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arXiv 2025年

作者： Wang, Guanghui Acharya, Krishna Lakshmikanthan, Lokranjan Muthukumar, Vidya Ziani, Juba College of Computing Georgia Institute of Technology United States School of Industrial and Systems Engineering Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States

Performative prediction models account for feedback loops in decision-making processes where predictions influence future data distributions. While existing work largely assumes insensitivity of data distributions to small strategy changes, this assumption usually fails in real-world competitive (i.e. multi-agent) settings. For example, in Bertrand-type competitions, a small reduction in one firm’s price can lead that firm to capture the entire demand, while all others sharply lose all of their customers. We study a representative setting of multi-agent performative prediction in which insensitivity assumptions do not hold, and investigate the convergence of natural dynamics. To do so, we focus on a specific game that we call the "Bank Game", where two lenders compete over interest rates and credit score thresholds. Consumers act similarly as to in a Bertrand Competition, with each consumer selecting the firm with the lowest interest rate that they are eligible for based on the firms’ credit thresholds. Our analysis characterizes the equilibria of this game and demonstrates that when both firms use a common and natural no-regret learning dynamic—exponential weights—with proper initialization, the dynamics always converge to stable outcomes despite the general-sum structure. Notably, our setting admits multiple stable equilibria, with convergence dependent on initial conditions. We also provide theoretical convergence results in the stochastic case when the utility matrix is not fully known, but each learner can observe sufficiently many samples of consumers at each time step to estimate it, showing robustness to slight mis-specifications. Finally, we provide experimental results that validate our theoretical findings. © 2025, CC BY.

关键词： Prediction models

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Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue Detection

Revolutionizing Road Safety: YOLOv8-Powered Driver Fatigue D...

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2023 International Conference on Sustainable Technology and engineering, i-COSTE 2023

作者： Xie, Shuaiyue Chuah, Joong Huang Ting Chai, Grace Mei University of Southampton Electronics and Computer Science SouthamptonSO171BJ United Kingdom University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia University of Southampton Malaysia Smart Manufacturing Systems Research Group Iskandar Puteri Johor79100 Malaysia

ISBN: (纸本)9798350329711

Driver fatigue is a significant cause of road accidents. Effective real-time fatigue detection systems are necessary to improve road safety. Utilizing a lightweight and fast model and creating an effective fatigue judgment rule is important in detecting fatigue in real-time. In this research, we propose a fatigue detection model based on You Look Only Once version 8 (YOLOv8) model to detect driver's facial features and use percentage of eyelid closure over the pupil over time (PERCLOS) and Percentage of Open Mouth (POM) indicators to identify fatigue. This research is divided into two parts. The first part involves creating, labeling, and preprocessing the dataset. We use the YawDD dataset as the original dataset and manually label a new dataset using the Roboflow platform. We then preprocessed the new dataset to train the YOLOv8 model for face features detection. In the second part, we established a multi-indicator fatigue determination rule that uses PERCLOS and POM indicators and set a threshold to determine whether the driver is fatigue. By combining the YOLOv8 model with the fatigue determination rules based on PERCLOS and POM, we successfully built a model to identify a driver's fatigue. Finally, the fatigue detection model based on YOLOv8 achieves a high accuracy rate of 96.9% and a high speed of 11.4 ms/frame in face features detection, surpassing other traditional models and previous generations of YOLO models. This model can effectively detect fatigue in both driver-driving pictures and videos. © 2023 IEEE.

关键词： Roads and streets

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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computer systems Science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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5G Use Cases in Metaverse Using Open Source Software

5G Use Cases in Metaverse Using Open Source Software

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International Technical Conference o Circuits/systems, computers and Communications (ITC-CSCC)

作者： Pisit Vanichchanunt Sirapop Saengthongkam Natthapong Waichampa Navy Wuttiananchai Pruk Sasithong Vitawat Sittakul Lunchakorn Wuttisittikulkij Sukritta Paripurana Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Electronic Engineering Technology College of Industrial Technology King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Designing, implementing, and testing a practical 5G use case usually consumes large amount of time, budget, and effort. To alleviate unexpected problems and also ensure the functionalities of a 5G project, this paper proposes the integration of 5G system and a virtual use case in metaverse for designing and pretesting by using open source software. This software-based approach in metaverse not only enhances the perspectives on the consideration for designing and testing but also escalates the project ready for 5G digital twin. A virtual smart warehouse is selected to demonstrate 5G use cases since it employs smart components/machines, such as Automated Guided Vehicles (AGVs) and autonomous forklifts, as well as uses various types of IoT devices including temperature sensors, humidity sensors, CCTV cameras, light detectors, and RFID cards. To enable such smart components and devices to function in metaverse, the 5G network slices are designed to support three types of IoT services including Broadband IoT, Critical IoT, and Massive IoT which require high data rate, low-latency, and massive connectivity respectively.

