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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Umamageswari, A. Mukund, P.U. Abhishek, V.B. Devi Vaishnavi, Y. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering With Specialization in Big Data Analytics Ramapuram Chennai India

ISBN: (纸本)9798331522988

The basis of this project is to investigate whether the YOLO, an object detection algorithm where 'You Only Look Once' constitutes the name, could be applied to develop FMCG management;together with the management of FMCG, it becomes the gateway to increasing retail precision, efficiency, and agility. Under real-time processing, the system detects and counts FMCG products on retail shelves with an accuracy rate of 92.5%. This accuracy ensures that the optimum stock levels are maintained and there will be no discrepancies. The system processes video feeds at 0.08 seconds per frame, thus allowing inventory data to be updated in real-time to enable quick, data-driven decisions. YOLO's flexibility ensures consistent performance across different store layouts and product types. This system integrates with other retail management tools and allows integrating inventory data in the real time. This means that it would be made available with invaluable insights which would prevent stock depletion while helping to curtail costs. This project takes YOLO ahead of just being a game changer in the FMCG inventory management while operating to reduce expenses and customer satisfaction due to product availability. © 2024 IEEE.

关键词： Efficiency

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Robust Local Light Field Synthesis via Occlusion-aware Sampling and Deep Visual Feature Fusion

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Machine Intelligence Research 2023年第3期20卷 408-420页

作者： Wenpeng Xing Jie Chen Yike Guo Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China Data Science Institute Imperial College LondonLondon SW72AZUK

Novel view synthesis has attracted tremendous research attention recently for its applications in virtual reality and immersive *** a locally immersive light field(LF)based on arbitrary large baseline RGB references is a challenging problem that lacks efficient solutions with existing novel view synthesis *** this work,we aim at truthfully rendering local immersive novel views/LF images based on large baseline LF captures and a single RGB image in the target *** fully explore the precious information from source LF captures,we propose a novel occlusion-aware source sampler(OSS)module which efficiently transfers the pixels of source views to the target view′s frustum in an occlusion-aware *** attention-based deep visual fusion module is proposed to fuse the revealed occluded background content with a preliminary LF into a final refined *** proposed source sampling and fusion mechanism not only helps to provide information for occluded regions from varying observation angles,but also proves to be able to effectively enhance the visual rendering *** results show that our proposed method is able to render high-quality LF images/novel views with sparse RGB references and outperforms state-of-the-art LF rendering and novel view synthesis methods.

关键词： Novel view synthesis light field(LF)imaging multi-view stereo occlusion sampling deep visual feature(DVF)fusion

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Improved UNET Architecture for Effective COVID-19 Severity Prediction from Pulmonary CT Images 1

Improved UNET Architecture for Effective COVID-19 Severity P...

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1st International Conference on Innovations in Communications, Electrical and computer Engineering, ICICEC 2024

作者： Ramla, M. Jasmine, R. Anita Ramesh, M. Bai, Femilda Josephin Joseph Shobana SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India SRM Institute of Science and Technology Department of Computer Science Chennai India Istinye University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350376517

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%. © 2024 IEEE.

关键词： COVID-19

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Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

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computer Systems science & Engineering 2023年第9期46卷 3471-3489页

作者： Jae-Seok Jang Hyuk-Yoon Kwon Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Korea Department of Industrial Engineering/Graduate School of Data Science Seoul National University of Science and TechnologySeoul01811Korea

Question-answering(QA)models find answers to a given *** necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data *** this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given *** studies for the approach performed on the entire dataset or datasets within a category that the question writer manually *** contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the *** to the text classification model,we can effectively reduce the search space for finding the answers to a given ***,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference ***,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding *** real-world QA data sets,we evaluate the performance of the proposed QA matching *** a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%***,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.

关键词： Question-answering text classification model data augmentation text embedding

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Automated Deep Learning Based Knee Osteoarthritis Joint Extraction and Classification 17

Automated Deep Learning Based Knee Osteoarthritis Joint Extr...

