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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Nakajima, Yoko Yuki, Masamune Ptaszynski, Michal Honma, Hirotoshi Akiba, Tomoyoshi Masui, Fumito Institute of Technology Kushiro Colloege Dept. of Creative Engineering Kushiro Japan School of Regional Innovation and Social Design Engineering of Kitami Institute of Technology Kitami Japan Toyohashi University of Technology Dept. of Computer Science and Technology and Engineering Toyohashi Japan

ISBN: (纸本)9798350383010

One of the significant problems faced by dementia group homes is the high caregiver turnover rate. In particular, there is an urgent need to prevent the turnover of new caregivers and work toward ensuring and improving the quality of care. To address this issue, we are developing a care method support system that utilizes two databases: a personal attributes database and a care case database ([1]-[3]). The personal attributes database extracts personal attributes such as hobbies and previous occupations of residents from care records and care conference minutes. The care case database extracts cases of resident problems, their solutions, and the original text containing them. The care method support system presents candidates for appropriate responses to problems faced by residents based on these databases. In this study, we generated two care suggestion models: Care Suggestion Model: This model applies text similarity to the care case database to suggest candidates for appropriate responses to unregistered problems. Personalized Care Suggestion Model: This model applies clustering to the personal attributes database and generates a care suggestion model that considers personal attributes. We compared the two models and tested the usefulness of the model. © 2024 IEEE.

关键词： Nursing

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FPGA Processor In Memory Architectures (PIMs): Overlay or Overhaul ?

FPGA Processor In Memory Architectures (PIMs): Overlay or Ov...

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International Conference on Field Programmable Logic and Applications

作者： MD Arafat Kabir Ehsan Kabir Joshua Hollis Eli Levy-Mackay Atiyehsadat Panahi Jason Bakos Miaoqing Huang David Andrews Department of Computer Science and Computer Engineering University of Arkansas Department of Computer Science and Computer Engineering University of South Carolina Department of Computer Science and Computer Engineering Cadence Design Systems

The dominance of machine learning and the ending of Moore's law have renewed interests in Processor in Memory (PIM) architectures. This interest has produced several recent proposals to modify an FPGA's BRAM architecture to form a next-generation PIM reconfigurable fabric [1], [2]. PIM architectures can also be realized within today's FPGAs as overlays without the need to modify the underlying FPGA architecture. To date, there has been no study to understand the comparative advantages of the two approaches. In this paper, we present a study that explores the comparative advantages between two proposed custom architectures and a PIM overlay running on a commodity FPGA. We created PiCaSO, a Processor in/near Memory Scalable and Fast Overlay architecture as a representative PIM overlay. The results of this study show that the PiCaSO overlay achieves up to 80% of the peak throughput of the custom designs with 2.56 x shorter latency and 25% - 43% better BRAM memory utilization efficiency. We then show how several key features of the PiCaSO overlay can be integrated into the custom PIM designs to further improve their throughput by 18%, latency by 19.5%, and memory efficiency by 6.2%.

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Smart Park - A Predictive and User Centric Parking Platform 8

Smart Park - A Predictive and User Centric Parking Platform

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Shivaji Rao, G. Devasenan, S. Gana Shree, C. Rathakrishnan, M. Swathi, S. Karpagam College of Engineering Department of Computer Science and Design Coimbatore641 032 India

ISBN: (纸本)9798331544607

Traditional city parking infrastructures cannot adapt to the demands of modern motorists, resulting in congestion, delays, and increased emissions. This project aims to optimize parking space utilization and user experience by employing machine learning algorithms. Based on historical data as well as real-time user-updated information, the system predicts parking availability and enables optimal parking management without extensive-scale IoT infrastructure. The app utilizes Firebase for a scalable and secure database and authentication mechanism, and Google Maps integration provides real-time navigation assistance for better parking directions. Flutter ensures cross-platform compatibility, making it possible for both Android and iOS users to have an intuitive and smooth UI/UX. The primary features include advance booking of parking lots, the possibility of time extension, and slot availability based on the hour, granting flexibility to users. A secure reservation system prevents double booking, and GPS-based navigation simplifies finding a parking space in an easy and convenient way. By preventing the use of costly hardware installations, this cost-effective, scalable, and eco-friendly solution enables sustainable urban mobility by reducing emissions and improving the overall parking experience. © 2025 IEEE.

关键词： Global positioning system

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Navigating the Interplay of Privacy, Security and Ethics on Social Media

Navigating the Interplay of Privacy, Security and Ethics on ...

