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检索条件"任意字段=International Conference on Advances in Intelligent Control and Computer Engineering"

共 47502 条记录，以下是141-150 订阅

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Enhanced Multimodal Depression Detection With Emotion Prompts

Enhanced Multimodal Depression Detection With Emotion Prompt...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Teng, Shiyu Liu, Jiaqing Sun, Hao Chai, Shurong Tateyama, Tomoko Lin, Lanfen Chen, Yen-Wei College of Information Science and Engineering Ritsumeikan University Shiga Japan College of Computer Science and Technology Zhejiang University Hangzhou China Department of Intelligent Information Engineering Fujita Health University Japan

ISBN: (纸本)9798350368741

Depression is a pervasive mental health disorder that remains frequently undiagnosed and untreated due to societal barriers and the subjective nature of its symptoms. Leveraging recent advances in large language models (LLMs), we propose a novel depression detection pipeline that generates emotion prompts tailored to individual data, enhancing detection accuracy. Our approach integrates cross-modality fusion via cross attention mechanisms to combine depressive and emotional features, creating a comprehensive representation of depression indicators. Evaluated on the E-DAIC and EATD datasets, our method outperforms state-of-the-art techniques, demonstrating its potential for more precise emotion-based depression detection. © 2025 IEEE.

关键词： Depression detection Emotion prompt Large language model Multi-modal fusion

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Fire Suppression Robot with Image Processing for Warehouse Safety 1

Fire Suppression Robot with Image Processing for Warehouse S...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Base, James Allen V. Dorosan, Jairus F. Ocampo, Kyle Joshua P. Puma, Aeron Joshua M. Mercado, Mark Angelo T. Niega, Juliet O. Nationial University Philippines Department of Computer Engineering Laguna Calamba City Philippines

ISBN: (纸本)9798331518578

Warehouses are highly vulnerable to fire hazards due to the presence of combustible materials, high-energy equipment, and operational complexities. In response, the proponents propose a solution to the pressing issue of warehouse fire which pose a significant threat globally, including Philippines. The prototype that the proponents developed introduces an advance approach to fire detection and suppression. It is an incorporation of the YOLO algorithm to ensure fire detection with high accuracy, the Raspberry Pi 5 for advanced image processing, and the Arduino Mega for effective hardware control. The overall performance capacity of the robot in terms of detection and suppression shows efficiency as it provides faster response with great productivity, compared to using traditional firefighting techniques with human intervention. With an average of 4.0, or interpreted as good, it confirms that it is effective and reliable in suppressing fire. Recommendations for future improvements include expanding the dataset and enhancing hardware scalability to optimize the system's real-world applications. © 2025 IEEE.

关键词： Fire extinguishers

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Spectrum Sensing Methodology Using Artificial Intelligence for Cognitive Radio Network 1

Spectrum Sensing Methodology Using Artificial Intelligence f...

引用

1st international conference on advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Bhavani, M. Monica Balamurugan, P. Sankaran, Vikram Nattamai Majumder, Sohom Rakshitha, L.P. Nandagopal, K. SRM University Data Science & Business Systems Department Tamil Nadu Chennai India Institute: Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mathematics Tamil Nadu Chennai India Department of Industry Experts AtlantaGA United States JIS College of Engineering Department of Business Administration West Bengal Kalyani Kolkata India Brindavan Group of Institutions Department of Mathematics Karnataka Bangalore India Mohan Babu University Department of Mathematics Andhrapradesh Tirupati India

ISBN: (纸本)9798331518578

Spectrum sensing is an essential part of cognitive radio networks (CRNs) that enables efficient utilisation of the radio frequency spectrum and dynamic spectrum access. Conventional spectrum sensing technologies, such as energy detection, matched filtering, and cyclostationary features recognition, are often computationally complex, noise-sensitive, and unable to detect signals at low signal-to-noise ratio (SNR) levels. Artificial intelligence (AI)-based approaches have become a viable substitute to deal with these issues. CRNs can provide more precise, flexible, and effective spectrum sensing owing to AI approaches. In intricate and dynamic situations, these algorithms can identify primary users, predict spectrum usage, and categorise spectrum accessibility. Major AI-driven techniques for spectrum sensing are reviewed in this work, with an emphasis on supervised, unsupervised, and reinforcement learning techniques. The suggested approach supports the creation of intelligent, independent CRNs for next-generation wireless networks by improving spectrum efficacy, cutting down on sensing time, and facilitating real-time decision-making. © 2025 IEEE.

关键词： Signal to noise ratio

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A Hybrid Approach for Cardiovascular Disease Risk Evaluation 6

A Hybrid Approach for Cardiovascular Disease Risk Evaluation

引用

6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Sesadri, U. Jumlesha, Shaik Raju, S. Hrushikesava Adinarayna, S. Choudhury, Nabanita Jadala, Vijaya Chandra Department of CSE Vardhaman College of Engineering Telangana Hyderabad India AP Tirupati517501 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Guntur522502 India Department of Computer Science and Systems Engineering College of Engineering Andhra University Visakhapatnam531162 India Faculty of Computer Technology Assam down town University Assam Guwahati India School of Computer Science and Artificial Intelligence SR University Ananthasagar Hasanparthy Hanumakonda Telangana Warangal506371 India

ISBN: (纸本)9798331522667

Nowadays, the risk estimators are to be applied based on the population characteristics of their country, which is termed as race attribute. There were specific tools to determine the risk of cardiovascular disease. Among many diseases, the mortality rate of the people is increasing due to a lack of quality lifestyle. In order to provide a guide to the individual based on their specific characteristics and lifestyle, would classify the risk into specific categories like severe (High), Moderate, and normal(Low) with pre-defined threshold constraints. The risk assessment estimators alert the individual to control certain modifiable attributes irrespective of their non-modifiable elements. The integration of random forests and intelligent rule mining would generate the CVD risk prediction. Efficiency as well as interpretability are important in this competitive hybrid model. The ultimate benefits assured are extending the lives of the patients by knowing the importance of daily work, habits, and their control food, presenting a powerful opportunity by practicing better personalization monitoring and improved patient care, and improved informed decisions for immediate action. The early prevention initiation by identifying the risk severity optimizes follow-up of better practices and treatment styles, resulting in improved decision making for better health care of the individual. © 2025 IEEE.

关键词： Accuracy Cardio-vascular disease Efficiency Hybridization Modifiable attributes Prediction Risk estimator

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Multi-agent Reinforcement Learning for Cooperative On-Ramp Merging of Connected Automated Vehicles 9th

Multi-agent Reinforcement Learning for Cooperative On-Ramp M...

引用

9th international conference on intelligent IoT as a Service

作者： Zhao, Boyuan Cui, Jianxun Harbin Inst Technol Sch Transportat Sci & Engn Harbin 150000 Peoples R China HIT Chongqing Res Inst 618 Liangjiang Ave Chongqing Peoples R China

ISBN: (纸本)9783031705069;9783031705076

Ramp merging areas on highways often serve as bottleneck areas, leading to frequent interactions and accidents between vehicles on the ramp and the arterial road. This results in severe congestion and reduced traffic performance. The emergence of Connected Autonomous Vehicles (CAVs) offers advanced solutions to address these issues and improve traffic operations at ramp merging areas. While previous studies have explored CAV decision-making approaches such as optimization control, model predictive control, and reinforcement learning, they face difficulties in accurately modeling the complex and dynamic scenarios of ramp merging. To overcome these challenges, this paper proposes a collaborative decision-making and control model based on Multi-agent Reinforcement Learning (MARL) for mixed vehicles (CAV-HDV) in multi-lane ramp merging scenarios on arterial roads. The paper introduces three novel MARL algorithms and conducts simulations in six different scenarios to evaluate traffic performance under various lane numbers and traffic densities. The results demonstrate the effectiveness of the proposed collaborative model for ramp merging vehicles. The proposed algorithms significantly reduce collision rates and improve traffic efficiency.

关键词： Highway Ramp Merging Multi Agent Reinforcement Learning Connected Automatic Vehicle Decision-making and control

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Revolutionizing Face Recognition with IIoT: Smarter, Faster, Connected

Revolutionizing Face Recognition with IIoT: Smarter, Faster,...

引用

2025 international conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Grover, Yash Sharma, Pulkit Kansal, Nishtha Vivekananda Institute of Professional Studies School of Engineering & Technology Technical Campus New Delhi India

ISBN: (纸本)9798331513894

The integration of facial recognition technology with Industrial Internet of Things (IIoT) systems offers significant potential to enhance operational efficiency, security, and safety within industrial environments. Automating processes like access control and attendance tracking minimizes human intervention, reduces errors, and streamlines workflows. From a security perspective, facial recognition strengthens access management, reduces unauthorized entries, and enhances real-time surveillance capabilities. In safety-critical industries, this technology can enforce compliance with protective protocols and monitor workers' well-being by detecting signs of fatigue or stress. Additionally, the use of artificial intelligence (AI) and machine learning facilitates real-time data analysis, providing insights to optimize operations and prevent system failures. This paper further examines the broader applications of IoT-driven facial recognition, highlighting promising research areas such as intelligent security systems, biometric authentication, automated surveillance, and smart access control. Practical implementations span smart homes, healthcare monitoring, financial transactions, and public safety, where real-time facial identification enhances both security and operational performance. Despite these advantages, challenges like data privacy concerns and the need for robust technological infrastructure remain critical. Strategies such as data encryption, edge computing, and adherence to privacy regulations like GDPR are essential to protect personal information. As advancements in AI, machine learning, and 5G networks continue, facial recognition systems integrated with IIoT will become increasingly accurate and efficient, paving the way for smarter, more secure, and highly efficient industrial and public environments. © 2025 IEEE.

关键词： Access control

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Decentralized Federated Learning with Auto-switchable Local Optimizers 13

Decentralized Federated Learning with Auto-switchable Local ...

