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Text Segmentation and Transliteration Using Deep learning 1

Text Segmentation and Transliteration Using Deep Learning

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Meena, P. Gnanasudharsan, A. Sreevarshan, S. Rithika, S.R. Guru Raghavendra, S. Umamaheswaran, S.K. Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350316469

Reading text from images is crucial for understanding image content. This can range from scanned documents with predominant text, video frames with captions, or photos with naturally occurring text. Recognizing text in images can be used in various ways, such as indexing multimedia, assisting drivers by recognizing signs, aiding visually impaired individuals with scene descriptions, or identifying vehicles via license plates. The surge in affordable digital cameras and high-quality mobile phone cameras has intensified the focus on extracting text from photographs in the field of computer vision. While there are many OCR (Optical Character recognition) systems, the preliminary step is to segment the text from the image. This project aims to extract and segment text from natural images, process it through an OCR, and enhance its usability through transliteration. It aims to enhance image-based text recognition by segmenting text from natural images, processing it with OCR, and amplifying its utility through transliteration, catering to diverse applications like multimedia indexing and aiding the visually impaired. © 2023 IEEE.

关键词： image Indexing OCR (Optical Character recognition) Text detection Text extraction Transliteration

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Comprehensive Review of Smart Healthcare Recommendation Systems Using Deep learning 1

Comprehensive Review of Smart Healthcare Recommendation Syst...

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1st international conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Bouslah, Soumeya Benharzallah, Saber Hamouma, Moumen Kahloul, Laid University of Batna 2 Lamie Laboratory Departement of Computer Science Batna Algeria University of Biskra Linfi Laboratory Departement of Computer Science Biskra Algeria

ISBN: (纸本)9798331541064

As smart cities advance and increasingly use data from Internet of Things (IoT) sources to enhance urban environments, smart healthcare has become a key focus area. This paper investigates the integration of Deep learning (DL) techniques in Smart Healthcare Recommendation Systems (SHRSs), presenting a foundational taxonomy, that categorizes these systems into four distinct types: Content-Based Filtering (CBF), Collaborative Filtering (CF), Context-Aware Filtering (CAF), and Hybrid Filtering (HF). These approaches, implemented through DL, enable advanced data processing, pattern recognition, and personalization. Additionally, the paper introduces a new classification based on the nature of intervention, dividing SHRSs into preventive, diagnostic, treatment-focused, and hybrid categories. This dual classification fills a critical gap in the comparative analysis of DL-driven healthcare, which demonstrates the efficacy of these systems in providing personalized health advice, leveraging real-time sensor data and optimizing the handling of complex health datasets. Furthermore, this research outlines future work and potential directions for advancing SHRSs, offering insights into the application and impact of sophisticated recommendation techniques within the context of urban health management. © 2024 IEEE.

关键词： Electronic health record

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Federated learning for Lung Sound Analysis 5th

Federated Learning for Lung Sound Analysis

引用

5th international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Farjana, Afia Makkar, Aaisha Univ South Dakota Dept Comp Sci Appl Res Lab 414 E Clark St Vermillion SD 57069 USA

ISBN: (纸本)9783031235986;9783031235993

Despite the general success of employing artificial intelligence (AI) to help radiologists perform computer-aided patient diagnosis, creating good models with tiny datasets at different sites is still tough. Medical image analysis is crucial for the quick and precise detection of lung disease and helps clinicians treat patients effectively while averting more fatalities. This is why real-time medical data management is becoming essential in the healthcare industry, especially for systems that monitor patients from a distance. To overcome this challenge, we propose a different approach by utilizing a relatively new learning framework. Individual sites may jointly train a global model using this approach, referred to as federated learning. Without explicitly sharing datasets, federated learning combines training results from various sites to produce a global model. This makes sure that patient confidentiality is upheld across all sites. Additionally, the additional supervision gained from partner sites' results enhances the global model's overall detection capabilities. This study's primary goal is to determine how the federated learning (FL) approachmay offer amachine learning average model that is robust, accurate, and unbiased in detecting lung disorders. For this aim, we analyze 325 Lung Sound audio recordings collected from https://***/and, transform this audio signal into Melspectrograms. Once the labeling and preprocessing steps were carried out, a Convolutional Neural Network (CNN)(FederatedNet) model was used to classify the respiratory sounds into healthy and unhealthy. We achieved the result of almost 88% validation accuracy. Furthermore, this paper discusses the application of FL and its overview. Lastly, we discuss the main challenges to federated learning adoption and potential future benefits.

