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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Sapna Yadav Pravin Mane Virendra Kumar Swarnkar Shital kumar Rawandale Alaknanda S. Patil Kiran S. Katke Niyati Bhat Entrepreneurship Mindset Curriculum State Council of Educational Research and Training Delhi Bharati Vidyapeeth (Deemed to be University) Institute of Management and Entrepreneurship Development Pune Department of Computer Science & Engineering Bharti Vishwavidyalaya Durg Chhattisgarh Dean Industry Institute Interaction PCET's Pimpri Chinchwad College of Engineering Pune Maharashtra India E&TC department JSPM NTC Pune Department of Computer Engineering Sanjivani College of engineering Kopergaon Ahmednagar Software Developer Master of Science california state university long beach CA USA

This study looks at how artificial intelligence (AI) is incorporated into healthcare policy, with an emphasis on how it affects administrative procedures, treatment optimization, especially clinical choice support. Using a deductive methodology and interpretive thinking as a philosophy, an exploratory design with secondary data collecting was used. In the present-day environment, AI is being used more frequently in healthcare legislation, especially within medical settings. Clinical decision-making systems driven by AI have shown to offer a great promise for lowering diagnostic mistakes and increasing treatment precision. Improving patient outcomes through individualized AI-driven treatments for treatment improvement appears promising. But there are issues with algorithm prejudices as well as information privacy, among other ethical and legal issues. Establishing thorough guidelines, ongoing education, and auditing procedures are among the suggestions that are made. In order to guarantee fair and efficient integration of health care legislation, future research should prioritize longitudinal research and improving artificial intelligence algorithms

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Privacy-Preserving Graph Convolutional Networks for Text Classification

arXiv

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arXiv 2021年

作者： Igamberdiev, Timour Habernal, Ivan Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany

Graph convolutional networks (GCNs) are a powerful architecture for representation learning on documents that naturally occur as graphs, e.g., citation or social networks. However, sensitive personal information, such as documents with people's profiles or relationships as edges, are prone to privacy leaks, as the trained model might reveal the original input. Although differential privacy (DP) offers a well-founded privacy-preserving framework, GCNs pose theoretical and practical challenges due to their training specifics. We address these challenges by adapting differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages. We propose a simple yet efficient method based on random graph splits that not only improves the baseline privacy bounds by a factor of 2.7 while retaining competitive F1scores, but also provides strong privacy guarantees of ϵ = 1:0. We show that, under certain modeling choices, privacy-preserving GCNs perform up to 90% of their non-private variants, while formally guaranteeing strong privacy measures. © 2021, CC BY-SA.

关键词： Classification (of information)

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Efficient Utilization of Large Pre-Trained Models for Low Resource ASR

arXiv

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arXiv 2022年

作者： Vieting, Peter Lüscher, Christoph Dierkes, Julian Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Unsupervised representation learning has recently helped automatic speech recognition (ASR) to tackle tasks with limited labeled data. Following this, hardware limitations and applications give rise to the question how to take advantage of large pre-trained models efficiently and reduce their complexity. In this work, we study a challenging low resource conversational telephony speech corpus from the medical domain in Vietnamese and German. We show the benefits of using unsupervised techniques beyond simple fine-tuning of large pre-trained models, discuss how to adapt them to a practical telephony task including bandwidth transfer and investigate different data conditions for pre-training and fine-tuning. We outperform the project baselines by 22% relative using pre-training techniques. Further gains of 29% can be achieved by refinements of architecture and training and 6% by adding 0.8 h of in-domain adaptation data. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

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What if it speaks like it was from the village? Effects of a Robot speaking in Regional Language Variations on Users’ Evaluations

What if it speaks like it was from the village? Effects of a...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Birgit Lugrin Elisabeth Ströle David Obremski Frank Schwab Benjamin Lange Human-Computer Interaction Institute of Computer Science University of Würzburg Media Psychology Instutute Human Computer Media University of Würzburg

ISBN: (数字)9781728160757

ISBN: (纸本)9781728160764

The present contribution investigates the effects of spoken language varieties, in particular non-standard / regional language compared to standard language (in our study: High German), in social robotics. Based on (media) psychological and sociolinguistic research, we assumed that a robot speaking in regional language (i.e., dialect and regional accent) would be considered less competent compared to the same robot speaking in standard language (H1). Contrarily, we assumed that regional language might enhance perceived social skills and likability of a robot, at least so when taking into account whether and how much the human observers making the evaluations talk in regional language themselves. More precisely, it was assumed that the more the study participants spoke in regional language, the better their ratings of the dialect-speaking robot on social skills and likeability would be (H2). We also investigated whether the robot's gender (male vs. female voice) would have an effect on the ratings (RQ). H1 received full, H2 limited empirical support by the data, while the robot's gender (RQ) turned out to be a mostly negligible factor. Based on our results, practical implications for robots speaking in regional language varieties are suggested.

