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Identifying Hand Pose Used in Sign Language Using Key-Point and Transfer learning Technique 1st

Identifying Hand Pose Used in Sign Language Using Key-Point ...

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1st international conference on Intelligent Computing and Big Data Analytics, ICICBDA 2024

作者： Ingoley, Shilpa N. Bakal, Jagdish W. Thadomal Shahani Engineering College Bandra Mumbai India Pillai HOC College of Engineering and Technology Rasayani India

ISBN: (纸本)9783031747007

Sign language recognition is now possible because of the advancements in processors, computer vision, image processing, machine and deep learning techniques. This development has made it feasible to interpret sign languages effectively by using cutting-edge technology. It has made communication possible for those with speech and hearing impairments. This will help them improve the quality of their lives when they need to connect with rest of the world. For the said cause, we have suggested a methodology. To extract the features, hand key points and transfer learning techniques with vgg16 model have been used. To prove its efficacy, we worked on three different datasets: (i) NUS hand posture datasets II (ii) ISL dataset from Kaggle and (iii) our own ISL dataset created with the help of multiple users in diverse backgrounds. Our methodology works efficiently on all three datasets and exhibits validation accuracy of 99.12, 99.89 and 99.78 respectively. Result demonstrates that it works effectively with various images and mentioned diverse datasets. Hence, we feel this approach is generalizable and can be effectively useful with different sign languages. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

PhD Symposium, Posters, Demos, and A Web for more inclusive,...

引用

PhD Symposium, Posters, Demos, and A Web for more inclusive, sustainable and prosperous societies, WEB-for-GOOD 2024 and 1st international Workshop on AI and Web Data Analytics, AIWDA 2024 form the 25th international conference on Web Information Systems Engineering, WISE 2024

ISBN: (纸本)9789819614820

The proceedings contain 36 papers. The special focus in this conference is on Web Information Systems Engineering. The topics include: Effective Transparent Monitoring of Personal Data;iCNN-LstM: An Incremental CNN-LstM Based Ransomware Detection System;a Mini Review on Purchaser Security in the Metaverse: Challenges and Solutions;low-Resource Dataset Synthetic Generation for Hate Speech Detection;Web Open Data to SDG Indicators: Towards an LLM-Augmented Knowledge Graph Solution;leveraging Sentence-Transformers to Overcome Query-Document Vocabulary Mismatch in Information Retrieval;time Distance Aware for Multi-component Graph Collaborative Filtering;scientific Documents Recommendation Based on Graph Convolutional Network;semantic Communication of images Using image Generation and image Captioning Models;leveraging Optimization Techniques for Effective Arabic Query Expansion;DURLLCON: Deep Reinforcement learning for URLLC Optimization in Multi-edge Networks;FMM-RNS: A Fast HMM Map Matching Method Based on Road Network Simplification;Context-Aware Selection of machine learning as a Service (MLaaS) in IoT Environments;GraphTFD: A Fraud Detection System Based on Graph Transformer;MLGE-AC-UFD: Multi-level Graph Embedding and Approximate Computation for Unsupervised Fraud Detection;SeCORE: Quantitative Security Assurance and Evaluation Platform;developing Geospatial Web Applications Using Question Answering Engines, Knowledge Graphs and Linked Data Tools;A Visual Query Builder for DBpedia;FL-PPELA: Partial Parameter Enhancement and Local Adaptive Aggregation for Personalized Federated learning;ORCPM: An Online Regional Core pattern Mining System;MTRM: A Web-Miner Multi-Threshold Mining Co-location patterns to Mitigate Redundancy;constrained Path Optimization on Time-Dependent Road Networks;prompt strategies for Sarcastic Meme Detection: A Comparative Analysis;Deepfake Detection in Cancer Medical Imaging Using CNN Architectures;strengthening Cybersecurity: The Influence o

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Lung Cancer Detection: A Pioneering machine learning Approach for Predictive Precision 1

Lung Cancer Detection: A Pioneering Machine Learning Approac...

