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6th International Conference on Applied Computational Intelligence in Information Systems, ACIIS 2023

作者： Elaklouk, Ahmed M. S. Zuarni Ramli, Ratna Edris, Ibrahim Padilla-Valdez, Nena Jumaily, Adel Al Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei Universiti Teknologi Mara Negeri College of Computing Informatics and Mathematics Seremban Malaysia Universiti Teknologi Brunei Centre for Communication Bandar Seri Begawan Brunei Faculty of Engineering University of Technology Brunei Brunei Department of Computer Science and Software Engineering School of Computing University of Western Australia Australia Ensta Bretagne University France

ISBN: (纸本)9798350317428

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study's objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient's motivation and engagement and increase the effectiveness of rehabilitation. © 2023 IEEE.

关键词： Serious games

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Improved Transportation Model with Internet of Things Using Artificial Intelligence Algorithm

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Computers, Materials & Continua 2023年第8期76卷 2261-2279页

作者： Ayman Khallel Al-Ani Shams Ul Arfeen Laghari Hariprasath Manoharan Shitharth Selvarajan Mueen Uddin Cybersecurity Research Group Faculty of Computing and InformaticsUniversity Malaysia SabahJalan UMSKota KinabaluSabah88400Malaysia National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaGelugorPenang11800Malaysia Department of Electronics and Communication Engineering Panimalar Engineering CollegePoonamalleeChennai600123TamilnaduIndia Department of Computer Science&Engineering Kebri Dehar UniversityKebri Dehar250Ethiopia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar

In this paper,the application of transportation systems in realtime traffic conditions is evaluated with data handling *** proposed method is designed in such a way as to detect the number of loads that are present in a vehicle where functionality tasks are computed in the *** to the existing approach,the design model in the proposed method is made by dividing the computing areas into several cluster regions,thereby reducing the complex monitoring system where control errors are ***,a route management technique is combined with Artificial Intelligence(AI)algorithm to transmit the data to appropriate central ***,the combined objective case studies are examined as minimization and maximization criteria,thus increasing the efficiency of the proposed ***,four scenarios are chosen to investigate the projected design’s *** all simulated metrics,the proposed approach provides better operational outcomes for an average percentage of 97,thereby reducing the amount of traffic in real-time conditions.

关键词： Transportation Artificial Intelligence(AI) data-driven Internet of Things(IoT)

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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Computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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Predicting Estimated Time of Arrival Using Boosting Models 6

Predicting Estimated Time of Arrival Using Boosting Models

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Kam, Say-Hong Chong, Yung-Wey Ibrahim, Noor Farizah Keoh, Sye-Loong Phon-Amnuaisuk, Somnuk Abdul Rahim, Sharul Kamal School of Computer Sciences Universiti Sains Malaysia Penang Malaysia National Advanced IPv6 Centre Universiti Sains Malaysia Penang Malaysia School of Computing Science University of Glasgow Glasgow United Kingdom School of Computing and Informatics Universiti Teknologi Brunei Brunei Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350344349

Estimating the time of arrival (ETA) in public transportation can be challenging due to incomplete data and the complex nature of the urban environment. This study aims to address persistent criticism of the poor punctuality problem in Malaysian buses through the modeling of bus arrival time predictions. The study uses geographical and time data to predict bus arrival times through several boosting models. The data cleaning method enhanced data quality by eliminating invariable entries, segmenting the bus route for a more granular analysis, and encoding the data for improved structure and reliability. Through the implementation of Boruta for feature selection, relevant variables crucial for prediction were identified, contributing to the model's precision. The results highlighted LightGBM's superiority over AdaBoost and XGBoost, exhibiting the highest accuracy and a balanced level of complexity. This integrated methodology not only presents a robust prediction model but also showcases a potential practical implementation. © 2024 IEEE.

