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Gui that Detects Liver Disease Using Machine Learning

Gui that Detects Liver Disease Using Machine Learning

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2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Ashok Kumar, K. Sri Rama Subrahmanyam, K. Srinivasulu Reddy, M. Sathyabama Institute of Scienceand Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309980

The liver is an important organ because it produces vitamins & substances necessary for the survival of the decaying body, including iron, toxins, proteins, & carbs. It aids in vitamin and mineral metabolism, tubular muscle growth, and blood coagulation. Factors including alcohol intake, opioid painkiller usage, poor nutrition, and underlying medical disorders have all been linked to the development of liver disease. The results of a patient's blood tests and physical exam may help physicians determine risk factors for liver disease. Data mining methods may help speed up the process of identifying liver illness. When more information is utilized, the vaccination becomes more precise. This design aims to address this issue by incorporating a method of device mastery into the process of evaluating liver disease. The primary motive of this system is to apply a wide angles set of concepts to perceive healthy folks that are honestly affected by liver disease. The design of this system also aims to evaluate and contrast different bracketing algorithms according to their operational characteristics. A Python-based graphical interface for Stoner is now being development to aid clinicians in the assessment of liver issues. The user interface may be used by soothsayers and medical interpreters as a web application for liver trials. © 2023 IEEE.

关键词： Blood

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Brain Tumor Detection Using Convolutional Neural Network 5

Brain Tumor Detection Using Convolutional Neural Network

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2023

作者： Uke, Shailaja Kaulwar, Mayuresh Kawtikwar, Yash Khadse, Samyak Khandare, Anurag Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798350338287

Tumor is the abnormal development of cells in the body. The fairly significant overgrowth of brain cells is known as a brain tumor and classification manually takes time and is only possible at a few diagnostic facilities. Therefore, it is necessary to create a system that can identify the type of brain tumor based solely on the input MR images. To evaluate these malignancies, a common imaging approach is magnetic resonance imaging (MRI), however, manual segmentation cannot be done in a timely manner due to the volume of data generated by MRI, restricting the application of exact quantitative measurements in clinical practice. This study presents a novel hybrid methodology for the classification of brain cancers based on the analysis of magnetic resonance imaging (MRI) data. The methodology compares the performance of different classifiers, namely Convolutional Neural Networks (CNNs), K-nearest neighbor, Support Vector Machine and Logistic Regression in predicting the presence of brain tumors. The CNN model is specifically employed to get features from the MRI data and apply them to the classifiers. The results of the study show that the different CNN models, along with KNN, SVM, LR, and Long Short-Term Memory Networks were taken for tumor classification. The achieved accuracies were 82.35%, 78.43%, 61.26% and 74.28% respectively. Among these algorithms, KNN demonstrated the highest accuracy, indicating its effectiveness in accurately classifying brain tumors based on the MRI data. By integrating these classifiers and utilizing the CNN model for feature extraction, this hybrid methodology presents a promising method for classifying brain tumors from MRI data. The findings highlight the potential of combining deep learning techniques like CNNs with traditional machine learning algorithms to increase the reliability of diagnosing brain tumors and contribute to the field of medical image analysis. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Data Quality Driven Design Patterns for Internet of Things 9th

Data Quality Driven Design Patterns for Internet of Things

引用

9th International Symposium on Applied Computing for Software and Smart systems, ACSS 2022

作者： Rath, Chouhan Kumar Mandal, Amit Kr Sarkar, Anirban Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India Department of Computer Science and Engineering SRM University AP Amaravati India

ISBN: (纸本)9789811967900

Many IoT applications are now using microservices design concepts and have developed as an emergent technology by leveraging containerization, modularity, autonomous deployment and loose coupling. The requirement of different software design patterns is essential to aid in the creation of scalable, interoperable and reusable solutions. In IoT systems and software development, several IoT patterns, such as IoT design patterns and IoT architectural patterns, have been studied. But, most of the studied design patterns are domain-specific, and they do not consider the impact of data quality in the design process. Also, in IoT environment data quality plays an important role while processing the data to produce accurate and timely decisions. Therefore, this paper presents a formal approach to incorporate the data quality dimensions in design patterns for the microservice based IoT applications. Here, data quality evaluation parameters are integrated with various microservice design patterns suitable to IoT applications such as event sourcing pattern, chained microservice pattern, API gateway pattern etc. to ensure the effective data communication and high-quality services provided by the IoT applications. Further, the proposed quality driven design patterns are systematically defined using Event-B language and validated through Rodin platform. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Robust Prediction of Alcoholism from EEG Signals Using Auto-Encoder 14

