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SSRN 2023年

作者： Xiong, Mingfu Xiong, Jiefan Wang, Zhongyuan Chen, Jia Hu, Ruimin Muhammad, Khan Xiong, Zixiang School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan430200 China Wuhan University Wuhan430072 China Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of The Department of Electrical and Computer Engineering Texas A&M University College StationTX7784 United States

Recently, person re-identification (person Re-ID) equipped with attention mechanism (AM) has gradually emerged as one of the prevalent study highlights. In fact, the existing attention-based methods still remain the challenges for recognizing the different persons with similar appearances in the unpredictable realistic environments: (1) the adaptability of the learned a single and local attention to the hostile scenes cannot be guaranteed;(2) most existing methods originated from the attention features usually try to find a simple linear transformation, which is easy to lost the function of distinguishing different pedestrians with similar appearance attributes. To tackle these issues, this paper proposed a spatial adaptive cooperative graph convolution attention (SACGCA) method to excavate the relevances of individual body local regions for each person. The proposed framework encompasses a dual-attention mechanism model (DAMM) and a spatial adaptive graph convolution model (SAGCM). In particular, the DAMM consists of the multi-region (local and global) association structure and a feature channel attention network to extract person features. Then, the SAGCM is primed for constructing the spatial nonlinear transformation relationships among the various body regions for each person by the virtue of graph optimization. Extensive experiments have conducted on the person Re-ID public datasets (Market-1501, DukeMTMC-reid and CUHK-03), which results indicate that the performance of our proposed algorithm is better than existing methods by 2%, 2%, 10% at Rank-1 or mAP respectively. © 2023, The Authors. All rights reserved.

关键词： Convolution

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On the Distortion of Committee Election with 1-Euclidean Preferences and Few Distance Queries

arXiv

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arXiv 2024年

作者： Fotakis, Dimitris Gourvès, Laurent Patsilinakos, Panagiotis School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece Archimedes Research Unit Athena RC Athens15121 Greece Université Paris Dauphine-PSL CNRS LAMSADE Paris75016 France Department of Informatics Athens University of Economics and Business Athens10434 Greece

We consider committee election of k ≥ 2 (out of m ≥ k + 1) candidates, where the voters and the candidates are associated with locations on the real line. Each voter's cardinal preferences over candidates correspond to her distance to the candidate locations, and each voter's cardinal preferences over committees is defined as her distance to the nearest candidate elected in the committee. We consider a setting where the true distances and the locations are unknown. We can nevertheless have access to degraded information which consists of an order of candidates for each voter. We investigate the best possible distortion (a worst-case performance criterion) wrt. the social cost achieved by deterministic committee election rules based on ordinal preferences submitted by n voters and few additional distance queries. For k = 2, we achieve bounded distortion without any distance queries;we show that the distortion is 3 for m = 3, and that the best possible distortion achieved by deterministic algorithms is at least n − 1 and at most n + 1, for any m ≥ 4. For any k ≥ 3, we show that the best possible distortion of any deterministic algorithm that uses at most k − 3 distance queries cannot be bounded by any function of n, m and k. We present deterministic algorithms for k-committee election with distortion of O(n) with O(k) distance queries and O(1) with O(k log n) distance queries. © 2024, CC BY.

关键词： Consensus algorithm

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National Institute of Standard and Technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Haider K. Hoomod Mohammad Alaa Al-Hamami Aqeel Kamil Kadhim Qutaiba Humadi Mohammed Jolan Rokan Niaf College of Education Mustansiriyah University Baghdad Iraq Department of Management Information Systems Applied Science University Manama Kingdom of Bahrain Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network.

