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RADAR: a rapid discovery algorithm for routes in SDN-based IoV

Digital Communications and Networks 2024年

作者： Zahid Khan Nauman Khan Anis Koubaa Adel Ammar Wadii Boulila College of Computer and Information Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia Department of Computer Science and Information Technology University of Malakand Chakdara (Dir lower) KPK 18800 Pakistan

Link failures are a critical issue that disrupts consistent communications in the fast-evolving Internet-of-Vehicles ( IoV ) environment. In response to the aforementioned limitation, we introduce the rapid discovery algorithm for routes ( RADAR ), a novel scheme designed for rapid route discovery in the event of abrupt link failures or topological changes. Built on a heuristic T h e t a ⁎ algorithm, RADAR operates within a three-layered Software-Defined Networking ( SDN ) architecture to ensure seamless communications. The first layer collects vital vehicle information, including location, speed, direction, and vehicle ID . The second layer uses an SDN edge controller to carry out local and intra-zone path discovery with the T h e t a ⁎ heuristic algorithm. The third layer introduces a global controller that oversees inter-zone route discovery, thereby expanding the network's reach and capabilities. To validate RADAR 's effectiveness, we compare its performance to those of Dijkstra -based and A ⁎ path planning algorithms across key metrics—Route Discovery Messages ( RDM ), Path Length ( PL ), and Route Discovery Time ( RDT ). Our analysis shows significant improvements with RADAR over Dijkstra , achieving up to an 80% increase in RDM , 20% in PL , and 70% in RDT . It also surpasses A ⁎ by up to 70% in RDM and 60% in RDT . These results indicate that RADAR enhances routing reliability in IoV systems, thereby improving overall network performance.

关键词： Route discovery Software defined networking (SDN) Link failure Intra-zone communications Inter-zone communications T h e t a ⁎

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Combining of extraction butterfly image using color, texture and form features 6

Combining of extraction butterfly image using color, texture...

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6th information Technology International Seminar, ITIS 2020

作者： Kartika, Dhian Satria Yudha Herumurti, Darlis Rahmat, Basuki Yuniarti, Anny Maulana, Hendra Anggraeny, Fetty Tri Veteran Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia Institut Teknologi Sepuluh Nopember Kampus ITS Sukolilo Faculty of Intelligent Electrical and Informatics Technology Department of Informatics Surabaya Indonesia Veteran Jawa Timur Faculty of Computer Science Department of Informatic Surabaya Indonesia

ISBN: (纸本)9781728177267

Combining the extraction results in digital image processing is an innovation in research. Previous research stated that the extraction was carried out separately for each feature, including color, shape, and pattern. Previous research for color feature extraction was used for koi fish, for shape feature extraction in flowers, and pattern feature extraction on batik. In this study, feature extraction results were combined using a dataset of insects, namely butterflies. The number of datasets used was 890 types of butterflies, which were divided into 10 classes. Before feature extraction and merging, the normalization process is carried out with the aim that the data used is clean from noise. The color feature extraction process uses the RGB and HSV methods by maintaining the color quality of the objects owned by faster computation and color quantization. The extraction of shape features using the LBPROT (local binary pattern rotational excess) method allows the dataset to be in multiple positions to obtain maximum results. The extraction of pattern features with region props allows the region of each object to be obtained. All of the feature extraction results are then tested to get an accuracy value of 75%, while the precision, recall, and f-measure are 76.4 for precision and 75 for recall, and 75.5 for f-measure. This research will continue to be tested with different methods and data. © 2020 IEEE.

关键词： Feature extraction

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Interaction effect of system, information and service quality on intention to use and user satisfaction 6

Interaction effect of system, information and service qualit...

