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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Girish, Deepank Chan, Yi Hao Gupta, Sukrit Xia, Jing Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore Indian Institute of Technology Department of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350351552

Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies focused on improving model performances for disease detection. An increasing number of such studies use their trained models to detect and evaluate salient features that could be potential biomarkers of these neuro-logical conditions. The evaluation of these salient features is often qualitative and limited to cross-referencing existing literature for similar findings. In this study, we present objective quantitative metrics to evaluate the robustness of these salient features. Building upon existing generic evaluation metrics, we propose metrics that capture topological properties known to be characteristic of brain functional connectomes. Using existing and newly proposed measures on a set of baselines and state-of-the-art graph neural networks (GNN) models, we found that when GNNExplainer is used with models that incorporate attention, the scores produced are relatively more robust than other combinations. On datasets of patients with Autism Spectrum Disorder (ASD) or Attention-deficit Hyperactivity Disorder (ADHD), our proposed metrics highlighted that salient features identified in both disorders are highly involved in functional specialization, while salient ASD features expressed stronger functional integration than ADHD. We package these existing and novel metrics together in the RE-CONFIRM framework that holds promise to set the foundations for the quantitative evaluation of salient features detected by future studies. © 2024 IEEE.

关键词： Graph neural networks

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Transforming Heart Disease Detection with BERT: Novel Architectures and Fine-Tuning Techniques

Transforming Heart Disease Detection with BERT: Novel Archit...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Javeed, Muhammad Usman Ali, Muhammad Shafqat Iqbal, Asma Azhar, Muhammad Aslam, Shafqat Maria Shabbir, Ifra Department of Computer Science Comsats University Islamabad Sahiwal Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong

ISBN: (纸本)9798331510503

The prediction of heart disease is crucial for effective prevention and treatment. However, extracting clinical information such as CAD, smoking, hypertension, hyperlipidemia, obesity, and family history of CAD from unstructured electronic health records (EHRs) poses significant challenges to clinicians. This research introduces a novel approach that leverages an ensemble of transfer learning algorithms combined with a multi-head attention mechanism to automatically extract heart disease risk factors from EHRs. Various deep learning models, including BERT, BioBERT, BioClinical BERT, RoBERTa, and XLNet, were initially trained on medical data sets and subsequently fine-tuned on the i2b2 clinical data set. Individual models delivered strong results, with RoBERTa achieving the highest accuracy of 95. 27% and an F1 score of 94. 94%. BioBERT, BioClinical BERT, XLNet, and BERT also performed well, with precision ranging from 94. 73% to 95. 03%. However, the proposed ensemble model with multi-head attention outperformed all, achieving an accuracy of 96.35% and the F1-score of 95.76%. These findings highlight the superior ability of the ensemble model to capture complex inter-dependencies between heart disease risk factors, making it a robust tool for clinical prediction. © 2024 IEEE.

关键词： Electronic health record

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Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fusion for Breakthrough Early Detection of Alzheimer's 9

Neuroimaging Precision: Crafting a Stacked VGG19-Logistic Fu...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Selvi, G. Anbu Karthick, T. Srmist Department of Computer Science And Engineering Chennai Kattankulathur India School of Computing Srmist Department of Data Science And Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350365092

Several medical investigations have demonstrated that Alzheimer's disease (AD) manifests itself long before the formal diagnosis of dementia. These studies have led to the identification of numerous ideal biomarkers associated with Alzheimer's symptoms. Consequently, the imperative for early diagnosis has become evident, necessitating a high-performance computational tool capable of efficiently processing vast amounts of data. Early detection of AD not only offers valuable insights into the condition's progression but also presents a significant opportunity to benefit from timely and effective treatment. This ground-breaking study pioneers an innovative approach to the early detection and diagnosis of AD by crafting a sophisticated model SVGGL, which blends the power of stacked VGG19 neural networks with the interpretability of logistic regression. Leveraging neuro-imaging data, specifically the insights provided MRI, and our model aims to discern subtle structural and functional alterations in the brains of individuals at risk for or in the early stages of AD. The combination of feature extraction from stacked VGG19 models and subsequent logistic regression classification yields a precise and efficient classifier with an outstanding accuracy of 98.24%. With such a high degree of accuracy, the model can reliably differentiate among healthy people and those with Alzheimer's, opening the door to potential early intervention and individualized treatment plans. The study not only sheds light on the intricate neuro pathological landscape of AD but also propels us toward a future where cutting-edge technology and data-driven insights, exemplified by our high-accuracy model, converge to tackle the challenges posed by this prevalent neurodegenerative disorder. © 2024 IEEE.

关键词： Neuroimaging

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Designing a Comprehensive Analysis Framework for University Student Stress

Designing a Comprehensive Analysis Framework for University ...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Lertnimitphan, Phattharakit Sumpaongen, Thanaporn Jiramongkonphat, Thananat Songmuang, Pokpong Kongkachandra, Rachada Thammasat University Data Science And Innovation College Of Interdisciplinary Studies Bangkok Thailand Thammasat University Department Of Computer Science Bangkok Thailand

ISBN: (纸本)9798350370096

In the dynamic landscape of higher education, there is a growing need to comprehend and address student stress. This demand has led to the development of a comprehensive Computational Analysis Framework for University Student Stress. The framework aims to develop the field by introducing innovative tools and systems for data collection, preparation, exploratory data analysis, principal component analysis, and clustering. Unlike existing methodologies, our approach pioneers a systematic and technologically advanced solution to analyze stress levels and coping strategies among university students. © 2024 IEEE.

