The high rate of urbanization has caused a high growth in the number of vehicles, which has produced a congestion, wastage on time, and fuel, as well as pollution to the environment. No longer applicable because of the dynamic character of modern urban traffic, the traditional traffic management systems based on the use of the non-informative control mechanisms and low real-time flexibility. The Intelligent Traffic Management Systems (ITMS) have become a very important part of an intelligent city system, as it intends to use the latest technologies that include Artificial Intelligence (AI), Internet of Things (IoT), machine learning, cloud computing and big data analysis to make traffic flow in the city more efficient and safer. In this paper, complete research on Intelligent Traffic Management Systems in smart cities has been made. It dwells upon the development of traffic management, the enabling technologies, system architecture, and methodologies. An elaborate literature review indicates the latest developments and outlines the gaps in research. The proposed approach will combine real-time data collection, predictive analysis, and responsive signal modulation to improve the traffic flow. The mathematical models and performance evaluation measures have been addressed to measure the system effectiveness. The findings indicate that intelligent systems are very effective in minimizing congestion, travelling time as well as emissions over traditional methods. Lastly, issues, constraints, and research prospects are given to facilitate long-term and viable implementation of ITMS in intelligent city setups.
Grace Ndlovu· International Journal of Mod...· 0 citations
The rapid proliferation of Internet of Things (IoT) devices has resulted in massive, continuous data generation, demanding scalable, low-latency, and energy-efficient processing methodologies. Traditional cloud-based machine learning (ML) inference pipelines often incur high energy consumption due to persistent server provisioning and inefficient resource utilization. This paper proposes an energy-efficient ML inference framework using serverless cloud functions that dynamically scale with IoT workloads. The architecture leverages event-driven execution, model optimization techniques (quantization, pruning, edge pre-filtering), and adaptive model selection based on workload intensity. Experimental evaluations conducted on widely used serverless platforms demonstrate significant reductions in energy consumption, cold-start latency, and operational cost while maintaining high inference accuracy. The study highlights the potential of serverless computing as a sustainable backbone for next-generation IoT–ML systems, offering guidelines for building carbon-aware and cost-efficient inference pipelines for real-world applications.
Silvia Diallo, Grace Ndlovu· International Journal of Art...· 0 citations
The rapid advancement of digital technologies has enabled organizations to generate and analyze vast amounts of data, making Data-Driven Decision Making (DDDM) a critical component of modern digital enterprises. DDDM leverages data analytics, business intelligence, machine learning, and predictive modeling to support evidence-based decision-making, improving operational efficiency, customer satisfaction, innovation, and competitive advantage. Organizations integrate data from enterprise systems, customer platforms, IoT devices, cloud environments, and digital transactions to gain actionable business insights and optimize strategic decisions. Despite its benefits, DDDM implementation faces challenges such as data quality issues, privacy concerns, system integration complexities, and organizational resistance. Successful adoption requires robust data governance, advanced analytical infrastructure, and a data-driven organizational culture aligned with business objectives. This study examines the role of DDDM in digital enterprises by exploring key technologies, implementation strategies, and analytical frameworks that support effective decision-making. A conceptual methodology is proposed to illustrate the transformation of organizational data into actionable business intelligence. The study also presents a quantitative evaluation of decision effectiveness across operational efficiency, customer satisfaction, revenue growth, and risk management. The findings indicate that organizations adopting data-driven strategies achieve improved decision accuracy, enhanced operational performance, and stronger competitive positioning. Furthermore, predictive analytics and real-time data processing significantly increase organizational responsiveness to dynamic market conditions. The study concludes that DDDM is a key driver of sustainable growth and innovation, with emerging technologies such as artificial intelligence and autonomous analytics expected to further transform enterprise decision-making.
I. Yusuf, Grace Ndlovu· International Journal of Com...· 0 citations
Stronger regulations, improved design, and better clinical validation are recommended to enhance digital mental health applications effectiveness, and the most effective approach is a hybrid model combining digital tools with professional care.
Grace Ndlovu, Samuel Johnson· International Journal of Inn...· 0 citations