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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Durga, Kanaparthi Dhathri, Kammarapalli Sri, Kanchukommula Sai Lavanya, Kandagaddala Naga Lakshmi School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350377972

This paper seeks to establish the energy conservation by using piezo-electric effect through some Modern Technology's and the project aims to investigate the application of modern technology in various fields to reduce energy consumption and increase energy efficiency. We focus on three key areas: buildings, transportation, and industry. In each of these areas, we identify the most significant energy consumption sources and potential opportunities for energy savings using modern technology. This aims to explore innovative solutions for sustainable energy generation by harnessing mechanical energy from human movement. This research focuses on integrating piezoelectric cells into everyday footwear and flooring tiles to convert mechanical stress into electrical energy. The project involves designing and fabricating prototypes of energy-harvesting shoes and tiles. The efficiency of energy conversion, the durability of the materials, and the overall feasibility of integrating such systems into daily life will be evaluated. The study will also address the scalability of this technology and its potential impact on reducing the reliance on traditional power sources. © 2024 IEEE.

关键词： Piezoelectricity

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Streamlining Software Development: A Comprehensive Study on CI/CD Automation 4

Streamlining Software Development: A Comprehensive Study on ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Baitha, Sanjay Soorya, Vijayakumar Kothari, Osho Rajagopal, Shinu M. Panda, Niharika Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidypeetham Bengaluru India

ISBN: (纸本)9798331540364

Continuous Integration and Continuous Deployment (CI/CD) automation have emerge as critical for modern-day software development. Despite its blessings, demanding situations including integration complexity, inconsistent trying out environments, and deployment troubles persist. Recent strategies like superior containerization, cloud-based scaling, and improved testing frameworks intention to address those issues. This take a look at implements an self sustaining CI/CD pipeline using gear such as Jenkins, GitHub, and SonarQube to automate processes which include coding, building, checking out, and deployment. By addressing those demanding situations, our proposed objective is to enhance efficiency and reliability in the software program development lifestyles cycle, allowing improvement teams to seamlessly add features and solve problems. © 2024 IEEE.

关键词： Software design

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Technology Enhanced Cradle by using Sensors and Internet of Things 5

Technology Enhanced Cradle by using Sensors and Internet of ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Thangam, S. Greeshma, Mogili Varun, Mekala Sanjana, N.V.S. Kumari, J. Jesy Janet Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331504403

Conventional cradles lack the ability to monitor and support infants continuously or adapt to varying needs. This study proposes a novel smart cradle equipped with advanced sensors and actuators, enabling parents to control environmental factors in real time, thereby ensuring the highest levels of safety and comfort for infants. Unlike previous models, the smart cradle automatically responds to stimuli, such as an infant's cry, and allows for remote monitoring via the Blynk app. Comparative analysis demonstrates that the proposed smart cradle offers faster response times and greater accuracy in environmental control compared to recent models, establishing it as a new benchmark in infant care products. This innovation addresses the deficiencies of current cradle designs and represents a significant advancement in automated infant care systems, ultimately conserving caregivers' resources and improving overall infant care. © 2024 IEEE.

关键词： Remote control

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Facial Grid Environment for Vitiligo Detection: A Deep Reinforcement Learning Perspective 2

Facial Grid Environment for Vitiligo Detection: A Deep Reinf...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Adwayan, P.C. Anfas Hassan, V. Suresh, Navneeth Nisha, K.S. Sha, Akhbar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350360523

This research addresses the challenging task of vitiligo detection within facial images through the lens of Deep Reinforcement Learning (DRL). Leveraging the Skin Disease dataset from Kaggle, our study focuses on the integration of three distinct convolutional neural network architectures - ResNet, VGG, and EfficientNet - within the DRL framework. Facial landmarks, crucial for spatial awareness, were extracted using OpenCV as a preprocessing step. The DRL agent navigates through a grid overlaying facial images, with states corresponding to grid cells, to reach the vitiligo-affected region and maximize cumulative rewards. Analysis of the models revealed ResNet's superior stability in the loss curve, indicative of its effective feature learning. The classification accuracy further reinforced ResNet's prominence, reaching an impressive 97%, outperforming VGG (94.5%) and EfficientNet (95.3%). While VGG and EfficientNet demonstrated competitive performances, ResNet's robustness and accuracy position it as the preferred architecture for vitiligo detection in facial images. This study contributes to the burgeoning field of dermatological diagnostics by showcasing the efficacy of DRL and different CNN architectures in automating vitiligo detection. The findings not only advance our understanding of the interplay between deep learning and dermatology but also lay the foundation for more accurate and efficient models in the broader landscape of skin disorder identification. © 2024 IEEE.

