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Deep Learning in Smart Healthcare: A GAN-based Approach for Imbalanced Alzheimer's Disease Classification

Procedia computer science 2024年 241卷 146-153页

作者： Hina Tufail Abdul Ahad Ira Puspitasari Ibraheem Shayea Paulo Jorge Coelho Ivan Miguel Pires Department of Computer Science University of Management and Technology Sialkot Pakistan Information System Study Program Faculty of Science and Technology Universitas Airlangga School of Software Northwestern Polytechnical University Xian Shaanxi P.R. China Department of Electronics and Communication Engineering Istanbul Technical University(ITU) Turkey Polytechnic University of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Coimbra Portugal Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Alzheimer's disease (AD) is a type of dementia that leads to memory loss and impairment, which afects patients’ lives badly. It is not curable yet, but its progression can be slowed down if detected at earlier stages. In this research study, we propose a transfer learning-based convolutional neural network (CNN) model to classify magnetic resonance imaging (MRI) into one of four stages of Alzheimer's disease. One of the major limitations of the deep learning-based classification model is the non-availability of healthcare datasets related to AD. The widely used Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset has a major class imbalance issue. We propose a generative adversarial network (GAN) based data augmentation technique to overcome the data imbalance. This promotes the investigation of applying GANs to generate synthetic samples for minority classes in Alzheimer's disease datasets to enhance classification performance. The results show the progression in the overall classification process of AD.

关键词： Alzheimer disease (AD) Deep learning Convolutional Neural Network (CNN) computer-aided diagnosis (CAD)

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Nikkei Stock Market Price Index Prediction Using Machine Learning 4

Nikkei Stock Market Price Index Prediction Using Machine Lea...

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4th International Conference on Computing and Applied Informatics 2019, ICCAI 2019

作者： Harahap, Lukman A. Lipikorn, Rajalida Kitamoto, Asanobu Department of Agricultural Engineering Faculty of Agriculture University of Sumatera Utara Medan Indonesia Machine Intelligence and Multimedia Information Technology Laboratory Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand Research Organization of Information and Systems Department of Informatics National Institute of Informatics Tokyo Japan

Stock market prediction has always been a difficult process, most of the prediction rely solely on the data of the corresponding stock market. Relationship of gold and oil price with stock market performance has been proven significant in some major world stock index. Prediction of stock market price index using machine learning methods is expected to perform well, with the ability of machine learning method to predict using nonlinear inputs. The methods were commonly able to predict relatively well in predicting the values of Japanese Nikkei 225 and Japanese Nikkei 400 indexes. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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Integrating cyber security factors with TAM framework for implementation in the Jordanian banks

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AIP Conference Proceedings 2023年第1期2979卷

作者： Mohammed Bani Baker Rami Sihwail Manar Mizher Department of Computer Information System Faculty of Computer Science and Informatics Amman Arab University Amman Jordan. Department of Cyber Security Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Department of Mobile Computing Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

The importance of cyber security in several areas of national security and the world trading system causes concern for many countries worldwide. This study is one of the few pioneering studies to examine the use of cybersecurity among banks in Jordan, a developing nation with the technology acceptance model (TAM). TAM is able to predict the acceptance and use of technology by its users. The two-pronged approach is adopted in the methodology to develop a comprehensive model. This work provides a framework that is developed based on integrating the organizational-human factors. It also focuses on the use of cyber security to provide insight into the influence level that each aspect has on the alert to using cybersecurity and the actual behavior of cybersecurity among Jordanian banks. Cyber security’s importance is expected to increase with the implementation of the framework, with organizational factors and human factors influencing its importance. Study results also indicated the necessity of paying attention to cyber security to improve employees’ actual behavior.

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The Locating-Chromatic Number of Certain Barbell Origami Graphs 3

The Locating-Chromatic Number of Certain Barbell Origami Gra...

