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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Parasar, Deepa Ali, Arif Pillai, N. Muthuvairavan Shahi, Archana Alfurhood, Badria Sulaiman Pant, Kumud Amity University Maharashtra Cse India Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India R.M.D Engineering College Computer Science and Business Systems Kavarapettai India Chitkara University Chitkara Business School India Princess Nourahbint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology's function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

关键词： Forecasting

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Parkinson’s Disease Detection from Resting State EEG using Multi-Head Graph Structure Learning with Gradient Weighted Graph Attention Explanations

arXiv

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arXiv 2024年

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud 22

URegM: a unified prediction model of resource consumption fo...

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Proceedings of the 2022 European Symposium on Software engineering

作者： Asif Imran Tevfik Kosar Department of Computer Science and Information Systems California State University San Marcos USA Department of Computer Science and Engineering The State University of New York at Buffalo (SUNY Buffalo) USA

ISBN: (纸本)9781450397308

The low cost and rapid provisioning capabilities have made the cloud a desirable platform to launch complex scientific applications. However, resource utilization optimization is a significant challenge for cloud service providers, since the earlier focus is provided on optimizing resources for the applications that run on the cloud, with a low emphasis being provided on optimizing resource utilization of the cloud computing internal processes. Code refactoring has been associated with improving the maintenance and understanding of software code. However, analyzing the impact of the refactoring source code of the cloud and studying its impact on cloud resource usage require further analysis. In this paper, we propose a framework called Unified Regression Modelling (URegM) which predicts the impact of code smell refactoring on cloud resource usage. We test our experiments in a real-life cloud environment using a complex scientific application as a workload. Results show that URegM is capable of accurately predicting resource consumption due to code smell refactoring. This will permit cloud service providers with advanced knowledge about the impact of refactoring code smells on resource consumption, thus allowing them to plan their resource provisioning and code refactoring more effectively.

关键词： unified regression modelling scientific application in cloud resource usage prediction

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IoT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time 3rd

IoT Enabled Driver Compatible Cost-Effective System for Drow...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Sarkar, Argha Kundu, Mayuri Pareek, Prakash Gupta, Nishu Reis, Manuel J. Cabral S. School of Computer Science and Engineering REVA University Karnataka Bangalore560064 India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of Electronic Systems Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway Engineering Department UTAD/IEETA Vila Real5001-801 Portugal

ISBN: (纸本)9783031289743

The present work researches driver drowsiness, which constitutes a huge problem, and can turn into fatal incidents potentially involving the losing of lives, being it while driving in a highway (car, bus, truck, etc.), in the railway, or any other transportation mean (ship, airplane, etc.). Recent technology has been involved in the study of the fatigue behaviour. The purpose of this work is to identify the in-driver’s drowsiness, contributing to get rid of the accidents, mainly during the night, and to ensure better safety in train and on the highways. A camera is used to capture images from the driver, and face detection is executed in real-time to find out the exact position of driver’s face. Here, the blinking of the driver’s eye is under consideration. The fundamental concept lies in the closing of the driver’s eye for a specific duration of time. If the closing time exceeds that certain duration, drowsiness is detected and an alarm is sounded for awareness. Python and OpenCV (using Haarcascade library) are the fundamental programming platforms for sensing the facial attributes. In this work, a cost-effective system for the driver’s drowsiness detection is aimed. Further, the result also indicates the reduction of response and processing time, which is ideal for real time detection. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Python

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Exploring Gender Dynamics in Motorcycle Traffic Violations Through Gender Detection Technology

Exploring Gender Dynamics in Motorcycle Traffic Violations T...

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International Conference on Intelligent Computing in Data sciences (ICDS)

作者： Ayoub Charef Zahi Jarir Mohamed Quafafou LAMIGEP Research Laboratory EMSI Moroccan School of Engineering Sciences Marrakech Morocco Laboratory of Computer Systems Engineering (LISI) Cadi Ayyad University Marrakech Marrakech Morocco Department of Computer Science University of Aix-Marseille Marseille France

ISBN: (数字)9798350351200

ISBN: (纸本)9798350351217

Urban traffic management faces significant challenges due to non-compliance with traffic rules, particularly among motorcycle riders. This study introduces an innovative approach employing the YOLOv9 object detection framework to monitor and analyze motorcycle violations at busy intersections in Marrakech. By leveraging advanced machine learning techniques, the study aimed to detect helmet usage, traffic light violations, and assess gender-based differences in violation rates. Data were collected using smartphone cameras at strategic locations during peak traffic times across selected days. The captured video footage was analyzed using the YOLOv9 model, which was pretrained on diverse traffic scenes to enhance its accuracy and reliability in real-time object detection. The findings reveal that the automated system not only aligns closely with manual counting but also offers greater consistency and efficiency. Key results indicated a pronounced gender disparity in violation frequencies and provided insights into the temporal patterns of traffic rule infractions. Moreover, the study highlighted minor discrepancies due to environmental factors, which were systematically addressed to refine the detection process. Contextual factors in interpreting detection results.

