Examination of Physician to Estimated Measurement Parameters for Malaria Mortality Using Modified State Estimation Model

This chapter considered monitoring human health condition as vital variable for well-being of man/society required input data for effective daily planning. Researchers have contributed to prediction of incidence/recovery rate for malaria mortality. Modified state-estimation model based (matrix-formulation, weighted sum of squares of errors) was applied. The instrument (sphygmomanometer, etc.) is manipulated for study under investigation to examined existing state of system. Four (4) measurements data were analyzed from different geographical locations for patients with malaria endemic cases. Physician measurement data are implemented into modified state-estimation equations to estimate degree of error(s) to classify as bad measurement. Results shows bad data estimation attributed to poor instrument calibration, aging, and poor physician measurement. These reveal discrepancies between actual (true-measurement) and patient-physical measurements. Four vital measurements include blood pressure (Bp), blood sugar level (BSL), body temperature (BT), and Plasmodium ViVax with relied validation test following chi-square distribution for 2-degree freedom with 99% significance level suspected as error measurement. Model-matrix coded in MATLAB gives state-estimation results x1=8.5225andx2=13.235, indicating strong variation between actual and physical measurements for some patients having low pulse rate under the measurement of blood pressure (Bp). Essentially, physicians’ measurements must be revalidated for accuracy before drugs prescription/administration to avoid under- or over-dose since patients’ body chemistry varies significantly for different persons

An Improvement of Load Flow Solution for Power System Networks using Evolutionary-Swarm Intelligence Optimizers

Load flow report which reveals the existing state of the power system network under steady operating conditions, subject to certain constraints is being bedeviled by issues of accuracy and convergence. In this research, five AI-based load flow solutions classified under evolutionary-swarm intelligence optimizers are deployed for power flow studies in the 330kV, 34-bus, 38-branch section of the Nigerian transmission grid. The evolutionary-swarm optimizers used in this research consist of one evolutionary algorithm and four swarm intelligence algorithms namely; biogeography-based optimization (BBO), particle swarm optimization (PSO), spider monkey optimization (SMO), artificial bee colony optimization (ABCO) and ant colony optimization (ACO). BBO as a sole evolutionary algorithm is being configured alongside four swarm intelligence optimizers for an optimal power flow solution with the aim of performance evaluation through physical and statistical means. Assessment report upon application of these standalone algorithms on the 330kV Nigerian grid under two (accuracy and convergence) metrics produced PSO and ACO as the best-performed algorithms. Three test cases (scenarios) were adopted based on the number of iterations (100, 500, and 1000) for proper assessment of the algorithms and the results produced were validated using mean average percentage error (MAPE) with values of voltage profile created by each solution algorithm in line with the IEEE voltage regulatory standards. All algorithms proved to be good load flow solvers with distinct levels of precision and speed. While PSO and SMO produced the best and worst results for accuracy with MAPE values of 3.11% and 36.62%, ACO and PSO produced the best and worst results for convergence (computational speed) after 65 and 530 average number of iterations. Since accuracy supersedes speed from scientific considerations, PSO is the overall winner and should be cascaded with ACO for an automated hybrid swarm intelligence load flow model in future studies. Future research should consider hybridizing ACO and PSO for a more computationally efficient solution model

Multivariate sample similarity measure for feature selection with a resemblance model

Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy