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检索条件"任意字段=IEEE International Conference on Computer Research and Development"

共 367929 条记录，以下是31-40 订阅

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A Novel Channel Selection Method for Near-Infrared Spectroscopy-based Brain-computer Interfaces 13

A Novel Channel Selection Method for Near-Infrared Spectrosc...

引用

13th international Winter conference on Brain-computer Interface, BCI 2025

作者： Lee, Hyung-Tak Hwang, Han-Jeong Dept. Electronics and Information Engineering Korea University Sejong Korea Republic of

ISBN: (纸本)9798331521929

Brain-computer interface (BCI) studies have focused on enhancing performance, reducing computational costs, and minimizing equipment size for practical applications. One effective approach to address these challenges is minimizing the number of recording channels. Among various neuroimaging modalities used in BCI development, near-infrared spectroscopy (NIRS) has unique characteristics, requiring two optodes to form a single channel. As a result, reducing the number of NIRS channels does not necessarily decrease the total number of optodes, potentially leaving the equipment bulky and inconvenient. In this study, we propose a novel channel selection algorithm, optode-based sequential forward selection (oSFS), which enhances the traditional SFS (tSFS) algorithm by accounting for the number of optodes. The proposed oSFS explores the optimal placement of optodes by simultaneously evaluating optode pairings and classification accuracy. Both tSFS and oSFS significantly improved classification accuracy compared to using all channels, but no significant difference was found between the two methods. Notably, tSFS reduced the number of optodes by only 16% compared to using all channels, whereas oSFS reduced by 52% when obtained the highest accuracy. Our study introduces the first channel selection method tailored to the unique characteristics of NIRS, making a significant step toward the practical application of practical application of NIRS-based BCIs. © 2025 ieee.

关键词： Near infrared spectroscopy

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Leveraging Artificial Intelligence for Colon Cancer: Trends, Challenges and Innovations 3

Leveraging Artificial Intelligence for Colon Cancer: Trends,...

引用

3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Takkar, Sakshi Rakhra, Manik Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798331542375

Artificial Intelligence (AI) has made huge enhancements in colon cancer research through its methods for diagnosis, treatment, and custom detection of one of the most common and fatal cancers suffered globally. Through AI deep learning and machine learning methods, tremendous possibilities have been realized in automating, for example, polyp detection in colonoscopy images, cancer classification from histopathological slides, and even predicting disease treatment outcomes. The current direction is towards transfer learning, hybrid model building and AI applications in surgery robots aimed at more accurate diagnosis and treatment procedures. Besides them, however, they have serious disadvantages, access to computer learning systems and datasets are limited, AI models come without explanation, embedding of artificial intelligence into clinical practices becomes troublesome. In future development, emphasize should be on creating databases, explainable ethics AI approaches, building bridges between developers and practitioners in medicine. The objective of the paper is how to incorporate research that has been done in moving colon cancer AI systems into practice, understanding the AI satellites put on colon cancer research. © 2025 ieee.

关键词： Deep learning

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A computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone 6

A Computer Vision Approach for Autonomous Cars to Drive Safe...

引用

6th ieee international conference on Image Processing, Applications and Systems, IPAS 2025

作者： Ahammed, Abu Shad Shahi Amran Hossain, Md Obermaisser, Roman University of Siegen Chair of Embedded Systems Siegen Germany

ISBN: (纸本)9798331506520

To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. The autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. This paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. The model developed with the YOLO framework achieved a mean average precision exceeding 94% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles. © 2025 ieee.

关键词： Autonomous vehicles

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Question Pair Similarity Detection and development of Question Answering Chatbot

Question Pair Similarity Detection and Development of Questi...

