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Harmonizing Music Recommendation: A Unified Approach with Hybrid, Collaborative, Content-Based, and Spotipy Modules 2

Harmonizing Music Recommendation: A Unified Approach with Hy...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Asmitha, M. Kavitha, C.R. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350372892

Recommendation systems play a crucial role in enhancing the music streaming experience, facilitating music discovery, and fostering user loyalty on platforms like Spotify. With the exponential growth of digital music platforms like Spotify, understanding and leveraging music consumption patterns has become essential for both users and platform providers. Recommendation systems are now seen multiple platform such as movie, social media etc. This study mainly aims an in-dept. analysis of preferences among Spotify users, coupled with the development of a recommendation system aimed at enhancing user experience and engagement. Furthermore the development of a robust recommendation system tailored to Spotify's platform, leveraging the identified patterns to provide personalized music recommendations to users. The recommendation system incorporates collaborative filtering, content-based filtering, and hybrid approaches to enhance recommendation accuracy and relevance. Through extensive experimentation and evaluation using real-world Spotify data, demonstrate the effectiveness and performance of the developed recommendation system. The system performance will be assessed based on accuracy in recommending, prediction of streams and popularity using RMSE, MAE, F1-score, Precision, Recall, Accuracy. © 2024 IEEE.

关键词： Collaborative filtering

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Silent Expressions Unveiled: Deep Learning for British and American Sign Language Detection 5

Silent Expressions Unveiled: Deep Learning for British and A...

引用

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

作者： Koyineni, Sai Niketh Sai, Gurram Kumar Anvesh, Kalwa Anjali, T. Dept. of Computer Science and Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Amritapuri India

Sign language is an efficient means of communication for the hearing impaired. The main goal of this research is to recognize both American and British sign languages using computer vision algorithms and neural networks. Our work addresses the critical need for inclusive and accessible communication for the Deaf community. For the study, we compiled a large dataset of her ASL and BSL gestures. Preprocess the data to improve feature extraction and reduce noise. Our model architecture uses a convolutional neural network (CNN) to capture the temporal patterns of finger gestures. This allows you to use Mediapipe keypoint detection within your model. The end result is a comprehensive model that can accurately and quickly recognize and classify sign language gestures. We further improve the usability of the system by integrating keyword recognition into the model, allowing sign language sentences to be decoded and translated into text. Our test results demonstrate how well our deep learning strategy works to achieve high efficiency and accuracy in both ASL and BSL detection. This program helps bridge the gap between those who use sign language and those who do not. Our new approach not only accurately recognizes American and British sign language, but also seamlessly integrates real-time feedback to help users make immediate improvements to improve their sign language skills. We can do better. Moreover, our approach is flexible enough to be used in a variety of settings, ensuring its usefulness in educational environments and providing sign language learners with a fun and dynamic learning environment. Explore and exploit the full potential of Mediapipe Keypoint using CNNs and also leverage the efficiency of computer vision. © 2024 The Authors. Published by Elsevier B.V.

关键词： computer vision

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Enhancing University Publication Access through Cloud-Edge Integration 5

Enhancing University Publication Access through Cloud-Edge I...

引用

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

作者： Manaswini, Poluri Shaik, Sajjad Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350376685

The integration of cloud and edge computing has the potential to transform how academic resources are accessed and managed. This study focuses on the development of a mobile application that enhances the accessibility of university publications by utilizing both cloud storage and edge computing, ensuring real-time data processing and minimal latency. The methodology includes data scraping from the university website, database creation, and the implementation of a search algorithm using the Flask framework. The results demonstrate significant improvements in the efficiency and accessibility of academic resources, highlighting the practical benefits of combining cloud and edge computing in educational settings. © 2024 IEEE.

关键词： Mobile edge computing

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Sentiment Analysis and Likes Prediction from Social Media Comments: An ML approach 15

Sentiment Analysis and Likes Prediction from Social Media Co...

