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Driving Behavior Safety Assessment in Edge Computing Using Multitask Discriminative TS Fuzzy Model

IEEE Transactions on Consumer Electronics 2024年

作者： Gu, Yi Chen, Zhiyu Wang, Lu Shen, Jinsong Bashir, Ali Kashif Wang, Wei Jiangnan University School of Artificial Intelligence and Computer Science Wuxi214122 China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi214122 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Jiangsu Keli Internet Information Technology Co. Ltd Jingjiang214500 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Woxsen University Woxsen School of Business Hyderabad502 345 India Lebanese American University Department of Computer Science and Mathematics Beirut Lebanon Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China

Driving behavior safety analysis in edge computing involves the real-time collection and processing of vehicle driving data. Fuzzy models have advantages in analyzing driving behavior due to their excellent ability to handle uncertain information. This study develops a multitask discriminative Takagi-Sugeno fuzzy model (MD-TS-FM) for driving behavior safety assessment. To comprehensively analyze the correlation and differences in multitask driving behaviors, the designed consequent part consists of two parts: task-shared part reflects the shared intrinsic structure information that are consistent across different driving tasks, and task-specific part reflects distinct characteristics and variations specific to each task, allowing the model to address the unique aspects of different driving behaviors. Accordingly, the task-shared consequent part is characterized by low-rank property, which mines global structural information within the fuzzy space;whereas the task-specific consequent part exhibits sparsity, which removes non-discriminative and irrelevant information. This sparsity ensures that the model focuses on the most critical features for each specific task. Furthermore, a discriminative diversity term is introduced to enhance the diversity between tasks, which explores the consistent information of task-shared consequents while reducing the overlap of task-specific consequents. Experimental results indicate that the MD-TS-FM model can be effectively applied to driving behavior safety assessment. © 1975-2011 IEEE.

关键词： driving behavior safety assessment edge computing fuzzy model multitask learning

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Textured growth and electrical characterization of Zinc Sulfide on back-end-of-the-line (BEOL) compatible substrates

arXiv

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arXiv 2025年

作者： Wu, Claire Surendran, Mythili Priyoti, Anika Tabassum Anilkumar, Gokul Wu, Cheng-Hsien Wang, Chun-Chen Kuo, Cheng-Chen Kumasubramanian, Harish Lin, Kenta Butler, Amari Kapadia, Rehan Bao, Xinyu Ravichandran, Jayakanth Mork Family Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA90089 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States Core Center for Excellence in Nano Imaging University of Southern California Los AngelesCA90089 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States Corporate Research Taiwan Semiconductor Manufacturing Company Ltd. Hsinchu Taiwan

Scaling of transistors has enabled continuous improvement in the performance of logic devices, especially with contributions from materials engineering. However, there is a need to surpass the horizontal limitations in chip manufacturing and incorporate the vertical or third dimension. To enable monolithic three-dimensional (M3D) integration of high-performance logic, one needs to solve the fundamental challenge of low temperature (1. Metal oxides, especially indium oxides alloyed with gallium and tungsten2,3, are promising n-type semiconductor channel materials;however there is a lack of p-type channel materials that can meet the stringent synthesis conditions of BEOL manufacturing. Zinc sulfide (ZnS), a transparent wide band-gap semiconductor, has shown room temperature p-type conductivity when doped with copper and nitrogen, and crystallizes below 400°C.4 Here, we report growth of crystalline thin films of ZnS by pulsed laser deposition (PLD) on a variety of amorphous and polycrystalline surfaces including silicon nitride (SiNx), thermal silicon dioxide (SiO2), yttrium oxide (Y2O3), hafnium dioxide (HfO2), sapphire (Al2O3), platinum (Pt), and titanium nitride (TiN). X-ray diffraction scans show out-of-the-plane texturing of ZnS on all surfaces. In-plane crystalline quality is investigated using grazing incidence wide-angle X-ray scattering measurements. Surface and interface quality is measured using X-ray reflectivity and atomic force microscopy measurements. Electrical characterization of the ZnS films is done by J-V measurements of ZnS on platinum and metal-oxide-semiconductor capacitor (MOSCAP) measurements of ZnS on SiO2 on heavily doped silicon. The J-V measurements indicate low leakage current on the order of 10-5 A/cm-2 with electric field of 0.40 MV cm-1 and the MOSCAP characteristics show bilayer capacitor behavior, which points to ZnS being highly intrinsic with very low unintentional, electrically active point defects. Further work on doping ZnS with

