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International Conference on Engineering technology and their Applications (IICETA)

作者： Dana Hdaib Noor Almajali Hiam Alquran Wan Azani Mustafa Waleed Al-Azzawi Ahmed Alkhayyat Department of Biomedical Engineering Jordan University Of Science And Technology Irbid Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Pauh Putra Campus Advanced Computing Centre of Excellence Universiti Malaysia Perlis (UniMAP) Pauh Putra Campus Arau Perlis Malaysia Medical technical college Al-Farahidi University Baghdad Iraq Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

One of the most common diseases in women of reproductive age is Polycystic Ovary Syndrome (PCOS). PCOS diagnosis can be tricky, because not everyone with PCOS has polycystic ovaries (PCO), nor does everyone with ovarian cysts have PCOS, hence the pelvic ultrasound as a stand-alone diagnosis is not sufficient. The full diagnostic plan is mainly a combination of a pelvic ultrasound besides blood tests of specific parameters that indicate the presence of PCOS. Since PCOS is a hard-to-diagnose widespread hormonal disorder, blood tests, symptoms, and other parameters with the help of a computer can form a new and easy method to diagnose it. Therefore, we had successfully built a high performing diagnostic model using MATLAB. The data was obtained from the website Kaggle, and the dataset is called Polycystic Ovary Syndrome. In this paper various machine algorithms were employed by utilizing seven classifiers. Results demonstrated that Linear Discriminant classifier exhibits the best performance in terms of accuracy, while in terms of sensitivity, the KNN classifier had the best result. Also, a comparison with four other research papers that exploited the same PCOS dataset was done in terms of implementation platforms, evaluation methods, classifiers, classes, accuracy, and precision of each classifier. Our research excelled among all in terms of accuracy and varied in precedence with precision. MATLAB had shown substantial results and a great model fitting embedded approaches, scoring a high accuracy and precision outcome compared to other studies. Other improvements on the overall PCOS prediction can involve employing preprocessed ultrasound images with the features presented in the dataset.

关键词： Solid modeling Ultrasonic imaging Sensitivity Fitting Predictive models Feature extraction Prediction algorithms

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Learning-Analytics based Intelligent Simulator for Personalised Learning

Learning-Analytics based Intelligent Simulator for Personali...

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2020 International Conference on Advancement in Data science, E-Learning and Information Systems, ICADEIS 2020

作者： Sharef, Nurfadhlina Mohd Azmi Murad, Masrah Azrifah Mansor, Evi Indriasari Nasharuddin, Nurul Amelina Omar, Muhd Khaizer Samian, Normalia Arshad, Noreen Izza Ismail, Waidah Shahbodin, Faaizah Universiti Putra Malaysia Faculty of Computer Science and Information Technology Selangor43400 Malaysia Positive Computing Research Center Institute of Autonomous Systems Universiti Teknologi Petronas Perak32610 Malaysia Universiti Sains Islam Malaysia Faculty of Science and Technology Bandar Baru Nilai Sembilan71800 Malaysia Centre for Academic Excellence and Scholarship Universiti Teknikal Malaysia Melaka Melaka76100 Malaysia

ISBN: (纸本)9781728182728

Personalised learning enables instructions to be tailored specific to students learning needs, while making sure learning outcomes are attained. Instructors require information that could facilitate them in adapting their pedagogy design so the learning delivery could be optimized. However, existing solutions are limited to descriptive analytic and intervention facilitation is confined to students at risk prediction based on their course engagement frequency. Tools to predict final grade is available but very scarce. Besides, realtime monitoring of reaction to learning events are not available. Therefore, this paper proposes a solution that integrates Internet of Things, learning analytic and chatbot to fill the said gaps. The paper also presents the experience of pilot developments towards the current version of solution. © 2020 IEEE.

关键词： Artificial Intelligence in Education Chatbot for Learning Learning Analytic Learning Experience Personalised Learning

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Analysis of inter-transaction time fluctuations in the cryptocurrency market

arXiv

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arXiv 2022年

作者： Kwapień, Jaroslaw Wątorek, Marcin Bezbradica, Marija Crane, Martin Mai, Tai Tan Drożdż, Stanislaw Complex Systems Theory Department Institute of Nuclear Physics Polish Academy of Sciences Radzikowskiego 152 Kraków31-342 Poland Faculty of Computer Science and Telecommunications Cracow University of Technology ul. Warszawska 24 Kraków31-155 Poland Adapt Centre School of Computing Dublin City University Glasnevin Dublin Ireland

We analyse tick-by-tick data representing major cryptocurrencies traded on some different cryptocurrency trading platforms. We focus on such quantities like the inter-transaction times, the number of transactions in time unit, the traded volume, and volatility. We show that the inter-transaction times show long-range power-law autocorrelations. These lead to multifractality expressed by the right-side asymmetry of the singularity spectra f (α ) indicating that the periods of increased market activity are characterised by richer multifractality compared to the periods of quiet market. We also show that neither the stretched exponential distribution nor the power-law-tail distribution are able to model universally the cumulative distribution functions of the quantities considered in this work. For each quantity, some data sets can be modeled by the former, some data sets by the latter, while both fail in other cases. An interesting, yet difficult to account for, observation is that parallel data sets from different trading platforms can show disparate statistical properties. Copyright © 2022, The Authors. All rights reserved.