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6th information Technology International Seminar, ITIS 2020

作者： Safitri, Eristya Maya Pratama, Arista Furqon, Muhammad Ariful Mukhlis, Iqbal Ramadhani Agussalim Faroqi, Asif University of Pembangunan Nasional Veteran Jawa Timur Faculty of Computer Science Department of Information System Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information System Surabaya Indonesia Informatics STIE Perbanas Surabaya Indonesia

ISBN: (纸本)9781728177267

The use of academic information systems is now essential in the teaching and learning process at every university. The Indonesian College of Economics (STESIA) is one of the universities that implement an academic information system to support the teaching and learning process at the university. An evaluation stage is needed to improve the performance of the academic information system. Improving the performance of academic information systems can be done by improving the quality of information, the quality of the system, and the quality of its services. These three aspects are related to increasing user attention and satisfaction. This study will evaluate following the conceptual model of the results of the development of the Delone Macline information system success model. The method used is a quantitative method using SPSS and WarpPLS. The results of the study state that the service quality aspect is the aspect that provides the most significant contribution in increasing attention to use and user satisfaction. © 2020 IEEE.

关键词： information systems

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An Empirical Study of Security Practices for Microservices systems

SSRN

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SSRN 2022年

作者： Nasab, Ali Rezaei Shahin, Mojtaba Raviz, Seyed Ali Hoseyni Liang, Peng Mashmool, Amir Lenarduzzi, Valentina School of Computer Science Wuhan University Wuhan430072 China School of Computing Technologies RMIT University Melbourne3000 Australia Department of Computer Science Bioengineering Robotics and System Engineering University of Genova Genova16126 Italy Faculty of Information Technology and Electrical Engineering University of Oulu OuluFI-90014 Finland

Despite the numerous benefits of microservices systems, security has been a critical issue in such systems. Several factors explain this difficulty, including a knowledge gap among microservices practitioners on properly securing a microservices system. To (partially) bridge this gap, we conducted an empirical study to manually analyze 861 security points collected from 10 GitHub open-source microservices systems and Stack Overflow posts concerning security in microservices systems, leading to a catalog of 28 microservices security practices. We then ran a survey with 63 microservices practitioners to evaluate the usefulness of these 28 practices. Our findings demonstrate that the survey respondents affirmed the usefulness of the 28 practices. We believe that the catalog of microservices security practices can serve as a valuable resource for microservices practitioners to more effectively address security issues in microservices systems. It can also inform the research community of the required or less explored areas to develop microservices-specific security practices and tools. © 2022, The Authors. All rights reserved.

关键词： Surveys

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Electrification of Transportation: Policy Framework, Technical Aspects and Challenges in Pakistan - A Case Study

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Masood, Arsalan Hassan, Syed Zulqadar Kamal, Tariq Khan, Salman Rizvi, Syed Asad Abbas Salman, Salman Department of Electrical Engineering Faculty of Engineering and Architecture University of Sialkot Pakistan Department of Computer Science Faculty of Information Technology and Computer Science University of Central Pubjab Lahore Pakistan School of Technology and Innovations Electrical Engineering University of Vaasa Vaasa Finland Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Estonia State Key Lab. for Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Beijing China Department of Electrical Engineering Balochistan University of Engineering and Technology Balochistan Khuzdar Pakistan

Pakistan faces severe air pollution and high dependency on imported fossil fuels for transportation, urgently needing solutions. Electrification of transportation could address these issues, but Pakistan lacks a clear roadmap for adopting electric vehicles. This paper examines the electrification of transportation in Pakistan, focusing on policies, technical aspects, and standards. It looks at best practices from developed countries and discusses incentives offered by the Pakistani government to boost electric vehicle adoption. The study also explores challenges and opportunities in adopting electric vehicles within existing power systems. A SWOT analysis is included, highlighting strengths, weaknesses, opportunities, and threats related to electric vehicle policies and market integration in Pakistan. By reviewing these factors, the paper provides insights into the obstacles and potential benefits of electric vehicle adoption. It also suggests ways for developing nations like Pakistan to encourage widespread use of electric vehicles, aiming to reduce fossil fuel dependency and meet environmental goals. © 2024

