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Decision Aid sciences and Application (DASA), International Conference on

作者： Abdallah Awad Mahmoud H. Qutqut Ali Ahmed Fatima Al-Haj Fadi Almasalha Faculty of Information Technology Applied Science Private University Amman Jordan Computer Science Department Center for Applied Mathematics and Bioinformatics (CAMB) Gulf University for Science & Technology West Mishref Hawalli Kuwait

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Artificial intelligence (AI) significantly influences the systems and applications that underpin modern life. Large datasets are generated from diverse sources. Hence, there is a need for effective data monitoring, processing, and reporting, which is crucial for making influential decisions. In developing complex software systems, AI has become essential for ensuring rigorous testing that aligns with business requirements within constrained timelines. The main advantage of AI in software testing lies in its ability to enhance accuracy and streamline repetitive tasks, ultimately reducing the time required for testing. To this end, this paper explores the role of AI in automated software testing, critically analyzing the limitations of traditional automated testing methods, which often struggle with inconsistent bug detection and have limited adaptability across different environments. By expanding test coverage and addressing these limitations, AI-driven tools can significantly enhance testing efficiency. Additionally, we investigate various AI-based tools designed to optimize software testing and strengthen quality assurance processes.

关键词： Software testing Automation Quality assurance Accuracy computer bugs Learning (artificial intelligence) Software systems Artificial intelligence Monitoring Business

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Stochastic Triangular Fuzzy Number (S-TFN) Normalization: A New Approach for Nonmonotonic Normalization 27

Stochastic Triangular Fuzzy Number (S-TFN) Normalization: A ...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Kizielewicz, Bartlomiej Dobryakova, Larisa Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology in Szczecin ul. Zolnierska 49 Szczecin71-210 Poland

Data normalization is essential in many fields, such as speech recognition, deep learning, machine learning, and optimization. Many researchers focus on developing various normalization techniques, such as min-max, score-based, maximum, vector, or sum, on processing data efficiently. However, classical normalization methods may need to be improved in Multi-Criteria Decision-Analysis (MCDA), where criteria are often non-monotonic. This paper proposes a new approach for identifying and normalizing non-monotonic criteria in MCDA. The method combines a stochastic optimization technique - a genetic algorithm - with normalization based on triangular fuzzy numbers. This paper compares the proposed approach with classical normalization techniques, such as min-max, maximum, vector, or non-linear, using the classical MCDA method - the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS). The study results showed that the proposed approach to normalizing non-monotonic criteria yields a better match between decision preferences and the actual reference model than traditional normalization methods. Optimization using the genetic algorithm helps identify the means of triangular fuzzy numbers, which generate more accurate results. Analysis of the R2 coefficient confirms a very good fit of the proposed model. Therefore, the new non-linear normalization approach in MCDA presented in this work has great potential in adjusting decision preferences to non-monotonic criteria. Future research should continue on the accuracy of this approach in identifying non-linear decision models and apply it to other MCDA methods and real-world decision problems. © 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： Decision analysis Fuzzy sets MCDA Normalization TOPSIS

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An Overview of Tools and Techniques in Network Forensics 25

An Overview of Tools and Techniques in Network Forensics

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25th International Arab Conference on Information technology, ACIT 2024

作者： Asassfeh, Mahmoud Al-Mousa, Mohammad Rasmi Al-Dweikat, Hasan Al-Mashagbeh, Malak Hamad Afaneh, Suha Samara, Ghassan Zaid, Ali Abu Khouj, Mohammed Al Daoud, Essam Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Cybersecurity and Cloud Computing Department Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

Network forensics pertains to a subfield of digital forensics that deals with network security. It is utilized in conjunction with computer network traffic monitoring and analysis, which acts as an intrusion detection system and firewall and facilitates the collection of data and digital proof for network security. This platform is designed to analyze and monitor data in a real-time network environment. It is a crucial instrument for identifying and resolving instances of real-time network misuse. This research shows the differences between network forensics and digital forensics as well as the tools and techniques used in network forensics. It also discusses building forensics platforms, developing network analysis methodologies, and talking about the future of network forensics. © 2024 IEEE.

关键词： Network intrusion

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Next-Generation Critical Infrastructure Security: A Framework for Autonomous Defense Systems

Next-Generation Critical Infrastructure Security: A Framewor...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Alauthman Ashraf Mashaleh Nauman Aslam Mouhammd Alkasassbeh Ammar Almomani Department of Information Security University of Petra Jordan Al-Balqa Applied University Al-Salt Jordan Department of Computer and Information Sciences Northumbria University U.K. Computer Science Department Princess Sumaya University for Technology Jordan Department of Computer Information Science Higher Colleges of Technology UAE Al-Balqa Applied University Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

As critical infrastructure systems become more interconnected and digitized, they face increasing cyber threats that can have severe consequences for national security, public safety, and economic stability. Traditional cybersecurity approaches, which rely heavily on human decision-making and static defense mechanisms, struggle to match the agility, scale, and sophistication of contemporary cyber attacks. In response, autonomous cyber defense systems have emerged as a promising approach to enhance the security and resilience of critical infrastructure. Leveraging artificial intelligence (AI), machine learning (ML), and advanced data analytics, autonomous defense systems can detect and respond to threats in real-time, often with minimal human intervention. This paper presents a comprehensive analysis of these technologies, examining the challenges that critical infrastructure operators face, exploring how autonomous defense mechanisms can address these problems, and proposing strategies for responsible implementation. Drawing on case studies in the energy, financial, and healthcare sectors, the paper demonstrates how autonomous capabilities can reduce detection and response times, enhance accuracy, and mitigate the impact of advanced persistent threats.

