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IEEE International Conference on Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)

作者： Oleksandr Kozachuk Pylyp Prystavka Department of Applied Mathematics Faculty of Computer Science and Technology National Aviation University Kyiv Ukraine

ISBN: (数字)9798331534141

ISBN: (纸本)9798331534158

Global visual localization is critical for UAVs operating in environments where global navigation satellite systems (GNSS) are unreliable or unavailable. While many methods, such as visual odometry (VIO), rely on optical flow for localization, they often require prior knowledge of the current coordinates. In this paper, we propose an enhanced UAV localization method using the vector of locally aggregated descriptors (VLAD) combined with a cycle generative adversarial network (GAN) architecture. Unlike traditional approaches, our method does not require knowledge of the current coordinates; it only requires a pre-downloaded flight map, enabling location determination through image matching.

关键词： Location awareness Global navigation satellite system Satellites Image recognition Image matching Autonomous aerial vehicles Generative adversarial networks Cameras Satellite images Drones

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IT-Security Risk Based Approach for Secure Operation of Distributed Data Platforms in Supply Chains

IT-Security Risk Based Approach for Secure Operation of Dist...

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2024 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2024

作者： Voß, Marvin Kallisch, Jonas Runge, Maxim Theus, Tobias Niemann, Karl-Heinz Wunck, Christoph Faculty I - Electrical Engineering and Information Technology University of Applied Sciences and Arts Hannover Germany Department of Engineering Section Electrical Engineering and Computer Science University of Applied Sciences Leer Emden Germany Faculty of Mathematics Informatics and Statistics Ludwig Maximilian University Munich Germany

ISBN: (纸本)9798350386097

This paper presents the development of a secure data platform designed to enhance operational efficiency and to facilitate cross-company collaboration within the manufacturing supply chain. The platform is designed to ease secure information exchange while maintaining the sovereignty of each participant, leveraging standardized security features and advanced technologies such as federated learning. Security measures are based on a comprehensive risk analysis, which methodically identifies, assesses, and mitigates potential threats. Moreover, federated learning models that are not transparent are utilized. This allows data owners to maintain control over sensitive information within the consortium. The described approach markedly enhances both security and operational collaboration across the industry, representing a crucial stride towards more integrated and efficient supply chain management in manufacturing. © 2024 IEEE.

关键词： Risk analysis

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Proposed Method of Seeding and Constraint in One-Parameter-At-a-Time Approach for t-way Testing

Proposed Method of Seeding and Constraint in One-Parameter-A...

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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Muazu, Aminu Aminu Hashim, Ahmad Sobri Sarlan, Aliza Maiwada, Umar Danjuma Universiti Teknologi Petronas Faculty of Science and Information Technology Computer & Information Sciences Department Malaysia Umaru Musa Yar'adua University Katsina Faculty of Natural and Applied Science Computer Sciences Department Nigeria

ISBN: (纸本)9798350397000

T-way testing has become a popular way to software testing since it can often deliver good error detection at a low cost, and the test case generation process is one of its biggest drawbacks. One difficulty in this field is the combinatorial explosion problem, which aims to discover the best final test suite that covers all possible combinations of interaction strength. Looking at this limitation, nowadays researchers are focusing more on t-way based strategies. As such, this research examines the present state-of-the-art and proposed an effective combinatorial t-way test case generation strategy that support seeding and constraint support based on One-Parameter-At-a-Time approach, as part of an effort to build an optimized strategy for paired testing. The performance of the proposed approach to achieve a minimal test suite size has been evaluated. The results have undergone statistical analysis using the Wilcoxon Rank and Friedman tests. The suggested approach, however, consistently produces outcomes that are competitive. © 2022 IEEE.

关键词： Software testing

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An Automation Perception for Cotton Crop Disease Detection Using Machine Learning 7

An Automation Perception for Cotton Crop Disease Detection U...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Aggarwal, Ritu Aggarwal, Eshaan Jain, Anurag Choudhury, Tanupriya Kotecha, Ketan Seth Jai Prakash Mukand Lal Institute of Engineering and Technology Computer Science and Engineering Radaur India MMU Mullana India School of Computer Science Uttarakhand Dehradun248007 India Maharashtra Pune412115 India India Symbiosis Centre for Applied Artificial Intelligence Maharashtra Pune412115 India

