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An Empirical Review for Character Recognition in Video Frames using Text Detection Algorithms 13

An Empirical Review for Character Recognition in Video Frame...

引用

13th International Conference on Cloud Computing, Data science and engineering, Confluence 2023

作者： Rajaboina, Nagendra Babu Prasad Sariki, Tulasi Kumar, Gunturu Bharadwaja Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462631

The characters in the text image or video is having more information for recovery and indexing applications. The recognition of characters from the video text images is a difficult task due to the complex backgrounds, grey-scale values, illuminations, and sizes. Hence, various text detection algorithms like feature extracting algorithms including texture and region-based, machine learning, and deep learning approaches are implemented. Optical Character Recognition (OCR) is implemented in detecting the characters from the text frames and gives the text information including the location of the frame for text in the video. The Natural Language Processing (NLP) technique results in high performance in detecting the different text languages from the video frames. Convolutional Neural Network (CNN) algorithms are used in the feature selection process for efficient recognition of text characters from the images. The deep learning algorithms are used in textual regions in the frames of the video for detecting the text. The LSTM combined CNN networks are also implemented for extracting the features from the image for efficient and high-quality output text. © 2023 IEEE.

关键词： Learning algorithms

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Enhancing the resolution of Brain MRI images using Generative Adversarial Networks (GANs)

Enhancing the resolution of Brain MRI images using Generativ...

引用

2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Ankitha, B. Srikanth, Ch. Venkatesh, D. Badrinath, D. Aditya, G. Akila Agnes, S. Computer Science and Engineering Gmr Institute of Technology Andhra Pradesh India

ISBN: (纸本)9798350323917

Generative Adversarial Networks (GANs) are an emerging technology in Artificial Intelligence (AI). They are an unsupervised learning framework that involves automatically learning patterns from input data to generate synthetic output that closely resembles the original input. GANs enable the creation of datasets by replicating copies of fake outputs based on given inputs. Using GANs, machines can generate unique content that is distinct from human-created data. GANs are widely used in various image-related areas, such as image-text conversion, photo-to-emoji conversion, face aging, new human face generation, super-resolution image conversion, and more. This paper focuses on enhancing the resolution of magnetic resonance images (MRI), which is a common method used in medical imaging. However, obtaining high-quality MRI scans often requires long waiting times and the use of lab equipment with restrictions. Additionally, there are health hazards associated with MRI radiation, resulting in lower-quality images. Super-resolution GANs (SRGANs) can potentially address this issue. Super-resolution is a method that can generate high-resolution images from lower-resolution ones. Previously, technologies such as bi-cubic interpolation, linear interpolation, and nearest neighbor interpolation were used to achieve higher resolution. However, the results obtained using the proposed SRGAN model outperform these conventional methods in terms of performance © 2023 IEEE.

关键词： Generative adversarial networks

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A Procedure of Weight Thresholding on Complex Networks 3

A Procedure of Weight Thresholding on Complex Networks

引用

3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： John, Jisha Mariyam Lekha, Divya Sindhu Indian Institute of Information Technology Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350300604

In recent years, a huge amount of data are available and can be characterized in the form of weighted networks. Analyzing real world weighted networks with large number of elements and links is still a challenging task specifically for extracting relevant features of the networks. The weight thresholding is a simple approach for sparsification of the networks. Here, we tried to define a procedure of weight thresholding based on a fixed fraction of maximum weight. © 2023 IEEE.

关键词： Complex networks

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Comparative Analysis on Solar Panel Defect Detection Using Deep Learning Approaches

Comparative Analysis on Solar Panel Defect Detection Using D...

引用

2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Raja, Viswanadhapalli Rakesh, Varanasi Kumar, Avinash Siva Sankari, S. Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350348910

Solar energy is one of the widely used renewable energy for commercial as well as house hold purposes. Even though solar energy is widely used, still one time installation change for the panel is high. The production of panel is not yet cost effective. In India, several panel manufacturers are trying to bring defect free product into the market. There are several methods adopted to make the panel defect free during manufacture as well as in working condition when installed in solar farms. Artificial Intelligence can be used to tackle the complex problems of designing solar plants and can save diverse power system issues like planning, control, scheduling, forecasting and so on. It can handle the demanding requirements that applications in current big power systems confront so as to meet increasing load demand. Solar farm owners usually used a team of employees to physically check solar panels for faults. This process is time-consuming, not cost effective and also less accurate. Maintenance visit to solar farms is very expensive and are simply not feasible to perform on daily basis for an entire solar development. The inspection process can be speedup by turning into an artificial intelligence powered inspection. It entails using algorithms to detect solar panel defects from photographs, which is quicker and more precise than human inspection. Eventhough artificial intelligence technology helps a lot to find the solar panel defect perfectly, it has some challenges that needs to be overcome. These challenges are addressed by using the deep learning methods like MobileNetV2, ResNetV1 and inceptionv3. The present paper analyses MobileNetV2, ResNetV1 and inceptionv3 for detecting solar panel defect. The simulation result shows MobileNetV2 provides better accuracy compared to others. © 2023 IEEE.