Robust Prediction of Alcoholism from EEG Signals Using Auto-...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Aarthi, B. Kulkarni, Raj Sanjay Chandra Kiran, K. Vinod, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The diagnosis of alcoholism (substance abuse) is crucial due to its impact on individuals, society, and healthcare systems worldwide. Electroencephalography (EEG) signals have been shown to contain valuable information about the effects of alcoholism on the brain, and can potentially be used as a non-invasive tool for early detection and monitoring. In this study, we proposed an auto-encoder model for predicting alcoholism from EEG signals, which achieved robust and accurate performance even in the presence of noise and variability in the data. We evaluated the model using multiple classification algorithms, including Logistic Regression, Random Forest Classifier, AdaBoost Classifier, and AutoEncoders. The auto-encoder model (consisting of Bi-LSTMs and ReLU, Sigmoid activation for Dense and output layers respectively) outperformed the other algorithms, achieving an accuracy of 65.5% on the test set, while Logistic Regression achieved an accuracy of 55%, Random Forest Classifier achieved 58%, and AdaBoost Classifier achieved 58.4%. These results demonstrate the effectiveness of auto-encoder models for biomedical signal processing and provide a promising avenue for future research in this area, including the investigation of more complex architectures and feature engineering techniques, and the use of larger and more diverse datasets for training and evaluation. This research contributes to the fields of biomedical signal processing, neurophysiology, and neuroimaging, offering insights into brain activity related to alcoholism and highlighting the importance of non-invasive diagnosis and early detection in tackling alcoholism disorders and their societal impact. © 2023 IEEE.

关键词： Long short-term memory

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Optimizing Super-Resolution Generative Adversarial Networks 1

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Jain, Vivek Annappa, B. Dodia, Shubham Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

ISBN: (数字)9789819932504

ISBN: (纸本)9789819932498

Image super-resolution is an ill-posed problem because many possible high-resolution solutions exist for a single low resolution (LR) image. There are traditional methods to solve this problem, they are fast and straightforward, but they fail when the scale factor is high or there is noise in the data. With the development of machine learning algorithms, their application in this field is studied, and they perform better than traditional methods. Many Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) have been developed for this problem. The Super-Resolution Generative Adversarial Networks (SRGAN) have proved to be significant in this area. Although the SRGAN produces good results with 4 upscaling, it has some shortcomings. This paper proposes an improved version of SRGAN with reduced computational complexity and training time. The proposed model achieved an PPSNR of 29.72 and SSIM value of 0.86. The proposed work outperforms most of the recently developed systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Generative adversarial networks

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Antarlekhaka: A Comprehensive Tool for Multi-task Natural Language Annotation 3

Antarlekhaka: A Comprehensive Tool for Multi-task Natural La...

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3rd Workshop for Natural Language Processing Open Source Software, NLP-OSS 2023

作者： Terdalkar, Hrishikesh Bhattacharya, Arnab Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9798891760455

One of the primary obstacles in the advancement of Natural Language Processing (NLP) technologies for low-resource languages is the lack of annotated datasets for training and testing machine learning models. In this paper, we present Antarlekhaka, a tool for manual annotation of a comprehensive set of tasks relevant to NLP. The tool is Unicode-compatible, language-agnostic, Web-deployable and supports distributed annotation by multiple simultaneous annotators. The system sports user-friendly interfaces for 8 categories of annotation tasks. These, in turn, enable the annotation of a considerably larger set of NLP tasks. The task categories include two linguistic tasks not handled by any other tool, namely, sentence boundary detection and deciding canonical word order, which are important tasks for text that is in the form of poetry. We propose the idea of sequential annotation based on small text units, where an annotator performs several tasks related to a single text unit before proceeding to the next unit. The research applications of the proposed mode of multi-task annotation are also discussed. Antarlekhaka outperforms other annotation tools in objective evaluation. It has been also used for two real-life annotation tasks on two different languages, namely, Sanskrit and Bengali. The tool is available at https://***/Antarlekhaka/code. © 2023 Association for Computational Linguistics.

