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

共 20250 条记录，以下是261-270 订阅

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A Novel Framework Combining CNN, LSTM, and Meta-learning for Real-Time Monitoring of Anesthetic-Induced Brain States 3

A Novel Framework Combining CNN, LSTM, and Meta-Learning for...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Jyothi, B.G. Arpitha, K. Adichunchanagiri University Bgs Institute of Technology Department of Computer Science Engineering B.G Nagar Mandya District India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515911

In order to improve our understanding of brain function, monitoring the depth of unconsciousness during anesthesia is essential for both clinical practice and neuroscience research. The electroencephalogram (EEG) is a frequently used objective instrument for monitoring the changed states of the brain caused by anesthesia in real time. The effects of various general anesthetics on brain electrical activity are not the same. However, because of the low Signal to Noise Ratio (SNR) of EEG data, traditional machine learning models have difficulty with it, particularly in office-based anesthetic settings. Applications of Brain-Computer Interface (BCI) for pattern recognition and classification have seen a rise in the use of deep learning models, which are renowned for their great generalization and resilience in the face of noise. Deep learning has shown effective in a number of BCI applications, although its use in categorizing brain states while anesthesia is still relatively new. This research presents a unique framework to characterize brain states under anesthesia that combines deep neural networks and meta-learning. The framework uses a Long Short-Term Memory (LSTM) network to capture temporal relationships, Convolutional Neural Networks (CNN) to extract power spectrum features, and a meta-learning technique to address large cross-subject variability. © 2025 IEEE.

关键词： Electroencephalography

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ML Based automated Assistance System for Efficient Crowd Control A detailed investigation 3

ML Based Automated Assistance System for Efficient Crowd Con...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Kumar, Ashwini Parikh, Swapnil Arka Jain University Department of Engineering& It Jharkhand Jamshedpur India Pit Parul University Department of Cse Gujarat Vadodara India

ISBN: (纸本)9798350399264

This section covers ML and automated assistance systems for crowd control. ML-based artificial intelligence (AI) lets software programmes enhance prediction accuracy without being designed to. ML techniques forecast correct output using past data. The next step towards truly automated beings is to develop methods for understanding and managing the currently pervasive community info gadgets since data is so important. In this part, we'll discuss ML's automatic crowd control. data availability is expanding learning for application across sectors. Computer vision has helped medical diagnosis, data presentation and processing, science, and research. Computer hardware, electronics, internet content, and cyber security have used these methods. The machine learning-based automatic assistance system for efficient cored management can now be concluded. © 2023 IEEE.

关键词： data acquisition

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A Robust Defense Algorithm for Backdoor Erasure Based on Attention Alignment in Federated learning 3

A Robust Defense Algorithm for Backdoor Erasure Based on Att...

引用

3rd international Joint conference on Information and Communication engineering (JCICE)

作者： Han, Haotong Dai, Xu Li, Dan Wang, Erfu Heilongjiang Univ Elect Engn Coll Harbin Peoples R China

ISBN: (纸本)9798350388732;9798350388725

The widespread adoption of deep learning has led to recurring data security issues. Consequently, there is an even greater need to preserve data privacy, resulting in data becoming increasingly challenging to share and creating what are often referred to as 'data islands', which FL effectively addresses. FL, a new deep learning paradigm, enables collaborative model training across multiple terminals distributively, facilitating data sharing and machine learning modeling while ensuring robust data privacy protection. Individual client could be backdoor attacked by poisoning the data. This paper introduces a robust defense method for backdoor attacks based on reverse trigger engineering, model hardening, and attention distillation mechanisms. In this paper, we conduct a lot of experiments on different classical datasets with different settings, and the results demonstrates that the proposed strategy improve robustness by reducing the attack success rate while preserving main task accuracy.

关键词： federated learning backdoor defense adversarial training attention distillation

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Advancing Phishing Detection with Random Forest: A Machine learning Approach for Enhanced Cybersecurity 3

Advancing Phishing Detection with Random Forest: A Machine L...

