In recent times, the phenomenal increase in the population of people and livestock in the world has placed an enormous pressure on water and land resources used by both crop farmers and herders alike. Desertification, deforestation and uncertainties in climatic conditions in Sub-Saharan Africa have led to massive movements of herders in search of pasture with resultant conflicts with local farm communities in the region. The inability to find a lasting solution to these problems has led to persistent cases of deteriorating relationships amongst crop farmers and herders which has continued to precipitate hostile consequences including the loss of lives, interruption and annihilation of the family units and in some cases, whole communities are destroyed. This research attempts to address the problem of inadequate grazing resources by the use of advances in Computational Intelligence Techniques in the determination of the optimum maturity of maize, so as to complement for the grazing of livestock in the region. Although the challenge inherent in determining the optimum maturity of maize is by no means trivial, the practice was hitherto based on human perception, which is a function of experience over time. This paper leverages on the use of Artificial Neural Networks (ANN) interfaced with image processing and Convolutional Neural Networks (pre-trained ResNet50 Network) in determining the optimum ripeness of the maize crop grown in Sub-Saharan Africa. Results obtained indicated a 3.5% improvement classification accuracy of pre-trained ResNet50 over ANN model, providing a stimulus for further research on the subject area. Therefore, this research posits that farmers could be sensitized on the possibility of utilizing image processing and neural networks technique in the determination of the maturity of maize in the nearest future when made operational.

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The increase in the number of vehicles on the road is evident by the rate of traffic congestions on daily basis. Problems of traffic congestions are difficult to be measured. Emission of dangerous substances are some of the worrisome effects on weather, theft and delays to motorist are other effects. More and better road network connections have been found to be effective. However, road networks often have intersection(s) which introduces conflicts to right-of-way. These are solved using road traffic light control systems. In this work, an attempt to improve upon an existing programmed stationary road traffic light control system of the Kaduna Refinery Junction (KRJ) is considered. The KRJ is the major road connection to Kaduna main town from the southern part of the state. During working days, motorist from other parts of the country, public and private servants, students, business men, etc. traveling to other parts of the country through the southern part of the state, meet at the KRJ. Trucks conveying petroleum products from the Kaduna Refinery, and vehicles transporting workers, and business men affect the flow of traffic at the junction. An efficient model of fuzzy logic (FL) technique is developed for the optimal scheduling of traffic light control system using TraCI4MATLAB and Simulation of Urban Mobility (SUMO). An average improvement of 2.74% over an earlier result was obtained. Considering priority for emergency vehicles, an improvement of 66.79% over the static phase scheduling was recorded. This shows that FL can be effective on traffic control system.

Selecting k out of a given set say n of sensors to accomplish some tasks or meet some well-defined objectives is often modelled and solved as an optimization problem or through an exhaustive search. The later solution strategy is ideal for some small sizes of both n and k. However, this simplistic method becomes quite hard and resource intensive considering a network of randomly deployed wireless sensor networks (WSNs) comprising a fairly large size of nodes (n) . In this paper, we employ and extend the conventional genetic algorithm (GA) technique by incorporating a more robust bivariate gene combination comprising both binary and continuous values to encode chromosomes in the solution space. Simulation results show the effectiveness of this method and serves to stimulate further research in the problem domain.

The challenges involved in the application of fuzzy logic in wireless sensors networks often stem from the limitation in processing and storage capabilities of the nodes . This anomaly can be overcome by using a centralized data sink, equipped with more storage and processing capabilities and which can also serve as the decider on the occurrence or otherwise of the event of interest based on selected readings of a subset of the deployed nodes. It is known that selecting a finite subset of a universal set can be intractable especially with relatively large size of the problem space. In this paper, we propose the application of T-norm Fuzzy Logic(TFL) to address the sensor selection problem and compare its performance to that of a standard Genetic Algorithm (GA). Extensive simulation results reveal the usefulness of this approach and how it is closely related to the GA technique.