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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Yulin Liu, Jing Chen, Qingqing Li, Yinhao Han, Xian-Hua Hu, Hongjie Lin, Lanfen Chen, Yen-Wei Ritsumeikan University Gradate School of Information Science and Engineering Shiga Japan Zhejiang Lab. Research Center for Healthcare Data Science Hangzhou China Zhejiang University School of Medicine Department of Radiology Sir Run Run Shaw Hospital Hangzhou China Ritsumeikan University College of Information Science and Engineering Shiga Japan Yamaguchi University Artificial Intelligence Research Center Yamaguchi Japan Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350371499

In contrast to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans can highlight discrepancies between abnormal and normal areas, commonly used in clinical diagnosis of focal liver lesions. However, the use of contrast agents in CE-CT scans imposes significant physical and economic burdens on patients in clinical practice. Recently, Generative Adversarial Networks (GANs)-based synthesis models offer an alternative approach that obtains CE-CT images from NC-CT images. However, poor coverage and mode collapse greatly limit their performance. Diffusion models (DMs)-based methods have demonstrated superior performance in natural image synthesis tasks. Nevertheless, our experiment shows that CE-CT images synthesized from DMs-based method exhibit higher overall quality but lower local quality. The quality of local areas, particularly those related to lesion areas, is crucial in medical image synthesis tasks. Hence, we propose a GANs-guided conditional diffusion model (GANs-CDM), combining the GANs and conditional diffusion model (CDM), to generate CECT images. In the proposed GANs-CDM, the GANs is to generate a preliminary CE-CT image, serving as conditional input for guiding the subsequent CDM to produce refined CE-CT images. Qualitative and quantitative evaluation on arterial and portal venous phase synthesis tasks demonstrates that our proposed GANs-CDM can significantly improve both the local and global quality of synthetic images. © 2024 IEEE.

关键词： Generative adversarial networks

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OBoctNet: Enhancing Ophthalmic Biomarker Detection Through Active Learning and Explainable ai in Radiological Analysis

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Acharja, Samya Hasan, Md. Zahid Chamok, Farjana Haque Fahim, Kayes Uddin Shuva, Taslima Ferdaus Bulbul, Abdullah Al-Mamun Khan, Risala Tasin Rahman, Md. Tanvir Kaiser, M. Shamim Mahmud, Mufti Moni, Mohammad Ali Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Savar Dhaka1341 Bangladesh School of Health and Rehabilitation Sciences The University of Queensland St Lucia BrisbaneQLD4072 Australia Electronics and Communication Engineering Discipline Khulna University Khulna9208 Bangladesh Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail1902 Bangladesh Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for AI King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia Interdisciplinary Research Center for Bio Systems and Machines King Fahd University of Petroleum and Minerals Dhahran31261 Saudi Arabia AI and Cyber Futures Institute Charles Sturt University BathurstNSW2795 Australia Rural Health Research Institute Charles Sturt University OrangeNSW2800 Australia School of Information Technology Washington University of Science and Technology AlexandriaVA22314 United States

Optical coherence tomography (OCT) scanning is crucial for the diagnosis of widespread ophthalmic diseases. Traditionally, experts manually identify diseases and biomarkers from OCT scans. Recently, modern medical imaging practices have increasingly utilized deep learning techniques to speed up and improve diagnostic accuracy in ophthalmology. However, obtaining accurately labeled datasets is a significant challenge in medical imaging due to the expertise required for precise annotation by trained professionals. This article presents a novel method, named OBoctNet, with a new two-stage training strategy to improve the identification of ophthalmic biomarkers using OCT scans from the OLIVES dataset, which contains only 12% labeled data. This approach leverages a robust methodology that effectively uses labeled and unlabeled data to enhance biomarker identification accuracy, achieving a cumulative performance increase of 23% across 50% of the biomarkers when compared to the previous studies. To better identify biomarkers, the OBoctNet employs an active learning strategy that uses unlabeled data and dynamically ensembles models based on their performance within each experimental setup. Additionally, the usage of Gradient weighted Class Activation Mapping (GradCAM) helps identify regions of interest associated with relevant biomarkers, enhancing interpretability and transparency for potential clinical adoption. © The Author(s) 2025.

关键词： Semi-supervised learning

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Multiscale Fusion Graph Convolutional Networks for Hyperspectral Image Classification 2

Multiscale Fusion Graph Convolutional Networks for Hyperspec...

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2022 2nd International Conference on Artificial Intelligence, Big data and Algorithms, CaiBDA 2022

作者： Wang, Lei Wang, Xili School of Computer Science Shaanxi Normal University Xi’an China School of Mathematics and Computer Application Shangluo University Shangluo China Engineering Research Center of Qinling Health Welfare Big Data Universities of Shaanxi Province Shangluo726000 China

ISBN: (纸本)9783800760268

Graph neural network (GNN) has shown remarkable performance in many fields, such as edge prediction, node classification, etc. However, the traditional graph neural network can only accept the full graph to train the model, which leads to a very high computational complexity. Besides, the lack of multi-scale mechanism limits the actual classification performance. In this paper, we propose a multiscale fusion model based on graph convolutional network (GCN) and convolutional neural network (CNN) for hyperspectral image (HSI) classification tasks. This model can first sample randomly from the full graph to subgraphs of different scales, then trains GCN on sub-graphs of different scales and extract features, and finally integrates the spatial-spectral features extracted by multi-scale CNN classifier and the structural features extracted by multi-scale GCN for end-to-end mini-batch joint training. Experiments on two real HSI data s demonstrate that the proposed method significantly improves the classification performance compared with other comparison methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Graph neural networks