关键词： Natural language processing systems

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ExpACVO-Exponential Anti Corona Virus Optimization Enabled Hybrid Deep learning for Tongue Image Segmentation Towards Diabetes Mellitus Detection Via CNN-LSTM Network 1

ExpACVO-Exponential Anti Corona Virus Optimization Enabled H...

引用

1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Mathew, Jimsha K Sathyalakshmi, S. Hindustan Institute of Technology & Science Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350330915

The surprising headways in biotechnology and public medical services foundations have prompted a pivotal creation of basic and touchy medical care information. By applying canny information examination methods, many fascinating examples are distinguished for the early and beginning discovery and avoidance of a few deadly infections. Diabetes Mellitus (DM) is a very dangerous illness since it adds to other deadly infections, i.e., heart, kidney, and nerve harm. The following stages have been proposed in this paper for the order, beginning phase ID, and expectation of diabetes: a) Data collection from tongue dataset of many images, b) Preprocessing using the gaussian filter for noise removal, c) Segmentation using U-Net-CRFRNN for segmenting region of interest and finally d) classification using the integration of Exponential Weighted Moving Average (EWMA) and Anti Corona Virus Optimization (ACVO) and also to increase the efficiency of the network, use of CNN-LSTM. The suggested architecture is more effective than other state-of-the-art models on many different parameters, based on the outcomes (accuracy: 0.94, sensitivity: 0.96, specificity: 0.96) © 2023 IEEE.

关键词： Anti Corona Virus Optimization Classification CNN-LSTM Deep learning Diabetes Mellitus Exponential Weighted Moving Average Tongue Segmentation

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Jayachitra, S. Prasanth, A. Prasannakiruba, G.S. Abbineni, Srichandana PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331528126

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the performance of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting approach of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature pertaining to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets. © 2024 IEEE.

关键词： Heuristic methods

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Damaged Image Repair using Masks with computer Vision Inpaint Method 2

Damaged Image Repair using Masks with Computer Vision Inpain...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ramakrishna, Doddi Purna Gana Emmanuel, G. S. Michael Selvan, Mercy Paul Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665456302

Image inpainting is the technique used to automatically fix damaged areas using data from sections that have been saved. With the development of deep learning in recent years, image drawing performance has substantially increased. This research study reviews the main methods used for automating image inpainting. This research study provides a brief overview of traditional techniques while concentrating on deep learning-based inpainting techniques, covering model categorization, strengths and drawbacks, scope of application, and performance comparison. Finally, the challenges and trends surrounding automated image inpainting are examined and foreseen. A tool called image inpainting uses the data from the remaining components to repair damaged areas. With the advancement of society, image inpainting has become a vital research area in the field of computer vision. It is extensively used in culture, daily life, and security, including object removal and the preservation of digital cultural assets. Conventional methods build geometric models based on geometric consistency and image content similarity, or they use texture generation to patch up small sections of damaged images. It partially solves the problem of loose coupling between high-level semantics and low-level image properties, enabling deep learning to gradually overtake traditional methods in computer vision. © 2023 IEEE.

关键词： computer vision

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Object Detection Algorithm based on Structural Modification in the Convolutional Neural Network for Remote Sensing Satellite İmages 8

Object Detection Algorithm based on Structural Modification ...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Singh, Abhimanyu Nene, Manisha J Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9798350334012

for machine vision to make sense of an image, object recognition techniques must first locate all of the items of interest within the frame of reference. Several strategies have been proposed to address this issue, many of which draw inspiration from computer vision and deep learning techniques. Existing methods, however, consistently underperform when trying to recognise small, dense objects and often fail altogether when trying to detect objects that have undergone random geometric alterations. In this research, we examine and compare state-of-the-art object detection techniques, then suggest a tweaked convolutional object detection network to address the problems with the status *** investigation shows superior performance than current best practices. For this purpose, we employ deep convolutional networks to collect multi-scaled features, and we tweak the convolutional structures to account for geo-metric transformations.. © 2023 IEEE.

关键词： Object detection

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