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1st IEEE international conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Gusain, Raj Dhami, Kavindra Singh Shekhar, Sushant Vidyarthi, Anurag Prakash, Rishi Gowri, R. University Department of Electronics & communication Engineering Dehradun India University Department of Civil Engineering Dehradun India Graphic Era Hill University Department of Computer Science Dehradun India

ISBN: (纸本)9798350377002

Lung Cancer has become serious health problem which affects all age groups from children to old age persons. An early-stage prognosis and diagnosis of lung cancer can save many humans from major causalities. X-rays, Ultrasounds and CT (Computerized Tomography) are further diagnostic tools. The goal of computer science, often known as machine learning (ML), is to develop algorithms that let computers pick up new practices from examples seen in the real world. There exist several machine learning methods which are used to categorize lung tumors which finally develop into lung cancer. Precision, Recall and F1 Score parameters are used for testing the model's capability. Using a test dataset of 200 epochs, the CNN system obtains a high accuracy of approx. 98.87% in identifying normal and cancerous cells. This study examines the use of optimized feature selection and image processing in the identification of initial phase of lung cancer. The rigorous evaluation is done on large dataset while taking in consideration healthy and malignant cells. The study includes training, pre-processing, model building and performance analysis of deep learning algorithm which gives better results in case of forecasting and curing lung cancer at the initial stage so that risk of causalities can be minimized as compared with conventional techniques. This paper also examines the benefits of machine learning techniques in healthcare, cancer prognosis, and detection. Many researchers have concentrated on creating cancer prediction systems that incorporate classification algorithms for accurate results and use supervised learning approaches in machine learning. The importance of deep learning and related algorithms in the healthcare industry is emphasized. The research recommends enhancing and expanding the lung cancer system through deep learning techniques in order to improve the accuracy of both lung cancer identification and prediction. With a view to implementing deep learning techniques

关键词： Lung cancer

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Automated Early Diagnosis of Lung Tumor Based on Deep learning Algorithms 1

Automated Early Diagnosis of Lung Tumor Based on Deep Learni...

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1st IEEE international conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Maheswari, M. Jothi, B. Devi, P. Gayathri Chitradevi, B. Pradeep, Kumar S SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Panimalar Engineering College Department of ECE Chennai India Department of Computer Science Engineering Perambalur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The main goals of this study are to identify the early stages of lung cancer and investigate the degree of accuracy of various machine learning algorithms. A thorough review of the literature revealed that certain classifiers have low accuracy while others have higher accuracy but are challenging to approach nearer to 100%. Low accuracy and high implementation costs as a result of inappropriate DICOM image handling Many various types of images are utilised in medical image processing, however CT scans are typically chosen since they have less noise. Deep learning has been shown to be the most effective technique for processing medical images, identifying and classifying lung nodules, extracting features, and predicting the stage of lung cancer. This system's initial stage employed image processing methods to separate lung sections. Using K Means, the segmentation is carried out. Segmented images are used to extract the features, which are then classified using a variety of machine learning algorithms. The accuracy, sensitivity, and specificity of the proposed techniques, as well as their classification time, are used to assess their performance. © 2023 IEEE.

关键词： Computerized tomography

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Feature learning by Least Generalization 1

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30th international conference on Inductive Logic Programming (ILP) held as part of the 1st international Joint conference on learning and Reasoning (IJCLR)

作者： Nguyen, Hien D. Sakama, Chiaki Univ Informat Technol Ho Chi Minh City Vietnam Vietnam Natl Univ Ho Chi Minh City Vietnam Wakayama Univ Wakayama Japan

ISBN: (数字)9783030974541

ISBN: (纸本)9783030974541;9783030974534

This paper provides an empirical study for feature learning based on induction. We encode image data into first-order expressions and compute their least generalization. An interesting question is whether the least generalization can extract a common pattern of input data. We introduce three different methods for feature extraction based on symbolic manipulation. We perform experiments using the MNIst datasets and show that the proposed methods successfully capture features from training data and classify test data in around 90% accuracies. The results of this paper show potentials of induction and symbolic reasoning to feature learning or pattern recognition from raw data.

