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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431-440 订阅

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Improved EEG-Based Epileptic Seizure Classification Using Fusion of Machine learning Model with Statistical Feature Integration 3

Improved EEG-Based Epileptic Seizure Classification Using Fu...

引用

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

作者： Sadangi, Rakesh Kumar Patra, Biswakalpa Gandhi, Tapan Kumar Sutar, Bijuni Charan Maity, Niladri Pratap Maity, Reshmi Mizoram Central University Dept. of Electronics and Communication Engineering Aizawl India Indian Institute of Technology Dept of Electrical Engineering New Delhi Delhi India Kmbb College of Engineering and Technology Dept. of Electronics and Communication Engineering Khordha India

ISBN: (纸本)9798331528201

Epilepsy is a neurological disorder characterized by recurrent seizures, often diagnosed through the analysis of electroencephalogram (EEG) signals. In this study, we propose a machine learning-based approach to classify EEG data into different brain states, focusing on epileptic seizure detection. Discrete Wavelet Transform (DWT) is employed to extract relevant time-frequency domain features from the EEG signals. Additionally, a set of statistical features - mean, standard deviation, variance, energy, entropy, coefficient of variation, root mean square, skewness, and kurtosis are incorporated to further enhance classification performance. Various machine learning models such as Random Forest, KNN, and LightGBM were evaluated. Results show that combining DWT with statistical features significantly improves the accuracy and robustness of the classification compared to using DWT alone. For example, the Random Forest model achieved an accuracy of 84.85% with DWT alone, which increased to 84.88% when combined with statistical features. Similarly, LightGBM improved from 84.87% to 85.10% in accuracy. This approach could serve as a valuable tool for aiding in the diagnosis and treatment of epilepsy. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Multi-Layered Approach to IoT Cybersecurity in Smart Cities 3

Multi-Layered Approach to IoT Cybersecurity in Smart Cities

引用

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

作者： Benjamine, Daniel Anto Madhavan, K. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (纸本)9798331527549

Internet of Things (IoT) has been rapidly growing over the past years. There have been many cybersecurity challenges faced by IoT-based smart cities in existing work for the energy, health and transportation sectors. These cybersecurity challenges are crucial to remove in the IoT sector to implement a safe and secure IoT based smart city. Vulnerabilities across layers in IoT are analyzed and a multi-layered model of security is proposed to enhance data integrity and communication security by using AI, blockchain and ML. This paper would also present several policy recommendations to stakeholders and IoT developers for building a safe urban IoT ecosystem. © 2025 IEEE.

关键词： Cybersecurity frameworks IoT smart city technologies urban

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Federal Hierarchical Mutual Assistance Based on Maritime intelligent Decision Recommendation 24

Federal Hierarchical Mutual Assistance Based on Maritime Int...

引用

3rd international conference on Algorithms, data Mining, and Information Technology, ADMIT 2024

作者： Liu, Xinru Kang, Jiabao Yang, Tingting Zhang, Shubo Navigation College Dalian Maritime University China School of Future Technology South China University of Technology China Peng Cheng Laboratory China

ISBN: (纸本)9798400718120

With the rapid development of artificial intelligence, edge computing and distributed collaborative processing, more effective solutions have been provided to solve the problems in the field of maritime communications. The surge in maritime activities and the growing number of vessels have precipitated an escalating demand for the real-time processing of oceanic data. However, the vast ocean environment and limited communication resources make the management and intelligent decision-making of heterogeneous data complex. In handling the collaborative processing of large-scale maritime fleet tasks, such as emergency responses to marine pollution, the network nodes of individual vessels are dispersed, and data timeliness is poor, which significantly impacts decision-making for ship operators. Additionally, the information exchanged between cooperating vessels must maintain privacy, making it difficult to develop personalized intelligent communication strategies. To address these potential issues, we propose a Federated Hierarchical Mutual Assistance (FHMA) algorithm for maritime recommendation systems. By integrating federated learning with layered privacy protection techniques, this approach ensures data privacy while also enabling model personalization. Additionally, it introduces compute power sharing and reward distribution mechanisms, incentivizing client participants to actively contribute their computational resources, thereby improving network efficiency. This algorithm not only efficiently handles large-scale, dynamically changing spatiotemporal data but also maintains high-quality recommendation results in parallel and distributed data processing environments, providing new directions for intelligent decision-making in the maritime communication field. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Comparative Analysis of Machine learning Models for Satellite Image Classification with Explainable AI Visualization 3

Comparative Analysis of Machine Learning Models for Satellit...