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3rd International Conference on Applied sciences Mathematics and Informatics, ICASMI 2020

作者： Irawan, A. Asmiati Suharsono, S. Muludi, K. Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Lampung Jl. Sumantri Brojonegoro no 1 Bandar Lampung Indonesia Information System Stmik Pringsewu Jl. Wisma Rini No.09 Pringsewu Lampung Indonesia Department of Computer Science Faculty of Mathematics and Natural Sciences Universitas Lampung Jl. Sumantri Brojonegoro no 1 Bandar Lampung Indonesia

The locating-chromatic number of a graph combined two graph concept, coloring vertices and partition dimension of a graph. The locating-chromatic number, denoted by χL (G), is the smallest k such that G has a locating k-coloring. In this paper, we discuss the locating-chromatic number for certain barbell Origami graphs. © Published under licence by IOP Publishing Ltd.

关键词： Location

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Unique English: An English Learning Application for Remedial Program Using Gamification Approach 2

Unique English: An English Learning Application for Remedial...

引用

2nd Joint International Conference on Emerging Computing Technology and Sports, JICETS 2019

作者： Yu, Yab Li Othman, Muhammad Fakri Bin Wahab, Mohd Helmy Abd Idrus, Syed Zulkarnain Syed Department of Multimedia in Faculty Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia Signal Imaging and Intelligence Focus Group Faculty of Electrical and Electronics Engineering Universiti Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Campus Arau Perlis02600 Malaysia Department of Multimedia in Faculty Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia

Literacy and Numeracy Screening (LINUS) that implemented in Malaysia as an educational program to improve literacy and numeracy among Year 1 to Year 3 pupils in all primary schools has been terminated in 2019. However, school-level remedial program still exists and its related application is limited in the market as there is no centralized syllabus for English remedial program yet. Therefore, an English learning application for remedial program called Unique English is designed with multimedia Mobile Content Development to overcome this problem by using previous LINUS centralized constructs. An application testing has been carried out in S.K. Pintas Raya, Parit Raja which involves pupils in remedial program and received satisfactory results with 83.2 % positive feedbacks obtained thus it can be concluded that Unique English is suitable to be implemented in remedial program. © Published under licence by IOP Publishing Ltd.

关键词： Software testing

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A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction

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Healthcare Analytics 2024年 6卷 100362-100362页

作者： Ahmed, Rasel Fahad, Nafiz Miah, Md Saef Ullah Hossen, Md. Jakir Morol, Md. Kishor Mahmud, Mufti Mostafizur Rahman, M. Faculty of Science and Technology American International University-Bangladesh Kuratoli Khilkhet Dhaka 1229 Bangladesh Faculty of Information Science and Technology (FIST) Multimedia University Melaka Malaysia Faculty of Engineering and Technology (FET) Multimedia University Jalan Ayer Keroh Lama 75450 Bukit Beruang Melaka Malaysia Department of Computer Science Nottingham Trent University Nottingham NG11 8NS United Kingdom Medical Technologies Innovation Facility Nottingham Trent University Nottingham NG11 8NS United Kingdom Computing and Informatics Research Centre Nottingham Trent University Nottingham NG11 8NS United Kingdom Department of Mathematics American International University-Bangladesh Dhaka 1229 Bangladesh Department of Computing and Information Science Cornell University NY United States EliteLab.Ai NY United States DataTech Research and Innovation Learning Lab Malaysia Faculty of Engineering Multimedia University Cyberjaya Malaysia

Dementia is a major global health issue that significantly impacts millions of individuals, families, and societies worldwide, creating a substantial burden on healthcare systems. This study introduces a novel approach for predicting dementia by employing the Logistic Regression (LR) model, enhanced with Recursive Feature Elimination (RFE), applied to a unique dataset comprising 1000 patients, with 49.60% male and 50.40% female. The LR model, recognized for its simplicity and effectiveness in binary classification tasks, is optimized through RFE, a technique that iteratively eliminates less significant features to improve model performance. The model's effectiveness was assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Kappa score. Furthermore, SHapley Additive exPlanations (SHAP) values were employed to increase the interpretability of the model, providing insights into the most influential features for dementia prediction. To address the issue of overfitting, a standardization technique was implemented, which enhanced the model's predictive performance. The findings of this study hold potential implications for early dementia detection, informing intervention strategies, and optimizing healthcare resource allocation. © 2024 The Authors