关键词： K-means clustering

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Unveiling Consumer Segmentation: Harnessing K-means Clustering Using Elbow and Silhouette for Precise Targeting 3rd

Unveiling Consumer Segmentation: Harnessing K-means Clusteri...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Saraswat, Shweta Agrohi, Vaibhav Kumar, Mahesh Lamba, Monica Kaur, Raminder Department of Artificial Intelligence and Data Science Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Arya College of Engineering Rajasthan Jaipur India Department of Computer Science and Engineering Department Graphics Era Deemed to Be University Dehradun India Department of Computer Science and Engineering Department JECRC University Jaipur India

ISBN: (纸本)9789819708918

Consumer segmentation is essential for accurate targeting and successful marketing efforts in today’s competitive business environment. Modern marketing groups individuals by interests and attributes. Segmentation drives targeting, personalization, and ROI. This article segments customers using K-means clustering. The Elbow approach and Silhouette score determine the appropriate number of clusters and increase segmentation accuracy. They also examine the possibility of precision targeting and customized marketing techniques across sectors. Businesses may optimize marketing, improve customer happiness, and increase profits by using K-means clustering. To compete in today’s market, this research helps marketers enhance targeting. Elbow and Silhouette K-means clustering may enhance client segmentation, engagement, loyalty, and economic success. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： K-means clustering

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Detecting Defamation Words from Social Media Comments

Detecting Defamation Words from Social Media Comments

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Patthong, Phetlada Doungdee, Teerapat Songmuang, Pokpong Kongkachandra, Rachada Thammasat University Data Science And Innovation College Of Interdisciplinary Studies Bangkok Thailand Thammasat University Department Of Computer Science Bangkok Thailand

ISBN: (纸本)9798350370096

The pervasiveness of social media has facilitated unprecedented communication and expression, but also fueled a surge in defamation cases. In Thailand, defamation carries significant legal weight, posing challenges for identifying and prosecuting perpetrators due to online anonymity and rapid content dissemination. This research proposed a novel machine learning approach to detect defamatory words in Thai social media comments. We utilized the state-of-the-art BERT model and the PyThaiNLP library, alongside a unique dataset of labeled comments from the Criminal Court of Thailand. This robust framework aimed to effectively identify potentially defamatory content, contributing to a safer and more responsible online environment. © 2024 IEEE.

关键词： Machine learning

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

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computers, Materials & Continua 2023年第9期76卷 2997-3011页

作者： Xinliang Tang Xiaotong Ru Jingfang Su Gabriel Adonis School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China Department of Computer Science and Information Systems Birkbeck Institute for Data AnalyticsLondonB100ABWC1E 7HXUK

On the transmission line,the invasion of foreign objects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separable convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-Excitation Networks(SENet)attention model is fused into the algorithm framework and a new detection scale suitable for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.

关键词： Transmission line YOLOv5 multi-scale integration SENet

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HealthBridge: A Decentralized and Interoperable Healthcare App for Rare Disease Communities

HealthBridge: A Decentralized and Interoperable Healthcare A...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Ziyuan Wang, Yiwei Liang, Anda Van Devanter, Wyn Zhang, Peng Vanderbilt University Department of Computer Science NashvilleTN United States Institute Vanderbilt University Department of Computer Science and Data Science NashvilleTN United States

ISBN: (纸本)9798350337488

Traditional centralized database systems share many weaknesses, including the overhead of sharing medical documentation, data fragmentation, and susceptibility to centralized data attacks. Those problems would especially influence rare disease patient communities given their higher need for medical data interoperability and security. In this paper, we present an alternative approach to managing healthcare data that is tailored to the rare disease community. Specifically, we design and implement HealthBridge, a decentralized and interoperable mobile application with distributed ledger technology. The underlying permissioned network utilizes Hyperledger Fabric and has a modular design, empowering efficient data transactions directly between trusted entities with predefined smart contracts that regulate access permissions. For the application design, we focus on the needs of the rare disease community, prioritizing ease of use without compromising data security. In particular, our application provides a delegate feature, aimed at supporting rare disease patients with additional needs. Through this feature, if needed, patients can grant pre-defined access permissions to delegates through smart contracts. While the overall implementation of HealthBridge is still in the developmental stages, it presents a proof of concept and a promising system to support the underfunded rare disease community in terms of healthcare interoperability through a permissioned blockchain network. © 2023 IEEE.

关键词： healthcare informatics interoperability mobile application permissioned blockchain rare disease

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Performance Evaluation of Coffee Bean Binary Classification Through Deep Learning Techniques 1

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International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

作者： Islamy, Fajrul Muchtar, Kahlil Arnia, Fitri Dawood, Rahmad Febriana, Alifya Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Master of Computer Science Program BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

ISBN: (数字)9783031290787

ISBN: (纸本)9783031290770

Coffee beans are one of the high-value commodities in Indonesia, but the sorting method for the quality of coffee beans still uses visual methods and sieves with mechanical machines. This study aims to provide an alternative to classifying coffee beans using Deep Learning algorithms, such as the ResNet-18 and MobileNetV2. The dataset used is USK-COFFEE which consists of two classes, the normal coffee bean class, and the defect coffee bean class. The models were trained using the balanced and imbalanced datasets, meaning that two scenarios were performed. From the results of this study, the accuracy value for the ResNet-18 model was 91.50% when trained on the balanced dataset and 90.87% on the imbalanced dataset. It outperformed the MobileNetV2 architecture, which only achieved 0.8125 and 0.8112 accuracy scores when trained on the balanced and imbalanced datasets, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： computer vision

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