引用

2025 ieee international Students' conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Gupta, Abhinay Tyagi, Praveen Kumar Siddiqui, Ebtasam Ahmad Poornima Institute of Engineering and Technology Rajasthan Jaipur302022 India Vit Bhopal University School of Computing Science and Engineering MP 466114 India

ISBN: (纸本)9798331529833

The development of a question-answer chatbot involves leveraging advanced embedding techniques to optimize language understanding and response generation. The process begins with curating a diverse dataset of questions and answers, followed by preprocessing steps such as tokenization, stemming, and stop-word removal. Various embedding techniques, including traditional methods like BoW and TF-IDF, as well as modern approaches such as Word2Vec, GloVe, and BERT, are explored to determine the most effective technique for chatbot optimization. Performance metrics such as accuracy, responsiveness, and scalability guide the evaluation process. The approach encompasses data collection, preprocessing, embedding, model training, and user interaction, aiming to enhance human-computer interactions and improve information retrieval. © 2025 ieee.

关键词： Embeddings

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Legal Aspects of Green Technologies in Achieving Sustainable development Goals: Regulatory Innovations, Compliance Challenges and Future Directions 1

Legal Aspects of Green Technologies in Achieving Sustainable...

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

作者： Thakur, Anupama Anuradha Sharma, Lakshay Agarwal, Shipra Singh, Vijaypal School of Law Graphic Era Hill University Dehradun India Student of LL.M. National Law University Nagpur India Graphic era Deemed to be University Dept. of Commerce Dehradun India University Institute of Legal Studies Chandigarh University Punjab Gharua India

ISBN: (纸本)9798331518578

This study extends the critical role of legal frameworks in encouraging green technologies to meet Sustainable development Goals (SDGs). It emphasizes the importance of international agreements like the Paris Agreement, Basel Convention and National laws such as the 'Environment Protection Act, 1986' and India's 'National Green Tribunal Act, 2010' in fostering compliance with environmental standards. This research highlights the need for regulatory innovations, including incentives and subsidies, to overcome economic and technological barriers. It also discusses challenges in enforcement and suggests future directions for harmonizing global regulations to support sustainable development goals. © 2025 ieee.

关键词： Green development

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Rarh Rainfall Analytics: Machine Learning Insights from NASA Data

Rarh Rainfall Analytics: Machine Learning Insights from NASA...

引用

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

作者： Murmu, Saachin Sain, Priyabrata Karmakar, Rahul The University of Burdwan Department of Computer Science Bardhaman India

ISBN: (纸本)9798331513894

Predicting rainfall is an essential part of meteorology and is crucial for solving problems in agriculture, water resource management, and disaster relief. In regions with significant climatic variability, such as the Rarh region of India, accurate rainfall forecasting is essential to support sustainable development and ensure agricultural productivity. This research focuses on predicting rainfall over the Rarh region of India, an area known for its diverse climatic conditions and agricultural reliance. By leveraging NASA's comprehensive satellite datasets, this study employs advanced ML techniques to increase the accuracy of rainfall forecasting in this region. Integrating various meteorological variables into the dataset enables the discovery of intricate patterns and relationships that traditional methods might not detect. This research adopts a systematic machine learning pipeline involving feature selection, correlation matrix analysis to understand relationships among features, 10 CV, hyperparameter passing, and three splitting ratios for training and validation. Five ML classifiers are employed to analyze performance among which Naïve Bayes gave the best accuracy score of 0.99. © 2025 ieee.

关键词： Rain

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The Utilization of Brain-computer Interfaces (BCI) with CLONALG to Amplify Exhibited Neurological Signals

The Utilization of Brain-Computer Interfaces (BCI) with CLON...

引用

2025 ieee international conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Sathiyandrakumar, Kaushik Ravenwood High School BrentwoodTN United States

ISBN: (纸本)9798331512965

Nearly 50 million individuals are diagnosed with neurodegenerative diseases every year. Moreover, this figure is expected to grow as the population grows older. These neurodegenerative diseases have had a profound impact on the neurological functioning of an individual. For instance, being diagnosed with cerebral palsy has been correlated with poor movement and coordination, atypical development patterns, and communication and swallowing difficulties. To help these struggling individuals, scientists have developed a plethora of technologies to assimilate them into day-to-day life. For example, botox injections are commonly used to relieve muscle spasms that have prevented swallowing difficulties. However, these existing treatments have all had disadvantages. For example, botox injections have commonly led to side effects, such as a crooked smile, watery and dry eyes, and flu-like symptoms. Overall, these side effects have disrupted individuals' psychological state, causing renewed calls for better treatments. Fortunately, brain-computer interfaces (BCIs) provide hope for these individuals. For example, to minimize the consequences of cerebral palsy, brain-computer interfaces (BCIs) allow for the usage of remaining muscular pathways as a substitute for paralyzed muscles. This substitution allows for the eyes and hands to have renewed control over communication devices. In this research paper, we will analyze techniques to optimize the strength of brain-computer interfaces (BCIs) by specifically analyzing how the CLONALG algorithm will enhance the strength of brain-computer interfaces. © 2025 ieee.

