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15th International Conference on ICT Innovations, ICT Innovations 2023

作者： Spasev, Vlatko Dimitrovski, Ivica Kitanovski, Ivan Chorbev, Ivan Faculty of Computer Science and Engineering Ss Cyril and Methodiuos University Skopje Macedonia

ISBN: (纸本)9783031543203

Semantic segmentation of remote sensing images is a vital task in the field of remote sensing and computer vision. The goal is to produce a dense pixel-wise segmentation map of an image, where a specific class is assigned to each pixel, enabling detailed analysis and understanding of the Earth’s surface. This paper provides an overview of semantic segmentation in remote sensing, starting with a definition of the task and its significance in extracting valuable information from remote sensing imagery. Various methods used for semantic segmentation in remote sensing are discussed, including traditional approaches such as region-based and pixel-based methods, as well as more recent deep learning-based techniques. Next, the paper delves into the available datasets for semantic segmentation of remote sensing images. Many available datasets are reviewed, highlighting their characteristics, including the number of images, image size, number of labels, spatial resolution, format and spectral bands. These datasets serve as valuable resources for training, evaluating, and benchmarking semantic segmentation algorithms in remote sensing applications. Furthermore, the paper highlights the broad range of applications enabled by semantic segmentation in remote sensing, including urban planning, land cover mapping, disaster management, environmental monitoring, and precision agriculture. Overall, this paper serves as a comprehensive guide to semantic segmentation of remote sensing images, providing insights into its definition, methods, available datasets and wide-ranging applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Semantic Segmentation

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2-Step RACH and Unsourced Massive Random Access in Cell-Free User-Centric Networks

2-Step RACH and Unsourced Massive Random Access in Cell-Free...

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Asilomar Conference on Signals, Systems & computers

作者： Eleni Gkiouzepi Burak Çakmak Giuseppe Caire Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

In this work we consider a scheme for unsourced random access (uRA) in cell free (CF) wireless networks, which is conceptually reminiscent of the 2-step RACH scheme defined in 3GPP for cellular networks. During the designated random access channel (RACH) slots, users engaged in random access transmit codewords drawn from a shared codebook. The distributed and CF nature of the network is adressed by partitioning the network coverage area into zones, referred to as “locations,” and assigning a location-specific access codebook to each location. We use the recently proposed multisource Approximated Message Passing (AMP) algorithm for joint detection of the access codewords and channel estimation. In particular, AMP generates two outputs:a list of active codewords and an estimate of the corresponding channel vectors. The aforementioned outputs facilitate the formation of a user-centric cluster. The impact of random access users and the overall system performance are assessed based on the sum spectral efficiency for uplink (UL) and downlink (DL) of the data transmission slots, under limited random activity of the users.

关键词： Fading channels Transmitters Spectral efficiency Wireless networks System performance Message passing Channel estimation Vectors Partitioning algorithms Uplink

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Empowering Software engineering Students: The Role of Gamification in Promoting Learning Independence 7

Empowering Software Engineering Students: The Role of Gamifi...

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7th International Conference on Vocational Education and Electrical engineering, ICVEE 2024

作者： Hidayat, Wahyu Nur Hamdan, Achmad Ichwanto, Muhammad Aris Muslihati Cakir, Gulsun Kurubacak Sari, Rahajeng Kartika Afni, Nur Hisyam, Mochammad Raffi Fadhil Universitas Negeri Malang Faculty of Engineering Department of Electrical Engineering and Informatics Malang Indonesia Universitas Negeri Malang Faculty of Vocational Department of Electronic Systems Engineering Technology Malang Indonesia Universitas Negeri Malang Faculty of Engineering Department of Civil Engineering and Planning Malang Indonesia Universitas Negeri Malang Faculty of Education Science Department of Guidance and Counseling Malang Indonesia Ankara Haci Bayram Veli University College of Communication Department of Journalism Ankara Turkey Smk Negeri 10 Malang Department of Computer Network and Telecommunication Engineering Malang Indonesia

