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Telecommunications Forum (TELFOR)

作者： Luka Jovanovic Nikola Jovic Aleksandar Petrovic Nebojsa Budimirovic Miodrag Zivkovic Nebojsa Bacanin Faculty of Informatics and Computing Singidunum University Belgrade Serbia Faculty of Technical Sciences Singidunum University Belgrade Serbia

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

As global energy demand grows, renewable sources offer a key alternative to fossil fuels. However, integrating these sources into power grids presents challenges, especially with supply unpredictability. This paper demonstrates how AI, specifically using a modified PSO algorithm to enhance GRU models, can predict solar energy output and improve grid balancing. Experiments on public datasets show that the optimized models achieve high accuracy, with the best models demonstrating a mean square error (MSE) of 0.024642 and an mean absolute percentage error (MAPE) of 0.000943 in hourly predictions, facilitating renewable energy integration.

关键词： Renewable energy sources Energy consumption Substations Accuracy Biological system modeling Solar energy Predictive models Prediction algorithms Fossil fuels Artificial intelligence

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The Use of LDAP for Query Operation in Trader Service

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Procedia Computer Science 2025年 259卷 297-305页

作者： Farkhana Binti Muchtar Mohd KuFaisal Bin Mohd Sidek Ismail Fauzi Bin Isnin Kiran Sanjay Degan Salmah Binti Fattah Muhammad Zafran Bin Muhammad Zaly Shah Nor Anita Fairos binti Ismail Zuraini binti Ali Shah Faculty of Computing Universiti Teknologi Malaysia 81310 Johor Bahru Johor Malaysia Symbiosis International University India Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

This research investigates the potential of the Lightweight Directory Access Protocol (LDAP) to optimize query operations in the Object Management Group (OMG) trading service, emphasizing the need for swift query responses in distributed systems. By leveraging LDAP’s inherent strengths in read-intensive environments, this study proposes a solution to enhance information retrieval and search efficiency. Through a comparative analysis involving a trading service configured with LDAP versus one using a conventional database backend, the results highlight superior performance in query response times when utilizing LDAP. The findings underscore the efficacy of LDAP as a backend storage mechanism, particularly in scenarios demanding rapid data access, and suggest its suitability for improving the operational efficiency of trading services in distributed computing contexts.

关键词： Object management group Lightweight directory access protocol Trading service

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Towards Swarm Intelligence Ontology for Formal Description of Metaheuristics Algorithms 4

Towards Swarm Intelligence Ontology for Formal Description o...

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4th International Conference on computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2023

作者： Tulicic, Domagoj Horvat, Marko Lovrencic, Sandra University of Applied Science in Information Technology Department of Computer System and Networks Zagreb Croatia University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia University of Zagreb Faculty of Organization and Informatics Foundations of Information Sciences Department of Theoretical and Applied Varaždin Croatia

ISBN: (纸本)9798350382754

Swarm intelligence is a class of nature-inspired metaheuristic algorithms, that is specifically derived from biological systems in nature with an emphasis on their social interactions. These algorithms have been primarily developed to solve complex combinatorial optimization problems and are extremely popular among researchers in metaheuristic algorithms. This is evident from the numerous published scientific papers describing algorithms inspired by biological systems, ranging from insects such as ants and bees to the largest animals on the planet like whales. All these algorithms share some common concepts and relationships that can be explicitly identified, named, and formalized. These shared concepts and relationships can be organized and represented through the construction of an ontology. The goal of this paper is to present a formal ontology that standardizes vocabulary, as well as a set of rules enabling precise communication and reasoning about these algorithms. Ultimately, this will improve our understanding, analysis, research processes, and utilization of these computational approaches. © 2023 IEEE.

关键词： Swarm intelligence

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Generative Adversarial Network for Data Augmentation and Substitution

Generative Adversarial Network for Data Augmentation and Sub...

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Marko Stankovic Nebojsa Bacanin Miodrag Zivkovic Luka Jovanovic Marko Sarac Milos Antonijevic Faculty of Technical Sciences Singidunum University Belgrade Serbia Faculty of Informatics and Computing Singidunum University Belgrade Serbia

ISBN: (数字)9798350349153

ISBN: (纸本)9798350349160

The healthcare industry undergoes a transformative shift with the integration of artificial intelligence (AI), particularly in medical imaging analysis. AI algorithms, trained on extensive datasets, demonstrate accuracy comparable to medical experts, promising earlier diagnoses and improved outcomes. However, challenges in widespread AI adoption include limited access to high-quality medical datasets and concerns about data privacy. These challenges hinder progress, as existing datasets often suffer from obsolescence. Overcoming these limitations is crucial for advancing AI research in healthcare. Leveraging Generative Adversarial Networks (GANs) addresses data scarcity by generating synthetic medical data that maintains essential features without compromising patient privacy. GANs present a solution to challenges related to data availability and quality, unlocking new opportunities for innovation in medical AI research and clinical practice.

