El-Sayed M. El-Alfy – författare

Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications.

Features:

This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

Video has rich information including meta-data, visual, audio, spatial and temporal data which can be analysed to extract a variety of low and high-level features to build predictive computational models using machine-learning algorithms to discover interesting patterns, concepts, relations, and associations. This book includes a review of essential topics and discussion of emerging methods and potential applications of video data mining and analytics. It integrates areas like intelligent systems, data mining and knowledge discovery, big data analytics, machine learning, neural network, and deep learning with focus on multimodality video analytics and recent advances in research/applications.

Features:

This book aims at researchers, professionals and graduate students in image processing, video analytics, computer science and engineering, signal processing, machine learning, and electrical engineering.

This book contains a selection of refereed and revised papers of Intelligent Informatics Track originally presented at the third International Symposium on Intelligent Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers selected for this Track cover several intelligent informatics and related topics including signal processing, pattern recognition, image processing data mining and their applications.

This book explores and discusses various aspects of intelligent systems technologies and their applications. Presenting the refereed post-conference proceedings of the 5th International Symposium on Intelligent Systems Technologies and Applications (ISTA 2019), held at the Indian Institute of Information Technology and Management-Kerala (IIITM-K), Trivandrum, India, on December 18–21, 2019, it covers a variety of topics, such as knowledge discovery, data mining, pattern recognition, signal processing, intelligent image processing, artificial vision, ad hoc and wireless sensor networks, business intelligence and big data analytics.

This book offers to readers a selection of refereed papers that were presented at the Sixth International Symposium on Intelligent Systems Technologies and Applications (ISTA’20). All submissions were evaluated on the basis of their significance, novelty, and technical quality. This book consists of 28 papers (19 regular and 9 short papers) that were virtually presented at the Symposium. The papers cover different areas such as big data analytics, security and privacy, Internet of things, machine and deep learning, health informatics, visual computing, signal processing, and natural language processing. The book is directed to the researchers and scientists engaged in various fields of intelligent systems. 

With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with artificial intelligence and machine learning and internet of things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services.

A “spatial data management system” is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with artificial intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services.

This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of artificial intelligence and machine learning for spatiotemporal data analysis and modeling. The book is composed of several chapters written by experts in their fields and focusing on several applications including recommendation systems, big data analytics, supply chains and e-commerce, energy consumption and demand forecasting,and traffic and environmental monitoring. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as data science and engineering, big data analytics and mining, artificial intelligence, machine learning and deep learning, cloud computing, and internet of things.