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science, Technology, Engineering and Management (ICSTEM), International Conference on

作者： Sri Ram M S Emmanuel Joy Leka Shree J Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

Web accessibility plays a significant role in shaping our online experiences in today's ever-changing digital world. People with vision impairmentsstill have trouble using most websites. This paper introduces WebSight an innovative AI-based Image Description Generator web extension to address these challenges. By seamlessly interfacing with a Flask server, Optical Character Recognition, and a dedicated Image Description Generator model, this browser extension systematically enhances web accessibility by methodically replacing ‘alt’ attributes of images with meticulously generated descriptions. This approach significantly contributes to web inclusivity, providing visually impaired users with accurate and contextually rich information. The extension's commitment to inclusivity is further demonstrated through the intelligent integration of Tesseract-OCR and Text-to-Speech ensuring a novel and far-reaching impact, offering an enhanced and tailored browsing experience for users with visual impairments.

关键词： Training Technological innovation Accuracy Visual impairment Optical character recognition Web pages Transformers

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ChatGPT-guided Semantics for Zero-shot learning

ChatGPT-guided Semantics for Zero-shot Learning

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Shubho, Fahimul Hoque Chowdhury, Townim Faisal Cheraghian, Ali Saberi, Morteza Mohammed, Nabeel Rahman, Shafin North South University Dept. of Electrical and Computer Engineering Bangladesh University of Adelaide Australian Institute for Machine Learning Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia University of Technology Sydney School of Computer Science and Dsi Australia

ISBN: (纸本)9798350382204

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method. © 2023 IEEE.

关键词： Zero-shot learning

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System for Identifying and Avoiding Obstructions on Railway Tracks in Forest Areas

System for Identifying and Avoiding Obstructions on Railway ...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P B Hemanthkumar Ruth Moly Benjamin Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Railways often faces the challenge of obstructions on the tracks especially passing through the forest area as not much precautions or announcements will be given to there also animals won't be able to understand the arrival of the train. Which results a significant safety risk to trains, travelling passengers and intruding creatures. This paper addresses for identifying the obstacles on railway tracks especially in forest areas which helps to take necessary actions to avoid those obstructions above. The system needs a camera, that has to be installed on the adjacent sides of the railway path which captures the real time data of the track. This captured data is then processed and analyzed to identify any potential obstruction on the tracks using Convolutional Neural Networks You Only Look Once version 8 is used. YOLOv8 is a high speed and accurate model; is trained with a custom dataset which is proposed in this paper. If any obstruction is detected it will inform to the train pilot and nearest stations for testing purpose mailing service is used which helps in taking necessary actions. With appropriate alerts will improve safety and efficiency on railway tracks in the forest areas by providing real time information about potential obstructions on the tracks. This directly helps in preventing accidents and delays. Ensures a safer and more reliable railway network.

关键词： YOLO Training Accuracy Computational modeling Forestry Streaming media Real-time systems data models Safety Object recognition

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An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models

arXiv

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arXiv 2024年

作者： Shiri, Fatemeh Guo, Xiao-Yu Far, Mona Golestan Yu, Xin Haffari, Gholamreza Li, Yuan-Fang Department of Data Science & AI Monash University Australia Australian Institute for Machine Learning University of Adelaide Australia School of Electrical Engineering and Computer Science University of Queensland Australia

Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs’ spatial understanding and reasoning capabilities. Our analyses on object-relationship and multi-hop reasoning reveal several important findings. Firstly, bounding boxes and scene graphs, even synthetic ones, can significantly enhance LMMs’ spatial reasoning. Secondly, LMMs struggle more with questions posed from the human perspective than the camera perspective about the image. Thirdly, chain of thought (CoT) prompting does not improve model performance on complex multi-hop questions involving spatial relations. Lastly, our perturbation analysis on GQA-spatial reveals that LMMs are much stronger at basic object detection than complex spatial reasoning. We believe our benchmark dataset and in-depth analyses can spark further research on LMMs spatial reasoning. © 2024, CC BY.

关键词： Visual languages

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Enhanced Radiological Anomaly Detection using Optimized YOLO-NAS Model

Enhanced Radiological Anomaly Detection using Optimized YOLO...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： Leka Shree J Emmanuel Joy Sri Ram M S Division of Data Science and Cyber Security Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Chest X-rays are commonly used to detect lung diseases. Chest radiographs often have a lot of perceptual errors. The application of image processing in diagnosing lung diseases has improved medical imaging. This has also enabled the creation of databases for classifying chest radiographs. However, current methods are not accurate enough to reliably detect anomalies. Aimed at improving the shortcomings of existing techniques, the proposed approach uses the Automated Neural Architecture Construction (AutoNAC) engine, a proprietary of Neural Architecture Search (NAS). Comparative evaluations against established models, including YOLOv3, YOLOv5, YOLOv7, and YOLOv8, showcase the superiority of YOLO-NAS in mean Average Precision (mAP@0.5), precision, and recall metrics. The originality of this study lies in introducing NAS specifically AutoNAC, to optimize neural network architectures for chest X-ray anomaly detection, marking a significant advancement in diagnostic capabilities in medical imaging.

