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1st IEEE International Conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Arora, Saksham Khayal, Inas S. Department of Computer Science Dartmouth College HanoverNH United States Dartmouth Institute & Biomedical Data Science Geisel School of Medicine at Dartmouth Dept. of Computer Science Dartmouth College HanoverNH United States

ISBN: (纸本)9798350371987

Improving patient-centered care necessitates accurate documentation of care preferences, a crucial aspect often underrepresented in administrative data. Most studies apply care documentation to specific patient populations, rather than the more appropriately broad population of ‘seriously ill’ patients. This study addresses this gap, first by matching decedent patients to non-decedent counterparts by using propensity score matching, accounting for important variables like age, gender, primary diagnoses, and patient co-morbidities, and then by leveraging transformer-based machine learning models, exhibiting an improvement over traditional keyword-based search methods. We trained and fine-tuned Bio_ClinicalBERT and ClinicalLongformer models on a large dataset of patient discharge summaries, enhancing their signal-to-noise ratio by focusing on key textual aspects, thereby capturing contextually nuanced mentions of care preference documentation. These models demonstrated high sensitivity and specificity compared to industry-standard keyword-search methods, proving adept at interpreting complex clinical concepts. This study highlights the potential of transformer-based models specifically trained for clinical domain tasks. Our findings not only contribute to enhancing end-of-life communication and aligning treatment with patients’ care objectives but also pave the way for future research in this promising domain, with potential implications for improving patient care quality. ©2024 IEEE.

关键词： Large datasets

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BERT and RoBERTa Models for Enhanced Detection of Depression in Social Media Text 9th

BERT and RoBERTa Models for Enhanced Detection of Depression...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kurniadi, Felix Indra Paramita, Ni Luh Putu Satyaning Pradnya Sihotang, Erna Fransisca Angela Anggreainy, Maria Susan Zhang, Rongfang Computer Science Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Statistic Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11530 Indonesia Department of Rehabilitation and Health Service University of North Texas United States

Depression is a disease that affects everyone, both young and old. This mental illness not only affects the surrounding environment but everyone. Depression is characterized by deep sadness, behavioral changes and many other actions that are risky for people. In this research we try to solve the problem of detecting depression using Natural Language Processing (NLP) approaches, these two methods are Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT Approach (RoBERTa), where these two methods are used to detect posts made in reddit. The dataset is taken from Kaggle. The results obtained found that the average use of BERT and RoBERTa resulted in a high accuracy value of around 98% and with a well balanced precision, recall and F1-Score ratio. This research shows that there is a possibility of using BERT and RoBERTa in depression detection. © 2024 The Authors.

关键词： BERT depression natural language processing RoBERTa

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A Machine Learning and Deep Learning based Approach to Generate a Speech Emotion Recognition System 18

A Machine Learning and Deep Learning based Approach to Gener...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Sumera Vaidehi, K. Nisha, Qamar Stanley College of Engineering and Technology for Women Department of Artificial Intelligence & Data Science and Computer Engineering Hyderabad India Stanley College of Engineering and Technology for Women Department. of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The most common and general medium via which we humans convey or communicate our thoughts, emotions, feelings or ideas artlessly is by speech or articulation. Blending of this artless way of speech with the technological advancements of AI, has given rise to the importance of building emotion recognition systems from speech today. Even more, the speech/articulation emotion recognition system presented here is also to contribute in and facilitate various emerging applications of today like, in detecting persons' physiological state (as in lie detectors), also be used in forensics, medicine. The proposed work identifies/associates an appropriate label/emotion for the respective emotion from speech presented in the form of an audio file (.wav format). About 4240 audio samples are taken. 1440, 2800 samples from RAVDESS and TESS datasets are considered respectively. After this process of data collection, features are separately extracted for each audio dataset mentioned above. Energy, pitch, ZCR, co-efficient of Mel frequency ceptrum (MFCC) are some of the features considered in this study. Furthermore, clubbing and merging of 2 datasets is performed resulting in a total of 4240 rows and 24 columns (features/characteristics including 1class label) of audio samples. The resulting 4240 samples of feature dataset is split/bifurcated into training and testing set by considering 3 different possibilities/instances viz;60%-40% ratio, 70%-30% ratio, 80%-30% ratio. The models namely CNN, Random forest and Support Vector Machine are trained to classify the dataset into 8 different emotions (neutral, calm, happy, sad, angry, fearful, disgust, surprise). An attempt to implement the models using two very essential disciplines of AI i.e. Machine Learning and Deep Learning is made here. The accuracy or results are depicted by generating confusion matrices on test data for CNN, RF and SVM models (Each model is trained and test across 3 different ratios viz;60%-40%, 70%-30%, 80%-20%). C