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

作者： Sree, M Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Belwal, Meena Amrita Vishwa Vidyapeetham Amrita School of Computing dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

The era of social media connectivity has produced a situation where people have platforms to receive approvals from the whole world whereas they used to be restricted to their local community, and in this work, a detailed analysis of the automated analysis of user-generated content on social media platforms with a primary focus on sentiment analysis of the comments on the posts will be presented. In the current era of social media dominated digital arena with people visiting social media for approval and in need of others' advice, it will not work to manually analyze the bulk of conversation. To address this challenge of sentimental analysis, we proposeLinear SVC, Logistic Regression, Gradient Boosting Classifier, Ada Boost Classifier, HistGradient Boosting Classifier with lexical analysis. Out of all HistGradient Boosting Classifier is the best model for sentiment analysis, with the highest accuracy mean of 0.935700 and ROC AUC mean of 0. 9 0 5 6 8 5, indicating superior performance in classification accuracy and class distinction. It is effective to use this method in analysis of the content of live social activity, and this allows to understand the emotions of the people and their engagement with the content, and this leads to the content optimization and the enhancement of the strategies of audience engagement. © 2024 IEEE.

关键词： Sentiment analysis

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IoT-Based Smart Gauntlet to Assist Physically Challenged and Fragile Mortals 7

IoT-Based Smart Gauntlet to Assist Physically Challenged and...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thangam, S. Gurupriya, M. Kumar, Kanderi Johith Shreya, Kuthati Divakarla, Lasya Priya Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350372816

Individuals who are bedridden or incapacitated struggle to do particular daily tasks because they have trouble with specific hand movements. Some people can need extra help to do jobs or communicate easily. In this situation, flex sensors and an Arduino microcontroller are two features that make IoT smart gloves useful. When a person moves their fingers, the sensors detect those motions, interpret the gestures, and provide the appropriate output to an LCD monitor. The buzzer will sound, the LED will glow, and the carer will receive an emergency alert in case of an emergency. With the assistance of the smart glove, the user can utilize hand motions to turn on the fan or light. © 2024 IEEE.

关键词： Liquid crystal displays

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Efficiency of Architectural Innovations in Deep Learning for Enhanced Age Prediction and Gender Classification 15

Efficiency of Architectural Innovations in Deep Learning for...

引用

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

作者： Vinod, Gowri Krishnendu, S. Panicker, Vinitha J. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Amritapuri India

ISBN: (纸本)9798350370249

Advancements in facial recognition technology have sparked significant interest in enhancing the precision of age and gender classification, crucial for applications ranging from personalized advertising to security systems. This paper introduces a sophisticated deep learning framework, utilizing inputs of facial imagery to deploy innovative architectural methods for age and gender classification. We detail our approach from data preprocessing, model training using Generative Adversarial Networks (GANs) and Transformer models, to the evaluation of model efficacy, ultimately achieving enhanced predictive *** meticulously navigates the landscape of existing methodologies to delineate the most efficacious models, spotlighting the transformative impact of architectural ingenuity on enhancing performance in such complex classification endeavors. The culmination of this research not only illuminates the expansive capabilities of deep learning in intricate classification scenarios but also paves the way for its application in scenarios demanding unparalleled precision in age and gender predictions, such as sophisticated security protocols in access control systems. Through a comprehensive exposition of the model's design, the intricacies of its training regimen, and the indispensable value of a heterogenous training dataset, this work enriches the existing corpus of knowledge, providing a beacon for forthcoming explorations and practical implementations in akin domains. © 2024 IEEE.

关键词： Age Prediction Computational Efficiency Convolutional Neural Networks Deep Learning Architectures Demographic Attribute Prediction Gender Classification Generative Adversarial Networks Recurrent Neural Networks Transformer Models

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AI-Enhanced Real-Time Accident Detection with Smart Emergency Response System

AI-Enhanced Real-Time Accident Detection with Smart Emergenc...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Bharathi Mohan, G. Sanikommu, Pranav Reddy Teja, Gudi Vishnu Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379525

Accidental deaths remain a significant threaten the lives of people worldwide, and recurrently cause severe injuries due to slow reportage and response. The existing methods employed to detect accidental occurrences rely on written reports or structured systems that are usually less reliable, thus slow reporting and high, false alarms rates. This paper proposes a real-time and automated accident detection system which aims at enhancing the identification process by using the deep learning YOLOv5 and YOLOv8. Using the object detection and vehicle tracking, the system can calculate the speed of vehicles and detect collision events, as well as bounding box movements with IoU (Intersection over Union) that let the system track constant collisions past defined thresholds. Contrary to traditional systems, this approach significantly reduces false positive while at the same time increasing detection accuracy. When an accident is found, the system uses geographic results from OpenStreetMap and the Haversine formula to notify the closest emergency services which include hospitals and police stations. Describing its use in real traffic proves that this scalable approach enhances both detection and response time rates. This research makes small advancements towards smarter transportation systems by improving the detection and timely alerting, thereby reducing accident death tolls by quicker responses. © 2024 IEEE.

