Improved secondary education is essential to the creation of effective human capital in any country (Evoh, 2007). The need for ICT in Nigerian higher schools cannot be overemphasized. Unfortunately, many developing counties, especially in Africa, are still low in ICT application and use .This paper focuses on ICT application in Nigerian higher schools. It particularly dwells on the importance of ICT and the causes of low levels of ICT application in Nigerian secondary schools. Recommendations for improvement are offered

The paper provides a good alternative method for quadratic fractional programming problem (QFPP) concern with non-linear programming problem (NLPP) because the technique is useful to apply on numerical problems, reduces the labor work and save valuable time.

The aim of this paper is to introduce a new class of closed sets in topological spaces called π^g-closed sets and obtain some of its characteristics. Also, the concept of continuity called   π^g-continuity is defined and obtained some of its properties.

One of the most successful approaches to obtain hedging with transaction cost is the utility based approach pioneered by Hodges and Neuberger (1989). Judging against the best possible trade off between the risk and cost of hedging strategy, this approach seems to achieve excellent empirical performance. However, the approach has one major drawback that prevents the broad application  of it in practice, which is lack of rehedging  function calibrated when the hedge ratio moves outside the prescribed tolerance. We overcome this draw back by presenting a simple efficient rehedging model and some other well known strategies and find that our model outperforms all others..

Data science algorithms are on the way to becoming an integral part of every company, and we can already see the effects in many corporations that have invented their own data science teams and also implemented the latest data science algorithms. To be able to work with all the different challenges that are emerging, new powerful data science tools have been developed (e.g. Python, R, H2O, Weka, Tensorflow, Spark, Flink, BigML or KNIME). One balance that companies that want to use these new tools have to face is the cost of implementation vs. the enhanced development that they give in return. Nowadays, most of the advanced algorithms are open source and available on multiple platforms and programming languages, which helps to minimize the development cost challenges that each company has to overcome.

Still, one of the main dangers lurking inside these development teams is that they do not know what the state of the art of advanced algorithms is and which problem they can address. To help mitigate this problem, a review of algorithms has been implemented in this paper. This review gives us a perspective on which algorithms are being developed and which problem areas they can address. With the development of more powerful data science algorithms, we are also enabling the possibility of tackling more complex and interesting problems. However, one characteristic of the review is that there are missing algorithms from the many that are currently being produced and frequently selected by the community as the best performers in many benchmark datasets.

The integration of data engineering and artificial intelligence (AI) has emerged as a transformative force in healthcare, enabling predictive analysis that significantly improves patient outcomes, operational efficiency, and cost management. This study proposes a robust predictive analysis framework that combines advanced data engineering techniques with AI models to address the inherent complexities of healthcare data. Healthcare systems generate vast and heterogeneous data from electronic health records (EHRs), imaging modalities, wearable devices, and laboratory results, presenting challenges such as data fragmentation, interoperability, and scalability. Leveraging data engineering, the framework ensures seamless data ingestion, preprocessing, and storage, creating a unified pipeline that supports real-time analytics. AI algorithms, including machine learning (ML) and deep learning models, are then employed to derive actionable insights for disease prediction, resource optimization, and personalized treatment strategies.The proposed framework is validated using diverse healthcare datasets, demonstrating high predictive accuracy, scalability, and practical applicability. It outperforms existing models by addressing critical limitations, such as handling data silos, ensuring data privacy, and adapting to varying clinical workflows. Furthermore, the study discusses the ethical implications and potential challenges, including data security and algorithmic biases, while suggesting future directions to refine the framework. This integration of data engineering and AI has the potential to revolutionize healthcare by enabling predictive, preventive, and precision medicine.

The convergence of Artificial Intelligence (AI) and Cloud Computing is reshaping the technological landscape, providing businesses with unprecedented opportunities to innovate, optimize operations, and scale their services. AI’s ability to process large datasets, generate predictive analytics, and automate complex tasks, combined with the scalability and accessibility of cloud platforms, offers transformative potential for organizations across industries. This paper explores how AI is redefining the capabilities of cloud computing by enhancing data processing, improving security protocols, and delivering cost-effective solutions. The integration of AI in cloud environments has enabled real-time analytics, intelligent automation, and personalized customer experiences, empowering businesses to make data-driven decisions faster and more efficiently.This study provides an in-depth analysis of the interplay between AI and cloud computing, presenting a comprehensive review of existing literature, methodologies, and real-world applications. By examining industry-specific case studies, we highlight the tangible benefits and strategic advantages for businesses adopting AI-powered cloud solutions. Furthermore, the paper discusses the challenges, including ethical concerns, data privacy issues, and the resource-intensive nature of implementing AI systems. The findings underline the pivotal role of this integration in driving digital transformation and fostering a competitive edge in an increasingly data-centric world.