The explosion in human population has left researchers scrambling for solutions on how to feed the world. Furthermore, rural-urban immigration has on the one hand left the farms in the rural areas devoid of farmers and on the other hand has left the urban areas over-populated. Hydroponics is a form of agriculture where crops are grown without soil. This technique allows the farms to follow the farmers to the urban area. In addition, the fact that no soil is needed, allows hydroponic system to be stacked vertically (also known as vertical farming) to save space. The final frontier in hydroponics is automation. It will allow one farmer to work more than one job and cultivate more than one farm simultaneously. This paper provides a comprehensive survey on smart hydroponic system developed to date.

With the progression of bioinformatics, applications of GE profiles on cancer diagnosis along with classification have become an intriguing subject in the bioinformatics field. It holds numerous genes with few samples that make it arduous to examine and process. A novel strategy aimed at the classification of GE dataset as well as clustering-centered feature selection is proposed in the paper. The proposed technique first preprocesses the dataset using normalization, and later, feature selection was accomplished with the assistance of feature clustering support vector machine (FCSVM). It has two phases, gene clustering and gene representation. To make the chose top-positioned features worthy for classification, feature reduction is performed by utilizing SVM-recursive feature elimination (SVM-RFE) algorithm. Finally, the feature-reduced data set was classified using artificial neural network (ANN) classifier. When compared with some recent swarm intelligence feature reduction approach, FCSVM-ANN showed an elegant performance.

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Due to spatial and temporal changes in climate, the incidences of COVID-19 is much more higher in some parts of America, Europe and Asia by comparing with Saharan and sub-Saharan Africa. Several studies show the link between climate factors (e.g., temperature rainfall and humidity) and COVID-19 occurrence will be used to aid intervention planning, prevention and control policies. Nigeria is a country that is sensitive to spatial and temporal variability in the occurrence of climate factors, and fully knowing it link with COVID-19 is crucial towards mitigation. In this study, we examined the link by firstly deployed convenience sampling to select three cities (Abuja, Kano and Lagos) where the international airports of Nigeria are situated and also the index case of the country came through Lagos. Secondly, we used the reported cases of COVID-19 from its onset in the country (22/02/2020) up to 21/05/2021. Thirdly, lagged regression was used to explore the link between weekly counts of COVID-19 cases and weekly recorded average of the climate data; including the google trend index as a measure of the populace health seeking behaviour. We found a significant influence of temperature, humidity and heath seeking trend, with a very negligible contributions of precipitation to the occurrence of the COVID-19 in the states investigated. This result will assist policy makers with a prior knowledge to plan for non-pharmaceutical interventions in anticipation of possible outbreak.

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