing interest in alternative medicine is often driven by conventional treatments' challenges and side effects. Nutraceuticals are becoming popular supplements for managing diabetic symptoms, as they are derived from food sources that provide health benefits beyond basic nutrition. This study explores the role of various nutraceuticals in preventing and managing diabetes mellitus, focusing on their mechanisms, effectiveness, and potential advantages. Additionally, the use of machine learning techniques to evaluate and predict the effectiveness of nutritional supplements in diabetes management is discussed. © 2024 IEEE.

关键词： Support vector machines Logistic regression Reviews Modulation Production Predictive models Diabetes Reliability Insulin intelligent systems

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Enhancing the BERT Model for Qur’anic Question Answering System 3

Enhancing the BERT Model for Qur’anic Question Answering Sy...

引用

3rd international conference on intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Alarabi, Ebtihal Ellabib, Issmail Computer Engineering Department University of Tripoli Tripoli Libya

ISBN: (纸本)9798331529222

Machine Reading Comprehension is a rapidly evolving field that aims to bridge the gap between human-like understanding and machine text processing. With advancements in deep learning and the development of sophisticated models, MRC systems are becoming increasingly capable of understanding context and providing accurate answers. Driven by the increasing demand for advanced question-answering (QA) systems on the Holy Qur'an, the inaugural Qur'an Question Answering shared task (Qur'an QA 2022 OSACT 2022), the second edition in Arabic NLP 2023. The Qur'an QA 2023 encompasses two essential sub-tasks: Passage Retrieval (PR) and Machine Reading Comprehension (MRC). The shared task aims to enhance the capabilities of QA systems and facilitate deeper engagement with the Qur'an. This research paper focuses on developing an improved BERT model, implemented using the KNIME platform, and tailored for Arabic. We aim to utilize this model for Qur'anic Question Answering, resulting in an Area under the Curve (AUC) of 0.5 and an accuracy rate of 0.997. ©2024 IEEE.

关键词： Deep learning

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Enhancing Spectrum Sensing in Cognitive Radio Networks using Deep learning Models: A Solution for Low-SNR Challenges 3

Enhancing Spectrum Sensing in Cognitive Radio Networks using...

引用

3rd international conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Ramakrishnan, P. Priyanga, V. Sathiya, A. Sharmiladevi, D. Samundeeswari, A. Annapoorani, B.T. Department of Electronics and Communication Engineering M.Kumaarsamy College of Engineering Tamilnadu Karur639113 India Department of Computer science Engineering Shreenivasa Engineering College Tamilnadu Dharmapuri635301 India M.Kumaarsamy College of Engineering Tamilnadu Karur639113 India Department of Electronics and Communication Engineering Sona College of Technology Tamilnadu Salem636005 India Department of Electronics and Communication Engineering Paavai Engineering College Namakkal637018 India Department of Electronics and Communication Engineering Kongu Engineering College Tamilnadu Erode638052 India

ISBN: (纸本)9798331532420

Spectrum sensing is a fundamental component of cognitive radio networks (CRNs), enabling efficient spectrum utilization by detecting and leveraging underutilized frequency bands. However, low Signal-to-Noise Ratio (SNR) environments present substantial challenges to reliable spectrum detection, leading to increased false alarms and missed detections. In this paper, we propose an enhanced deep learning approach utilizing advanced neural networks, including Generative Adversarial Networks (GANs), to improve spectrum sensing in low-SNR regimes. By generating high-fidelity, synthetic training data across varying SNR levels, our model gains robustness against noise interference, enabling superior feature extraction and detection accuracy. Comparative evaluations with other deep learning models demonstrate that the GAN-augmented spectrum sensing framework achieves significant performance improvements in low-SNR conditions, offering a promising solution to enhance spectrum reliability in CRNs. This work underscores the potential of GAN-based data augmentation and denoising in addressing noise-related challenges in spectrum sensing, with implications for the next generation of intelligent, adaptive CRN architectures. © 2024 IEEE.

关键词： Signal to noise ratio

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Hybrid feature selection method based semi-supervised for transformer fault diagnosis 3

Hybrid feature selection method based semi-supervised for tr...

