27 research outputs found
PHOTOCATALYTIC DEGRADATION OF CONGO RED Over 1wt% CuO-ZnO COMPOSITE CATALYST
No full textAbstract: In this paper, the photocatalytic removal efficiency of this dye by a 1wt % CuO-ZnO nanocomposite was studied. The synthesized CuO-ZnO catalyst was characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM) and UV-Visible spectroscopy.The degradation of Congo red was monitored using UV-Visible spectrophotometer. The XRD analysis of the catalyst revealed a hexagonal wurtzite structure. The effect of operating variables such as initial Congo red concentration, catalyst dosage and pH of the solution were studie.
Keywords: Photosynthetic, pH, Variable, Wurtzite, Degradation, Nano composite.
Title: PHOTOCATALYTIC DEGRADATION OF CONGO RED Over 1wt% CuO-ZnO COMPOSITE CATALYST
Author: Shamsu Musa Sayaya, Dr Abdulfatah SM, Salim Aliyu Yusuf
International Journal of Novel Research in Physics Chemistry & Mathematics
ISSN 2394-9651
Vol. 10, Issue 2, May 2023 - August 2023
Page No: 18-31
Novelty Journals
Website: www.noveltyjournals.com
Published Date: 15-June-2023
DOI: https://doi.org/10.5281/zenodo.8042208
Paper Download Link (Source)
https://www.noveltyjournals.com/upload/paper/PHOTOCATALYTIC%20DEGRADATION-15062023-2.pdfInternational Journal of Novel Research in Physics Chemistry & Mathematics, ISSN 2394-9651, Novelty Journals, Website: www.noveltyjournals.co
WEB BASED HEART DISEASE PREDICTION MODEL USING MACHINE LEARNING TECHNIQUE
No full textThe cases of heart diseases are increasing at a rapid rate and it’s very important to take precaution to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on wen based heart disease prediction technique based on various medical attributes. Heart disease prediction system were prepared to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and Naïve Bayes to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using Naïve Bayes and Logistic Regression which showed a good accuracy in comparison to the previously used classifier such as naive bayes etc. So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The Given heart disease prediction system enhances medical care and reduces the cost. This project gives us significant knowledge that can help us predict the patients with heart disease.
Keywords: Web Based, Heart, Disease, Prediction Model, Machine Learning
Wavelet Transform Technique Applied to Satellite Image Denoising
No full textSatellite 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
Ensemble Model for Heart Disease Prediction
No full textFor 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
SUBJECT AND AUTHOR INDEXS
No full textSUBJECT INDEX IJBE VOLUME 2access credit, 93acquisition, 177AHP, 61, 82, 165arena simulation,43BMC, 69Bojonegoro, 69brand choice, 208brand image, 208brand positioning, 208bullwhip effect, 43burger buns, 1business synergy and financial reports, 177capital structure, 130cluster, 151coal reserves, 130coffee plantation, 93competitiveness, 82consumer behaviour, 33consumer complaint behavior, 101cooking spices, 1crackers, 1cross sectional analytical, 139crosstab, 101CSI, 12direct selling, 122discriminant analysis, 33economic value added, 130, 187employee motivation, 112employee performance, 112employees, 139EOQ, 23farmer decisions, 93farmer group, 52financial performance evaluation, 187financial performance, 52, 177financial ratio, 187financial report, 187fiva food, 23food crops, 151horticulture, 151imports, 151improved capital structure, 177IPA, 12leading sector, 151life insurance, 165LotteMart, 43main product, 61marketing mix, 33, 165matrix SWOT, 69MPE, 61multiple linear regression, 122muslim clothing, 197Ogun, 139Pangasius fillet, 82Pati, 93pearson correlation, 101perceived value, 208performance suppy chain, 23PLS, 208POQ, 23portfolio analyzing, 1product, 101PT SKP, 122pulp and papers, 187purchase decision, 165purchase intention, 33remuneration, 112re-purchasing decisions, 197sales performance, 122sawmill, 52SCOR, 23sekolah peternakan rakyat, 69SEM, 112SERVQUAL, 12Sido Makmur farmer groups, 93SI-PUHH Online, 12small and medium industries (IKM), 61socio-demographic, 139sport drink, 208stress, 139supply chain, 43SWOT, 82the mix marketing, 197Tobin’s Q, 130trade partnership, 52uleg chili sauce, 1 AUTHOR INDEX IJBE VOLUME 2Achsani, Noer Azam, 177Andati, Trias, 52, 177Andihka, Galih, 208Arkeman, Yandra, 43Baga, Lukman M, 69Cahyanugroho, Aldi, 112Daryanto, Arief, 12David, Ajibade, 139Djoni, 122Fahmi, Idqan, 1Fattah, Muhammad Unggul Abdul, 61Hakim, Dedi Budiman, 187Harianto, 93Hartoyo, 101Homisah, 1Hubeis, Musa, 112Hutagaol, M. Parulian, 93Jaya, Stevana Astra, 93Juanda, Bambang, 52Kirbrandoko, 122, 208Manurung, E. Batara, 130Mukhlis Yusuf, Ahmad, 1Najib, Mukhamad, 165, 197Noor Yuliati, Lilik, 208Nugraha, Artadi, 23Nuryartono, Nunung, 151Oktaviani, Rina, 122Prasetyo, Andrie, 101Primadona, Fitry, 165Qaulan Tsaqiela, Bimahri, 43Ramadhan, Arfin, 82Ridho Nurrochmat, Dodik, 33Rifin, Amzul, 23, 151Rusolono, Teddy, 12Sanim, Bunasor, 43Saptono, Imam Teguh, 130Satria, Arief, 61Setiawan, Indarto, 197Simanjuntak, Mangatas, 12Siregar, Hermanto, 130Suharjo, Budi, 197Sukandar, Dadang, 187Sukardi, 23Sumarwan, Ujang, 33, 165Suprayitno, Gendut, 61Suwandi, Ruddy, 82Syahran, Rinaldi, 187Syarief, Rizal, 69Tinaprila, Netti, 101Trilaksani, Wini, 82Ulfah, Iffatul, 33Wahyudi, Imam, 151Widyatami, Sarah, 52Wijayanto, Hari, 112Wiska, Friesgina, 69Zen, Novian, 177</p