关键词： Feature learning Least generalization Interpretable machine learning

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Retinotopic image Encoding by Samples of Counts 10th

Retinotopic Image Encoding by Samples of Counts

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11th international conference on pattern recognition Applications and Methods (ICPRAM)

作者： Antsiperov, Viacheslav Kershner, Vladislav RAS Kotelnikov Inst Radioengn & Elect Mokhovaya 11-7 Moscow 125009 Russia

ISBN: (纸本)9783031245374;9783031245381

The article is devoted to the synthesis of image encoding methods based on the images data themselves. The proposed approach is based on a previously developed special representation of images by samples of counts (sampling representations). Since the sampling representations are essentially random constructions, the synthesis of encoding methods is carried out strictly within the framework of the generative paradigm. In essence, the approach proposed treats the image coding within generative model as a special case of the classical statistical problem of probability distribution density estimation. In the paper we restrict ourselves to the class of parametric estimation procedures, which imply some parametric family of probability distributions. Namely, we propose to use the model of a parametric mixture of simple distribution components. Accordingly, a set of component weights estimates calculated from a sampling representation, considering as input data, is interpreted as an encoded image - output data. In this context, optimal coding is synthesized with the maximum likelihood method. For the algorithmic implementation of the coding procedure the mixture model is equipped with the structure of receptive fields, that is a well-known organizing principal for receptors in the human eye retina. On this basis, we synthesize a relatively simple recurrent coding algorithm, which turned out to be close to the popular in machine learning EM algorithm. The paper presents interpretation of several features of the algorithm from the point of view of well-known facts about the image processing in the periphery of the visual system, discusses options for the algorithm implementation, and presents the results of numerical simulation of its operation.

关键词： Perceptual image coding Generative models machine learning

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Various Active learning strategies Analysis in image Labeling: Maximizing Performance with Minimum Labeled Data 6th

Various Active Learning Strategies Analysis in Image Labelin...

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6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Tyagi, Arnav Aditya, Harshvardhan Shelke, Nitin Arvind Khandelwal, Rishabh Singh, Jagendra Jadeja, Yagna Turukmane, Anil V. School of Computer Science Engineering and Technology Bennett University Greater Noida India College of Science and Engineering University of Derby Derby United Kingdom School of Computer Science and Engineering VIT - AP University Amaravati India

ISBN: (纸本)9783031530814

The use of active learning in supervised machine learning is proposed in this study to reduce the expenses associated with labeling data. Active learning is a technique that includes iteratively selecting the most informative unlabeled data points and asking a human expert to label them. Active learning can achieve high accuracy while utilizing fewer labeled examples than typical supervised learning algorithms by selecting the most informative data points. This study conducts and provides an in-depth examination and analysis of numerous active learning algorithms and their applications to various machine learning labeling problems, especially focusing on image classification. The experiments are carried out using Fashion MNIst as a benchmark dataset. This study compares the performance of five popular active learning methods BALD, DBAL, coreset, least confidence and ensemble varR for the given problem. The best performing algorithm was BALD with a mean classification accuracy of 91.31%, when 50% of the data is considered labeled, closely followed by all other techniques, making each suitable for specific use cases. The trials conducted by the study illustrates how active learning may lower the time and cost of data labeling while also maintaining high accuracy. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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Smart Diet Management Through Food image and Cooking Recipe Analysis 21st

Smart Diet Management Through Food Image and Cooking Recipe ...

