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Artist-Specific Analysis of Spotify using BI Tools 14

Artist-Specific Analysis of Spotify using BI Tools

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sharma, Varsha Atwal, Hetal Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350335095

Spotify is one of the world's largest music audio streaming and media service provider with over 489 million monthly active users, including 205 million paying subscribers as of December 2022. The music from various artists around the globe can be found on Spotify where songs from these artists compete daily in order to top the charts. In this paper, we present an analysis of the music charts using many features that describe a song such as acousticness, danceability, etc., and present artist-specific suggestions that can help the artists know the genre of the songs that is most popular among the listeners and therefore, produce songs accordingly and hence help the artists gain popularity among the audience. © 2023 IEEE.

关键词： Audio acoustics

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Brain Tumor Detection and Classification Using Deep Neural Network and Interpretation Using XAI Techniques

Brain Tumor Detection and Classification Using Deep Neural N...

引用

2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Anitha, A. Nair, Anjana Kamaraj, Balakrishnan Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore India Madurai Medical College Tamilnadu Madurai India

ISBN: (纸本)9798350376135

Clinical decision-making relies heavily on accurate brain tumor diagnosis from MRI imaging. Manual interpretation is time-consuming and error-prone. To enhance efficiency, automated techniques using deep neural networks (DNNs), particularly Convolution Neural Networks (CNNs), are proposed. This study incorporates explainable artificial intelligence (XAI) for interpretability. By addressing the need for automated and accurate diagnosis, this research aims to improve patient care and treatment outcomes, providing medical professionals with a dependable tool for brain tumor identification in clinical practice. © 2024 IEEE.

关键词： Deep neural networks

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A Simple Algorithm to Secure Data Dissemination in Wireless Sensor Network 1

引用

1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Sonai, Veeramani Bharathi, Indira Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Wireless sensor networks are heavily utilized in numerous applications in both industry and science. As the original data is transferred from the sensor node to the sink node, unauthorized individuals attempt to access it, breaching confidentiality. The first step in enhancing the security of data transmission in a wireless sensor network (WSN) is figuring out the best method for encrypting data as it moves from the sensors to the central node. In order to move encrypted data from different sensor nodes to sink nodes efficiently in wireless sensor networks, this paper introduces a simple data encryption technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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Prediction and Analysis of Sentiments of Reddit Users towards the Climate Change Crisis 1

Prediction and Analysis of Sentiments of Reddit Users toward...

引用

1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Ray, Sujana Senthil Kumar, A.M. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

Climate crisis is one of the most talked about issues in the world today. In spite of a global agreement on the necessity to protect the earth from global warming, people still lack awareness on the graveness of the situation. Social media platforms such as Twitter, Facebook, Instagram, Reddit and others offer immense opportunities for people to become vocal and participate with their opinions and thoughts on these practical challenges by exchanging information and talking about them. By looking at their attitudes and the issues they discuss, it is possible to determine in this research how users of Reddit, one of the most well-known and popular social media platforms in the world, feel about climate change. Retrieved comments and posts are classified into two sentiment classes: Positive and Negative. To understand the sentiments, we find sentiment targets by comparing two neural networks CNN and RNN and using the more accurate model to predict sentiments of the comments in the test dataset and analyse the nature of climate change discussion over time. Although the computational maximal accuracy for the two models is comparable, it was discovered that the CNN model scored marginally better than the RNN in terms of average precision, average accuracy, and average loss. The examination of Reddit users' opinions demonstrates that the general attitude is negative, particularly when people acknowledge extreme weather events that have the potential to impact the public wellbeing framework. © 2023 IEEE.

