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Dynamic Analysis and Circuit Design of a New 3D Highly Chaotic System and its Application to Pseudo Random Number Generator (PRNG) and Image Encryption

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SN Computer Science 2024年第4期5卷 1-17页

作者： Sambas, Aceng Benkouider, Khaled Kaçar, Sezgin Ceylan, Nurdoğan Vaidyanathan, Sundarapandian Sulaiman, Ibrahim Mohammed Mohamed, Mohamad Afendee Ayob, Ahmad Faisal Mohamad Muni, Sishu Shankar Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu Kuala 22200 Malaysia Department of Mechanical Engineering Universitas Muhamadiyah Tasikmalaya Tamansari Gobras Tasikmalaya 46196 Indonesia Department of Electronics Faculty of Technology Badji-Mokhtar University B.P. 12 Sidi Ammar Annaba 23000 Algeria Department of Electrical and Electronics Engineering Sakarya University of Applied Sciences Serdivan Sakarya 54050 Turkey Department of Mechanical Engineering Adıyaman University Merkez Adıyaman 02040 Turkey Centre for Control Systems Vel Tech University Tamil Nadu Chennai 600062 India School of Quantitative Science University Utara Malaysia Sintok Kedah Bukit Kayu Hitam 06010 Malaysia Faculty of Education and Arts Sohar University Sohar 311 Oman Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Gong Badak Terengganu Kuala Terengganu 21300 Malaysia Department of Physical Sciences Department of Physical Sciences Indian Institute of Science Education and Research Mohanpur Kolkata 741246 India

In this paper, a new 3D dynamical system with four quadratic nonlinear terms is presented. It is shown that the proposed chaotic system has two saddle-foci, unstable, equilibrium points. Thus, the proposed chaotic system exhibits self-excited chaotic attractors. Dynamical analysis methods such as Lyapunov exponents spectrum, bifurcation diagrams, and phase portraits are used to explore the complex dynamical behaviors of the proposed chaotic system and analyze its basic qualitative properties. It is shown that the maximal Lyapunov exponent (MLE) of the new chaotic system is 7.196, which is a high value. The proposed highly chaotic system exhibits high complexity and it will be very useful for applications in cryptography, encryption and secure communications. The physical feasibility of the proposed system is verified by implementing its electronic circuit schematic using Multisim software. Additionally, a PRNG has been developed using the derived state variables and analyze with NIST-800–22 test. Lastly, we developed a PRNG-based image encryption and employ it for an encryption. The experimental outcomes obtained prove that the 3D chaotic system-based encryption application presented in this section has a very good performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Chaos Image encryption Pseudo random number generator

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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Computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Identifying Gamified Teaching Elements in Computer Science Course 9

Identifying Gamified Teaching Elements in Computer Science C...

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9th International Conference on Platform Technology and Service, PlatCon 2023

作者： Tanalol, Hasnah Hashim, Hazmi Turumogan, Punitha Noor, Noorsidi Aizuddin Mat Baharum, Aslina Deris, Farhana Diana Universiti Malaysia Sabah Preparatory Center for Science & Technology Kota Kinabalu Malaysia University Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Tunku Abdul Rahman University of Management and Technology Department of Computing and Information Technology Penang Malaysia Universiti Teknologi Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Faculty of Built Environment and Surveying Real Estate Department Johor Johor Bahru81310 Malaysia School of Engineering and Technology Sunway University Computing and Information System Bandar Sunway Selangor Malaysia Universiti Teknologi Malaysia Universiti Malaysia Sabah Faculty of Social Sciences and Humanities Johor Johor Bahru81310 Malaysia

ISBN: (纸本)9798350305999

Traditional education methods, having said, lack motivation and self-regulation. The 5G and 6G technology have improved education;one such is gamification. Gamification applies gaming mechanisms to non-gaming platforms. Sadly, the use of gamification is still limited, especially in Malaysia. Gamification affects user experience, so it is intriguing to see if it can solve education problems. A study was conducted to identify elements of gamification. A systematic literature review (SLR) determines the gamification elements. Results show the list of identified 30 elements as guidelines for developing gamified teaching in Computer Science Courses. Thus, it is hoped that the improved teaching and learning process can affect students' performance. © 2023 IEEE.

关键词： Curricula

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When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis

arXiv

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arXiv 2024年

作者： Moradizeyveh, Sahar Tabassum, Mehnaz Liu, Sidong Newport, Robert Ahadizad Beheshti, Amin Ieva, Antonio Di Lab Macquarie Medical School Faculty of Medicine Health and Human Sciences Macquarie University Sydney Australia School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Sydney Australia

Eye-gaze tracking research offers significant promise in enhancing various healthcare-related tasks, above all in medical image analysis and interpretation. Eye tracking, a technology that monitors and records the movement of the eyes, provides valuable insights into human visual attention patterns. This technology can transform how healthcare professionals and medical specialists engage with and analyze diagnostic images, offering a more insightful and efficient approach to medical diagnostics. Hence, extracting meaningful features and insights from medical images by leveraging eye-gaze data improves our understanding of how radiologists and other medical experts monitor, interpret, and understand images for diagnostic purposes. Eye-tracking data, with intricate human visual attention patterns embedded, provides a bridge to integrating artificial intelligence (AI) development and human cognition. This integration allows novel methods to incorporate domain knowledge into machine learning (ML) and deep learning (DL) approaches to enhance their alignment with human-like perception and decision-making. Moreover, extensive collections of eye-tracking data have also enabled novel ML/DL methods to analyze human visual patterns, paving the way to a better understanding of human vision, attention, and cognition. This systematic review investigates eye-gaze tracking applications and methodologies for enhancing ML/DL algorithms for medical image analysis in depth. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye tracking

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Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro 28

Digital Innovations Development: Causal factors and relation...

