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LSTM-Based Classification of Psychiatric Disorders Using Ultra-Short Raw ECG Signals

LSTM-Based Classification of Psychiatric Disorders Using Ult...

引用

2025 IEEE Symposium on Computational Intelligence in Health and Medicine, CIHM 2025

作者： Tsakmaki, Paraskevi V. Tasoulis, Sotiris Georgakopoulos, Spiros V. Plagianakos, Vassilis P. Dept. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece Dept. of Mathematics University of Thessaly Lamia Greece

ISBN: (纸本)9798331508333

Psychiatric disorders, including bipolar disorder, depression, and schizophrenia, pose substantial public health challenges due to the intricate nature of their diagnosis and management. Electrocardiogram (ECG) signals offer a noninvasive method to assess autonomic nervous system function, which is frequently dysregulated in these conditions. However, conventional ECG analysis techniques often require prolonged recordings and extensive preprocessing, limiting their applicability in real-time or large-scale settings. This study presents an approach leveraging Long Short-Term Memory (LSTM) networks for medical data classification, specifically for the identification of psychiatric disorders using ultra-short, raw ECG signals. Our methodology efficiently captures the temporal dynamics of ECG signals within brief windows, significantly reducing the need for extensive preprocessing and manual feature extraction. The proposed LSTM-based classifier achieved an impressive mean accuracy of 97.12% ± 0.006, underscoring its precision and efficacy in distinguishing between different psychiatric conditions. Our findings indicate that LSTM networks, with their capacity to effectively model complex temporal patterns in short ECG recordings, represent a promising advancement in the diagnostic landscape of psychiatry. © 2025 IEEE.

关键词： Electrocardiography

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Enhanced Approaches to Kingfisher Species Identification Using Machine Learning and Deep Learning 5

Enhanced Approaches to Kingfisher Species Identification Usi...

引用

5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Vidanagamachchi, C.S. Chathuranga, W.G.D. Chathuranga, L.L.G. Jayathilake, K.T.A.S. Yeshani, R.B. Vidanagamachchi, S.M. University of Ruhuna Dept. of Computer Science Matara Sri Lanka University of Ruhuna Dept. of Zoology Matara Sri Lanka

ISBN: (纸本)9798331530983

This study addresses the critical challenge of bird species recognition within the domains of deep learning, emphasizing its significant ecological and conservation implications. Focusing on Sri Lanka's avian biodiversity, particularly the Kingfisher family, this research systematically analyzes bird identification methodologies. The methodology encompasses data collection, pre-processing, feature extraction, classification, and analysis of results. The study employs various deep learning models, including VGG16, VGG19, SVM, a custom CNN, and a hybrid CNN-SVM model. The custom CNN model, specifically optimized for kingfisher species identification, achieved a testing accuracy of 81.21%, outperforming traditional models. However, the hybrid CNN-SVM model demonstrated slightly lower testing accuracy at 79.35%. These findings highlight the effectiveness of the proposed deep learning models in identifying Kingfisher species, particularly when dealing with complex variations in plumage and environmental factors. The results indicate that the custom CNN model is highly reliable for ornithological applications, especially for nonspecialists. The research highlights the complexity of Kingfisher species identification and underscores the critical role of advanced machine learning techniques in addressing these challenges. The findings have significant implications for wildlife conservation, enabling enhanced monitoring and preservation of bird populations. Additionally, the results reveal the potential for developing a mobile application for bird species recognition, particularly for Kingfishers, addressing the lack of existing computer-based identification solutions. © 2025 IEEE.

关键词： Feature extraction

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Integrated Word2Vec-based Speech Annotations for Enhanced EEG Decoding of Speech Intentions 13

Integrated Word2Vec-based Speech Annotations for Enhanced EE...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Jang, Se-Na Park, Hyeong-Yeong Kim, Seong-Wook Yu, Seong-Hyun Lee, Minji Jeong, Ji-Hoon Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. This study aims to decode human speech intentions by analyzing EEG signals recorded during actual and imagined speech. EEG data were collected using a 64 channels system, with preprocessing to remove artifacts. Automatic speech recognition (ASR) was used to extract precise speech onset times, generating time-specific speech annotations corresponding with EEG data. Pretrained Word2Vec embeddings were integrated to provide semantic context, combining neural signals with high-level linguistic features. Support vector machine (SVM), linear discriminant analysis (LDA) were employed for decoding. The results demonstrate that integrating speech annotations improves decoding accuracy, even for imagined speech, highlighting the potential of BCI technology for advanced applications in communication and rehabilitation. © 2025 IEEE.

