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Machine Learning Classification of Shoulder Pain Pathologies: A Scoping Review

Machine Learning Classification of Shoulder Pain Pathologies...

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International Conference on Computing and Networking technology (ICCNT)

作者： Garima Shukla Khushi Bhagat Vedant Chaudhari E Navaneeth Krishnan Mansi Chauhan Bhadauria Yash Chouksey Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Mumbai Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Shoulder problem is a widely known musculoskeletal disorder that impacts heavily on the life of patients. Effective treatment for this must be preceded by accurate and timely diagnosis but traditional diagnostic techniques have always been subjective and less reliable. In recent years, machine learning (ML) techniques have shown promising results in improving the accuracy of diagnosing shoulder pain, addressing the limitations of traditional diagnostic methods. Nonetheless, there is a failure to translate these ML models into practical clinical tools that can easily be employed by healthcare professionals. In addition, this paper will present an overview of current understanding and methodologies employed for diagnosing and managing shoulder pain along with reviewing the use of ML techniques in medical image diagnostics. It also suggests development of a user-friendly and accessible diagnosis tool for shoulder pathologies aimed at linking modern research and daily practice in the medical field.

关键词： Pathology Accuracy Pain Reviews Shoulder Standardization Medical services Feature extraction Data models Medical diagnostic imaging

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SkinSage - Lesion Diagnosis Using Deep Learning Techniques

SkinSage - Lesion Diagnosis Using Deep Learning Techniques

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Geetika Paarth Bhardwaj Nimish Vaibhav Bhargava Shivendra Singh Department of Computer Science and Engineering Amity School of Engineering and Technology Noida India

The alarming rise in instances of skin cancer in recent years, one of the most prevalent malignancies worldwide, emphasises how important early and accurate identification is. The SkinSage project, which combines the precision of deep learning models and technologies. Earlier, dermatologists relied on eye exams to spot skin lesions. But with datasets like HAM10000 and digital photography, it became easy to record a variety of skin conditions. SkinSage uses cutting-edge neural architectures like CNN, ResNet, DenseNet, and InceptionV3 to recognise patterns and nuances that are much above the capabilities of the human eye. In addition to being a computational marvel, the SkinSage project also exemplifies accessibility by making its analytical capabilities available via a smartphone application in future. High-quality skin lesion diagnostics will be accessible to everyone thanks to this platform, regardless of location or degree of training. With the confluence of these developments, SkinSage becomes more than just a tool for diagnosing skin cancer; it signals a paradigm shift that has the potential to democratise early detection and save countless lives in the future.

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Statistical Exploration and Sentiment Analysis of WhatsApp Chats using Supervised Learning 23

Statistical Exploration and Sentiment Analysis of WhatsApp C...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Aakarsh Sharma Shweta Sinha Amity School of Engineering and Technology Amity University Gurugram India Computer Science & Engineering Amity University Haryana India

ISBN: (纸本)9798400709418

This paper presents the sentiment analysis of WhatsApp chat data while also evaluating user activity, employing VADER and supervised learning. The study encompasses both sentiment classification utilizing VADER's lexicon-based approach and supervised sentiment analysis using the Naive Bayes and SVM model. The research extends to statistical exploration like identifying the most active user within the group chat dataset and other statistical information. By contrasting the efficiency and accuracy of these techniques, the paper aids in method selection based on specific analysis goals. Results offer insights into sentiment trends and user engagement for informed decision-making.

关键词： Keywords. Naïve Bayes

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C-SPIN: Classification of Satellite Images for Prediction Network

C-SPIN: Classification of Satellite Images for Prediction Ne...

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Sumit Kumar Devanshu Mittal Vibha Nehra Department of Computer Science Engineering Amity School of Engineering and Technology Noida India

Satellite image classification and prediction are crucial, with applications in domains ranging from environmental monitoring to urban planning, geological exploration, mapping, disaster response management, and agriculture. The paper provides a comprehensive analysis of current methods and techniques for object classification and prediction through satellite image data. The current work introduces the Classification of Satellite Images for Prediction Network (C-SPIN), which is meant to use both the spatial-geographical and temporal information inherent in satellite data. Image resolutions, compression, bit depth, sensor types, and spectral bands are among some of the variables, datasets, and factors considered in the research. Accurate object classification, detection, and prediction from satellite images are crucial for realising the full potential of the proposed model. The study gives insight on the strengths and limits of current models in various circumstances and highlights the potential capabilities of C-SPIN to enhance satellite image processing, image classification, and object prediction.

