Fatima Zahra Ouariach – författare

The book provides a complete overview of forensic science, AI, and data management in law and business. It reviews the applications of AI and advanced data management methods across various legal and forensic domains. Topics discussed include digital forensics, predictive modelling, automatic classification, anomaly detection, cold case analysis, metadata analysis, and pattern recognition using blockchain technology.The book explores the cutting-edge intersection of fields such as ethical AI in legal systems, forensic science, data privacy, and forensic accounting laws. It examines the ethical implications of integrating computational intelligence including AI and machine learning into legal and judicial systems. The challenges and opportunities of applying AI to decision-making in human rights contexts are analysed, with emphasis on transparency, accountability, and fairness in algorithmic decision-making within legal frameworks.Further, it evaluates the intersection of data privacy and human rights with respect to biometric data collection, discussing the implications of mass surveillance, data harvesting, and profiling on individual privacy and civil liberties. The book also investigates how computational intelligence can be used to analyse large-scale datasets and enhance legal research and decision-making within the framework of human rights and the rule of law.Key Features: Studies the use of AI in criminal scene management Reviews the use of AI in data handling Reviews anomaly detection in companies Evaluated the use of AI in legal assistance Covers emotion and sentiment analysis by AI in criminal matters.

The book provides a complete overview of forensic science, AI, and data management in law and business. It reviews the applications of AI and advanced data management methods across various legal and forensic domains. Topics discussed include digital forensics, predictive modelling, automatic classification, anomaly detection, cold case analysis, metadata analysis, and pattern recognition using blockchain technology.The book explores the cutting-edge intersection of fields such as ethical AI in legal systems, forensic science, data privacy, and forensic accounting laws. It examines the ethical implications of integrating computational intelligence including AI and machine learning into legal and judicial systems. The challenges and opportunities of applying AI to decision-making in human rights contexts are analysed, with emphasis on transparency, accountability, and fairness in algorithmic decision-making within legal frameworks.Further, it evaluates the intersection of data privacy and human rights with respect to biometric data collection, discussing the implications of mass surveillance, data harvesting, and profiling on individual privacy and civil liberties. The book also investigates how computational intelligence can be used to analyse large-scale datasets and enhance legal research and decision-making within the framework of human rights and the rule of law.Key Features: Studies the use of AI in criminal scene management Reviews the use of AI in data handling Reviews anomaly detection in companies Evaluated the use of AI in legal assistance Covers emotion and sentiment analysis by AI in criminal matters.

The book provides a complete overview of forensic science, AI, and data management in law and business. It reviews the applications of AI and advanced data management methods across various legal and forensic domains. Topics discussed include digital forensics, predictive modelling, automatic classification, anomaly detection, cold case analysis, metadata analysis, and pattern recognition using blockchain technology.The book explores the cutting-edge intersection of fields such as ethical AI in legal systems, forensic science, data privacy, and forensic accounting laws. It examines the ethical implications of integrating computational intelligence including AI and machine learning into legal and judicial systems. The challenges and opportunities of applying AI to decision-making in human rights contexts are analysed, with emphasis on transparency, accountability, and fairness in algorithmic decision-making within legal frameworks.Further, it evaluates the intersection of data privacy and human rights with respect to biometric data collection, discussing the implications of mass surveillance, data harvesting, and profiling on individual privacy and civil liberties. The book also investigates how computational intelligence can be used to analyse large-scale datasets and enhance legal research and decision-making within the framework of human rights and the rule of law.Key Features:Studies the use of AI in criminal scene managementReviews the use of AI in data handlingReviews anomaly detection in companiesEvaluated the use of AI in legal assistanceCovers emotion and sentiment analysis by AI in criminal matters.

This comprehensive guide examines the restructuring of instructional design principles and practices for digital learning environments. It combines established pedagogical principles with the latest technological innovations to help practitioners create effective, learner‑centered online experiences.The volume grounds its recommendations in foundational instructional design models, including ADDIE, TPACK, and the Community of Inquiry framework, to demonstrate how these approaches are being reconfigured for digital transformation. It explores multimedia learning design through Cognitive Load Theory, Dual Coding Theory, and Mayer’s Cognitive Theory of Multimedia Learning, offering guidance on optimizing the integration of text, images, audio, video, and interactive elements. The text presents architectural frameworks for flipped classrooms using Small Private Online Courses and examines the integration of synchronous and asynchronous modalities in blended higher education. Accessibility is addressed through Universal Design for Learning and Web Content Accessibility Guidelines implementation strategies, while emotional design principles illustrate the creation of meaningful learner‑content connections. The volume explores emerging technologies, including GenAI integration within learning management systems for personalized feedback and intelligent tutoring, AI‑driven adaptive learning systems, and virtual reality and augmented reality applications that foster embodied cognition and situated learning across disciplines, from medical education to historical reconstruction.This collection is designed for instructional designers, educational technologists, and faculty members seeking evidence‑based strategies for digital course development. It will help graduate students, curriculum developers, and policymakers in promoting equitable access to quality online education.

This book gives an insight into the laws, regulations and ethics involved in adopting digital technologies to promote the energy transition. The current global development requires a deep understanding and a well-adopted strategy. The energy transition is an essential lever for global development, whether in terms of energy consumption or the protection of biodiversity in order to combat climate change, which is threatening living beings. Climate change is not only threatening living beings, it is also threatening natural resources and, in particular, causing water stress, which is currently a major issue. The energy transition is therefore the key to solving these challenges by adopting purely renewable consumption to reduce the impact of fossil fuels. But how is energy transition contributing to development? Clearly, digitalisation is the current trend. So the idea is to use digital tools to achieve what we call an innovative energy transition. This edited book aims to present how digital technologies contribute to the energy transition. It discusses the approaches, challenges and opportunities of applying digitalisation in the energy sector to ensure an innovative energy transition. This digitisation can then be used to optimise energy efficiency using intelligent energy technologies. To ensure an innovative energy transition, the book encourages readers to discover artificial intelligence and energy simulation programmes. Finally, the book presents the concept of the digital enterprise in the energy sector and the emerging trends in smart energy professions up to 2050.