关键词： Dementia prediction Early detection Logistic regression Recursive feature elimination Shapley Additive exPlanations (SHAP)

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Digital forensics domain and metamodeling development approaches

arXiv

引用

arXiv 2021年

作者： Ameerbakhsh, Omair Ghabban, Fahad M Alfadli, Ibrahim AbuAli, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad College of Computer Science and Engineering Information system department Taibah University Madina Saudi Arabia School of Computing Faculty of Engineering University Technology Malaysia Johor Malaysia Faculty of Computer & Information Technology Al-Madinah International University Shah Alam Malaysia

Metamodeling is used as a general technique for integrating and defining models from different domains. This technique can be used in diverse application domains, especially for purposes of standardization. Also, this process mainly has a focus on the identification of general concepts that exist in various problem domain and their relations and to solve complexity, interoperability, and heterogeneity aspects of different domains. Several diverse metamodeling development approaches have been proposed in the literature to develop metamodels. Each metamodeling development process has some advantages and disadvantages too. Therefore, the objective of this paper is to provide a comprehensive review of existing metamodeling development approaches and conduct a comparative study among them-eventually selecting the best approach for metamodel development in the perspective of digital forensics. © 2021, CC BY.

关键词： Digital forensics

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CIPM: Common identification process model for database forensics field

arXiv

引用

arXiv 2021年

作者： Alfadli, Ibrahim Ghabban, Fahad M Ameerbakhsh, Omair AbuAli, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad College of Computer Science and Engineering Information system department Taibah University Madina Saudi Arabia Faculty of Engineering School of Computing University Technology Malaysia Johor Malaysia Faculty of Computer & Information Technology Al-Madinah International University Shah Alam Malaysia

Database Forensics (DBF) domain is a branch of digital forensics, concerned with the identification, collection, reconstruction, analysis, and documentation of database crimes. Different researchers have introduced several identification models to handle database crimes. Majority of proposed models are not specific and are redundant, which makes these models a problem because of the multidimensional nature and high diversity of database systems. Accordingly, using the metamodeling approach, the current study is aimed at proposing a unified identification model applicable to the database forensic field. The model integrates and harmonizes all exiting identification processes into a single abstract model, called Common Identification Process Model (CIPM). The model comprises six phases: 1) notifying an incident, 2) responding to the incident, 3) identification of the incident source, 4) verification of the incident, 5) isolation of the database server and 6) provision of an investigation environment. CIMP was found capable of helping the practitioners and newcomers to the forensics domain to control database crimes. © 2021, CC BY.

关键词： Database systems

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Rice crop management expert system with forwarding chaining method and certainty factor 9

Rice crop management expert system with forwarding chaining ...

引用

9th International Seminar on New Paradigm and Innovation of Natural sciences and Its Application, ISNPINSA 2019

作者： Kharisma Adi, K. Isnanto, R.R. Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

Rice plants are one of the focuses on food sustainability so that an increase in rice production needs to be done. Farmers need information and knowledge to improve rice crop production by practicing good planting methods that begin with the selection of superior varieties of seeds and handling plant pests properly. The problem that arises is that assistance from agricultural specialists is not always available when farmers need it, making it difficult for farmers to consult experts according to their needs at any time. Therefore, in this study, an expert system was proposed which was able to help farmers to obtain information according to their needs. This research builds an expert system for the management of rice plants including the selection of rice seed varieties and handling pests of rice plants. The method used in this expert system is forward chaining and certainty factor. Knowledgebase data was obtained from experts in agricultural extension, experts on plant pest organisms and literature from the agricultural service. From this study, an expert system was produced that was able to provide information on the selection of seed varieties and pest of rice plants according to the conditions and needs of the users. © Published under licence by IOP Publishing Ltd.

关键词： Expert systems

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