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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Kazmi, Syed Hussain Ali Qamar, Faizan Hassan, Rosilah Nisar, Kashif Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Selangor Malaysia University Malaysia Sabah Faculty of Computing and Informatics Sabah Malaysia

ISBN: (纸本)9781665451628

Global interest in drones is prone to surface challenges related to collision avoidance in dense constellation. Existing collision avoidance mechanism, known as Automatic Dependent Surveillance-Broadcast (ADS-B), contains serious limitations of interference effects due to broadcasting. Therefore, we propose a novel Discrete Sequence Spread Spectrum (DSSS) enabled Minimum Shift Keying (MSK) modulation for Three Dimension (3D) position sharing in collision avoidance mechanism. Our proposed scheme avoids extra processing through physical layer addressing and provides convergence to further reduced broadcast rate. We analyzed the performance of the proposed mechanism in the spectrum completely covered with Gaussian noise. The MATLAB based analysis results indicate the proposed scheme as a potential solution to address the challenges faced in drone to drone communication for collision avoidance in dense swarms of drones or Unmanned Aerial Vehicles (UAV). Moreover, the proposed scheme outperforms the traditional demodulation approach compare to direct correlation without demodulation. Further, we discussed possible future research directions in subject solution. © 2022 IEEE.

关键词： Drones

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Efficient Resource Allocation Algorithm in Uplink OFDM-Based Cognitive Radio Networks

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Computers, Materials & Continua 2023年第5期75卷 3045-3064页

作者： Omar Abdulghafoor Musbah Shaat Ibraheem Shayea Ahmad Hamood Abdelzahir Abdelmaboud Ashraf Osman Ibrahim Fadhil Mukhlif Herish Badal Norafida Ithnin Ali Khadim Lwas Electronic&Telecommunications Engineering Department The American University of KurdistanDuhokKurdistan RegionIraq CTTC BarcelonaSpain Faculty of Electrical and Electronics Engineering Istanbul Technical UniversityIstanbulTurkey Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia R&D Ministry of Industry and MineralsBaghdadIraq

The computational complexity of resource allocation processes,in cognitive radio networks(CRNs),is a major issue to be ***,the complicated solution of the optimal algorithm for handling resource allocation in CRNs makes it unsuitable to adopt in real-world applications where both cognitive users,CRs,and primary users,PUs,exist in the identical geographical ***,this work offers a primarily price-based power algorithm to reduce computational complexity in uplink scenarioswhile limiting interference to PUs to allowable ***,this paper,compared to other frameworks proposed in the literature,proposes a two-step approach to reduce the complexity of the proposed mathematical *** the first step,the subcarriers are assigned to the users of the CRN,while the cost function includes a pricing scheme to provide better power control algorithm with improved reliability proposed in the second *** main contribution of this paper is to lessen the complexity of the proposed algorithm and to offer flexibility in controlling the interference produced to the users of the primary networks,which has been achieved by including a pricing function in the proposed cost ***,the performance of the proposed power and subcarrier algorithm is confirmed for orthogonal frequency-division multiplexing(OFDM).Simulation results prove that the performance of the proposed algorithm is better than other algorithms,albeit with a lesser complexity of O(NM)+O(Nlog(N)).

关键词： Cognitive radio resource allocation OFDM pricing

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Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces 14

Deep Learning-Based Malaysian Sign Language (MSL) Recognitio...

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14th International Conference on Computer and Knowledge Engineering, ICCKE 2024

作者： Moung, Ervin Gubin Suwek, Precilla Fiona Sufian, Maisarah Mohd Liaw, Valentino Farzamnia, Ali Khong, Wei Leong Faculty of Computing and Informatics University Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Computing and Engineering University of Huddersfield West YorkshireHD1 3DH United Kingdom School of Engineering Monash University Malaysia Bandar Sunway Selangor Malaysia

ISBN: (纸本)9798331511272

Sign language is one form of communication for this group of people to communicate with each other. Not only for people with hearing problems but sign language is also useful for people who are mute or have problem speaking. The most used sign language is the American Sign Language (ASL) that is widely used in English speaking countries. In Malaysia, Bahasa Isyarat Malaysia (BIM) or Malaysian Sign Language (MSL) is still a new teaching to the community in Malaysia. Hence, in this project, a dataset with 5980 images of the signed alphabet is used to train models to recognize what the signs mean. The problem this project aims to address is the limited research and the availability of datasets in the field of Malaysian Sign Language (MSL) recognition using deep learning and various color spaces. Two deep learning models that are used are Convolutional Neural Network (CNN) and Residual Network 18 (ResNet18). The images are also converted into different color spaces which are RGB, YCbCr, Grayscale and the combination of RGB and YCbCr. The findings reveal that RGB is the most effective color space for CNNs, achieving up to 83.9% accuracy, while YCbCr performed best with ResNet18, achieving 88.3% accuracy. These results demonstrate the importance of color space selection in sign language recognition and contribute to the growing body of research on MSL. Key metrics such as precision, recall, and F1-score further underscore the robustness of the proposed system. ©2024 IEEE.

