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Globally, recent research are focused on developing appropriate and robust algorithms to provide a robust healthcare system that is versatile and accurate. Existing malaria models are plagued with low rate of convergence, overfitting, limited generalization due to restriction to binary cases prediction, and proneness to local minimum errors in finding reliable testing output due to complexity of features in the feature space, which is a black box in nature. This study adopted a stacking method of heterogeneous ensemble learning of Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms to predict multiclass, symptomatic, and climatic malaria infection. ANN produced 48.33 percent accuracy, 60.61 percent sensitivity, and 45.58 percent specificity. SVM with Gaussian kernel function gave better performance results of 85.60 percent accuracy, 84.06 percent sensitivity, and 86.09 percent specificity. Consequently, to improve prediction performance, a stacking method was introduced to ensemble SVM with ANN. The proposed ensemble malaria model was tuned on different thresholds at a threshold value of 0.60, the ensemble model gave an optimum accuracy of 99.86 percent, sensitivity 100 percent, specificity 98.68 percent, and mean square error 0.14. The ensemble model experimental results indicated that stacked multiple classifiers produced better results than a single model. This research demonstrated the efficiency of heterogeneous stacking ensemble model on effects of climatic variations on multiclass malaria infection classification. Furthermore, the model reduced complexity, overfitting, low rate of convergence, and proneness to local minimum error problems of multiclass malaria infection in comparison to previous related models.

Outpatients receive medical treatment without being admitted to a hospital. They are not hospitalized for 24 hours or more but visit hospital, clinic or associated facility for diagnosis or treatment [1]. But the problems of keeping their records for quick access by the management and provision of confidential, secure medical report that facilitates planning and decision making and hence improves medical service delivery are vital issues. This paper explores the challenges of manual outpatient records system for General Hospital, Minna and infers solutions to the current challenges by designing an online outpatient's database system. The main method used for this research work is interview. Two (2) doctors, three (3) nurses on duty and two (2) staff at the record room were interviewed. Fifty (50) sampled outpatient records were collected. The combination of PHP, MYSQL and MACROMIDIA DREAMVEAVER was used to design the webpage and input data. The records were implemented on the designed outpatient management system and the outputs were produced. The finding shows these challenges facing the manual system of inventory management system. Distortion of patient's folder and difficulty in searching a patient's folder, difficulty in relating previous complaint with the new complains because of volume of the folder, slow access to patient diagnosis history during emergency, lack of back up when an information is lost, and preparation of accurate and prompt reports make it become a difficult task as information is difficult to collect from various register. Based on the findings, this paper highlights the possible solutions to the above problems. An online outpatient database system was designed to keep the outpatients records and improve medical service delivery.

Distributed Denial of Service (DDoS) attacks has been one of the persistent forms of attacks on information technology infrastructure connected to public networks due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS which overwhelms the target with a large number of request packets. However, compared to slow DDoS, limited number of research has been executed on mitigating slow DDoS. Attackers have resorted to slow DDoS because it mimics the behaviour of a slow legitimate client thereby causing service unavailability. This paper provides the scholarly community with an approach to boosting service availability in web servers under slow Hypertext Transfer Protocol (HTTP) DDoS attacks through attack detection using Genetic Algorithm and Support Vector Machine which facilitates attack mitigation in a Software-Defined Networking (SDN) environment simulated in GNS3. Genetic algorithm was used to select the Netflow features which indicates the presence of an attack and also determine the appropriate regularization parameter, C, and gamma parameter for the Support Vector Machine classifier. Results obtained showed that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and a false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. Also, the algorithm for subsequent implementation of the selective adaptive bubble burst mitigation mechanism was presented. This study contributes to the ongoing research in detecting and mitigating slow HTTP DDoS attacks with emphasis on the use of machine learning classification and meta-heuristic algorithms.

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