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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kaushik, M. Kandala, Mahita Vignesh, G.S. Vithyatharshana, N. Palaniswamy, Suja Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331528201

Physical activity is an essential component of physical and mental health-being. Although physical activity offers numerous advantages, maintaining proper posture could be difficult, especially when it comes to complex movements or poses. This task is even challenging for the ones who exercise at home, those who have disabilities, or social anxiety. In order to deal with this issue, this work proposes an AI gym tracker app powered by advanced pose estimation technology through OpenCV and Mediapipe which are two popular frameworks being used in computer vision to access and solve several real-life problems through pose-estimation. This paper describes an AI-powered gym tracker app built with MediaPipe and OpenCV. It uses real-time pose estimation and joint angle calculation to provide feedback on exercise form as well as expanded rep count. This application employs a unique method by using dynamic angle thresholds and joint distance computations to provide precise feedback about user posture. It demonstrates the application's effectiveness in assisting users in maintaining proper form and tracking their workout progress by demonstrating a counter. Future work is to broaden its functionality to embrace more exercises and to further add other forms of decision feedback system, thus exploring the possible incorporation of this application into various fitness apps which would in-turn enhance the overall experience of the users. © 2024 IEEE.

关键词： mHealth

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TweetFeel: Analyzing Emotions in the Twittersphere

TweetFeel: Analyzing Emotions in the Twittersphere

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Adithya Gokulan, S. Reddy, Mahati Varshith, M. Venugopalan, Manju Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The majority of people on the planet now have access to the Internet for text, image, audio, and video communication. Through social media, people from many backgrounds share knowledge about current events and express their own opinions on them. Analyzing people's emotions is necessary to comprehend and identify the behavior of such vast amounts of textual data about them. The study focuses on information gathered from Twitter, one of the most widely used social media platforms, by examining both historical and real-time feeds and extracting emotions from them. The necessary English-language Twitter data is transformed into a vector of six emotions, and supervised learning methods like Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest, Gradient Boosting, and Multi-Layer Perceptron are utilized to identify the label of one of the basic human emotions. Gradient Boosting and SVM are the top-performing models that reported the highest F1-Score of 0.88 among the other models. Sadness and joy came out to be the best-performing emotions whereas it was comparatively difficult to predict surprise emotion. © 2024 IEEE.

关键词： Tweets

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An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 1165-1186页

作者： Ali, Rifaqat Pal, Arup Kumar Kumari, Saru Sangaiah, Arun Kumar Li, Xiong Wu, Fan Dhanbad826004 India Department of Mathematics Ch. Charan Singh University Uttar Pradesh Meerut250004 India School of Computing Science and Engineering VIT University Vellore Tamil Nadu632014 India School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Engineering Xiamen Institute of Technology Xiamen361021 China

With the rapid growth of wireless medical sensor networks (WMSNs) based healthcare applications, protecting both the privacy and security from illegitimate users, are major concern issues since patient’s precise information is vital for the proper diagnosis procedure. So, authentication protocol is one of the efficient mechanisms to deal with trustworthy and authentic users. Several authentication protocols have been proposed in WMSNs environment. However, the most of these protocols are so susceptible to security threats and not suitable for practical use. In this article, recently proposed Amin et al.’s authentication scheme is reviewed and some vulnerabilities like off-line password guessing attack, user impersonation attack, known session-key temporary information attack, the revelation of secret parameters, and identity guessing attack are pointed out. To overcome all the above mentioned vulnerabilities, we have proposed an enhanced three-factor based remote user authentication protocol in WMSNs environment. Further, the proposed protocol is validated using Burrows–Abadi–Needham logic and then simulated using Automated Validation of Internet Security Protocols and Applications tool. Moreover, the security analysis ensures that the proposed protocol is well protected from various types of malicious attacks. In addition, the performance evaluation shows better efficiency and suitability of our protocol over other related protocols. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Authentication

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Code Optimization for Python using Large Pre-Trained Model 5

Code Optimization for Python using Large Pre-Trained Model

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Adithya Gokulan, S. Reddy, Mahati Varshith, M. Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Optimizing Python code is essential for enhancing performance and efficiency. This project investigates the use of large pre-trained language models, specifically GPT (Generative Pre-trained Transformer), for Python code optimization. By leveraging the advanced capabilities of these models, we aim to improve traditional optimization techniques. Our approach involves preprocessing Python code, feeding it into the pre-trained model, and generating optimized code sequences. Experimental results show significant improvements in code efficiency and execution time, validating the effectiveness of our method. Additionally, we explore the practical implications, challenges, and future directions of incorporating large pre-trained models into code optimization, aiming to bridge the gap between standard optimization techniques and Python programming for a more intelligent and efficient software development practice. © 2024 IEEE.

关键词： Python

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Crop Recommendation and Yield prediction Using Machine Learning based Approaches 5

Crop Recommendation and Yield prediction Using Machine Learn...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Padmavathi, A. Gupta, Arnab Prakash, Koppadi Bhanu Sai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa VidyaPeetham Chennai India

ISBN: (纸本)9798350351378

India is an agricultural country, supporting a significant portion of the population. Agriculture is a source of income for millions of Indians. But due to lack of knowledge and the state's diverse climate and soil conditions often leave farmers grappling with the critical decisions of which crops to cultivate and what yields to expect. This in turn makes the farmers suffering from losses as they cultivate a crop hoping they will get a good harvest but in reality, they don't get the expected amount of production. To address these challenges prior researchers have used soil and climatic conditions data to recommend crops for a particular soil and predict its expected yield using various types of machine learning and deep learning algorithms. This paper presents a survey of previous works where deep learning and machine learning techniques are used and performs a comparative examination of some bagging and boosting machine learning techniques including random forest, gradient boost, XG boost, Ada boost, light bgm and cat boost for effective recommendation of crop and prediction of its expected yield. © 2024 IEEE.