关键词： Data privacy Technological innovation Accuracy Neural networks Medical services Generative adversarial networks Data models

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Modeling uncertainties associated with decision-making algorithms based on similarity measures of possibility belief interval-valued fuzzy hypersoft setting

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Neutrosophic Sets and Systems 2025年 77卷 282-309页

作者： Romdhini, Mamika Ujianita Al-Sharqi, Faisal Al-Obaidi, R.H. Rodzi, Zahari Md. Department of Mathematics Faculty of Mathematics and Natural Science University of Mataram Mataram83125 Indonesia Department of Mathematics Faculty of Education for Pure Sciences University of Anbar Ramadi55431 Iraq Fuel and Energy Techniques Engineering Department College of Engineering and Technologies Al-mustaqbal University Babylon51001 Iraq College of Computing Informatics and Mathematics UiTM Cawangan Negeri Sembilan Kampus Seremban Negeri Sembilan 72000 Malaysia

Hypersoft sets (HSSs) were initiated as an extension of soft sets (SSs) to address real-life scenarios involving multiple disjoint sets with different traits. One such extension is the interval-valued fuzzy hypersoft set (IVFHSS), which has proven effective in decision-making (DM). However, the IVFHSS model lacks a mechanism to incorporate the degree of acceptance of DM opinions, which is crucial for accurate decision-making. To overcome this limitation, our work aims to develop a novel hyperstructure called a possibility interval-valued fuzzy hypersoft set (PIVFHS-set). We begin by introducing essential operations and their properties, such as PIVFHS-subset, PIVFHS-null set, PIVFHS-absolute set, and complement of a PIVFHS-set. These concepts are illustrated through numerical examples to demonstrate their practical applications. Next, we delve into set-theoretic operations of PIVFHS sets, including union, intersection, AND, OR, and relevant laws. These operations are further elucidated through numerical examples, matrix representations, and graphical illustrations. Additionally, we present two algorithms based on AND and OR operations, providing step-by-step explanations and showcasing their effectiveness through illustrative examples. Furthermore, we introduce a similarity measure to facilitate pattern recognition in PIVFHS-sets, aiding users in recruitment processes. Alongside an analytical study of the advantages and disadvantages of this model, we provide suggestions for future research based on the identified limitations. © 2025, University of New Mexico. All rights reserved.

关键词： Fuzzy sets

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Metaheuristics Algorithm for Tuning of PID Controller of Mobile Robot System

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Computers, Materials & Continua 2022年第8期72卷 3481-3492页

作者： Harsh Goud Prakash Chandra Sharma Kashif Nisar Muhammad Reazul Haque Ag.Asri Ag.Ibrahim Narendra Singh Yadav Pankaj Swarnkar Manoj Gupta Laxmi Chand Indian Institute of Information Technology NagpurIndia School of Computing&Information Technology Manipal UniversityJaipurRajasthanIndia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Maulana Azad National Institute of Technology BhopalIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurRajasthanIndia Department of Computer Science and Engineering Hanyang UniversitySeongdong-guSeoul04763Korea

Robots in the medical industry are becoming more common in daily life because of various advantages such as quick response,less human interference,high dependability,improved hygiene,and reduced aging *** is why,in recent years,robotic aid has emerged as a blossoming solution to many challenges in the medical *** this manuscript,meta-heuristics(MH)algorithms,specifically the Firefly Algorithm(FF)and Genetic Algorithm(GA),are applied to tune PID controller constraints such as Proportional gain Kp Integral gain Ki and Derivative gain *** controller is used to control Mobile Robot System(MRS)at the required set *** FF arrangements are made based on various pre-analysis.A detailed simulation study indicates that the proposed PID controller tuned with Firefly Algorithm(FF-PID)for MRSis beneficial and suitable to achieve desired closed-loop system *** FF is touted as providing an easy,reliable,and efficient tuning technique for PID *** most suitable ideal performance is accomplished with FF-PID,according to the display in the time ***,the observed response is compared to those received by applying GA and conventional off-line tuning *** comparison of all tuning methods exhibits supremacy of FF-PID tuning of the given nonlinear Mobile Robot System than GA-PID tuning and conventional controller.

