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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Prachi Siddhi Deepa Manju Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bangalore India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394474

This computer age has changed content consumption, every individual can access infinite collection of information at their fingertips. The change from conventional media to internet-based media has enabled consumers to select content, nurturing a varied and worldwide informational panorama and entertainment. The rise of trendsetters and community-driven content has more enhanced the effect of social media, generating new types of internet-based celebrity and modifying the way users and enterprises involve their viewers. YouTube has progressed into a massive video-sharing medium, presenting content covering from entertainment and tutorial to comprehensive informative materials. YouTube's easy to use interface and computational suggestions produce customized viewing experience, contributing to platform's accomplishment within its enormous user base. Revenue-generating initiatives on YouTube aid to a noteworthy financial source for content creators, cultivating the creation of varied and high-quality videos that captivate to both specific spectators and worldwide viewer base. The drive behind conducting view count prediction exists in its capability to enable content creators, marketers and platform planners. We use broad variety of attributes in this predictive analysis challenge to project the number of views on YouTube videos. The dataset is imported from kaggle. Features used are likes, view-velocity, comments, subscribers, total views, categories, tags. © 2024 IEEE.

关键词： Predictive analytics

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A Comparative Analysis of ML & DL Models for Fetal Health Classification Using Cardiotocographic Data

A Comparative Analysis of ML & DL Models for Fetal Health Cl...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Kaushik, Tata Sri Vani, Tunuguntla Aasritha Vithyatharshana, N. Khare, Sangita Amrita Vishwa Vidyapeetam Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798350361537

Traditional diagnostic methods in fetal health are frequently hindered by class imbalance and complex data, which puts early intervention and optimal perinatal outcomes at risk. This study fills this important gap by conducting a thorough comparative analysis of several ML and DL models that have been effective in fetal health research. This project goes beyond the obvious applications and investigates how model performance is affected by Principal Component Analysis (PCA) and Chi-Square for feature extraction and feature selection respectively and Conditional Generative Adversarial Networks (CGANs) for data augmentation. This project seeks to determine the best model or ensemble for dependable and accurate fetal health prediction by methodically assessing model performance across all its forms, CGAN-augmented versions, integrated CGAN - PCA and CGAN - Chi-Square configurations. This will provide medical professionals with a data-driven tool for early diagnosis and well-informed clinical decisions. This thorough study has the potential to greatly enhance perinatal outcomes and improve the health of moms and their unborn children. It is positioned to transform prenatal care. In addition to identifying the best model, this comparative analysis lays the foundation for future research examining the integration of explainable AI techniques to gain deeper insights into fetal health dynamics and to understand the internal decision-making processes of the model. © 2024 IEEE.

关键词： Generative adversarial networks

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Automated Classification and Size Estimation of Fetal Ventriculomegaly from MRI Images: A Comparative Study of Deep Learning Segmentation Approaches 5

Automated Classification and Size Estimation of Fetal Ventri...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Gopikrishna, K. Niranjan, N.R. Maurya, Siddharth Uday Krishnan, V.G. Surendran, Simi Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Fetal ventriculomegaly is one of the major risks in prenatal diagnosis, which is an enlargement of the ventricles of the developing fetus's brain. Timely prediction of these brain disorders helps patients and healthcare providers make informed decisions and treatment plans. This paper investigates the classification of ventriculomegaly from MRI images using a deep learning approach. Segmentation of ventricles is performed using DeeplabV3+ and U-Net architectures and the accuracy, dice coefficient, and loss of these two approaches are compared. Additionally, a novel approach for size estimation was developed, utilizing contour detection to delineate ventricle boundaries and compute their areas by enclosing them within rectangles. A threshold-based classification method was then applied to differentiate between abnormal ventriculomegaly and normal cases. This research contributes to the assessment and comparison of the segmentation algorithms for aiding in the identification and classification of ventriculomegaly cases, offering potential implications for clinical diagnosis and treatment planning. © 2024 The Authors. Published by Elsevier B.V.

关键词： Clinical research

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Can Technical Festivals Help in Attaining Undergraduate engineering Programme Outcomes? 13

Can Technical Festivals Help in Attaining Undergraduate Engi...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Dhanalakshmy, Dhanya M Murali, Ritwik Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350376234

The curriculum framework of an undergraduate engineering programme contains clearly defined learning outcomes. It is expected that students who graduate from a specific degree / diploma are able to attain these goals. Towards this, while not mandatory, every institution is encouraged to also host technical events such as technical festivals, workshops, technical competitions, hackathons, etc. This work explores the challenges and merits in leveraging undergraduate technical festivals to enable an evaluation of students towards the attainment of qualitative Programme Outcome. Using qualitative analysis strategies including surveys and one-on-one interviews, this work shows that technical festivals provide unparalleled hands-on experience in medium to large-scale project management, encompassing crucial disciplines that are often overlooked in the classroom evaluation rubrics. The major takeaways from organizing a technical festival can be summarized as large-scale organizing of events, better time and people management, improved communication skills, and the positive attitude / ability to learn new things. This work also mapped the specific programme outcomes to the actual learning that could be attained though the organization of a technical festival. This work also highlights the importance of such events in order to bridge the gap between industry & academia while being able to assess qualitative outcomes effectively. © 2024 IEEE.

