Wavelet Transform Technique Applied to Satellite Image Denoising

Satellite images either digital or analog must have certain elements that are accidentally introduced during the processing of capturing as a result of weather or system sensor known as electronic noise. However, several attempts and advances have been made by academicians, industries and intelligent security agencies to remove this noise. It has been a nagging problem in the area of computer vision, image processing and artificial intelligence to denoise satellite images and noise removal is among the significant components in satellite image analysis. The aim of this research work was to denoise the satellite image of Sambisa forest using the wavelet transform technique. Satellite images of Sambisa forest captured by Landsat satellite in 2007, 2013, 2014, 2019 and 2021 respectively with their associated Geo-referenced 11.2503° N Longitude and 13.4167° E Latitude were downloaded from the United States Geological Survey (USGS) website. The images are acquired as Zipped Geo-referenced Tagged Image File Format (GeoTIFF). Color Composite bands of natural colors (bands 2, 3 and 4) are combined using the ArcGIS software and RGB image were obtained. Wavelet transforms denoising technique was used to filter noise from the images, which was implemented using the wdenoise2() function in MATLAB 2021

EXPERT SYSTEM FOR DIAGNOSIS OF MALARIA AND TYPHOID

An expert system is a computer program designed to solve problems in a domain that has human expertise. The knowledge built into the system is usually obtained from experts in the field. Based on this knowledge, an expert system can replicate the thinking process of the human experts and make logical deductions accordingly. Malaria and Typhoid are major health challenge in our society today (Nigeria), its symptoms can lead to other illness which include prolonged fever, fatigue, headaches, nausea, abdominal pain and constipation or diarrhea. People in endemic areas are at risk of contracting both infections concurrently. According to the world malaria report 2011, there were about 216 million cases of malaria and typhoid and estimated 655,000 deaths in 2010. (WHO report, 2011). The main challenging issue confronting the healthcare is lack of quality of service at minimal cost implying from diagnosing to predicting patients correctly. This issue can sometimes lead to an unfortunate clinical decision that can result in devastating consequences that are unacceptable. Although many studies were carried out by different researchers in the medical domain using various data techniques. In this research work, an efficient expert system that diagnoses patients with malaria and typhoid was developed. A secondary data was collected from university of Maiduguri teaching hospital for the period of four years which ranges from 2017 to 2020. The work explored the potential benefits of proposing a new model for prediction and diagnosis of malaria and typhoid using symptoms. The model adopted the Naive bayes and was implemented using the python. The system diagnoses a patient in real time (within 30 minutes) without necessarily visiting the laboratory for a test. Three algorithms were used these are, Support vector machine, Artificial neural network and Naïve bayes. From our finding, it is observed that Naïve bayes and support vector machine give the best result which is 100% in terms of accuracy of diagnosis. Keywords: Diagnosis, Prediction, Expert System, Typhoid, Malari

THE PREDICTION OF HEPATITIS B VIRUS (HBV) USING ARTIFICIAL NEURAL NETWORK (ANN) AND GENETIC ALGORITHM (GA)

The hepatitis B virus causes a liver infection called hepatitis B (HBV). It might be severe and go away on its own. Some kinds, however, can be persistent, leading to cirrhosis and liver cancer. HBV can be transmitted to others without the individual being aware of it; some persons have no symptoms, while others only have the first infection, which later resolves. Others develop a chronic illness as a result of their condition. In chronic cases, the virus attacks the liver for an extended period of time without being detected, causing irreparable liver damage. The manual approach has a high number of errors due to human decision-making, and visual screening is time-consuming, tiresome, and costly in terms of manpower. To predict the occurrence of Hepatitis virus (HBV), this research project thesis suggested an algorithm; Artificial Neural Network (ANN), and genetic algorithm (GA). To develop, evaluate and validate the performance of the model developed using ANN. Medical records of nine hundred patients were collected in the Northern Senatorial District (Mubi South), Central Senatorial District (Hong), and Southern Senatorial District (Ganye) regions of Adamawa state, Nigeria. Three hundred (300) patient records were collected from each general hospital, for a total of 900 patient records. The success of the proposed technique is demonstrated when ANN is paired with GA, Accuracy (66.30%), Specificity (66.33%), and Sensitivity (77.53%) were discovered. In this study, hepatitis B virus (HBV) was predicted using Artificial Neural Network (ANN) classifier and Genetic algorithm optimization tool were used to select the features that are responsible for hepatitis B virus (Sex, Loss of Appetite, Nausea and vomiting, Yellowish skin and eye, Stomach pain, Pain in muscles and joint). The prediction was found to have acceptable performance measures which will reduce future incidence of the outbreak and aid timely response of medical experts. Keywords: Hepatitis B Virus (HBV), Prediction, Features, Classification

Ensemble Model for Heart Disease Prediction

For the identification and prediction of different diseases, machine learning techniques are commonly used in clinical decision support systems. Since heart disease is the leading cause of death for both men and women around the world. The heart is one of the essential parts of the human body, therefore, it is one of the most critical concerns in the medical domain, and several researchers have developed intelligent medical decision support systems to enhance the ability to diagnose and predict heart disease in humans. However, there are few studies that look at the capabilities of ensemble methods in developing a heart disease detection and prediction model. In this study, the researcher looks at how to use the ensemble model, which proposes a more stable performance than the use of a base learning algorithm and these lead to better results than other heart disease prediction models. The University of California, Irvine (UCI) Machine Learning Repository archive was used to extract patient heart disease data records. To achieve the aim of this study, the researcher Bagging meta-algorithm. The ensemble model is a superior solution in terms of high predictive accuracy and diagnostics output reliability, according to the results of the experiments. An ensemble heart disease prediction model is also presented in this work as a valuable, cost-effective, and timely predictive option with a user-friendly graphical user interface that is scalable and expandable. From the finding, the researcher suggests that Bagging is the best ensemble classifier to be adopted as the extended algorithm that has a high prediction probability score in the implementation of heart disease prediction

Modeling of fuzzy logic controller and membership functions for humanizing synthetized music data in multimedia applications

Multimedia applications, especially those that synthesize music data, are designed to operate based on algorithmic composition models. One of such models is the Hybridized Interactive Algorithmic Composition (HIAC) model. Algorithmic composition models are known to generate and synthesize music devoid of the natural human emotional dynamics. Such synthesized musical themes are considered mechanical in nature because they are precision driven. However, human composed music is driven by certain degree of uncertainty and approximations as emotions help in creating dynamics such as very soft, soft, medium, loud and very loud notes. Therefore, this paper is proposing the use of fuzzy logic in humanizing computer generated and synthesized music notes. This shall help multimedia application designers and developers to create software that will generate and synthesize natural sounding music.Keywords: Fuzzy Logic, HIAC Model, Membership Functions, Multimedia Applications, Music Dynamics and Soft Computin