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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. The resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. These findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

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Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models 31

Entropy Guided Extrapolative Decoding to Improve Factuality ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Das, Souvik Jin, Lifeng Song, Linfeng Mi, Haitao Peng, Baolin Yu, Dong Department of Computer Science and Engineering University at Buffalo NY United States Tencent AI Lab BellevueWA United States

ISBN: (纸本)9798891761964

Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination - generating content ungrounded in the realities of training data. Recent work has focused on decoding techniques to improve factuality during inference by leveraging LLMs' hierarchical representation of factual knowledge, manipulating the predicted distributions at inference time. Current state-of-the-art approaches refine decoding by contrasting early-exit distributions from a lower layer with the final layer to exploit information related to factuality within the model forward procedure. However, such methods often assume that the final layer is the most reliable and the lower-layer selection process depends on it. In this work, we first propose the extrapolation of critical token probabilities beyond the last layer for more accurate contrasting. We additionally employ layer-wise entropy-guided lower-layer selection, decoupling the selection process from the final layer. Experiments demonstrate strong performance, surpassing state-of-the-art on multiple different datasets by large margins. The analyzes show that different kinds of prompt respond to different selection strategies. Our source code will be available on GitHub. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Improving Medical Imaging Diagnostics with Deep Convolutional Networks for Early Detection and Treatment

Improving Medical Imaging Diagnostics with Deep Convolutiona...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Krishnakumar, K. Gurrapu, Omprakash Reshma, P. Krishnammal, P. Muthu Khan, Huma Qamar Amane, Ninad Saveetha School of Engineering Chennai India Volvo Trucks North America NC United States Dhanalakshmi Srinivasan College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India VIT-AP University Amaravati India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Bms Institute of Technology and Management Department of Ai&ml Bangalore India

ISBN: (纸本)9798331538538

Diagnostic imaging is an important technique which helps in diagnosing several diseases at the initial stage in order to take necessary treatments. Nevertheless, conventional techniques tend to have issues with regard to precision, speed, and quantitatively assessing multidimensional medical information. In this paper, we reveal the potential of deep convolutional networks (DCNs) in the field of medical image diagnostic. It means that, with the help of deep learning algorithms DCNs can learn meaningful features of medical images and improve diagnostic accuracy and speed. The near proposed technique involves the use of deep convolutional network model to analyse medical images for diagnosis of diseases such as cancer, heart diseases and neurological disorders. The obtained experimental results demonstrate that the presented model has the highest accuracy of 96.5% with very high sensitivity and specificity coefficients compared to other methods. Further, the interpretability of the proposed DCN model and different aspects of it are discussed to support clinical validation. Through this research, the effectiveness of deep learning in revolutionizing the diagnosis for medical imaging diagnostics while early disease detection and treatment is established. © 2025 IEEE.

关键词： Neuroimaging

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Comparative Analysis of ai Models for Accurate Early Diabetes Detection

Comparative Analysis of AI Models for Accurate Early Diabete...

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Machine Learning and Autonomous Systems (ICmlAS), International Conference on

作者： Ratnam Dodda Mohd Miskeen Ali Prateeka Munugala Roshini Rajan Desu Om Keerthi Computer Science & Engineering (AI&ML) CVR College of Engineering Hyderabad India Computer Science & Engineering Sphoorthy Engineering College Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Diabetes is a common long-term disease with significant health implications, necessitating early and accurate diagnosis to manage and mitigate its impact. The use of deep learning (DL) and machine learning (ml) algorithms is investigated in this study to predict diabetes based on patient-specific factors, including medical history and lifestyle habits. By leveraging various algorithms, the research evaluates and compares their prediction accuracy to identify the most effective model. The findings aim to improve the early detection of diabetes, providing insights into the performance and feasibility of ai-driven diagnostic approaches in healthcare. In the experimental study we have compared machine learning and deep learning algorithms with the same dataset and found that convolutional neural network outperformed other algorithms.

关键词： Deep learning Accuracy Machine learning algorithms Gaussian processes Prediction algorithms Diabetes Convolutional neural networks Decision trees Random forests Medical diagnostic imaging

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Cerebral cortex contributions to Parkinson's disease tremor:what exactly generates the tremor?

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Neural Regeneration Research 2025年第12期20卷 3535-3536页

作者： Manuel Bange Hao Ding Muthuraman Muthuraman Informatics for Medical Technology Institute of Computer ScienceUniversity AugsburgAugsburgGermany Department of Neurology Neural Engineering with Signal Analytics and Artificial Intelligence(NESA-AI)University Clinic WürzburgWürzburgGermany

Pathological tremor is one of the cardinal symptoms in Parkinson's disease (PD).Tremor is comprised of involuntary,rhythmic,a nd oscillating movements that can vary according to the circumstances under which they occur,the body parts that are involved,and the frequency at which they *** example,tremors can be mild to severe,are stress sensitive,and can affect arms,legs,or the head (Dirkx and Bologna,2022).