关键词： Electronic money

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

arXiv

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

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Development of biomass-fueled cogeneration systems for off-grid electrification 2

Development of biomass-fueled cogeneration systems for off-g...

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2nd International Conference on Smart Power and Internet Energy Systems, SPIES 2020

作者： Bagherian, Mohammad Ali Mehranzamir, Kamyar Ahmed, Jubaer Nafea, Marwan Nabipour-Afrouzi, Hadi Wooi, Chin-Leong University of Nottingham Malaysia Department of Electrical and Electronic Engineering Selangor Malaysia Swinburne University of Technology Sarawak Faculty of Engineering Computing and Science Kuching Malaysia Universiti Malaysia Perlis Centre of Excellence for Renewable Energy School of Electrical Systems Engineering Perlis Malaysia

ISBN: (纸本)9781728166117

Over the past centuries, discovery and utilization of fossil fuels has revolutionized the world pushing societies towards modernization. This source of energy that is being referred as black gold, has faced extensive consumption which consequently started to underpin the acceptance of the negative effects of our carbon society and its likely demise. Fossil fuels have costed the safety and overall health of our environment, causing serious consequences such as global warming and unsustainability. To this end, in the search for alternative generation, the potential of renewables was realized and hence, exploited in the energy sector. In recent times, development of efficient prime movers and possible utilization of renewable energy resources for both power and heat production introduced the concept of cogeneration to the energy sector. Accordingly, this comparative research elaborates on utilization of the most available and versatile renewable energy, biomass, in combined heat and power technology. There is no doubt that the integration of bioenergy and cogeneration is associated with significant advantages, namely: lower environmental impacts and primary energy savings. In this context, four different biomass-fueled configurations are developed for the purpose of electrifying the Masset village in Canada. Two well-known methods of: Direct Combustion and Gasification are considered in the model development. Further, a comparative conclusion is drawn from economic perspective. © 2020 IEEE.

关键词： Biomass

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An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis

arXiv

引用

arXiv 2023年

作者： Han, Luyi Zhang, Tianyu Huang, Yunzhi Dou, Haoran Wang, Xin Gao, Yuan Lu, Chunyao Tan, Tao Mann, Ritse Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Amsterdam Netherlands GROW School for Oncology and Development Biology Maastricht University P. O. Box 616 Maastricht6200 MD Netherlands Faculty of Applied Science Macao Polytechnic University Macao999078 China Institute for AI in Medicine School of Automation Nanjing University of Information Science and Technology Nanjing China School of Computing University of Leeds Leeds United Kingdom School of Medicine University of Leeds Leeds United Kingdom

Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-based methods have achieved good performance in combining multiple available sequences for missing sequence synthesis. Despite their success, these methods lack the ability to quantify the contributions of different input sequences and estimate the quality of generated images, making it hard to be practical. Hence, we propose an explainable task-specific synthesis network, which adapts weights automatically for specific sequence generation tasks and provides interpretability and reliability from two sides: (1) visualize the contribution of each input sequence in the fusion stage by a trainable task-specific weighted average module;(2) highlight the area the network tried to refine during synthesizing by a task-specific attention module. We conduct experiments on the BraTS2021 dataset of 1251 subjects, and results on arbitrary sequence synthesis indicate that the proposed method achieves better performance than the state-of-the-art methods. Our code is available at https://***/fiy2W/mri_seq2seq. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Voice Pathology Detection Using Machine Learning Technique 5

Voice Pathology Detection Using Machine Learning Technique

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5th IEEE International Symposium on Telecommunication Technologies, ISTT 2020

作者： Al-Dhief, Fahad Taha Latiff, Nurul Mu'azzah Abdul Malik, Nik Noordini Nik Abd. Sabri, Naseer Baki, Marina Mat Albadr, Musatafa Abbas Abbood Abbas, Aymen Fadhil Hussein, Yaqdhan Mahmood Mohammed, Mazin Abed Universiti Teknologi Malaysia School of Electrical Engineering Johor Bahru Malaysia Sohar University Computing and Information Technology Sohar Oman Universiti Kebangsaan Malaysia Medical Centre Faculty of Medicine Kuala Lumpur Malaysia Universiti Kebangsaan Malaysia Cait Faculty of Information Science and Technology Bangi Malaysia University of Anbar Planning and Follow Up Department Anbar Iraq

