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Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

Computer Systems science & engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

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Modeling and simulation study of oxygen permeation in La0.8Ca0.2Fe0.95O3-δ-Ag hollow fiber membrane module

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Materials Today: Proceedings 2022年 64卷 1549-1556页

作者： Yeo, Jason Yi Juang Li, Claudia Sunarso, Jaka Research Centre for Sustainable Technologies Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga KuchingSarawak93350 Malaysia

Mixed ionic-electronic conducting (MIEC) ceramic membranes present an attractive alternative oxygen separation technology to cryogenic distillation and pressure swing adsorption. The model of Tan and Li has been extensively adopted in the oxygen permeation modeling through MIEC hollow fiber membranes. To evaluate the performance of the La0.8Ca0.2Fe0.95O3-δ-Ag hollow fiber membrane module here, the oxygen permeation is simulated numerically by feeding the air into the shell while passing sweep gas or creating a vacuum in the permeate side under different configuration. The simulation results show that countercurrent flow configuration is preferable in the sweep gas mode given its capability to separate oxygen completely under moderate flow rate of feed air. For vacuum mode, both cocurrent and countercurrent flow configurations result in a similar oxygen permeation performance, which lies between the sweep gas mode with countercurrent and cocurrent flow configurations. When abundant oxygen presence is created in the feed side by elevating its flow rate, the sweep gas mode loses its oxygen separation efficiency and is thus outperformed by the vacuum mode. © 2022

关键词： Oxygen

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Optical fiber integrated photoelectrochemical sensing patch: towards non-invasive blood glucose detection

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IEEE Sensors Journal 2025年第11期25卷 18741-18749页

作者： Wen, Xingyue Ge, Zhongxuan Ma, Minghua Yang, Xinghua Wang, Shengjia Tian, Fengjun Liu, Zhihai Zhang, Yu Teng, Pingping Gao, Shuai Zhu, Zheng Zhang, Yang Jones, Adam Zhang, Bo Sivanathan, Sivagunalan Li, Kang Harbin Engineering University Key Laboratory of In-Fiber Integrated Optics Ministry of Education College of Science Harbin150001 China Department of Anesthesiology First Hospital of Harbin Harbin150000 China University of South Wales Faculty of Computing Engineering & Science CF37 1DL United Kingdom Henan Academy of Special Optics Ltd Xinxiang453000 China

In recent years, non-invasive blood glucose detection has received increasing attention. Among them, sweat glucose detection is expected to become an alternative to blood glucose detection due to advantages such as its samples easily to obtain. This study was proposed and preliminarily verified a prototype of photoelectrochemical sensing patch based on an optical fiber modified by ITO/ZnO/polydopamine/glucose oxidase and printed electrode, enabling close contact with human skin. In this innovative design, the optical fiber to serve both as a light transmission medium and a working electrode, while the printed electrode functions as the counter electrode and reference electrode. The electrode materials were characterized by SEM, EDS, XRD and XPS, and the reaction kinetics of the optical fiber electrode studied through SWV, EIS, and Mott-Schottky analysis. A linear relationship was established between the photocurrent and the logarithm of glucose concentration in the concentration range from 10 μM to 1 mM, with a detection limit of 3.5 μM, and a response time of less than 1 s. In addition, the specificity and stability of the sensing patch were evaluated, and an attempt was made to detect real sweat glucose in situ. This work presents a novel prototype for the development of wearable sensors, with promising applications in biochemical analysis and health monitoring. © 2001-2012 IEEE.

关键词： Glucose sensors

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StrideSense: Enriching Lower Extremity and Kinetics in ACLR Patients via Sonic Insights

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IEEE Internet of Things Journal 2025年第12期12卷 19549-19560页

作者： Ahmed, Awais Yang, Panlong Butt, Abdul Haleem Rizwan, Muhammad Angin, Pelin Khan, Taha Nanjing University of Information Science and Technology University of Science and Technology of China China University of Bologna Department of Biomedical and Neuromotor Sciences Italy Air University Islamabad Department of Creative Technologies Faculty of Computing and Artificial Intelligence Pakistan University of Science and Technology of China School of Computer Science and Technology China Middle East Technical University Department of Computer Engineering Turkey Detectivio AB Sweden