关键词： Forecasting

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Electronic Circuit in the Two-Scroll Hyperchaotic System with a Unique Saddle-Point Rest Point and its Synchronization via Integral Sliding Mode Control

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IAENG International Journal of Applied Mathematics 2024年第3期54卷 452-464页

作者： Mujiarto Ayob, Ahmad Faisal Mohamad Vaidyanathan, Sundarapandian Sambas, Aceng Benkouider, Khaled Nawawi, Mohd Kamal Mohd Khalid, Kamal Madi, Elissa Nadia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Department of Mechanical Engineering Universiti Malaysia Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Research and Development Centre Vel Tech University Avadi Tamil Nadu Chennai600062 India Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Kuala TerengganuGong Badak Malaysia Department of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Indonesia Department of Electronics Faculty of technology Badji-Mokhtar University Annaba23000 Algeria Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia UUM SintokKedah 06010 Malaysia Institute of Strategic Industrial Decision Modeling School of Quantitative Sciences Universiti Utara Malaysia UUM SintokKedah 06010 Malaysia

A new 4-D two-scroll hyperchaotic system with three quadratic nonlinearities is detailed in this research paper. It is shown that the new hyperchaotic system has a unique saddle-point rest point at the origin and it exhibits self-excited attractor. A detailed stability analysis is carried out for the new hyperchaotic *** is also proved that the new hyperchaotic system has multistability with coexisting attractors. Next, we invoke integral sliding mode control for the global self-synchronization of the new hyperchaotic system taken as leader-follower *** electronic circuit design of the new two-scroll hyperchaotic system is developed in MultiSim, which is beneficial for engineering applications. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Sliding mode control

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Nuclei Classification in ER-IHC Stained Histopathology Images using Deep Learning Models

Nuclei Classification in ER-IHC Stained Histopathology Image...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Wan Ahmad, Wan Siti Halimatul Munirah Hasan, Md Jahid Fauzi, Mohammad Faizal Ahmad Lee, Jenny T.H. Khor, See Y. Looi, Lai M. Abas, Fazly Saleh Center for Visual Computing Faculty of Computing and Informatics Faculty of Engineering Multimedia University Cyberjaya Malaysia Multimedia University Faculty of Engineering Cyberjaya Malaysia Sarawak General Hospital Department of Pathology Kuching Malaysia Queen Elizabeth Hospital Department of Pathology Kota Kinabalu Malaysia University Malaya Medical Center Department of Pathology Kuala Lumpur Malaysia Multimedia University Faculty of Engineering and Technology Melaka Malaysia

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

Breast cancer treatment is highly dependent on the carcinoma stage, which was obtained by evaluating the pathological slides and the estrogen receptor status. The Allred score has been manually calculated by the pathologists to represent the percentage and intensity of tumor nuclei. The task can be automated by enabling digital pathology, by classifying the nuclei using learning-based method. We present here a comprehensive analysis of 32 pretrained deep learning models from DenseN et, EfficientN et, InceptionResN et, Inception, ResN et, MobileNet, NasNet, VGG and Xception. The aim of this exper-iment is to identify the best pre-trained model for classifying the negative, weak, moderate and strong nuclei taken from 44 whole slide images of estrogen receptor immunohistochemistry stained histopathology. The highest test accuracy is achieved by DenseNet169 with the measure of 94.91 %. This study will be a basis for the future development of more complex deep learning models with cascading or any combination of the tested models. © 2022 IEEE.

关键词： Diseases

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12 COVID-19 #2: Compliant data processing from your home office

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Spectroscopy Europe 2021年第1期33卷 32-34页

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

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On Handcrafted Machine Learning Features for Art Authentication

On Handcrafted Machine Learning Features for Art Authenticat...

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IEEE International Conference on Signal and Image Processing Applications (ICSIPA)

作者： M. Bwanali H. Ugail Z. Mnasri E. Jensen Z. Omar Centre for Visual Computing and Intelligent Systems University of Bradford Bradford United Kingdom Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Accurate art authentication is crucial for ensuring correct attribution, cultural preservation, and financial valuation of artworks. Machine learning tools can significantly complement established techniques such as connoisseurship, provenance research, and scientific analysis (e.g., pigment analysis and radiographic imaging). This study explores a more objective and explainable approach to machine learning in art authentication. Specifically, we investigate the use of handcrafted features, such as image entropy, energy, texture patterns, and image similarities, to train a OneClassSVM model based on known authentic images of paintings by a given artist. To demonstrate the versatility of our method, we present results for three selected artists: John Constable, Vincent van Gogh, and Jean-Michel Basquiat. We further show by way of an example how our method can be utilised to study a given painting in terms of its attribution to a given artist. These examples, along with the accuracy of our results, indicate the potential of the proposed method as a flexible and efficient tool for art authentication alongside more established methods.

