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International Symposium on Parallel and Distributed Computing

作者： Andreas Andreou Constandinos X. Mavromoustakis Jordi Mongay Batalla Ciprian Dobre Evangelos Markakis George Mastorakis Departement of Computer Science University of Nicosia and University of Nicosia Research Foundation Nicosia Cyprus Warsaw University of Technology Warsaw Poland Automatic Control and Computers University Politehnica of Bucharest National Institute for Research and Development in Informatics Bucharest Romania Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Department of Management Science and Technology Hellenic Mediterranean University Agios Nikolaos Greece

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area.

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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

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engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

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An Analysis of the Features Considerable for NFT Recommendations

arXiv

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arXiv 2022年

作者： Piyadigama, Dinuka Poravi, Guhanathan Computer Science and Engineering University of Westminster London United Kingdom Department of Computing Informatics Institute of Technology Colombo06 Sri Lanka

This research explores the methods that Nonfungible Token (NFT)s can be recommended to people who interact with NFT-marketplaces to explore NFTs of preference and similarity to what they have been searching for. While exploring past methods that can be adopted for recommendations, the use of NFT traits for recommendations has been explored. The outcome of the research highlights the necessity of using multiple Recommender Systems to present the user with the best possible NFTs when interacting with decentralized systems. © 2022, CC BY.

关键词： Data mining

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Developing a Multi-site Hyperscanning Procedure for Investigating Interbrain Synchrony Underlying Remote Social Interaction 17th

Developing a Multi-site Hyperscanning Procedure for Investig...

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17th International Conference on Brain informatics, BI 2024

作者： Limtragooltongchai, Sirapakit Yamtuan, Kawin Gracia, Javier O. Chunharas, Chaipat Sriravanasan, Ramesh Chaisilprungraung, Thitaporn Itthipuripat, Sirawaj Neuroscience Center for Research and Innovation King Mongkut’s University of Technology Thonburi Bangkok Thailand Institute of Field Robotics King Mongkut’s University of Technology Thonburi Thonburi Thailand DEVCOM Army Research Laboratory Humans in Complex Systems Division AdelphiMD United States Chula Neuroscience Center King Chulalongkorn Memorial Hospital Thai Red Cross Society Bangkok10330 Thailand Cognitive Clinical and Computational Neuroscience Department of Internal Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Department of Cognitive Sciences University of California IrvineCA United States Department of Biomedical Engineering University of California IrvineCA United States Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok10600 Thailand

ISBN: (纸本)9789819632930

EEG hyperscanning provides novel insights into inter-brain connectivity during social interactions by enabling the simultaneous recording of neural activity from multiple individuals. This research aims to develop and validate a new protocol in which dyads engage in a synchronized button-pressing task across both same-location and multi-location setups. Our findings reveal significantly higher interpersonal neural synchronization during the interaction period compared to the baseline. Notably, the increased neural synchronization is comparable across the same- and multi-location setups, spanning frontal, central, and posterior electrodes. Interestingly, the level of synchronization in the multi-location setup is greater than in the same-location setup over electrodes positioned above the right superior temporal gyrus (STG), a region associated with social cognition and the mirror neuron system (MNS). These findings validate our methodology for studies across different locations and open new avenues for research into inter-brain synchronization in remote environments. Our experimental protocol could enhance the design of experiments and applications involving social interaction, particularly in remote settings, which have gained popularity due to COVID-19. It also advances our understanding of the neural mechanisms that support social communication in the new normal era. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Economic and social effects

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MADP-IIME: malware attack detection protocol in IoT-enabled industrial multimedia environment using machine learning approach

MADP-IIME: malware attack detection protocol in IoT-enabled ...

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作者： Pundir, Sumit Obaidat, Mohammad S. Wazid, Mohammad Das, Ashok Kumar Singh, Devesh Pratap Rodrigues, Joel J. P. C. Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun 248 002 India College of Computing and Informatics University of Sharjah UAE with King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan University of Science and Technology Beijing Beijing 100083 China Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Federal University of Piauí (UFPI) PI Teresina 64049-550 Brazil Instituto de Telecomunicações Covilhã 6201-001 Portugal

