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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Selvaraj, Arikumar K. Naveen Akash, P. Venkata Swamy, B. Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

The COVID-19 pandemic has ravaged public health in a manner that is unprecedented: millions of infections and fatalities worldwide. Now that the immediate crisis seems to recede, there is growing concern over long-term health consequences faced by survivors, commonly referred to as "long COVID." This study aims to systematically explore the multifaceted health effects experienced by individuals who have recovered from COVID-19, focusing on physical, mental, and cognitive health issues that have emerged in the aftermath of the infection. Employing a data-driven approach, the collected extensive survey data from a diverse cohort of COVID-19 survivors to assess their post-recovery health status. The survey included questions targeting various health dimensions, such as fatigue, pain, anxiety, depression, and cognitive function. To analyze the data, utilized several machine learning algorithms to identify significant health complications and to determine the interrelationships among various health factors. © 2025 IEEE.

关键词： COVID-19

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H2O Guard: Predicting Water Potability with Machine Learning and Blockchain Integration 3

H2O Guard: Predicting Water Potability with Machine Learning...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nair, Rahul Aalim, Ansab Manhaas, Ishaan Rastogi, Kshitij Kalpana, A.V. Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798331509675

Access to clean drinking water is critical to public health and well - being. Water compatibility refers to the safety of drinking water for human consumption and includes various physical, chemical and biological parameters. Contaminated water can cause serious health problems, particularly among vulnerable populations due to water-borne diseases. Traditionally, water quality assessment relies on time - consuming laboratory tests that can delay responses to contamination. The H2OGuard project presents a novel approach: It uses deep learning and machine learning algorithms to predict water sustainability based on key environmental factors such as pH, turbidity and chemical contaminants. With these advanced methods, we aim to improve the efficiency and accuracy of water quality testing. In addition, the integration of blockchain technology is intended to ensure secure and immutable storage of water quality data and promote integrity and transparency. This paper discusses the methods of the H2OGuard project, evaluates the performance of various predictive models, and highlights the critical importance of blockchain in securing data. With this innovative framework, we aim to enable more efficient and timely assessment of drinking water safety, ultimately contributing to improved public health outcomes. © 2025 IEEE.

关键词： Electronic health record

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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Media Power Measuring via Emotional Contagion

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Journal of Social Computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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MeDict-GP: An Accurate Entity Recognition Model Combining Medical Domain Knowledge and Globalization Ideas 23

MeDict-GP: An Accurate Entity Recognition Model Combining Me...

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9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Zhang, Yixuan Liu, Junzhen Lu, Wei Faculty of Data Science City University of Macau Macau China School of Aliyun Big Data Applications Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9781450399029

In order to extract medical terms from the vast amount of medical literature, such as diseases, treatment modalities, and drug names, clinical named entity recognition (CNER), a crucial step and foundation in medical natural language processing, aims to increase the effectiveness and caliber of clinical research. Medical named entity identification has given rise to a number of recognition models, however these algorithms struggle to recognize nested connections in medical texts and multilingual annotations in Mandarin and English. For this reason, we provide the sensible and precise MeDict-GP model, which fuses the lexicon with the globalization framework GlobalPointer to predict the labels and output the prediction sequences. It solely employs the medical domain as input to the lexicon embedding model. We discovered that the MeDict-GP model attained an F1-Score value of 68.171 percent in the assessment tests on the CMeEE medical dataset, an improvement of 1.17 percent compared to the manual annotation findings. We discovered through a series of comparative ablation experiments that adding domain lexicons and adversarial training can boost entity recognition accuracy. Our next step will be to investigate models that are more particular and have better capacity to demarcate entity borders because we also discovered that the models frequently failed to recognize certain medical entities. © 2023 Copyright held by the owner/author(s).

关键词： Clinical research

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A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

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Journal of Computer science & technology 2023年第6期38卷 1203-1222页

作者：刘嘉聿汪飞马海平黄振亚刘淇陈恩红苏喻 School of Data Science University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Cognitive Intelligence Hefei 230088China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230026China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Computer Science and Artificial Intelligence Hefei Normal UniversityHefei 230061China

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge *** existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning *** this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis ***,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness ***,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model *** UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

关键词： cognitive diagnosis probabilistic graphical model item response theory(IRT) stochastic process expectation maximization(EM)algorithm

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Comparison of Out-of-Distribution Detection Performance of CLIP-based Fine-Tuning Methods

Comparison of Out-of-Distribution Detection Performance of C...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Kim, Jeonghyeon Kim, Jihyo Hwang, Sangheum Seoul National University of Science and Technology Department of Data Science Korea Republic of

ISBN: (纸本)9798350371888

In recent years, large-scale vision-language models such as CLIP have shown remarkable performance on various zero-shot classification tasks. Inspired by these pretrained models, many studies have proposed effective fine-tuning methods to exploit the models' pre-trained knowledge. One common approach is to fine-tune the entire model to transfer the pre-trained knowledge to target tasks. On the other hand, given the high cost of fine-tuning large-scale models, parameter-efficient fine-tuning methods are also being explored. While there have been performance comparisons of existing fine-tuning methods, they often focus solely on classification performance. Such a singular focus is not sufficient to assess the quality of the transferred pre-trained knowledge comprehensively. For a more rigorous evaluation, other metrics, such as the detection of out-of-distribution samples, should be considered, given their importance for model reliability. However, the comparison of fine-tuning methods concerning model reliability has been less explored. Therefore, we aim to fill this gap by offering a comprehensive comparative analysis on the out-of-distribution detection performance of CLIP-based fine-tuning methods along with their in-distribution classification performance. Our experimental results on the OpenOOD v1.5 benchmark dataset suggest that fine-tuning the entire model provides superior performance in both classification and out-of-distribution detection in a few-shot setting. © 2024 IEEE.

关键词： Classification (of information)

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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of Computer science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

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Novel Rifle Number Recognition Based on Improved YOLO in Military Environment

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Computers, Materials & Continua 2024年第1期78卷 249-263页

作者： Hyun Kwon Sanghyun Lee Department of Artificial Intelligence and Data Science Korea Military AcademySeoulKorea Graduate School of Information Security Korea Advanced Institute of Science and TechnologyDaejeonKorea

Deep neural networks perform well in image recognition,object recognition,pattern analysis,and speech *** military applications,deep neural networks can detect equipment and recognize *** military equipment,it is necessary to detect and recognize rifle management,which is an important piece of equipment,using deep neural *** have been no previous studies on the detection of real rifle numbers using real rifle image *** this study,we propose a method for detecting and recognizing rifle numbers when rifle image data are *** proposed method was designed to improve the recognition rate of a specific dataset using data fusion and transfer *** the proposed method,real rifle images and existing digit images are fusedas trainingdata,andthe final layer is transferredto theYolov5 *** detectionand recognition performance of rifle numbers was improved and analyzed using rifle image and numerical *** used actual rifle image data(K-2 rifle)and numeric image datasets,as an experimental *** was used as the machine learning *** results show that the proposed method maintains 84.42% accuracy,73.54% precision,81.81% recall,and 77.46% F1-score in detecting and recognizing rifle *** proposed method is effective in detecting rifle numbers.

关键词： Machine learning deep neural network rifle number recognition detection

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