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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Yan Long, Qingmin Tang, Qiang He, Shiming Xiong, Bing Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410004 China National University of Defense Technology Science and Technology on Information System Engineering Laboratory Changsha410073 China

ISBN: (纸本)9798331509712

It is well-established that terrestrial communication systems may fail during emergencies such as earthquakes, tsunamis, and floods. Fortunately, with the rapid advancement of unmanned aerial vehicle (UAV) network technology, deploying UAV nodes as aerial base stations (BSs) is assuming an increasingly crucial role in facilitating downlink transmissions and restoring ground communication capabilities. However, a single UAV node is not sufficient to meet the requirements. Inspired by distributed communication, we introduce a performance analysis framework based on stochastic geometry to analyze the distributed coordinated multi-point (CoMP) UAV-assisted communication network. Specifically, we assume that all UAV nodes follow a homogeneous Poisson point process (PPP) and maintain a constant altitude. The entire space is tessellated by multiple hexagons, with multiple UAV nodes within each hexagonal region working together to serve terrestrial user equipments (UEs). For this region-centric cooperative model, we derive an exact expression for the coverage probability to quantify the performance improvement enabled by UAVs, analyze the upper bound of the coverage probability, and provide a simplified approximation. We then compare this model to a user-centric model. Our numerical findings demonstrate that the cooperation of UAV nodes can significantly enhance the coverage probability and save spectrum resources. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optimal planning of a green hydrogen fueling station

Optimal planning of a green hydrogen fueling station

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Coelho, António Soares, Filipe Iria, José Inesc Tec Center for Power and Energy System Porto Portugal Australian National University College of Engineering and Computer Science Canberra Australia

ISBN: (纸本)9789531842976

As the global community transitions towards decarbonization and sustainable energy, green hydrogen is emerging as a key clean energy carrier. This paper addresses the role of hydrogen in transportation, emphasizing the European Union's additionality principle for renewable energy sources in green hydrogen production. It introduces a model for optimally designing hydrogen fueling stations, considering electrolyzers, hydrogen storage, fuel cells, PV systems, and batteries. This model also considers the participation in electricity (energy and secondary reserve), hydrogen, and oxygen markets, and it is evaluated under different additionality policy scenarios. Results indicate that stricter additionality policies reduce the internal rate of return. However, participation in secondary reserve markets significantly boosts operational revenues and compensates for higher investment costs. © 2024 IEEE.

关键词： Clean energy

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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jabbar, Abdul Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia

ISBN: (纸本)9798350369359

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities. © 2023 IEEE.

关键词： Ant colony optimization

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Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach 4

Identifying The Coconut Leaf Disease Using Deep Transfer Lea...

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4th IEEE International Conference on Smart Information systems and Technologies, SIST 2024

作者： Tuhin, Hemayet Hossain Kayum, Md Abdul Rahmatul Kabir Rasel Sarker, Md. Tanvir Siddiquee, Shah Md Pandey, Bishwajeet Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Intelligent System and Cyber Security Astana IT University Kazakhstan

ISBN: (纸本)9798350374865

Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have_ been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry. © 2024 IEEE.

关键词： coconut leaf disease Inception v3 MobileNet v2 nutrient deficiency pest attacks ResNet50 VGG19

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Machine learning classifiers with pre-processing techniques for rumour detection on social media: an empirical study

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International Journal of Cloud Computing 2022年第4期11卷 330-344页

作者： Al-Sarem, Mohammed Al-Harby, Muna Saeed, Faisal Hezzam, Essa Abdullah College of Computer Science and Engineering Taibah University Medina Saudi Arabia Saba'a Region University Mareb Yemen Information System Department College of Computer Science and Engineering Taibah University Medina Saudi Arabia

The rapid increase in popularity of social media helped the users to easily post and share information with others. However, due to uncontrolled nature of social media platforms, such as Twitter and Facebook, it becomes easy to post fake news and misleading information. The task of detecting such problem is known as rumour detection. This task requires data analytics tools due to the massive amount of shared content and the rapid speed at which it is generated. In this work, the authors aimed to study the impact of different text pre-processing techniques on the performance of classifiers when performing rumour detection. The experiments were performed on a dataset of tweets on emerging breaking news stories which cover several events of Saudi political context (EBNS-SPC). The results have shown that pre-processing techniques have a significant impact on increasing the performance of machine learning methods such as support vector machine (SVM), multinomial naïve Bayes (MNB), and K-nearest neighbour (KNN) classifiers. However, the classifiers react differently when different combinations of pre-processing techniques were used. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Support vector machines

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GHVC-Net: Hypervolume Contribution Approximation Based on Graph Neural Network

GHVC-Net: Hypervolume Contribution Approximation Based on Gr...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Wu, Guotong Nan, Yang Shang, Ke Ishibuchi, Hisao Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665410205

This paper proposes a framework called GHVC-Net that uses the graph neural network (GNN) model to approximate each solution's hypervolume contribution (HVC). GHVC-Net is permutation invariant and can handle solution sets of arbitrary size, similar to the properties of GNN. Compared to HVC-Net (i.e., a machine learning model for HVC approximation), GHVC-Net achieves better accuracy with less training time. GHVC-Net is also compared with traditional approximation methods, such as line-based and point-based methods, to demonstrate its ability to identify the solution with the smallest (largest) HVC. © 2024 IEEE.

