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7th International Conference on Medical and Health Informatics, ICMHI 2023

作者： Das, Utpal Chandra Le, Ngoc Thien Benjapolakul, Watit Vitoonpong, Timporn Pluempitiwiriyawej, Charnchai Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Chulalongkorn University Bangkok10330 Thailand Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok10330 Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand

ISBN: (纸本)9798400700712

Stroke is a significant cause of mortality and disability globally, with its occurrence in the human brain and motor function being linked to various parts of the human body. Stroke victims often experience disabilities or mobility problems in affected body parts, either on one or both sides of the body. Physiotherapy exercises are the primary treatment and medication for stroke patients, necessitating daily monitoring by a physiotherapist. However, this approach is expensive, and rehabilitation centers and physicians are scarce. Numerous research studies have been conducted to address this issue from various perspectives. This study proposes a Convolutional Neural Network (CNN) based pose net machine learning (ML) model for stroke home rehabilitation using pose detection and classification with a skeleton base model and human pose estimation drawing. We trained our ML model using different human pose images for this model. The tested accuracy of our CNN model is 100% for our exercise pose image test case. Later we build and mobile application for remote rehabilitation. Tested the application with ten different subjects, achieving a 98% for elbow extension, and in the case of elbow flexion and Normal position of balancing both sides, all the persons achieved a 100 % accuracy rate under a laboratory environment. © 2023 ACM.

关键词： Patient treatment

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Celebrity profiling through linguistic analysis of digital social networks

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Computational Social Networks 2021年第1期8卷 1-36页

作者： Moreno-Sandoval, Luis G. Pomares-Quimbaya, Alexandra Alvarado-Valencia, Jorge A. Center of Excellence and Appropriation in Big Data Analytics (CAOBA) Bogota Colombia Department of System Engineering Pontificia Universidad Javeriana Bogota Colombia Department of Industrial Engineering Pontificia Universidad Javeriana Bogota Colombia

Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame. © 2021, The Author(s).

关键词： Author profile Celebrity profile Demographic features Influential feature Natural Language Processing

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SIU2023-NST - Hate Speech Detection Contest

SIU2023-NST - Hate Speech Detection Contest

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IEEE Signal Processing and Communications Applications (SIU)

作者： İnanç Arın Zeynep Işık Seçilay Kutal Somaiyeh Dehghan Arzucan Özgür Berrin Yanikoğlu Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Center of Excellence in Data Analytics (VERIM) Sabanci University Istanbul Turkey Department of Computer Engineering Bogazici University Turkey

Hate speech spread on social media can strongly affect people and societies, especially when it targets a specific group of people in terms of religion, culture, or a specific social situation, such as refugees. For this reason, the detection and elimination of hate speech in social networks have attracted the attention of natural language processing researchers in recent years. A competition is organized to benchmark progress in Turkish hate speech recognition, within SIU2023 Computational Social Sciences special session, with four different tasks. A total of 20 teams registered for the competition, while eight teams submitted results at the end. The details of the winning models and their results are explained in this paper.

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AgriLLM:Harnessing Transformers for Framer Queries

arXiv

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arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

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Enhancing Communication Efficiency in Adam Optimizer for Distributed Deep Learning

Enhancing Communication Efficiency in Adam Optimizer for Dis...

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International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA)

作者： Fransiskus Febryan Suryawan Achmad Imam Kistijantoro School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Natural Language Processing & Big Data Analytics (U-CoE AI-VLB) University Center of Excellence on Artificial Intelligence for Vision

Deep learning is a popular technique used to solve various problems in many domains. Deep learning models consists of learnable parameters. The development of more advanced deep learning models are headed towards increasing the number of parameters. Increase in the number of parameters will also increase communication overhead in distributed deep learning. Therefore, this paper will discuss about existing techniques to reduce communication overhead by reducing the communication rounds and compressing the communications. The reduction in communication uses the idea from CADA [1], while the compression uses the idea from Efficient-Adam [2]. The result are a technique which could reduce communication to as much as 0.97 times that of CADA, while reducing communication data size to 0.29 times the communication size of CADA.