关键词： Classification CNN Confusion matrices Extraction of features MFCC RF Speech/articulation Emotion Recognition SVM

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A Novel Approach for Heart Disease Classification using Cascaded XGBoost Algorithm 3

A Novel Approach for Heart Disease Classification using Casc...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajitha, M. Megala, S. Santhana School of Computer Studies RVS College of Arts and Science Sulur Coimbatore India Artificial Intelligence and Data Science SNS College of Engineering Coimbatore India Department of Computer technology Hindustan College of Arts and Science Coimbatore India

ISBN: (纸本)9798331532420

Heart disease remains a leading cause of mortality worldwide. Accurate and timely diagnosis is crucial for effective treatment and prevention. This research proposes a novel approach using a cascaded XGBoost model to improve the accuracy and efficiency of heart disease diagnosis. With gradient boosting, the cascaded XGBoost model effectively handles complex interactions within the dataset, leading to superior performance compared to traditional machine learning techniques like logistic regression, support vector machines, and decision trees. The proposed model was evaluated on a standard heart disease dataset, demonstrating significant improvements in terms of accuracy, precision, and recall. Furthermore, the model's ability to identify key factors contributing to heart disease provides valuable insights for clinical decision-making. This research highlights the potential of machine learning techniques, particularly cascaded XGBoost, in advancing the field of heart disease diagnosis and improving patient outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Identifying the best common factor model via exploratory eactor analysis

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Applied Mathematics(A Journal of Chinese Universities) 2024年第1期39卷 24-33页

作者： HE Bao-hua TANG Rui TAGN Qi-yi School of Big Data Fuzhou University of International Studies and TradeFuzhou 350202China School of Mathematics Sun Yat-Sen UniversityGuangzhou 510275China Department of Computer Science University of BathUK Institute of Insect Science Zhejiang UniversityHangzhou 310058China.

Currently,there is no solid criterion for judging the quality of the estimators in factor *** paper presents a new evaluation method for exploratory factor analysis that pinpoints an appropriate number of factors along with the best method for factor *** proposed technique consists of two steps:testing the normality of the residuals from the fitted model via the Shapiro-Wilk test and using an empirical quantified index to judge the quality of the factor *** are presented to demonstrate how the method is implemented and to verify its effectiveness.

关键词： factor analysis Shapiro-Wilk normality residuals

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Using an extended technology acceptance model to predict enterprise architecture adoption in making cities smarter

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Environment Systems and Decisions 2023年第1期43卷 36-53页

作者： Jnr, Bokolo Anthony Petersen, Sobah Abbas Department of Applied Data Science Institute for Energy Technology Os Alle 5 Halden 1777 Norway Department of Computer Science Norwegian University of Science and Technology NTNU Trondheim NO-7491 Norway

Presently cities are undergoing changes and transformations due to the adoption of information and communications technology. Enterprise Architecture (EA) is one of the approaches adopted by practitioners and researchers to facilitate smart city development as it can enhance the effectiveness of cities’ digital resources and sustainability capabilities. But, despite several literature on EA, studies on the adoption of EA to improve the sustainability of cities are still at the early stage. Besides, there are fewer studies that provided evidence on the adoption of EA to make cities sustainable grounded on established theoretical models and quantitative data. Therefore, this study aims to provide an understanding on the adoption of EA by different practitioners involved in a smart city project. Knowledge transfer and support services are integrated as new external variables needed to improve practitioners’ behavior intention and actual adoption of EA in making cities smarter. A model is developed grounded on an extension of Technology Acceptance Model (TAM), and data were collected via a cross-country survey. Partial least squares-structural equation modeling was employed to analyze the data. Findings from this study offer implications for research and practice and provide opportunities for future research. © 2022, The Author(s).

关键词： Enterprise architecture adoption Extended technology acceptance model Knowledge transfer Smart cities Support service Sustainable cities

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Segmentation of Libraries, CMS, and PHP Frameworks Based on Code Characteristics: Implementation of Clustering Using K-Means

Segmentation of Libraries, CMS, and PHP Frameworks Based on ...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Parulian, Onesinus Saut Aji, Indra Budi Patria, Omega Joel Nugroho, Ariyanto Adi Susanto, Nanang Mariyanto, Totox Salsabila, Nurul Jannah Haris, Muhammad Manik, Lindung Parningotan Universitas Nusa Mandiri Jakarta Computer Science Department Indonesia Universitas Nusa Mandiri Jakarta Research Center for Data and Information Sciences Badan Riset dan Inovasi Nasional Computer Science Department Indonesia

ISBN: (纸本)9798350376210

Rapid development of software significantly facilitates developers in all phases of software development. Developers can easily leverage frameworks, content management systems (CMS), and libraries available in each programming language. With the large and rapid addition of frameworks, CMS, and libraries, developers must be able to choose precisely which ones to use based on the requirements of their software development projects. The utilization of machine learning for segmentation or clustering enables developers to identify groups of frameworks, CMS, and libraries by leveraging features obtained from static analysis, such as the size of codebases, complexities, structures, and other code characteristics. One algorithm that can be used for segmentation is K-means clustering. However, the availability of datasets is crucial as it can offer information about the code characteristics of each software. The use of machine learning on datasets can be an ongoing research area in software development related to utilizing code characteristics in software analysis. This study provides a novel dataset for the domains of software development and machine learning. The dataset we created contains 30 instances and 68 features that can be utilized for related or future research. This research utilizes the Silhouette score to determine the optimal k value. © 2024 IEEE.