引用

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

作者： Jennyfer Susan, M.B. Nishanthini, S. Avinashilingam Institute for Home Science and Higher Education for Women Centre for Machine Learning and Intelligence Coimbatore India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

Satellite image classification is vital for applications like environmental monitoring, urban planning, and resource management. Manual methods are time-consuming and error-prone, highlighting the need for automated solutions. This study explores classifying satellite imagery with four distinct classes using sensor data and Google Map snapshots. Four machine learning models - Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), and Artificial Neural Network (ANN) - were implemented and assessed. The SVM model achieved the highest accuracy at 99%, followed by GBM with 98% accuracy, while KNN attained 94%. Although ANN had the lowest accuracy of 58%, it displayed fewer misclassifications. Explainable AI (XAI) tools like LIME and SHAP were used to interpret model decisions, highlighting how features influenced classifications. The results show machine learning's ability to automate satellite image classification with accuracy and actionable insights. © 2025 IEEE.

关键词： Resource allocation

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Integration and Implementation of intelligent Circuit System in Internet of Things Equipment 3

Integration and Implementation of Intelligent Circuit System...

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3rd international conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Sang, Yike Han, Xintong Zhao, Songling Chang, Mingsheng Weifang Engineering Vocational College Shandong Qingzhou262500 China

ISBN: (纸本)9798331519032

The integration and implementation of intelligent circuit system in Internet of Things (IoT) devices is one of the key technologies in the current digital era. With the continuous improvement of the intelligence of IoT equipment, intelligent circuit system, as its core component, undertakes the heavy responsibility of data acquisition, processing and transmission, and gives the equipment the ability of independent learning and decision-making. However, there are still many challenges to effectively integrate the intelligent circuit system into IoT devices and achieve efficient and stable operation. In this paper, the integration method and implementation technology of intelligent circuit system in IoT equipment are deeply discussed. In the aspect of hardware integration, the research emphasizes the importance of standardization and universal interface design, and how to reduce energy consumption through intelligent sleep technology and energy management strategy. Software integration involves selecting the appropriate operating system, communication protocol and data processing strategy to ensure that the intelligent circuit system can seamlessly interface with IoT devices and realize efficient data processing. In addition, the paper also discusses the selection and application of embedded system and microcontroller, as well as the selection and optimization of communication technology to meet the needs of different IoT devices. Through case analysis, this paper shows the integration and realization process of intelligent circuit system in smart home system, including hardware selection, software development, application of communication technology and application of data analysis and processing technology. The analysis of performance, stability and security shows that the smart home system performs well in real-time control, data processing and personalized experience. At the same time, it also points out the potential risks of the system in extreme cases and puts

关键词： Decision making

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Ensemble and Attention-Driven Deep learning Model for Grapevine Leaf Variety Identification 3

Ensemble and Attention-Driven Deep Learning Model for Grapev...

引用

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

作者： Kaur, Jashanpreet Singh, Gurpreet Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798331515911

The research paper introduces an advanced Mo- bileNetV3Large model incorporating attention mechanisms and ensemble learning, which will be applied in identifying grapevine leaf varieties with high accuracy. The approach enhances classification accuracy by applying attention mechanisms that target critical features within the image and by using ensemble learning to further stabilize predictions and prevent overfitting. The model was trained on and tested against a dataset that consisted of five varieties of grapevine leaves, and in that the model's classification accuracy is 98. Extensive metrics that accounted for precision, recall, and the F1 score all average in at 0.98. Comparisons with state of art models, such as MobileNetV2, PNN, and Deep CNN, show the adequacy of the proposed method over these methods with respect to accuracy. The results have revealed that the capabilities of attention mechanisms combined with ensemble learning capabilities introduce additional robustness to the model and results which make it a real world based applicability to grapevine leaf variety classification. The proposed method would provide a promising solution to precision agriculture, with an accurate identification of different grapevine leaf varieties that would eventually lead to further improvements in vineyard management and crop quality. © 2025 IEEE.