引用

3rd international conference on Energy and Electrical Power Systems (ICEEPS)

作者： Tan, Xuemin Zhang, Jianglin Guo, Chao Zhuang, Huimin Xie, Xiaona Li, Yanxia Chengdu Univ Informat Technol Coll Automat Chengdu Peoples R China Chengdu Power Supply Co State Grid Chengdu Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The task of collecting labeled Dissolved Gas Analysis (DGA) data is challenging due to the time and cost involved in identifying transformer faults at substations. However, unlabeled DGA data is more readily available. To leverage the limited labeled DGA data and the abundant unlabeled data to enhance the transformer fault diagnosis rate, it is crucial to explore semi-supervised learning methods for DGA fault diagnosis that can address real-world challenges. This paper introduces an innovative Hybrid Feature Selection Method based Semi-supervised learning (HFSM-SSL) that employs a filter-based method combined with wrapper-based in SSL to identify the most optimal DGA features. A XYZ strategy is proposed to augment the training dataset and select optical features in the filter-based process. The method is validated using the IEC TC10 dataset and is compared with standard and single optimization feature criterion diagnostic model. The findings demonstrate the effectiveness of the proposed algorithm and XYZ strategy in optimizing DGA features and its robustness in tackling classification problems with small DGA sample sizes.

关键词： Dissolved Gas Analysis fault diagnosis semi-supervised learning Hybrid Feature Selection

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Application of Artificial Neural Network in Renewable Energy-Based Building Cooling Systems 3

Application of Artificial Neural Network in Renewable Energy...

引用

3rd IEEE international conference on Power Electronics, intelligent Control and Energy Systems, ICPEICES 2024

作者： Singh, Gaurav Das, Ranjan Singh, Adityabir Indian Institute of Technology Ropar Department of Mechanical Engineering Punjab Rupnagar140001 India

ISBN: (纸本)9798350387193

This paper introduces an artificial neural network (ANN) assisted machine learning model for a vapour absorption radiant air-conditioning system (V ARACS). The V ARACS was primarily operated with solar and natural gas/biogas energies. ANN outputs include, necessary cooling capacities of the chillers as well as necessary thermal loads imposed on the gas boiler, solar collector and the electric heater. Input data sets under variable ambient conditions were taken from EnergyPlus simulations. Three parameters: radiative intensity, ambient air temperature, and environment's relative humidity, were considered as inputs to the ANN. A total of 2591 input and output datasets were used, of which 80 % data were consumed for training, whereas 10% each were involved for validating and testing. The proposed ANN offered prediction of the cooling capacity with correlation coefficient of above 90 %. However, due to more irregular pattern of the heating load, its prediction accuracy was lower for this case. © 2024 IEEE.

关键词： Input output programs

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Pre-sleep anxiety detection by multimodal signal 3

Pre-sleep anxiety detection by multimodal signal

引用

3rd international conference on Biomedical and intelligent Systems, IC-BIS 2024

作者： Cai, Banghao Liu, Miao Li, Binwen Li, Jiayu College of Electronic and Information Engineering Southwest University CO Chongqing400715 China

ISBN: (纸本)9781510681279

Anxiety significantly impacts individuals' lives. This study employs a machine learning framework to detect anxiety among college students in their daily environments, focusing particularly on pre-sleep anxiety. data encompassing electrocardiogram (ECG) and triple-axis acceleration (T-ACC) were gathered from 45 college students across 254 days, and calculated the continuous RR intervals in the ECG data. Subsequently, 30 linear and non-linear RR parameters, along with T-ACC parameters, were extracted to form the physiological model features indicative of anxiety. Feature selection was conducted using Sequential Forward Selection and Leave-One-Out Cross Validation methods. A binary classification model was then developed using these selected features through a classical machine learning algorithm, achieving an optimal accuracy of 68.42%. Statistical analysis of the features indicated that anxiety elevates parasympathetic nervous activity and significantly influences heart rate variability on certain features. © 2024 SPIE.

关键词： Electrocardiograms

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A Study on Network Intrusion Detection Systems by Employing Memory-Based learning Techniques 3

A Study on Network Intrusion Detection Systems by Employing ...