A DIAGNOSTIC MODEL FOR THE PREDICTION OF LIVER CIRRHOSIS USING MACHINE LEARNING TECHNIQUES
No full textLiver cirrhosis is the most common type of chronic liver disease in the globe. The ability to forecast the onset of liver cirrhosis sickness is critical for successful treatment and the prevention of catastrophic health implications. As a result, the researchers created a prediction model using machine learning techniques. This study was based on a dataset from the Federal Medical Centre, Yola, which included 583 patient instances and 11 attributes. The proposed model for the prediction of liver cirrhosis sickness employed Nave Bayes, Classification and Regression Tree (CART), and Support Vector Machine (SVM) with 10-fold cross-validation. Accuracy, precision, recall, and F1 Score were used to evaluate the model's performance. Among all the strategies used in this study, the Support Vector Machine (SVM) technique produces the best results, with accuracy of 73%, precision of 73%, recall of 100%, and F1 Score of 84%. Based on medical data from FMC, Yola, this study shows that machine learning methods, specifically the Support Vector Machine, provide a more accurate prediction for liver cirrhosis sickness. This approach can be used to help doctors make better clinical decisions
THE PREDICTION OF HEPATITIS B VIRUS (HBV) USING ARTIFICIAL NEURAL NETWORK (ANN) AND GENETIC ALGORITHM (GA)
No full textThe 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
EXPERT SYSTEM FOR DIAGNOSIS OF MALARIA AND TYPHOID
No full textAn 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
A MODEL FOR PREDICTION OF DRUG RESISTANT TUBERCULOSIS USING DATA MINING TECHNIQUE
No full textThe rate of mortality in the recent time because of tuberculosis disease is so alarming. Drug-Resistant Tuberculosis is a communicable disease very dangerous that attack lungs, many victims were not identified due to weak health systems facilities, poor doctor-patient relationship, and inefficient mechanisms for predicting of the disease. Data mining can be applied on medical data to foresee novel, useful and potential knowledge that can save a life, reduce treatment cost, increases diagnostic and prediction accuracy as well as delay taking during prediction which reduce the treatment cost of a patience. Several data mining technique such as classification, clustering, regression, and association rule were used to enhance the prediction of tuberculosis. In this project I used Naïve Bayes Classifier to design a model for predicting tuberculosis. I considered the following parameters; Gender, Chills, Fever, Night sweat, Fatigue, Cough with Blood, Weight loss, and Loss of Appetite for classification phase 1. While Gender Chest Pain, Sputum, Contact DR, Weight Loss, In-adequate treatment for classification phase 2 as the clinical symptom. The Naïve Bayes Classifier has the advantage of attribute independency, it is easy in construction, can classify categorical data, and can work on high dimensional data effectively. The model designed using Naïve Bayes Classifier is divided o into classification phase 1 and classification phase 2 and implemented using Phython 3.2 Programing Language. The result shows that Naïve Bayes Classfier was suitable in predicting drug resistant tuberculosis with performance accuracy of 82%, 98% and area under curve (AUC) is 88%.
Keywords: Model Prediction, Tuberculosis. Drug, Resistant, Data Mining
Analysis of emerging trends in artificial intelligence for education in Nigeria
No full textAbstract In the domain of education, the integration of Artificial Intelligence (AI) has ushered in a paradigm shift towards a more technologically-driven landscape, demonstrating its efficacy as an emergent strategy. The pervasive influence of computer technology has catalyzed a surge in online learning within the country, yielding positive educational outcomes. Despite these advancements, a considerable number of educational institutions in Nigeria have yet to leverage AI technologies. Recognizing the expanding significance of AI in education, this study seeks to align with this trajectory by aggregating instances of AI implementation in education from developed countries. The methodology employed involves a comprehensive review of current advancements in AI applications within the Nigerian educational context. The review process, spanning papers retrieved from four digital libraries published between 2008 and 2022, culminated in the inclusion of 74 papers. These selected papers demonstrated the utilization of AI software tools and technologies, adhering to predefined exclusion and inclusion criteria. The findings of the study reveal a prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning. This discernible trend indicates a growing demand for the application of AI technology in the educational landscape of Nigeria. However, the study highlights a discrepancy: sophisticated AI techniques like intelligent tutoring systems, learnable robots, web-based educational systems, and advanced learning management systems are infrequently applied in Nigeria. The study suggests that Nigerian educational institutions should adopt AI practices from advanced nations to enhance student learning and bridge the gap in AI integration