关键词： Neural network models

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Optimizing Broadcast Utilization for Efficient Disaster Management Using Wireless Ad Hoc Networks and Novel Energy-Saving Algorithms

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第20期18卷 142-156页

作者： Alzyoud, Faisal Y. Tarawneh, Monther Almaghthawi, Ahmed Altalidi, Askar Asiri, Layla Alrehaili, Meaad Department of Computer Science Faculty of Information Technology Isra University Amman Jordan Department of Information Technology College of Information Technology and Communications Tafila TechnicaUniversity Tafila Jordan Department of Computer Science College of Science & Art at Mahayil King Khalid University Abha Saudi Arabia Department of Information Science College of Science & Art at Mahayil King Khalid University Abha Saudi Arabia Departement of Computer Information System Applied college at Mahayil King Khalid University Abha Saudi Arabia Department of Computer Sciences and Artificial Intelligence Collège of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

Globalization, industry, and population growth is considered the main reasons for climate change, so when some natural calamity or disaster occurs in any area, it leads to isolating this area from the rest of the region. Wireless ad hoc networks are important in disaster management to reduce losses and costs as they reduce the response, aiding time to save lives. However, they suffer from limited energy, which controls the lifetime of the sensor. Different methods are applied to save energy and increase battery life, such as sleep-awake schedules which assume similar initial energy for all nodes. Domestic partition in a unit disk graph is the production of the maximum possible number of such disjoint dominants. However, it assumes different initial energy levels for all nodes, which is another problem. In this paper, a new broadcast approach based on minimum set data broadcasting is proposed to mini-mize the number of broadcasting messages to increase the battery life during disasters and enhance rescue operations. The proposed minimum rebroadcast algorithm (MSRA) method balances coverage and utilization efficiency by dynamically adjusting node selection based on network conditions. The results show that MSRA significantly reduces power consumption and transmission latency, opening the way to find a new solution for disaster management using wireless ad hoc networks. © 2024 by the authors of this article.

关键词： Mobile ad hoc networks

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Enhanced Object Detection and Classification via Multi-Method Fusion

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computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

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Ultra-large mode area multi-core orbital angular momentum transmission fiber designed by neural network and optimization algorithms

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Optoelectronics Letters 2023年第12期19卷 744-751页

作者： GU Zhiwei HUANG Wei ZHANG Ran FAN Junjie SONG Binbin Key Laboratory of Computer Vision and Systems(Ministry of Education) School of Computer Science and EngineeringTianjin University of TechnologyTianjin 300384China Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China

A large mode area multi-core orbital angular momentum(OAM)transmission fiber is designed and optimized by neural network and optimization *** neural network model has been established first to predict the optical properties of multi-core OAM transmission fibers with high accuracy and speed,including mode area,nonlinear coefficient,purity,dispersion,and effective index *** the trained neural network model is combined with different particle swarm optimization(PSO)algorithms for automatic iterative optimization of multi-core structures *** to the structural advantages of multi-core fiber and the automatic optimization process,we designed a number of multi-core structures with high OAM mode purity(>95%)and ultra-large mode area(>3000µm^(2)),which is larger by more than an order of magnitude compared to the conventional ring-core OAM transmission fibers.

关键词： network fiber OAM

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aDBF: an autonomous electromagnetic noise filtering mechanism for industrial environments 32

aDBF: an autonomous electromagnetic noise filtering mechanis...

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32nd International Conference on computer Communications and Networks, ICCCN 2023

作者： Herrero, Angel C. Sanguesa, Julio A. Martinez, Francisco J. Garrido, Piedad Calafate, Carlos T. University of Zaragoza Dept. of Computer Science and System Engineering Teruel Spain Universitat Politecnica de Valencia Dept. of Computer Engineering Valencia Spain

ISBN: (纸本)9798350336184

The use of proprietary systems in Industry 4.0 often involves high economic costs. To address this issue, using low-cost devices with similar capabilities is becoming an increasingly popular alternative. However, these devices are prone to suffer the negative effects of electromagnetic interference (EMI) due to their placement in electrical panels alongside other electromechanical devices. To solve this problem, this article presents the autonomous Data Base Filter (aDBF). aDBF is an enhanced electromagnetic interference filtering mechanism capable of eliminating erroneous signals generated by EMI. aDBF has been specifically designed to autonomously (i.e., without the need for operator supervision or intervention) determine both the number of different product types elaborated in a production line, and the time instants when their manufacturing process starts and ends. In particular, aDBF goes through three stages: (i) pre-filtering, (ii) product change detection, and (iii) identification of valid signals. The results obtained after validating our proposal in three different manufacturing shifts demonstrate that the aDBF filtering mechanism works very accurately, as the maximum error introduced is of 0.93%. © 2023 IEEE.

关键词： Signal interference

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