关键词： Reinforcement learning

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Optimising Energy Efficient Routing protocols in Wireless Sensor Networks - A comparative study 2

Optimising Energy Efficient Routing protocols in Wireless Se...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manideep, Thotapalli Sri Surya Rohan, Gamidi Saragadam, Sneha Sahay, Apurvanand Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368413

This study presents a comparative overview of optimization techniques that include, among others, Ant Colony Optimization, Firefly Algorithm, Bat Algorithm, Bee Colony Optimization, Q-learning, Dynamic Programming, and Branch and Bound Algorithm. Addressing each domain in detail, explanations will be offered for the methods described: for example, ACO's foraging behavior, Firefly's light attraction, Bat's echolocation, and BCO's collaborative approach. The Travelling Salesman Problem (TSP) is treated with the DP algorithm and Branch and Bound. Different tests in each algorithm are made, and their efficiency and weaknesses are approximated to guide the selection of a proper approach to be used in future optimizations. This research highlights the strengths and drawbacks of each method, thus enhancing the understanding of how these optimization techniques operate in various fields and contributing to the informed selection of algorithms for specific problems. The results provide practical insights and suggest directions for further improvement and application in real-world scenarios. © 2024 IEEE.

关键词： Ant colony optimization

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Modeling Temple Run Gameplay with Finite State Machines 3

Modeling Temple Run Gameplay with Finite State Machines

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

作者： Datta, P. Tapan Lalith Kumar, D. Tanoj, S. Radha, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

The ambitious task of implementing the popular Temple Run game in a new approach that is used to create a version of the Temple Run game that is represented in Non-Deterministic Finite Automata (NFA) and Deterministic Finite Automata (DFA). Temple Run, celebrated for its dynamic gameplay and constantly changing obstacles, offers an ideal platform for these automata models in gaming mechanics. The initiative focuses on using Non-Deterministic Finite Automata to generate diverse and evolving pathways within the game environment, ensuring players encounter a variety of challenges and opportunities. Conversely, Deterministic Finite Automata will regulate the behavior of in game elements like obstacles, abilities, and enemies, establishing clear rules and patterns for their movements and interactions. Through the integration of both NFA and DFA models, an immersive Temple Run experience is aimed to be created, seamlessly combining unpredictability with structured gameplay, ensuring each gaming session is both engaging and rewarding for players. © 2024 IEEE.

关键词： Digital elevation model

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Telugu Language Analysis with XLM-RoBERTa: Enhancing Parts of Speech Tagging for Effective Natural Language Processing

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SN Computer science 2025年第2期6卷 1-13页

作者： Bharathi Mohan, G. Prasanna Kumar, R. Krishna Jayanth, K. Doss, Srinath Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Faculty of Engineering and Technology Botho University Gaborone Botswana

This research investigates the efficacy of XLM-RoBERTa, a potent deep learning architecture rooted in transformer networks, for Part-of-Speech (POS) tagging—a foundational task in Natural Language Processing (NLP). The study narrows its focus to address the formidable UCD Telugu dataset, renowned for its intricate morphological characteristics that pose challenges to accurate tagging. Through meticulous fine-tuning of the pre-trained XLM-RoBERTa model. This study adeptly discerns and exploit intricate syntactic and semantic patterns underpinning the interplay between lexical elements and their respective POS tags. The rigorous evaluation regimen, complemented by a comprehensive statistical comparison with the Multilingual BERT model, conclusively establishes XLM-RoBERTa’s superiority. This advanced model attains a remarkable accuracy score of 91%, decisively surpassing the Multilingual BERT model, which achieves an accuracy of 88%. These findings not only underscore XLM-RoBERTa’s prowess in precise POS tagging, especially in the context of morphologically rich languages like Telugu, but also carry significant implications for the broader NLP community. This research unlocks valuable insights and positions XLM-RoBERTa as an exemplar of reliability and robustness in POS tagging. Furthermore, it extends an invitation for further exploration and innovation in deep learning architectures within the NLP domain. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bert Multilingual Bert NLP Parts-of-speech tagging Telugu XLM-RoBERTa

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Real-Time Speech-to-Text Holographic Communication for the Deaf Children and Elderly 4

Real-Time Speech-to-Text Holographic Communication for the D...