引用

13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Li, Ting Chang, Zhongyi Pan, Zhiyao Yang, Shaofu Xu, Wenying Chen, Zhongying School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9798331516147

In this paper, we propose a novel decentralized learning algorithm over networks, termed as DLAGD, which combines the consensus mechanism with an auto-switchable local optimizer. Specifically, each node updates its local model parameter based on either gradient descent algorithm or Adam-like algorithm, which depends on a local switching function. Such switchable local optimizer can better capture the structure information of local loss function than existing algorithms relying on a fixed local optimizer, then has the potential to accelerate the convergence rate and improve the generalization performance. We theoretically prove that DLAGD can converge to the neighborhood of a stationary point at a sublinear rate for non-convex problems. Finally, numerical experiments are presented to show the superior performance of DLAGD in terms of the training rate and test accuracy. © 2025 IEEE.

关键词： Federated learning

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Classifying Land Cover Using Hyperspectral Image and LiDAR Data 13

Classifying Land Cover Using Hyperspectral Image and LiDAR D...

引用

13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Zhang, Nian Thompson, Lara A. Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States

ISBN: (纸本)9798331516147

This paper proposes an advanced approach to land cover classification by integrating hyperspectral and LiDAR data to leverage their complementary strengths. Hyperspectral sensors capture detailed reflectance across numerous spectral bands, providing unique spectral signatures that differentiate various land cover types. On the other hand, LiDAR technology offers high-resolution elevation data, enabling accurate characterization of surface structures. This paper details the process of converting unorganized LiDAR point clouds into organized formats for effective analysis, followed by a robust data fusion technique that combines spectral and spatial information. Utilizing a land cover classification methodology on the MUUFL Gulfport dataset, collected in November 2010, we evaluate the effectiveness of our approach by comparing classification accuracies of multiple machine learning algorithms, including kernel classification model, support vector machines, and others. Our results demonstrate significant improvements in classification accuracy when hyperspectral and LiDAR data are fused, highlighting the efficacy of this integrated methodology for complex land cover analysis. © 2025 IEEE.

关键词： Support vector machines

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EchoNet Dynamic: Deep Learning-driven Advancements in Dynamic Heart Function Analysis through Echocardiography 3

EchoNet Dynamic: Deep Learning-driven Advancements in Dynami...

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

作者： Vidhya, K. Nagarajan, B. Jenefa, A. Catherine Joy, R. Cherian, Minu Franz Kingstein, N. School of CST Karunya Institute of Tech. and Sci Coimbatore India Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Division of ECE Karunya Institute of Tech. and Sci Coimbatore India Computer science and engineering department Amal Jyothi college of engineering kanjirapally Kottayam India

ISBN: (纸本)9798331527549

advances in echocardiography have significantly improved the diagnostic processes for heart function analysis. However, traditional imaging techniques often struggle with the dynamic complexities of cardiac activities. Existing methods fail to capture detailed variations in cardiac function over time, leading to suboptimal clinical decision-making and patient outcomes. Traditional echocardiography relies heavily on manual interpretation, which can be subjective and varies significantly between observers. This research presents EchoNet Dynamic, a novel method for improving echocardiographic data analysis and interpretation based on deep learning and the Transformer model. The EchoNet Dynamic dataset used in this study comprises 15,000 B-mode echocardiographic images from 750 patients, divided into training, validation, and testing sets for comprehensive model evaluation. The Transformer model demonstrated remarkable effectiveness, achieving an accuracy of 98.5%, an AUC of 0.992, and an F1 score of 0.972, surpassing traditional diagnostic tools by a significant margin. The EchoNet Dynamic model demonstrates a promising enhancement in detecting and diagnosing cardiac functions, offering substantial improvements over conventional echocardiographic methods and setting a new benchmark in cardiac healthcare. © 2025 IEEE.

关键词： Medical imaging

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Tensor Decomposition-Based Feature Transfer Learning for BCI Applications 13

Tensor Decomposition-Based Feature Transfer Learning for BCI...

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13th international conference on intelligent control and Information Processing, ICICIP 2025

作者： Bonab, Zahra Sohrabi Shamsollahi, Mohammad Bagher Department of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9798331516147

Event-Related Potential (ERP) detection and enhancement remain significant challenges in EEG signal processing due to the complex and multidimensional nature of ERP signals. This paper addresses these challenges by proposing a novel framework that combines low-rank tensor representation with heuristic feature transfer learning for efficient ERP processing. By leveraging tensor decomposition, we capture intricate multidimensional patterns in ERP data, facilitating the extraction of informative features. These features greatly reduce processing time, making the method particularly suitable for online Brain-computer Interface (BCI) applications. We also present a feature transfer learning mechanism utilizing tensor decomposition, which facilitates the efficient adaptation of features trained on multiple subjects to new subjects, requiring only a small amount of calibration data. This approach leverages shared classification structures across subjects, allowing for rapid deployment and minimizing the need for extensive retraining. Our results demonstrate that the proposed method achieves reasonable ERP detection accuracy while drastically reducing computational overhead, presenting a robust solution for real-time BCI systems. © 2025 IEEE.

关键词： Event Related Potentials (ERPs) Feature Extraction Feature Transfer Learning Tensor Decomposition

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