ISBN: (纸本)9798350383065

Cotton crop disease detection and classification based on images of leaves is a significant objective in agriculture. Cotton crops play a vital role in India. Every year, due to the attack of diseases, cotton production is decreasing. The main reasons for disease in plants are the pests, insects, and pathogens used;if they are not controlled on time, they affect productivity. In advancing digital image processing technologies, machine learning and other techniques that identify early disease detection in plants are proposed. This paper focuses on improving disease detection in cotton crop plants and leaves. Machine learning is used to identify and predict cotton plant disease using images and leaves collected in an uncontrolled environment. This study uses machine learning to review the different problems identified regarding plant disease. Various experimental configurations are investigated to analyze the impacts of different leaf classes, plant disease combinations, and their categories. Cotton crops are detected to classify the various types of cotton plants. Cotton plant diseases have a wide range of illnesses, from bacterial deficiency to bacterial, fungal, viral, and vitamin and nutrient deficiency. The proposed approach indicates the cotton crop plant in the leaf disease by their implementation results. © 2023 IEEE.

关键词： Cotton

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Optimization of Vehicular Congestion Control in Traffic Signals using YOLO algorithm

Optimization of Vehicular Congestion Control in Traffic Sign...

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Smart Systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), International Conference on

作者： D. Sivaganesan S. Barath Kumar S. Krishnadharani G. Rashmitha Dept. of Computer Science and Engg PSG Institute of Technology and Applied Research Coimbatore India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The increase in the volume of vehicles utilizing our roads has highlighted the seriousness of the ongoing problem of traffic congestion. This problem is even worse at intersections, where a line of cars waits patiently for their turn to move forward. The traditional traffic light systems, which were originally intended to handle a less intense traffic load, are now having difficulty keeping up with the increasing number of vehicles, which further complicates the situation. We have adopted cutting-edge technologies, particularly those found in the fields of computer vision and machine learning, to tackle this modern dilemma. Our methodology is based on the application of a state-of-the-art system powered by a deep Convolutional Neural Network, which we call You Only Look Once (YOLO). With the help of this cutting-edge technology, we can quickly and thoroughly examine real-time data at signalized road crossings to gain a sophisticated understanding of the constantly changing traffic environment. Once we have a thorough understanding of the complex traffic dynamics thanks to YOLO's capabilities, we use this knowledge to make the most out of traffic light phases. We focus on closely examining important data elements, like the average wait time for a car and the number of cars in the line. The main goal is to create a coordinated and efficient traffic flow. This reduces the amount of time that drivers must wait in traffic overall and gives priority to safe and effective vehicle movement through intersections, resulting in a more responsive and fluid urban mobility experience.

关键词： YOLO Roads Computational modeling Real-time systems Classification algorithms Convolutional neural networks Automobiles

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The inverse nullity pair problem and the strong nullity interlacing property

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Linear Algebra and Its Applications 2024年 699卷 539-568页

作者： Abiad, Aida Curtis, Bryan A. Flagg, Mary Hall, H. Tracy Lin, Jephian C.-H. Shader, Bryan Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Department of Mathematics: Analysis Logic and Discrete Mathematics Ghent University Ghent Belgium Department of Mathematics Iowa State University AmesIA50011 United States Department of Mathematics Statistics and Computer Science University of St. Thomas HoustonTX United States Hall Labs LLC ProvoUT United States Department of Applied Mathematics National Sun Yat-sen University Kaohsiung80424 Taiwan University of Wyoming Colorado United States

The inverse eigenvalue problem studies the possible spectra among matrices whose off-diagonal entries have their zero-nonzero patterns described by the adjacency of a graph G. In this paper, we refer to the i-nullity pair of a matrix A as (null(A),null(A(i)), where A(i) is the matrix obtained from A by removing the i-th row and column. The inverse i-nullity pair problem is considered for complete graphs, cycles, and trees. The strong nullity interlacing property is introduced, and the corresponding supergraph lemma and decontraction lemma are developed as new tools for constructing matrices with a given nullity pair. © 2024 Elsevier Inc.

关键词： Matrix algebra

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Vibrating Particles System Algorithm for Solving Classification Problems

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computer Systems science & Engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Enhancing Web Application Efficiency: Exploring Modern Design Patterns within the MVC Framework

Enhancing Web Application Efficiency: Exploring Modern Desig...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Kumar, Ankit Pandey, Saroj Kumar Prakash, Sunny Singh, Kamred Udham Singh, Teekam Kumar, Gaurav Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Applied Science and Humanities India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

The growing demand for web applications across various sectors has led to challenges in developing efficient and high-performance online apps. The traditional approach to web app development may struggle to meet the performance requirements of modern online applications. To address this, the Model-View-Controller (MVC) framework has become an essential tool for creating interactive online apps. While the MVC framework has limitations, several new design patterns have emerged from its architecture that offers improvements. This paper explores some of the thoroughly researched design patterns of the MVC framework. The Composite View pattern merges several views to facilitate the development of complex user interfaces, while the Intercepting Filter design pattern filters and processes requests before they reach the controller, enhancing security and speed. The Service Layer design pattern creates an abstraction layer between the data access layer and the controller to increase scalability and maintainability. Employing MVC-based design patterns can help developers enhance the functionality and efficiency of web apps, ensuring they meet the expectations of modern consumers. © 2023 IEEE.