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TraSeTR: Track-to-segment transformer with contrastive query for instance-level instrument segmentation in robotic surgery

arXiv

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arXiv 2022年

作者： Zhao, Zixu Jin, Yueming Heng, Pheng-Ann Department Of Computer Science And Engineering The Chinese University Of Hong Kong Hong Kong Guangdong-Hong Kong-Macao Joint Laboratory Of Human-Machine Intelligence-Synergy Systems Shenzhen Institute Of Advanced Technology Chinese Academy Of Sciences China Department Of Computer Science University College London United Kingdom

Surgical instrument segmentation - in general a pixel classification task - is fundamentally crucial for promoting cognitive intelligence in robot-assisted surgery (RAS). However, previous methods are struggling with discriminating instrument types and instances. To address above issues, we explore a mask classification paradigm that produces per-segment predictions. We propose TraSeTR, a novel Track-to-Segment Transformer that wisely exploits tracking cues to assist surgical instrument segmentation. TraSeTR jointly reasons about the instrument type, location, and identity with instance-level predictions i.e., a set of class-bbox-mask pairs, by decoding query embeddings. Specifically, we introduce the prior query that encoded with previous temporal knowledge, to transfer tracking signals to current instances via identity matching. A contrastive query learning strategy is further applied to reshape the query feature space, which greatly alleviates the tracking difficulty caused by large temporal variations. The effectiveness of our method is demonstrated with state-of-the-art instrument type segmentation results on three public datasets, including two RAS benchmarks from EndoVis Challenges and one cataract surgery dataset CaDIS. © 2022, CC BY-NC-SA.

关键词： Surgical equipment

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Video Face Clustering with Self-Supervised Representation Learning

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2020年第2期2卷 145-157页

作者： Sharma, Vivek Tapaswi, Makarand Saquib Sarfraz, M. Stiefelhagen, Rainer Department of Informatics Institute for Anthropomatics and Robotics Computer Vision for Human-Computer Interaction Lab Karlsruhe Institute of Technology Karlsruhe76131 Germany University of Toronto TorontoONM5S 3G4 Canada Massachusetts Institute of Technology CambridgeMA02139 United States Vector Institute TorontoON Canada Inria Paris75012 France

Characters are a key component of understanding the story conveyed in TV series and movies. With the rise of advanced deep face models, identifying face images may seem like a solved problem. However, as face detectors get better, clustering and identification need to be revisited to address increasing diversity in facial appearance. In this paper, we propose unsupervised methods for feature refinement with application to video face clustering. Our emphasis is on distilling the essential information, identity, from the representations obtained using deep pre-trained face networks. We propose a self-supervised Siamese network that can be trained without the need for video/track based supervision, that can also be applied to image collections. We evaluate our methods on three video face clustering datasets. Thorough experiments including generalization studies show that our methods outperform current state-of-the-art methods on all datasets. The datasets and code are available at https://***/vivoutlaw/SSIAM. © 2019 IEEE.

关键词： computer vision

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A systematic study on the role of SentiWordNet in opinion mining

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Frontiers of computer science 2021年第4期15卷 165-183页

作者： Mujtaba HUSNAIN Malik Muhammad Saad MISSEN Nadeem AKHTAR Mickael COUSTATY Shahzad MUMTAZ V.B.Surya PRASATH Department of IT Faculty of ComputingThe Islamia University of BahawalpurBahawalpur 63100Pakistan Laboratoire Informatique Image et Interaction(L3i)Facultés des Sciences et TechnologiesUniversity of La RochelleLa Rochelle 17000France Division of Biomedical Informatics Cincinnati Children’s Hospital Medical CenterCincinnati 45229USA Department of Pediatrics University of Cincinnati College of MedicineCincinnati 45229USA Department of Biomedical Informatics College of MedicineUniversity of CincinnatiCincinnati 45267USA Department of Electrical Engineering and Computer Science University of CincinnatiCincinnati 45221USA

Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment *** lexicons play an important role in performing several different opinion mining *** efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the ***,one has to explore the available sentiment lexicons and then select the most suitable *** available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion *** SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of *** this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive *** survey will be useful for the researchers contributing to the field of opinion *** features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers *** the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.

