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检索条件"机构=MTech Computer Science and Engineering School of Computing Science and Engineering"

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Exploring the Landscape of Lung Diseases: A Comprehensive Literature Survey

Exploring the Landscape of Lung Diseases: A Comprehensive Li...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： E. Soumya S. Santhanalakshmi Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The lung is the sensitive organ of our body. Modern developments in deep learning and machine learning have completely changed how medical imaging is used to diagnose and classify lung problems. Consequently, there is a wealth of literature exploring the application of these technologies in this domain. This study undertakes a broad survey of the utilization of deep learning and machine learning methods for diagnosing lung illnesses in medical images. Its primary objectives include delineating the taxonomy of cutting-edge deep learning-driven systems for detecting lung diseases, interpreting recent research trends, and identifying persistent challenges and potential future directions. By proposing a machine learning-based classification of contemporary lung diseases, this research aims to furnish a framework crucial for researchers across various domains to structure their contributions and investigations. This classification encompasses several common survey attributes, including the types of data utilized, the spectrum of lung diseases addressed, and the collection of machine learning and deep learning procedures employed. Such a framework is pivotal for augmenting the efficacy and accuracy of machine learning in the precise identification and categorization of lung disorders, thereby minimizing diagnostic errors.

关键词： Surveys Deep learning Accuracy Lungs Pulmonary diseases Taxonomy Aerospace electronics Market research Object recognition Medical diagnostic imaging

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computer Vision Based Algorithms for Detecting and Classification of Activities for Fall Recognition on Real Time Video

Computer Vision Based Algorithms for Detecting and Classific...

引用

Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： Tirumala Kumar Kandukuru Senthil Kumar Thangavel G Jeyakumar Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Falls among the elderly population present significant risks, including serious injuries and even death. Prompt detection of falls is crucial for immediate assistance and notification to relevant parties. However, current fall detection systems face challenges and privacy concerns. To address this, a new system is proposed using a Convolutional Neural Network (CNN) and optimizing Optical Flow images through preprocessing in spatial and frequency domains. The system incorporates the Viola-Jones algorithm for human face and action recognition. It is designed for detecting falls in home or room environments, covering various postures - standing, sitting, walking, and falling. Surveillance cameras in public spaces capture relevant data for training the model. The system achieves impressive accuracy and precision, highlighting its effectiveness in fall detection applications.

关键词： Training Privacy Face recognition Surveillance Sociology Streaming media Real-time systems

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Adding Constructs to ‘C’ Through Flex and Bison

Adding Constructs to ‘C’ Through Flex and Bison

引用

International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Nachiketh Arroju Nallabothula Vamsi N. Yeshwanth Reddy Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Developing a unique language for coding if and for loops involves using creatively designed markers such as “ifff” and “4”. Meeting the three acceptance criteria requires coding loops using these markers, where “ifff” signifies an if loop and “4” represents a for loop. Coding loops conventionally with “0” and “4” is considered unacceptable. Additionally, the language requires modifiers for loops and their respective lengths to be placed on one line. The language also encompasses formatting rules, such as combining multiple lowercase letters and ensuring that the letter “c” appears three times for successful compilation. Repeated occurrence of certain letters is allowed and accepted within the language. Furthermore, specific responses like “np” for “no problem,” “sry” for “sorry,” and “fq” for “thank you” have been formulated based on input.

关键词： Flexible printed circuits Encoding Information technology

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Detection and Classification of Types of Cracks Using Deep Learning

Detection and Classification of Types of Cracks Using Deep L...

引用

Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Siva Charan Kadagala Harshith Reddy Konda Prudhvi Sri Harsha Krovvidi Nalini Sampath Department of Computer Science and Engineering Bengaluru Amrita Vishwa Amrita School of Computing Vidyapeetham India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

