El-Sayed M. El-Alfy - Böcker

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: Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics. Explores important applications that require techniques from both artificial intelligence and computer vision. Describes multimodality video analytics for different applications. Examines issues related to multimodality data fusion and highlights research challenges. Integrates various techniques from video processing, data mining and machine learning which has many emerging indoors and outdoors applications of smart cameras in smart environments, smart homes, and smart cities.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: Provides up-to-date coverage of the state-of-the-art techniques in intelligent video analytics. Explores important applications that require techniques from both artificial intelligence and computer vision. Describes multimodality video analytics for different applications. Examines issues related to multimodality data fusion and highlights research challenges. Integrates various techniques from video processing, data mining and machine learning which has many emerging indoors and outdoors applications of smart cameras in smart environments, smart homes, and smart cities.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.

With the rapid evolution of cyberspace, computing, communications and sensing technologies, organizations and individuals rely more and more on new applications such as fog and cloud computing, smart cities, Internet of Things (IoT), collaborative computing, and virtual and mixed reality environments. Maintaining their security, trustworthiness and resilience to cyber-attacks has become crucial which requires innovative and creative cyber security and resiliency solutions. Computing algorithms have been developed to mimic the operation of natural processes, phenomena and organisms such as artificial neural networks, swarm intelligence, deep learning systems, biomimicry, and more. The amazing characteristics of these systems offer a plethora of novel methodologies and opportunities to cope with emerging cyber challenges.This edited book presents a timely review of the fundamentals, latest developments and diverse applications of nature-inspired algorithms in cyber security and resiliency. Topics include bio-inspired collaboration and cyber security; immune-based defense and resiliency; bio-inspired security and resiliency of network traffic; nature inspired machine learning approach for cyber security; nature-inspired algorithms in A.I. for malicious activity detection; DNA-inspired characterization and detection of novel social Twitter spambots; nature-inspired approaches for social network security; bio-inspired cyber-security for smart grid; nature-inspired cryptography and cryptanalysis, and more.

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.

The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017.

The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017.

The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017.

The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constituts the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017.

The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017.

The six volume set LNCS 10634, LNCS 10635, LNCS 10636, LNCS 10637, LNCS 10638, and LNCS 10639 constitues the proceedings of the 24rd International Conference on Neural Information Processing, ICONIP 2017, held in Guangzhou, China, in November 2017.

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). This book consists of 28 papers (19 regular and 9 short papers) that were virtually presented at the Symposium.

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.