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

作者： Chandra, Sucheta Das, Priyanka Mandal, Ankita Chakroborty, Sriparno Chowdhury, Ishika Ghosh, Kathamrita University of Engineering & Management Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798331523893

Phishing attacks remain a critical threat to cybersecurity, leveraging deception to steal sensitive information from individuals and organizations. Traditional detection methods often fail to keep up with evolving phishing tactics, highlighting the need for advanced solutions. This paper investigates the use of the Random Forest algorithm, a powerful machine learning technique, to enhance phishing detection systems. By leveraging the ensemble learning approach of Random Forest, we improve accuracy and efficiency in classifying phishing attempts, while reducing false positives. The methodology includes data preprocessing, feature extraction, and model implementation, showcasing its effectiveness in real-time detection. The study also emphasizes the importance of continuous learning, allowing the model to adapt to new phishing strategies, thereby offering a robust defense against emerging threats. This research contributes to advancing cybersecurity by providing a dynamic, scalable approach to phishing detection. © 2025 IEEE.

关键词： Random forests

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Smart Hackathon Management System with Machine learning Integration 3

Smart Hackathon Management System with Machine Learning Inte...

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3rd international conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Konkepudi, Bhagya Rekha Peddarapu, Rama Krishna Singam, Vamshi Krishna Katanguri, Sai Shashidhar Reddy Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331515911

In the rapidly growing landscape of hackathons and competitive coding events, managing large-scale participation and evaluating diverse submissions have become increasingly challenging. Traditional methods of handling hackathons are often inefficient, particularly in assessing and summarizing large volumes of participant data, presentations, and project documentation. This paper introduces a Smart Hackathon Management System that integrates advanced machine learning techniques to address these challenges, focusing on text summarization using seq2seq models and image-based text extraction. The proposed system automates the summarization of textual content, such as project proposals and idea presentations, allowing juries to make quick, informed decisions. Additionally, it incorporates a robust image text extraction module, designed to accurately retrieve and analyze text from images, further enhancing the system's capability to handle various forms of data. This integration of machine learning models within a hackathon management platform not only streamlines the evaluation process but also improves accuracy and reduces the manual workload involved in event management. The effectiveness of the proposed system is validated through extensive experiments, highlighting its potential for broader applications in event management and other domains requiring efficient handling of large datasets. © 2025 IEEE.

关键词： Optical character recognition

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Research on ventilation and noise reduction technology for substations based on deep learning 3

Research on ventilation and noise reduction technology for s...

引用

3rd international conference on Energy and Power engineering, Control engineering (EPECE)

作者： Zhu, Li Zhu, Zixuan Jiang, Wenxiu State Grid Shanghai Econ Res Inst Shanghai 200000 Peoples R China Power China Shanghai Elect Power Engn Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798350366105;9798350366099

With the accelerated urbanization process and the continuous growth of power demand, substations are a critical component of the power system. However, the main noise sources in substations, such as main transformers, reactors, and cooling fans, have a significant impact on the surrounding environment. Therefore, this paper proposes an optimization design method for ventilation and noise reduction in substations based on deep learning. Firstly, the finite element method is used to simulate the ventilation and noise data of substations under multiple operating conditions to obtain sufficient samples. Secondly, the construction of the Convolutional Neural Network (CNN) model is completed, and the obtained data is used for model training. Finally, the optimization solution of parameter design is achieved using the SAC algorithm. The results show that the designed parameters for substations using this method can effectively reduce noise and comply with national standards.

关键词： transformer station deep learning ventilation and noise reduction

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Surveillance Information Processing Strategy of Distribution Network Under Power Supply Reliability 3

Surveillance Information Processing Strategy of Distribution...

