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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Imed Eddine Bouramoul Soumia Zertal Makhlouf Derdour Imene Zenbout Department of Mathematics & Computer Science IAOA Laboratory Larbi ben M’hidi University oum el Bouaghi Algeria Dept. of Fundamental Informatics and its Application Misc Laboratory University Constantine 2 Constantine Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

Advancements in systems based on the Internet of Things (IoT) have led to a significant transformation across various sectors. However, the security of IoT networks remains a major concern due to the diversity and ubiquity of connected devices. This paper introduces an innovative intrusion detection method for IoT systems, combining the bioinspired features selection algorithms with artificial neural network, emphasing a special focuse on Grey wolf optimisation algorithm (GWOA). Bio-inspired algorithms select the most relevant features from a dataset used in intrusion detection evaluation, while machine learning/deep learning (ML/DL) techniques ensure accurate classification of attacks. This approach provides an effective solution for enhancing IoT network security by identifying and responding to threats with precision and speed, thereby contributing to the protection of critical infrastructures against cyberattacks. The obtained results showed promising performances in intrusion detection with the optimal set of features. In which GWO achived a performance above 90% with approximately 20% of the global features set.

关键词： Deep learning Analytical models Machine learning algorithms Heuristic algorithms Biological system modeling Diversity reception Intrusion detection

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Mamba-Based Super-Resolution and Segmentation Network for UAV-Captured Blueberry Farmland Imagery

Mamba-Based Super-Resolution and Segmentation Network for UA...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Fan Zhao Jian Song Jiaqi Wang Yijia Chen Dianhan Xi Xinlei Shao Y ongying Liu Bangzhang Ma Yinyin He Guochen Zhang Qingyang Wu Zhengyue Tu Mengya Wu Yulun Chen Jiaolin Guo Chenyu Zhang Graduate School of Frontier Science The University of Tokyo Kashiwa Japan Dept. Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong Dept. of Computer and Information Sciences Hosei University Tokyo Japan Dept. Environmental Health Sciences University of California Los Angeles LA CA US Dept. Mechanical Engineering The University of Tokyo Bunkyo Japan Dept. Environmental Science Southwest Forestry University Kunming China Yunnan Yijiu Agricultural Development Co. Ltd. Yuxi City China Dept. Geographic Information Science Fujian Normal University Fuzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Traditional methods of inspecting blueberry maturity primarily rely on manual observation, which is inefficient and prone to visual errors. The high similarity in shape and color between immature blueberries and blueberry flowers poses significant challenges for accurate visual assessment. This study proposes a novel method for evaluating the overall maturity of a blueberry plantation using super-resolution reconstruction (SRR) and semantic segmentation. We designed and evaluated various SRR models, including SRCNN, EDSR, RCAN, Real-ESRGAN, SwinIR, HAT, and MambaIR, to enhance the detailed representation of blueberries at different maturity stages in low-resolution images. These enhanced images are then processed by the proposed semantic segmentation network to accurately segment blueberry flowers, leaves, and blueberries at different maturity stages. Experimental results demonstrate that MambaIR achieves 82.26% SSIM in the SRR task, while the image segmentation model developed based on the Mamba algorithm attains an average intersection over union (mIoU) of 80.48. In summary, the proposed method shows significant accuracy and efficiency advantages in assessing blueberry maturity.

关键词： Visualization Accuracy Shape Semantic segmentation Superresolution Plantations Manuals Flowering plants Image reconstruction Optimization

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Auto-Adaptive Stator Ground Fault Protection for Synchronous Generators in Diesel-Electric Locomotives

Auto-Adaptive Stator Ground Fault Protection for Synchronous...