关键词： Convolutional neural networks

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UAV Path Planning using Rotated TOR in Structured Environment 4

UAV Path Planning using Rotated TOR in Structured Environmen...

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4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Wahab, Shaliza Hayati A. Saudi, Azali Saad, Nordin Chekima, Ali Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Path planning is one of the crucial criteria for an unmanned aerial vehicle (UAV) in providing a safe path to fly from any location to the specified goal. Global approach to autonomous path planning often used the harmonic potentials technique to guide the planner. The harmonic potentials are essentially the solutions of Laplace's equation that are employed to model the environment. The computational resources required to obtain these harmonic potentials often involve a large number of mathematical calculations. In the previous work, fast iterative methods that apply the use of full-sweep iteration were suggested. This study examines a fast-iterative numerical approach, namely Rotated Two-Parameter Successive Over-Relaxation (RTOR), to UAV path planning. The algorithm is implemented in a self-developed UAVPlanner Simulator where hills are generated randomly in grid areas. The proposed method was tested using several simulation scenarios which demonstrated the efficiency of the algorithm in finding the path, where it was evaluated in terms of the number of iterations and computational time efficiency with the different number of outdoor static obstacles. The results show that RTOR outperformed the previous methods and gives faster computational time and iteration in generating a path for UAVs. © 2022 IEEE.

关键词： Motion planning

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Interactive Multimodal Learning: Towards Using Pedagogical Agents for Inclusive Education

Interactive Multimodal Learning: Towards Using Pedagogical A...

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2023 IEEE International Humanitarian Technology Conference, IHTC 2023

作者： Al Omoush, Muhammad H. Salih, Sumaia E. Kishore, Sameer Mehigan, Tracey Dublin City University Faculty of Engineering and Computing Dublin Ireland Dublin Ireland Middlesex University Department of Computer Engineering and Informatics Dubai United Arab Emirates

ISBN: (纸本)9798350314311

A pedagogical agent is an animated interface in an interactive online learning environment. Its role involves guiding users through instructions and participating in direct discussions. Numerous research studies underline the advantages of pedagogical agents in delivering instruction, tailoring learning experiences, and promoting inclusivity. The authors utilize machine learning techniques to develop an interactive multimodal application (combining visuals and audio) designed as a pedagogical agent. This application is intended to assist in teaching primary school students (aged 6 to 8) the recognition of colors and letters. Additionally, it incorporates voice interaction to assess their learning progress. The overarching objective of this pedagogical agent is to seamlessly integrate artificial intelligence (AI) into educational settings, thereby amplifying student engagement and motivation during the learning process. This paper will explore the journey of creating a machine learning-based application and its potential utility in supporting educators. Furthermore, the authors will investigate potential augmentations that could render the application more adept at aiding students with dyslexia. The paper will emphasize the significance of human-computer interaction (HCI), as it significantly shapes pedagogical agents' design and operational aspects. © 2023 IEEE.

关键词： Graphical user interfaces

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A Comparison Study on Generative Adversarial Networks (GANs) for Satellite Image Upscaling 12

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

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

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. © 2024 IEEE.

关键词： Generative adversarial networks

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User Authentication with Keystroke Dynamics: Performance Evaluation in Neural Network

User Authentication with Keystroke Dynamics: Performance Eva...

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IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

作者： Xin-Jin Kek Yu-Beng Leau Soo Fun Tan Cybersecurity Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (数字)9798350389692

ISBN: (纸本)9798350389708

In the current context of research, it investigates user authentication through keystroke dynamics using the neural network model which focuses on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and Long Short-Term (LSTM). As of above, four distinct experiments were conducted: (1) evaluating 3 different algorithms, (2) layering configuration for CNN and GRU fusion, (3) number of filters and neurons as hyperparameters, (4) assessing combinations of events counts and classes. In justification, the final outcome demonstrates an achieved accuracy of 87%. Based on the experiments that were carried out, optimal hyperparameters were identified: window size of 40, 256 for the number of filters and neurons and 2500 as the optimal number of events with 30 as the number of classes. These findings underscore a significance of considering diverse combinations of a number of events and classes by providing insights into the determination of optimal hyperparameters for keystroke dynamics in performance evaluation.

