A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively

Affective e-learning approaches, technology and implementation model: a systematic review

A systematic literature study including articles from 2016 to 2022 was done to evaluate the various approaches, technologies, and implementation models involved in measuring student engagement during learning. The review’s objective was to compile and analyze all studies that investigated how instructors can gauge students’ mental states while teaching and assess the most effective teaching methods. Additionally, it aims to extract and assess expanded methodologies from chosen research publications to offer suggestions and answers to researchers and practitioners. Planning, carrying out the analysis, and publishing the results have all received significant attention in the research approach. The study’s findings indicate that more needs to be done to evaluate student participation objectively and follow their development for improved academic performance. Physiological approaches should be given more support among the alternatives. While deep learning implementation models and contactless technology should interest more researchers. And, the recommender system should be integrated into e-learning system. Other approaches, technologies, and methodology articles, on the other hand, lacked authenticity in conveying student feeling

Smart transit payment for university campus transportation using RFID card system

In the transportation business, we aim to be cost-efficient and effective in our customer service but with the traditional transit payment system, it is not so. Lately, transit companies all over the world are moving towards superior client service, nimbleness, receptiveness to necessities that diverge at a time scale that was absurd even two decades ago. The aim of this study was to create an electronic transit payment system that will allow for full pliability and solutions functionality that Covenant Universities and Nigerian transit companies should adopt to become more effective and efficient. We achieved this with the use of radio frequency identification (RFID) smart cards and card readers aiding a computer program that was programmed using C#. In addition, the program was simple and not expensive to implement in order to eliminate the mismanagement of ticket funds, loiter paper in bus stations, and so on. Together all this became our payment system

A Prediction Model for Bank Loans Using Agglomerative Hierarchical Clustering with Classification Approach

Businesses depend on banks for financing and other services. The success or failure of a company depends in large part on the ability of the industry to identify credit risk. As a result, banks must analyze whether or not a loan application will default in the future. To evaluate if a loan application was eligible for one, financial firms used highly competent personnel in the past. Machine learning algorithms and neural networks have been used to train class-sifters to forecast an individual's credit score based on their prior credit history, preventing loans from being provided to individuals who have failed on their obligations but these machine learning approaches require modification to solve difficulties such as class imbalance, noise, time complexity. Customers leaving a bank to go to a competitor is known as churn. Customers who can be predicted in advance to leave provide a firm an edge in client retention and growth. Banks may use machine learning to predict the behavior of trusted customers by assessing past data. To retain the trust of those clients, they may also introduce several unique deals. This study employed agglomerative hierarchical clustering, Decision Trees, and Random Forest Classification techniques. The data with decision tree obtained an accuracy of 84%, the data with the Random Forest obtained an accuracy of 85% and the clustered data passed through the agglomerative hierarchical clustering obtained an accuracy of 98.3% using random forest classifier and an accuracy of 98.1 % using decision tree classifier

Enhanced dimensionality reduction methods for classifying malaria vector dataset using decision tree

RNA-Seq data are utilized for biological applications and decision making for classification of genes. Lots of work in recent time are focused on reducing the dimension of RNA-Seq data. Dimensionality reduction approaches have been proposed in fetching relevant information in a given data. In this study, a novel optimized dimensionality reduction algorithm is proposed, by combining an optimized genetic algorithm with Principal Component Analysis and Independent Component Analysis (GA-O-PCA and GAO-ICA), which are used to identify an optimum subset and latent correlated features, respectively. The classifier uses Decision tree on the reduced mosquito anopheles gambiae dataset to enhance the accuracy and scalability in the gene expression analysis. The proposed algorithm is used to fetch relevant features based from the high-dimensional input feature space. A feature ranking and earlier experience are used. The performances of the model are evaluated and validated using the classification accuracy to compare existing approaches in the literature. The achieved experimental results prove to be promising for feature selection and classification in gene expression data analysis and specify that the approach is a capable accumulation to prevailing data mining techniques

Machine Learning–Based Predictive Farmland Optimization and Crop Monitoring System

E-agriculture is the integration of technology and digital mechanisms into agricultural processes for more efficient output. This study provided a machine learning–aided mobile system for farmland optimization, using various inputs such as location, crop type, soil type, soil pH, and spacing. Random forest algorithm and BigML were employed to analyze and classify datasets containing crop features that generated subclasses based on random crop feature parameters. The subclasses were further grouped into three main classes to match the crops using data from the companion crops. The study concluded that the approach aided decision making and also assisted in the design of a mobile application using Appery.io. This Appery.io then took in some user input parameters, thereby offering various optimization sets. It was also deduced that the system led to users’ optimization of information when implemented on their farmlands

A Linear Discriminant Analysis and Classification Model for Breast Cancer Diagnosis

Although most cases are identified at a late stage, breast cancer is the most public malignancy amongst women globally. However, mammography for the analysis of breast cancer is not routinely available at all general hospitals. Prolonging the period between detection and treatment for breast cancer may raise the likelihood of proliferating the disease. To speed up the process of diagnosing breast cancer and lower the mortality rate, a computerized method based on machine learning was created. The purpose of this investigation was to enhance the investigative accuracy of machine-learning algorithms for breast cancer diagnosis. The use of machine-learning methods will allow for the classification and prediction of cancer as either benign or malignant. This investigation applies the machine learning algorithms of random forest (RF) and the support vector machine (SVM) with the feature extraction method of linear discriminant analysis (LDA) to the Wisconsin Breast Cancer Dataset. The SVM with LDA and RF with LDA yielded accuracy results of 96.4% and 95.6% respectively. This research has useful applications in the medical field, while it enhances the efficiency and precision of a diagnostic system. Evidence from this study shows that better prediction is crucial and can benefit from machine learning methods. The results of this study have validated the use of feature extraction for breast cancer prediction when compared to the existing literature