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IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED)

作者： Kumar Mahtani José M. Guerrero Luis F. Beites Carlos A. Platero Automation Electrical and Electronic Engineering and Industrial Informatics Dept. Universidad Politécnica de Madrid Madrid Spain Electronics and Computer Science Dept. Mondragon Unibertsitatea Arrasate-Mondragon Spain Automation Electrical and Electronic Engineering and Industrial Informatics Department Universidad Politécnica de Madrid Madrid Spain

Diesel-electric locomotives are equipped with one or more synchronous generators driven by diesel engines, that provide the necessary electrical energy for their operation. These generators are usually provided with some protection relays, depending on their rated power and their type of service. Standard stator-ground fault protection has a setting for rated conditions. In case of variable speed and voltage operation, the protection does not cover all the operation range of the machine. In addition, unwanted tripping may occur due to incorrect filtering of the third harmonic. In this paper, an auto-adaptative stator ground protection function is presented, for the operation of synchronous machines at variable voltage and frequency. A specific case study is tackled, focused on the main generators of a diesel-electric locomotive. Numerous simulations are carried out to verify the proposed protection method. The obtained results are promising, and they can be used in other synchronous machines operating at variable speed.

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Using the recurrence plots as indicators for the recognition of Parkinson’s disease through phonemes assessment

Using the recurrence plots as indicators for the recognition...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasileios Skaramagkas Anastasia Pentari Dimitrios I. Fotiadis Manolis Tsiknakis Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Crete Greece Institute of Computer Science Foundation for Research and Technology Hellas (FORTH) Heraklion Crete Greece Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology (FORTH) Ioannina Greece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece

Parkinson’s disease (PD) is considered to be the second most common neurodegenerative disease which affects the patients’ life throughout the years. As a consequence, its early diagnosis is of major importance for the improvement of life quality, implying that the severe symptoms can be delayed through appropriate clinical intervention and treatment. Among the most important premature symptoms of PD are the voice impairments of articulation, phonation and prosody. The objective of this study is to investigate whether the voice’s dynamic behavior can be used as possible indicator for PD. Thus in this work, we employ the recurrence plots (RPs) which derive from the analysis of the three modulated vowels /a/, /e/ and /o/, which belong to the PC-GITA dataset, and are fed as input images to a 3-channel Convolutional Neural Network-based (CNN) architecture, which, finally, differentiates the 50 PD patients from 50 healthy subjects. The experimental results obtained provide evidence that the RP-based approach is a promising tool for the recognition of PD patients through the analysis of voice recordings, with a classification accuracy achieved equal to 87%.

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Boosting the performance of MALT lymphoma classification in patients with primary Sjögren’s Syndrome through data augmentation: a case study

Boosting the performance of MALT lymphoma classification in ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vasileios C. Pezoulas Themis P. Exarchos Nikolaos S. Tachos Andreas Goules Athanasios G. Tzioufas Dimitrios I. Fotiadis Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina Greece Dept. of Informatics Ionian University Corfu Greece Dept. of Pathophysiology Faculty of Medicine National and Kapodistrian University of Athens Athens Greece FORTH Biomedical Research Institute (BRI) Ioannina Greece

Sjögren’s Syndrome (SS) patients with mucosa associated lymphoid tissue lymphomas (MALTLs) and diffuse large B-cell lymphomas (DLBCLs) have 10-year survival rates of 80% and 40%, respectively. This highlights the unique biologic burden of the two histologic forms, as well as, the need for early detection and thorough monitoring of these patients. The lack of MALTL patients and the fact that most studies are single cohort and combine patients with different lymphoma subtypes narrow the understanding of MALTL progression. Here, we propose a data augmentation pipeline that utilizes an advanced synthetic data generator which is trained on a Pan European data hub with primary SS (pSS) patients to yield a high-quality synthetic data pool. The latter is used for the development of an enhanced MALTL classification model. Four scenarios were defined to assess the reliability of augmentation. Our results revealed an overall improvement in the accuracy, sensitivity, specificity, and AUC by 7%, 6.3%, 9%, and 6.3%, respectively. This is the first case study that utilizes data augmentation to reflect the progression of MALTL in pSS.

