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Types of Machine Learning algorithms Source: https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/
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The paper reviews how human-centered artificial intelligence and security primitive have influenced life in the modern world and how it’s useful in the future. Human-centered A.I. has enhanced our capabilities by the way of intelligence, human informed technology. It has created a technology that has made machines and computer intelligently carry t...
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A network intrusion detection system (NIDS) is an important technology for cyber security. Recently, machine learning based NIDSs are being actively researched as various machine learning techniques are proposed. However, existing NIDSs have limitation in terms of generality because they have been designed based on specific characteristics obtained...
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... AI is the study and design of algorithms that perform activities or exhibit behaviours like humans that require intellect Mathew (2020). Examples AI systems include, Alexa, smart assistants, and autonomous cars Riedl (2018). ...
Cyber-attacks are on the rise, becoming more sophisticated as technology advances and negatively impacting people as well organisations globally. Advanced approaches to managing cyber-attacks are required to mitigate cyber-risk. One of the advanced approaches to managing cyber-attacks is the use of artificial intelligence (AI) in cybersecurity. Cybersecurity is a broad field, and this study includes identity and access management (IAM) as IAM is the entry point to IT systems. The adoption of AI in the financial services sector is slow, globally. This study develops and proposes guidelines for adopting AI in IAM within the financial services. A narrative literature review is performed, and guidelines for adopting AI in IAM are designed based on literature and frameworks available. The guidelines are composed of nine layers which include environment, organisation, technology, IAM life cycle, IAM processes, AI user requirements, AI architecture, AI implementation approach, and implementing AI. Furthermore, key stakeholders at each layer are defined in the guidelines. The guidelines will increase the possibility of successfully adopting AI in IAM leading to a better security posture in banks.
Bioreactors are essential tools in tissue engineering and drug delivery research, providing controlled environments for cell growth, tissue development, and optimization of manufacturing parameters. There are various types of bioreactors, including static, dynamic, perfusion, and rotating systems, each offering unique advantages depending on the application. Key design considerations for bioreactors include the size, geometry, components, materials, and operating conditions needed to support the cultured tissue or organ. Stimuli-responsive materials have emerged as essential components in the design of bioreactors and the fabrication of scaffolds for various applications in tissue engineering and drug delivery. These intelligent materials possess the ability to modulate their properties and functionalities in direct response to external stimuli such as temperature, pH, light, electric or magnetic fields, and biochemical signals. This inherent responsiveness affords precise control over the spatiotemporal manipulation of physical and chemical cues, thereby influencing cellular behavior and facilitating controlled release of therapeutic agents. Commonly employed stimuli-responsive polymers encompass thermoresponsive, pH-responsive, light-responsive, and redox responsive materials.3D printing techniques allow fabrication of complex, customized scaffolds using digital designs and living cell-laden bio-inks. Bioprinting combined with stimuli-responsive materials enables 4D printing of dynamic scaffolds that transform over time when triggered. Ongoing research aims to optimize bioreactor design, develop novel smart biomaterials, achieve multi-material 4D printing, and enhance responsiveness to internal stimuli for advanced tissue engineering and drug delivery applications.