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4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Charan, Sreevathsa Sree Srihitha, Vemula Palaniswamy, Suja Chethana, Savarala Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350375466

In the field of assistive technologies, innovative solutions that cater to the needs of individual with disabilities are paramount for fostering inclusive communication environments. The proposed work introduces an innovative real-time speech-to-text conversion system using holographic display technology to aid communication for the deaf and hard-of-hearing (DHH). The system incorporates two complementary speech recognition approaches as each contribute uniquely to improve the system's overall performance and accuracy in different ways and also reduce the latency. First approach is a conventional speech recognition module and another is an enhanced version of the Wav2vec2 model, which has been retrained to improve its efficacy with diverse speech patterns. Hosted on a Raspberry Pi and interfaced with an USB microphone, the system captures spoken language effectively. Upon recognition, the text is instantly displayed on a holographic screen made up of LED matrices and set against an acrylic glass surface, creating a visually engaging representation of floating text. The integration of these two recognition methods enhances the system's accuracy and reliability in processing real-time speech. By providing a visual output of spoken words, this system not only bridges communication gaps but also enriches the interaction experience within various social and professional settings. The application of holographic technology in this context is pioneering, offering substantial promise for developing accessible communication tools that are both functional and inclusive. This work underscores the potential of advanced display and speech processing technologies to transform interactions for individuals with hearing impairments, fostering greater inclusivity and engagement in everyday communications. © 2024 IEEE.

关键词： Speech recognition

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Deep Learning Approach for Automated Data Augmentation and Multi-class Classification of Pap Smear Images 2

Deep Learning Approach for Automated Data Augmentation and M...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nayar, Sanjana Priya, Devi Vinitha Panicker, J. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

AI is now used to analyze large datasets, which can subsequently be used to predict outcomes and provide patient insights. In this paper, we aim to address the challenge of insufficient medical data, which in turn hinders ML and AI from being used in medicine. By producing additional and related medical data, data augmentation will make it easier to develop machine learning or deep learning models to automate diagnosis. This study aims to utilize Generative Adversarial Networks to create synthetic images and to develop a model to effectively classify the dataset, including the newly generated images, into NILM, HSIL, LSIL, or SCC, which were the four types of cancer cells provided in the dataset. The LBC dataset for multi-class classification has been classified with a state-of-the-art accuracy of 99.1% in our study. The testing accuracy achieved using traditional methods is 98.8%, whereas using GAN is 99.1%, demonstrating that employing synthetic images created using GANs can be a better augmentation method than conventional ways. © 2024 Elsevier B.V.. All rights reserved.

关键词： Convolutional neural networks

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Deciphering Poxvirus Strains: Feature-Driven classification of Monkeypox 4

Deciphering Poxvirus Strains: Feature-Driven classification ...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Mehul, Daripelli Sasikala, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

This research outlines a method for monkeypox virus classification from images, emphasizing the effective management of augmented datasets to ensure balanced class distribution and provide a robust foundation for model training. The study harnesses the capabilities of advanced pre-trained deep learning models, specifically ResNet50, EfficientNetB3, and DenseNet169, utilizing transfer learning techniques to adapt them to the task at hand. Techniques such as batch normalization and dropout are used to reduce overfitting and improve the generalization capacity of the models. Then, using the optimizer and categorical cross-entropy loss, these models are fine-tuned for monkeypox virus classification. One noteworthy addition is the inclusion of interactive learning, which gives users dynamic control over the training process by enabling them to intervene at predetermined intervals. Performance metrics such as accuracy, confusion matrix are used in the evaluation of the model. In order to guarantee a trustworthy evaluation of the models' performance, an independent test dataset is utilized. The most impressive accomplishment is the ensemble learning method, which combines the predictions from all three models,ResNet50,EfficientNetB3 and DenseNet169 with a simple averaging procedure. The classification report shows that this all-encompassing methodology consistently produces excellent results, with precision, recall, and F1-score consistently exceeding 0.97. In a time when healthcare challenges are constantly changing, this method shows promise as a useful tool for image-based diagnosis. © 2024 IEEE.

关键词： DenseNet169 EfficientNetB3 Monkeypox ResNet50 Virus

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