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Dr. Surbhi R. Khare is an Assistant Professor in the Department of Information Technology at Priyadarshini College of Engineering, Nagpur, India. Her research interests include cybersecurity, artificial intelligence, cloud computing, and network security.
The escalating intricacy of global security issues, cyber threats, natural disasters, geopolitical hazards, and vital infrastructure vulnerabilities has expedited the integration of Artificial Intelligence (AI) inside defence, security, and risk management sectors. Resolving these complex difficulties necessitates robust cross-sector collaborations involving governmental bodies, defence entities, private enterprises, academic institutions, technology suppliers, and international organizations. These collaborations facilitate the amalgamation of AI, machine learning, deep learning, cybersecurity, Internet of Things (IoT), cloud computing, big data analytics, autonomous systems, and digital twin technologies to bolster situational awareness, augment threat detection, refine decision-making, and assist in proactive risk mitigation. This chapter examines the principles, frameworks, technologies, applications, challenges, and future trajectories of cross-sector partnerships in AI-driven defence, security, and risk management, highlighting the significance of collaborative innovation, ethical governance, resilience, and strategic leadership.
The swift expansion of internet commerce has revolutionised the manner in which companies engage with customers, oversee product stocks, tailor shopping experiences, and formulate strategic choices. The growing accessibility of customer profiles, browsing histories, buy records, product evaluations, search enquiries, and clickstream information has generated prospects for utilising machine learning (ML) to comprehend consumer behaviour and enhance corporate efficacy. This chapter explores the essential principles, algorithms, frameworks, applications, and assessment techniques related to machine learning-based e-commerce and consumer analytics. It examines supervised learning, unsupervised learning, deep learning, natural language processing, collaborative filtering, content-based filtering, hybrid recommendation, and sequential recommendation. A theoretical structure is proposed for the amalgamation of data collection, preprocessing, consumer modelling, suggestion formulation, assessment, and ongoing feedback. Mathematical models for forecasting customer behaviour, rating recommendations, and assessing models are presented.