The identification and classification of cracks are vital for maintaining the safety and integrity of buildings and transportation infrastructure. Conventional manual inspection methods are often time-consuming and labor-intensive, underscoring the necessity for automated systems leveraging computer vision techniques. This study proposes two distinct methodologies: one based on digital image processing and the other on deep learning, to facilitate the development of crack inspection systems. The deep learning approach employs a pre-trained model serving as a feature extractor, coupled with a fully connected neural network classifier, to facilitate the detection and classification of cracks. Specifically, detection is accomplished utilizing Convolutional Neural Networks (CNNs), while classification is conducted employing ResN et, addressing the distinct tasks of identifying cracks and categorizing them accurately. Evaluation on publicly available crack datasets demonstrates the superior accuracy of the proposed method in detecting surface cracks, surpassing existing state-of-the-art techniques. Furthermore, a supervised deep learning model is introduced for the detection and classification of pavement cracks, adept at tackling the complexities associated with diverse backgrounds and variations in crack patterns. Results from this model exhibit promise accurately classifying individual image patches within collected datasets. These methodologies hold considerable potential for practical implementation in non-destructive testing, enabling the detection and classification of various types of cracks across diverse structural components.

关键词： Deep learning Measurement Training Accuracy Inspection Feature extraction Safety

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Deep Learning for Automated Air Quality Classification Using Image Data: A Comparative Study of GoogleNet and MobileNet

Deep Learning for Automated Air Quality Classification Using...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Maanav Thalapilly Ketone Agasti G Kisor T Anjali Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The following study contributes to the improvement of environmental monitoring by providing an approach for the estimation of air quality in real-time based on deep learning models GoogleNet and MobileNet. With either compromised realtime processing capability or spatial resolution in conventional approaches, these two models can serve as practical and scalable alternatives. Empirical results demonstrate that both GoogleNet and MobileNet can classify AQI levels effectively and transfer learning significantly improves the effectiveness and generalization of comparison for tiny datasets. Class labels include “AQI-Good” to “AQI-Hazardous/Severe” Pollution information collected at sites in cities of India and Nepal along with location, time, pollutant concentration. A detailed data collection enhances the capability of models to generate accurate predictions over a range of scenarios and furthers our knowledge of temporal and spatial variability of air quality. Evaluation criteria like recall, accuracy, precision, and F1score portray the stability and reliability of the method. These measures assure their steady performance in a variety of contexts and thus guaranteeing their applicability in real life. Hence, GoogleNet and MobileNet are a few significant developments in the real-time air quality monitoring process, which provides information on would be crucial in dealing with the problems associated with air pollution.

关键词： Deep learning Analytical models Accuracy Atmospheric modeling Transfer learning Predictive models Air quality Data models Real-time systems Indexes

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A Machine Learning Based Classification of Students' Algebraic Responses Using MathBERT Embeddings

A Machine Learning Based Classification of Students' Algebra...

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Emerging Technology (INCET), International Conference for

作者： Anees Sajid Injeti G. Neha Rupsica G. Praneetha Reddy Roshni M Balakrishnan Peeta Basa Pati Dept of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Conduction of assessments and analysing the students' knowledge is the most important part of any teaching learning process. Traditional manual grading of written mathematical solutions can be time-consuming and laborious, especially for a larger group of students. It takes lot of time and effort to correct these answer scripts. In order to evaluate students' open responses for algebraic inquiries automatically, this project explores the use of MathBERT embeddings, an advanced natural language processing technique with different machine learning classifiers. The model tries to classify the student's responses to either correct or wrong. XGBoost Classifier on the MathBERT embeddings exhibited the best accuracy for this model with 75.3% mean accuracy with standard deviation of 0.016.

关键词： Training Accuracy Machine learning Manuals Feature extraction Mathematical models Natural language processing

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Advanced Knee Osteoarthritis Detection using Deep Learning

Advanced Knee Osteoarthritis Detection using Deep Learning

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International Conference for Convergence of Technology (I2CT)

作者： Chetlapalli Amritha Krishna R Bhuvaneswari Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This present research investigates the use of machine learning and deep learning models for the detection of knee osteoarthritis using the X-Ray images in depth. knee osteoarthritis is one of the most common joint disorders which affects numerous percentages of people across the world. And its detection of it at an early stage is very critical for effective management. This paper presents valuable dataset of X-ray images with a dual focus on detection of knee joint and KellgrenLawrence grading [kl]. The methodology which is proposed in this paper includes basic steps such as data loading and preprocessing. It also includes feature extraction and utilization of models of both machine and deep learning. In the phase of machine learning models which are used are Decision tree, KNN, MLP, Random Forest and ensemble methods. While in deep learning models used for evaluation are Nasnetmobile, VGG19, Darknet53, Darknet19 and Googlenet. The output of all the above-mentioned models are compared and evaluated based on four most important metrics of performance. The final output of the research shows that Random Forest and the Ensemble methods have outperformed in the phase of machine learning and when it comes to deep learning Googlenet and Darknet53 have done well in comparison. This study helps in the early detection of the knee osteoarthritis and also helps in management of it by giving valuable insights to medical practitioners and researchers.

