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Renewable energy sources are needed globally to support the available non-renewable energy sources our day-to-day living. There is high demand for renewable energy sources in both the developed and developing economies. Solar power is a good example of renewable energy source and people are currently embracing it globally for both domestic and industrial uses. Generally, these energy sources are meant to support the hydro, thermal and other energy sources that are available in different countries of the world. With the popularity of solar energy for both domestic and industrial usage, it can be argued that the estimation of the production level of such energy source is necessary so as to achieve proper planning and management. Due to the fact that the availability of the solar energy power depends largely on a number of environmental and weather conditions, predicting its production or generation can be very important. This study surveyed different works in the area of using machine learning techniques for solar power production prediction. The articles sourced were from notable research repositories. This study focuses on articles that were published between 2013 and 2023 on the subject matter. Different types of machine learning (ML) algorithms that have been used to build models from solar energy datasets are reported in this study. It is believed that the work can provide better insights for the researchers working in the problem area. Thus, the insights in this study can lead to building of improved machine learning-based models for solar power forecasting

The revival of rail transport service in Nigeria in recent years came at a critical moment of insecurity in Nigeria, it promised not only to serve as an alternative to overloaded vehicles on the roads but was also thought to be a safer means of transportation. Due to kidnapping on many roads, both high and low-class Nigerians patronized rail transport. However, train attacks and terrorism are major challenges that have impacted the sector negatively. Though the government has tried to improve surveillance along train routes, however, the result has not been impressive. The current level of insecurity in Nigeria is beyond the use of traditional surveillance and monitoring systems. It requires the adoption of technology to fight attacks and to monitor the health of train facilities. While the insecurity challenges that have overwhelmed the security forces will not permit the assignment of more personnel to the rail tracks that stretch several kilometres across the country, the incorporation of IoT and edge computing can be an optimal solution to the challenges of constant security problems. Among the trending technologies, IoT is a viable option that the country can adopt to improve security in the sector. It will increase the confidence of passengers and improve revenue and growth in rail transport.

Agriculture plays a crucial role in Nigeria's economy, serving as a vital source of sustenance and livelihood for numerous Nigerians. With the escalating impact of climate change on crop yields, it becomes imperative to develop models that can effectively study and predict rice output under varying climatic conditions. This study collected rice yield data from Katsina state, spanning the years 1970 to 2017, sourced from the Nigeria Bureau of Statistics. Additionally, climatic data for the same period were obtained from the World Bank Climate Knowledge portal. Logistic Regression (LR), Artificial Neural Network (ANN), Random Forest (RF), Random Trees (RT), and Naïve Bayes (NB) were employed to develop rice yield prediction models utilizing this dataset. The findings reveal that random forest and random trees exhibited superior classification performance for yield prediction. The developed models offer a promising tool for predicting future rice yields, facilitating proactive measures to ensure food security for the people of the state.

The limitation of signature-based Intrusion Detection systems has given rise for the popularity of Machine learning (ML) approaches r for building such intrusion detection systems (IDSs). ML is a sub-filed of Artificial Intelligence that enables algorithms to learn from data and its applications have been widely accepted and used in many domains. To achieve a promising ML-based model that can identify attacks and intrusions in networks and the cyber space, different stages of machine learning approach like pre-processing, attribute selection, model building, hyper parameter tuning can be very important. CICIDS2017 intrusion dataset was used for all the experimentations. This study focuses on building cyber threat detection model based on the ensemble feature selection and classification method. Innovative approaches were used for the analysis and pre-processing of the dataset. Thereafter, XGboost algorithm was used for selecting relevant features from the default input attributes in each of the captures. Thereafter, the reduced features were employed in the identification of cyber intrusions. The average accuracy achieved in the 8 captures of the dataset is 98% while precision is 0.98. Also, recall is 0.98, f1-score is 0.98 while AUC ROC score is 0.99. The study concluded that XGBoost-based model was able to achieve promising results based on the proper dataset encoding, feature importance-based feature selection and tuning of the algorithm for intrusion identification.

Microteaching and teaching practice are important and compulsory twin course for any teacher trainee student in Nigeria before becoming a professional teacher. The former pave the way for the later and it is intended to prepare teacher trainee for the real teaching practice exercise. This study investigate whether microteaching successfully predict the performance of physics teacher trainees in teaching practice. This investigation is a quantitative research which adopt the ex post facto research design. It involved a cohort of 638 final year Bachelor of Science Education (Physics) students spanning four academic sessions (2015/2016- 2018/2019). The only research question and hypothesis raised were answered and tested using mean/standard deviation and t-test statistics respectively. The result reveal a moderate positive relationship between the performance of physics teacher trainees in microteaching and teaching practice. This study concludes that micro-teaching viably predicted student’s performance in teaching practice.

African countries have been severely harmed by terrorism, insurgency, secession, instability, violence and religious crises since the beginning of the 21st century. Countries are on the verge of collapse, wars engulf society, and instability remains a recurring experience since the collapse of colonial rule and subsequent independence from African nations. The increasing incidence of the insecurity crisis in Nigeria is worrying enough to emphasize that the West African regional organizations that have legal responsibility for insecurity management have not been able to adequately address this issue. Nigeria's security challenges seem to be getting worse, threatening the fundamental development and health and education challenges of our nation especially for you almajiri. National security has become a troubling and challenging issue. Therefore, the main purpose of this study is to prove the truth of the existence of a negative correlation between the transmission of covid19, education and other related hazards by the almajiri system. This study uses qualitative research methods with several analytical techniques and several ways of collecting data. This study resulted in recommendations for constitutional reform. Primary and secondary data collection sources are used to provide direction and give credence to the originality of this work and recommendations will be offered for policy makers to consider future steps.