关键词： Accuracy Decision making Standards organizations Machine learning Threat assessment Critical infrastructure Time factors Faces Resilience Testing

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Secure Key Management in 5G Networks Using Elliptic Curve Integrated Encryption Scheme (ECIES) for Low-Power IoT Devices 4

Secure Key Management in 5G Networks Using Elliptic Curve In...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Chitra Kiran, N. Maindola, Meenakshi Kumara, Bharath Kaliappan, S. Bhatt, Pooja Maranan, Ramya Alliance University Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

The rapid proliferation of the Internet of Things (IoT) has led to a surge in cyber threats, demanding the development of robust security frameworks. This paper introduces an advanced threat detection model, leveraging Artificial Intelligence (AI) and Cyber Twin Technologies for enhanced IoT security. The proposed framework integrates a Cyber Twin-a digital replica of physical IoT devices-with real-time data analytics to detect, predict, and respond to sophisticated cyber-attacks. The Cyber Twin continuously monitors IoT networks, identifying abnormal behaviors and enabling the implementation of dynamic security measures through AI-driven intrusion detection systems (IDS). A hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is utilized to enhance anomaly detection and threat classification accuracy. The system's ability to simulate various attack scenarios within a digital environment facilitates the development of effective countermeasures while minimizing the impact on actual IoT operations. Experimental results demonstrate significant improvements in detection accuracy, reduced false positives, and enhanced system resilience. The proposed AI and Cyber Twin-based threat detection model sets a new benchmark in safeguarding IoT infrastructures against emerging cyber threats. © 2024 IEEE.

关键词： Network intrusion

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An overview of common emotion models in computer systems 45

An overview of common emotion models in computer systems

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45th Jubilee International Convention on Information, Communication and Electronic technology, MIPRO 2022

作者： Horvat, M. Stojanovic, A. Kovacevic, Z. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Zagreb University of Applied Sciences Department of Computer Science and Information Technology Zagreb Croatia

ISBN: (纸本)9789532331035

Emotions are an omnipresent and important factor in the interaction and communication between people. Since emotions are an indispensable part of human life, it would accelerate the progress of artificial intelligence and other fields of science that require data about emotions if they could be adequately described by computer systems. Today there are many different theories of affect, but few of them are used in affective computing. Other areas of computing also benefit from structured and expressive data models of the affective domain, such as human-computer interaction and brain-computer interfaces. Typical tasks include automated recognition and analysis of emotional states, mental fatigue, individual motivation, vigilance and stress resilience. In this paper four often used models of emotion and cognitive behavior are listed and their properties explained: discrete, dimensional, appraisal and action tendency models. For each model, algorithms are provided for similarity measures that can be used to determine the relatedness between different stimulation and estimation artefacts in their respective emotion spaces. The goal of this article is to help professionals find the optimal emotion model for their research and quickly become familiar with data modelling of affective states. © 2022 Croatian Society MIPRO.

关键词： Brain computer interface

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Enhancing Vegetable Quality Prediction with Fuzzy Interference System 2

Enhancing Vegetable Quality Prediction with Fuzzy Interferen...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Al-Rajab, Murad Asif, Muhammad Chuhan, Saad Hussain Mustafa, Muzzamil Ilyas, Amna Kamran, Rukshanda Ahmad Abazeed, Ashraf Riad Abu Saima, Mahmoud Geeta, Sai Abu Dhabi University Computer Science and IT College of Engineering United Arab Emirates NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Universiti Malaysia Faculty of Compter Science and Information Technology Sarawak94300 Malaysia ADVETI Abu Dhabi Vocational Education and Training Institute United Arab Emirates United Arab Emirates University College of Science United Arab Emirates Institute of Management Technology Dubai Head QAIE and Accreditation United Arab Emirates

ISBN: (纸本)9798350335644

We are living in this hurly burly world of Tumult and Turmoil where many problems arises who have not any exact solution/answer like we have health and nutrition problems people facing difficulties to select best vegetable for their health and if they select best vegetable for themselves they don't know about the quality of the vegetable which they have selects. In the vegetable processing industry, some manufacturers add extra ingredients to prolong the shelf life and maintain the quality of their products. However, excessive amounts of these ingredients can have negative health implications such as palpitations, headaches, allergies, and even cancer. Therefore, it is crucial to implement a system to assess the quality of vegetables being used, which can provide consumers and patients with information regarding their quality content. This system is particularly beneficial for addressing human-related issues, especially in determining the percentage of quality. Various methods have been established to achieve optimal solutions in response to rapidly changing living conditions. This paper proposes the development of a fuzzy-based system that takes inputs such as season, time, and condition to detect the quality of vegetables. The output of this system is determined using Kappa statistics. © 2023 IEEE.

