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International Conference on Big Data and Smart computing (BIGCOMP)

作者： Mahe Zabin Syed Tasnimul Karim Ayon Farhan Md Siraj Mehedi Hasan Shuvo Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Republic of Korea

ISBN: (数字)9798331529024

ISBN: (纸本)9798331529031

The deep learning models are computationally burdensome, and their accuracy depends on the number of labeled datasets used in training. The scarcity of the labeled dataset in industrial machine diagnosis is a significant concern as it is pretty impossible to collect data from faulty machines in industrial environments. To address this issue, in this paper, we presented a few-shot learning-based machine fault diagnosis model on a lightweight SqueezeNet architecture using generated texture images from audio sensor data. Subsets of two public audio datasets, MIMII and ToyADMOS, containing limited data- 10 images per class were used to validate the model. The experimental results demonstrate that the model has exhibited 89.6% and 98.3% accuracy for MIMII and ToyADMOS, respectively, with only ten labeled images of each fault class. As the model has only 73 thousand trainable parameters and requires only 300 KB of memory, it is expected to be deployable in a portal device for machine fault diagnosis.

关键词： Fault diagnosis Training Accuracy Sensitivity Computational modeling Memory management Data models Few shot learning Spectrogram Portals

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Artificial Intelligence in Health Care computing and Assessments in Oman: an Effectual Study 2

Artificial Intelligence in Health Care Computing and Assessm...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Jabri, Jaber Saleh Salim Al Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Kattankulathur India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Health care plays a vital responsibility in a human life span. World countries targets to provide better care for their citizens. Digital transformations and innovations in Artificial Intelligence (AI) technologies helps in deep for the novel solutions to speed-up the decision making process in health management and patient consulting. AI branches including Machine Learning (ML), Deep Learning (DL), Neural Networks (NN), Natural Language Processing (NLP) and so forth can support innovations in heal care domain. This paper presents the comprehensive and noble review on recent existing tools and techniques and also recommends advanced techniques for health care industry that are to be used in Sultanate of Oman. Furthermore, overcoming AI challenges are resulted and presented through the study. This work mainly aimed to produce the multiple dimensions and challenges that helps to develop AI powered real-life applications in health care support system. © 2025 IEEE.

关键词： Nutrition

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Speaker-Independent Depression Detection based on Adversarial Training Method

Speaker-Independent Depression Detection based on Adversaria...

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European Signal Processing Conference (EUSIPCO)

作者： Minggang WANG Shohei KATO Wen GU Jun YAN Fenghui REN Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan School of Computing and Information Technology University of Wollongong Wollongong Australia Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Depression, a worldwide mental health problem, significantly affects people's lives and may even lead to suicide in severe cases. As speech contains a large number of features that can reflect the speaker's mental condition, research has been conducted on depression detection based on speech analysis recently. However, personalized features from various speakers in speech can be mingled with depression-related features, causing a personalization bias in the features and reducing feature generalization. In this paper, we propose an adversarial training method based on the gradient reversal technique to remove speaker characteristics from speech signals, eliminating the personalization bias caused by speaker characteristics on depression features. Experiments carried out on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WoZ) dataset, demonstrate that our method improved the detection performance through more generalized depression feature extraction.

关键词： Training Analytical models Speech analysis Europe Mental health Signal processing Depression Feature extraction Interviews

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MINIMUM CONSISTENT SUBSET IN TREES AND INTERVAL GRAPHS

arXiv

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arXiv 2024年

作者： Banik, Aritra Das, Sayani Maheshwari, Anil Manna, Bubai Nandy, Subhas C. Krishna Priya, K.M. Roy, Bodhayan Roy, Sasanka Sahu, Abhishek National Institute of Science Education and Research An OCC of Homi Bhabha National Institute Odisha Bhubaneswar752050 India Theoretical Computer Science The Institute of Mathematical Sciences India School of Computer Science Carleton University Canada Department of Mathematics Indian Institute of Technology Kharagpur India Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India

In the Minimum Consistent Subset (MCS) problem, we are presented with a connected simple undirected graph G = (V, E), consisting of a vertex set V of size n and an edge set E. Each vertex in V is assigned a color from the set {1, 2, . . ., c}. The objective is to determine a subset V ′ ⊆ V with minimum possible cardinality, such that for every vertex v ∈ V , at least one of its nearest neighbors in V ′ (measured in terms of the hop distance) shares the same color as v. The decision problem, indicating whether there exists a subset V ′ of cardinality at most l for some positive integer l, is known to be NP-complete even for planar graphs. In this paper, we establish that the MCS problem for trees, when the number of colors c is considered an input parameter, is NP-complete10. We propose a fixed-parameter tractable (FPT) algorithm for MCS on trees running in O(26cn6) time, significantly improving the currently best-known algorithm whose running time is O(24cn2c+3). In an effort to comprehensively understand the computational complexity of the MCS problem across different graph classes, we extend our investigation to interval graphs. We show that it remains NP-complete for interval graphs, thus enriching graph classes where MCS remains intractable. Copyright © 2024, The Authors. All rights reserved.

