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A Method for Continuous Health Survey in an Elderly Community and the Relationship Between Social Capital and Health data Collection

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Procedia Computer science 2024年 246卷 1518-1526页

作者： Jun Takeo Akira Masuo Saki Nakamura Takuto Sakuma Yoshihiro Kawade Tadashi Suzuki Kohei Watanabe Hiroyasu Akatsu Shohei Kato Faculty of Technology International Professional University of Technology in Nagoya Aichi 450–0002 Japan NITech Artificial Intelligence Research Center Nagoya Institute of Technology Aichi 466–8555 Japan Graduate School of Medical Science Nagoya City University Aichi 467–8601 Japan Seijoh University Aichi 476–8588 Japan Graduate School of Engineering Nagoya Institute of Technology Aichi 466–8555 Japan Graduate School of Pharmaceutical Sciences Nagoya City University Aichi 467–8603 Japan Graduate School of Health and Sport Sciences Chukyo University Aichi 470–0393 Japan National Center for Geriatrics and Gerontology Aichi 474–8511 Japan

Early detection and treatment of diseases are crucial in preventing and controlling their progression. Developing methods for a super-early screening is highly desirable. We have continuously conducted health survey sessions for elderly adults, and have collected information on their health, such as their living environment and physical functions. This paper presents the design of the measurement session. At the latest measurement session, we focus on DLB (Dementia with Lewy Bodies). We then try to construct a screening method for Parkinsonism, which is one of the prodromes of DLB. In the survey, we collected not only health information but also gait measurements. It attempts to realize a highly accurate screening tool with modality fusion; each modality is based on the gait analysis, in addition to measurements, questionnaires, and interviews. Next, we show the difference between the active and inactive groups in the last five sessions of information. The results show each of the two groups made contact with different types of communities. The results also show that the active group excelled in activities of daily living and working memory and had few concerns about dementia.

关键词： Screening for Dementia DLB Parkinsonism Health survey Gait analysis Social capital

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Orientation and Context Entangled Network for Retinal Vessel Segmentation

SSRN

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SSRN 2022年

作者： Wei, Xinxu Yang, Kaifu Bzdok, Danilo Li, Yongjie MOE Key Lab for Neuroinformation School of Life Sciences and Technology University of Electronic Science and Technology of China China McGill University MontrealQC Canada Department of Biomedical Engineering Faculty of Medicine McGill University MontrealQC Canada Mila - Quebec Artificial Intelligence Institute MontrealQC Canada

Most of the existing deep learning based methods for vessel segmentation neglect two important aspects of the retinal vessel, one is the orientation information of vessels, and the other is the contextual information of the whole fundus region. In this paper, we propose a robust Orientation and Context Entangled Network (denoted as OCE-Net), which has the capability of extracting complex orientation and context information of the blood vessels. To achieve complex orientation aware, a Dynamic Complex Orientation Aware Convolution is proposed to extract complex vessels with multiple orientations for improving the vessel continuity. To simultaneously capture the global context information and emphasize the important local information, a Global and Local Fusion Module is developed to simultaneously model the long-range dependency of vessels and focus sufficient attention on local thin vessels. A novel Orientation and Context Entangled Non-local module is proposed to entangle the orientation and context information together. In addition, an Unbalanced Attention Refine Module is proposed to deal with the unbalanced number of vessels among background, thick and thin *** experiments were performed on several commonly used datasets (DRIVE, STARE and CHASEDB1) and some more challenging datasets (AV-WIDE, UoA-DR, RFMiD and UK Biobank). The ablation study shows that the proposed method achieves promising performance on maintaining the continuity of thin vessels and the comparative experiments demonstrate that our OCE-Net can achieve state-of-the-art performance on retinal vessel segmentation. © 2022, The Authors. All rights reserved.