关键词： Crops

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Exploring Eye Gaze Patterns in Three Dimensions: An Innovative Visualization Dashboard 2

Exploring Eye Gaze Patterns in Three Dimensions: An Innovati...

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Akshay, S. Yashwanth, M.A. Manohar, N. Jipeng, Tian Amudha, J. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Karnataka Mysore India Computer Science College Zhongyuan University of Technology Department of Computer Science China School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350383652

Eye tracking is a method of measuring and analyzing the movements of the eyes as they move and focus on different objects in the visual field. This technique has been used in a wide range of fields, including psychology, neuroscience and human-computer interaction. Eye tracking provides valuable insights into how individuals process visual information, including attentional biases, cognitive processing, and decision-making. Traditionally, eye tracking has relied on licensed software and specialized hardware, making it costly and less accessible for some researchers and applications. However, with recent technological advancements and by leveraging the capabilities of webcams, researchers, and practitioners can now perform eye-tracking experiments without the need for expensive equipment. By utilizing webcams, researchers can conduct eye-tracking experiments in a cost-effective manner, making the method more accessible to a broader range of researchers and practitioners. © 2024 IEEE.

关键词： Human computer interaction

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MazeSolver: Exploring Algorithmic Solutions for Maze Navigation 15

MazeSolver: Exploring Algorithmic Solutions for Maze Navigat...

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

作者： Narendran, Advik Hothri, Anantha Saga, Hemanth Sahay, Apurvanand Bangalore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

The initiative examines the efficiency of various maze-solving algorithms, including breadth-first search (BFS), depth-first search (DFS), A∗ search, Markov decision processes with policy iteration (MDP PI), value iteration (MDP VI), and an enhanced wall-follower algorithm. The study seeks to discover the best algorithm for the autonomy of navigation in harsh and non-obvious environments, which is a crucial step for robotics applications in logistics, exploration, and rescue operations. In a comparative analysis within a controlled maze environment, each algorithm's performance is evaluated based on path length, execution time, and memory usage. The study highlights the benefits of sophisticated decision-making and optimized pathfinding, particularly in MDP implementations and heuristic-based A∗ searches, addressing traditional limitations such as high computational costs and susceptibility to specific maze configurations. Results from this study are anticipated to enhance autonomous navigation systems, provide valuable educational insights, and improve operational efficiencies in critical applications. © 2024 IEEE.

关键词： Markov processes

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Variational Auto encoders for Improved Breast Cancer Classification 5

Variational Auto encoders for Improved Breast Cancer Classif...

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

作者： Sreelekshmi, V. Nair, Jyothisha J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Among women breast cancer is the second leading cause of cancer. Emergence of Artificial Intelligence(AI) in the medical care leads to good survival rate by diagnosing and effective prognosis of the breast cancer patients. Scientific findings show that supervised deep learning model is highly dependent on the size of the training set, which must be manually labeled by experienced radiologists. The freely available biomedical imaging dataset are small in size mostly. In addition, obtaining large medical image files is difficult due to privacy and legal reasons. Thus the deep learning models tend to overfit and fail to provide a general result. Also the recent studies indicate that early detection of breast cancer (BC) is crucial to achieve favorable treatment results and reduce associated mortality. Data augmentation is the most widely used approach to address the aforementioned problem. Our proposed architecture consist of the pectoral muscle removal of the dataset, then the variational auto encoders used for data augmentation and then the U-Net and its varients used for breast cancer classification. The dice similarity score obtained for the model after the pectoral muscle segmentation is 97.53% and the classification rate accuracy is 98.5% by use of the Attention Residual U-Net than the other two models. © 2024 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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Emotion Recognition Using Meta-Learning based on Facial Expressions 5

Emotion Recognition Using Meta-Learning based on Facial Expr...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Prashanth, V.J. Sri Naga Jathin, P. Gopinath, Suraj Udith, S. Kavitha, C.R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Emotion recognition is essential in many real-life problems and applications such as computer-human interaction, health monitoring, etc. In this project, we propose a meta-learning approach for developing a computer visionbased emotion recognition system. The aim of the work is to find human emotions from visual inputs and sort them accordingly. Meta learning techniques are used to allow the model to quickly adapt itself to emotion patterns using little training data that is short and compact. Therefore, meta learning technique is important since it allows the algorithm to perform well among different datasets with diverse data sets including unseen emotions (unknown emotions). Emotion recognition should address generalization problems while having ability to handle adaptation issues. It therefore seeks transparency in emotion recognition in addition to overcoming challenges of adaptability. Its objective is to offer answers to practical cases where emotion detection can be applied within dynamic environments. Thus at the end of this project we expect a reliable, faster, more dynamic way of classifying and recognizing human emotions based on visual inputs which highly valuable in understanding human emotional state for multiple other applications. © 2024 IEEE.

关键词： Contrastive Learning

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