关键词： Performance evaluation Accuracy Filters Sensitivity Keystroke dynamics Neurons Authentication Convolutional neural networks Long short term memory Biological neural networks

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Secure Blockchain Model for Iomt Smart Mobility System 3rd

Secure Blockchain Model for Iomt Smart Mobility System

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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Farahat, Ibrahim Shawky Elhoseny, Mohamed Elmougy, Samir Abualkishik, Abedallah Zaid Aladrousy, Waleed Tolba, Ahmed Elsaid Faculty of Computers and Information Luxor University Luxor85951 Egypt Faculty of Computers and Information Mansoura University Mansoura35516 Egypt College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates American University in the Emirates Dubai United Arab Emirates

ISBN: (纸本)9789819726707

With technological advancements, electronic health records (EHRs) have replaced paper records to improve patient treatment quality and effectiveness in civil hospitals and military treatment facilities. These records must access by doctors and nurses to help in treating the patients through the Internet. Also, the enormous quantity of medical data for each patient makes it difficult to secure it. These reasons make it possible for medical data to be stolen by hackers. So, organizing and securing patients’ medical data in both civil and military hospitals is one of the main issues in the healthcare field. So, a new blockchain technique is proposed to secure the patient’s medical data. By making it decentralized, this proposed blockchain uses smart mobility technology. A smart Internet of Medical Things (IoMT) mobility system is used to collect patient vital signs and then store them in the blockchain. A modified version of the Nik-512 algorithm is utilized to connect the blocks in the blockchain. This modified version of the Nik-512 uses the LZ4 algorithm to compress data. This work aims to minimize the hash code computation time by preventing the hash code from beginning with zeros. Also, we build an authentication parity technique that checks the identification of the doctor and patient before the transaction happens. Also, in this work, a new master contract system is developed to guarantee transferring the money from the patient’s wallet to the doctor’s wallet after the doctor sends the treatment to the patient. The experimental results prove that the proposed work using Nik-512 is faster than all pervious compared blockchain versions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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A COMPREHENSIVE FRAMEWORK FOR EVALUATING AUTONOMOUS VEHICLES IN SMART AND SUSTAINABLE URBAN TRANSPORTATION 51

A COMPREHENSIVE FRAMEWORK FOR EVALUATING AUTONOMOUS VEHICLES...

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51st International Conference on Computers and Industrial Engineering, CIE 2024

作者： Mohamed, Mona Elsayed, Assmaa Sallam, Karam M. Radwan, Ibrahim Abdel-Basset, Mohamed Higher Technological Institute 10th of Ramadan City44629 Egypt Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computer Science Department Collage of Computing and Informatics University of Sharjah Sharjah United Arab Emirates University of Canberra Canberra Australia

Recently, a plethora of thoughts and terminologies have surfaced intending to protect the environment and public health as sustainability and smart cities. Wherein the relationship between these terminologies has an explicit link. The implementation of smart city regulations has the potential to enhance the standard of living for citizens by incorporating sustainable practices. Smart and green urban transportation is one of the sustainable strategies and practices. The newest innovations in Smart and ecological urban transportation are autonomous vehicles (AuVs), which can move around with minimal help from humans. Thanks to AuVs the transportation costs, pollution in cities, and dramatically the number of accidents and fatalities on the roads have lessened. Also, all social groups have better access. Opting for AuV which is optimal, and efficient is one of the toughest obstacles. This process is conducted based on a set of criteria. Hence, Multicriteria Decision Making (MCDM) techniques contribute to constructing a three-tiered decision-making paradigm (TTDM) for appraising AuV and recommending optimal AuV. Each tier is responsible for a certain mission. The first tier is aggregating data and forming it into decision matrices based on decision makers (DMs). The second tier is analyzing the criteria to obtain their weights by embracing Criteria Importance Through Intercriteria Correlation (CRITIC). The third tier recommends the optimal AuV based on Multi-Attributive Border Approximation Area Comparison (MABAC). All these tiers are implemented under the authority of Type 2 Neutrosophic Numbers (T2NNs) as branch of uncertainty theory. Hence, TTDM can treat mysterious circumstances and inaccurate information. © 2024 Computers and Industrial Engineering. All rights reserved.

关键词： Sustainable city

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John Hollerton: a life in industrial analytical spectroscopy, part 2

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Spectroscopy Europe 2023年第1期35卷 21-26页

作者： Davies, Antony N. Cashyap, Mohan Hollerton, John SERC Sustainable Environment Research Centre Faculty of Computing Engineering and Science University of South Wales United Kingdom MASS Informatics Harpenden United Kingdom Hollerton Scientific Software Consultancy St Albans United Kingdom

An interview with John Hollerton, who retired from a long career at GSK, is presented. Among other things, Hollerton discusses the biggest innovation he has seen that advanced his science, the biggest hyped innovation which failed to deliver as promised, and the go-to instrument development techniques from the start of his career that are now seldom used.

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