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AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations

AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogene...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Alain Bertrand Bomgni Ribot Fleury Téné Ceskoutsé Kevin Jordan Njike Njingang Thomas Bouetou Bouetou Venkataramana Gadhamshetty Etienne Gnimpieba Z. Math Science and Technology Oglala Lakota College Kyle SD USA Ecole Nationale Supérieure Polytechnique University of Yaounde 1 Yaounde Cameroon Dept. of Computer Science University of Dschang Dschang Cameroon Dept. of Civil and Environmental Engineering South Dakota School of Mines and Technology Rapid City SD USA Dept. of Biomedical Engineering University of South Dakota Vermillion SD USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The synthesis of small molecules is a crucial task across multiple scientific domains, including drug discovery, materials science, and sustainable chemistry. As AI and Machine Learning (ML) continue to advance, these technologies offer transformative potential in molecular synthesis, enhancing efficiency and expanding synthetic possibilities. In particular, small molecule synthesis has profound implications for creating novel therapeutic agents, high-performance materials, and greener chemical processes, but a major challenge remains in designing efficient synthetic routes for target molecules. Retrosynthesis, the process of mapping out synthetic pathways by working backwards from a target molecule, represents a vital step; however, traditional retrosynthesis methods often struggle to predict complex or novel transformations. To address this limitation, we introduce AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations, a model that leverages graph neural network techniques to enhance retrosynthetic predictions. AIM-HKR integrates information from heterogeneous knowledge graphs, capturing the intricate relationships and analogical reasoning required for retrosynthesis. This model generates type-specific embeddings that reflect both network topology and semantic connections across different entity types, such as molecules, reactions, and functional groups. AIM-HKR’s unique capacity to leverage heterogeneous graphs allows it to propose synthetic pathways that extend beyond established precedents, enabling predictions of chemically feasible but previously uncharted transformations. We believe AIM-HKR has the potential to significantly advance molecular synthesis through AI-driven retrosynthesis, establishing a new paradigm in AI-assisted chemistry.

关键词： Technological innovation Chemistry Accuracy Scalability Semantics Machine learning Predictive models Reliability engineering Drug discovery Chemicals

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Synergistic dynamics of heart rate variability and systolic blood pressure revealed by dual Poincaré plot analysis

Synergistic dynamics of heart rate variability and systolic ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Prachi Agarwal Yu Guo Payam Gharibani Poorna Prakash Nitish V Thakor Dept. of Electrical and Computer Engineering At Johns Hopkins University School of Medicine Baltimore MD USA Dept. of Biomedical Engineering At Johns Hopkins University School of Medicine Baltimore MD USA Dept. of Neurology At Johns Hopkins University School of Medicine Baltimore MD USA Thomas Jefferson High School for Science and Technology Alexandria VA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cardiac arrest (CA) survivors often suffer secondary brain injury due to impaired autonomic nervous system (reflected by heart rate HR) and hemodynamic function (reflected by blood pressure BP and baroreflex regulation). This study proposes a Dual Poincaré Plot, a novel method analyzing coupled variability in HR - BP dynamics to assess these impairments. Ten rats were categorized into good and poor neurological outcome groups in a rat model of CA. Dual Poincaré plot analysis, squared partial directed coherence, and sequence method were used to characterize the outcome. Results revealed differences in variability of HR, BP, and baroreflex, as well as coupling strength, between groups. Good outcome subjects exhibited 1) increased variability in both BP and HR, 2) enhanced baroreflex regulation, and 3) weakened cardiovascular coupling. Conversely, subjects with poor outcomes displayed 1) decreased HR-BP variability, 2) impaired baroreflex, and 3) stronger coupling. The Baroreflex Index from the Dual Poincaré plot showed a high correlation with the traditional sequence method (R 2 = 0.91). These results imply that Dual Poincaré offers a real-time assessment of autonomic-hemodynamic interaction, effectively stratifying post-CA neurological recovery and potentially enhancing prognostic accuracy for timely interventions.

关键词： Couplings Visualization Baroreflex Reliability engineering Regulation Real-time systems Blood pressure Heart rate variability biomedical monitoring Monitoring

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Neural Rendering for Stereo 3D Reconstruction of Deformable Tissues in Robotic Surgery

arXiv

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arXiv 2022年

作者： Wang, Yuehao Long, Yonghao Fan, Siu Hin Dou, Qi Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Dept. of Biomedical Engineering The Chinese University of Hong Kong Hong Kong