关键词： Federated learning Lung sound machine learning

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Maxillary Sinus Disease Detection and Analysis Approaches in Deep learning: Survey 1

Maxillary Sinus Disease Detection and Analysis Approaches in...

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1st IEEE international conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Vimala, R. Preethi, D.M.D. Government College of Engineering Department of Computer Science and Engineering Bodinayakkanur Theni India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350372748

The intend of this literature survey is to lessen the problems faced by dentists in the field of maxillary sinus diagnosis in image processing and to serve as a valuable reference to the literature related application. The odontogenic diseases may be diagnosed with atypical symptoms or it might mimic other conditions. This can create difficulty to disembark an accurate diagnosis. Sinusitis or temporomandibular joint disorder possibly is a symptom that resemble an odontogenic infection. Some odontogenic diseases may have overlapping symptoms, making it difficult to differentiate between them when based solely on clinical presentation. For instance, both a periapical abscess and a periodontal abscess can cause localized pain, swelling, and sensitivity. Diagnosing maxillary sinus issues through digital imaging, such as panoramic dental Xray, Cone Beam Computed Tomography (CBCT) and Computed Tomography (CT) scans, can be challenging due to the complex anatomy and the potential for overlapping structures. Radiologists utilize assorted computerized methods for maxillary sinus disease detection. CT scan analysis uses algorithms for segmentation and feature extraction, aiding machine learning algorithms in pattern recognition. CBCT provides detailed three-dimensional images, enabling comprehensive assessments of maxillary sinus anatomy and pathology. MRI utilizes signal intensity variations and texture analysis to identify potential diseases. Moreover, the integration of ultrasound, analysis of endoscopic video, and reporting of automated systems utilizing techniques of deep learning such as Convolutional Neural Networks and Recurrent Neural Networks, enhances precise detection by combining information from various imaging modalities. Interpreting dental radiographs can be complex, and certain conditions may not be clearly visible or may appear differently on different imaging modalities. It requires expertise and experience to accurately interpret radiographic findings and

关键词： Diseases

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A survey on machine learning Methods used for Classification of Cardiovascular disease using ECG and image processing Techniques 1

A survey on Machine Learning Methods used for Classification...

引用

1st IEEE international conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Upamanyu, M.S. Chakki, Karun Kulkarni, Varun P. Matthew, A. Seshikala, G. School of electronics and Communication REVA University Bangalore India

ISBN: (纸本)9798350371802

This research explores critical research gaps in cardiovascular disease detection and prediction, emphasizing the urgent need for further investigation. The identified gaps encompass interpretability issues in machine learning models, insufficient comparisons between contemporary and traditional diagnostic methods, and a lack of attention to generalization performance across diverse patient populations. The review underscores the importance of addressing gaps in model interpretability, real-world challenges of Convolutional Neural Network algorithms, and the exploration of optimal feature sets for predictive modelling. Recognizing limitations such as small datasets and the opaque nature of certain models, the imperative for future studies to prioritize explainable AI frameworks and address broader challenges in data overfitting, cost-effectiveness, and model bias is emphasized. The research advocates for the exploration of alternative algorithms to enhance accuracy and interpretability in cardiovascular disease detection and prediction. © 2024 IEEE.

关键词： Electrocardiograms

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A machine learning Approach to image Inpainting 1

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14th international conference on Soft Computing and pattern recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Naveenkumar, T. Kulkarni, Harsha Kulkarni, Makrand Raghavendra, H. Guru Jayashree, R. Department of CSE PES University Bengaluru India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Although image inpainting, or the art of restoring old and degraded photographs/images, has been around for a long time, it has lately acquired popularity as a consequence of technical advancements in image processing technologies. Because of the developments in image processing tools and the versatility of picture editing, automatic image inpainting has become a significant and demanding subject in image processing analysis, with important applications in computer vision. The goal of this article is to develop a hybrid inpainting model that could outperform existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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End-to-End Hyperspectral image Classification Using Hybrid GCNN with SWIN Transformer 1

End-to-End Hyperspectral Image Classification Using Hybrid G...

