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27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Kolodziejczyk, Joanna Tarasenko, Yuliia Sulikowski, Piotr Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Department of Information Systems Engineering Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Particle Swarm Optimization (PSO) is a robust stochastic optimization algorithm for solving complex and constrained optimization problems. This paper aims to systematically investigate the influence of diverse random number distributions on the learning parameters of PSO and evaluate the algorithm's sensitivity based on these parameters. To this end, several exhaustive numerical experiments were conducted on several test functions, including asymmetric Sphere, Easom, and Csendes. Initially, appropriate random distributions were identified for the learning parameters and proceeded to compare these distributions using all potential combinations. The sensitivity of PSO to these parameters was evaluated using the mean best score, the relative proximity of the best particle to the theoretical optimum, and the average objective function evaluations. The most significant contribution of this research lies in the insights derived from the experimental data. The discovery was that highly skewed and long-tailed distributions such as Chi, Chi-square, Lomax, and Exponential often underperform. On the other hand, distributions that are bounded proportionally to the problem domain consistently surpass the performance of the standard uniform distribution. These findings significantly enhance the understanding of PSO algorithms and provide valuable guidelines for their practical deployment. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Particle Swarm Optimization random number distribution stochastic optimization

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Data Trading and Monetization: Challenges and Open Research Directions

arXiv

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arXiv 2024年

作者： Ramadan, Qusai Boukhers, Zeyd AlShaikh, Muath Lange, Christoph Jürjens, Jan Institute of Software Engineering University of Koblenz Germany Fraunhofer Institute for Applied Information Technology FIT Uniklinik Köln Germany College of Computing and Informatics Saudi Electronic University Saudi Arabia Fraunhofer Institute for Applied Information Technology FIT RWTH Aachen University Germany University of Koblenz Fraunhofer-Institute for Software and Systems Engineering ISST Germany

Traditional data monetization approaches face challenges related to data protection and logistics. In response, digital data marketplaces have emerged as intermediaries simplifying data transactions. Despite the growing establishment and acceptance of digital data marketplaces, significant challenges hinder efficient data trading. As a result, few companies can derive tangible value from their data, leading to missed opportunities in understanding customers, pricing decisions, and fraud prevention. In this paper, we explore both technical and organizational challenges affecting data monetization. Moreover, we identify areas in need of further research, aiming to expand the boundaries of current knowledge by emphasizing where research is currently limited or lacking. © 2024, CC BY.

关键词： Commerce

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Comp-RPKI: A Decentralized Protocol for Full Route Origin Validation

Comp-RPKI: A Decentralized Protocol for Full Route Origin Va...

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International Conference on Big Data Computing and Communications (BIGCOM)

作者： Yanwei Wu Yun Wang Jack DeJulius Riad Abboud Melvin Woitel Bohdan Osadchyy Kyree Richardson Computer Science and Software Engineering University of Wisconsin Platteville WI USA Computer Science and Information Systems Bradley University Peoria IL USA

The Resource Public Key Infrastructure (RPKI) is an essential technology for securing the Border Gateway Protocol (BGP) routing on the Internet. Although the global RPKI-ROV (Route Origin Validation) rate of unique Prefix-Origin Pairs(IPv4) has been improved from 17% in June 2019 (pre-pandemic) to 41.12% in March 2023 (post-pandemic) [1] – [3], the overall RPKI-ROV rate remains below 50%. This indicates a significant insufficiency in RPKI-ROV deployment for ensuring secure BGP routing and persistent security challenges continue to persist in the Internet. To address this, we first develop software to analyze and visualize the relationships among Autonomous systems (ASes), degree distribution, and their RPKI validation status, given a region in IL. Next, we investigate the RPKI validation rates along real-time routing paths from Peoria, IL to all 50 states in the USA. Based on the analysis, we propose a novel lightweight, decentralized, easy-to-deploy, offline protocol called Comp-RPKI, which aims to complement and address the partial deployment of RPKI. Comp-RPKI aims to ensure every segment of a routing path is validated either through online RPKI or secured by offline Comp-RPKI, thereby achieving full RKPI-ROV coverage and enhancing BGP security.