引用

3rd international conference on Sentiment Analysis and Deep learning, ICSADL 2024

作者： Leena Jenifer, L. Jishnu Vardan, S.R. Karthik, R. Rajalakshmi Engineering College Dept. of Information Technology Chennai India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396157

Computer networks must be shielded from numerous security threats and assaults. Network security can be improved by using systems that detect intrusions on networks. As security breaches become more sophisticated and frequent, there is an increasing demand for advanced analytics techniques to enhance NIDS's detection as well as reaction capabilities. The creation and application of analytics techniques for identifying intrusions on networks of computers is the main goal of this research. Applying these methods to NIDS will improve its ability to identify and handle security-related problems. The research begins with a discussion of the principles of discovering network intrusions, covering the many kinds of assaults and the difficulties in identifying them. Signature-based systems and anomaly-based systems, two common types of NIDS models, each have their own set of benefits and drawbacks, which are discussed here. The major goal of this software is to employ analytical methods to further improve network intrusion detection systems (NIDS). In the face of constantly developing cyber threats, the proposed approaches have the potential to improve attack detection accuracy, reduce false positives, enable efficient huge data processing, and facilitate automated incident response. © 2024 IEEE.

关键词： Network security

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How Deep learning is Implemented in Manufacturing Quality Control 3

How Deep Learning is Implemented in Manufacturing Quality Co...

引用

3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Li, Dongjin Chen, Xinyan Tan, Peiwen Jia, Jie School of Electronic Information Shanghai Dian Ji University Shanghai201306 China

ISBN: (纸本)9798350365443

This study aims to explore the application and effect of deep learning technology in manufacturing quality control. The study deployed a convolutional neural network (CNN) model to analyze images and sensor data during the manufacturing process to achieve automatic detection and classification of defects. The experiments were conducted in a simulated high-speed production line environment, and a large amount of production data, including images and sensor readings, were collected and processed. The results showed that the accuracy of the deep learning model in identifying serious, moderate and minor defects reached 98%, 95% and 90% respectively, which was significantly higher than the traditional quality control system. The conclusion shows that deep learning technology can significantly improve the quality control accuracy and efficiency of the manufacturing industry, provide strong technical support for the field of intelligent manufacturing, and indicate broad application prospects in advanced manufacturing systems that require higher levels of automation and intelligence. © 2024 IEEE.

关键词： Smart manufacturing

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Development of an Expert System for Advanced Podiatric Treatments and Ergonomics 3

Development of an Expert System for Advanced Podiatric Treat...

引用

3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vigneshwaran, P. Pandian, V Buvanesh Radha, M. Mohan, B. Krishnan, R Santhana Raj, J Relin Francis P.S.R. Engineering College Department of Electronics and Communication Engineering Sivakasi India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Krishnankoil India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331523893

An expert system tailored for foot pressure analysis and imprint detection, with a focus on enhancing podiatric treatments and ergonomics. The system comprises three key components: a foot pressure analyser, an image processing-based foot imprint analyser, and a machine learning-driven data analyser. The foot pressure analyser employs an array of FSR sensors with piezo resistive material Velostat, accommodating various foot types. The collected data is processed using thermal image algorithms to create foot pressure images. The image processing unit of the system utilizes edge detection techniques to extract relevant features from the foot pressure images, forming a dataset for machine learning. A neural network is trained using this dataset to identify lower limb abnormalities accurately. The expert system encompasses an extensive knowledge base, storing foot anatomy, pressure point norms, common foot conditions, footwear information, and also analyses lower limb extremities like arthritis and synovial accumulations etc., especially helps in geriatric care. The system generates comprehensive reports summarizing the analysis results, facilitating treatment planning for specific applications, age groups, genders, and foot shapes and giving real-time feedback and recommendations based on the Expert's knowledge to aid in treatment planning, ensuring accessibility and data security. Future developments include 3D printing integration, research support, and machine learning enhancements. The system promises to advance foot health diagnostics and treatments significantly, benefiting healthcare professionals and patients. © 2025 IEEE.

关键词： Geriatrics

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Enhancing Smart Grid Security with Quantum Cryptography: A BB84-based Framework 3

Enhancing Smart Grid Security with Quantum Cryptography: A B...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Garg, Shail Gayatri, Yerukola Surya Kausthub, A. Rajagopal, Shinu M. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The study investigates how quantum cryptography can be employed to improve security in smart grid communication networks. Attempting to use the BB84 QKD protocol, it allows secure creation and management of those cryptographic keys that cannot be broken with the help of quantum and traditional hacking techniques. It depends on symmetric encryption mechanisms like AES to provide high data confidentiality during transmission. Regarding the key management, the system provides a key management server that distributes keys to the grid nodes and performs key updating to ensure control with the managing authority. It also presents reliability under practical conditions through performance analogs such as Quantum Bit Error Rates (QBER), and system latency. This approach shows that quantum cryptography can provide a highly secure and efficient communication network for the next-generation smart grid. © 2025 IEEE.

关键词： Smart power grids

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