关键词： Silicon nitride

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Toward Automated Detection of Microbleeds with Anatomical Scale Localization: A Complete Clinical Diagnosis Support Using Deep Learning

arXiv

引用

arXiv 2023年

作者： Kim, Jun-Ho Noh, Young Lee, Haejoon Lee, Seul Kim, Woo-Ram Kang, Koung Mi Kim, Eung Yeop Al-Masni, Mohammed A. Kim, Dong-Hyun Department of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of Neuroscience Research Institute Gachon University Incheon Korea Republic of Department of Neurology Gachon University College of Medicine Gil Medical Center Incheon Korea Republic of Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Department of Radiology Seoul National University Hospital Seoul Korea Republic of Department of Radiology Seoul National University College of Medicine Seoul Korea Republic of Department of Radiology Gachon University College of Medicine Gil Medical Center Incheon Korea Republic of Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul05006 Korea Republic of

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular diseases depending on their anatomical location, including cognitive decline, intracerebral hemorrhage, and cerebral infarction. However, manual detection of CMBs is a time-consuming and error-prone process because of their sparse and tiny structural properties. The detection of CMBs is commonly affected by the presence of many CMB mimics that cause a high false-positive rate (FPR), such as calcification and pial vessels. This paper proposes a novel 3D deep learning framework that does not only detect CMBs but also inform their anatomical location in the brain (i.e., lobar, deep, and infratentorial regions). For the CMB detection task, we propose a single end-to-end model by leveraging the U-Net as a backbone with Region Proposal Network (RPN). To significantly reduce the FPs within the same single model, we develop a new scheme, containing Feature Fusion Module (FFM) that detects small candidates utilizing contextual information and Hard Sample Prototype Learning (HSPL) that mines CMB mimics and generates additional loss term called concentration loss using Convolutional Prototype Learning (CPL). For the anatomical localization task, we exploit the U-Net segmentation network to segment the brain anatomical structures. This task does not only tell to which region the CMBs belong but also eliminate some FPs from the detection task by utilizing anatomical information. We utilize Susceptibility-Weighted Imaging (SWI) and phase images as 3D input to efficiently capture 3D information. The results show that the proposed RPN that utilizes the FFM and HSPL outperforms the vanilla RPN and achieves a sensitivity of 94.66% vs. 93.33% and an average number of false positives per subject (FPavg) of 0.86 vs. 14.73. Also, the anatomical localization task further improves the detection performance by reducing the FPavg to 0.56 while

关键词： Brain

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A holistic review of the effects of dust buildup on solar photovoltaic panel efficiency

Solar Compass

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Solar Compass 2025年 13卷

作者： Yakubu, Sufyan Samikannu, Ravi Gawusu, Sidique Wetajega, Samuel Dodobatia Okai, Victor Shaibu, Abdul-Kadir Seini Workneh, Getachew Adam Sustainable Energy Center of Excellence Addis Ababa Science and Technology University P.O. Box 16417 Addis Ababa Ethiopia Department of Industrial Chemistry Addis Ababa Science and Technology University P.O. Box 16417 Addis Ababa Ethiopia Pan African University Institute of Water and Energy Sciences-University of Tlemcen Tlemcen Algeria Department of Electrical and Electronic Engineering Bolgatanga Technical University Bolgatanga Ghana Department of Electrical Computer & Telecommunications Engineering Botswana International University of Science and Technology Palpaye Botswana Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Whiting School of Engineering Johns Hopkins University Baltimore MD United States Department of Chemistry Joseph Sarwuan Tarka University Benue State Makurdi Nigeria Department of Electrical and Electronic Engineering Dr Hilla Limann Technical University Wa Ghana

Understanding the drivers of photovoltaic system performance is very important in the backdrop of the increasingly significant role that solar energy plays in mitigating carbon emissions. Dust accumulation on surface of photovoltaic panel may result in a high degradation of PVs' efficiency with losses ranging from 10% in mild conditions to over 40% in arid regions. This review systematically explores the effects of dust deposition on PV performance, emphasizing the role of environmental factors such as wind speed, precipitation, humidity, and dust composition. Dust particles impede light transmission, raise cell temperatures, and increase resistive losses, leading to reduced output power. Notable efficiency reductions are linked to specific dust types, such as coal dust (up to 64% losses), fine sand (32%), and gypsum (30%), as highlighted by global case studies. This review further underlines how dust accumulation patterns are influenced by complex interactions of environmental factors such as wind precipitation and humidity. The installation characteristics, such as the height of the panels and their orientation, further exacerbate or mitigate the impact of soiling. This study has been designed to put a great deal of significance on the maximisation of solar PV efficiency, given the critical requirements for global renewable energy targets. It flags some promising cleaning methods and emphasises a holistic approach to the optimisation of PV system design and maintenance practices to empower the widespread adoption of this transformative clean energy solution. © 2024 The Author(s)

关键词： Cleaning techniques Dust Efficiency Performance degradation Solar photovoltaic

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Employee Attrition Prediction in Industries using Machine Learning Algorithms

Employee Attrition Prediction in Industries using Machine Le...