关键词： Blood vessels

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Orthogonal Embedding Hashing with Semantic Reinforcement for Modal Matching

SSRN

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SSRN 2022年

作者： Fang, Yixian Wang, Canwei Yuan, Feng Sun, Qian Ren, Yuwei School of Information Engineering Shandong Management University Shandong Province Jinan250357 China School of Information Science and Engineering Shandong Normal University Shandong Province Jinan250014 China Faculty of Mathematics and Artificial Intelligence Qilu University of Technology Shandong Academy of Sciences Shandong Province 250353 China

Cross-modal hashing (CMH) is a fast modal association technique in multimodal learning community. Current CMH techniques, however, are susceptible to scaling in binarization, resulting in semantic deficiency of hash codes. It is mainly caused by the redundancy of raw features and the loss of binary quantization. For these occasions, a novel orthogonal embedding hash with semantic reinforcement (SR-OEH) is proposed to alleviate these limitations. To be specific, SR-OEH firstly leverages the associated tags to guide feature semantic condensation by optimized adjustment weights, which are trained by maximizing the sample edge, so as to highlight the relevant features with detrimental redundancy degradation. What's more, we construct an efficient iterative optimization algorithm to learn a strong semantic hash dictionary by supervised rotation factor step by step orthogonal, so as to effectively offset the quantization loss induced by the mixed integer optimization. Finally, two common hash functions are employed to concatenate the hash book and the deepened semantic features, thus accomplishing cross modal matching. The promising results obtained on five public data sets bear out the superiority of proposed strategy against the representative state-of-the-art cross-modal hashing baselines. © 2022, The Authors. All rights reserved.

关键词： Semantics

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Implementation of fuzzy multi criteria decision making for determining student rank (Case study: Bimbel plus IKA)

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AIP Conference Proceedings 2023年第1期2536卷

作者： Yutika Amelia Effendi Nania Nuzulita Robotics and Artificial Intelligence Engineering Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya Indonesia Information Systems Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia

The concept of a Decision Support System (DSS), one of the branches in artificial intelligence, is widely used. There are many methods in DSS which are useful to find the optimum solution or alternative solution for a problem. One such method is Fuzzy Multi-Criteria Decision Making (FMCDM). This method will help decision makers in situations where there are many alternative decisions with several criteria available. This paper presents a new way to define student rank by implementing Fuzzy Multiple Criteria Decision Making (FMCDM) method with problem solving using the Simple Addictive Weighting Method (SAW). The results of this study were the ranking process obtained by the sum of the multiplication of normalized matrix with a weight vector so that the greatest value is chosen as the best alternative as the solution for determining student rank in Bimbel Plus IKA.

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Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models

Stress Prediction in Higher Education Students Using Psychom...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Sarthak Sharma Suman Vij RVS Praveen S. Srinivasan Dharmendra Kumar Yadav Raj Kumar V S Management Shri Guru Ram Rai University Dehradun Uttarakhand India School of Management and Commerce Studies Shri Guru Ram Rai University Dehradun Uttarakhand India Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Advanced Computing Sciences AMET University Chennai Tamilnadu India University Department of Mathematics Lalit Narayan Mithila University Darbhanga Bihar India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Research backs up the common perception that the years preceding up to college are the most difficult, showing that students report higher levels of discomfort and worse self-esteem during this switch. Positive personality qualities such as optimism, hope, and happiness were examined in this system along with 215 first-semester Israeli university students' ratings of functional impairment, psychological distress, and self-esteem. The three stages that make up this suggested method are data preprocessing, model training, and feature selection. In order to process data that would be infeasible to process without data preprocessing, the data can be adjusted to meet the specifications of each data mining technique. Beginning with different forms of feature classification or aggregation and progressing to individual activity levels, several levels of feature granularity were investigated in order to employ a feature selection technique. The proposed approach used an AOA-CNN-XGBoost for the whole model training phase. This novel method outperforms CNN and AOA with an average accuracy of 89.27%.

关键词： Training Accuracy data preprocessing Psychology Human factors Switches Predictive models Feature extraction Prediction algorithms data mining

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Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

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Informatics in Medicine Unlocked 2022年 28卷 100840页

作者： Hasan, Md. Imran Rahman, Md Habibur Islam, M. Babul Islam, Md Zahidul Hossain, Md Arju Moni, Mohammad Ali Department of Computer Science and Engineering Islamic University Kushtia 7003 Bangladesh Department of Electrical and Electronic Engineering University of Rajshahi 6205 Bangladesh Department of Information and Communication Technology Islamic University Kushtia 7003 Bangladesh Department of Biotechnology and Genetic Engineering Mawlana Bhashani Science and Technology University Tangail 1092 Bangladesh Department of Computer Science and Engineering Pabna University of Science and Technology Pabna 6600 Bangladesh Artificial Intelligence & Digital Health Data Science School of Health and Rehabilitation Sciences Faculty of Health and Behavioural Sciences The University of Queensland St Lucia QLD 4072 Australia