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2024 28th International Conference on Information Technology, IT 2024

作者： Ognjanovic, Ivana Lakovic, Luka Sendelj, Ramo Bokor, Laszlo Kara, Peter A. Reich, Christoph Madan, Manav Roganovic, Milovan Zouilas, Emmanouil Mantas, John Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro

ISBN: (纸本)9798350369618

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts. © 2024 IEEE.

关键词： Ecosystems

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Optimizing Continuous-Wave Pumped Entanglement-based QKD in Noisy Environment

arXiv

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arXiv 2025年

作者： Kuniyil, Hashir Al-Kuwari, Saif Ali, Asad Czerwinski, Artur Arslan, Syed M. Qatar Centre for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Torun ul. Grudziadzka 5 Torun87-100 Poland

Quantum key distribution (QKD) has emerged as a promising solution to protect current cryptographic systems against the threat of quantum computers. As QKD transitions from laboratories to real-world applications, its implementation under various environmental conditions has become a pressing challenge. Major obstacles to practical QKD implementation are the loss of photons in the transmission media and the presence of extreme noise, which can severely limit long-range transmission. In this paper, we investigate the impact of extreme noise on QKD system parameters, including detector timing uncertainty (jitter), measurement timing shifts, variations in detector deadtime, and rate-dependent detector efficiency. Contrary to manufacturers’ specifications, which assume these parameters to be constant, we demonstrate that these parameters exhibit significant variations in extreme noise conditions. We show that changes in these parameters play a key role in determining system performance in noisy environments. To address these non-idealities, we develop a robust model that can address and adapt to various detector characteristics. In particular, our model is independent of source parameters and can be implemented using data from the detection unit. Our results show that our model is well suited for characterizing and optimizing the performance of the QKD system under noisy conditions. © 2025, CC BY-NC-SA.

关键词： Quantum computers

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Discrepancy and Thematic Bibliometric Analyses of the Remaining Limitations in Artificial Intelligence

Discrepancy and Thematic Bibliometric Analyses of the Remain...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling

A Comparison Study on Generative Adversarial Networks (GANs)...

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Brandon Kah Wei Li Muhammed Basheer Jasser Bayan Issa Samuel-Soma M. Ajibade Hui Na Chua Richard T.K. Wong Muzaffar Hamzah Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Research Centre For Human-Machine Collaboration (HUMAC)School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model’s effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image.

关键词： Training Photography Measurement Web services Generative adversarial networks Transformers Satellite images Image restoration Particle swarm optimization Image reconstruction

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DAFNI: A computational platform to support infrastructure systems research

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

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Unveiling the Psychology of Human-Computer Interaction: Bridging the Gap for Future Advancements 9

Unveiling the Psychology of Human-Computer Interaction: Brid...

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9th International Conference on Platform Technology and Service, PlatCon 2023

作者： Baharum, Aslina Rahim, Emelia Abdul Ismail, Rozita Noor, Noorsidi Aizuddin Mat Daruis, Dian Darina Indah Deris, Farhana Diana Samat, Jessellah School of Engineering and Technology Sunway University Computing and Information System 5 Jalan Universiti Bandar Sunway Selangor 47500 Malaysia Universiti Teknologi MARA Faculty of Business Management Sarawak Kota Samarahan94300 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Kajang Selangor 43000 Malaysia Universiti Teknologi Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Faculty of Built Environment and Surveying Real Estate Department Johor Johor Bahru81310 Malaysia Universiti Pertahanan Nasional Malaysia Faculty of Engineering Kuala Lumpur57000 Malaysia Universiti Teknologi Malaysia Faculty of Social Sciences and Humanities Johor Johor Bahru81310 Malaysia SMK Keningau 2 Keningau Sabah 89008 Malaysia

ISBN: (纸本)9798350305999

This paper brings some light to the importance of psychology as a concept, its initial role in human-computer interaction, and exactly how necessary it is to maintain this aspect when spearheading the future of the field. The method used is to decipher what psychology gives to Human-Computer Interaction (HCI), including the primary components, the task, user, and computer. The results of this study discuss the framework of previous research, and how it stands today considering modern sub-theories. In the end, the verdict stands that psychology as one of the feature of HCI applies primarily to authors in the United States, and very much less when it comes to European researchers, who would generally take different approaches to their research. It is necessary to have a consistent conversation on the gravity of connecting the researchers of HCI and advancements with the full extent and realm of studies that exist. One of the biggest things that this paper brings to the field is some focus on the necessity that there is for in-depth analysis of psychology as a method of approaching HCI. © 2023 IEEE.

关键词： Human computer interaction

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