关键词： Controllers

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GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images

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Mathematical Biosciences and Engineering 2024年第8期21卷 6847-6869页

作者： Aggarwal, Sonam Gupta, Isha Kumar, Ashok Kautish, Sandeep Almazyad, Abdulaziz S. Mohamed, Ali Wagdy Werner, Frank Shokouhifar, Mohammad Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Model Institute of Engineering and Technology J&K Jammu India Chandigarh University Punjab Mohali140413 India Department of Computer Engineering College of Computer and Information Sciences King Saud University P.O. Box 51178 Riyadh11543 Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza12613 Egypt Applied Science Research Center Applied Science Private University Amman11931 Jordan Faculty of Mathematics Otto-von-Guericke University Magdeburg39016 Germany Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam

Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equivalent to or exceeding those of medical specialists. This capability is particularly crucial in the realm of gastrointestinal disorders, where even experienced gastroenterologists find the automatic diagnosis of such conditions using endoscopic pictures to be a challenging endeavor. Currently, gastrointestinal findings in medical diagnosis are primarily determined by manual inspection by competent gastrointestinal endoscopists. This evaluation procedure is labor-intensive, time-consuming, and frequently results in high variability between laboratories. To address these challenges, we introduced a specialized CNN-based architecture called GastroFuse-Net, designed to recognize human gastrointestinal diseases from endoscopic images. GastroFuse-Net was developed by combining features extracted from two different CNN models with different numbers of layers, integrating shallow and deep representations to capture diverse aspects of the abnormalities. The Kvasir dataset was used to thoroughly test the proposed deep learning model. This dataset contained images that were classified according to structures (cecum, z-line, pylorus), diseases (ulcerative colitis, esophagitis, polyps), or surgical operations (dyed resection margins, dyed lifted polyps). The proposed model was evaluated using various measures, including specificity, recall, precision, F1-score, Mathew’s Correlation Coefficient (MCC), and accuracy. The proposed model GastroFuse-Net exhibited exceptional performance, achieving a precision of 0.985, recall of 0.985, specificity of 0.984, F1-score of 0.997, MCC of 0.982, and an accuracy of 98.5%. ©2024 the Author(s), licensee AIMS Press.

关键词： Endoscopy

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A survey and recent advances in black widow optimization: variants and applications

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Neural Computing and Applications 2024年 1-21页

作者： Shehab, Mohammad Shambour, Mohd Khaled Yousef Abu Hashem, Muhannad A. Al Hamad, Husam Ahmad Shannaq, Fatima Mizher, Manar Jaradat, Ghaith Sh. Daoud, Mohammad Abualigah, Laith College of Computer Sciences and Informatics Amman Arab University Amman11953 Jordan The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research Umm Al-Qura University Makkah Saudi Arabia Department of Geomatics Architecture and Planning Faculty King Abdulaziz University Jeddah Saudi Arabia College of Engineering Al Ain University Abu Dhabi United Arab Emirates Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Applied science research center Applied science private university Amman11931 Jordan College of Engineering Yuan Ze University Taoyuan32003 Taiwan

Artificial Intelligence techniques, such as optimization algorithms, have become essential for success in many fields. Therefore, most researchers, especially in computer and engineering sciences, focused their efforts and abilities on adapting the optimization algorithms for solving various problems. This review introduces one of the recent nature-inspired optimization algorithms, called black widow optimization (BWO). This review highlights the main procedures of BWO, its strengths and weaknesses, the growth of BWO, the variants of BWO (i.e., binary, modification, and hybridization), and the efficiency of BWO in different fields and applications (i.e., feature selection, medical, image processing, engineering, IoT, power and energy, and networks). As a result, the review seeks to assist researchers who are interested in using the BWO to address their challenges. For instance, it could help manage large pilgrim crowds, enabling smoother ritual performances and overcoming potential difficulties they encounter. Also, it suggests new possible ways of enhancing the efficiency of the basic BWO. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Optimization

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