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1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Ghotekar, Rahul Shaw, Kailash Rout, Minakhi Kiit University Department of Computer Science and Engineering Odisha India Department of Artificial Intelligence and Machine Learning Maharashtra Pune Lavale India

ISBN: (纸本)9798350316469

Hyperspectral image (HSI) classification holds immense significance in remote sensing applications like land cover mapping and environmental monitoring. This study introduces Graph Convolutional Neural Networks (GCNN) with the SWIN Transformer model (G_SwTr) to enhance HSI classification accuracy. GCNNs are utilized to incorporate spatial information through graph representations, capturing spectral-spatial dependencies. Graph convolutions, effectively harnesses spectral and spatial attributes, further the integration of the SWIN Transformer, recognized for its proficiency in capturing long-range dependencies via shifted windows and hierarchical processing, elevates feature extraction and contextual understanding. The Proposed G_SwTr model is evaluated across three HSI datasets (Pavia-University, Indian-Pines, and Salinas) and is compared against traditional machine learning and Deep learning methods. The results achieved outperform state-of-art methods, with an outstanding Overall Accuracy (O.A) of 99.7, 99.9, and 99.5, an average accuracy (A.A) of 99.5, 99.6, and 98.2, and a kappa coefficient (K) of 99.5, 99.6, and 98.3 for all three datasets. © 2023 IEEE.

关键词： Deep learning Graph Convolution Neural Network Hybrid Model Hyperspectral image Classification SWIN Transformer

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A Transformer Based Indian Signed Language recognition 1

A Transformer Based Indian Signed Language Recognition

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Saproo, Varun Aggarwal, R.K. National Institute of Technology Kurukshetra Dept. of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350365016

Effective communication is essential to human interaction. The people with hearing and speaking disability primarily rely on Signed Language for communication. Each Sign Language has its own grammar and vocabulary. Individuals who can speak and hear proficiently often struggles to understand and respond to Sign Language gestures, creating a barrier to meaningful communication. This paper attempts to bridge the communication gap by using sophisticated deep learning models like transformers. INCLUDE is a dataset tailored for the Indian population, focusing specifically on Indian Sign Language. Our work involves breaking down videos into frames and employing image processing techniques. Furthermore, we utilize pre-trained models, such as VGG16, EfficientNet, and MobileNet, to extract latent embedding, which are then processed through Transformer network for classification. Hyperparameter tuning is used to optimize model performance. This allowed us to reach an accuracy of 92 % which is better than existing approaches. The proposed method was also tested on LSA64 dataset giving an impressive accuracy of 99.34%. © 2024 IEEE.

关键词： Deep learning

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Enhanced Camouflage Detection Using Advanced image processing Techniques for Real-Time Object recognition and pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy image processing Surveillance image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Design of a Multisensor System for a Smart Cooking Assistant 1

Design of a Multisensor System for a Smart Cooking Assistant

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1st IEEE international conference on Artificial Intelligence for Medicine, Health and Care, AIMHC 2024

作者： Ruiz, Gustavo George, Kiran Kilambi, Sai Chaitanya Soni, Pratishtha Panangadan, Anand Electrical and Computer Engineering Department California State University Fullerton FullertonCA92831 United States Computer Science Department California State University Fullerton FullertonCA92831 United States

ISBN: (纸本)9798350371987

Homeless people and people who have only recently escaped homelessness are at risk of being malnourished due to several factors. Personalized cooking recipe recommendation systems are one technology-based approach to provide information to such populations that can assist them with making more informed choices about their food. Current recipe recommendation systems do not provide any guidance on how to cook for a person who has limited experience with cooking. This work describes a system that acts as a smart cooking assistant. The main idea is for the system to observe the user perform the steps of cooking based on a recipe, and then provide automatic reminders on when to move to the next step. The system consists of both hardware and machine learning-based software components. The hardware consists of a camera, infrared thermal camera, and temperature sensor. These are integrated around a Raspberry Pi mini-computer. This suite of sensors is mounted over a stovetop and constantly monitors the cooking area, specifically the area where a cooking pot is over the stovetop. Convolutional Neural Network-based image processing algorithms are used to analyze the sequence of images from the cameras to identify the stage of cooking that the user is performing. ©2024 IEEE.

关键词： machine learning

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