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Abbas, Muhammad Sohail Jamil, Sonain Khurshid, Atif Department of Data Science and Artificial Engineering Islamabad Pakistan Department of Computer Science Gjovik2815 Norway

ISBN: (纸本)9798350381320

Knee osteoarthritis (KOA) is a widespread global condition, impacting over 300 million individuals as per the World Health Organization (WHO). Particularly prevalent among older adults, knee OA is a prominent cause of disability. Its occurrence increases with age, especially after 50, and is more frequent in women, particularly post-menopause. Several studies have been carried out so far for automated grading and classification of knee osteoarthritis (KOA), but none of them built strong foundations enough to make this system automated. This study focuses on machine-controlled knee joint extraction and grading classification with improved accuracy and performance. We used the osteoarthritis initiative (OAI) dataset of X-ray images for our study. Initially, a single-stage detector is used for joint extraction of the knee area as the X-ray images contain entire knees with both joints. Enhanced osteoarthritis feature extraction (OAFE) and osteoarthritis dimensionality reduction (OADR) blocks are used for grading classification. We have significantly improved state-of-The-Art results. We have acquired joint extraction with a mean average precision (map) of 95.3% and grading classification accuracy of 78.93%. Furthermore, the performance due to the dimensionality reduction block has improved by a huge factor. © 2023 IEEE.

关键词： Extraction

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List Sample Compression and Uniform Convergence 37

List Sample Compression and Uniform Convergence

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37th Annual Conference on Learning Theory, COLT 2024

作者： Hanneke, Steve Moran, Shay Waknine, Tom Department of Computer Science Purdue University United States Departments of Mathematics Computer Science and Data and Decision Sciences Technion Google Research Israel Department of Mathematics Technion Israel

List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical principles related to generalization within the context of list learning. Our primary goal is to determine whether classical principles in the PAC setting retain their applicability in the domain of list PAC learning. We focus on uniform convergence (which is the basis of Empirical Risk Minimization) and on sample compression (which is a powerful manifestation of Occam's Razor). In classical PAC learning, both uniform convergence and sample compression satisfy a form of 'completeness': whenever a class is learnable, it can also be learned by a learning rule that adheres to these principles. We ask whether the same completeness holds true in the list learning setting. We show that uniform convergence remains equivalent to learnability in the list PAC learning setting. In contrast, our findings reveal surprising results regarding sample compression: we prove that when the label space is Y = {0, 1, 2}, then there are 2-list-learnable classes that cannot be compressed. This refutes the list version of the sample compression conjecture by Littlestone and Warmuth (1986). We prove an even stronger impossibility result, showing that there are 2-list-learnable classes that cannot be compressed even when the reconstructed function can work with lists of arbitrarily large size. We prove a similar result for (1-list) PAC learnable classes when the label space is unbounded. This generalizes a recent result by Pabbaraju (2023). In our impossibility results on sample compression, we employ direct-sum arguments which might be of independent interest. In fact, these arguments raise natural open questions that we leave for future research. Our findings regarding uniform convergence rely on a coding theoretic perspective. © 2024 S. Hanneke, S. Moran & T. Waknine.

关键词： Contrastive Learning

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Interactive Physiotherapy Game Using React Three Fiber for Hand Motion Rehabilitation

Interactive Physiotherapy Game Using React Three Fiber for H...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Yadav, Ram Kumar Soni, Ashish Kumar Sakare, Apeksha Manipal University Department of Data Science and Engineering Jaipur India G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331529833