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease's initiation and progression as well as developing drug therapeutics. © 2022 The Authors

关键词： Biomarker signatures Blood common gene COVID-19 Differentially expressed genes Gene ontology PBMCs cell

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Progressive LiDAR Adaptation for Road Detection

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 693-702页

作者： Zhe Chen Jing Zhang Dacheng Tao IEEE the UBTECH Sydney Artificial Intelligence Centre and the School of Computer Science Faculty of Engineering and Information Technologies University of Sydney the School of Automation Hangzhou Dianzi University the University of Technology Sydney

Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection,because LiDAR data is less susceptible to visual noises. However,the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection(PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-theart road detection models and is currently top of the publicly accessible benchmark leader-board.

关键词： Autonomous driving computer vision deep learning LiDAR processing road detection

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Jacobian Kolmogorov-Arnold Networks for Cervical Cancer Cell Classification

SSRN

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SSRN 2025年

作者： Khare, Smith K. Nadimi, Esmaeil S. Blanes-Vidal, Victoria Applied AI and Data Science Unit Mærsk Mc-Kinney Møller Institute Faculty of Engineering University of Southern Denmark Denmark Centre for Clinical Artificial Intelligence Odense University Hospital Denmark

Cervical cancer is a primary cause of death in women throughout the world, and early identification using cell classification is essential for increasing survival rates. Timely and accurate detection of cancer cells is critical for promoting personalized treatment and clinical diagnostics. This paper introduces Conv-Jacobian Kolmogorov–Arnold Network (ConvJKAN), a novel deep-learning architecture specifically designed for high-accuracy cervical cancer cell classification from cytological images. The Conv-JKAN uniquely integrates convolutional neural networks with the Jacobian Kolmogorov–Arnold network, enabling enhanced feature extraction while maintaining computational efficiency. The model employs just three convolutional layers and three JKAN layers to capture intricate spatial and contextual patterns across varying resolutions, ensuring superior recognition of subtle cytopathological changes. The novelty of ConvJKAN lies in its hierarchical decomposition approach, where JKAN dissects high-dimensional functions into low-dimensional components using Jacobian polynomial-based sensitivity analysis. This novel technique enhances both interpretability and gradient flow, addressing common challenges in deep-learning-based medical diagnostics. To validate its robustness, Conv-JKAN was rigorously tested on three public cytology datasets, achieving high classification performance: 100%, 99.75%, and 96.52% accuracy in binary classification, and 99.7%, 98.73%, and 96.35% in multiclass classification on the Mendeley LBC, SIPaKMeD, and Herlev datasets, respectively. Furthermore, we integrated explainable artificial intelligence (XAI) techniques within Conv-JKAN to provide transparent and interpretable insights into the model’s decision-making, which is crucial for real-world clinical adoption. © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Camouflaged Object Detection via Context-aware Cross-level Fusion

arXiv

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions

Deep Learning Approaches for Signal and Image Processing: St...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： S Deepan Jaideep Gera Anupam Pareek Sheela Chinchmalatpure Gouri Sankar Nayak Jagadeesh Bodapati School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department Of Cse St Marys Group Of Institutions Guntur Chebrolu (V) Guntur (Dist) Faculty of Management of Commerce Poornima University IS-2027-2031 Ramchandrapura P.O. Vidhani Vatika Sitapura Extension Jaipur Rajasthan India AIDS Vishwakarma Institute of Technology Punez Maharashtra India Ai&Ds Vignan’s Institute Of Information Technology Duvvada Visakhapatnam Andhra Pradesh India Electronics and Communication Engineering BVC College of Engineering (A) Rajamundry

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has revolutionized signal and image processing by enabling the creation of complex algorithms with many applications. This study examines deep learning signal and image processing optimization and hardware acceleration strategies. Experimentally evaluating stochastic gradient descent (SGD) and Adam optimization algorithms determines their convergence speed and effectiveness. We also examine how GPUs may accelerate model execution and deep learning inference. Our study reveals that deep learning optimization methodologies and platforms provide several practical challenges and trade-offs. This work may help signal and image processing researchers and practitioners design scalable and efficient solutions. Additionally, their methods illuminate deep learning.

关键词： Deep learning Image processing Signal processing algorithms Optimization methods Stochastic processes Streaming media Inference algorithms Hardware acceleration Standards Convergence

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