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International Conference on Computing and Networking Technology (ICCNT)

作者： Dharmesh Dhabliya N. Thangarasu B Reddy D. Nirmala Yuvraj Parmar Subbulakshmi Ganesan Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Function extraction is a vital step in file photo analysis. It enables to reduction in the size of the statistics, similar capabilities from exclusive files can be identified, and the traits of the report may be better understood. Various techniques were advanced to extract features from record pictures, such as Counting Neighboring Pixels (CNP), aspect Detection (ED), grey-stage Co-occurrence Matrix (GLCM), neighborhood Binary styles (LBP), Wavelet rework (WT), and Zernike second Invariants (ZMI). Each method has its blessings and drawbacks, which makes it suitable for particular responsibilities or files. Counting Neighboring Pixels (CNP) is an easy but powerful approach for characteristic extraction. CNP works by means of counting the range of pixels that can be comparable (in phrases of brightness, color, contrast, and so forth.) to a target pixel. It is a great technique for report pictures as it can be used to pick out the structural functions of the report with no prior understanding. However, CNP could be more sturdy to photograph noise. Aspect detection (ED) is a technique for extracting features from a picture. It’s used to locate the rims or outlines of an item.

关键词： Text analysis Image analysis Image color analysis Brightness Feature extraction Indexes Colored noise

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Review of Serious Games for Medical Operation

arXiv

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arXiv 2022年

作者： Ning, Huansheng Guo, Zhijie Hamzaoui, Raouf Li, Rongyang Farha, Fadi Mao, Lingfeng The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083 China The School of Engineering and Sustainable Development De Montfort University The Gateway LeicesterLE1 9BH United Kingdom

Objective: Medical operations(MOs) are essential in healthcare, and they are also a big concept that includes various operations during the perioperative period. Traditional operation exposes its limitations during the perioperative period, reflected in medical training, surgical preparation, and postoperative rehabilitation. Serious Games for Medical Operation (SGMO) offer new ways and complementary solutions to support MOs. Methods: As a review, this paper analyzes the development of SGMO and considers various aspects of the SGMO, such as interface, functions, and technologies. Results and conclusion: By combining MO and serious games’ characteristics, the paper classifies SGMO and analyzes their features and functions for different groups of users and at various stages of the perioperative period (before, during, and after an MO). Interactive technologies used in SGMO are presented from a visual, haptic, and auditory perspective. Significance: This paper reviews the development of SGMO, summarizes its functions and technologies. Besides, it presents representative products and suggests future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Serious games

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Load Balancing Optimization in Software-Defined Wide Area Networking (SD-WAN) using Deep Reinforcement Learning

Load Balancing Optimization in Software-Defined Wide Area Ne...

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International Symposium on Electronics and Telecommunications (ISETC)

作者： Mohamed Amine Ouamri Gordana Barb Daljeet Singh Florin Alexa Computer Communication LIG Laboratory DRAKKAR Teams Grenoble Alpes university Grenoble France Faculty of Electronics Telecommunication and Information Technologies Politehnica University Timisoara Timisoara Romania Department of Research and Development Centre for Space Research School of Electronics and Electrical Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9781665451512

Software-Defined Wide Area Network (SD-WAN) holds tremendous potential to provide multi-cloud multi-network interconnection and prevent channel congestion. However, traffic among Customer Premises Edge (CPE) and controllers continuously increases, requiring pre-emptive load balancing in the control plane. In this paper, we study the flow migration problem in SD-WANs when the controller presents a limited processing capacity. Specifically, the data plane may include one or more CPE deployed at a site where service traffic is forwarded. To address this narrow, we propose a new approach based on a Deep Reinforcement Learning (DRL) strategy to optimize the balancing process under a latency constraint. As far as we can tell, we have not observed any pertinent research published in this context. The obtained simulation results revealed that our proposed approach decreases the load balancing and outperforms other baseline methods.

关键词： Deep learning Wide area networks Costs Simulation Process control Reinforcement learning Load management

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A Data-Driven Near-Optimization Approach for Smart Parking Management Platforms 18

A Data-Driven Near-Optimization Approach for Smart Parking M...