关键词： Color

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Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Gammatone Spectrogram with Limited Labeled Audio Dataset

Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Ga...

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2025 IEEE International Conference on Big Data and Smart computing, BigComp 2025

作者： Zabin, Mahe Ayon, Syed Tasnimul Karim Siraj, Farhan Md Shuvo, Mehedi Hasan Choi, Ho-Jin Uddin, Jia Korea Advanced Institute of Science and Technology School of Computing Daejeon Korea Republic of BRAC University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Department of Computer Science and Engineering Dhaka Gazipur Bangladesh Woosong University AI and Big Data Department Endicott College Daejeon Korea Republic of

关键词： Deep learning

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Analysing various algorithms applied to Alzheimer’s disease dataset to help the Future Generation

Analysing various algorithms applied to Alzheimer’s disease...

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Intelligent and Innovative Technologies in computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Athinarayanan K. Navaz Tamilselvi. P Shajeena J Vishwa Priya V M. Robinson Joel Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Department of CSE School of Computing Veltech Rangarajan Dr. Sakunthala R&D Institute of Science & Technology Department of Computer Science Vels Institute of Science Technology and Studies Chennai Department of Information Technology Kings Engineering College Chennai India

A sort of brain illness that affects the brain is Alzheimer's. Nowadays, older adults are more likely to have these ailments. People who have this illness have very poor memories and decreased memory. Additionally, their brain isn't working correctly. As a result, their regular practice is progressively beginning to decline. Alzheimer's is a kind of dementia for which there is no definitive treatment. This essay will help us learn more about Alzheimer's illness. We shall learn more about its causes, symptoms, and remedies. Additionally, we investigate several illness prediction methods employing Graph Neural Networks denoted as GNNs. Decision Tree, Random Forest algorithms, and more. However, the Random forest classifier gave good results with 86% accuracy and good recall values.

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Machine Fault Diagnosis Using EMD-Gammatone Texture Representation and A Lightweight Self-Attention SqueezeNet

Machine Fault Diagnosis Using EMD-Gammatone Texture Represen...

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International Conference on Big Data and Smart computing (BIGCOMP)

作者： Mahe Zabin Anika Nahian Binte Kabir Muhammad Khubayeeb Kabir Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science & Technology KAIST Daejeon South Korea Department of Computer Science and Engineering Brac University Dhaka Bangladesh Artificial Intelligence and Big Data Department Woosong University Daejeon South Korea

Machine fault diagnosis involves the intricate process of detecting and isolating faults, which is particularly challenging due to various sources of noise and the complex nature of the faults. In automatic fault diagnosis, feature engineering becomes daunting because of the time-varying characteristics of the fault signals. As artificial intelligence continues to advance, deep learning models have been increasingly employed for machine fault classification. However, a significant limitation of state-of-the-art models is their high computational complexity, making them unsuitable for deployment on portable devices. In this paper, a lightweight fault diagnosis model is proposed that consists of a self-attention SqueezeN et architecture along with a hybrid texture representation technique using empirical mode decomposition (EMD) and gammatone-spectrogram (GS) filter. In the model, initially, the dominant signal is extracted from the ID audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD and then the dominant signals are converted to 2D texture maps applying the GS filter. Then, the generated texture maps are fed to the modified self-attention SqueezeNet classifier, with reduced model width and depth as input for training and validation. In the experimental evaluation, two public benchmark datasets- ToyADMOS and MIMII are used to validate the model. Finally, the model classifies the machine audio faults using the trained features. The experimental results demonstrated that the hybrid EMD-Gammatone texture imaging outperforms the other state-of-the-art methods like MFCC, Gammatone, and Hilbert Huang Transform with a self-attention (SA) based SqueezeNet architecture. The EMD-Gammatone spectrum-based feature extraction accurately detected the faults by exhibiting an accuracy of 89.32% and 96.46% for MIMII and ToyADMOS datasets, respectively. In addition, with the EMD-Gammatone Spectrogram images, the proposed model outperforms the conventional

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Fine-grained Background Representation for Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Yin, Xu Im, Woobin Min, Dongbo Huo, Yuchi Pan, Fei Yoon, Sung-Eui The School of Computing Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Faculty of the Department of Computer Science and Engineering Ewha Womans University Seoul03760 Korea Republic of The State Key Lab of CAD and CG Zhejiang University China Zhejiang Lab 310058 China The School of Computer Science Engineering University of Michigan United States The Faculty of School of Computing Korea Advanced Institute of Science and Technology Deajeon34141 Korea Republic of

