Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)

As we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp

Cognitive Informatics

Cognitive Informatics (CI) is a contemporary field of basic studies on the brain, computational intelligence theories and underpinning denotational mathematics. Its applications include cognitive systems, cognitive computing, cognitive machine learning and cognitive robotics. IEEE ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This paper reports the plenary panel (Part I) at IEEE ICCI*CC'17 held at Oxford University. The summary is contributed by invited keynote speakers and distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and cognitive computing

Research Study on basic Understanding of Artificial Neural Networks

Artificial neural networks are a computing system inspired by human neuron, designed to simulate the way human brain analyzes and processes information. They are the foundation of artificial intelligence and machine learning technology. This research paper focuses on the basic understanding of Artificial neural networks. ANN create a lots of excitement in Machine learning research and that results a huge development on many AI and machine learning systems like text processing, speech recognition, image processing. Neural networks consist of input and output layers, in many cases hidden layer consisting of units that transform the input into something that the output layer can use. They are essential tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize

Research Study on basic Understanding of Artificial Neural Networks

Artificial neural networks are a computing system inspired by human neuron, designed to simulate the way human brain analyzes and processes information. They are the foundation of artificial intelligence and machine learning technology. This research paper focuses on the basic understanding of Artificial neural networks. ANN create a lots of excitement in Machine learning research and that results a huge development on many AI and machine learning systems like text processing, speech recognition, image processing. Neural networks consist of input and output layers, in many cases hidden layer consisting of units that transform the input into something that the output layer can use. They are essential tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize