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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Nag, Sauradip Ramachandra, Raghavendra Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Kankanhalli, Mohan Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781728128610

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets. © 2019 IEEE.

关键词： Sports

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Analysis of Models of Security to Mitigate the Risks, Vulnerabilities and Threats in a Company of Services of Telecommunications

Analysis of Models of Security to Mitigate the Risks, Vulner...

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International Conference on Information and computer Technologies (ICICT)

作者： Segundo Moisés Toapanta Toapanta Luis Enrique Mafla Gallegos María José Chévez Morán Javier Gonzalo Ortiz Rojas Salesian Polytechnic University Guayaquil Ecuador Faculty of Engineering System National Polytechnic School (EPN) Quito Ecuador Derpartment Computer Science Salesian Polytechnic University (UPS) Guayaquil Ecuador

ISBN: (数字)9781728172835

ISBN: (纸本)9781728172842

Different models in Information and Communication Technologies were analyzed for the creation of an alternative model for mitigating information security risks within telecommunications organizations. The main objective is to define a concept model prototype for the management of process and information security in this type of organizations. Was used the deductive and exploratory research method to analyze the information of the references. It is research in an information security prototype through a risk matrix, a conceptual security prototype. It was concluded that the prototypes presented as results are an alternative to improve the security of the processes and the flow of information and that these can be used as a reference in this type of organizations.

关键词： Security Companies Time-frequency analysis Analytical models Telecommunications Standards organizations

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Digital Reference: A quasi-standard for digitalization in the domain of semiconductor supply chains 3

Digital Reference: A quasi-standard for digitalization in th...

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3rd IEEE Conference on Industrial Cyberphysical systems, ICPS 2020

作者： Ehm, Hans Schoitsch, Erwin Van Der Weit, Jan Wytze Ramzy, Nour Luo, Lanyingzhu Gruetzner, Daniel Louis Infineon Technologies Ag Supply Chain Innovations Munich Germany Austrian Institute of Technology Center for Digital Safety and Security Vienna Austria Philips Consumer Lifestyle Philips Drachten Netherlands Leibniz University Faculty of Electrical Engineering and Computer Science Hannover Germany The Technical University of Munich Faculty of Information System Munich Germany Epf École d'Ingénieur-e-s Formation Franco-Allemande Sceaux France

ISBN: (纸本)9781728163895

This paper is about the installation of trust in the Semantic Web content of the Digital Reference (DR). The DR was developed in H2020/ECSEL/Productive4.0, which contains ontologies to model production and Cyber Physical systems addressing the whole industrial automation and control area. It should serve as a quasi-standard for digitalization in the domain of semiconductors and corresponding supply chains to overcome the partial incompatibility problem of existing *** Web is a success story in the B2C (Business to consumer) environment as it is understandable for both humans and machines alike. To transfer this success of easy data exchange into the complex industrial B2B (Business to business) environment, a much higher quality is required-a standardization of the classes and properties. The confidence in standards usually comes from human verification, validation and confirmation, which has the disadvantage of being slow. Machine validation could be faster but often lacks confidence. Thus, how can trust be installed to ensure the "correctness"of the used classes and properties in a speedy process?We propose a human and machine blended approach based on peer review and Blockchain technology built on decentralized peer-to-peer networks to solve this problem. The consensus process combines transparency, traceability, immutability and speed of Blockchain with the fidelity of human experts. © 2020 IEEE.

关键词： Blockchain

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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

arXiv

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： Data mining

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Challenges and Recommendation of the Information Technologies Application in Hazardous Medical Waste Management amidst Pandemic Covid-19

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Journal of Physics: Conference Series 2021年第1期1844卷

作者： P Rahayu S Rohajawati S Fairus H Saragih H Akbar Information System Computer Science Faculty Universitas Mercu Buana Jakarta Indonesia Information System Informatics and Environmental Engineering Universitas Bakrie Jakarta Indonesia. Informatics Computer Science Faculty Universitas Esa Unggul Jakarta Indonesia

Handling of Covid-19 patients requires more medical equipment than normal conditions, increasing the amount of hazardous medical waste. The management of hazardous medical waste has many challenges; therefore, it needed a strategy to solve it using information technology. Based on a filter of 376 articles, this review adopted a systematic Literature Review approach to evaluating the recent challenge and recommendation in the field of Hazardous Medical Waste Management amidst Covid-19. Through a four-phase workflow consisting of searching, screening, excluded, and included literature search, this study identified the most influential journals, scholars, and articles that have been influential in the domain of Hazardous Medical Waste Management. These literature review results are four challenges in Hazardous Medical Waste Management, including Regulation, Technology, Financial and Awareness. The other finding is IT application recommendations such as IoT, Big Data, DSS, AI and GIS. By providing the latest research about the challenges and recommendations in the domain of Hazardous Medical Waste Management amidst Covid-19, the paper serves as a preliminary recommendation for practitioners and researchers to link current research to future trends.