关键词： Sentiment analysis

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Smartphone recommendation using popularity and collaborative filtering-based models 6

Smartphone recommendation using popularity and collaborative...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Paul, Aniket Kumar Nath Pandey, Trilok Kumar, Aswathi Manoj Moloparambil, Savio Sajan Kartheek, Surya Singh, Suraj Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350394702

With so many options available in the smartphone market, the question of which model is to be purchased, arises. The model proposed in our article will be trained with large datasets which can then suggest the perfect model with a given set of inputs without any hassle to search for hours on the internet in order to decide which phone is best suited to the customer in accordance with his/her needs. In this paper, it should be assumed that "Users"and "Reviewers"are used interchangeably in this paper as it is assumed that phone reviewers and users are the same. Therefore, our smartphone's recommendation system is designed to create a system that can suggest the best suited smartphones based on the choices or behaviors of the consumers. © 2023 IEEE.

关键词： Smartphones

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A Comprehensive Review on Early Diagnosis of Alzheimer's Disease Detection

A Comprehensive Review on Early Diagnosis of Alzheimer's Dis...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, G.S. Nachiyappan, S. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

A progressive neurodegenerative Alzheimer's Disease (AD) shrinks (atrophy) the brain and kills brain cells. On a global scale, Finland and the United Kingdom have the highest prevalence of AD, with 54.7 and 42.7 cases per 100, 000 inhabitants respectively. In India, there are 14.60 AD cases for every 100, 000 people and it is ranked 137th in the world and 13th in India among the 50 causes of death. At the beginning of the disease, a person may forget things, or have trouble remembering recent conversations. As the disease progresses, a person may experience severe memory loss and difficulty performing everyday tasks. Consequently, the only way to predict AD at an early stage is to use treatments that prevent the disease from progressing. This paper provides an overview of the current approaches to AD diagnosis and detection, with a focus on the use of Biomarkers and discussed the advantages and disadvantages of Machine Learning (ML)/Deep Learning (DL) techniques in early detection of AD. It also reviews the collection of AD datasets and the pre-processing techniques used in the studies. Although DL methods gives the best outcome measures such as accuracy, precision, F-measures, recall and Area Under the Curve (AUC) still DL techniques have some limitations that can be overcome via Quantum Computing (QC). QC is currently emerging new technology based on quantum theory where qubits are used to store the data instead of bits. It accomplishes 1000 times faster and also solves the complex problem within a fraction of time than the classical computers. © 2023 IEEE.

关键词： Alzheimer's Disease Biomarkers Datasets Deep Learning Machine Learning Quantum Computing

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Stroke Disease Prediction Using Adaboost Ensemble Learning Technique 4th

Stroke Disease Prediction Using Adaboost Ensemble Learning T...

引用

4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Ganachari, Sreenidhi Battula, Srinivasa Rao School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh Amaravati India

ISBN: (纸本)9789819920990

This paper presents an approach to predict stroke utilizing a better model with Adaboost algorithm. Today the utilization of opportunely trained machine learning algorithms can be significantly utilized in fields such as medicine and data science to identify and provide solutions to quandaries that do not have a solution or if the current solutions are not efficacious. A stroke is a condition where blood arteries in the brain burst, harming the brain. The rigor of a stroke can be abated by early apperception of numerous stroke warning symptoms. Previous works have utilized sundry machine learning algorithms to determine stroke. Despite many research works in the literature indicating successful outcomes in preclinical stroke models, there has been no significant contribution to the medical field. Since one weak classifier cannot reliably predict an object’s categorization by itself, adaptive boosting, also known as AdaBoost, combines numerous weak classifiers in progressive learning to create a strong predictive classifier. It uses ensemble learning techniques to predict the accuracy which makes it better than the other algorithms discussed in the paper. The dataset used emanates from Kaggle with sundry parameters that have been taken into consideration. The machine learning algorithms considered are random forest algorithm, decision tree algorithm, SVM, logistic regression algorithm, KNN, Naïve Bayes, and AdaBoost algorithm to train different models and compare the results for the best model. Amongst all the algorithms AdaBoost algorithm gives us the best precision of 98.8%. Thus, the main aim of this paper is to propose a classifier—AdaBoost classifier that can be utilized by medical practitioners to detect the ailment in its early stages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Jaya integrated binary whale optimized algorithm for preserving medical data

Jaya integrated binary whale optimized algorithm for preserv...