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A Survey of Text-to-Image Diffusion Models in Generative AI

A Survey of Text-to-Image Diffusion Models in Generative AI

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International Conference on Confluence The Next Generation Information technology Summit (Confluence)

作者： Siddharth Kandwal Vibha Nehra Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Noida Uttar Pradesh

From dreamscapes to photorealistic portraits, text-to-image generation pushes the boundaries of AI creativity. This survey navigates diverse techniques, such as GANs, VAEs, and Diffusion models, uncovering their potential for transforming textual descriptions into captivating visuals. These models have significantly advanced the field, but they are not without their limitations. One notable issue is data bias, which could potentially lead to a deficiency in variety and cultural awareness in the produced visuals. Furthermore, recognising the significance of mitigating data bias in generative models, this report offers insights and strategies to address this pressing issue. It explores approaches that leverage inclusive datasets, fairness-aware training techniques, and ethical considerations. These methods aim to bridge the gap between the technological advancements in image generation and the imperative need for inclusivity and cultural sensitivity.

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Comparing the Efficacy of Machine Learning Models on Potentially Hazardous Objects 2

Comparing the Efficacy of Machine Learning Models on Potenti...

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2nd International Conference on Technological Advancements in Computational sciences, ICTACS 2022

作者： Chhibber, Manasi Bhatia, Madhulika Chaudhary, Aniket Stewart, Christopher Amity School of Engineering & Technology Amity University Noida India Ohio State University Computer Science and Engineering Department OH United States

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

An asteroid or a comet is termed as a potentially hazardous object (PHO) if its orbit is such that it can closely approach the Earth and is huge enough to cause serious regional damage in the event of an impact. There are currently nearly 1,500 asteroids documented that have the potential to cause serious damage, but these are solely the ones we know about. In an event like this, it becomes a necessity to keep a check on all celestial bodies near earth so that we can take appropriate measures to minimize the damage. This project aims at analyzing data gathered by NASA about various asteroids or PHOs and predicting whether an Earth approaching body is hazardous or not. On finding an appropriate dataset, extensive data pre-processing was done on Machine Learning workspace on Azure. The Azure platform for machine learning algorithm provide a quick and easy way to apply machine learning algorithms as well as for modelling of data. Experimental results obtained by using diverse Machine Learning models were compared. The Two-Class Decision Forest provided the highest accuracy, a ROC-AUC score of 0.99. © 2022 IEEE.

关键词： Asteroids

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Prevalence of GitOps, DevOps in Fast CI/CD Cycles

Prevalence of GitOps, DevOps in Fast CI/CD Cycles

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2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022

作者： Gupta, Saumya Bhatia, Madhulika Memoria, Meenakshi Manani, Preeti Amity University Computer Science & Engineering Amity School of Engineering & Technology Noida India Uttaranchal University Department of Computer Science & Engineering Uttarakhand Dehradun India Dayalbagh Educational Institute Agra India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Today, when we are surrounded by technology, software and applications are making our lives more efficient and easy because of the operations and processes controlled by them. Since the technology is constantly changing at a very high pace, improving and modifying with each development, there is a constant need of is faster and more frequent delivery of the software and applications. Today, most software and applications are built in such a way that they could run across multiple operating environments. Software development is performed using agile principles and the most critical part of it is Continuous Delivery and Continuous Integration (CI/CD). CI/CD aims at automating the process of testing, building, and deploying the commitments made by the developer to the code repository. The use of Container-based applications solves a number of complex problems that are found on CI/CD such as portability, elasticity, visibility, and version control. This modular approach enables a simpler, faster, more secure, and more efficient way of development by more focused teams responsible for specific containers. In this context, a new point of interest in the development process, GitOps, which is more agile, reliable, fast, and efficient in its approach towards better performance levels with cloud-native. The main objective of this paper is to understand the Kubernetes GitOps process by day 2 operations, to access the benefits of implementing GitOps in the Kubernetes environment, and to implement Kubernetes GitOps on AWS. © 2022 IEEE.