This article describes an interactive physical activity system designed to use React Three Fiber to assist patients with hand rehabilitation. The game simulates a virtual environment where rods controlled by motion-sensing devices represent the patient's hand. Through the "Create"function, a sphere can be created from the previous position and the patient must hold their hand firmly within the sphere's boundaries. Adjustable sliders allow optimal control of hold time and sensitivity to adapt to the patient's growth and treatment needs. The game provides immediate visual feedback, encouraging patients to practice stability and mobility, which are essential elements of good physical therapy. The app simulates real-world movements in a virtual environment, creating a controlled environment that provides immediate visual feedback to encourage patients to take action. This technology uses React Three Fiber to create a 3D environment, including writing information for precise control of the stick. This interactive model is designed to not only develop motor skills through consistent, guided exercises, but also to improve overall patient compliance. Through this application, we explore the intersection of play and physical therapy by examining the potential for digital therapy tools to complement and enhance traditional practices This approach uses the appeal of interactive games to potentially increase treatment compliance, making it an important tool in digital therapy. This article discusses the use of React Three Fiber's techniques, methods, and therapeutic potential for physical therapy. © 2025 IEEE.

关键词： Virtual environments

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Tourist Destination Recommendation System based on Machine Learning 24

Tourist Destination Recommendation System based on Machine L...

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9th International Conference on Big data and Computing, ICBDC 2024

作者： Kongpeng, Sumitra Hanskunatai, Anantaporn Data Science and Computational Intelligence Lab Department of Computer Science School of Science King Mongkut's Institute of Technology Ladkrabang Thailand

ISBN: (纸本)9798400718205

Thailand has a wide variety of tourist attractions, making it difficult for tourist to choose where to go on vacation. The tourist destination recommendation system is a challenge for creating a system to help recommend tourist destinations that are appropriate for personal. Therefore, the principal aims of this research encompass two distinct objectives: firstly, to create a recommendation system for tourist destinations in Thailand by applying machine learning algorithms;and secondly, to analyze factors influencing tourists' choices of destinations. The dataset was gathered from an online survey conducted via Google Forms, comprising responses from 429 tourists in Thailand. In the experiments, three different types of feature selection methods were applied in a data preprocessing step. In the modeling process, four machine learning algorithms, namely Decision Tree, Random Forest, k-Nearest Neighbors (k-NN), and Multi-Layer Perceptron (MLP), were used to construct the model and compare the predictive performance of the recommendation system based on hit rate and NDCG. The experimental results showed that suggesting tourist destinations in the Central region was the most effective, with the highest hit rate and NDCG compared to other regions. The average hit rate and NDCG for the five regions were 0.8 and 0.59, respectively. In addition, there has been an analysis of key factors influencing destination selection, such as activity, travel month, travel budget, and the age of tourists, to understand their impact on travel choices in each region of Thailand. © 2024 Owner/Author.

关键词： Adversarial machine learning

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Word-Diffusion: Diffusion-Based Handwritten Text Word Image Generation 27th

Word-Diffusion: Diffusion-Based Handwritten Text Word Image ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Gurav, Aniket Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031784941

Generating realistic handwritten word images that closely resemble a target style remains a challenging task in document image analysis. In recent years, deep learning techniques, such as Latent Diffusion Models (LDM), have shown promise in generating styled handwritten text. However, these models face significant challenges when creating images for ‘Out of Vocabulary’ (OOV) words, impacting their overall effectiveness. In this paper, we introduce an extended diffusion-based Handwritten generation method that incorporates a novel conditioning mechanism. It is based on the Pyramidal Histogram of Shapes (PHOS) representation, which takes into account the spatial and structural characteristics of the target handwriting style. By conditioning the diffusion model on input text, PHOS vector, and writer ID, our approach enables the generation of handwritten word images. Notably, our approach outperforms the original diffusion model, which only uses text and writer ID as conditions, in generating both in-sample and out-of-sample. Furthermore, we have developed a faster inference method that significantly reduces the number of steps required for generating the output. Through qualitative and quantitative evaluations, we demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Open problem: Direct Sums in Learning Theory 37

Open problem: Direct Sums in Learning Theory

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37th Annual Conference on Learning Theory, COLT 2024

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