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Shreya Gupta Gargi Bhardwaj Megha Arora Ritu Rani Poonam Bansal Ravinder Kumar Computer Science Engineering with Specialization in Artificial Intelligence Indira Gandhi Delhi Technical University For Women New Delhi India Center of Excellence- Artificial Intelligence Indira Gandhi Delhi Technical University for Women Kashmere Gate Delhi India Skill department of CS/IT Shri Vishwakarma Skill University Palwal

Employee attrition is a major issue among all employee-related issues in the current setting, notwithstanding changes in the external environment. Attrition is defined as a gradual decrease in the number of employees due to seclusion, death, renunciation, and resignations. This research employs logistic regression to forecast whether or not a firm employee will leave. We evaluate job involvement, average monthly hours worked, years employed, gender, education level, and job satisfaction, among other things, as our features. K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and decision trees are more methods for solving this issue. The dataset was divided, with 80% of it being used to train the algorithm and 20% of it being used to test it, yielding a Logistic Regression (LR) accuracy of 88.0952%.

关键词： Support vector machines Radio frequency Industries Machine learning algorithms Computational modeling Education Forestry

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A Comprehensive Review of Techniques, Algorithms, Advancements, Challenges, and Clinical Applications of Multi-modal Medical Image Fusion for Improved Diagnosis

arXiv

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arXiv 2025年

作者： Zubair, Muhammad Hussain, Muzammil Al-Bashrawi, Mousa Ahmad Bendechache, Malika Owais, Muhammad Interdisciplinary Research Center for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Information Systems and Operations Management King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia ADAPT Research Centre School of Computer Science University of Galway GalwayH91 TK33 Ireland Department of Mechanical and Nuclear Engineering Khalifa University Abu Dhabi127788 United Arab Emirates

Multi-modal medical image fusion (MMIF) is increasingly recognized as an essential technique for enhancing diagnostic precision and facilitating effective clinical decision-making within computer-aided diagnosis systems. MMIF combines data from X-ray, MRI, CT, PET, SPECT, and ultrasound to create detailed, clinically useful images of patient anatomy and pathology. These integrated representations significantly advance diagnostic accuracy, lesion detection, and segmentation. This comprehensive review meticulously surveys the evolution, methodologies, algorithms, current advancements, and clinical applications of MMIF. We present a critical comparative analysis of traditional fusion approaches, including pixel-, feature-, and decision-level methods, and delves into recent advancements driven by deep learning, generative models, and transformer-based architectures. A critical comparative analysis is presented between these conventional methods and contemporary techniques, highlighting differences in robustness, computational efficiency, and interpretability. The article addresses extensive clinical applications across oncology, neurology, and cardiology, demonstrating MMIF’s vital role in precision medicine through improved patient-specific therapeutic outcomes. Moreover, the review thoroughly investigates the persistent challenges affecting MMIF’s broad adoption, including issues related to data privacy, heterogeneity, computational complexity, interpretability of AI-driven algorithms, and integration within clinical workflows. It also identifies significant future research avenues, such as the integration of explainable AI, adoption of privacy-preserving federated learning frameworks, development of real-time fusion systems, and standardization efforts for regulatory compliance. This review organizes key knowledge, outlines challenges, and highlights opportunities, guiding researchers, clinicians, and developers in advancing MMIF for routine clinical use and promotin

关键词： Neurophysiology

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software Testing Issues Improvement in Quality Assurance

Software Testing Issues Improvement in Quality Assurance

引用

Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Mohammed Salahat Raed A. Said Khalid Hamid Usama Haseeb Elsaid Abdel Maguid Abdel Ghani Abedallah Abualkishik Muhammad Waseem Iqbal Mohammad Inairat College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Faculty of Management Canadian University Dubai Dubai UAE Department of Computer Science Superior University Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Department of Software Engineering Superior University Lahore Pakistan Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates VICE CHANCELLOR Skyline University College Dubai

Quality assurance ensures that the project is carried out following the agreed-upon supplies, principles, and then purposes. The goal of type and its calibration investigation remains to better comprehend the multifaceted course of software analysis. Current software rehearses have some errors such as analysis techniques, user arrogances, and logistic culture. Altogether of these subjects, including such rushed testing, way to quickly times, totally inadequate supporting documents, and so on others must be addressed together. Describe a solution to the significant trouble of product testing in quality managing. We will discuss about the solutions of testing problems in quality management. propose a solution to the glitches mentioned above.

关键词： software testing Quality assurance Philosophical considerations Process control Documentation Production software

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SCALING-UP DEEP LEARNING PREDICTIONS OF HYDROGRAPHY FROM IFSAR DATA IN ALASKA

SCALING-UP DEEP LEARNING PREDICTIONS OF HYDROGRAPHY FROM IFS...