关键词： Solar energy

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Predictive Modeling for Medical Insurance Malpractice Using Random Forest and XGBoost

Predictive Modeling for Medical Insurance Malpractice Using ...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Carmel Mary Belida, M.J. Begum, Almas Alex David, S. Kannan, E. Senthil, K. Ruth Naveena, N. Saveetha Institute Of Medical And Technical Sciences Saveetha School Of Engineering Department Of Computer Science And Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India Hindustan Institute Of Technology & Science Department Of Mathematics Chennai India

ISBN: (纸本)9798350352689

The study promotes a thorough research effort to create a predictive model specifically for medical malpractice litigation, highlighting the intricate relationships between insurance and medical malpractice claims. The model that has been suggested incorporates sophisticated machine learning methods, particularly the Random Forest and XGBoost algorithms, to improve accuracy and forecasting skills. The study emphasizes the need for a comprehensive strategy that combines state-of-the-art prediction technology with a well-defined theoretical framework. The article provides a comprehensive overview of the topic by carefully examining the underlying factors that give rise to medical malpractice claims, which might range from poor recordkeeping to communication problems. It highlights the financial strain that healthcare providers and patients experience and suggest ways that insurance companies can better control risks, evaluate claims, and encourage preventive treatment. The performance of the suggested Random Forest and XGBoost models is presented in the results and comments, with XGBoost showing better predicting skills. The residual distribution and the accuracy of the models are clearly shown by the visualizations. To sum up, this study lays the groundwork for the development and application of predictive models and provides insurance companies with important information to help them negotiate the intricate terrain of medical malpractice in the healthcare industry. © 2024 IEEE.

关键词： Patient treatment

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DCNN Model for Automatic Detection of Polyps using UNet

DCNN Model for Automatic Detection of Polyps using UNet

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Devananda Rao, B. Bhavana, T. Madhu Babu, T. Neha Chouhan, L. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350348217

Colon cancer is one of the most common types of malignant cancer. It initially grows as a polyp, which is essentially an outgrown from the colon's surface. Thus, early detection of polyps and cancers is one of the major goals of endoscopy and colonoscopy. DCNN methodologies based on U-Net architecture is used, so that it could significantly improve the robustness and effectiveness of colorectal cancer screening, as well as improve segmentation, accuracy, lesion detectability, and histological characterization accuracy. Data augmentation is used to increase the size of our dataset which helps U-Net to have more information resulting in precise detection. Photos of polyps obtained during colonoscopy and endoscopy have non uniform polyp presence, uniform colour pattern, effect of reflections. This can lead to over and under segmentation. Taking these factors into account, segmentation method is proposed based on a model of a polyp's presence, which is described as a prominent shape enclosed within a polyp. The existence of edges and valleys can be used to identify an area. To improve polyp detection accuracy, post-processing methods such as morphological transformations to smooth the segmentation boundaries and combining nearby objects that originally belonged to a large polyp in the output segmentation chart. Each pixel is divided into a polyp and non-polyp instances. This enables the polyp will be identified more easily, quickly, and accurately by the clinician. Automated polyp segmentation can be beneficial in the creation of a CADx (computer-Aided Diagnosis) system. Therefore, we are putting forth an efficient U-Net strategy for DCNN design and approach for automated detection of polyps. © 2023 IEEE.