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18th IEEE International Conference on Networking, Sensing and Control, ICNSC 2021

作者： Bai, Mingyan Zhong, Shenghua Yan, Pengyu Chen, Zhibin Zhang, Zhixian University of Electronic Science and Technology of China School of Management and Economics Chengdu China Chengdu Communication Investment Group Intelligent Parking Industrial Development Company Ltd Chengdu China NYU Shanghai Division of Engineering and Computer Science Center for Data Science and Artificial Intelligence Shanghai China China Telecom Research Institute Shanghai China

ISBN: (纸本)9781665440486

This paper addresses an allocation optimization problem of parking slots in a real-time parking reservation platform in which parking demands randomly show up. For each decision period of a finite horizon, the reservation platform allocates the available parking slots at hand for the random demands with different types specified by the parking duration to maximize the total revenue over the whole horizon. This paper presents a real-time reservation framework and formulates the problem as a stochastic programming model, considering the different type demands with unknown probability distributions. We propose a data-driven near-optimization approach entitled a two-sample average approximation (2-SAA) to determine the allocation scheme over the horizon. In the 2-SAA approach, the confidence interval of the total revenue is established by the out-of-sample resampling method. The results of the numerical experiment validate the performance of the proposed 2-SAA algorithm. © 2021 IEEE.

关键词： Stochastic systems

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Driving Fatigue Detection Combining Face Features with Physiological Information 7

Driving Fatigue Detection Combining Face Features with Physi...

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7th IEEE International Conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Zeng, Lingqiu Wang, Yang Han, Qingwen Zhou, Kun Ye, Lei Long, Yang Chongqing University School of Computer Science Chongqing400044 China Research and Development Center of Transport Industry of Self-driving Technology Chongqing400000 China School OfMicroelectronics and Communication Engineering Chongqing University Chongqing400044 China Chongqing Key Laboratory of Space Information Network and Intelligent Information Fusion College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China

ISBN: (纸本)9781665405829

Fatigue driving is one of the main reasons that cause sever accidents. It's necessary to detect fatigue state and warn drivers to avoid life-threatening accidents. There are many related technologies to detect fatigue, some of which based on physiological information or face features. However, biological indicators are difficult to analyze in real-time and the signal sensor is invasive while image-based approaches have relatively strong subjective. Hence, in this paper, a method combined physiological information and face features is employed. We use near-infrared spectroscopy (fNIRS) on behalf of physical states and eye and mouth condition representing face states. Firstly, Multi-Task Convolutional Neural Network (MTCNN) was used to extract image features and then a lightly classifier was designed to recognize the state of face states. Finally, we use Long Short-Term Memory (LSTM) model to fuse these characters and predict fatigue. Experiment results show that the method proposed have a high accuracy about 95.8% and fast speed about 6.12ms to detect fatigue. © 2021 IEEE.

关键词： Long short-term memory

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Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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Multimedia tools and applications 2022年第20期81卷 28799-28800页

作者： Amir Masoud Rahmani Elham Azhir Morteza Naserbakht Mokhtar Mohammadi Adil Hussein Mohammed Aldalwie Mohammed Kamal Majeed Sarkhel H Taher Karim Mehdi Hosseinzadeh Future Technology Research Center National Yunlin University of Science and Technology Douliu Yunlin Taiwan. Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran. Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran. Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq. Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq. Information Technology Department Faculty of Applied Science Tishk International University Erbil Iraq. Computer Department College of Science University of Halabja Halabja Iraq. Computer Networks Department Sulaimani Polytechnic University Technical College of Informatics Sulaymaniyah Iraq. Computer Science University of Human Development Sulaymaniyah Iraq.

[This corrects the article DOI: 10.1007/s11042-022-12952-7.].

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Spatial and Spatial-Spectral Morphological Mamba for Hyperspectral Image Classification