关键词： Accuracy Motorcycles Manuals Object detection Streaming media Cameras Real-time systems Safety Reliability Monitoring

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An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1756-1767页

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada Computer School Hubei University of Arts and Science Xiangyang441000 China Future Technology Research Center National Yunlin University of Science and Technology Douliu City64002 Taiwan Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan Riken Center for Advanced Intelligence Project Riken Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization. © 2005-2012 IEEE.

关键词： Bandwidth

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Exact recovery and bregman hard clustering of node-attributed stochastic block model 23

Exact recovery and bregman hard clustering of node-attribute...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Maximilien Dreveton Felipe S. Fernandes Daniel R. Figueiredo School of Computer and Communication Sciences EPFL Lausanne Switzerland Systems Engineering and Computer Science Federal University of Rio de Janeiro Rio de Janeiro Brazil

Network clustering tackles the problem of identifying sets of nodes (communities) that have similar connection patterns. However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (edges) and node information (attributes) can be jointly leveraged to design high-performance clustering algorithms. Under a general model for the network and node attributes, this work establishes an information-theoretic criterion for the exact recovery of community labels and characterizes a phase transition determined by the Chernoff-Hellinger divergence of the model. The criterion shows how network and attribute information can be exchanged in order to have exact recovery (e.g., more reliable network information requires less reliable attribute information). This work also presents an iterative clustering algorithm that maximizes the joint likelihood, assuming that the probability distribution of network interactions and node attributes belong to exponential families. This covers a broad range of possible interactions (e.g., edges with weights) and attributes (e.g., non-Gaussian models), as well as sparse networks, while also exploring the connection between exponential families and Bregman divergences. Extensive numerical experiments using synthetic data indicate that the proposed algorithm outperforms classic algorithms that leverage only network or only attribute information as well as state-of-the-art algorithms that also leverage both sources of information. The contributions of this work provide insights into the fundamental limits and practical techniques for inferring community labels on node-attributed networks.

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Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging

Optimal Control of Connected Automated Vehicles with Event-T...

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Control Technology and Applications (CCTA),

作者： Ehsan Sabouni H.M. Sabbir Ahmad Wei Xiao Christos G. Cassandras Wenchao Li Department of Electrical & Computer Engineering Division of Systems Engineering Boston University Boston MA USA Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

We address the problem of safely coordinating a network of Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network. Such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved online over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may limit the controller’s application to real CAVs. We tackle this limitation by adopting an event-triggered control approach for CAVs such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed developed to emulate merging roadways using mobile robots as CAVs. We present results to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety.

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

Investigation on Structures of Axial Gap Type Magnetic Multi...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Kamijo, Taiga Aiso, Kohei Akatsu, Kan Aoyama, Yasuaki Shibaura Institute of Technology Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan Yokohama National University Division of Intelligent Systems Engineering Department of Faculty of Engineering Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

ISBN: (纸本)9798350314274

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis. © 2023 IEEE.

关键词： Electric vehicles

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Lightweight and Efficient Distributed Cooperative Intrusion Detection System for Intelligent Swarms

Lightweight and Efficient Distributed Cooperative Intrusion ...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Li, Zhaoyang Zhang, Zhiwei Chen, Zehan Duan, Hao Li, Hongjun Ren, Baoquan School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Institute of Network Information Academy of Systems Engineering Academy of Military Sciences Beijing100141 China Institute of Systems General Academy of Systems Engineering Academy of Military Sciences Beijing100101 China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

Intrusion Detection systems (IDSs) have been wildly used in various environments to actively detect internal and external attacks with high accuracy. Unfortunately, the traditional IDSs cannot distinguish new or unknown attacks from abnormal behaviors effectively, and that makes them infeasible to protect the emerging dynamic open information systems. Subsequently, artificial intelligence (AI) algorithms are introduced into IDSs to support the recognization of unexplored malicious behaviors. However, most of the existing AI-driven IDSs are not able to be directly applied to intelligent swarm scenarios, which are typically employed to aggregate heterogeneous or homogeneous elements (e.g., autonomous vehicles, drones) to solve complex problems that the individual members cannot deal with, due to the characteristics of mobility and complexity of intelligent elements. Therefore, in this paper, we propose a lightweight and efficient distributed cooperative IDS (DCIDS) for intelligent swarms. On one hand, to efficiently detect the malicious behaviors among swarm elements, we design a collaborative detection model which utilizes multi-dimension features including the swarm elements' position, storage-computing resource consuming levels, network traffics, et al. On the other hand, to predict the movement trends and detect attacks of resource-limited swarm elements, we construct a concrete DCIDS scheme by employing the Kalyan Filter algorithm and Long Short Term Memory Network (LSTM) algorithm. Furthermore, our experimental results demonstrate that the proposed DCIDS scheme outperforms the previous IDS schemes on attack detection/classification accuracy and efficiency in intelligent swarm environments and also achieves an accuracy of 98.00%. © 2022 IEEE.

关键词： Kalman filters

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