Internet of Things (IoT) is one of the fastest-growing technologies. With the deployment of massive and faster mobile networks, almost every daily-use item is connected to the Internet. IoT-enabled industrial multimedia environment is used for the collection and analysis of different types of multimedia data (i.e., images, videos, audios, etc.). This multimedia data is generated by various types of smart devices like drones, robots, smart controller, smart surveillance system which are deployed for the industrial monitoring and control. The multimedia data is generated in the enormous amount which can be considered as the big data. This data is further utilized in various types of business needs for example, chances of fire accidents in the industrial plant, overall machine health, etc., which can be predicted through the application of big data analytics. Therefore, IoT-enabled industrial multimedia environment is very helpful to the concerned authorities as they come to know the important information in advance. However, all the smart devices are connected and controlled through the Internet. It further causes severe threats to the communication happens in an IoT-enabled industrial multimedia environment. It is vulnerable to various types of attacks such as replay, man-in-the-middle, impersonation, secret information leakage, sensitive information modification, and malware injection (i.e., mirai). Therefore, it is important to prevent the communication of such an environment against the different types of possible attacks. These days, the attacks performed by botnets (i.e., malware attacks such as mirai and reaper) have drawn attention to the researchers. Under the influence of such attacks, the communication of IoT-enabled industrial multimedia environment is disrupted. Moreover, the attackers may also control the smart devices remotely and can change their functionalities. Hence, we need some robust mechanism to detect the presence of the malware attacks in such

关键词： Internet of things

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A Machine Learning Approach for Analyzing Texture and Morphometry Markers of Glioblastoma TERT Promoter Mutations in DAPI Fluorescence Images

A Machine Learning Approach for Analyzing Texture and Morpho...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Afsari, Fatemeh Naglah, Ahmed Gupta, Akshita Lohaan, Harshit Onubogu, Ugoma Janiszewska, Michalina Sarder, Pinaki Division of Nephrology Hypertension and Renal Transplantation-Quantitative Health Section Department of Medicine College of Medicine University of Florida GainesvilleFL32603 United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL32611 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Department of Molecular Medicine The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology JupiterFL33458 United States

ISBN: (纸本)9781510686045

Advanced molecular and imaging techniques, such as STAR-FISH, are frequently required to identify mutations in tissue specimens to properly diagnose Glioblastoma (GBM), an aggressive type of brain tumor. Mutations to the telomerase reverse transcriptase promoter (TERTp) are common in cancer and present in approximately 80% of all GBM tumors. We hypothesize that machine learning can stratify cells by their TERTp mutation status using DAPI fluorescence images since the STAR-FISH technique can be costly and inaccessible for some healthcare facilities. In this study, we present a machine learning-based approach to recognize nuclei harboring TERTp mutations solely based on the DAPI fluorescence images of GBM tissues. The proposed pipeline integrates a convolutional neural network (CNN)-based deep learning model to segment cells, extract hand-engineered features, and feed them into fully connected (FC) layers within the neural network or other machine learning classifiers to identify TERTp mutant cells. This approach involves extracting DAPI features, including both morphological and textural image features, followed by an ensemble of machine learning modules to predict TERTp mutant cells. We evaluate our approach on a cohort of 18 patients (70 pairs of DAPI and STAR-FISH processed images). Our experimental results show that the FC layers within the neural network achieve the highest AUC, F1-Score, and recall of 0.77, 0.69, and 0.86, respectively, with the integrated CNN model achieving a Dice score of 0.83, accurately segmenting cells. Our work is one of the earliest attempts that we are aware of to use machine-learning models for TERTp mutation identification using DAPI fluorescence images. Our results indicate the possibility of improved GBM diagnosis accuracy. To improve patient diagnosis and treatment planning for GBM, future research aims will expand the cohort and enhance the models to provide more precise and diagnostically valuable models for the interpretation o

关键词： Image segmentation

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Smart Education with DNA Testing: A Pathway to Personalized academic Success

Smart Education with DNA Testing: A Pathway to Personalized ...

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International Conference on ICT For Smart Society (ICISS)

作者： Davy Ronald Hermanus Sasmoko Yasinta Indrianti Computer Science Department Research Interest Group in Educational Technology Bina Nusantara University School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Primary Teacher Education Department Faculty of Humanities Research Interest Group in Educational Technology Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Universitas Agung Podomoro Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This paper examines the incorporation of DNA testing into intelligent education systems to develop customized learning plans that respond to the specific needs of each student. Through the examination of distinct genetic indicators associated with personality traits, talent, and inborn abilities, educators can formulate approaches that optimize academic achievement and boost the overall welfare of students. This approach not only enhances learning outcomes but also promotes a supportive and inclusive educational environment. The study critically examines the present state of literature regarding the correlation between genetics and learning. It digs into the ethical implications surrounding this topic and shows case studies that provide evidence of the effectiveness of teaching methods informed by DNA analysis. The results underscore the capacity of DNA testing to transform education by enhancing its adaptability and responsiveness to the distinct genetic composition of individual students. This underscores the need for additional research and ethical guidelines to guarantee responsible integration in educational environments.

关键词： Ethics Data privacy Correlation Law Scalability DNA Transforms Educational technology Testing Guidelines

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Comparing the Effectiveness of Generative Adversarial Networks for Clustering Tasks 19

Comparing the Effectiveness of Generative Adversarial Networ...