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Analysis of performance comparison between Software-Based iSCSI SAN and Hardware-Based iSCSI SAN

Analysis of performance comparison between Software-Based iS...

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Universitas Riau International Conference on science and Environment 2019, URICSE 2019

作者： Kurniawan, Dwi Ely Ahmad, Imam Ridho, Muhammat Rasid Hidayat, Fendi Anggra Js, Ahmad Department of Informatics Engineering Politeknik Negeri Batam Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Department of Information System Universitas Putera Batam Indonesia Department of Information System Universitas Batam Batam Indonesia Department of Multimedia and Network Engineering Politeknik Negeri Batam Indonesia

Nowadays computer data grows rapidly and make a lot of institutions and companies looking for storage solutions that are safe, reliable and trustworthy. One of the solutions is to implement a Storage Area Network (SAN). In this study, the authors chose the iSCSI protocol as an object for analyzing the performance of the SAN server based on software and hardware. The RAID type used are RAID 5 and RAID 10, while the parameters used are Input Output per Second (IOPS), throughput and average latency with workloads 4 KB, 8 KB, and 256 KB. The tests performed on the network running Link Aggregation (LAG) or network without LAG. From the test results, it is seen that a SAN network running LAG have increased throughput by 89.57 MBps. The best IOPS performance was achieved by Software-Based iSCSI SAN with a value of 26729.48 IOPS. The best throughput performance is achieved by Hardware-Based iSCSI SAN with a value of 118.55MBps. The best latency performance achieved by Software-Based iSCSI SAN with a value of 2.01ms. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Multi-modal remote sensory learning for multi-objects over autonomous devices

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Frontiers in bioengineering and biotechnology 2025年 13卷 1430222页

作者： Aysha Naseer Naif Almudawi Hanan Aljuaid Abdulwahab Alazeb Yahay AlQahtani Asaad Algarni Ahmad Jalal Hui Liu Department of Computer Science Air University Islamabad Pakistan. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia. Department of Computer Science College of Computer Science and Information System Najran University Najran Saudi Arabia. Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia. Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia. Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea. Guodian Nanjing Automation Co. Ltd. Nanjing China. Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and technology Nanjing China. Cognitive Systems Lab University of Bremen Bremen Germany.

Introduction:There has been an increasing focus on object segmentation within remote sensing images in recent years due to advancements in remote sensing technology and the growing significance of these images in both military and civilian realms. In these situations, it is critical to accurately and quickly identify a wide variety of objects. In many computer vision applications, scene recognition in aerial-based remote sensing imagery presents a common issue. Method:However, several challenging elements make this work especially difficult: (i) Different objects have different pixel densities; (ii) objects are not evenly distributed in remote sensing images; (iii) objects can appear differently depending on viewing angle and lighting conditions; and (iv) there are fluctuations in the number of objects, even the same type, in remote sensing images. Using a synergistic combination of Markov Random Field (MRF) for accurate labeling and Alex Net model for robust scene recognition, this work presents a novel method for the identification of remote sensing objects. During the labeling step, the use of MRF guarantees precise spatial contextual modeling, which improves comprehension of intricate interactions between nearby aerial objects. By simultaneously using deep learning model, the incorporation of Alex Net in the following classification phase enhances the model's capacity to identify complex patterns in aerial images and adapt to a variety of object attributes. Results:Experiments show that our method performs better than others in terms of classification accuracy and generalization, indicating its efficacy analysis on benchmark datasets such as UC Merced Land Use and AID. Discussion:Several performance measures were calculated to assess the efficacy of the suggested technique, including accuracy, precision, recall, error, and F1-Score. The assessment findings show a remarkable recognition rate of around 97.90% and 98.90%, on the AID and the UC Merced Land datasets,

关键词： autonomous devices deep learning computer vision likelihood estimation multi-modal multi-objects posterior probability remote sensing scene analysis

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Digital Health and Deep Learning: Opportunities, Challenges, and the Imperative of Data Protection in Wearable Devices