Reconstruction of the soft tissues in robotic surgery from endoscopic stereo videos is important for many applications such as intraoperative navigation and image-guided robotic surgery automation. Previous works on this task mainly rely on SLAM-based approaches, which struggle to handle complex surgical scenes. Inspired by recent progress in neural rendering, we present a novel framework for deformable tissue reconstruction from binocular captures in robotic surgery under the single-viewpoint setting. Our framework adopts dynamic neural radiance fields to represent deformable surgical scenes in MLPs and optimize shapes and deformations in a learning-based manner. In addition to non-rigid deformations, tool occlusion and poor 3D clues from a single viewpoint are also particular challenges in soft tissue reconstruction. To overcome these difficulties, we present a series of strategies of tool mask-guided ray casting, stereo dept.-cueing ray marching and stereo dept.-supervised optimization. With experiments on DaVinci robotic surgery videos, our method significantly outperforms the current state-of-the-art reconstruction method for handling various complex non-rigid deformations. To our best knowledge, this is the first work leveraging neural rendering for surgical scene 3D reconstruction with remarkable potential demonstrated. Code is available at: https://***/med-air/EndoNeRF. © 2022, CC BY-NC-ND.

关键词： Tissue

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Coach, Data Analyst, and Protector: Exploring Data Practices of Collegiate Coaching Staff 25

Coach, Data Analyst, and Protector: Exploring Data Practices...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Brewer, Mollie Childs, Kevin Thomas, Spencer Wilkins, Celeste Boyer, Kristy Elizabeth Nichols, Jennifer A. Butler, Kevin R.B. Beatty, Garrett F. Ferris, Daniel P. Department of Computer & Information Science & Engineering University of Florida Gainesville United States University Athletic Association GainesvilleFL United States J. Crayton Pruitt Family Dept. of Biomedical Engineering University of Florida GainesvilleFL United States College of Health & Human Performance University of Florida GainesvilleFL United States

ISBN: (纸本)9798400713941

A rapidly emerging research community at the intersection of sport and human-computer interaction (SportsHCI) explores how technology can support physically active humans, such as athletes. At highly competitive levels, coaching staff play a central role in the athlete experience by using data to enhance performance, reduce injuries, and foster team success. However, little is known about the practices and needs of these coaching staff. We conducted five focus groups with 17 collegiate coaching staff across three women's teams and two men's teams at an elite U.S. university. Our findings show that coaching staff selectively use data with the goal of balancing performance goals, athlete emotional well-being, and privacy. This paper contributes design recommendations to support coaching staff in operating across the data life cycle through gathering, sharing, deciding, acting, and assessing data as they aim to support team success and foster the well-being of student-athletes. © 2025 Copyright held by the owner/author(s).

关键词： SportsHCI

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Multi-label topic classification of patient generated content in a breast-cancer community forum 20

Multi-label topic classification of patient generated conten...

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4th International Conference on Medical and Health informatics, ICMHI 2020

作者： Athira, B. Idicula, Sumam Mary Jones, Josette Dept. of Computer Science Cochin University of Science and Technology Cochin India Dept. of BioHealth Informatics School of Informatics and Computing Indiana University IN United States

ISBN: (纸本)9781450377768

The research community has been noticing the importance of the online forums in healthcare in understanding the nuances of health-related problems. Breast Cancer is the most common malignance among women worldwide and its survival rate is steadily increasing thanks to early diagnosis and timely and effective treatment. Still how they manage the disease and maintain the quality of daily life are worth understanding. And it is in this context that the online posts of patients with breast cancer, discussing several topics, become patient generated content. The multi-label nature with these posts arising out of the combined contents of these posts can bring out a multitude of divulging conclusions. The resulting classification issues of these online posts under various categories of topics is examined in the present work. A semi-supervised multi-label classification, followed by refinement of multiple assignments of labels based on fuzzy logic and a neighborhood technique is proposed in the paper. Multiplicity of labels, occurs during the assignment of labels to clusters based on proximity. While extending the refined label set to a greater number of unlabeled posts clustered on the basis of proximity, it is observed that the proposed method could bring out more information on the description of posts. The results thus convey that the most discussed topic in the posts is about diagnosis, along with tangential reference to adverse drug effect, presumably to offer support in terms of information or viewpoint. The results show how the diverse nature of multiple labels can be effectively harnessed to draw conclusion from the potential of social media posts of patients' experience in critical health problems. © 2020 ACM.

关键词： Fuzzy logic

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