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19th IEEE International Conference on computer Science and Information Technologies, CSIT 2024

作者： Lytvynenko, Volodymyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution 'Kundiiev Institute of Occupational Health of the National Academy of Medical Sciences of Ukraine' Kyiv Ukraine

ISBN: (纸本)9798331542627

This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silhouette Score, Davies-Bouldin Index, Adjusted Rand Index, and Mutual Information. WGANs demonstrated the best clustering performance, while GANs showed promising results but were outperformed by WGAN. Contrary to expectations, WGAN-GP performed the worst. The research underscores the potential of GANs in clustering and highlights the need for further optimization based on specific datasets. © 2024 IEEE.

关键词： adjusted mutual information Davies-Bouldin index generative adversarial networks adjusted rand index mutual information silhouette index

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Lightweight Deep Learning for Resource-Constrained Environments: A Survey

arXiv

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arXiv 2024年

作者： Liu, Hou-I Galindo, Marco Xie, Hongxia Wong, Lai-Kuan Shuai, Hong-Han Li, Yung-Yui Cheng, Wen-Huang Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130000 China Faculty of Computing and Informatics Multimedia University Cyberjaya63100 Malaysia Hon Hai Research Institute Taipei114 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan

Over the past decade, the dominance of deep learning has prevailed across various domains of artificial intelligence, including natural language processing, computer vision, and biomedical signal processing. While there have been remarkable improvements in model accuracy, deploying these models on lightweight devices, such as mobile phones and microcontrollers, is constrained by limited resources. In this survey, we provide comprehensive design guidance tailored for these devices, detailing the meticulous design of lightweight models, compression methods, and hardware acceleration strategies. The principal goal of this work is to explore methods and concepts for getting around hardware constraints without compromising the model's accuracy. Additionally, we explore two notable paths for lightweight deep learning in the future: deployment techniques for TinyML and Large Language Models. Although these paths undoubtedly have potential, they also present significant challenges, encouraging research into unexplored areas. Copyright © 2024, The Authors. All rights reserved.

关键词： computer hardware

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Developing a wireless stereoscopic optical palpation probe towards cost effective intraoperative breast cancer detection 12

Developing a wireless stereoscopic optical palpation probe t...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Jones, Rhys Fang, Qi Bharakhda, Ankit Sanderson, Rowan W. Zilkens, Renate Saunders, Christobel M. Kennedy, Brendan F. BRITElab Harry Perkins Institute of Medical Research QEII Medical Centre Nedlands Centre for Medical Research The University of Western Australia CrawleyWA6009 Australia Department of Electrical Electronic & Computer Engineering School of Engineering The University of Western Australia CrawleyWA6009 Australia Medical School The University of Western Australia CrawleyWA6009 Australia Department of Surgery Melbourne Medical School The University of Melbourne ParkvilleVIC3010 Australia The Royal Melbourne Hospital 300 Grattan Street ParkvilleVIC3050 Australia Australian Research Council Centre for Personalised Therapeutics Technologies Australia Institute of Physics Faculty of Physics Astronomy and Informatics Nicolaus Copernicus University in Toruń ul. Grudziądzka 5 Toruń87-100 Poland

ISBN: (纸本)9781510683907

Optical coherence tomography (OCT)-based elastography techniques, such as optical palpation, have shown promise for breast tumor margin assessment. However, the high cost and bulk of OCT systems may limit their clinical feasibility. Here, we present a wireless optical palpation probe utilizing stereoscopic cameras, offering a cost-effective and portable alternative. This stereoscopic optical palpation (SOP) probe measures the deformation of a silicone layer embedded with phosphorescent microparticles. Under UV illumination, these particles emit visible light, allowing the cameras to capture 2D strain images of the layer when compressed against the tissue. These strain images are then converted into stress maps based on the pre-characterized mechanical properties of the silicone layer. The probe is constructed with a 3D-printed case, housing the cameras, a Wi-Fi transceiver, and a rechargeable battery. Real-time video feeds are transmitted to a laptop for stress image processing. We demonstrated the SOP probe on silicone inclusion phantoms, identifying the location of embedded stiff silicone inclusions based on elevated stress measurements caused by increased mechanical contrast. The SOP probe offers a compact, cost-effective solution for real-time breast tumor margin assessment on excised specimens and also holds potential for in vivo detection of residual tumor in the surgical cavity. The low cost, portability, and accessibility of the probe make it suitable for widespread application, including in resource-limited settings. Future work will focus on enhancing measurement accuracy and estimating tissue elasticity to further improve clinical utility. © 2025 SPIE.

关键词： Stress measurement

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