关键词： opinion mining lexical based resources Senti-WordNet opinion strength

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Generative Data Imputation for Sparse Learner Performance Data Using Generative Adversarial Imputation Networks

SSRN

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SSRN 2025年

作者： Zhang, Liang Lin, Jionghao Sabatini, John Zapata-Rivera, Diego Forsyth, Carol Jiang, Yang Hollander, John Hu, Xiangen Graesser, Arthur C. Institute for Intelligent Systems University of Memphis MemphisTN United States Department of Electrical and Computer Engineering University of Memphis MemphisTN United States Faculty of Education The University of Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Centre for Learning Analytics Monash University MelbourneVIC Australia Department of Psychology University of Memphis MemphisTN United States Educational Testing Service PrincetonNJ United States Department of Psychology and Counseling Arkansas State University JonesboroAR United States Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

As human learners respond to questions in Intelligent Tutoring Systems (ITSs), their performance data (correct or incorrect answers) is crucial for assessing and predicting their knowledge states. However, data sparsity, often arising from skipped or incomplete responses, hampers accurate assessment and personalized instruction. To address this challenge, we propose a Generative Adversarial Imputation Networks (GAIN) method using a three-dimensional representation (learners, questions, and attempts) with Convolutional Neural Networks (CNNs) in the input and output layers. We evaluate our method on three ITS datasets, AutoTutor ARC, ASSISTments, and MATHia, where it outperforms baselines, including tensor factorization methods and other GAN variants, across multiple maximum attempt settings. To confirm imputation effectiveness for learner modeling, we applied Bayesian Knowledge Tracing (BKT) and observed significant reductions in RMSE compared to original sparse data, indicating substantial improvements in modeling accuracy. Moreover, analyzing divergence of key BKT parameters (initial knowledge \(P(L_0)\), learning rate \(P(T)\), guess rate \(P(G)\), and slip rate \(P(S)\)) via Kullback-Leibler (KL) divergence shows that the imputed data preserves essential learning characteristics. Our findings highlight GAIN’s potential for mitigating data sparsity in ITSs, enabling more adaptive, individualized instruction and fostering more effective AI-driven education. © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Novel Lightweight Convolutional Neural Network for EEG-Based Miniature-Event-Related Potentials

A Novel Lightweight Convolutional Neural Network for EEG-Bas...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Chang Liu Wenxiao Zhong Yang Di Liang Zhao Xingwei An Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin P. R. China Tianjin Key Laboratory of Brain Science and Neuroengineering academy of medical engineering and translation of medicine Tianjin University Tianjin Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration State Key Laboratory of Advanced Medical Materials and Devices Tianjin University Tianjin Tianjin P. R. China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

Miniature asymmetric visual evoked potential (aVEP) induced by very small lateral visual stimuli is a comfortable stimulation paradigm. It could only induce a miniature potential about 0.5 μV in amplitude, which could be challenging to detect. Most decoding methods based on miniature aVEP are machine learning approaches, and deep learning methods have not yet been introduced. Therefore, this paper introduces deep learning techniques for the first time and designs a lightweight convolutional neural network. Specifically, depthwise convolution is employed to reduce the number of network parameters and extract temporal and spatial features from EEG signals. The model achieves an average classification accuracy of 78.48% on the miniature aVEP dataset, which is 1.14% higher than the previous state-of-the-art model, DCPM. Through comparison with other deep learning models in terms of parameter count, computational complexity, and classification accuracy, it is evident that the proposed model surpasses them in all these aspects The results suggest that the CNN model proposed in this study achieves high classification accuracy while demanding fewer computational resources. Although the network model we proposed is very simple, it can achieve good results in processing miniature aVEP. In future research, we will continue to improve this lightweight network to enhance its decoding performance for miniature aVEP.

关键词： Deep learning Visualization Accuracy Computational modeling Neural networks Virtual environments Brain modeling

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An evolutionary lion optimization algorithm-based image compression technique for biomedical applications

An evolutionary lion optimization algorithm-based image comp...

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作者： Geetha, Karuppaiah Anitha, Veerasamy Elhoseny, Mohamed Kathiresan, Shankar Shamsolmoali, Pourya Selim, Mahmoud M. Department of Computer Science & Engineering MIET Engineering College Tamilnadu India Department of Computer Science and Engineering Imayam College of Engineering Anna University ChennaiTamilnadu India Faculty of Computers and Information Mansoura University Mansoura Egypt Scientific Group for Research and Technology Cairo Egypt Department of Computer Applications Alagappa University Karaikudi India Huawei Technologies Corporate Shanghai China. & Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Mathematics Al-Aflaj College of Science and Human Studies Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image. © 2020 John Wiley & Sons Australia, Ltd

关键词： Vector quantization

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