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Customers' sentiment on food delivery services: An Arabic text mining approach

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International Journal of information Management Data Insights 2024年第2期4卷

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa 13133 Jordan Department of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid 21163 Jordan Department of Software Engineering Jordan University of Science & Technology Irbid 21110 Jordan

The Covid-19 pandemic has accelerated the shift in organizations' strategies toward innovative online services. Customer reviews on platforms for online ordering and delivery are a vital source of information about how well a business is performing. Businesses that provide food delivery services (FDS) seek to leverage consumer input to locate areas where customer satisfaction could be raised. Sentiment analysis (SA) has been the subject of an enormous amount of English-language research. Despite Arabic's increasing popularity as a writing language on the Internet, not much study has been conducted on sentiment analysis of Arabic up to this point, with a limited number of publicly available resources for Arabic SA such as datasets and lexicons. The present study collects FDS-related reviews in Arabic to conduct extensive emotion mining, taking advantage of Natural Language Processing, feature selection, and Machine Learning techniques to elicit personal judgments, identify polarity, and recognize customers’ feelings in the FDS domain. To demonstrate that the proposed approach is suitable for analyzing human perceptions of FDS, we designed and carried out excessive experiments that assess the utility of each phase. Our highest categorization accuracy was 90 % using Mutual information with the SVM classifier. The study's findings provide various managerial insights for improving their plans and service delivery, as well as revealing the main reasons for consumer complaints. It also demonstrates how future academics might harness the power of online business reviews in Arabic using a variety of text-mining approaches. © 2024 The Author(s)

关键词： Arabic dialects Arabic sentiments resources Consumer sentiment Features selection Food delivery services Machine learning

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Fitness Approximation through Machine Learning

arXiv

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arXiv 2023年

作者： Tzruia, Itai Halperin, Tomer Sipper, Moshe Elyasaf, Achiya The Software and Information Systems Engineering Department Ben-Gurion University of the Negev Israel The Computer Science Department Ben-Gurion University of the Negev Israel

We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine-learning (ML) models, through dynamic adaptation to the evolutionary state. Maintaining a dataset of sampled individuals along with their actual fitness scores, we continually update a fitness-approximation ML model throughout an evolutionary run. We compare different methods for: 1) switching between actual and approximate fitness, 2) sampling the population, and 3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than that of the fully run GA-depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators-where fitness computation is costly-our approach is generic and can be easily applied to many different domains. © 2023, CC BY.

关键词： Genetic algorithms

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Prediction of the Mechanical Characteristic of Bamboo Using Deep Neural Network

Prediction of the Mechanical Characteristic of Bamboo Using ...

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Electrical, Computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Raviduth Ramful Mohammad Shoaib Casseem Mechanical & Production Engineering Department University of Mauritius Réduit Mauritius Department of Software and Information Systems University of Mauritius Réduit Mauritius

The prediction of the mechanical characteristics of bio-based materials, which are indispensable in engineering-related applications, are restrained by numerous natural-occurring and environmental factors. The aim of this study is to use artificial intelligence (AI) technology to estimate with a high level of accuracy the mechanical characteristics of bamboo materials for use in construction based on their inherent physical characteristics. The biological-inspired technique of deep neural network (DNN) was considered as the specific technology in the major sub-fields of AI and the Adam optimizer was selected as optimization function in the proposed algorithm. The model was trained and conceptualized to predict the mechanical properties of bamboo materials in construction by considering extensive datasets based on the condition assessment and physical properties of the materials in context. A dataset consisting of a total of 600 entries was considered for this study. The proposed model in this study, which is still in its developmental stage, has revealed glimpses of the powerful ability of the DNN model to predict the mechanical characteristics of bio-based materials such as bamboo.

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DomainDemo: a dataset of domain-sharing activities among different demographic groups on Twitter

arXiv

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arXiv 2025年

作者： Yang, Kai-Cheng Goel, Pranav Quintana-Mathé, Alexi Horgan, Luke McCabe, Stefan D. Grinberg, Nir Joseph, Kenneth Lazer, David Network Science Institute Northeastern University BostonMA United States Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Be’er-Sheva Israel Computer Science and Engineering Department University at Buffalo BuffaloNY United States

Social media play a pivotal role in disseminating web content, particularly during elections, yet our understanding of the association between demographic factors and political discourse online remains limited. Here, we introduce a unique dataset, DomainDemo, linking domains shared on Twitter (X) with the demographic characteristics of associated users, including age, gender, race, political affiliation, and geolocation, from 2011 to 2022. This new resource was derived from a panel of over 1.5 million Twitter users matched against their U.S. voter registration records, facilitating a better understanding of a decade of information flows on one of the most prominent social media platforms and trends in political and public discourse among registered U.S. voters from different sociodemographic groups. By aggregating user demographic information onto the domains, we derive five metrics that provide critical insights into over 129,000 websites. In particular, the localness and partisan audience metrics quantify the domains’ geographical reach and ideological orientation, respectively. These metrics show substantial agreement with existing classifications, suggesting the effectiveness and reliability of DomainDemo’s approach. © 2025, CC BY.

关键词： Tweets

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Improving the Flexural Behaviour of Small Clear 3D-Printed PLA Specimens Through Generative Design

Improving the Flexural Behaviour of Small Clear 3D-Printed P...