关键词： Transport protocols Network intrusion detection Machine learning Routing Data transfer Data models Delays Classification algorithms Internet of Things Intelligent sensors

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An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing

arXiv

引用

arXiv 2023年

作者： Peng, Le Luo, Gaoxiang Zhou, Sicheng Chen, Jiandong Xu, Ziyue Zhang, Rui Sun, Ju Department of Computer Science and Engineering University of Minnesota MinneapolisMN United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Institute for Health Informatics University of Minnesota MinneapolisMN United States Nvidia Corporation Santa ClaraCA United States Division of Computational Health Sciences Department of Surgery University of Minnesota MinneapolisMN United States

Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP). However, the medical field faces challenges in training LMs due to limited data access and privacy constraints imposed by regulations like the Health Insurance Portability and Accountability Act (HIPPA) and the General Data Protection Regulation (GDPR). Federated learning (FL) offers a decentralized solution that enables collaborative learning while ensuring data privacy. In this study, we evaluated FL on 2 biomedical NLP tasks encompassing 8 corpora using 6 LMs. Our results show that: 1) FL models consistently outperformed models trained on individual clients' data and sometimes performed comparably with models trained with polled data;2) with the fixed number of total data, FL models training with more clients produced inferior performance but pre-trained transformer-based models exhibited great resilience. 3) FL models significantly outperformed large language models using zero-/one-shot learning and offered lightning inference speed. © 2023, CC BY.

关键词： Data privacy

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Low-Cost Air Box Application for Indoor Environment Classification and LSTM Microclimate Prediction

Low-Cost Air Box Application for Indoor Environment Classifi...

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International Conference on Control and Robots (ICCR)

作者： Hu Hsun-Jui Chiu Yu-Hsien Yang Pei-Ching Master Degree Program in Information and Design Chang Jung Christian University Tainan Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering Chang Jung Christian University Tainan Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

Air pollution is a significant issue because it burdens human health and the environment. Particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) can penetrate the lungs and enter the bloodstream, seriously affecting the cardiovascular system, causing strokes, and impacting the respiratory system. Modern individuals spend 70% to 90% of their time indoors, making indoor air quality even more important than outdoor air quality. To estimate and predict indoor air quality, this study utilizes low-cost, self-assembled air boxes based on the Internet of Things to collect various air quality data. The collected data is then used in an indoor air quality forecasting system that combines Long Short-Term Memory (LSTM) and K-means clustering methods to compare the overall performance of the algorithms. The experimental results are analyzed and statistically described based on each model method prediction accuracy and clustering results. These methods are integrated to develop an indoor air quality forecasting system to help indoor populations prevent issues in advance or optimize energy usage.

关键词： Temperature measurement Temperature sensors Atmospheric modeling Predictive models Air quality Data models Forecasting Long short term memory Monitoring Indoor air quality

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A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

arXiv

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arXiv 2024年

作者： Yang, Songnan Zhang, Xiaohui Zhang, Shiliang Ma, Xuehui Bai, Wenqi Li, Yushuai Huang, Tingwen The School of Automation and Information Engineering Xi’an University of Technology Shaanxi 710048 China The Institute of Advanced Navigation and Electromagnetics Xi’an University of Technology Shaanxi 710048 China The Department of Informatics University of Oslo Oslo0316 Norway The Department of Computer Science Aalborg University Aalborg9220 Denmark Texas A&M University at Qatar Doha23874 Qatar

Various animals exhibit accurate navigation using environment cues. The Earth’s magnetic field has been proved a reliable information source in long-distance fauna migration. Inspired by animal navigation, this work proposes a bionic and data-driven approach for long-distance underwater navigation. The proposed approach uses measured geomagnetic data for the navigation, and requires no GPS systems or geographical maps. Particularly, we construct and train a Temporal Attention-based Long Short-Term Memory (TA-LSTM) network to predict the heading angle during the navigation. To mitigate the impact of geomagnetic anomalies, we develop the mechanism to detect and quantify the anomalies based on Maximum Likelihood Estimation. We integrate the developed mechanism with the TA-LSTM, and calibrate the predicted heading angles to gain resistance against geomagnetic anomalies. Using the retrieved data from the WMM model, we conduct numerical simulations with diversified navigation conditions to test our approach. The simulation results demonstrate a resilience navigation against geomagnetic anomalies by our approach, along with precision and stability of the underwater navigation in single and multiple destination missions. © 2024, CC BY-NC-ND.