关键词： Neurodegenerative diseases

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Hands Free Mouse Using Facial Expression for Physically Disabled People 4

Hands Free Mouse Using Facial Expression for Physically Disa...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Jain, Rushabhkumar Bhavana, Murahari Naga Kale, Aditya Rastogi, Suvan Thakur, Payel Pillai College of Engineering Department of Computer Engineering New Panvel India

ISBN: (纸本)9798331523923

This research study explores the development of a Hands-Free Mouse system designed to empower physically disabled individuals through facial expression recognition. In the domain of assistive technology, where the focus is on enhancing accessibility, we delve into the novel application of facial expression recognition techniques. Commonly employed techniques in this domain include computer vision and machine learning algorithms, with diverse approaches such as deep neural networks and feature-based methods. © 2025 ieee.

关键词： Machine vision

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From classical techniques to convolution-based models: A review of object detection algorithms 6

From classical techniques to convolution-based models: A rev...

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6th ieee international conference on Image Processing, Applications and Systems, IPAS 2025

作者： Neha, Fnu Bhati, Deepshikha Shukla, Deepak Kumar Amiruzzaman, Md Kent State University Dept. of Computer Science KentOH United States Rutgers University Rutgers Business School NewarkNJ United States West Chester University Dept. of Computer Science West ChesterPA United States

ISBN: (纸本)9798331506520

Object detection is a fundamental task in computer vision and image understanding, with the goal of identifying and localizing objects of interest within an image while assigning them corresponding class labels. Traditional methods, which relied on handcrafted features and shallow models, struggled with complex visual data and showed limited performance. These methods combined low-level features with contextual information and lacked the ability to capture high-level semantics. Deep learning, especially Convolutional Neural Networks (CNNs), addressed these limitations by automatically learning rich, hierarchical features directly from data. These features include both semantic and high-level representations essential for accurate object detection. This paper reviews object detection frameworks, starting with classical computer vision methods. We categorize object detection approaches into two groups: (1) classical computer vision techniques and (2) CNN-based detectors. We compare major CNN models, discussing their strengths and limitations. In conclusion, this review highlights the significant advancements in object detection through deep learning and identifies key areas for further research to improve performance. © 2025 ieee.

关键词： Convolutional neural networks

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research on Target Tracking Control of Soccer Robots Based on Fuzzy Algorithm 5

Research on Target Tracking Control of Soccer Robots Based o...

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5th ieee international conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Yi, Shao Yue, Wang Shanghai Customs College Shanghai China East China University of Science and Technology Shanghai China

ISBN: (纸本)9798331533694

With the continuous development of soccer robot technology, target tracking control has become one of its core technologies. Traditional target tracking methods often exhibit poor robustness and flexibility when facing complex and dynamic environments. To address this issue, this paper proposes a target tracking control method for soccer robots based on fuzzy algorithms. By introducing a fuzzy logic controller, this study effectively handles the uncertainty of targets and the dynamic changes of the environment. First, the kinematic model of the soccer robot is analyzed, and a fuzzy controller suitable for target tracking requirements is designed. Second, the proposed method is validated through experiments and simulations, demonstrating superior performance in terms of tracking accuracy, response speed, and robustness compared to traditional PID control and model predictive control methods. Finally, the limitations of the method are discussed, and directions for future improvements are proposed. The research results show that the target tracking control method based on fuzzy algorithms has strong adaptability and high control performance, providing an effective solution for soccer robots to track targets in dynamic environments. © 2025 ieee.

关键词： Robustness (control systems)

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