关键词： Software design

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Digital Twin (DT) Smart City for Air Quality Management 8

Digital Twin (DT) Smart City for Air Quality Management

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Ariansyah, Dedy Isnan, Mahmud Rahutomo, Reza Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Air pollution is a pressing issue in cities, and managing air quality poses a challenge for urban designers and decision-makers. This study proposes a Digital Twin (DT) Smart City integrated with Mixed Reality technology to enhance visualization and collaboration for addressing urban air pollution. The research adopts an applied research approach, with a focus on developing a DT framework. A use case of DT development for Jakarta, the capital of Indonesia, is presented. By integrating air quality data, meteorological information, traffic patterns, and urban infrastructure data, the DT provides a comprehensive understanding of air pollution dynamics. The visualization capabilities of the DT, utilizing Mixed Reality technology, facilitate effective decision-making and the identification of strategies for managing air quality. However, further research is needed to address data management challenges to build a DT for Smart City at scale. © 2023 The Authors. Published by Elsevier B.V.

关键词： Augmented Reality Digital Twin Mixed Reality Smart City

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DCNN and LSTM based Plant Leaf Disease Detection Model For Edge IoT Device 5

DCNN and LSTM based Plant Leaf Disease Detection Model For E...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Selvam, L. Mary, S. Roseline Manimuthu, A. Pandian, M. Faculty of Science and Humanities Srmist Department of Computer Applications chengalpattu Kattankulathur India Loyola College Department of Mathematics Chennai India Loyola College Department of Data Science Chennai India

ISBN: (纸本)9798350383522

Edge Computing is an incredibly practical and affordable alternative IoT device for cloud computing that uses a revolutionary methodology. Not only does edge processing reduce the cost of systems administration frameworks, but it also improves edge-cloud latency, which is essential for all critical applications. Plant diseases have a catastrophic impact on food production security and can lead to a sharp decline in the quantity and quality of agricultural products. In severe cases, plant diseases may continue to be the cause of partial grain failure. As a result, an automated method for diagnosing and detecting plant diseases is vitally needed for all agricultural data. In this paper, we have proposed a plant disease detection method based on transfer learning and tested the model's performance on IoT edge device. Long-Short Term Memory (LSTM) is used in the proposed system to classify plant leaf diseases based on data extracted by CNN. An internal memory within the LSTM network allows it to learn from critical experiences with long-term conditions. Therefore, the combination of CNN and LSTM layout features significantly enhances classification. It is suggested to use a Deep Convolutional Neural Network based on a pre-trained MobileNetV1 feature extractor and an LSTM classifier. The proposed model's performance is contrasted with that of other pre-trained models, including NASNetMobile and MobileNetV2. The proposed model outperformed other models, achieving a validation accuracy of 0.9892. © 2024 IEEE.

关键词： Edge Computing MobileNet Plant Disease Detection Transfer Learning

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Prior-Guided Parallel Residual Bi-Fusion Network in USV Obstacle Detection

Prior-Guided Parallel Residual Bi-Fusion Network in USV Obst...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Hsu, Chih-Chung Yang, Sophia Hou, Xiu-Yu Jhang, Yu-An Institute of Data Science National Cheng Kung University Taiwan National Tsing Hua University Interdisciplinary Program of Electrical Engineering and Computer Science Taiwan

ISBN: (纸本)9798350324174

In this paper, we propose a novel Prior-Guided Parallel Residual Bi-Fusion Feature Pyramid Network (PPRB-FPN) for accurate obstacle detection in unmanned surface vehicle (USV) sailing. Our method tackles the challenge of detecting small objects, which are prone to information vanishing. To the end, we leverage the PRB-FPN for small object detection and YOLOv7 as a single-stage object detector to effectively identify obstacles. Our experimental results on the Obstacle Detection Challenge dataset at the 1st Workshop on Maritime computer Vision (MaCVi) demonstrate that our method outperforms both Mask R-CNN (mrcnn) and YOLOv7, achieving an F-avg score of 0.514. © 2023 IEEE.

关键词： Unmanned surface vehicles

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