Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.