Redeer Avdal Saleh’s scientific contributions

Transforming Enterprise Systems is impact concept when researching the ways in which enterprise systems are being impacted by the revolutionary effects of web technology, cloud computing, digital marketing, and machine learning, the objective of this study is to investigate the ways in which these trends are affecting enterprise systems. The purpose of this paper is to give businesses with practical advice by doing an analysis of the benefits, challenges, and integration strategies connected with the technologies being discussed. The findings of this research draw attention to the fact that these technologies have the potential to enhance the effectiveness, scalability, and competitiveness of businesses. The purpose of this paper is to provide a comprehensive understanding of the role that these technologies play in the enterprise systems that are currently in use. In addition to providing insights into the actual implementations of these technologies, the paper's objective is to convey this knowledge.

The transformative power of AI has only just begun to redefine how businesses function and relate to their customers within e-commerce and digital marketing. In fact, AI really does help firms adjust to changes in consumer preference and market fluctuations by improving operational efficiencies. Big data analytics, aided by artificial intelligence, really boosts the understanding of the customer journey-hence, optimizing and finally allowing for tailor-made marketing campaigns in real time. This leads to great growth for the business. The COVID-19 pandemic pushed companies into adopting AI-driven solutions in the quest for their resilience; this consequently led to an increase in the need for effective digital marketing strategies. E-commerce activities are integrated with 396 artificial intelligence in order to better understand consumer behavior, support market dynamics forecasting, and enhance risk management strategies. Hence, it becomes an indispensable aspect. It is relevant that ethical frameworks and further research address the problems of data privacy and scalability in order to optimize the intrinsic potential of AI. A focus on innovative applications of AI, alongside interdisciplinary collaboration, can empower an organization to develop genuinely inclusive and effective marketing strategies. Embracing the AI-driven initiatives, this will result in long-term relationship building with the customer for growth in a sustainable manner and maintaining competitiveness at an exponential pace in changing digitization.

The greatest technical achievement of the twenty-first century is machine learning (ML). The application of machine learning to detect cybersecurity vulnerabilities is a significant advancement in information security. A void exists in the field since the widespread application of machine learning technologies in cybersecurity remains distant. The primary cause of this gap is that contemporary technology has rendered it challenging for people to comprehend the role of machine learning in cybersecurity. The review seeks to furnish readers with a comprehensive analysis of machine learning's relevance across several facets of information security, especially for individuals interested in cybersecurity. It highlights the benefits of machine learning compared to human-operated detection methods and the diverse cybersecurity tasks it can do. This research elucidates various fundamental issues that impact real-world machine learning applications in cybersecurity. Ultimately, it examines how diverse businesses might advance machine learning in cybersecurity in the future, as this is crucial for the field's further growth. This study analyzes the contribution of machine learning to the enhancement of cybersecurity, highlighting the necessity of safeguarding sensitive information from theft and loss, as well as protecting critical assets against cyberattacks.

Recent years have seen the proposal of numerous routing algorithms for potential use in a variety of application areas. In many network types, such as Wireless Sensor Networks (WSNs), Mobile Ad Hoc Networks (MANETs), and other dynamic contexts, routing is a crucial difficulty. By fusing the benefits of proactive (table-driven) and reactive (on-demand) routing techniques, hybrid routing algorithms have become a notable breakthrough. Researchers have focused on hybrid routing algorithms since traditional ones frequently fail to adjust to the changing network conditions present in MANETs. These novel methods strive to maximize speed while reducing overhead by combining the best features of proactive and reactive routing strategies. It provided a thorough analysis of these algorithms in this work, emphasizing their mechanisms, benefits, limitations, and security features. Also focused especially on the analysis of hybrid routing algorithms in a range of applications.