Название: IoT, Cloud and Data Science Автор: S. Prasanna Devi, G. Paavai Anand, M. Durgadevi, Golda Dilip Издательство: Trans Tech Publications Год: 2023 Страниц: 911 Язык: английский Формат: pdf (true) Размер: 68.5 MB
One of the most significant characteristics of the evolving digital age is the convergence of technologies that includes sensors (Internet of Things: IoT), data storage (cloud), information management (databases), data collection (Big Data), data applications (analytics), knowledge discovery (Data Science), algorithms (Machine Learning), transparency (open data) and API services (micro services, containerization).
Nowadays face recognition system is widely used in every field of computer vision applications such as Face lock-in smartphones, surveillance, smart attendance system, and driverless car technology. Because of this, the demand for face recognition systems is increasing day by day in the research field. The aim of this project is to develop a system that will recommend music based on facial expressions. The face-recognition system consists of object detection and identifying facial features from input images, and the face recognition system can be made more accurate with the use of convolutional neural networks. Layers of convolution neural network are used for the expression detection and are optimized with Adam to reduce overall loss and improve accuracy. YouTube song playlist recommendation is an application of a face recognition system based on a neural network. We use streamlit-webrtc to design the web frame for the song recommendation system. For face detection, we used the Kaggle-FER2013 dataset, and images in the dataset are classified into seven natural emotions of a person. The system captures the emotional state of a person in real-time and generates a playlist of Youtube songs based on that emotion.
Time series data and its practical applications lie across diverse domains: Finance, Medicine, Environment, Education and more. Comprehensive analysis and optimized forecasting can help us understand the nature of the data and better prepare us for the future. Financial Time series data has been a heavily researched subject in the present and in the previous decades. Statistics, Machine Learning (ML) & Deep Learning (DL) models have been implemented to forecast the stock market and make data informed decisions. However, these methods have not been thoroughly explored, analysed in context of the Indian Stock Market. In this paper we attempt to implement evaluate the avant-garde statistical, Machine Learning methods for Financial Time Series Analysis & Forecasting.
A software bug is some sort of a fault in the source code or a computer program. These bugs work in unusual and unintended ways which is a serious problem for a programmer and the company. Detecting bugs in software has been tried and tested through multiple means, the most recent of which is Machine Learning algorithms. Using a revolutionary dataset consisting of real software code snippets of the C language into key values, we were able to train the algorithms based on numerical parameters. This in turn simplified our algorithmic process. Furthermore, we run multiple classification algorithms to gain precision, recall and Fn scores, and improve upon these scores using key hyperparameter tuning techniques. Our observations revealed an increase in accuracy and were able to create an end module which can directly take the source code as an input from which the metrics and features are extracted and give the output if the code has a software bug or not.
Cloud computing, a next-generation computing technique, enables extremely flexible, scalable, and cost-effective computing that is available on demand. Organizations can expect increased agility, improved business interaction, and continuity as a result of cloud technologies. However, with increased demand, the need for data confidentiality, integrity, and availability has become one of the most crucial challenges in cloud computing. Various cryptographic approaches are employed in the cloud to guarantee that these needs are met, such as safeguarding against data breaches and malicious attacks. This paper outlines cloud infrastructure, cloud security requirements, and their challenges. We then present an overview of various cryptographic techniques such as AES, DES, RSA, etc. that have been used to ensure cloud security. We also reviewed hybrid algorithms proposed and implemented by various authors and mentioned their advantages and shortcomings.
Preface Chapter 1: Machine Learning to Image Processing and Computer Vision Chapter 2: Computational Linguistics Chapter 3: Machine Learning to Financial Data Analysis Chapter 4: Machine Learning on Other Types of Datasets Chapter 5: Blockchain Technology Chapter 6: Cloud Computing Chapter 7: WEB and Network Security Chapter 8: Internet of Things and Networking
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IoT, Cloud and Data Science 
Название: IoT, Cloud and Data Science
