Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this paper, we investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation and wind speed. This was carried out using Artificial Neural Network and Decision Tree algorithms and meteorological data collected between 2000 and 2009 from the city of Ibadan, Nigeria. A data model for the meteorological data was developed and this was used to train the classifier algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive Neural Network model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting and climate change studies.

Cloud computing has maintained popularity and attraction in the ICT industry. Many business organizations have embraced the benefits that it offers and many more are still working towards cloud computing. One of the many concerns many organizations and individuals have about cloud computing from its inception is how to test the services and opportunities offered by cloud computing. This concern is as a result of the cost of running experiments on real cloud system and the fact that real infrastructure limits experiments within the size of the infrastructure, thereby making it really difficult to reproduce test. The alternative solution to this concern is the use of cloud simulation tools which offer a cloud environment to test cloud services in a repeated and controlled manner at no cost. Many cloud simulation tools have been developed since the inception of cloud computing. These tools have their strengths and weakness in modelling and simulating cloud system. This paper reviews some simulation tools and compares them in terms of their underlying framework, programming language, GUI, availability, cost modelling, energy modelling, simulation time, federation policy and communication model

Accurate interpretation of chest radiographs outcome in epidemiological studies facilitates the process of correctly identifying chest-related or respiratory diseases. Despite the fact that radiological results have been used in the past and is being continuously used for diagnosis of pneumonia and other respiratory diseases, there abounds much variability in the interpretation of chest radiographs. This variability often leads to wrong diagnosis due to the fact that chest diseases often have common symptoms. Moreover, there is no single reliable test that can identify the symptoms of pneumonia. Therefore, this paper presents a standardized approach using convolutional neural network (CNN) and transfer learning technique for identifying pneumonia from chest radiographs that ensure accurate diagnosis and assist physicians in making precise prescriptions for the treatment of pneumonia. A training set consisting of 5,232 optical coherence tomography and chest X-ray images dataset from Mendelev public database was used for this research and the performance evaluation of the model developed on the test set yielded 88.14% accuracy, 90% precision, 85% recall and F1 score of 0.87.

Security of information in this Information Technology (IT) era has been one of the challenges facing individuals and organisations. Among the measures developed by security experts to counter security threats is the Intrusion Detection System (IDS). Despite earlier research efforts to develop formidable IDSs, the existing systems still suffer from a high false alarm and inability to detect new (novel) attacks because of the high volume of features in network traffic. Therefore, this study aimed at developing IDS with an enhanced feature selection and classification method using two stages of attack identification. The feature selection phase employed Particle Swarm Optimization (PSO) to optimally select relevant features from Principal Component Analysis (PCA)'s projected principal space. The reduced dataset was passed into the misuse detector using C4.5 to classify network traffic into normal and attack. The "assumed" normal traffic further passed to the anomaly detector, the second-level classifier using Support Vector Machine (SVM) for detecting new attacks that the misuse detector has not previously detected. The proposed model was demonstrated on the KDD Cup'99 and NSL-KDD intrusion datasets, with the system achieving a false alarm rate of 0.53% and detection rate of 99.43% for NSL KDD dataset. The results show that enhancing the feature selection phase and classification method reduces the false alarm and improves the system's ability to detect zero-day attacks.

Breast cancer is a deadly disease affecting women around the world. It can spread rapidly into other parts of the body, causing untimely death when undetected due to rapid growth and division of cells in the breast. Early diagnosis of this disease tends to increase the survival rate of women suffering from the disease. The use of technology to detect breast cancer in women has been explored over the years. A major drawback of most research in this area is low accuracy in the detection rate of breast cancer in women. This is partly due to the availability of few data sets to train classifiers and the lack of efficient algorithms that achieve optimal results. This research aimed to develop a model that uses a machine learning approach (convolution neural network) to detect breast cancer in women with significantly high accuracy. In this paper, a model was developed using 569 mammograms of various breasts diagnosed with benign and maligned cancers. The model achieved an accuracy of 98.25% and sensitivity of 99.5% after 80 iterations.