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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Mazlan, Aiman Syabil Suhaizan, Sofea Nur Batrisyia Hishamudin, Zariq Izni, Nor Aziyatul Aslam, Siti Nor Aini Mohd Saruchi, Sarah'Atifah College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Negeri Sembilan Kampus SerembanNegeri Sembilan Seremban70300 Malaysia Universiti Teknologi MARA Cawangan Selangor Centre of Foundation Studies Kampus Dengkil Selangor Dengkil43800 Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Manufacturing and Mechatronic Engineering Technology Pahang Pekan26600 Malaysia

ISBN: (纸本)9798331528553

Exercise and repetitive movement fatigue are crucial to healthcare and rehabilitation. To improve the quality of rehabilitation and healthcare, precise evaluations and focused interventions are needed. Electromyography (EMG) is an essential technique used to measure muscle activity during various tasks. Despite its popularity, previous studies have yet to determine which EMG features are critical in classifying muscle contraction patterns across different physical activities and obtaining optimal performance. Therefore, this study aims to classify muscle contraction patterns during stepping exercises from sEMG and investigate the efficiency of Artificial Neural Networks as a classifier of this muscle contraction pattern. Sixty participants voluntarily participated in this stepping exercise and their EMG signal was recorded. The raw EMG signal undergoes filtering, rectification, and linear enveloping. Then, each signal from each participant was segmented into a relax signal and a contract signal. Time domain and frequency domain features were then retrieved from every relax and contract signal and fed into the ANN model. The results indicate that ANN yielded 96.5% accuracy performance in classifying the muscle contraction patterns from the sEMG signal. The findings from this study could contribute valuable insights to inform more effective and personalized rehabilitation services in identifying muscle contractions autonomously, thereby improving the overall mobility and quality of life for individuals facing muscle fatigue and financial problems. This study could help in strengthening the healthcare services mentioned in Ekonomi Madani's 17-Big Bolds, Malaysia. © 2024 IEEE.

关键词： Electromyography

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Ensemble Based Machine Learning Model for Heart Disease Prediction 3

Ensemble Based Machine Learning Model for Heart Disease Pred...

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3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Ambrews, Ashley Bryan Gubin Moung, Ervin Farzamnia, Ali Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Sabah Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Sabah Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

The World Health Organization reports that more than 10 million people worldwide die each year due to heart disease. In this project, an ensemble-based machine learning (ML) model is proposed for the prediction of heart disease. The proposed method was evaluated on two datasets: UCI heart disease and Framingham heart study. Various ML such as K-Nearest Neighbors, Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) were carried out to predict the outcome of heart disease. Additionally, the Stacking and Voting ensemble approaches are also employed for heart disease prediction. All ML classifiers evaluated on both datasets have their performance metrics analyzed and compared. The results of all ML classifiers assessed on the Framingham dataset is inferior than that of the UCI heart disease dataset. For individual ML classifier, SVM achieved the highest accuracy for UCI heart disease with 91.09%, whereas LR achieved the highest accuracy for the Framingham heart study with 85.38%. However, it should be noted that RF trained on the UCI dataset is the best ML for prioritizing Sensitivity (92.52%). Finally, with the exception of Sensitivity, the Voting approach is strongly suggested because it outperforms the other classifiers noticeably where it achieved Accuracy (91.96%), F1 (91.69%), Sensitivity (91.72%), Specificity (90.77%), and Precision (92.40%) on UCI dataset. © 2022 IEEE.

关键词： Forecasting

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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Computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Internet-of-Things for Smart Dryers: Enablers, State of the arts, Challenges, and Solutions 4

Internet-of-Things for Smart Dryers: Enablers, State of the ...

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

作者： Rizalman, Mohd Khairulanwar Moung, Ervin Gubin Dargham, Jamal Ahmad Jamain, Zuhair Yaakub, Nurul'azah Mohd Omatu, Sigeru Universiti Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Science and Natural Resources Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Sustainable Agriculture Sandakan Malaysia Education Research Centre Hiroshima University Higashi-Hiroshima Japan

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Smart devices have grown to becoming an indispensable part of many individuals' lives. This is due to cloud computing and connectivity, that enabled wireless controls and real-time monitoring that benefits various industries. Soft-computing techniques that has been integrated to many online services such as Amazon allows machine to understand consumer activity and autonomously cater to a personalized experience for each user. In the agricultural industries, smart system is a tool to enhance controls and increase farming precision to improve yield and product quality. Challenges in implementing smart agriculture system are still apparent to this day;(i) Mathematical Modelling of Operation, (ii) Lack of Data and Attention in Smart Farming System, (iii) Complexity of Drying Process. In this paper, state of the art in the smart system in agricultural field, challenges of executing smart farming system are further described and the proposed solution is presented. © 2022 IEEE.