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3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Song, Youle Liang, Yong Xie, Jinlong Yunnan Power Grid Co. Ltd. Electric Power Science Research Institute Yunnan Kunming China Yunnan Power Grid Co. Ltd. Yuxi Xinping Power Supply Bureau Yunnan Yuxi China

ISBN: (纸本)9798350380989

In order to solve problems of computer delay and low information batch storage load of the intelligent distribution network system, a machine learning-based distribution network monitoring information batch processing method is proposed. The method uses the HDFS distributed file system to measure the data transmission records, builds the distribution network monitoring information scheduling cluster, measure the latency of the computer system, and fills system delay time with the data response task. It plans the processing mode of the monitoring information interface, divides the large-scale flow calculation of machine learning, and builds the monitoring information batch processing model. It increases and counts the information batch accumulation nodes, and expands the distribution network to develop the learning path of intelligent analysis machine through the cloud computing service level. It calculates the telemetry information and effective event information, plans the layout of telemetry coordination nodes through HDFS. It divides the storage space of monitoring information, monitors the storage space with remote control metabase, and completes the batch processing and storage of accumulation V002 distribution network monitoring information with the manager. The experimental results show that the highest storage load rate of the information cluster processing is 95%, and the batch processing effect of the distribution network monitoring information is guaranteed. © 2024 IEEE.

关键词： distribution network machine learning monitoring information batch processing

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Research on learning Terminal System for English Teaching Practice in Artificial Intelligence 3

Research on Learning Terminal System for English Teaching Pr...

引用

3rd IEEE international conference on Electrical engineering, Big data and Algorithms, EEBDA 2024

作者： Cao, Yu Xi'an Eurasia University Global Language Center Xi'an710065 China

ISBN: (纸本)9798350380989

This paper is mainly for the English test terminal research and development. It has realized the functions of question bank, examination, examination statistics and so on. The main feature of the system is that the whole process of using cryptography and digital signature technology, the paper generation process and the content of the examination questions are more fully processed, and also provides a safe and flexible authorized access control mechanism, a complete function of dynamic query and statistical analysis, intelligent paper design. It converts speech recognition and English into Chinese intonation, and designs and develops English translation system at the same time. The deviation between the basic frequency curve of the established tone system and the original sound is only 0.02, and the basic frequency curve is very close. This method can obtain good speech production results, and the generated sounds are diversified and accurate. The CPU usage of this system is 27.8%. Compared with the mobile terminal system and the WEB system, the CPU usage decreased by 19.0%. © 2024 IEEE.

关键词： Artificial intelligence English teaching English test learning terminal system spoken language

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Clustering method for forming student groups based on the cognitive performance measured using eye responses 24

Clustering method for forming student groups based on the co...

引用

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

作者： Mishra, Unnati Parikh, Saurin Sharad School of Technology Nirma University Gujarat Ahmedabad India

ISBN: (纸本)9798400707308

In our past study, Eye and Pupil responses were analyzed to measure the ability of the students to learn the meaning of a novel word or concept from the given contextual information in the passage. Moreover, in this paper, the eye response data of several subjects is used to form student groups of varying cognitive levels. Student clusters are formed using clustering methods such as K-Means, K-Medoids, and variants of hierarchical clustering. Moreover, the purity of the clusters is measured using the Silhouette Score (SC), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). When two or three clusters are formed using various types of feature sets, the hierarchical clustering method forms good-quality clusters in comparison to K-Means, and K-Medoids. Hence, when student groups are formed based on their cognitive, language, and learning skills, it helps to provide real-time assistive, supplementary learning content to the students on a need basis and may assist in student-team formation based on their cognitive abilities. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive learning

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Deep learning based Brain Tumor Detection using YOLO11n and Segmentation using SAM2 Model 3

Deep Learning based Brain Tumor Detection using YOLO11n and ...

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

作者： Pasunoori, Krupa Chary Rajendra Prasad, Ch. Raj Kumar, K. Dept. of ECE SR University Warangal506371 India

ISBN: (纸本)9798331527549

The central nervous system (CNS) contains the brain, spinal cord, and it governs all essential functions. These functions encompass cognition, verbal communication, and locomotion. When a tumor develops in the CNS, it can disrupt an individual's cognitive functions, speech, and motor skills. A brain tumor is a condition resulting from the proliferation of atypical cells inside the brain. Any disorder in the brain might be life-threatening. This paper presents deep learning based YOLO11n algorithm for tumour prediction. Then the Segment Any Model2 is employed for tumor segmentation. The proposed model significantly improve that segmentation Dice Score (96.3%) and IOU Score(93.03%) compared with previous models. © 2025 IEEE.

关键词： Deep learning

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