关键词： Vehicle detection

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Comparative Analysis of Blockchain based Digital Currency Transactions and UPI 2

Comparative Analysis of Blockchain based Digital Currency Tr...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Sharma, Ch Rahul A N Harsha Krovvidi, Prudhvi Sri Kadagala, Siva Charan Rajagopal, Shinu M. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

In the rapidly evolving landscape of digital transactions, Blockchain, a decentralized ledger technology, is at the forefront. This research seeks to offer a comprehensive comparison with the widely adopted UPI, aiming to dissect the strengths and challenges of blockchain in real-world scenarios. In the context of the Unified Payments Interface (UPI), leveraging the power of blockchain through tools like Ganache and Truffle involves delving into critical parameters such as Transaction Volume, Overhead, Busy Server, Failed Transactions, and Concurrent User Interaction. The primary objective is to evaluate the performance of blockchain, equipped with Ganache and Truffle, in terms of transaction efficiency and user interactions, providing valuable insights into its practical applications in the realm of digital transactions. Moreover, this project aligns with the United Nations Sustainable Development Goals (SDGs), contributing to financial inclusion, innovation, and sustainable economic growth by exploring how blockchain can address challenges in existing payment systems, fostering transparency, security, and accessibility. This research aligns with the global agenda of leveraging technology to create a more inclusive and sustainable financial ecosystem. © 2024 IEEE.

关键词： Blockchain Digital Currency Ganache Smart Contracts Truffle Unified Payments Interface

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Study of the contextual factors of a project affecting the build performance in Continuous Integration

Study of the contextual factors of a project affecting the b...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Benjamin, Jetty Mathew, Juby Jose, Rubin Thottupurathu Amal Jyothi College of Engineering Dept. of Computer Applications Kerala Kanjirappally686 518 India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram695016 India Amal Jyothi College of Engineering Dept. of Computer Science and Engineering Kerala Kanjirappally686 518 India

ISBN: (纸本)9798350303452

Continuous Integration (CI) has a significant and substantial impact on software engineering. CI optimize the development processes, enhance the code quality and facilitate a collaborative environment resulting in the effective release of high quality software features to the end users. The performance and outcome of CI build is a major concern in CI environment. In this paper we address the impact of contextual factors of software projects that can influence the performance of build in Continuous Integration/Continuous Deployment (CI/CD) pipeline. We studied 588 large-scale GitHub projects that uses the Travis CI build system. An experimental study is conducted on these projects to leverage the history of prior build success and failures. Three ML models are trained on the data. The aim of the study was to identify the contextual factors of a project, influencing the performance of build. We used Logistic Regression, Decision Tree and Extreme Gradient Boost model to predict the build outcome. We identified that the contextual factors has a significant impact and the study will further helps to develop techniques to preemptively detect when a CI build environment is most likely to trigger a failure. © 2023 IEEE.

关键词： Decision trees

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Self-Healing Email Systems: Autonomous Recovery from Cyber Attacks

Self-Healing Email Systems: Autonomous Recovery from Cyber A...

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

作者： Singh, Harsh Kumar Prabhu, H. Suhas Reddy, Bedadala Mohaneeshwar Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

Email systems are critical for communication and collaboration, yet cyber attacks are a major threat. Traditional approaches to security heavily depend upon human intervention, which can be slow and ineffective against advanced attacks. This paper proposes self-healing email systems as a novel solution for autonomous recovery from cyber attacks. These systems can use machine learning and artificial intelligence to automatically detect, diagnose, and recover from attacks, minimizing downtime and disruption. Key components of self-healing email systems, including anomaly detection, fault localization, recovery mechanisms, and adaptive learning, have been discussed. The challenges and opportunities associated with this technology have been explored. © 2024 IEEE.

关键词： Cyber attacks

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