关键词： Contrastive learning

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Design of an intelligent College English Teaching Management Platform Based on Association Rules Mining 3rd

Design of an Intelligent College English Teaching Management...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Wu, Dong School of Foreign Language Sichuan Vocational and Technical College Sichuan Suining629000 China

ISBN: (纸本)9783031631412

The intelligent university English teaching management platform uses association rule mining algorithms to mine high-frequency vocabulary in English learning data, providing intelligent services for student learning and teaching resource management. This platform is divided into three modules: data collection, association rule mining, and resource recommendation. The data collection module collects student learning data, while the association rule mining module uses the Apriori algorithm and FP Growth algorithm to discover high-frequency vocabulary and related resources related to English learning. The resource recommendation module provides optimal learning resource recommendations based on student learning data and mined association rules. In addition, the platform also includes a score prediction module and a personalized learning path module to help students analyze and predict their final grades and learning paths, improving English learning efficiency. This platform fully utilizes artificial intelligence technology to integrate teaching resources, manage student learning, improve English learning efficiency, and cultivate students’ autonomous learning ability, providing strong support for the intelligence and modernization. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Association rules

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Design and Simulation Approaches of Resistive-Random Access Memory 3

Design and Simulation Approaches of Resistive-Random Access ...

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

作者： Nayak, Abhijith Jayarajendra Bhoomika, V. Chaithra, P. Divyashree, S. Jamuna, S. Dayananda Sagar College of Engineering Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331515911

Memory refers to a physical device storing data in bits. It may be 0 or 1. Storage and retrieval of information is essential for computer operation and memory acts as the primary foundation. Non-volatile memory is a type of storage that does not lose the data it contains after a power loss, just as volatile memory does. Re-RAM is one of the nonvolatile memories. Re-RAM is another outstanding non-volatile category that applies resistance switching from a high resistive state to a low resistive state and vice versa in metal-oxide layers to store data more quickly and at higher density. For instance, it has a low power rating, high endurance, and good data retention characteristics, and is thus suitable for energy-constrained mobile and embedded systems. This Paper demonstrates the simulation of Re-RAM which can be done using Verilog-A in Cadence Virtuoso and through code-level analysis with MATLAB. © 2025 IEEE.

关键词： MATLAB

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Video enabled crowd monitoring system and analysis 3

Video enabled crowd monitoring system and analysis

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

作者： Mehra, Gunjan Joshi, Aditya Singh, Aakriti Shuaib, Mohd Diwakar, Manoj Singh, Prabhishek Graphic Era Deemed to be University Department of Computer Applications Dehradun India Graphic Era Deemed to be University Department of Cse Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India

ISBN: (纸本)9798331515911

One of the most victorious strategies for slowing the spread of the COVID-19 virus has been proposed: social isolation. A computer vision-based AI-assisted approach for aiding with social distance norms compliance is utilized. The solution consists of modules for person detection, tracking, and distance violations detection. You are given the option to select a tool-based mode for camera calibration. In this paper, the various measures for determining the likelihood of social distance violations and how to tell them apart from temporary violations from persistent ones are demonstrated. On countless real-world issues, AI/Deep learning has in the past shown promising results. In this paper, how to observe crowds in public areas and workplaces using Python, image processing, and computer vision is discussed in depth. © 2025 IEEE.

关键词： Deep learning

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Smart Agriculture using IoT 3

Smart Agriculture using IoT

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

作者： Das, Archana Nagendra, R. Preetham, H.S. Keerthi, K. Diwakar, V. Abhishek, N. Department of Information Science And Engineering New Horizon College of Engineering Affiliated to VTU Bangalore India Department of electronics And Communication Engineering New Horizon College of Engineering Affiliated to VTU Bangalore India

ISBN: (纸本)9798331527549

Crop yields are heavily reliant on efficient irrigation management, making agriculture essential to economic growth. This study presents an Internet of Things (IoT)-based system that continuously monitors soil and plant conditions to modernize farming. Real-time environmental data is gathered by sensors like the DHT-11 for temperature and humidity, an LDR for light intensity, and a soil moisture sensor. Based on these readings, a water pump that is managed by an L293D motor driver IC runs automatically, maximizing water use and cutting waste. By enabling remote monitoring and control via an online platform, the system improves user accessibility. To increase efficiency over time, it also includes a database to hold historical data and predicts irrigation requirements using the Partial Least Squares Regression (PLSR) algorithm. This solution provides a farming method that is both sustainable and scalable. © 2025 IEEE.

关键词： Smart agriculture

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