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Landscape of sign language research based on smartphone apps: coherent literature analysis, motivations, open challenges, recommendations and future directions for app assessment

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Universal Access in the Information Society 2024年第2期23卷 687-702页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Zaidan, B.B. Zaidan, A.A. Garfan, Salem Ismail, Amelia Ritahani Albahri, A.S. Alsinglawi, Belal Malik, R.Q. Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates International Islamic University Malaysia Kuala Lumpur Malaysia Baghdad Iraq School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq

Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Smartphones

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IoT data Validation Using Blockchain and Dedicated Cloud Platforms 1

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International Conference on Cyber Security, Privacy and Networking, ICSPN 2022

作者： Carratù, Marco Colace, Francesco Gupta, Brij B. Marongiu, Francesco Pietrosanto, Antonio Santaniello, Domenico DIIn University of Salerno SA Fisciano Italy International Center for AI and Cyber Security Research and Innovations & Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Lebanese American University Beirut1102 Lebanon

ISBN: (数字)9783031220180

ISBN: (纸本)9783031220173

The Internet of Things world has grown exponentially in recent years, projecting the future toward an increasingly automated reality in which human presence results less and less and decisions about processes are made directly by machines, which exchange information, operating to maximize a goal. Such complex and automated systems pose quite a few challenges from the standpoint of securing cyber-physical spaces, particularly tampering with the data of specific components and/or sensors, which can go a long way in impacting the operation of the entire system and causing damage. This work aims to reduce the possibility of data tampering by using Blockchain systems integrated into cloud platforms for data collection and analysis so that in the case of critical decisions, this information can be verified in a certified way and thus proceed safely. An application case study based on ThingsBoard and HyperLedger Busu technologies was proposed showing how these technologies can integrate and collaborate to achieve effective results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

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computers, Materials & Continua 2024年第7期80卷 1347-1379页

作者： Ahmad Ababneh Yousef Sanjalawe Salam Fraihat Salam Al-E’mari Hamzah Alqudah Department of Computer Science Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Department of Cybersecurity Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346Ajman13306United Arab Emirates Information Security Department Faculty of Information TechnologyUniversity of PetraAmman11196Jordan Department of Data Science and Artificial Intelligence Faculty of Information TechnologyAmerican University of MadabaAmman11821Jordan

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and *** conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size *** proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing *** research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model *** compare OWS-based retrieval models against others using different schemes,including tf-idf variations and *** reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index *** highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

关键词： Information retrieval orbit weighting scheme semantic text analysis Tf-Idf weighting scheme vector space model

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on computer System, Information Technology, and Electrical engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms

Self-Improving Safety Performance of Reinforcement Learning ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Resul Dagdanov Halil Durmus Nazim Kemal Ure Department of Aeronautical Engineering ITU Artificial Intelligence and Data Science Research Center Istanbul Technical University Turkey Department of Electronics and Communication Engineering Eatron Technologies Istanbul Technical University Turkey Department of Computer Engineering ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Turkey

In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods. RL algorithms have become popular in AD applications in recent years. However, the performance of existing RL algorithms heavily depends on the diversity of training scenarios. A lack of safety-critical scenarios during the training phase could result in poor generalization performance in real-world driving applications. We propose a novel framework in which the weaknesses of the training set are explored through black-box verification methods. After discovering AD failure scenarios, the RL agent's training is re-initiated via transfer learning to improve the performance of previously unsafe scenarios. Simulation results demonstrate that our approach efficiently discovers safety failures of action decisions in RL-based adaptive cruise control (ACC) applications and significantly reduces the number of vehicle collisions through iterative applications of our method. The source code is publicly available at https://***/data-and-decision-lab/self-improving-RL.

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Decentralized and Network-Aware UAV Service Deployment for Dependency-Driven applications

Decentralized and Network-Aware UAV Service Deployment for D...

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IEEE Conference on Wireless Communications and Networking

作者： Talip Tolga Sarı Christian Quadri Gökhan Seçinti Angelo Trotta Department of Computer Science and Engineering University of Bologna Italy Department of Computer Engineering Istanbul Technical University Turkey Department of Computer Science Università degli Studi di Milano Italy BTS - Digital Twin Application and Research Center Istanbul Technical University Turkey

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Unmanned Aerial Vehicle (UAV) swarms enable the rapid deployment of IoT services in dynamic and challenging environments. While these swarms offer flexibility and close proximity to sensing and actuation points, efficiently deploying interdependent services at scale remains a core challenge. Traditional centralized methods struggle to handle the complexity of large UAV networks, leading to increased latency and limited reliability. In this paper, we propose a decentralized approach to service deployment in UAV swarms. Our method relies on local information at each node, allowing UAVs to make their own assignment decisions. Over time, these decisions are iteratively refined as nodes exchange status updates and adapt to network changes. This process avoids the bottlenecks of centralized coordination and enables more responsive resource allocation. Simulation results show that our approach supports the successful deployment of a high number of tasks while maintaining low latency. These findings indicate that decentralized methods with local resource knowledge, improves both scalability and responsiveness in UAV-based IoT systems.

关键词： Simulation Scalability Autonomous aerial vehicles Robustness Sensors Internet of Things Resource management Vehicle dynamics Optimization Testing

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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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computer Modeling in engineering & sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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