关键词： Vegetables

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Enhanced Intrusion Detection System for an EH IoT Architecture Using a Cooperative UAV Relay and Friendly UAV Jammer

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IEEE/CAA Journal of Automatica Sinica 2021年第11期8卷 1786-1799页

作者： Van Nhan Vo Hung Tran Chakchai So-In Institute of Research and Development Duy Tan UniversityDa Nang 550000Vietnam Faculty of Information Technology Duy Tan UniversityDa Nang 550000Vietnam Applied Network Technology Laboratory Department of Computer ScienceFaculty of ScienceKhon Kaen UniversityKhon Kaen 40002Thailand Faculty of Computer Science Phenikaa UniversityHanoi 100000Vietnam

In this paper,the detection capabilities and system performance of an energy harvesting(EH)Internet of Things(Io T)architecture in the presence of an unmanned aerial vehicle(UAV)eavesdropper(UE)are *** communication protocol is divided into two *** the first phase,a UAV relay(UR)cooperates with a friendly UAV jammer(UJ)to detect the UE,and the UR and UJ harvest energy from a power beacon(PB).In the second phase,a ground base station(GBS)sends a confidential signal to the UR using non-orthogonal multiple access(NOMA);the UR then uses its harvested energy to forward this confidential signal to IoT destinations(IDs)using the decode-and-forward(DF)***,the UJ uses its harvested energy to emit an artificial signal to combat the detected UE.A closed-form expression for the probability of detecting the UE(the detection probability,DP)is derived to analyze the detection ***,the intercept probability(IP)and throughput of the considered IoT architecture are ***,we identify the optimal altitudes for the UR and UJ to enhance the system and secrecy *** Carlo simulations are employed to verify our approach.

关键词： Detection probability(DP) energy harvesting(EH) friendly UAV jammer Internet of Things(IoT) system performance UAV detection

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ANN-Based Modeling and Control of a Pick and Place Manipulator 1

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Mostafa, Mohamed Essam Mostafa, Aya Essam Ammar, Hossam Hassan Shalaby, Raafat School of Engineering and Applied Science NU Giza Egypt School of Information Technology and Computer Science NU Giza Egypt SESC Center School of EAS NU Giza Egypt Faculty of Electronic Engineering Menofia University Menouf Egypt

ISBN: (数字)9789819930913

ISBN: (纸本)9789819930906

Pick-and-place robots are used in the manufacturing industry. This paper will focus on one of the most used functionalities in the industry. In addition, it will focus on this functionality and how to achieve more precision with the least cost. The robot is used for packaging, picking, placing factory jobs, and inspection. This paper is focusing on the design of the linear pick-and-place robot. The target of this paper is to tackle the design, construction, control, and analysis of the linear pick-and-place robot. The main target of this paper is to find the best design and the most cost-effective way to develop it. It will be done by comparing several delta robots designs to find which one will be the most economical. It will be done via a survey. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cost effectiveness

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Impact of Blockchain technology and Fintech on Sustainable Performance 2

Impact of Blockchain Technology and Fintech on Sustainable P...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Almahadin, Hamed Ahmad Shehadeh, Maha Al-Gasaymeh, Anwar S. Abu-Alsondos, Ibrahim A. Atta, Anas Ahmad Bani Applied Science Private University Finance and Banking Science Department Amman Jordan American University in the Emirates College of Computer Information Technology Dubai United Arab Emirates Middle East University Department of Accounting and Finance Amman Jordan

ISBN: (纸本)9798350335644

The financial industries have grown concerned over the last ten years about the rapid development of financial technology, or Fintech, and the conversation surrounding sustainable performance. Achieving cutting-edge performance through ongoing product and process development is encouraged by global competition, and blockchain technology knowledge is the most compelling tool for long-term sustainability and business growth. The main goal of this research was to examine the potential application of blockchain technology and fintech in the insurance industry, which may need to manage client data and transactions with a secure and reliable blockchain record in order to maintain sustainable performance. A descriptive research was incorporated using simple convenient sampling technique. Data recorded for 196 employees working in insurances companies based in Dubai UAE. data gathered through online questionnaire and evaluated through SmartPLS software by applying hypothesis testing using PLS-SEM Algorithm, Reliability and validity test for model validation. As a result, a positive significant relationship of the proposed construct further expands the implementation of blockchain technology will result in significant efficiency gains, cost savings, transparency, quicker payouts, and a reduction in fraud while allowing data to be shared in real-time across several parties in a trustworthy and verifiable manner. © 2023 IEEE.

关键词： Blockchain

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