关键词： Semantics

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Real-Time Traffic Prediction And Optimization using Machine Learning 3

Real-Time Traffic Prediction And Optimization using Machine ...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vineetha, Kundrapu Tharun Reddy, M. Jaffer Ali, Md. Prathima, Narisetty Rajagopal, Shinu M. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The Real-Time Traffic Prediction and Optimization System may be described as enriched, spacious, algorithm-oriented, and designed to help contribute to the anti-symptomatic deprievement of increasingly congested urban traffic streams. The system with the help of integrating real time data of numerous sources like GPS tracker, traffic sensor as well as weather API, the system using advanced machine learning algorithm in order to predict traffic accurately and optimize routes based on it. This predictive capability then greatly improves the flow of vehicles, the time taken, and the occurrence of choke points. The structure of the system is fully based in the cloud, which makes it easy to scale and very compatible with other systems to amalgamate a vast array of data inputs with high dependability. Fully customizable and intuitive control panel gives live view of traffic, offers actionable stats, and instant control mechanisms to traffic operators to make correct decisions quickly. Congestion in urban areas has an influence on time taken to cover a certain distance, fuel consumption and emission to the atmosphere. On the basis of the above analysis, this work extends a Real-Time Traffic Prediction and Optimization System (RTPOPS) using machine learning approach to predict the traffic density and to further optimize traffic signals and routes. The used system demonstrated successful management and reduction of vehicle density by 30%, and increasing speed limits by 18% and proved that Intelligent Urban Mobility has the potential to be achieved. © 2025 IEEE.

关键词： Traffic congestion

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A Study on the Evaluation of Cyber Security Vulnerabilities in Medical Software

A Study on the Evaluation of Cyber Security Vulnerabilities ...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Woo, Jeong-Hyun Koo, Jee-Hyun Seo, Byung-Suk Son, Sun-Young Ko, Kwang-Man Sang-Ji University Dept. of Computer Engineering Korea Republic of Sang-Ji University Dept. of Bigdata Science Korea Republic of Sang-Ji University Dept. of Electrical and Electronic Engineering Korea Republic of

ISBN: (纸本)9798331510756

In this paper, we examine the cybersecurity vulnerability assessment method of medical software. Medical software processes patient sensitive data and is linked to various medical devices and systems in real time. Due to these characteristics, medical software is highly likely to be exposed to various cybersecurity threats such as ransomware, data leakage, and medical device hacking. Based on the international standard IEC TS 60601-4-5, we propose threat modeling, vulnerability scanning, and penetration testing as a methodology for assessing the security vulnerabilities of medical software. Through this, we can identify security vulnerabilities in advance and prepare measures to respond quickly. We can prevent security threats and improve the safety of medical software through response strategies such as security patches and updates, network separation, data encryption, and security education. In conclusion, strengthening the security of medical software is essential to maintain patient safety and the reliability of the medical system, and systematic security assessment and continuous response are required. © 2025 IEEE.

关键词： Cyber attacks

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Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review 3

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

引用

3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Venkatesan, Iyyappan Dharanish, M.B. Muthumari Lakshmi, S. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported. © 2025 IEEE.

关键词： Weather forecasting

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 IEEE.

关键词： Network intrusion

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices 3

Integrating Artificial Intelligence and Educational Technolo...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ilayaraja, Lathika Yukthika, S.S. Prabakaran, Subramani Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation. © 2025 IEEE.

关键词： Federated learning

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C/C++ Vulnerability data Set Generation Framework using a Parser and LLM 4

C/C++ Vulnerability Data Set Generation Framework using a Pa...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Nguyen, Minh Dao Carlisle, Martin Texas A&M University College Station Dept. of Computer Science TX United States

ISBN: (纸本)9798331518882

Open-source software can reduce the cost of code use and development;however, it can also introduce vulnerabilities. Machine learning methods show promise for finding and fixing vulnerabilities in source code, but require a large amount of labeled data for training. We provide a data set generation framework for C C++ code that will identify code commits that patch vulnerabilities and provide the patched and unpatched function. The framework uses a large language model to determine if the commit patches a vulnerability, then a custom parser to extract the function before and after the patch. Our data set provides over 18,000 functions from 12 repositories. Random sampling and human verification estimate the accuracy at 8 9%. © 2025 IEEE.

关键词： Adversarial machine learning

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