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21st international conference on image Analysis and processing (ICIAP)

作者： He, Yinchao Hakguder, Zeynep Shi, Xu Univ Calif San Diego La Jolla CA 92093 USA Prorize LLC Marietta GA 30062 USA Univ Nebraska Lincoln NE 68508 USA

ISBN: (纸本)9783031133213;9783031133206

Food monitoring has become an indispensable practice for personal health management in increasingly growing populations. To facilitate this process, advanced image processing and AI technology have empowered automated recognition of food items and nutrients using food images taken by smart mobile devices. However, precision is often compromised for convenience, which is also applicable in food logging. In this study, we have explored new solutions that can help improve food recognition accuracy with a particular focus on domestic cooking, by leveraging advanced machine learning and natural language processing techniques, in conjunction with comprehensive food nutrient profiles in the knowledge base, as well as contextual ingredient information parsed from publicly available recipes. The optimized models were proved to be effective and have been integrated into an Android app named "FoodInsight" .

关键词： image recognition Recipe analysis Transfer deep learning Natural language processing Recipe recommendation Food intake monitoring

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Fusion of Multi-Level CNN With LBP Features For Facial Emotion recognition 31

Fusion of Multi-Level CNN With LBP Features For Facial Emoti...

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31st international conference on Electrical Engineering, ICEE 2023

作者： Bahmanabady, Ehsan Imani, Maryam Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350312560

A facial emotion recognition framework is proposed in this work. The convolutional neural network (CNN) has high ability in extraction of hierarchical spatial features from low level texture characteristics to high level contextual features. A simple CNN model with three layers is suggested in this paper where the features extracted in all layers containing multi-level features are activated and used for classification. In addition, the local binary pattern (LBP) descriptor is used to extract discriminative features from the spatial structure of the input image. Therefore, four feature sources are provided by multi levels of CNN and the LBP descriptor. Each feature source is used for facial emotion recognition by applying to the support vector machine (SVM) classier. Finally, the majority voting rule is used for decision fusion to provide the final emotional label of each given face image. The proposed method with 84% overall accuracy, 83% weighted F1-score and 81% kappa coefficient provides the best performance compared to LBP, multi-level CNN and two-dimensional principal component analysis (2DPCA) methods. © 2023 IEEE.

关键词： Emotion recognition

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Rescaled Hinge Loss Function based Convolutional Neural Network for Identification of Art Forgery by image Analysis 1

Rescaled Hinge Loss Function based Convolutional Neural Netw...

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1st international conference on Software, Systems and Information Technology, SSITCON 2024

作者： Sundravadivelu, K. Karthika, S. Al-Fatlawy, Ramy Riad Monisha, Jothi R. Sundari, Mandapati Venkata Rama Madurai Kamaraj University School of Information Technology Department of Computer Science Madurai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Hyderabad India

ISBN: (纸本)9798350352931

The rapid growth of Information Technology (IT) as well as Computer Science becomes an important evolution in marketable painting applications. The systematic authentication of paintings embraces the implication into the monarchy of art gathering. Performing the Convolutional Neural Network (CNN) approach for the identification of the reliable as well as forged painting arts according to the colour images is a feasible but trivial selection. However, this is challenging due to variations in artistic styles, textures, and subtle details which ambiguous forgery detection. Hence, this research proposes the Rescaled Hinge Loss Function based CNN (RHLF-CNN) approach for the identification of art forgery. The RHLF improves the model's capability to discriminate between genuine and forged artworks, leads to more precise classification. The high-definition and hyperspectral image dataset of Chinese characters is utilized to estimate the effectiveness of the RHLF-CNN approach. The Local Binary pattern (LBP) approach is used for the feature extraction process. The Chi-square approach is utilized for extracting the relevant features from the extraction process. The experimental results demonstrates that the proposed RHLF-CNN approach attains the better accuracy of 93.16% as compared to the existing approaches like Pulse-Coupled Neural Network (PCNN). © 2024 IEEE.

关键词： Convolution

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