Generating reliable pseudo masks from image-level labels is challenging in the weakly supervised semantic segmentation (WSSS) task due to the lack of spatial information. Prevalent class activation map (CAM)-based solutions are challenged to discriminate the foreground (FG) objects from the suspicious background (BG) pixels (a.k.a. co-occurring) and learn the integral object regions. This paper proposes a simple fine-grained background representation (FBR) method to discover and represent diverse BG semantics and address the co-occurring problems. We abandon using the class prototype or pixel-level features for BG representation. Instead, we develop a novel primitive, negative region of interest (NROI), to capture the fine-grained BG semantic information and conduct the pixel-to-NROI contrast to distinguish the confusing BG pixels. We also present an active sampling strategy to mine the FG negatives on-the-fly, enabling efficient pixel-to-pixel intra-foreground contrastive learning to activate the entire object region. Thanks to the simplicity of design and convenience in use, our proposed method can be seamlessly plugged into various models, yielding new state-of-the-art results under various WSSS settings across benchmarks. Leveraging solely image-level (I) labels as supervision, our method achieves 73.2 mIoU and 45.6 mIoU segmentation results on Pascal Voc and MS COCO test sets, respectively. Furthermore, by incorporating saliency maps as an additional supervision signal (I+S), we attain 74.9 mIoU on Pascal Voc test set. Concurrently, our FBR approach demonstrates meaningful performance gains in weakly-supervised instance segmentation (WSIS) tasks, showcasing its robustness and strong generalization capabilities across diverse domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Webcam-Based OCR System with Text-to-Speech Conversion

Webcam-Based OCR System with Text-to-Speech Conversion

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Rekha R Nair Tina Babu Kishore S Poornima J Madduru Sambasivudu Manchikalapudi Bhargavi Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte(Deemed to be University) Mangalore India Department of Information Science and Engineering Brindavan College of Engineering Bengaluru India Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Hyderabad India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The project develops an OCR system that converts webcam-captured text to speech, featuring user registration for personalized experiences. Key components include image capture via webcam, text extraction using OCR, database storage, and text-to-speech conversion. The system employs Python, OpenCV for image processing, Tesseract for OCR, and Google Text-to-Speech for audio output. Main objectives are to streamline text digitization and assist visually impaired users. The modular design allows for future enhancements like language translation. Comprehensive testing ensures reliability and effectiveness. The system demonstrates an efficient solution for converting printed text to digital and audio formats, with applications in document digitization, education, and accessibility services. It reduces manual effort in text digitization and improves access to written information for visually impaired individuals, showcasing potential across various fields.

关键词： Translation Webcams Scalability Optical character recognition User interfaces Streaming media Real-time systems Text to speech Reliability Testing

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AI-Enhanced Child Handwriting Analysis: A Framework for the Early Screening of Dyslexia and Dysgraphia

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SN computer science 2025年第5期6卷 1-26页

作者： Rangasrinivasan, Sahana Sumi Suresh, M.S. Olszewski, Abbie Setlur, Srirangaraj Jayaraman, Bharat Govindaraju, Venu Department of Computer Science and Engineering University at Buffalo Buffalo 14260 NY United States Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India College of Education and Human Development University of Nevada-Reno Reno 89557 NV United States Amrita Institute of Advanced Research Elburn 60119 IL United States

Dyslexia and dysgraphia are two specific learning disabilities (SLDs) that are prevalent among children. To minimize the negative impact these SLDs have on a child’s academic and social-emotional development, it is crucial to identify dyslexia and dysgraphia at an early age, enabling timely and effective intervention. The first step in this process is screening, which helps determine if a child requires further instruction or a more in-depth assessment. Current screening tools are expensive, require additional administration time beyond regular classroom activities, and are designed to screen exclusively for one condition, not for both dyslexia and dysgraphia, which often share some common behavioral characteristics. Most dyslexia screeners focus on speech and oral tasks and exclude writing activities. However, analyzing children’s writing samples for behavioral signs of dyslexia and dysgraphia can offer valuable insights into the screening process, which can be time-consuming. As a solution, we propose a co-designed framework for building artificial intelligence (AI) tools that could boost the efficiency of screening and aid practitioners such as speech-language pathologists (SLPs), occupational therapists, general educators, and special educators by simplifying their tasks. This paper reviews current screening methods employed by practitioners, the use of AI-based systems in identifying dyslexia and dysgraphia, and the handwriting datasets available to train such systems. The paper also outlines a framework for developing an AI-integrated screening tool that can identify writing-based behavioral indicators of dyslexia and dysgraphia in children’s handwriting. This framework can be used in conjunction with current screening tools like the Dysgraphia and Dyslexia Behavioral Indicator Checklist (DDBIC). The paper also proposes a methodology for collecting children’s offline and online handwriting samples to build a valuable dataset for developing AI solutions. The pr

关键词： Artificial intelligence Child handwriting Dysgraphia Dyslexia Early screening

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