关键词： Temperature sensors 5G mobile communication Metaverse Robot vision systems Cameras Digital twins Internet of Things

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Data-Driven Assessment of Operational and Harsh Environmental Stress Factors for Solar Inverters Using Field Measurements

Data-Driven Assessment of Operational and Harsh Environmenta...

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IEEE General Meeting Power& Energy Society

作者： Yi Luo Liming Liu Yuxuan Yuan Zhaoyu Wang Feng Qiu Shijia Zhao Murat Yildirim Electrical and Computer Engineering Iowa State University Ames IA USA Energy Systems Division Argonne National Laboratory Lemont IL USA Industrial and Systems Engineering Wayne State University Detroit MI USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

The operation of inverters in distributed photovoltaic (PV) systems within distribution systems is critical for ensuring the reliability and durability of the PV system, particularly when challenged by diverse operational and environmental stress conditions. This study delves into analyzing the inverter stress factors to assess the impact of under-sizing and varying environmental stresses on the reliability and service life of PV inverters. A new operational stress metric, equivalent under-sizing rate (EUR), is proposed to quantify the actual inverter stress from the under-sizing problem. Along with the designed weather and extreme event metrics, the proposed EUR is evaluated in a real-world PV inverter dataset. To handle the censored working time variable, the Cox proportional hazards model is adopted to conduct the data-driven stress factor analysis. The regression results reveal aside from the high temperature and large temperature variance, the inverter under-sizing problem is also a significant factor that could influence the lifetime of inverters. Meanwhile, further applications and potential future directions for the proposed work are explored.

关键词： Temperature measurement Measurement Photovoltaic systems Analytical models Temperature Inverters Hazards Reliability Stress Stress measurement

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Exploring the Possibility of Improving the Design of a Fuel Level Measuring Device Using a Compensating Sensor 22

Exploring the Possibility of Improving the Design of a Fuel ...

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22nd International Symposium on electrical Apparatus and Technologies, SIELA 2022

作者： Simeonov, Stanislav Gospodinova, Ekaterina Assen Zlatarov University Department of Computer Systems and Technologies Burgas8010 Bulgaria Technical University of Sofia Faculty of Engineering and Pedagogy-Sliven Department of Electrical Engineering Automation and Information Technology Sliven8800 Bulgaria

ISBN: (数字)9781665411394

ISBN: (纸本)9781665411394

The choice of systems for level measurements determined by the properties of the fluid, the design features of the tank, the type of level reading and other factors. This article discusses the results of the experimental study of two automated measuring systems and analyzes the errors in measuring the level of light fuels in the laboratory and real conditions. After research in this work, we have proposed an improvement in the construction of the level measuring system, width a compensating sensor is used. © 2022 IEEE.

关键词： Fuels

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Comparative Analysis of Deep Learning Architectures for Blood Cancer Classification

Comparative Analysis of Deep Learning Architectures for Bloo...

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IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： Saad Ahmed Syed Humaira Nisar Lee Yu Jen Saeed Mian Qaisar Shahnawaz Talpur Department of Electronic Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Kampar Perak Malaysia Electrical and Computer Engineering Department Effat University Jeddah Saudi Arabia Department of Computer Systems Engineering Mehran Univeristy of Engineering and Technology Jamshoro Pakistan