关键词： Deep learning Measurement Ensemble learning Osteoarthritis Task analysis X-ray imaging Random forests

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A Holistic Approach to Handling Metallic Waste for Environmental Sustainability

A Holistic Approach to Handling Metallic Waste for Environme...

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Advancement in Technology (ICONAT), International Conference for

作者： Thangam Itreddy Shyam Sundar Reddy Kakarla Bhanu Prakash Panyam Hemanth Reddy Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Metal waste management includes the gathering, moving, handling, recycling, and getting rid of waste made of metal. Metallic waste comprises a wide range of materials, such as alloys, composites, and ferrous and nonferrous metals. Numerous activities, including industrial processes, manufacturing, construction, and demolition, can result in the production of metallic trash. Handling metallic waste improperly can contaminate the environment and be harmful to human health. Effective metal waste management strategies combine disposal, recycling, reuse, and trash reduction. Reducing the production of metallic waste at the source is the goal of waste reduction. Reuse is the process of finding new uses for metallic waste materials without reprocessing them. Recycling is the process of turning waste metal into new products, like alloys or new metal objects. Disposal is the controlled placement of metallic waste in landfills or other approved locations.

关键词： Waste reduction Waste materials Metals Production Solar energy Turning Recycling Solar panels Sustainable development Sorting

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Premature Birth Prediction Using Machine Learning Algorithms – A Comparative Analysis

Premature Birth Prediction Using Machine Learning Algorithms...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： A Padmavathi Laxmi Maurya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Premature birth (PB) presents a significant global health challenge, often leading to enduring developmental issues and neonatal mortality. Approximately 4-16% of new-born worldwide is born prematurely. Leveraging machine learning, a predictive model has been developed to anticipate preterm birth risk. This model evaluates maternal characteristics and other pertinent factors to provide timely alerts to healthcare providers. Through the analysis of extensive datasets, the system offers valuable insights for forecasting and classifying preterm birth risks. First and foremost, this study aims to enhance prenatal care and outcomes for high-risk pregnancies by harnessing artificial intelligence to refine the accuracy and promptness of preterm birth prediction. Employing machine learning algorithms, our study achieves a remarkable 99% accuracy rate throughout all the techniques.

关键词： Pregnancy Pediatrics Machine learning algorithms Accuracy Computational modeling Mortality Machine learning Predictive models Public healthcare Informatics

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XAI – Empowered Ensemble Deep Learning for Deepfake Detection

XAI – Empowered Ensemble Deep Learning for Deepfake Detecti...

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International Conference on computing and Networking Technology (ICCNT)

作者： Amidela Anil Kumar S J Dheepthi Priyangha P Meghana Muppalla Dheeraj R Aarthi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Recent technologies have evolved rapidly, allowing the creation of fake videos/images that are realistic. These artificial media pieces have provoked grave worries about the possibilities of their misuse in different areas like politics, journalism, and communication. Consequently, the demand for deepfake detection methods is increasing. In response to this growing threat, there is an increasing demand for robust deepfake detection methods. This paper proposes an ensemble-based approach for deepfake detection, leveraging deep learning techniques. Specifically, we employ a combination of CNN models, including VGG, Xception, RegularizedConvNet, and RegularizedConvDenseNet, designed for image classification. Our approach integrates predictions from multiple models to enhance detection accuracy and reliability. Specifically, the base is formed by the ensemble of RegularizedConvNet and RegularizedConvDenseNet, which cooperate to prevent the spread of deepfakes and protect digital integrity. Experimental results show that our Ensemble method achieve better detection values with accuracy of 94.5% and AUC of 0.98.

关键词： Deep learning Deepfakes Accuracy Computational modeling Neural networks Training data Media Predictive models Ensemble learning Reliability

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