ISBN: (纸本)9798331505103

The role of learning media greatly supports the learning process, increasing motivation and interest in the process. Learning media can also be a place to improve competence optimally through bootcamp training menus and the like with more specific training objectives. The main objective of this study is to develop a gamification-based website to enhance students' preparedness for their Final Competency Test by tackling challenges in comprehending programming absent practical experience. The learning method implemented in this website is case-based learning that integrates dynamic coding activities. The research method uses the Plomp approach with 3 main stages, namely preliminary research, prototyping, and assessment stage. The trial of the gamification website began with media and content validation followed by small group testing and large group testing. Validation by media and material experts led to the execution of small group trials (95.7%) and large group trials (91.7%), producing exceptionally positive outcomes. The results of the small group test with 12 respondents produced a very suitable value with a score of 95.7 and the large group test with 27 respondents produced a score of 9 1. 7 % with very suitable criteria. This indicates that the web-based platform is highly viable for assisting vocational students in acquiring software development skills, enhancing their preparedness for practical IT positions such as software developers and system analysts. Subsequent study involved the incorporation of gamification, such as computer-supported collaborative learning. © 2024 IEEE.

关键词： Software testing

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Streamlining Software Release Process and Resource Management for Microservice-based Architecture on multi-cloud 25

Streamlining Software Release Process and Resource Managemen...

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25th International Multi Topic Conference, INMIC 2023

作者： Herath, Isuru Pathum Jayawardena, Samuditha Fadhil, Ahamed Kodagoda, Nuwan Arachchillage, Udara Srimath S.Samaratunge Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Sri Lanka

ISBN: (纸本)9798350317701

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry's automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components. © 2023 IEEE.

关键词： Long short-term memory

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RETRACTED ARTICLE: Dynamic Multilevel Scheduling Strategy (MSS) mechanism for commercial multi-cloud surroundings

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Distributed and Parallel Databases 2023年第1-2期41卷 159-159页

作者： Selvapandian, D. Santhosh, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Coimbatore India

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The Way to Check the Reliability Test in Smart Grids for the Smart Protection System

The Way to Check the Reliability Test in Smart Grids for the...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Hannah Jessie Rani, R. Goyal, Manish Kumar Mishra, Awakash Faculty of Engineering and Technology Department of Electrical and Elceltronics Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

The complex operation of contemporary power systems is closely linked to the widespread integration of computer networks, or cyber networks. These applications fall into two general categories: direct and indirect interdependencies, which are the result of how they communicate with power networks. Past studies explored the area of direct interdependencies in cyber-power networks and presented a state mapping-based model that was carefully designed to assess its impact on power system dependability. The investigation of indirect relationships between cyber and electricity networks is the new emphasis of this article. A wide range of applications involving indirect interdependencies in modern power systems are examined, laying the foundation for the release of a model that is based on state updating. This model is carefully crafted to provide a numerical evaluation of cyber-power networks' dependability in the face of indirect interdependencies. Two separate models are used to maximize data communication inside the cyber network and minimize load limitation in the power network, respectively, in order to maximize the functioning of these integrated networks. In order to demonstrate the efficacy of the suggested solution model, the research focuses on a high-voltage converter equipped with both protection and monitoring devices. By taking a comprehensive approach, the research makes a substantial contribution to a deeper understanding of the complex interactions between cyber and electricity networks, providing priceless insights for enhancing the dependability of these interconnected systems. © 2023 IEEE.

关键词： Cyber-Power Interdependencies Dependability Indices and Optimization Framework Indirect Relationships Monitoring Systems P- Table Protection Systems Reliability Assessment Smart Grid State Updating

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Study of Feature Fusion Models for Human Activity Recognition on Point Clouds from mmWave Radar 9

Study of Feature Fusion Models for Human Activity Recognitio...

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9th IEEE Smart World Congress, SWC 2023

作者： Kong, Lin Zhou, Houpu Zhang, Guochen Huang, Runhe Ma, Yanbo Luo, Yunlong Su, Yun Pontosense Inc. Canada Southwest Jiaotong University School of Information Science and Technology Chengdu China Northwest Normal University College of Computer Science and Engineering Gansu Lanzhou China Hosei University Faculty of Computer and Information Sciences Tokyo Japan

ISBN: (纸本)9798350319804

Human Activity Recognition (HAR) has gained significant attention as a technological response to the ever-increasing global need to monitor the health of elderly populations. This research focused on millimeter-wave (mmWave) radar technology for HAR because of its comparative advantages over competing technologies in privacy protection, superior penetration, and lack of a requirement for ambient light. This research proposes a feature fusion model for detecting activities that incorporates both voxel and Doppler information embedded in pixels. The proposed model achieves high accuracy of 95.70% for recognizing human activities, which is a significant improvement in performance over the performance using a single model, particularly in distinguishing between challenging activity categories. This paper provides an overview of the HAR system based on mmWave radar, including the design of two submodels and the composition of the feature fusion model, as well as an accuracy analysis of the proposed model through experimentation. The study also identifies several challenges that need to be addressed in future research, such as the improvement of dataset categories, the pre-processing of point cloud data, and the development of models with greater accuracy in recognizing similar activity categories. © 2023 IEEE.