关键词： tremor cortex exactly

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Harnessing Tech Innovations for Diversified Multigenerational Work Force and Work Life Balance

Harnessing Tech Innovations for Diversified Multigenerationa...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sharma, Vishal Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

This research aims to examine how technologically innovative breakthroughs may help a multigenerational heterogeneous workforce to maintain a good work and life balance. We investigate how technology can be used to strategically raise up the rate of participation and align with the particular needs of different age groups. The research findings show how ai powered personal learning, collaborative digital communication tools and flexible remote working solutions promote a healthy and mature culture in the workplace. The findings explain how key it is to utilize technology in a refined manner such that all workers can thrive and maintain a sustainable balance between their work and home life. © 2025 IEEE.

关键词： Collaborative learning

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Imaging – based Detection of Bone Injuries using Hybrid Models

Imaging – based Detection of Bone Injuries using Hybrid Mod...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Vineela Madireddy Bhagya Lakshmi Medam Sai Vardhan Reddy Manne Navaneeth Yadav Madhugani Dept. Computer Science and Engineering -AI&ML Vignan Institute of Technology and Science Nalgonda India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Bone fractures are among the most common physical injuries, affecting individuals across all age groups and requiring prompt and precise diagnosis. However, radiologists often face challenges in accurate fracture identification due to variations in image quality and complexity. The present work investigates a robust system for the automatic detection and classification of fractures in X-radiation (X-ray) images. The proposed model integrates Convolutional Neural Networks (CNN) and MobileNet with a hybrid approach that combines MobileNet and Random Forest to improve classification accuracy. The system is designed to be user-friendly, incorporating modules for registration, data processing, and real-time classification of fractures into 11 distinct types, including avulsion fracture, impacted fracture, longitudinal fracture, comminuted fracture, greenstick fracture, oblique fracture, pathological fracture, spiral fracture, and others. Experimental results demonstrate that the hybrid model significantly enhance diagnostic accuracy, underscoring its potential application in medical settings.

关键词： Pathology Accuracy Spirals Computational modeling Bones Real-time systems Convolutional neural networks X-ray imaging Random forests Injuries

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Deep Reinforcement Learning for Optimizing Route Planning in Urban Traffic

Deep Reinforcement Learning for Optimizing Route Planning in...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Mittal, Mudit Sehgal, Archana Varshney, Neeraj Kumar, Sunil Prashanth Boob, Nandini Shirish Reddy, R. Akhilesh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India MLR Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798331512965

Urban traffic networks are efficient route design problems due to the intricacy and dynamic character of contemporary cities. Conventional methods hamper real time adjustment to continually changing traffic conditions, thereby providing suboptimal routes, a greater burden on the roadways, and greater air pollution. This thesis presents a novel deep reinforcement learning (DRL) method to optimize the route planning for urban traffic networks. With this rendering of real time traffic data, we use DRL to make autonomous decisions to reduce travel time and congestion of routes. One advantage of using sophisticated neural networks for feature extraction and policy learning is that, since the model is so adaptable to changing traffic patterns, the model guarantees that. And, in comparison to common routing algorithms, the results of simulations using real world traffic data show that this method is able to greatly enhance route efficiency and overall traffic flow management. The suggested DRL based system is feasible as a smart city program based on intelligent transportation system which can scale to grand metropolitan regions. The research presents that DRL can enhance the commuter run experience, reduce the environmental effect, and alter urban transportation. Future study will also investigate how this system can be better optimized when integrated with traffic infrastructure enabled by the Internet of Things. © 2025 IEEE.

关键词： Deep reinforcement learning

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A Comprehensive Analysis of Word Sense Disambiguation in a Regional Language

A Comprehensive Analysis of Word Sense Disambiguation in a R...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Aditi Barai Manami Das Priyanka Bhowmick Udita Dey Prianka Dey Sagarika Chowdhury Department of Computer Science and Business Systems Narula Institute of Technology Kolkata India Department of Computer Science and Engineering (AI ML) Narula Institute of Technology Kolkata India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Word Sense Disambiguation(WSD) is a Natural Language Processing(NLP) technique that tries to disambiguate ambiguous words by finding the right sense of a word in a particular context. It is a classification task that assists in mapping a word to one or more potential senses based on its sentence. Understanding the intended sense in a particular context is crucial for proper communication and accurate language processing. But WSD is especially crucial in Bengali for a number of reasons: Similar to other Indic languages, Bengali possesses a rich and complex system of word formation. A root word can be modified according to aspects like tense, gender, number, and case. An attempt is made in this working paper where authors will be working on WSD but on the Bengali language, where words have different meanings. The Bengali language has plenty of words with multiple meanings. Even though WSD has been propagated the world over, the language Bengali has a lot of challenges since there are very few resources available and research due to its complex syllable structure.

关键词： Surveys Sensitivity Computational modeling Evaluation models Natural languages Computational efficiency Complexity theory Object recognition Optimization Overfitting

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Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

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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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