关键词： YOLO Pulmonary diseases X-rays Reliability Diagnostic radiography Anomaly detection X-ray imaging

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data Assimilation for Microstructure Evolution in Kinetic Monte Carlo 152nd

Data Assimilation for Microstructure Evolution in Kinetic Mo...

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152nd Annual Meeting and Exhibition of The Minerals, Metals and Materials Society, TMS 2023

作者： Tran, Anh Wang, Yan Rodgers, Theron Scientific Machine Learning Sandia National Laboratories AlbuquerqueNM United States Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States Computational Materials and Data Science Sandia National Laboratories AlbuquerqueNM United States

ISBN: (纸本)9783031225239

Modeling grain growth has been a subject of interest in computational material science, as it occurs in thermal-based processing methods such as annealing and sintering. Kinetic Monte Carlo with Potts model is often used as an integrated computational materials engineering (ICME) grain growth model and can generate high-fidelity synthetic microstructures. In this paper, we offer a data-driven stochastic calculus perspective on the kinetics of grain growth and model the microstructure evolution through the lens of stochastic differential equations, based on Langevin dynamics and Fokker-Planck equation to forecast the grain size distribution. We demonstrate that our proposed approach agrees reasonably well with the hybrid Potts-phase field model using SPPARKS in forecasting the long-term evolution of grain size distribution. © 2023, The Minerals, Metals & Materials Society.

关键词： Grain growth

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TC-PDM: Temporally Consistent Patch Diffusion Models for Infrared-to-Visible Video Translation

arXiv

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arXiv 2024年

作者： Doan, Anh-Dzung Phan, Vu Minh Hieu Gupta, Surabhi Wagner, Markus Chin, Tat-Jun Reid, Ian Australian Institute for Machine Learning The University of Adelaide Australia Department of Data Science and Artificial Intelligence Monash University Australia Safran Electronics and Defense Australasia Australia

Infrared imaging offers resilience against changing lighting conditions by capturing object temperatures. Yet, in few scenarios, its lack of visual details compared to daytime visible images, poses a significant challenge for human and machine interpretation. This paper proposes a novel diffusion method, dubbed Temporally Consistent Patch Diffusion Models (TC-DPM), for infrared-to-visible video translation. Our method, extending the Patch Diffusion Model, consists of two key components. Firstly, we propose a semantic-guided denoising, leveraging the strong representations of foundational models. As such, our method faithfully preserves the semantic structure of generated visible images. Secondly, we propose a novel temporal blending module to guide the denoising trajectory, ensuring the temporal consistency between consecutive frames. Experiment shows that TC-PDM outperforms state-of-the-art methods by 35.3% in FVD for infrared-to-visible video translation and by 6.1% in AP50 for day → night object detection. Our code is publicly available at https://***/dzungdoan6/tc-pdm Copyright © 2024, The Authors. All rights reserved.

关键词： Infrared imaging

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Smart Finance: An overview of Artificial Intelligence Integration in Fintech 2

Smart Finance: An overview of Artificial Intelligence Integr...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Akre, Parth Dhananjay Pacharaney, Utkarsha Siraskar, Warsha Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Maharashtra Nagpur441110 India

ISBN: (纸本)9798331528713

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion. © 2024 IEEE.

关键词： Federated learning

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DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Aids

DFingerNet: Noise-Adaptive Speech Enhancement for Hearing Ai...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tsangko, Iosif Triantafyllopoulos, Andreas Müller, Michael Schröter, Hendrik Schuller, Björn W. EIHW - Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Technical University of Munich Germany MCML - Munich Center for Machine Learning Munich Germany WS Audiology Research and Development Erlangen Germany GLAM - Group on Language Audio & Music Imperial College London United Kingdom MDSI - Munich Data Science Institute Munich Germany

ISBN: (纸本)9798350368741

The DeepFilterNet (DFN) architecture was recently proposed as a deep learning model suited for hearing aid devices. Despite its competitive performance on numerous benchmarks, it still follows a 'one-size-fits-all' approach, which aims to train a single, monolithic architecture that generalises across different noises and environments. However, its limited size and computation budget can hamper its generalisability. Recent work has shown that in-context adaptation can improve performance by conditioning the denoising process on additional information extracted from background recordings to mitigate this. These recordings can be offloaded outside the hearing aid, thus improving performance while adding minimal computational overhead. We introduce these principles to the DFN model, thus proposing the DFingerNet (DFiN) model, which shows superior performance on various benchmarks inspired by the DNS Challenge. © 2025 IEEE.

关键词： Context Adaptation Deep learning Hearing Aids Speech Denoising Speech Enhancement

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A Review of machine learning Algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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