关键词： 2D-CNN 3D-CNN Feature Fusion LSTM Point Cloud Voxel

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A Review on Building Blocks of Decentralized Artificial Intelligence

arXiv

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arXiv 2024年

作者： Kersic, Vid Turkanovic, Muhamed The Faculty of Electrical Engineering and Computer Science University of Maribor Maribor2000 Slovenia

Artificial intelligence is transforming our lives, and technological progress and transfer from the academic and theoretical sphere to the real world are accelerating yearly. But during that progress and transition, several open problems and questions need to be addressed for the field to develop ethically, such as digital privacy, ownership, and control. These are some of the reasons why the currently most popular approaches of artificial intelligence, i.e., centralized AI (CEAI), are questionable, with other directions also being widely explored, such as decentralized artificial intelligence (DEAI), to solve some of the most reaching problems. This paper provides a systematic literature review (SLR) of existing work in the field of DEAI, presenting the findings of 71 identified studies. The paper’s primary focus is identifying the building blocks of DEAI solutions and networks, tackling the DEAI analysis from a bottom-up approach. In the end, future directions of research and open problems are proposed. © 2024, CC BY.

关键词： Cryptography

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Analysis and Prediction of Location based Criminal Behaviors Through Machine Learning

Analysis and Prediction of Location based Criminal Behaviors...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Pandya, Darshanaben Dipakkumar Amarawat, Geetanjali Jadeja, Abhijeetsinh Degadwala, Sheshang Vyas, Dhairya Visnagar India Faculty of Computer Engineering Swaminarayan University Kalol India Computer Engineering Department Sigma Inst. of Engineering Gujarat Vadodara India Computer Science and Engineering The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Criminal Behaviors may be predicted, identified, and prevented via data mining. The research of criminal activity and its features is known as criminology. To be more accurate, crime analysis entails investigating and detecting crimes and their connections to convict. Because of the vast number of criminal record and intricacy of the linkages between different types of data, criminology is an excellent place to use data mining techniques. The first stage in doing more research is to identify criminal traits. This study offered an overview of current work in the field of crime prevention or prediction using data mining and machine learning technologies. For the system to learn, a year's worth of crime data is fed into it from a reliable Indian internet source that focuses on murder, kidnapping and abduction as well as dacoits and burglars and rape. To anticipate crime rates in different states, a regression model is created using Indian statistics, which provides data on a wide range of crimes over the last few years. A variety of Machine Learning techniques, including unsupervised, semi-supervised, and supervised learning, are used to improve the accuracy of crime predictions. Local police stations will benefit from this effort with regards to crime avoidance. The results may help policymakers better understand crime prediction and prevention. © 2022 IEEE.

关键词： Crime

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Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

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computers, Materials & Continua 2022年第2期70卷 3281-3294页

作者： Nazar Hussain Muhammad Attique Khan Usman Tariq Seifedine Kadry Muhammad Asfand E.Yar Almetwally M.Mostafa Abeer Ali Alnuaim Shafiq Ahmad Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Data Science Noroff University CollegeNorway Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543KSASaudi Arabia Department of natural and engineering sciences College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11421Saudi Arabia Faculty of Engineering Industrial Engineering DepartmentKing Saud UniversityRiyadh11421Saudi Arabia

Agriculture is an important research area in the field of visual recognition by *** diseases affect the quality and yields of ***-stage identification of crop disease decreases financial losses and positively impacts crop *** manual identification of crop diseases,which aremostly visible on leaves,is a very time-consuming and costly *** this work,we propose a new framework for the recognition of cucumber leaf *** proposed framework is based on deep learning and involves the fusion and selection of the best *** the feature extraction phase,VGG(Visual Geometry Group)and Inception V3 deep learning models are considered and *** fine-tuned models are trained using deep transfer *** are extracted in the later step and fused using a parallel maximum fusion *** the later step,best features are selected usingWhale Optimization *** best-selected features are classified using supervised learning algorithms for the final classification *** experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%.A comparison with recent techniques shows the significance of the proposed method.

关键词： Cucumber diseases database preparation deep learning parallel fusion features selection

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