关键词： Electric transportation Electric vehicles Key challenges Policy SWOT analysis

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Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction

arXiv

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arXiv 2023年

作者： Wang, Xinshun Zhang, Wanying Wang, Can Gao, Yuan Liu, Mengyuan the National Key Laboratory of General Artificial Intelligence Peking University Shenzhen Graduate School China the School of Intelligent Systems Engineering Sun Yat-sen University China the Multimedia Information Processing Laboratory the Department of Computer Science Kiel University Germany Peking University China the Unit of Computing Sciences CS Faculty of Information Technology and Communication Sciences ITC Tampere University Finland

Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN. To solve both problems, this paper presents a Dynamic Dense Graph Convolutional Network (DD-GCN), which constructs a dense graph and implements an integrated dynamic message passing. More specifically, we construct a dense graph with 4D adjacency modeling as a comprehensive representation of motion sequence at different levels of abstraction. Based on the dense graph, we propose a dynamic message passing framework that learns dynamically from data to generate distinctive messages reflecting sample-specific relevance among nodes in the graph. Extensive experiments on benchmark Human 3.6M and CMU Mocap datasets verify the effectiveness of our DD-GCN which obviously outperforms state-of-the-art GCN-based methods, especially when using long-term and our proposed extremely long-term protocol. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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E-government and E-commerce issues in Malaysia 2

E-government and E-commerce issues in Malaysia

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Kanaan, Anas Ghassan Pathmanathan, P. Ravindran Alawi, Nahg Abdulmajid Hatamleh, Amer Jusoh, Julaily Aida Aledinat, Lowai Saleh Aseh, Khairi Bamansoor, Samer UniSZA Faculty of Informatics and Computing Malaysia Petra University Faculty of Managment and Financial Sciences Business and E-Commerce Depaetment Amman Jordan International Institute of Applied Science Swiss School of Management Netherlands Geomatika University College Malaysia University of Aden Yemen Irbid National University Faculty of Financial and Business Science Head of Management Information System Department Irbid Jordan UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer and Information Technology MEDIU Malaysia University of Aden Aden Yemen

ISBN: (纸本)9781665432221

In Malaysia, an e-commerce company is still considered a new middleman. As well as the development of information and communication technology strongly affects business companies at the present time in terms of management, business strategies, production, sales and marketing, and other aspects that underpin business profitability in general. E-commerce, known as e-commerce, refers to a general term for a commercial activity using modern technologies, or a commercial transaction that includes the exchange of data via electronic frameworks, especially the Internet, as well as many modern systems used in personal computers, smart phones, etc. It is not just limited to doing business between companies but also related to online services between governments and individuals. Various investigations related to e-commerce have identified obstacles related to information technology (IT) organization, internet use, financial situation, government and administrative activities as important determinants of doing business online. information and Communication Technology (ICT) has revolutionized businesses around the world. In this paper, e-commerce practices in Malaysia are analyzed by their characteristics, drawbacks, openings and risks. This paper provides a general review of the current issues considered by e-commerce organizations today. It also suggests the challenges and problems facing e-commerce companies at the present time and how to solve them using e-commerce concepts. © 2021 IEEE.

关键词： Smartphones

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Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

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computers, Materials & Continua 2020年第9期64卷 2043-2055页

作者： Congcong Wang Pengyu Liu Kebin Jia Xiaowei Jia Yaoyao Li Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijing100124China Department of Computer Science and Engineering University of MinnesotaTwin CitiesUSA

Weather phenomenon recognition plays an important role in the field of ***,weather radars and weathers sensor have been widely used for weather ***,given the high cost in deploying and maintaining the devices,it is difficult to apply them to intensive weather phenomenon ***,advanced machine learning models such as Convolutional Neural Networks(CNNs)have shown a lot of promise in meteorology,but these models also require intensive computation and large memory,which make it difficult to use them in *** practice,lightweight models are often used to solve such ***,lightweight models often result in significant performance *** this end,after taking a deep dive into a large number of lightweight models and summarizing their shortcomings,we propose a novel lightweight CNNs model which is constructed based on new building *** experimental results show that the model proposed in this paper has comparable performance with the mainstream non-lightweight model while also saving 25 times of memory *** memory reduction is even better than that of existing lightweight models.