关键词： Project management

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Weather-Integrated Recommendations for Food Choices with SHAP Explanation 1

Weather-Integrated Recommendations for Food Choices with SHA...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jain, Nikunj Hawari, Jawed Jha, Priyanshu Dinesh Kumar, K. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350372748

In the context of today's rapidly evolving technological landscape and the urbanization of Tier 1 and Tier 2 cities, cloud kitchens have emerged as pivotal players, offering quick and convenient meals without the need for dine-in facilities. The demand prediction for the food has become quite tough and could be considered as one of the significant challenges. Cloud kitchens face challenges in managing stock due to the absence of in-house diners, potentially resulting in wasted food. Moreover, adverse weather conditions often hinder the seamless delivery of online. To address these challenges, this project employs machine learning techniques to predict weather patterns and correlate them with food preferences according to that weather. The key algorithms found to deal with this problem were Random Forest, kNN, Decision tree and Naïve Bayes and Support Vector Machine. This serves the first half of the statement and then for recommendation a suitable system was developed. The pivotal role of the system is to reduce the food wastages for cloud kitchens by fostering adaptive strategies for efficient decision making. The key inputs from the market were found to be that 30-40% of the predicted sales in food industry doesn't meet due to various reasons. The ultimate goal is to empower cloud kitchen operations, ensuring adaptive and informed decision-making in response to changing weather dynamics. The union of both of these are explained using Interpretability tool SHAP for a convenient study. © 2024 IEEE.

关键词： Decision trees

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A Multi-Class Classification Framework with SMOTE Based Data Augmentation Technique for Alzheimer's Disease Progression

A Multi-Class Classification Framework with SMOTE Based Data...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Saketh, C.H. Ramasamy, Gayatri Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350371567

The detection of Alzheimer's disease is a critical task in medical diagnostics due to its rapid progression and profound impact on cognitive function. Deep learning (DL) offers unprecedented capabilities in analyzing medical imaging data, particularly in neuroimaging like MRI scans. However, class imbalances within the dataset persist, making it difficult to discern intricate patterns indicative of Alzheimer's pathology. This project aims to overcome this challenge by integrating advanced DL techniques with data augmentation methodologies, specifically SMOTE. This strategic integration aims to overcome the limitations of class imbalances within the initial MRI dataset, enhancing the precision and reliability of the classification model. This interdisciplinary exploration aims to redefine Alzheimer's detection capabilities, enhancing diagnoses and offering the potential for broader applications in understanding and managing complex neurodegenerative disorders. © 2024 IEEE.

关键词： Classification (of information)

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Autonomous Car Driving: Advanced Maneuvers Training 3

Autonomous Car Driving: Advanced Maneuvers Training

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Chethana, Savarala Charan, Sreevathsa Sree Srihitha, Vemula Radha, D. Amudha, J. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering India

ISBN: (纸本)9798350364569

As the demand for autonomous driving systems continues to rise, the need for proficient highway navigation becomes paramount. This study presents a comprehensive approach to training autonomous cars for proficient highway driving using deep reinforcement learning. The research focuses on critical maneuvers of switching lanes on the road. A sophisticated simulation environment is then employed for training, enabling safe and efficient iterations. The primary objective of this project is to develop a more adaptive and efficient navigation system using Deep Reinforcement Learning (DRL). Utilizing the HighwayEnv environment and Stable Baselines' Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) algorithms, the project aims to create a model capable of real-time decision-making and multi-objective optimization, thereby improving the overall efficiency and safety of highway navigation. A comparative analysis has been performed on these two algorithms to know the best algorithm suitable for the task of switching lanes. The developed system demonstrates promising results in mastering complex highway scenarios, showcasing the potential for a safer and more efficient autonomous driving future. © 2024 IEEE.

关键词： Deep reinforcement learning

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Unified Detection Framework for Robbery Events: Integrating YOLOv8, Fast R-CNN, and RetinaNet with Explainable AI Validation and Real-time Android Application Integration

Unified Detection Framework for Robbery Events: Integrating ...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Sai Mrudhun, P. Keerthana, M.C. Guru Nithysh, K. Akash, T. Manjusha, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350376876

This research proposes a Unified Detection Framework for Robbery Events by combining YOLOv8, Fast R-CNN, and RetinaNet models. The framework incorporates Explainable AI Validation for transparency and real-time detection capabilities. Additionally, an Android application integration is implemented for practical deployment. Through experimentation and evaluation, the effectiveness of the framework in detecting robbery events is demonstrated. The fusion of multiple object detection models enhances accuracy and efficiency, while the integration of explainable AI validation ensures interpretability of results. The proposed framework shows promising results for addressing security concerns in real-world scenarios. © 2024 IEEE.

关键词： Crime

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Dynamic Face Interaction: Simulating 3D Spatial Relationships with Mesa and OpenCV 15

Dynamic Face Interaction: Simulating 3D Spatial Relationship...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Podupuganti, Ravi Charan Injeti, Hemanth Kandula, Ashish Paul Jaiswal, Rishu Inuguri, Anantha Hothri Khanna, Manju Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

The dynamic face interaction model simulates real-time spatial connections by calculating distances between recognized faces and a camera by merging Mesa and OpenCV. It leverages OpenCV's Haar cascades to offer dynamic face interactions depending on proximity in 3D space through the use of FaceAgent and FaceDistanceModel classes. It is useful for social dynamics, surveillance, and human-computer interaction. It employs LBPH facial recognition and Haar cascades to precisely modify agent placements. The purpose-built dataset addresses difficulties in comprehending social dynamics and strengthens the originality of research while promoting ethical research practices. This creative method highlights the transformational potential of combining simulation and AI technologies, and it encourages researchers to carefully examine the dataset in quest of significant breakthroughs while being aware of biases. © 2024 IEEE.

关键词： Face recognition

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An Iterative PRISMA Review of GAN Models for Image Processing, Medical Diagnosis, and Network Security

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computers, Materials & Continua 2025年第2期82卷 1757-1810页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Sujatha Canavoy Narahari Parvathaneni Naga Srinivasu Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad501301India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravati522503India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Inti International University Nilai71800Malaysia

The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some

关键词： GAN CGAN WGAN DCGAN image analysis

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