关键词： Metaheuristic algorithm genetic algorithm PID controller mobile robot system firefly algorithm

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Message from Advisor

2023 International Conference on Emerging Trends in Networks...

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2023 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2023 - Proceedings 2023年 VIII-IX页

作者： Stanley, Colin Faculty of Computing and Informatics Namibia University of Science and Technology Namibia

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Memory forensic: detecting unusual intrusion activity in dump of RAM memory using FTK imager

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International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Pandey, Bishwajeet Kumar, Amit Acharya, Deepak Bhaskar Pandey, Pushpanjali Bakar, W.A.W.A. GL Bajaj Institute of Technology and Management Greater Noida India Gyancity Research Consultancy Greater Noida India The University of Alabama in Huntsville Huntsville AL United States Faculty of Informatics and Computing Universiti Sultan Zainul Abidin (UniSZA) Kuala Terengganu Malaysia

Memory forensics helps the forensic investigator to detect any unusual activity. In this paper, we have discussed memory forensics and how to dump the content of primary memory RAM (Random Access Memory) using the FTK (Forensic Tool Kit) Imager tool. This memory dump helps to detect unusual activity in the systems, and we have also demonstrated the detection of hacker activities (find the traces of ping commands initiated by a hacker, get the traces of credentials used by the hacker) in the memory dump. Our proposed memory forensics methodology is based on the Identification, Prevention, Analysis, Documentation, and Presentation methodology. Our proposed technique will help law enforcement agencies, government organisations, and cyber forensic investigators to solve high-level cybercrimes. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： FTK imager Intrusion activities Memory forensic Random-access memory (RAM) Windows

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G-PAT: A Group Peer Assessment Tool to Support Group Projects 7

G-PAT: A Group Peer Assessment Tool to Support Group Project...

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7th International Conference on Research and Innovation in Information Systems, ICRIIS 2021

作者： Tiew, Han-Bin Chua, Fang-Fang Chan, Gaik-Yee Multimedia University Faculty of Computing Informatics Cyberjaya Malaysia

ISBN: (纸本)9781665408059

Peer assessment is a type of assessment that allows students in taking their initiative to manage their study under the supervision of their peers to achieve the learning outcome. Students constantly need to reflect on themselves to understand the progress they made and what learning aspects they are currently lacking. As learning is a step-by-step process, therefore to achieve better consistency and learning experience, educational institutions are slowly transitioning summative learning to the combination of both formative and summative learning. Assessment is used to evaluate the progress of learning. However, existing peer assessment tool has limitation. It is unable to fit into all the use cases for different subjects. Moreover, the biasness of the instructor or peers has caused some of the students facing discrimination when doing the peer assessments. Integration of both formative and summative assessment into the peer assessment system could help instructors better understanding their students through feedback provided hence could improve learning strategies. In this paper, the authors proposed a web application, G-PAT, a peer assessment tool that enhances the flexibility of the template of the existing systems to cater different use cases. The SPA and SAPA factors from SPARKLUS are used in the calculation of performance and feedback of the students. The implementation results of G-PAT have shown that more than 90% of the students agree that the tool helps them to evaluate their performance and they are willing to continue to reuse it as their learning companion. © 2021 IEEE.

关键词： Students

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A Hybrid Classification Approach For Artificial Speech Detection 5

A Hybrid Classification Approach For Artificial Speech Detec...

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5th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2023

作者： Tan, Choon Beng Ahmad Hijazi, Mohd. Hanafi Ellyza Nohuddin, Puteri Nor Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Institute of IR4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Higher Colleges of Technology Faculty of Business Sharjah United Arab Emirates

ISBN: (纸本)9798350304152

The emergence of voice biometrics has revolutionized user authentication methods by delivering enhanced security and convenience, steadily replacing less secure authentication techniques. However, the automatic speaker verification (ASV) systems remained vulnerable to spoofing attacks, especially artificial speech attacks. This is because artificial speech can be generated rapidly and in abundance using state-of-the-art speech synthesis and voice conversion algorithms. Recently, a one-class learning-based countermeasure known as the AIR-ASVspoof was introduced for artificial speech detection, which has proven to be the best-performing non-fusion system. As AIR-ASVspoof focuses solely on the bona fide class as the target in artificial speech detection, its practicality in a real-world environment may be restricted. The reason for this limitation lies in the fact that bona fide speech can be recorded in different environments, thereby increasing the risk of encountering false negatives. Hence, this paper proposes a hybrid approach to mitigate the issue, named Hybrid AIR-ASVspoof (HAIR-ASVspoof), by hybridizing end-to-end learning and classic machine learning. In this approach, deep features will be extracted from the dataset using the trained AIR-ASVspoof model and classified using a classic machine learning model such as Support Vector Machine (SVM). The objectives are: (i) to extract deep features using the AIR-ASVspoof;and (ii) to compare the performance of the AIR-ASVspoof and the proposed hybrid approach. An experiment was conducted to find the appropriate backend classifier for the proposed hybrid approach. Experimental results showed that the proposed approach outperformed the original AIR-ASVspoof, with an Equal Error Rate (EER) of 0.57%. © 2023 IEEE.

关键词： Support vector machines

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