关键词： Containers

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Harnessing the Potential of the Internet of Things: Navigating Choices Between Fog and Cloud Infrastructure in Agriculture 1

引用

2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Singh, Malikhan Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Ather, Danish Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic era Hill University Dehradun India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

In recent years, the Internet of Things (IoT) has become a pivotal force in transforming various sectors, with agriculture being a prominent beneficiary. The integration of smart devices, sensors, and data-driven decision-making tools has led to the emergence of a more efficient and sustainable farming landscape. Central to this transformation is the decision of where and how to process and store the vast amounts of data generated by these devices. This paper delves into the practical implications of fog and cloud computing systems in the agricultural domain taking Delhi NCR and region as a sample area by considering factors like efficiency, scalability, and real-world challenges faced by farmers. The aim here is to provide a comprehensive guide to stakeholders in making an informed choice between fog and cloud infrastructure for their IoT-enabled agricultural endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Deep Learning Scheme for Modulation Classification under Fading Channels 7

Deep Learning Scheme for Modulation Classification under Fad...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Ghauri, Sajjad A. Sarfraz, Mubashar Chhabra, Gunjan Kaushik, Keshav Zafar, Asim Khan, Inam Ullah Isra University School of Engineering & Applied Sciences Islamabad Pakistan Numl University Faculty of Engineering and Computing Islamabad Pakistan Graphic Era Hill University Graphic Era Deemed to be University Department of Cse Uttarakhand Dehradun India Amity University Amity School of Engineering and Technology Punjab India National University of Technology Department of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350350067

The field of automatic modulation classification using deep neural networks has undergone significant development in recent years. In this research, M-QAM and N-PSK modulation formats have been considered for classification. The deep neural network architecture is directly fed with raw signals that are often corrupted with channel noise and fading effects such as Rayleigh and Nakagami. The proposed classifier structure also incorporates normalized cumulants of higher order from the received signal. The proposed DNN architecture's performance is evaluated by comparing its efficiency for raw data and extracted features-based classification. We have validated the classifier efficiency through three different cases. Our proposed classifier demonstrates remarkable capability in efficiently classifying modulation formats. © 2024 IEEE.

关键词： Signal modulation

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Malware Detection Using Genetic Cascaded Support Vector Machine Classifier in Internet of Things 2

Malware Detection Using Genetic Cascaded Support Vector Mach...

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2nd International Conference on computer science, engineering and Applications, ICCSEA 2022

作者： Gupta, Shashi Kant Pattnaik, Birajashis Agrawal, Vineet Boddu, Raja Sarath Kumar Srivastava, Archana Hazela, Bramah Integral University Computer Science and Engineering UP Lucknow India Amity University Mechanical & Automation Engineering Amity School of Engineering & Technology Kolkata India Babu Banarasi das Institute of Technology and Management Department of Computer Science and Engineering UP Lucknow India Lenora College of Engineering Department of Cse Andhra Pradesh Rampachodavaram India Bbd University School of Computer Applications UP Lucknow India Amity University Computer Science and Engineering Amity School of Engineering and Technology Lucknow Lucknow Campus India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

The Internet of Things (IoT) is a network of computing devices that can transmit and obtain data across a network without human intervention. In the last couple of decades, software and communication technology have advanced tremendously, resulting in a considerable increase in IoT devices. The rapid expansion has raised security and privacy issues. Threats and malware attacks on IoT devices have increased dramatically recently. Hence, in this paper, we proposed a novel malware detection framework based on machine learning in IoT using a Genetic Cascaded Support Vector Machine (GC-SVM) classifier. We introduce the Chaotic Binary Coded Cuckoo Search Optimization Algorithm (CBC-CSOA) for optimizing the detection process. The performance of the proposed method is evaluated and compared with various conventional methodologies. The proposed method produced accurate outputs this approach may be used to forecast and identify malware in IoT-based systems, according to the study. © 2022 IEEE.

关键词： Internet of things

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