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2022 Free and Open Source software for Geospatial, FOSS4G 2022

作者： Stanislawski, L.V. Shavers, E.J. Duffy, A.J. Thiem, P. Jaroenchai, N. Wang, S. Jiang, Z. Kronenfeld, B.J. Buttenfield, B.P. U.S. Geological Survey Center of Excellence for Geospatial Information Science 1400 Independence Road RollaMO65401 United States University of Missouri Science & Technology RollaMO United States Department of Geography and Geographic Information Science University of Illinois at Urbana Urbana-Champaign United States Computer & Information Science & Engineering University of Florida United States Department of Geology and Geography Eastern Illinois University United States Department of Geography University of Colorado-Boulder United States

The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data. However, deriving hydrography through flow-routing methods is a complex process that needs to be tailored to different geographic conditions, which can lead to varying solutions. To address this problem, this paper evaluates automated deep learning and its transferability to extract hydrography from interferometric synthetic aperture radar (IfSAR) elevation data spanning a range of geographic conditions in Alaska. © Copyright:

关键词： Surface waters

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Reinforcement-Learning Based Method for Accelerating Functional Coverage Closure of Traffic Light Controller Dynamic Digital Design

Reinforcement-Learning Based Method for Accelerating Functio...

引用

International Conference on computer Theory and Applications (ICCTA)

作者： Youstina M. Halim Khaled A. Ismail Mohamed A. Abd El Ghany Sameh A. Ibrahim Youssef M. Halim Mixel Egypt. Electronics and Communications Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt GSI Helmholtz Center for Heavy-Ion Research GmbH (GSI) Germany Electronics Department German University in Cairo Egypt. Integrated Electronic Systems Lab TU Darmstadt Germany Electronics and Communications Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Engineering and Software Systems Dept. Faculty of Engineering Ain Shams University Cairo Egypt

Functional verification of digital designs is an increasingly complex and time-consuming endeavor. One of the major challenges in functional verification is achieving functional coverage closure in a timely manner. Verification experts have moved from using directed stimulus, towards constrained random stimulus and finally, towards using machine learning to direct stimulus. In this work, reinforcement learning (RL) was used, incorporating three different kinds of RL agents to direct stimulus generation for a traffic light controller system. Two of the agents are based on Q-learning (DQN and SARSA), and one is based on Monte-Carlo method (CEM). The maximum reduction in simulation time needed to achieve a targeted percent of coverage was 97%. The average reduction of simulation time across all testcases and agents used was 70%. The solution is highly reusable, the architecture was integrated with different agents and testcases.

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FedMonitor: An Implementation Architecture for Monitoring of Resources and Events'Notifications on Federated Cloud Computing Infrastructures

FedMonitor: An Implementation Architecture for Monitoring of...

引用

International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Emmanuel Adetiba Uche Nnaji John S. Wejin Festus Ojesanmi Matthew B. Akanle Priscilla O. Ajayi Faith O. Sweetwiliams Surendra C. Thakur Ezekiel F. Adebiyi Department of Electrical and Information Engineering and Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Computer and Information Science & CApIC-ACE Covenant University Ota Nigeria Department of Electrical and Information Engineering & CApIC-ACE Covenant University Jalingo Nigeria Department of Computer Science Tareba State University Jalingo Nigeria Covenant Applied Informatics & Communication African Center of Excellene(CApIC-ACE) Covenant University Ota Nigeria Department of Electrical and Information Engineering Covenant University & CApIC-ACE Covenant University Ota Nigeria Center for Systems and Information Services & CApIC-ACE Covenant University Ota Nigeria Department of Information Technology and KZN e-Skills Co-Laboratory Durban University of Technology Durban South Africa Covenant University Bioinformatics Research (CUBRe) Covenant University Ota Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala Uganda

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

In the evolving landscape of cloud computing, federated cloud infrastructures present unique challenges and opportunities for resource and application monitoring. Monitoring the diverse array of cloud resources and applications within a federated cloud environment is crucial for delivering consistent Quality of Service (QoS) and continuous oversight across various cloud applications and infrastructure. Despite the various successes documented in the literature on multi-cloud monitoring, existing frameworks have not fully explored or provided enough insights into centralized monitoring of resources, applications, and network devices in a federated cloud environment. This paper introduces FedMonitor, an implementation architecture designed to enhance the monitoring and notification capabilities in federated cloud environments by building on the theoretical ideals in our earlier work (i.e. FEDARGOS-V1). FedMonitor leverages Prometheus and Grafana to crawl, manage and visualize metrics from diverse software services, hardware resources, and network devices within a federated cloud environment. The results demonstrate hat FedMonitor improves the effectiveness of resource and application monitoring in federated cloud environments.

关键词： Measurement Cloud computing Visualization computer architecture Quality of service Production software Real-time systems Stakeholders Monitoring

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