Tearing the anterior cruciate ligament requires repair and rehabilitation to restore lower limb functionality fully. This study outlines a method for monitoring rehabilitation after knee surgery by analyzing footstep sounds and applying deep learning techniques. The process involves examining gait sounds during the initial four weeks of rehabilitation. The suggested system, StrideSense, recognizes walking sounds, enabling seamless and ongoing monitoring of patients' gait rehabilitation. The research proposes a novel method for event detection by analyzing walking patterns using dynamic time-warping and sequential footstep duration. It leverages physiological data like gait sound and energy descriptors to develop a deep-learning model for gait improvement assessment, evaluated by a physiotherapist through lower extremity functional scores. The suggested model, AtdNet, which utilizes Densenet169 and attention mechanisms, achieves 96% accuracy in classifying walking on various post-surgical days. It also predicts lower extremity functional scores with a mean absolute error of 4.63%. A deeper analysis of bone-conducted footstep sounds enriched the acoustic sensing method. We assessed the suggested acoustic model alongside existing methods, showing that rehabilitation monitoring driven by acoustics outperforms traditional clinic-based approach assessments. Future efforts will focus on validating the model with a larger dataset and integrating it into smart homes. © 2014 IEEE.

关键词： Physiological models

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Spatio-Temporal Clustering of Forest Fire Hotspots in the Wallacea Region in 2012 – 2022 Using Chronnet 11

Spatio-Temporal Clustering of Forest Fire Hotspots in the Wa...

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11th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2024

作者： Ismanto, Rido Dwi Vatresia, Arie Wahyuni, Intan Nuni Utama, Ferzha Putra Gusmanto, Rizki Anees, Mohd Talha Research Center for Computing National Research and Innovation Agency Bogor Indonesia Informatics-Engineering Faculty University of Bengkulu Bengkulu Indonesia Information System-Engineering Faculty University of Bengkulu Bengkulu Indonesia Department Geology Faculty of Science Universiti Sains Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9798331542313

Data from the Ministry of Environment and Forestry of Indonesia show that millions of hectares were burned by fires in Indonesia with 2015 being the greatest incidence in the last of ten years. Therefore, a research is required to examine the distribution pattern of hotspots for forest fires. This study used Chronnet to display the clustering of forest fire hotspot data in the Wallacea Region (North Maluku) between 2012 and 2022. The NASA FIRMS provided the MODIS data used in the study. Several findings have been made from the implemented Chronnet method. The degree of connectivity map shows a medium degree which suggests that the North Maluku frequently experiences forest and land fires of a moderate intensity. The strength map shows a complex pattern of fire activity, with high-intensity flames creating major concerns in some, particularly in West Halmahera Regency and low to moderate fire events occurring in most other places. The results from the Chronnet cluster show that most fires occurred in production forests, indicating the need for stronger fire prevention and regulations. While protected forests had fewer incidents, there are still several clusters that require attention. Additionally, the frequent fires in non-forest areas emphasize the need for better land-use planning and coordination. Finally, although conservation forests remained unaffected, it is crucial for the government to prioritize maintaining this record. © 2024 IEEE.

关键词： Premixed flames

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A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm

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International Journal of Hybrid Intelligent Systems 2024年第3期20卷 207-221页

作者： Subbiah, Priyanga Tyagi, Amit Kumar Krishnaraj, N. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperfor

关键词： deep learning disease detection enhanced OPTICS algorithm Image processing image segmentation infection severity tomato plant diseases transfer learning

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METRICALLY REGULAR MAPPING AND ITS UTILIZATION TO CONVERGENCE ANALYSIS OF A RESTRICTED INEXACT NEWTON-TYPE METHOD

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Journal of Computational Mathematics 2022年第1期40卷 44-69页

作者： Mohammed Harunor Rashid Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China Department of Mathematics Faculty of ScienceUniversity of RajshahiRajshahi-6205Bangladesh

In the present paper,we study the restricted inexact Newton-type method for solving the generalized equation 0∈f(x)+F(x),where X and Y are Banach spaces,f:X→Y is a Frechet differentiable function and F:X■Y is a set-valued mapping with closed *** establish the convergence criteria of the restricted inexact Newton-type method,which guarantees the existence of any sequence generated by this method and show this generated sequence is convergent linearly and quadratically according to the particular assumptions on the Frechet derivative of ***,we obtain semilocal and local convergence results of restricted inexact Newton-type method for solving the above generalized equation when the Frechet derivative of f is continuous and Lipschitz continuous as well as f+F is metrically *** application of this method to variational inequality is *** addition,a numerical experiment is given which illustrates the theoretical result.

