Preeti Malik – författare

At the heart of this evolution is the concept of sustainability. The book stresses the importance of adopting a balanced approach that prioritizes both technological advancements and ethical considerations. While AI and automation offer remarkable efficiencies, the text underscores that these innovations must align with core ethical principles, including privacy, legal constraints, and responsible data use. The book delves into the growing necessity of ensuring that digital forensic practices remain adaptable to the ever-evolving legal landscape. Ultimately, Sustainable Digital Forensics presents a forward-thinking vision for the future of digital investigations, where technology, ethics, and sustainability intersect. It offers a roadmap for forensic professionals, policymakers, and technologists, urging them to adopt innovations in AI and automation without losing sight of the broader implications for privacy, legal integrity, and the environment. The book serves as a comprehensive guide for shaping a digital forensic practice that not only excels in efficiency but also upholds the values of responsibility and sustainability.

Algorithms play a central role both in the theory and in the practice of computing. The goal of the authors was to write a textbook that would not trivialize the subject but would still be readable by most students on their own. The book contains over 120 exercises. Some of them are drills; others make important points about the material covered in the text or introduce new algorithms not covered there. The book also provides programming projects. From the Table of Contents: Chapter 1: Basic knowledge of Mathematics, Relations, Recurrence relation and Solution techniques, Function and Growth of functions. Chapter 2: Different Sorting Techniques and their analysis. Chapter 3: Greedy approach, Dynamic Programming, Branch and Bound techniques, Backtracking and Problems, Amortized analysis, and Order Statics. Chapter 4: Graph algorithms, BFS, DFS, Spanning Tree, Flow Maximization Algorithms. Shortest Path Algorithms. Chapter 5: Binary search tree, Red black Tree, Binomial heap, B-Tree and Fibonacci Heap. Chapter 6: Approximation Algorithms, Sorting Networks, Matrix operations, Fast Fourier Transformation, Number theoretic Algorithm, Computational geometry Randomized Algorithms, String matching, NP-Hard, NP-Completeness, Cooks theorem.