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PUSH-FORWARD SIGNED DISTANCE FUNCTIONS ENABLE INTERPRETABLE AND ROBUST CONTINUOUS SHAPE QUANTIFICATION

arXiv

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arXiv 2024年

作者： Rouatbi, Roua Suarez, Juan Esteban Sbalzarini, Ivo F. Dresden University of Technology Faculty of Computer Science Germany Max-Planck Institute of Molecular Cell Biology and Genetics Germany Center for Systems Biology Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig Germany Cluster of Excellence "Physics of Life" TU Dresden Germany

We introduce the Push-Forward Signed Distance Morphometric (PF-SDM), a novel method for shape quantification in biomedical imaging that is continuous, interpretable, and invariant to shapepreserving transformations. PF-SDM effectively captures the geometric properties of shapes, including their topological skeletons and radial symmetries. This results in a robust and interpretable shape descriptor that generalizes to capture temporal shape dynamics. Importantly, PF-SDM avoids certain issues of previous geometric morphometrics, like Elliptical Fourier Analysis and Generalized Procrustes Analysis, such as coefficient correlations and landmark choices. We present the PF-SDM theory, provide a practically computable algorithm, and benchmark it on synthetic data. © 2024, CC BY-NC-ND.

关键词： Topology

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Detecting Online Outlier for data Streams using Recursive Residual 7

Detecting Online Outlier for Data Streams using Recursive Re...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Dani, Yasi Gunawan, Agus Yodi Indratno, Sapto Wahyu Institut Teknologi Bandung Industrial and Financial Mathematics Research Group Faculty of Mathematics and Natural Sciences Bandung Indonesia University Center of Excellence on Artificial Intelligence for Vision Natural Language Processingand Big Data Analytics Institut Teknologi Bandung Faculty of Mathematics and Natural Sciences Department Statistics Research Division Bandung Indonesia

ISBN: (纸本)9798350345711

Outlier analysis is a statistical procedure that involves the identification of anomalous observations. Outlier detection is important in many fields and involved in numerous applications since if the outliers are not detected then it can lead us to a wrong decision. There are a wide variety of techniques that can be used to identify outliers in datasets. Most of the existing methods are based on static data, while in case of data stream these methods are computational inefficiency and require big memory storage. This is due to the data stream being a dataset that is generated in a real-time data stream and requires real-time data analysis. The recursive least squares approach is mainly used in many regression analysis and the outlier detection process is referred to as supervised outlier detection. In this paper, we propose an online outlier detection algorithm using recursive residuals via recursive least squares method. The recursive residuals are sequentially calculated on the distance of the observation and the prediction values. To estimate online parameters in this research, we apply a recursive least squares method to predict an iterative model. In recursive least squares method a single new data point is analyzed each algorithm iteration to improve the estimation of our model parameters. Then, our simulation is performed on historical data for Tesla stock for a given period. Results that our online algorithm can demonstrate its ability to maintain high accuracy and find better outliers with low computational cost for anomaly detection. © 2022 IEEE.

关键词： Anomaly detection

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Fast Variational Boosting for Latent Variable Models

arXiv

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arXiv 2025年

作者： Gunawan, David Nott, David Kohn, Robert School of Mathematics and Applied Statistics University of Wollongong Australia School of Economics University of New South Wales Australia Australian Center of Excellence for Mathematical and Statistical Frontiers Australia Department of Statistics and Data Science National University of Singapore Singapore Institute of Operations Research and Analytics National University of Singapore Singapore

