One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer.

Cloud computing, the technology that makes it possible for computing resources to be provisioned to clients / subscribers over long distances, as well as its component technology – virtualization, which makes it possible for multiple guest systems to co-reside on a single host machine and share the computing resources of the host, have both become very popular technologies that have witnessed giant improvements in the 21^st century. This is a review paper that presents an overview of the technologies of virtualization and cloud computing, tracing their history and evolution down a timeline to what they have today, and providing pointers to prospects of future advancements to virtualization and cloud computing.

Exponential technological development as seen in the 21st century has led to the generation of the very large volume of data in different formats and at a very high speed, such that traditional processing methods cannot be used to process these data. With the evolution of big data and its processing techniques, useful information has been derived from these volumes of data that has been useful in decision making in various domains, education inclusive. In the developed economy land stride has been made in the use of big data analytics especially academic and learning analytics, but such cannot be said in the developing economy like sub-Saharan Africa. Researchers have done thorough work on the benefits and challenges of big data as a trend and in extension big data analytics in education. This work systematically reviewed works in big data, academic analytics, learning analytics, identifying generalized challenges of big data and it extends to identifying salient factors that are hindering adoption of big data in educationin sub-Saharan Africa in line with the 4th sustainable development goal 2030Development Agenda of the United Nations; using online sources as it bulk of data collection. These issues that formed the gap are addressed in the different sections of the article.

The cultivation of maize round the year is a great challenge to both subsistence and mechanized farmers in Abia State owing to the changes in climatic conditions especially precipitation, relative humidity, and temperature during the two traditional seasons, which affect the growth and yield of the cereal crop. This paper is the first of a two-part study aimed to evolving an internet-based remote monitoring and messaging system for farmers in the Umueze-Umuchi communities and other connected areas in Osisioma Ngwa Local Government of Abia State to enable monitoring of vital climatic conditions that are much likely to affect their maize farms during the dry season. This study is descriptive and presents succinct information on maize cultivation in the communities with recourse to topography, relief and drainage, climate, soil and vegetation of the area. Data were collected through observation and interview of selected farmers. The vital atmospheric conditions required for maize farming such as temperature, vapour pressure, and relative humidity were noted to vary during the seasons: rainy and dry seasons, respectively. Data from farmers showed that maize cultivation begins in early march following early rainfall and actively ends around June when the volume of precipitation is at its peak. No maize cultivation is done during the dry season beginning from early November owing to low precipitation regardless of the presence of the Aba River across these communities. Consequent upon the findings, the authors are led to examining the option of all year-round maize cultivation aided by an internet of things (IoT)-enabled climate monitoring system in order to boost maize production in the aforementioned communities. It is submitted that the use of the monitoring device will enable the farmer know when to complement the adverse climatic conditions during the dry season thereby enhancing maize cultivation round the year. Key words: Osisioma Ngwa, Abia State, maize farming, internet of things (IoT), food production, climate monitoring system.