关键词： Quality control

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Analyzing darknet traffic through machine learning and neucube spiking neural networks

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Intelligent and Converged Networks 2024年第4期5卷 265-283页

作者： Iman Akour Mohammad Alauthman Khalid M.O.Nahar Ammar Almomani Brij B.Gupta Information Systems Department College of Computing&InformaticsUniversity of SharjahSharjahUnited Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmman 11196Jordan the Faculty of Computer Studies Arab Open UniversityRiyadh 11681Saudi Arabia Department of Information Technology Al-Huson University CollegeAl-Balqa Applied UniversityIrbid 19117Jordan the Department of Computer Information Science Higher Colleges of TechnologySharjahUnited Arab Emirates the Department of Computer Science and Information Engineering Asia UniversityTaichung 413ChinaSymbiosis Centre for Information TechnologySymbiosis International(Deemed University)Pune 412115India Centre of Inter-disciplinary Research&Innovation University of Petroleum and Energy StudiesDehradun 248007India

The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication *** detection of clandestine darknet traffic is therefore critical yet immensely *** research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen *** diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed *** on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural *** pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet *** proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.

关键词： darknet traffic machine learning deep learning spiking neural network PCA

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Real-Time Segmentation of IHC Images From Microscope Using Deep Learning Architecture

Real-Time Segmentation of IHC Images From Microscope Using D...

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Biomedical engineering Conference (NBEC), IEEE National

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

Segmentation of nuclei in digital histopathology image analysis plays a critical role in the early assessment of breast cancer and may enable patients to get appropriate treatment. In this paper, we create a real-time application that thoroughly examines the effectiveness of various deep learning models, including U-Net, SegNet, ResNet50-Unet, and ResNet50-SegNet, in the domain of real-time segmentation. For real-time implementation, we use an industrial machine vision camera mounted to the microscope, stream the image from the microscope glass slide and segment it using the model. This experiment aims to identify the best deep-learning model for real-time segmentation of nuclei for immunohistochemistry (IHC)-stained glass slides. The models are evaluated in offline mode using test images from estrogen receptor IHC stains, taken from whole-slide images. The effectiveness of the model for real-time work is based on its segmentation computational time. For offline evaluation, the highest F1-score and Jaccard index is achieved by ResNet50-SegNet (85.21%) and ResNet50-Unet (0.725) accordingly. These findings support the proof of concept that deep learning models can effectively segment nuclei in real-time from IHC-stained glass slides. This research serves as a foundation for the future construction of fast and efficient deep learning models for real-time histopathological analysis directly from the microscope.

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Deep Learning Assisted Kidney Organ Image Analysis for Assessing the Viability of Transplantation 14

Deep Learning Assisted Kidney Organ Image Analysis for Asses...

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14th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2022

作者： Elmhamudi, Ali Abubakar, Aliyu Ugail, Hassan Thomson, Brian Wilson, Colin Turner, Mark Manas, Derek Tingle, Samuel Colenutt, Sam Sen, Gourab Hunter, James Sun, Meng Scully, Jackie University of Bradford Centre for Visual Computing Faculty of Engineering and Informatics Bradford United Kingdom Nihr Blood and Transplant Research Unit in Organ Donation and Transplantation at Cambridge Newcastle Universities Newcastle upon Tyne United Kingdom University of Oxford Nuffield Department of Surgical Sciences Oxford United Kingdom Disability Innovation Institute Unsw University of New South Wales Kensington Australia

ISBN: (纸本)9781665493345

The kidney is a vital organ in humans that removes toxic waste from the body and maintains the balance between water, minerals, and salts. Malfunctioning of this vital organ has become one of the significant public health concerns in recent years. The most viable way to treat patients with acute kidney failure is via transplantation. A healthy substitute is required from a healthy donor, which goes through rigorous examination by experienced clinicians to ascertain its vitality. However, the whole procedure is time-consuming, not reliable, and has high intra-observer and inter-observer variations. For these reasons, we proposed a machine learning-based approach using photographic samples to assess the health of the donor organ. Deep learning models, VGG-16 and DenseNet121, were used for feature extraction from 120 organs labelled 1,2,3,4 and 5, where scores 1 and 2 are good, score 3 is fair (uncertain), and 4 and 5 as poor. Random Forest Regressor and Support Vector Regressor were trained and then used to predict the surgeon-derived score labels, determining whether an organ is transplantable or should be discarded. The results indicate an algorithm of this nature could go a long way show in deciding the transplantability of a kidney organ. © 2022 IEEE.