关键词： Generalized equation Restricted inexact Newton-type method Metrically regular mapping Partial Lipschitz-like mapping Semilocal convergence.

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Supervised Machine Learning Approach to Housing Market

Supervised Machine Learning Approach to Housing Market

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Mohd Ismayatim, Izat Hakimi Najiah Zakaria, Hanis Ramadhani, Ihsanul Arifin Abdullah, Nurzulaikah Ridzuan, Fakhitah Mustafa, Wan Azani Al-Farouni, Mohammed Universiti Malaysia Kelantan Faculty of Data Science and Computing Kota Bharu Malaysia Universitas Mercu Buana Faculty of Computer Science Jakarta Indonesia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau02600 Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350336474

Understanding the multifaceted factors influencing housing prices is crucial for facilitating informed decision-making among diverse stakeholders, including homebuyers, sellers, investors, and policymakers. Therefore, the primary objective of this research is to develop a predictive model for housing prices utilizing regression analysis. By leveraging machine learning techniques, such as regression and classification analysis, this study aims to uncover the intricate relationships between various factors and housing prices, ultimately providing valuable insights to stakeholders involved in the real estate market. Through rigorous data analysis and model development, this research seeks to enhance understanding and contribute to more accurate predictions in the realm of housing price dynamics. © 2023 IEEE.

关键词： Investments

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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2023 International Research Conference on Smart computing and Systems engineering, SCSE 2023

作者： Arunoda, D.P.H. Walpola, S.R. Piumira, S.M.I. Athukorala, A.D.M.P. Thilakarathna, Thusithanjana Chandrasiri, Sanjeevi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy. © 2023 IEEE.

关键词： Students

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Graph Low-Rank Non-Negative Matrix Factorization with Auto-Encoders for Fault Detection 14

Graph Low-Rank Non-Negative Matrix Factorization with Auto-E...

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14th International Conference on Information science and Technology, ICIST 2024

作者： Liu, Yabing Yu, Shanshan Guo, Wei Leung, Man-Fai School of Electronic and Information Engineering Southwest University Chongqing China South-Central Minzu University State Ethnic Affairs Commission Key Laboratory of Cyber-Physical Fusion Intelligent Computing Wuhan430074 China School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom

ISBN: (纸本)9798350353334

Fault detection is the process of detecting and diagnosing faults or abnormalities in a system by analyzing its operational data. However, with the complexity of modern industrial processes, some faults are difficult to be detected in a timely manner due to various factors such as noise and data nonlinearity. Therefore, data-driven Fault Detection (FD) has become a widely used method to detect abnormal events in functional modules. Non-negative Matrix Factorization (NMF), as an efficient dimensionality reduction technique, has not had potential applications in fault detection (FD) thoroughly explored. In order to improve the FD methods based on NMF, we have developed a new approach, named graph low-rank non-negative matrix Factorization with auto-encoders (GLNMFA). GLN-MFA integrates the Laplacian operator effectively identifies t he l ocal s tructure a mong d ata p oints, enhancing the performance of dimensionality reduction algorithms. It also introduces the nuclear norm to find a low-rank approximation to the original matrix, thereby constraining sparsity. Additionally, auto-encoders are incorporated to learn a low-dimensional representation of the data and extract key features, which are subsequently applied for fault detection purposes. We employ an optimization algorithm based on Alternating Direction Method of Multipliers (ADMM) to optimize this model. Two test statistics T2 (Hotelling's T-squared), SPE (Squared Prediction Error) are used to evaluate detection efficiency. K ernel Density Estimation (KDE) are used to estimate control limits for fault detection. The effectiveness of GLNMFA is validated on two benchmark datasets. © 2024 IEEE.

关键词： Non-negative matrix factorization

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