The presence of AI and advanced data engineering has transformed ITS and physical infrastructure and resolved main issues to urban mobility. This article is aimed at revealing the role of AI solutions in improving traffic flow, safety levels and emissions. Smart traffic signals, big data technology, and predictive algorithms have gone a long way to negate traffic jams, as well as emissions and enhance travel time and security. Real examples illustrate the applicability of AI when it comes to traffic control in cities and in self-driving cars.

The modern nature of the business requires openness and connectivity, making it difficult to manage ever-increasing volumes of data in enterprises. Application of Artificial Intelligence in automation of data pipelines is a game changer in data engineering that seeks to close data to intelligence chasm. This gives AI the ability to co-ordinate machine learning, natural language processing and advanced orchestration tools to create complex and sustainable pipelines which allows for real-time data ingestion, transformation and delivery.

This article aims to demonstrate the change that AI brings into organizations’ daily vocabulary by automating mostly clerklike tasks, making use of various methodologies like, Anomaly detection, Predictive analytics and Dynamic resource allocation. Through case studies, large scale implementation of AI DP in areas of financial fraud detection, IoT based smart manufacturing, and smart retail experiences are shown.

The foremost advantage is that, using MEAN, it becomes possible to decrease latency, improve scalability, and increase the overall operational efficiency as a result of proper integration and better inbuilt functions of corresponding supplying tools and technologies Nevertheless, there are still crucial problems such as data security, ethical approaches to the usage of AI, and integration of MEAN with legacy systems. The article also expands to future possibilities such as linking of quantum computing and generative AI to create optimizing pipelines. In doing so, this research also draws focus on the future of data engineering powered by AI automation to promote intelligent decision making and innovation in the industries.

Benign lymphoepithelial cyst (BLEC) or better known as cystic lymphoid hyperplasia is a lesion of the bilateral parotid glands and upper cervical lymph nodes, painless and enlarges slowly. BLEC is defined as a single or multiple cysts in the lymph nodes of the parotid gland. The typical clinical sign that is often found is bilateral painless enlargement of the parotid, enlarged gradually with diffuse cervical lymphadenopathy. The diagnosis of HIV-associated BLEC can be formed from blood tests with HIV immunoserology and anatomical pathology in patients with asymptomatic enlargement of the parotid gland. In this case, we reported a 54-year-old male patient with HIV infection will be discussed and diagnosed with HIV-associated BLEC through an anatomical pathology biopsy examination.

Globalization and the expanded nature of business operations are driving organizations to use enterprise resource planning software. Since home-grown enterprise resource planning software are developed to fit organizations’ requirements and procedures, organizations are better in accepting these systems than commercial enterprise resource planning packages. However, home-grown enterprise resource planning software engineers, especially in the context of developing countries, have been facing threats from organizations, customers and managers. This study aimed at identifying the determinants and threats of home-grown enterprise resource planning software engineers by using grounded theory. In this study, grounded theory appaoch was used in the collection, analysis and interpretation of the home-grown enterprise resource planning software engineers in Ethiopia. Having inductively analysed the data gathered by using semi-structured interview and field observation, six themes of determinants and threats, namely requirement dynamics, resistance to change, mismanagement, conflict of interest, poor planning, and licensure gaps, were emerged. A total of nineteen subcategories were also found from those critical threats. Those emerged determinants and threats of enterprise resource planning software could be used by software engineering and research practices. The findings can be applicable to other developing countries having similar contextual characteristics with Ethiopia.

The rapid spread of AI across various fields like IoT, autonomous systems, and real-time analytics has brought to light the need for smarter ways to handle data processing both in the cloud and at the edge. While cloud computing is known for its powerful processing capabilities, it often depends on stable internet connections and it can deal with high latency, making it less suitable for applications that need in stant responses. On the other hand, edge devices excel in providing quick responses but typically face limits in processing power and energy. This research dives into techniques that can effectively connect the strengths of both cloud and edge computing. By looking at strategies like dynamic workload distribution, model compression, and flexible resource management, the aim is to find a balance between speed, cost, and energy use. Ultimately, the goal is to create a scalable and adaptable framework for AI that allows for the smooth operation of models across different systems.