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sudha, I. Ramesh, P.S. Jagadeesan, S. Murugan, Guru Vimal Kumar Gokulnath, C. Poongavanam, N. Department of Computer Science and Engineering SIMATS School of Engineering SIMATS Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India School of information Technology and Engineering Vellore Institute of Technology Tamilnadu Vellore India

ISBN: (纸本)9781032669663

Cloud Computing has the source of information to various sectors that the future generation duly depends on the resources of the cloud. Users can access a variety of network, storage, and platform features through the cloud. The users exploit the cloud’s resources in accordance with their needs, and the data is centralized for cloud storage. Hence, The main aim proposes a method called Jaya Binary Whale Optimization (JBWO), which combines the Binary Whale Optimization and Jaya algorithm (JBWOA) and modifies the Fully homomorphic encryption (FHE) Organism exhausting Advanced Encryption Standard (AES), for the cloud to start securely transmitting data.. By applying the proposed JBWO algorithm to generate the Data Protection coefficient (DP), the original data is retained. For the purpose of selecting the best resolution, privacy and utility parameters are used to calculate fitness. By combining the key vector and the Key Information Product (KIP) matrix with the EX-OR operation, the sanitized data are produced. By utilizing the key that the data owner provides, users can access the unique data. The strategy produced improved results in the analysis utilizing the Cleveland datasets and is capable of managing sensitive information with clearly specified privacy. The suggested privacy protection techniques effectively safeguard data while maintaining privacy, resulting in satisfied customers. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Cloud storage

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Suicide: An Analysis and Prediction Using Pyspark and Machine Learning Models

Suicide: An Analysis and Prediction Using Pyspark and Machin...

引用

2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Akhila, Pujala Priyadarshini, R. Banerjee, Shalini Thomas, Beryl Abey School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350305708

The World Health Organization (WHO) reported in 2021 that more over 700, 000 people had committed suicide. Suicide can be stopped, but most efforts have so far been ineffective. However, the application of machine learning presents fresh chances to improve prediction accuracy and advance the cause of suicide prevention. This study uses data-driven methodologies to analyze and predict suicide, and presents the same in this report. Machine learning algorithms and PySpark programs are used to analyze a dataset of demographic, socioeconomic, and psychological characteristics related to suicide instances from a Kaggle dataset comprising of 237519 rows and 7 columns. The study creates prediction models like linear regression, decision tree, decision tree regressor and bagging regressor. The results support efforts to prevent suicide by assisting in the prioritization of resources and focused interventions. © 2023 IEEE.

关键词： bagging decision tree linear regression prediction models pyspark suicide analysis

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Interactive Learning Environment for Networking Topics based on Augmented Reality

Interactive Learning Environment for Networking Topics based...

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International Conference on computer Vision and Internet of Things 2023, ICCVIoT 2023

作者： Kanimozhi, S. Sairamesh, L. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering St. Joseph's Institute of Technology Chennai India

ISBN: (纸本)9781839539848

Augmented Reality (AR) implemented on mobile devices has emerged as a prominent subject of study within the field of mobile applications and human-machine interaction. The mobile augmented reality (AR) technique integrates intelligent display, registration tracking, virtual and reality convergence, and human-computer interactions using portable devices or intelligent terminals. This enables the seamless integration of the 3D virtual object with the customer's real-life environment, thereby enhancing the perceived depth and breadth of the experience. Augmented reality has been extensively utilized across diverse domains, including the field of education. In the domain of education, augmented reality is employed to enhance the level of engagement and appeal in the learning process. Beginning with the issue of acquiring knowledge about computer networks, it is observed that the conventional approach involves the introduction of network devices, TCP/IP models, and OSI models. This research addresses the problem by creating a learning media solution that utilizes augmented reality (AR) technology. AR technology integrates two-dimensional or three-dimensional virtual objects into a real environment and displays these virtual objects in real-time. The novelty of this work implies on the design of augmented reality (AR) applications for teaching the OSI model in the field of computer networks. The aim is to enhance comprehension, stimulate motivation, and generate student interest in learning. © The Institution of engineering & technology 2023.

关键词： Augmented reality

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