关键词： Endoscopy

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Securing Medical Image Transmission using Memetic Algorithm 2

Securing Medical Image Transmission using Memetic Algorithm

引用

2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Thangapalani, L. Dharini, R. Keerthana, R. Sri Sairam Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350315905

In recent times, medical industry faces adverse issue in terms of protecting the medical records of people all over the world. In remote areas, in case of severe health issues, if they want to contact the doctors in urban areas, the new technology has been introduced is teleradiology where you can send the data through IoT based distributed systems. Securing the medical images while transferring the medical records is an important task to maintain the privacy. Then for securing these records, we have to implement two techniques that i steganography and cryptography. These technologies act as a second layer security in the IoT based distributed systems. Here we will be using "Memetic algorithm", a bio-inspired genetic algorithm. This algorithm is used effectively to encrypt and decrypt the *** paper proposes a novel approach called "Memetic Optimization"for intensifying the security and efficiency of Medical Data(MD) transmission through cryptography. The proposed method involves using memetic algorithms to optimize the key generation process and improve the randomness of the keys used for encryption. By incorporating memetic optimization techniques into the cryptographic process, the security of MD transmission is strengthened, making it more resistant to attacks by unauthorized users. The results of our experiments show the effectual of the proposed method, and its potential for improving the security and efficiency of MD transmission. © 2023 IEEE.

关键词： Steganography

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Chinese Long Text News Summary Based on BERTSUM-BART

Chinese Long Text News Summary Based on BERTSUM-BART

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Sun, Haowen Yao, Feng School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (纸本)9781510666597

For time-sensitive and large-scale text information such as news, traditional abstract summarization methods may lead to deviation from the subject, distortion and incompleteness of information. To this end, we propose an improved scheme based on automatic text summarization technology, which combines the characteristics of BERTSUM(BERT-based Text Summarization) and BART(Bidirectional and Auto-Regressive Transformers)models. Specifically, we input the same text information into the BERTSUM and BART models respectively, and obtain the key sentence granules of relevant text information through the BERTSUM model and the summary text processed by the BART model, and combine the processing results of the two models into a mixed data Set, and generate a summary through the BART model again to get the final summary result. The effectiveness of the BERTSUM-BART method was verified through experiments based on news long text data. The average recall rate of ROUGE-1, ROUGE-2 and ROUGE-L reached 39.83, 18.05 and 36.46. © 2023 SPIE.

关键词： Text processing

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Human Stress Detection in and through Sleep - A Deep Learning Approach

Human Stress Detection in and through Sleep - A Deep Learnin...

引用

2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Altaf, Hunain Yadav, Pramod Kumar National Institute of Technology Department of Computer Science and Engineering Srinagar India

ISBN: (纸本)9798350319088

Stress refers to the man's ability to respond to any external or internal threat or pressure and has a direct or indirect impact on one's health. Many health-related disorders are linked to stress and thus monitoring, measuring, and managing stress is simply a lifesaving remedy. Due to the automatic ability of deep neural networks to learn features, this study is proposed based on the four-layer Feed Forward Neural Network (4-FFNN) model for the detection and classification of physiological signals obtained from a smart yoga pillow (SaYoPillow) into five distinct classes: namely: normal, medium low, medium, medium high, high. The proposed FFNN model consists of an input layer to accept 8 distinct features followed by 2 hidden layers back-to-back and lastly an output SoftMax layer of 5 neurons. The accuracy of the proposed stress detection model is compared with a few machine learning models as well as other stress detection-based studies. The average accuracy of 99.9% is certainly better than the traditional approaches for detecting stress and the usage of the smart pillow makes the model more robust and easier to use. © 2023 IEEE.

关键词： Stresses

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A Novel Function Fusion Approach for Serverless Cold Start

A Novel Function Fusion Approach for Serverless Cold Start

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2023 International Conference on Communication, Circuits, and Systems, IC3S 2023

作者： Tirkey, Kanchan Kumari, Anisha Mohanty, Sagarika Sahoo, Prof. Bibhudatta National Institute of Technology Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350325904

Serverless computing is a new cloud technology that is popular due to its fast reaction time and scale-to-zero features, where users are only charged for the actual quantity of resource utilization and the code can work closer to the users to minimize the delay. The code that can run independently on a different container serves as the processing element in serverless computing. A container needs to be set up and prepared each time a request for function implementation is made. This is referred to as a "cold start,"and it takes some time before the actual execution *** cold start issue substantially impacts the overall response time of a workflow that consists of functions because it can happen in any of the functions. One method for reducing a workflow's cold start delay is function fusion. When two functions are combined to form one function, the cold start of the second function is removed. However, if parallel functions are fused, the workflow reaction time can be increased because the parallel functions run sequentially even though the second function's cold start is eliminated. This study provides a method for using function fusion and taking into account a parallel run to reduce the cold start latency of a workflow. We first identify the three latencies that have an impact on response time, then we show a workflow response time model that takes the latency into account. Finally, we effectively locate a fusion solution that can improve response time on a cold start. © 2023 IEEE.

关键词： Containers

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