We consider the problem of estimating complex statistical latent variable models using variational Bayes methods. These methods are used when exact posterior inference is either infeasible or computationally expensive, and they approximate the posterior density with a family of tractable distributions. The parameters of the approximating distribution are estimated using optimisation methods. This article develops a flexible Gaussian mixture variational approximation, where we impose sparsity in the precision matrix of each Gaussian component to reflect the appropriate conditional independence structure in the model. By introducing sparsity in the precision matrix and parameterising it using the Cholesky factor, each Gaussian mixture component becomes parsimonious (with a reduced number of non-zero parameters), while still capturing the dependence in the posterior distribution. Fast estimation methods based on global and local variational boosting moves combined with natural gradients and variance reduction methods are developed. The local boosting moves adjust an existing mixture component, and optimisation is only carried out on a subset of the variational parameters of a new component. The subset is chosen to target improvement of the current approximation in aspects where it is poor. The local boosting moves are fast because only a small number of variational parameters need to be optimised. The efficacy of the approach is illustrated by using simulated and real datasets to estimate generalised linear mixed models and state space models. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic systems

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Elevating the photodecolorization efficiency for synthetic and actual colored wastewater through the integration of chitosan and zinc oxide layers on a fiberglass flat sheets

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Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Subagyo, Riki Saepurahman, Saepurahman Zain, Elfirza Hartati, Sri Zhang, Lei Kurnia, Kiki Adi Arramel, Arramel Ediati, Ratna Akhlus, Syafsir Kusumawati, Yuly Department Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Research Center for Chemistry National Research and Innovation Agency (BRIN) Center of Excellence Applied Physics and Chemistry Nano Center Indonesia Departement of Physics National University of Singapore Department of Chemical Engineering Bandung Institute of Technology

This study reported a novel technique for spray coating zinc oxide (ZnO) on one side of fiberglass (FG) substrate and chitosan (CS) on the opposite side, resulting in the production of ZnO/FG/CS composite. This strategy alters the morphology of the pure FG, creating smooth and rough surfaces on the CS and ZnO sides, respectively. ZnO/FG/CS demonstrated remarkable photoremoval activity in a combined solution of methylene blue-methyl orange and real wastewater with the addition of hydrogen peroxide and facilitated moderate reusability over five cycles. This strategy revealed an insight for developing an integrated adsorption and photocatalytic process with high-performance capabilities. © 2024 The Authors

关键词： Chitosan Composite Photodecolorization Wastewater ZnO

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Seeing Your Stories:Visualization for Narrative Medicine

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Health data Science 2024年第1期4卷 390-405页

作者： Hua Ma Xiaoru Yuan Xu Sun Glyn Lawson Qingfeng Wang Faculty of Science and Engineering University of NottinghamNingbo 315100China Digital Art Department Art&Design Technology InstituteSuzhou 215104China National Key Laboratory of General Artiffcial Intelligence and School of Intelligence Science and Technology Peking UniversityBeijing 100871China Health Data Visualization and Visual Analytics Research Center National Institute of Health Data Science at PKUBeijing 100191China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo ChinaNingbo 315100China Human Factors Research Group Faculty of EngineeringUniversity of NottinghamNottingham NG72RDUK Nottingham University Business School China University of NottinghamNingbo 315100China

Importance:Narrative medicine(NM),in which patient stories play a crucial role in their diagnosis and treatment,can potentially support a more holistic approach to patient care than traditional scientiffc ***,there are some challenges in the implementation of narrative medicine,for example,differences in understanding illnesses between physicians and patients and physicians’increased workloads and overloaded *** paper ffrst presents a review to explore previous visualization research for narrative medicine to bridge the gap between visualization researchers and narrative medicine experts and explore further visualization ***:The review is conducted from 2 perspectives:(a)the contexts and domains in which visualization has been explored for narrative medicine and(b)the forms and solutions applied in these *** applied domains are deffned,including understanding patients from narrative records,medical communication,medical conversation training in education,and psychotherapy and emotional wellness ***:A future work framework illustrates some opportunities for future research,including groups of speciffc directions and future points for the 4 domains and 3 technological exploration opportunities(combination of narrative and medical data visualization,taskaudience-based visual storytelling,and user-centered interactive visualization).Speciffcally,3 directions of future work in medical communication(asynchronous online physician-patient communication,synchronous face-to-face medical conversation,and medical knowledge dissemination)were concluded.

关键词： directions education visualization

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