ISBN: (纸本)9781728181615

Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection of voice pathologies. However, most proposed systems in the detection of voice disorder utilized limited database. Furthermore, low accuracy rate is still the one of the most challenging issues for these techniques. This paper presents a voice pathology detection system using Online Sequential Extreme Learning Machine (OSELM) to classify the voice signal into healthy or pathological. In this work, the voice features are extracted by using Mel-Frequency Cepstral Coefficient (MFCC). The voice samples for the vowel /a/ were collected equally from Saarbrücken voice database (SVD). The proposed method is evaluated by three widely used measurements which are accuracy, sensitivity and specificity. The obtained results show that the maximum accuracy, sensitivity and specificity are 85%, 87% and 87%, respectively. According to the experimental results, the performance of OSELM algorithm is able to differentiate healthy and pathological voices effectively. © 2020 IEEE.

关键词： Pathology

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Scandium-doped barium ceria ferrites-based composite mixed conducting hollow fiber membranes for H 2 and O 2 permeation

引用

Journal of Industrial and Engineering Chemistry 2022年 107卷 100-108页

作者： Jian Song Claudia Li Shao Zhang Kai Wang Bo Meng Xiaoyao Tan Jaka Sunarso Shaomin Liu Department of Chemical Engineering Shandong University of Technology Zibo 255049 PR China State Key Laboratory of Separation Membranes and Membrane Processes Department of Chemical Engineering Tiangong University Tianjin 300387 PR China Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia WA School of Mines: Minerals Energy and Chemical Engineering Curtin University Perth Western Australia 6102 Australia College of Chemical Engineering Beijing University of Chemical Technology Beijing 100029 PR China

BaCeO 3 -based ceramics are a unique class of perovskites that exhibit protonic and oxygen ionic conduction, which are ideal for high temperature hydrogen separation applications. This work features Sc-doped BCFe composites with nominal composition of BaCe 0.70 Fe 0.30-x Sc x O 3−δ (BCFSc) (x = 0, 0.05, 0.1, 0.15, and 0.2) that exhibit enhanced thermal, chemical, and mechanical stability. The BCFSc20 (x = 0.20) hollow fiber membranes demonstrated the highest hydrogen and oxygen fluxes among the Sc-doped composites of up to 0.32 and 0.20 mL min −1 cm −2 , respectively, at 1000 °C. The BCFSc20 hollow fibers also displayed excellent stability under the hydrogen and oxygen permeation environments, while the permeation performance of the other BCFSc samples with low Sc content gradually deteriorated over the 120-h stability test.

关键词： Hollow fiber membrane Hydrogen permeation Oxygen permeation Phase inversion Scandium doping

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Thermo-Kinetic Study of Co-Pyrolysis of Empty Fruit Bunches (Efb) and Disposable Face Mask (Dfm) Wastes Via Thermogravimetric Analysis

SSRN

引用

SSRN 2022年

作者： Wee, Melvin Xin Jie Chin, Bridgid Lai Fui Saptoro, Agus Chew, Jiuan Jing Sunarso, Jaka Yusup, Suzana Sharma, Abhishek Department of Chemical and Energy Engineering Faculty of Engineering and Science Curtin University Malaysia CDT 250 Sarawak Miri98009 Malaysia Energy and Environment Research Cluster Faculty of Engineering and Science Curtin University Malaysia CDT 250 Sarawak Miri98009 Malaysia Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga Sarawak Kuching93350 Malaysia Research Sdn Bhd Kawasan Institusi Penyelidikan No. 1 Jln Ayer Hitam Selangor Kajang43000 Malaysia Department of Chemical Engineering Manipal University Jaipur Rajasthan Jaipur303007 India

Recent innovative approach to reduce the palm oil industry biomass is via the co-pyrolysis process, by introducing a high hydrogen containing co-feedstock, such as polyolefin plastics. Following this, the novel coronavirus (COVID-19) has greatly exacerbated the plastic waste generation due to the surge of disposable face masks (DFM) consumption. Hence, the co-pyrolysis characteristics and kinetics of empty fruit bunch (EFB) and DFM were investigated in this study. The EFB and DFM samples were blended at a weight ratio of 1:1, and the thermogravimetric analysis (TGA) of the mixture were conducted at heating rates of 10, 20, 50 and 100 °***-1. The TGA results were fitted into the distributed activation energy model (DAEM). The activation energy and pre-exponential factor of the EFB-DFM blends were determined as 62.71 kJ mol-1 and 1.45 x107 s-1, respectively. Moreover, the thermo-kinetic parameters of the co-pyrolysis blends can be ranked from EFB-DFM-ZSM-5 > EFB-DFM-Ni > EFB-DFM. © 2022, The Authors. All rights reserved.

关键词： Thermogravimetric analysis

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arXiv

引用

arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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