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Sustainable Technology and engineering (i-COSTE), International Conference on

The interest to look into alternative engineered materials, which would maximize the efficiency and performance attributes in numerous modern engineering application as well as to push the boundary imposed by present-day material science in the field engineering, is ever so high. One commonly searched feature in engineered materials is their high strength-to-weight ratio. The new AI (Artificial Intelligence) -driven technique of generative design has revolutionized the method of design refinement in Computer-Aided Design (CAD) by enabling advanced optimization techniques which was formerly-unavailable. This study seeks to apply the technique of generative design to improve the flexural behaviour of small clear Polylactic acid (PLA) specimens produced through the additive manufacturing process. The results obtained have shown that despite the optimized material geometry and weight, the refined model was still able to bear significantly high bending loads. Further analysis via the Finite Element Method (FEM) was conducted to substantiate the experimental investigation. Findings of this study illustrates the benefits of generative design which can seamlessly provide advanced design refinements all while preserving or enhancing mechanical performance of the material.

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Web Application to predict Skin Disease: An Approach Using Deep Learning Algorithm

Web Application to predict Skin Disease: An Approach Using D...

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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Eelanila Thayalan Ayingaran Arumugavel Sharujan Ganeshan Makenthiran Dalaxshan Harinda Fernando Kaushalya Rajapakse Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The Disease Prediction System revolutionizes healthcare with advanced machine learning techniques for early detection of skin diseases, notably focusing on skin cancer. Through image processing and Transfer Learning, it excels in identifying potential malignancies, enabling timely interventions. Its capabilities extend to benign tumors and growth disorders, utilizing the ResNet50 Model for precise identification. Additionally, it predicts skin reactions associated with various dermatological conditions, such as Urticaria Hives and Warts, leveraging the efficientnetB0 Model. Integration of the VGG16 Model enhances diagnostic accuracy for inflammatory skin conditions. This holistic approach prioritizes patient-centric care, leveraging diverse datasets and intricate pattern recognition in medical images. The system’s proactive nature embodies personalized solutions for early detection, timely intervention, and improved patient outcomes. Its versatility and accuracy underscore itstransformative potential in healthcare delivery. By harnessing diverse datasets and recognizing intricate patterns within medical images, it heralds a new era of personalized healthcare solutions. In essence, the Disease Prediction System exemplifies the transformative potential of machine learning in healthcare, ensuring the highest standards of diagnostic accuracy and efficacy, while prioritizing patient well-being and quality oflife.

关键词： Transfer learning Medical services Skin Pattern recognition Medical diagnostic imaging Standards Robots

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Deep reinforcement learning-based algorithms selectors for the resource scheduling in hierarchical Cloud computing

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Journal of Network and Computer Applications 2022年第0期208卷

作者： Zhou, Guangyao Wen, Ruiming Tian, Wenhong Buyya, Rajkumar School of Information and Software Engineering University of Electronic Science and Technology of China China Cloud Computing and Distributed Systems Lab School of Computing and Information Systems The University of Melbourne Australia

Cloud computing environment is becoming increasingly complex due to its large-scale information growth and increasing heterogeneity of computing resources. Hierarchical Cloud computing dividing the system into multi-levels with multiple subsystems to support the adaptability to abundant requests from users has been widely applied and brings great challenges to resource scheduling. It is critical to find an effective way to address the complex scheduling problems in hierarchical Cloud computing, whose scenarios and optimization objectives often change with the types of subsystems. In this paper, we propose a scheduling framework to select the scheduling algorithms (SFSSA) for different scheduling scenarios considering no algorithm well suitable to all scenarios. To concretize SFSSA, we propose deep learning-based algorithms selectors (DLS) trained by labeled data and deep reinforcement learning-based algorithms selectors (DRLS) trained by feedback from dynamic scenarios to complete the algorithms selection regarding the scheduling algorithms as selectable tools. Then, we apply strategies including pre-trained model, long experience reply and joint training to improve the performance of DRLS. To enable the quantitative comparison of selectors, we introduce a weighted cost model for the trade-off between solution and complexity. Through multiple sets of experiments in hierarchical Cloud computing with multi subsystems for five types of scheduling problems and varying weights of cost, we demonstrate DLS and DRLS outperform baseline strategies. Compared with random selector, greedy selector, round-robin selector, single best selector, virtual best selector and single fast selector, DLS reduces the cost by 47.4%, 46.1%, 33.9%, 47.9%, 19.3%, 18.8% under stable parameter ranges, and DRLS reduces the cost by 41.1%, 40.6%, 11.7%, 42.3%, 11.5%, 12.5% in dynamic scenarios respectively. In experiments, we also validate DRLS has stronger adaptability than DLS in dynamic schedulin

关键词： Reinforcement learning

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