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

An increased growth in the blood cancer necessitates the development of efficient, cost effective, timely, and accurate diagnosis. Traditional diagnosis methods are often invasive, expensive and time-consuming. The rapid artificial intelligence (AI) assistive advancement in the digital healthcare permits the realization of effective solutions in this framework. Specifically, the deep learning (DL), seems promising in an automated diagnosis. However, still a critical gap needs to be covered by understanding that which DL architecture performs better for the blood cancer detection. To address this crucial need, this paper presents a comprehensive comparative analysis of the key DL methods, used in an automated categorization of the blood cancer. The considered DL architectures are the MobileNetV2, DenseNet121, VGG16, ResNet50, and Inception *** applicability is tested using two blood cancer datasets namely the Acute Lymphoblastic Leukemia (ALL) dataset and American Society of Hematology (ASH) dataset. Each model is meticulously trained and evaluated on the ALL dataset for binary classification and the ASH image bank for multi-class classification. The categorization performance is evaluated based on accuracy, precision, recall, F1 score, and latency. Results have shown an out performance of the MobileNetV2 compared to the counter DL architectures with a mean accuracy of 91.26%, precision of 92.94%, recall of 91.27%, F1 score of 90.58% and latency of 104.16 mins for ALL dataset and 88.11 accuracy, 90.23% precision, recall of88.11 %, 87.98% F1 score and latency of 11.16 mins for ASH dataset.

关键词： Deep learning Accuracy Computational modeling computer architecture Ash Robustness Artificial intelligence

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KinGloves: Rapid Prototyping of Usability Enhanced Sensing Gloves for Parkinson's Disease

KinGloves: Rapid Prototyping of Usability Enhanced Sensing G...

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2024 International Conference on the Challenges, Opportunities, Innovations and Applications in Electronic Textiles, E-Textiles 2024

作者： Ravichandran, Vignesh Chomal, Shubham Arruda, Rachel Al Rumon, Md Abdullah Dupre, Anne-Marie Fapohunda, Kofoworola Tandon, Puneet Owens, Jim Macht, Gretchen Kosiba-Quiterio, Carolyn Solanki, Dhaval Mankodiya, Kunal University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI United States Indian Institute of Information Technology Design and Manufacturing Department of Mechanical Industrial and Systems Engineering KingstonRI United States University of Rhode Island Department of Mechanical Industrial and Systems Engineering KingstonRI United States University of Rhode Island Department of Textiles Fashion Merchandising and Design KingstonRI United States University of Rhode Island Department of Physical Therapy KingstonRI United States Nautilus Defense PawtucketRI United States Rock Steady Boxing East GreenwichRI United States

ISBN: (纸本)9783000804298

Parkinson's Disease (PD) is a progressive neurological movement disorder causing motor (movement) impairments, leading to difficulty in performing activities of daily living such as eating, writing, cutting vegetables, buttoning a shirt, tying shoelaces, brushing teeth, etc. Although sensing clothes has gained popularity in monitoring disease progression in home settings, they pose usability challenges arising from movement/cognitive symptoms that hamper their adoption and adherence. In this study, we employ a user-centered design process with an aim to improve the wearability of gloves through a multi-phase study involving 23 people with PD (PwPD). The goal was to design gloves with suitable wrist closure and finger insertion mechanisms to enable independent upper-body movement assessments. The first phase conducted with 4 PwPD helped identify preferred and undesired glove-wearing mechanics through 12 daily living tasks. Next, a rapid prototyping process for textiles, using a laser cutter, produced three design concepts for evaluation by 9 PwPD. The best-performing prototype design was chosen for capacitive sensing thread integration and design revision. The capacitive sensing threads play a crucial role in accurately measuring finger flexion and extension, providing high-resolution data on hand movements that are critical for assessing finger movements associated with Parkinson's Disease symptoms. After incorporating participant feedback, the improved design had an average reduction of 23% in time to wear and 250% in time to take off when tested with 18 PwPD. In particular, features such as a view window for fingers and use of zippers had been reported by participants to improve glove usability. © 2024 Fraunhofer IZM.

关键词： User centered design

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying 4

Talk to your data: Enhancing Business Intelligence and Inven...

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4th International Conference on Data Analytics for Business and Industry, ICDABI 2023

作者： Zhu, Jerry Bazaz, Saad Ahmed Dutta, Srimonti Anuraag, Bhavaraju Haider, Imran Bandopadhyay, Srijita University of Waterloo Faculty of Mathematics ON Canada National University of Computer and Emerging Sciences Dept. of Computer Science Islamabad Pakistan Indian Institute of Technology Guwahati School of Agro and Rural Technology Guwahati India Vellore Institute of Technology Dept. of Electronics and Communication Engineering India Indian Institute of Technology Kharagpur Dept. of Industrial and Systems Engineering Kharagpur India University of Engineering and Management Dept. of Electronics and Communication Engineering Kolkata India

ISBN: (纸本)9798350369786

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT's precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape. © 2023 IEEE.

关键词： Structured Query Language

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer Science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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