关键词： Toxic materials

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A review of mechanistic learning in mathematical oncology

arXiv

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arXiv 2023年

作者： Metzcar, John Jutzeler, Catherine R. Macklin, Paul Köhn-Luque, Alvaro Brüningk, Sarah C. Intelligent Systems Engineering Luddy School of Informatics Computing and Engineering BloomingtonIN United States Informatics Luddy School of Informatics Computing and Engineering BloomingtonIN United States Zürich Switzerland Schulthess Klinik Zürich Switzerland Lausanne Switzerland Oslo Centre for Biostatistics and Epidemiology Faculty of Medicine University of Oslo Oslo Norway Oslo Centre for Biostatistics and Epidemiology Research Support Services Oslo University Hospital Oslo Norway

Mechanistic learning, the synergistic combination of knowledge-driven and data-driven modeling, is an emerging field. In particular, in mathematical oncology, the application of mathematical modeling to cancer biology and oncology, the use of mechanistic learning is growing. This review aims to capture the current state of the field and provide a perspective on how mechanistic learning may further progress in mathematical oncology. We highlight the synergistic potential of knowledge-driven mechanistic mathematical modeling and data-driven modeling, such as machine and deep learning. We point out similarities and differences regarding model complexity, data requirements, outputs generated, and interpretability of the algorithms and their results. Then, organizing combinations of knowledge- and data-driven modeling into four categories (sequential, parallel, intrinsic, and extrinsic mechanistic learning), we summarize a variety of approaches at the interface between purely data- and knowledge-driven models. Using examples predominantly from oncology, we discuss a range of techniques including physics-informed neural networks, surrogate model learning, and digital twins. We see that mechanistic learning, with its intentional leveraging of the strengths of both knowledge and data-driven modeling, can greatly impact the complex problems of oncology. Given the increasing ubiquity and impact of machine learning, it is critical to incorporate it into the study of mathematical oncology with mechanistic learning providing a path to that end. As the field of mechanistic learning advances, we aim for this review and proposed categorization framework to foster additional collaboration between the data- and knowledge-driven modeling fields. Further collaboration will help address difficult issues in oncology such as limited data availability, requirements of model transparency, and complex input data. Copyright © 2023, The Authors. All rights reserved.

关键词： Oncology

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Hypothyroidism Prediction and Detection Using Machine Learning 5

Hypothyroidism Prediction and Detection Using Machine Learni...

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5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Almahshi, Hebatullah Mohammad Almasri, Esraa Abdallah Alquran, Hiam Mustafa, Wan Azani Alkhayyat, Ahmed Jordan University of Science and Technology Department of Biomedical Engineering Irbid21163 Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid21163 Jordan Faculty of Electrical Engineering Technology Pauh Putra Campus Perlis Arau02600 Malaysia Advanced Computing Centre of Excellence Pauh Putra Campus Perlis Arau02600 Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The world is facing an increase in the incidence of thyroid diseases, including in Jordan as well, especially hypothyroidism. This condition can be diagnosed through blood samples to know the percentage of hormones (TSH, T3, and T4) and take the appropriate decision after that. The major goal of this study is to make diagnosis easier and reduce the risk of misdiagnosis, which happens all too often. Methods: The use of machine learning in the prediction of disease is a crucial step in the process of data analysis. This paper presents an analysis and classification model that takes into account the various factors involved in the prediction of disease. We applied a number of machine learning algorithms like support vector machine (SVM), Nave Bayes, decision trees, and ensemble to get the best prediction. The decision tree algorithm showed the highest accuracy and lowest cost with a percentage of 97.6 percent and 90, respectively. The results appeared in the form of three classes (compensated hypothyroidism, primary hypothyroidism, and negative). Conclusion: The best results were obtained at the milestone point in computer-aided diagnosis of hypothyroidism. Future work is considered to increase the number of cases that can be detected and diagnosed using machine learning. © 2022 IEEE.

关键词： Decision trees

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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

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Computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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