SSRN

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SSRN 2024年

作者： Norman, Azah Anir Eke, Christopher Ifeanyi Phiri, Musa Al-Garadi, M.A. Ahmed, Abdulaziz Yeh, Wan-Ju Department of Information System FCSIT University of Malaya Malaysia Department of Computer Science Faculty of Computing Federal University of Lafia Nasarawa state Nigeria Department of Engineering and Technology Mulungushi University PO Box 80415 Kabwe Zambia Department of Biomedical Informatics School of Medicine Vanderbilt University 1161 21st Ave S # D3300 NashvilleTN37232 United States Department of Health Services Administration School of Health Professions University of Alabama at Birmingham 1716 9th Ave S BirminghamAL35233 United States National Cheng-Kung University Department of Law No. 1 University Road Tainan City70 Taiwan

The rapid expansion of digital health technologies has been revolutionized by the combination of deep learning and machine learning models with wearable devices, making it possible to perform sophisticated data analytics, predictive health analytics, and real-time monitoring. The paper examines the fascinating possibilities of deep learning, wearable technology, and digital health while emphasizing the challenges and urgent need for data protection. Deep learning and digital health hold great promise for individualized treatment plans, early disease identification, and ongoing health monitoring. Large volumes of health data can be analyzed by machine learning/deep learning models, which can then produce findings and recommendations that are timely and instructive for patients, doctors, and scholars. However, these opportunities also provide significant challenges such as security and privacy. It becomes critical to protect data against misuse, unauthorized access, and data breaches. Strong data protection protocols, compliance with legal requirements, and ethical considerations to uphold user trust are necessary for ensuring the responsible use of machine learning/algorithms in the healthcare industry. This article also highlights the significance of encouraging transparency, guaranteeing accountability among business players, and developing data protection rules and guidelines for wearable devices. Collaboration between policymakers, healthcare professionals, researchers, and technology developers are essential to address the ethical, legal, and technical complexities surrounding data protection in wearable healthcare devices. By addressing the challenges and imperative of data protection, stakeholders can shape a responsible and secure landscape for digital health and deep learning applications, ensuring the long-term success and adoption of wearable devices in healthcare. © 2024, The Authors. All rights reserved.

关键词： Wearable technology

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Glaucoma identification with retinal fundus images using deep learning: systematic review

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Informatics in Medicine Unlocked 2025年 56卷

作者： Meedeniya, Dulani Shyamalee, Thisara Lim, Gilbert Yogarajah, Pratheepan Department of Computer Science and Engineering University of Moratuwa Moratuwa 10400 Sri Lanka First-Year Division Faculty of Computing Sri Lanka Institute of Information Technology Malabe 10115 Sri Lanka SingHealth AI Health Program SingHealth Group Singapore 168582 Singapore School of Computing Engineering and Intelligent System Ulster University Londonderry BT48 7JL United Kingdom

Glaucoma is a leading cause of blindness, affecting millions of people worldwide. It is a chronic eye condition that damages the optic nerve and, if left untreated, can lead to vision loss and a decreased quality of life. Therefore, there is a need to explore practical and reliable mechanisms for glaucoma identification. This study systematically reviews deep-learning approaches for glaucoma identification using retinal fundus images from 2018 to 2024. Compared to existing survey studies, we cover the latest research, including several public retinal fundus image datasets, and focus on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The findings of this study, including comparisons of existing methods and key insights, will assist researchers and developers in identifying the most suitable techniques for glaucoma detection. © 2025 The Authors

关键词： Artificial intelligence Classification Deep learning Explainability Glaucoma Segmentation Trustworthiness

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Distributed Coordinated Emergency Frequency Control Strategy for Renewables-Rich Power systems Based on Multi-Agent Reinforcement Learning

SSRN

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SSRN 2025年

作者： Sun, Zhenglong Han, Junpeng Chen, Weihan Zhang, Rui Liu, Yong Levron, Yoash Belikov, Juri Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Akadeemia Tee 15a Tallinn12618 Estonia

With the rapid growth of renewable energy in the power system and the implementation of large-capacity LCC-HVDC projects, the frequency support capability of the grid has been reduced. Simultaneously, the renewables-rich grid faces the risk of high-power shocks due to LCC-HVDC blocking. Existing centralized, single-measure approaches and the model-based emergency frequency control (EFC) methods are facing tremendous challenges. To fill this gap, this paper proposes a frequency emergency control method for renewables-rich grids that coordinates LCC-HVDC emergency power support and load shedding. The coordination of the two stabilization measures is modeled as a decentralized partially observable Markov decision process, and a multi-agent reinforcement learning (MADRL) approach is employed to optimize the power of measures for each stabilization control substation. An MADRL algorithm that integrates the system frequency response model is developed to overcome the problem of inaccurate evaluation of centralized action value in the MADRL algorithm used for EFC. This approach allows for the global optimization of the distributed coordination strategy through centralized training. The effectiveness and adaptability of the proposed method are demonstrated through simulations of the modified IEEE 39-bus system. © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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