关键词： Deep learning convolution neural networks lightweight models weather identification

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Smart Home Gas Sensor Optimization Using Kalman Filter for Data Processing

Smart Home Gas Sensor Optimization Using Kalman Filter for D...

引用

International Conference on Informatics and Computing (ICIC)

作者： Somantri Muhamad Ikhsan Thohir Alun Sujjada Deni Setiawan Ilham Komara Fikri Ardiansyah Efendi Muhamad Muslih Zubaile Bin Abdullah Mohd Zainuri Saringat Department of Software Engineering University Tun Hussein Onn Malaysia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Informatics Engineering Study Program Nusa Putra University Sukabumi Indonesia Department of Software Engineering University Tun Hussein Onn Malaysia Sukabumi Indonesia Department of Information System Nusa Putra University Sukabumi Indonesia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Johor Bahru Malaysia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The integration of Internet of Things (IoT) technologies into modern homes has enhanced safety and comfort, particularly in detecting gas leaks, which pose serious fire hazards. Gas leaks can often be detected by smell, but this method fails when no one is present. Previous research using microcontrollers and sensors to detect gas leaks faced challenges with accuracy due to noise interference. This paper proposes the use of a Kalman filter, developed by Rudolf Emil Kalman in 1960, to improve gas leak detection accuracy by filtering out noise. The system comprises an Arduino Nano, ESP8266 WiFi module, MQ-2 gas sensor, buzzer, and ThingSpeak cloud platform. By applying the Kalman filter, noise and data oscillations are reduced, enhancing detection accuracy. Experimental results show the system effectively detects gas leaks, provides real-time data, and triggers alarms. Future improvements could include additional sensors and features to further increase the system’s reliability and functionality.

关键词： Accuracy Noise Sensor phenomena and characterization Sensor systems Real-time systems Kalman filters Internet of Things Reliability Leak detection Gas detectors

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High-precision brain tumor diagnosis using SECNN-MNet framework and explainable AI

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International Journal of Data science and Analytics 2025年

作者： AbdelAziz, Nabil M. Said, Wael AbdelHafeez, Mohamed M. Ali, Asmaa H. Information System Department Faculty of Computers and Informatics Zagazig University Zagazig44511 Egypt Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig44511 Egypt

Accurate diagnosis of brain tumors from MRI scans is crucial for effective treatment planning and patient outcomes. Deep learning techniques have shown promise in automating this process, but the interpretability of their decisions remains a challenge. This paper introduces the Squeeze-and-Excitation Convolutional Neural Network-MobileNet (SECNN-MNet), a novel deep learning model designed for precise and interpretable classification of brain tumors in MRI images. SECNN-MNet combines the robust feature extraction capabilities of SE-CNN with the computational efficiency of MobileNet. By integrating SE blocks, the model dynamically recalibrates feature maps to capture crucial tumor characteristics, enhancing diagnostic accuracy. MobileNet’s depthwise separable convolutions optimize computational resources while preserving image detail fidelity. Our model achieves exceptional performance, demonstrating an accuracy of 99.6% in classifying brain tumors. Leveraging explainable artificial intelligence (XAI), SECNN-MNet provides insights into decision-making processes, enhancing its interpretability and utility for clinicians. This study underscores the potential of SECNN-MNet in advancing automated brain tumor diagnosis, offering both high accuracy and interpretability crucial for clinical adoption and patient care. © The Author(s) 2025.

关键词： Convolutional neural networks

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