arXiv

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arXiv 2024年

作者： Ahmad, Muhammad Butt, Muhammad Hassaan Farooq Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Usama, Muhammad Roy, Swalpa Kumar Chanussot, Jocelyn Hong, Danfeng Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Institute of Artificial Intelligence School of Mechanical and Electrical Engineering Shaoxing University Shaoxing312000 China School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Department of Computer Science and Engineering Alipurduar Government Engineering and Management College West Bengal 736206 India Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Grenoble38000 France The Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Recent advancements in transformers, specifically self-attention mechanisms, have significantly improved hyperspectral image (HSI) classification. However, these models often suffer from inefficiencies, as their computational complexity scales quadratically with sequence length. To address these challenges, we propose the morphological spatial mamba (SMM) and morphological spatial-spectral Mamba (SSMM) model (MorpMamba), which combines the strengths of morphological operations and the state space model framework, offering a more computationally efficient alternative to transformers. In MorpMamba, a novel token generation module first converts HSI patches into spatial-spectral tokens. These tokens are then processed through morphological operations such as erosion and dilation, utilizing depthwise separable convolutions to capture structural and shape information. A token enhancement module refines these features by dynamically adjusting the spatial and spectral tokens based on central HSI regions, ensuring effective feature fusion within each block. Subsequently, multi-head self-attention is applied to further enrich the feature representations, allowing the model to capture complex relationships and dependencies within the data. Finally, the enhanced tokens are fed into a state space module, which efficiently models the temporal evolution of the features for classification. Experimental results on widely used HSI datasets demonstrate that MorpMamba achieves superior parametric efficiency compared to traditional CNN and transformer models while maintaining high accuracy. The code will be made publicly available at https://***/mahmad000/MorpMamba. © 2024, CC BY.

关键词： Hyperspectral imaging

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Optimal Reordering Trace Files for Improving Software Testing Suitcase

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computers, Materials & Continua 2021年第4期67卷 1225-1239页

作者： Yingfu Cai Sultan Noman Qasem Harish Garg Hamïd Parvïn Kim-Hung Pho Zulkefli Mansor School of Measurement and Communication Harbin University of Science&TechnologyHarbinChina Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia Department of Computer Science Faculty of Applied ScienceTaiz UniversityTaizYemen School of Mathematics Thapar Institute of Engineering and TechnologyDeemed UniversityPatiala147004India Institute of Research and Development Duy Tan UniversityDa Nang550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Department of Computer Science Nourabad Mamasani BranchIslamic Azad UniversityMamasaniIran Fractional Calculus Optimization and Algebra Research GroupFaculty of Mathematics and StatisticsTon Duc Thang UniversityHo Chi Minh CityVietnam Fakulti Teknologi dan Sains Maklumat Universiti Kebangsan MalaysiaUKM BangiSelangor43600Malaysia

An invariant can be described as an essential relationship between program *** invariants are very useful in software checking and *** tools that are used to detect invariants are invariant *** are two types of invariant detectors:dynamic invariant detectors and static invariant *** software is an available computer program that implements a special case of a dynamic invariant detection *** proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical *** method has some *** of its biggest drawbacks is its overwhelming time *** is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace *** is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this *** findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

关键词： Dynamic invariant detection software testing genetic algorithm

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Accounting for the Special Role of Infrastructure-assisted Collective Perception

Accounting for the Special Role of Infrastructure-assisted C...

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International Conference on Intelligent Transportation

作者： Florian Alexander Schiegg Anna–Lisa Rueeck Jörg Gamerdinger Hugues Tchouankem Edmir Xhoxhi Georg Volk Distributed Intelligence and Systems Corporate Research Robert Bosch GmbH Hildesheim Germany Function Development 3D Surround View Engineering Parking Functions Robert Bosch GmbH Feuerbach Germany Department of Computer Science Embedded Systems Group University of Tübingen Tübingen Germany Institute of Communication Technology Leibniz University Hanover Hanover Germany

The number of traffic participants has increased exponentially over the past century, making a traffic accident the most likely cause of death for children and young adults today. Vehicle-to-Everything (V2X) communication is regarded as one of the most promising emerging technologies to enhance road safety. It allows traffic participants to exchange relevant information to increase their environmental perception. Cooperative Awareness, a V2X service that is already being deployed in Europe, enables road users to share their dynamic states. Collective Perception (CP), currently in the final stage of standardization in the different regions of the world, enables the exchange of sensor-detected objects. The latter is thus not only relevant for connected vehicles but can further be used by sensor-equipped infrastructure to support vehicular perception, opening a whole new range of possibilities. The objective of this work is to investigate mechanisms to account for the special role of Infrastructure-assisted Collective Perception (ICP). Packet duplication is introduced for ICP to enhance the communication reliability. Additionally, different message generation rules prioritizing ICP are proposed and compared based on the enabled environmental perception. Simulations show significant improvements of the average precision, reaching an average increase of about 60% for ICP in comparison to V2V -based CP in scenarios with low connected vehicle densities.

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