关键词： Animals

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis

Digital Technology in Health Education Trend Mapping through...

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International Conference on informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Fairuz Iqbal Maulana Dian Lestari Vandha Pradwiyasma Widartha Nirwana Haidar Wahyu Dirgantara Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Medicine Airlangga University Surabaya Indonesia Information System of Industrial Engineering Faculty Telkom University Bandung Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Department of Electrical Engineering University of Merdeka Malang Malang Indonesia

ISBN: (纸本)9781665473286

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012–2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9 % ), Conference Paper (4.2 % ), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social Sciences (7.8%), engineering (3.9%), computer Science (2.6 % ), Environmental Science (2.6 % ), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector.

关键词： Training Pediatrics Instruments Education Bibliometrics Transportation Psychology

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Explainable machine learning versus known nomogram for predicting non-sentinel lymph node metastases in breast cancer patients: A comparative study

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computers in Biology and Medicine 2025年 184卷 109412-109412页

作者： Fattahi, Asieh Sadat Hoseini, Maryam Dehghani, Toktam Nezhad Noor Nia, Raheleh Ghouchan Naseri, Zeinab Ebrahimzadeh, Amirali Mahri, Ali Eslami, Saeid Department of Surgery Faculty of Medicine Endoscopic and Minimally Invasive Research Center Mashhad University of Medical Sciences Mashhad Iran General Surgeon Department of Surgery Endoscopic and Minimally Invasive Research Center Mashhad University of Medical Sciences Mashhad Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Computer Engineering Sharif University of Technology Tehran Iran Endoscopic and Minimally Invasive Research Center Mashhad University of Medical Sciences Mashhad Iran Pharmaceutical Research Center Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran Department of Medical Informatics UMC-Location AMC University of Amsterdam Amsterdam Netherlands

Introduction: Axillary lymph node dissection (ALND) is the standard of care for breast cancer patients with positive sentinel lymph nodes (SLN), which are the first lymph nodes that drain the breast. However, many patients with positive SLNs may not have additional positive nodes, making the prediction of non-sentinel lymph node (NSLN) metastasis challenging. Reliable prognostic tools are essential for accurately assessing NSLN metastasis. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram has demonstrated effectiveness in this context, but it requires further evaluation within the Iranian breast cancer population. While ALND remains the gold standard, its unnecessary application in patients without evidence of additional positive nodes raises concerns due to potential complications such as lymphedema, nerve injury, and shoulder joint dysfunction. Furthermore, integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques presents an opportunity to enhance the precision of NSLN metastasis predictions. Method: This study conducts an extensive comparative analysis between the MSKCC nomogram and various ML models to predict NSLN metastasis, utilizing a dataset of Iranian breast cancer patients. Employing eXplainable Artificial Intelligence (XAI) methodologies, we analyzed 16 clinical features across a cohort of 183 patients. Our methodology includes rigorous statistical evaluations and the training and validation of ML models to assess the precision and robustness of these models compared to the MSKCC nomogram. Results: Our analysis revealed that the Random Forest (RF) model outperformed the MSKCC nomogram, achieving an accuracy of 72.2 % and an AUC of 0.77, compared to the nomogram's AUC of 0.73. Logistic Regression (LR) also demonstrated competitive performance with an accuracy of 65 % and an AUC of 0.73. The RF model exhibited high sensitivity (75 %) and precision (73 %), effectively identifying critical predictors of NSLN metastasis, includi

关键词： Diseases

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Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model 2

Latent fingerprint enhancement and segmentation technique ba...

引用

2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Miaikil, Othman A.M. Mahmoud Altrad, Abdullah Baker El-Ebiary, Yousef A. Hilles, Mohanad M Contreras, Jennifer Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia University College of Applied Science Information Technology Department Gaza Palestine FEU Institute of Technology Department of Information Technology Manila Philippines

ISBN: (纸本)9781665432221

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE.

关键词： Image enhancement

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