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

共 20248 条记录，以下是131-140 订阅

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Investigation on Attack Detection in IoT Networks: A Study and Analysis of the Existing Machine learning and Deep learning Techniques 3

Investigation on Attack Detection in IoT Networks: A Study a...

引用

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

作者： Saini, Kamaljit Singh Chaudhary, Sumit Uttaranchal University Uttaranchal India

ISBN: (纸本)9798331527549

Despite revolutionizing fields like eHealth, smart cities, and autonomous systems, the Internet of Things (IoT) has presented serious security challenges due to its widespread usage. This study gives an extensive review of current deep learning (DL) and machine learning (ML) techniques aimed at improving IoT network security. It examines different methods for identifying and thwarting a range of security risks, including malware and botnet attacks, evaluating their effectiveness, computational needs, and dataset consumption. The outcome of the survey helps in understanding the drawbacks of the current existing approaches such as high complexity, unbalanced datasets, and scalability problems. Additionally, the study examines new developments in ensemble models and hybrid architectures to increase detection efficiency and accuracy. The study provides a thorough grasp of the state of IoT security solutions today and their suitability for dealing with changing cyber threats, thanks to this examination. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

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Early Diagnosis of Alzheimer's Disease Using Machine learning in MCI Individuals 3

Early Diagnosis of Alzheimer's Disease Using Machine Learnin...

引用

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

作者： Rahman, Amreen Singh, Dinesh Galgotias University Department of Electrical Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9798331528201

To find the Alzheimer's Disease biomarkers K-means clustering, Principal Component Analysis and hierarchical clustering are employed in categorization of patients. In Machine learning introductory results manifest that unsupervised learning is effective in differentiating between healthy people and Mild Cognitive Impairment patients. Clusters are provided using this method which shows further the metabolic abnormalities and brain shrinkage related to Alzheimer's Disease. Functional changes and brain shrinkage before clinical symptoms are revealed by anomaly detection. Employing these methods could help doctors determine patients at risk at the initial stage and improve illness management. To advance neurodegenerative disease identification this study emphasizes the need to integrate and develop Machine learning technologies into the diagnostics. Machine learning for early diagnosis of disease is promising for clinical application and research. Later this study adds to the data supporting machine learning in the patient's medical diagnostics that shows the patient's outcomes and care. © 2024 IEEE.

关键词： Adversarial machine learning

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Employability Prediction based on Student Performance using CNN Model 3

Employability Prediction based on Student Performance using ...

引用

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

作者： Vasumathi, M.T. Asha, V. Prasad, Arpana Bhavya, M. Chaithra, V. Bhanurekha, N. Department of MCA New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

Unemployment among graduates is a persistent issue, largely because traditional academic evaluations don't always reflect the skills employers seek. This study explores a new approach to predict employability based on students' exam performance using Convolutional Neural Networks (CNNs). We utilized the "Student Performance" dataset, which includes a variety of academic, demographic, and socioeconomic factors. By comparing CNNs with other machine learning models such as SVM, Decision Trees, and XGBoost, we observed that SVM obtained an accuracy of 94.33%, whereas Decision Trees and XGBoost obtained 96.00% and 97.00%, respectively. CNN obtained the highest accuracy of 99.50%. CNN is sometimes misclassified but its strength lies in unearthing complex patterns in student data. This research underscores how advanced machine learning models, especially CNNs, can enhance employability predictions, enabling educational institutions to refine their curricula and better prepare students for the evolving job market. © 2025 IEEE.

关键词： Convolutional neural networks

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Integrating Bi-LSTM and Extended Geological Constraints Method for Automatic Logging Curve Stratification 3

Integrating Bi-LSTM and Extended Geological Constraints Meth...

引用

3rd international conference on Cloud Computing, Big data Application and Software engineering, CBASE 2024

作者： Li, Xiang Zhang, XiaLin Li, ZhangLin Weng, ZhengPing School of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9798331506582

Logging curve stratification plays a crucial role in the interpretation of geological data. As the volume of logging data increases, logging curve stratification becomes increasingly challenging and time-consuming. Current automated log stratification methods primarily rely on pattern recognition techniques, which are limited to processing single curves and overlook the complex nonlinear relationships between different logging curves, resulting in suboptimal performance in thin layer stratification. In this study, we propose a deep learning-based approach that simultaneously handles multiple logging curves while incorporating geological constraints to enhance the impact of spatial features on logging curve stratification. Furthermore, we optimize the loss function and introduce a new evaluation metric to better assess the model's performance. Experimental results demonstrate that our proposed method performs better in terms of stratification error and thin layer detection. © 2024 IEEE.

关键词： Deep learning

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Advancements, Methods, and Obstacles in Emotion Recognition with Multimodal Approaches 3

Advancements, Methods, and Obstacles in Emotion Recognition ...

引用

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

作者： Arunkumar, M. Athiban, R. Selva Jothi, M. Pavithra, S. Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331527549

Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field. © 2025 IEEE.

关键词： Contrastive learning

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The Usage of Machine learning on Penetration Testing Automation 3

The Usage of Machine Learning on Penetration Testing Automat...

