Computer Science Review最新文献

Artificial Intelligence (AI) and Machine Learning (ML) are being used more and more to handle complex tasks in many different areas. As a result, interconnected information systems are growing, which means that autonomous systems are needed to help them adapt, find complex patterns, and make better decisions in areas like cybersecurity, finance, healthcare, authentication, marketing, and supply chain optimization. Even though there have been improvements in self-learning methods for complex pattern recognition in linked information systems, these studies still do not have a complete taxonomy that sorts these methods by how they can be used in different areas. It is hard to fully understand important factors and do the comparisons that are needed to drive the growth and use of autonomous learning in linked systems because of this gap. Because these methods are becoming more important, new study is looking into how they can be used in different areas. Still, recent study shows that we do not fully understand the environment of other uses for independent learning methods, which encourages us to keep looking into it. We come up with a new classification system that puts applications into six groups: finding cybersecurity threats, finding fraud in finance, diagnosing and monitoring healthcare, biometric authentication, personalized marketing, and optimizing the supply chain in systems that are all connected. The latest developments in this area can be seen by carefully looking at basic factors like pros and cons, modeling setting, and datasets. In particular, the data show that Elsevier and Springer both put out a lot of important papers (26.5 % and 11.8 %, respectively). With rates of 12.9 %, 11 %, and 8 %, respectively, the study shows that accuracy, mobility, and privacy are the most important factors. Tools like Python and MATLAB are now the most popular ways to test possible answers in this growing field.

With the amplification of digitization, the surge in multimedia content, such as text, video, audio, and images, is incredible. Concomitantly, the incidence of multimedia tampering is also apparently increasing. Digital watermarking (DW) is the means of achieving privacy and authentication of the received content while preserving integrity and copyright. Literature has produced a plethora of state-of-the-art DW techniques to achieve the right balance between its performance measuring parameters, including high imperceptibility, increased watermarking ability, and tamper-free recovery. Meanwhile, during the vertex of DW, scientific advances in quantum computing led to the emergence of quantum-based watermarking. Though quantum watermarking (QW) is in its nascent stage, it has become captivating among researchers to dive deep inside it. This study not only investigates the performance of existing DW techniques but also extensively assesses the recently devised QW techniques. It further presents how the principles of quantum entanglement and superposition can be decisive in achieving superior immunity against several watermarking attacks. To the best of our knowledge, this study is the unique one to present a comprehensive review of both DW as well as QW techniques. Therefore, the facts presented in this study could be a baseline for the researchers to devise a novel DW or QW technique.

Artificial Intelligence (AI) plays a crucial role in the digital transformation of organizations, with the influence of AI applications expanding daily. Given this context, the development of these AI systems to guarantee their effective operation and usage is becoming more essential. To this end, numerous international standards have been introduced in recent years. This paper offers a broad review of these standards (mainly those defined by ISO/IEC), with a particular focus on the software aspects: at the level of process and product quality; and at the level of data quality of applications integrating AI systems.