关键词： Art Accuracy Authentication Machine learning Feature extraction Entropy Anomaly detection

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Smart Skim Pinjaman Buku Teks (SBPT) School Operation System in Malaysia

Smart Skim Pinjaman Buku Teks (SBPT) School Operation System...

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2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021

作者： Ismail, Nor-Syahidatul N Zakaria, Muhammad Ashraf Bin Muhammad, Nor Aishah Binti Yunus, Farizah Binti Faculty of Computing College of Computing and Applied Sciences Pahang Malaysia Wireless Communication Centre School of Electrical Engineering Faculty of Engineering Malaysia Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9781665403436

Skim Pinjaman Buku Teks (SPBT) is a textbook loan program for elementary and secondary schools in Malaysia. This program is an initiative system by the Malaysia Ministry of Education to help students in the school by providing them with textbooks for their learning purpose. The current SPBT system is handled by storing all the records into a book or a file that can cause data missing, data redundant and incorrect data stored in the system. This project presents a new system for the textbook loan system known as Smart Skim Pinjaman Buku Teks (SBPT) School Operation system (SSSOS). This system upgrades the conventional method that manually records the loan information to a systematic approach using a web-based management system. The rapid application development (RAD) cycle is used as a methodology to develop the SSSOS system. The proposed system can efficiently handle borrowing and returning processes and reduce the textbook loan process period. This system also provides a complete report related to the textbook, such as the current status of a book and the previous information of the borrower. The proposed system can be used to enhance the conventional SPBT system in handling the textbook loan program. The system has been tested in Sekolah Kebangsaan Gambang, Pahang, where an SPBT coordinator acts as a client for the system. © 2021 IEEE.

关键词： Textbooks

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Nuclei Segmentation in ER-IHC Stained Histopathology Images using Mask R-CNN

Nuclei Segmentation in ER-IHC Stained Histopathology Images ...

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2022 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2022

作者： Hasan, Md Jahid Wan Ahmad, Wan Siti Halimatul Munirah Fauzi, Mohammad Faizal Ahmad Lee, Jenny Tung Hiong Khor, See Yee Looi, Lai Meng Abas, Fazly Saleh Multimedia University Faculty of Engineering Cyberjaya Malaysia Center for Visual Computing Faculty of Computing and Informatics Cyberjaya Malaysia Sarawak General Hospital Department of Pathology Kuching Malaysia Queen Elizabeth Hospital Department of Pathology Kota Kinabalu Malaysia University Malaya Medical Center Department of Pathology Kuala Lumpur Malaysia Multimedia University Faculty of Engineering and Technology Cyberjaya Malaysia

ISBN: (纸本)9798350332421

Breast cancer is the leading cause of mortality among women in both developing and underdeveloped countries. The nuclei segmentation in digital histopathology image analysis plays a crucial role in breast cancer in the early stages of its development and may allow patients to have proper treatment. Nuclei overlap and complex structural organisation of the breast tissue in biopsy images make nuclei segmentation and feature extraction challenging. To mitigate the aforementioned problems, this paper employed a mask region-based convolution neural network (Mask R-CNN) to segment immunohistochemistry breast cancer images. The mask R-CNN algorithm introduces advanced Regional Proposal Network architecture that precisely addresses the object location to generate candidate regions. The Mask R-CNN used resnet50 as the backbone and applied Feature Pyramid Network (FPN) to fully explore multiscale feature maps. And then, Region Proposal Network (RPN) was used to propose candidate bounding boxes. The robustness of the Mask R-CNN model is enhanced by training the model with our collected dataset. The proposed architecture has the average of 72% precision, 84.2% recall, 77.62% F1-score, and Jaccard Index overall score of 0.59. The proposed model can be beneficial in assisting pathologist for a routine exam, as well as a second opinion for breast cancer segmentation from whole slide images. Since the process is fully automated, it can be done without supervision and only the final result will be attended by the pathologists. © 2022 IEEE.

关键词： Diseases

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