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Communication & Computing (WCONF), World Conference on

作者： R Rajagopal M Senbagavalli Deepak Raj D.M Riya Kumari Deepanshu Gupta Anusha Gupta Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

Social media has changed the way people interact with the digital world, communicate, and exchange information. It has also brought privacy, security, and ethics into sharper relief. A fundamental concern is privacy, which includes people's rights to manage their personal data. Social networking sites are repositories of user data, which gives rise to worries about illegal access and privacy violations. Furthermore, complex algorithms make it more difficult to maintain privacy by obfuscating the distinction between invasive surveillance and individualized experiences. To protect user data and retain confidence, security is important. Social media users are vulnerable to online risks like phishing and identity theft, though, because of their interconnectedness. It's always difficult to strike a balance between security precautions and consumer comfort. The ethical implications of data monetization, algorithmic biases, and disinformation are particularly significant. Platforms face societal challenges such as digital harassment and political manipulation in addition to problems with data ownership, transparency, and responsibility. To overcome all of these, we have come forward with a solution that has 3 features. With the help of machine learning algorithms and python libraries, we made a solution that helps the users with these three concerns of social media.

关键词： Ethics Privacy Data privacy Machine learning algorithms Navigation Blogs Cyberbullying Passwords Libraries Security

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Effective Blockchain-Based Management of Agri-Food Supply Chains Using Deep Reinforcement Learning 8

Effective Blockchain-Based Management of Agri-Food Supply Ch...

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8th International Conference on Trends in Electronics and Informatics, ICOEI 2025

作者： Shivaji Rao, G. Ilakkiya, S. Lakshmi Priya, G. Sneha, S. Vijay Raj, S. Karpagam College of Engineering Department of Computer Science and Design Tamil Nadu Coimbatore India

ISBN: (纸本)9798331544607

Blockchain based agri food supply chains augmented with Deep Reinforcement Learning (DRL) optimize important problems like perishability, seasonality as well as demand variability. The traditional supply chain models, which are mostly static, fail to respond to the dynamic supply chain conditions and hence there is a strong need for the introduction of new ideas. In this work, blockchain is integrated for secure and transparent data management, and data for automation, like inventory management, route planning and price adjustment, is optimized in real time using DRL. The blockchain is a phenomenon of decentralization that improves trust and traceability, and the flexibility of DRL guarantees resource utilization in a way that can adapt to unexpected settings. The proposed system is piloted and a case studied to show its capability of enhancing supply chain resilience, recycle wasted products and scale efficiently. It specifies the transformative outlook of the mixture of blockchain and AI technologies in agricultural and Supply chain administration. © 2025 IEEE.

关键词： Information management

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AI -Driven Detection of Gastrointestinal Disease based on Advanced Deep Learning Techniques 4

AI -Driven Detection of Gastrointestinal Disease based on Ad...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Izankar, Sakshi V. Puri, Chetan Burle, Rushikesh Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798331523923

Esophageal, gastric, and colorectal cancers (CRC) are some of the gastrointestinal (GI) cancers threatening global health with CRC being particularly concerning due to its high fatality rate. While early diagnosis is crucial in improving treatment efficiency, barriers such as image noise and visual subjective assessments during diagnostics such as colonoscopies often render it inconceivable. For better diagnostic accuracy in detecting gastrointestinal disease via endoscopic images, the research explores whether highly advanced artificial intelligence (AI) models such as Vision Transformers (ViTs) and pre-trained DenseNet201 can be effective. We compare these models to popular deep learning techniques such as Support Vector Machines (SVM), ResNet50, VGG19, and GoogleNet. At a training accuracy of 99.9% and a validation accuracy of 98.8%, experiments on the KVASIR dataset indicate that Vision Transformer is superior to other models since it can learn long-range dependencies. The robustness of deep learning models for identifying GI disease is exemplified by the superior performance of DenseNet201, ResNet50, and InceptionV3, all of which possess high accuracy, precision, and recall values. The paper illustrates the potential of Vision Transformers, when combined with transfer learning and pre-trained CNN models, to revolutionize endoscopic diagnoses and provide a valuable resource to help physicians manage gastrointestinal conditions more effectively. © 2025 IEEE.