引用

3rd international conference on Electronic and Electrical engineering and intelligent System, ICE3IS 2023

作者： Clintswood Lie, Delbert Giovanni Kuswandana, Lexiandy Nadia Achmad, Said Suhartono, Derwin School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

Penetration testing is a method to assess the security within a network by performing or simulating a real-world cyber-attack on the network. It has been one of the best ways preferred by organizations to strengthen their network defenses against cyber threats. However, penetration testing proves that it has several drawbacks such as requiring a significant number of skills and time to perform. A survey made by The international Information System Security Certification Consortium also shows that the world is lacking cybersecurity workforces. To address this problem, this paper conducts research regarding the usage of machine learning to automate penetration testing activity. To start off, automated penetration testing tools will be created using machine learning algorithms, specifically reinforcement learning and deep reinforcement learning algorithms. The second step is to put the tools in a learning stage where it will be provided with the MITRE ATT&CK Framework. This framework will be the base for the attacks and exploitations used within the tools. The third step is to conduct a series of automated penetration testing over different target networks. The result will be then compared and analyzed with a manual penetration testing report to see how efficient and better it is compared to a manual penetration test. Lastly, the final data will then be collected for future developments and recommendations. © 2023 IEEE.

关键词： Cybersecurity

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intelligent sensing and adaptive modulation: the key technologies of future communication systems 24

Intelligent sensing and adaptive modulation: the key technol...

引用

3rd international conference on Frontiers of Artificial Intelligence and Machine learning (FAIML)

作者： Zhang, Pengqin Zhu, Hongjie Guangzhou Coll Technol & Business Guangzhou 510850 Guangdong Peoples R China

ISBN: (纸本)9798400709777

With the continuous progress of the times and technology, the way of communication between people has evolved into an efficient way of information transmission based on the Internet, and the future communication system can only be more efficient and accurate. Therefore, this paper believes that intelligent perception and adaptive modulation can become the main technologies of future communication development, because intelligent perception can efficiently collect information, and adaptive modulation can independently choose the best modulation format, thereby improving bandwidth utilization. In addition, this paper also verifies the idea of this paper through comparative experiments at the end, and the experimental index to measure the performance is the data transmission rate. In the experiments repeated up to 50 times, the average data transmission efficiency of the intelligent communication system using the proposed method is 96.93Mdbs, while that of the ordinary communication system is 66.78Mdbs. It can be seen that there is a big gap between the two, which fully reflects the great improvement of the work efficiency of the communication system brought by the method in this paper.

关键词： intelligent sensing Adaptive modulation Future communication data transmission rate Signal-to-noise ratio

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Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

引用

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

作者： Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. Thota, Ragupathi Keerthanadevi, R. Department of Computer Information Systems University of the Central Missouri Warrensburg United States Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including AI-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences. © 2025 IEEE.

关键词： Inventory control

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Epilepsy Seizure Detection based on EEG Signals using Machine learning 3

Epilepsy Seizure Detection based on EEG Signals using Machin...

引用

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

作者： Goel, Aadya School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

ISBN: (纸本)9798331515911

Seizure detection is a critical aspect of epilepsy management, and accurate identification can significantly improve patient outcomes. This study explores the application of machine learning techniques, specifically the Random Forest classifier, for the detection of seizures using electroencephalogram (EEG) data. By employing a systematic approach that includes feature extraction and channel reduction, we enhanced the model's performance. Relevant features were extracted from EEG signals, including time-domain statistics and frequency-domain characteristics, which are vital for effective classification. The model was evaluated using standard classification metrics such as precision, recall, F1-score, and accuracy, yielding high performance, particularly in identifying non-seizure events. These findings highlight the potential of ensemble learning methods for real-time seizure monitoring and underscore the importance of this research in developing reliable diagnostic tools. Overall, our results demonstrate that machine learning approaches can significantly contribute to improved patient outcomes in epilepsy management, paving the way for future advancements in clinical applicability. © 2025 IEEE.

关键词： Electroencephalography

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Research and Design of intelligent Hydropower SCADA 3

Research and Design of Intelligent Hydropower SCADA

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

作者： Li, Zheng Wang, Yuhang Wang, Boyu Zhang, Huan Yalong River Hydropower Dev Co Ltd Chengdu Sichuan Peoples R China Nari Grp Corp State Grid Elect Power Res Inst Nanjing Jiansu Peoples R China

ISBN: (纸本)9798350366105;9798350366099

The necessity of intelligent transformation of hydropower monitoring was analyzed in response to a large amount of alarm information and insufficient intelligent analysis level faced by traditional computer monitoring systems in hydropower stations. Then, an intelligent hydropower monitoring system based on hierarchical device objects was proposed. Through the graphical configuration of alarm strategies, the digital integration, inference, and hierarchical fault diagnosis of traditional monitoring data information were achieved, thereby achieving rapid determination of fault location and providing fault diagnosis-aided decision-making to achieve real-time and predictive alarms for equipment safety. Based on the research on the demand for intelligent transformation of hydropower monitoring, combined with the research and application of intelligent monitoring systems in hydropower plants, the overall functions and key technologies were introduced, and their impacts were summarized.

关键词： Hydropower station SCADA intelligence alarm early warning device model

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