关键词： Endoscopy

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Shadow-point Enhanced Inexact Accelerated Proximal Gradient Method with Preserved Convergence Guarantees

arXiv

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arXiv 2025年

作者： Yang, Lei Luo, Liwei Lin, Meixia School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-sen University China School of Computer Science and Engineering Sun Yat-sen University China Engineering Systems and Design Singapore University of Technology and Design Singapore

We consider the problem of optimizing the sum of a smooth convex function and a nonsmooth convex function using the inexact accelerated proximal gradient (APG) method. A key limitation of existing approaches is their reliance on feasible approximate solutions to subproblems, which is often computationally expensive or even unrealistic to obtain in practice. To address this limitation, we develop a shadow-point enhanced inexact accelerated proximal gradient method (SpinAPG), which can eliminate the feasibility requirement while preserving all desirable convergence properties of the APG method, including the iterate convergence and an o(1/k2) convergence rate for the objective function value, under suitable summable-error conditions. Our method also provides a more flexible and computationally efficient inexact framework for the APG method, with a fairly easy-to-implement error criterion. Finally, we demonstrate the practical advantages of our SpinAPG through numerical experiments on a relaxation of the quadratic assignment problem, showcasing its effectiveness while bypassing the explicit computation of a feasible point. Copyright © 2025, The Authors. All rights reserved.

关键词： Numerical methods

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Optimization method for a radar situation interface from error-cognition to information feature mapping

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Journal of Systems engineering and Electronics 2022年第4期33卷 924-937页

作者： WU Xiaoli WEI Wentao CALDWELL Sabrina XUE Chengqi WANG Linlin School of Design Art&Media Nanjing University of Science&TechnologyNanjing 210094China School of Mechanical Engineering Southeast UniversityNanjing 211189China College of Mechanical and Electrical Engineering Hohai UniversityChangzhou 211100China School of Computer Science Australian National UniversityCanberra 2600Australia

With the rapid development of digital and intelligent information systems, display of radar situation interface has become an important challenge in the field of human-computer interaction. We propose a method for the optimization of radar situation interface from error-cognition through the mapping of information characteristics. A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Based on the mapping relationship between the domain of error-cognition and the domain of design information, a cross-correlational analysis is carried out between error-cognition and design *** obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the radar situation interface.

关键词： radar situation interface error-cognition information feature mapping visual information display

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Analytic Hierarchy Process of Digital Book Content Display Method on Large Displays 4

Analytic Hierarchy Process of Digital Book Content Display M...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Koyama, Junnosuke Yamada, Toshiva Temma, Seiya Nakamichi, Noboru Graduate School of Engineering Fukuyama University Fukuyama Japan NTT TechnoCross Corporation Tokyo Japan Fukuyama University Faculty-of-Engineering Department of-Computer Science Fukuyama Japan Fukuyama University ANKER DESIGN Inc. Fukuyama Japan

ISBN: (纸本)9798350395914

The development of information technology and the digitization of books have led to the widespread use of electronic bookshelves that display a group of books on a display and the use of larger displays. In this study, we investigate the display method of digital book contents on a large-screen wall displays, an environment with few restrictions on the display size of book contents. We set up eight display patterns by combining four image display patterns that combine images of the front cover, spine, and back cover, and two image size patterns of actual and standard sizes. As a result of the pairwise comparisons with 52 participants, it was confirmed that the 'Front cover and Spine' display method was highly evaluated by those who placed importance on the ease of identification. © 2024 IEEE.

关键词： bookshelves digital twin library museum

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Predictive Analytics in Smart Transportation: Integrating AI and IoT to Forecast Traffic Volumes

Predictive Analytics in Smart Transportation: Integrating AI...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Parampreet Kaur Ved Prakash Mishra Akash Badhan School of Computer Science and Engineering LPU India School of Engineering Architecture and Design Amity University Dubai School of Engineering Architecture and Interior Design Amity University Dubai UAE School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies presents a transformative opportunity to come up with smart transportation systems. This research basically explores the application of these technologies that could help in predictive analysis of traffic volumes, thereby enhancing urban mobility management and reducing congestion. In this a predictive model has been developed using linear regression based on synthesized data which includes different variables such as time of day, day of week, and peak traffic hours due to high congestion on roads, reflecting typical urban traffic conditions. The objective is to improve the accuracy and reliability of traffic prediction by leveraging real-time data from IoT sensors and historical traffic patterns. The model's performance was evaluated through metrics such as the Mean Squared Error (MSE) and R-Squared values, with results which indicate a strong predictive capability. Additionally, error distribution analysis heighted areas for potential improvements and model refinement in future. This paper discusses the implications of our findings for traffic management systems and proposes directions for future research to optimize the hybridization of AI and IoT technologies in enhancing the adaptability and efficiency of urban transportation networks.

关键词： Adaptation models Accuracy Smart cities Predictive models Real-time systems Smart transportation Reliability Artificial intelligence Predictive analytics Autonomous vehicles

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