Transactions on Machine Learning and Artificial Intelligence

EVALUATION OF MACHINE LEARNING CLASSIFICATION TECHNIQUES IN PREDICTING SOFTWARE DEFECTS

2020-11-15T14:56:08+00:00

The advances in technology has brought about a significant rise in the number of software being developed and deployed on daily basis. This has brought about more dependencies on software and any defect in the software can lead to calamitous issues due to the type of data stored on the software or the type and importance of the functions the software perform. It is necessary to make sure that the all the defects are properly identified before deployment for use. The purpose of this study is to evaluate classifiers on software defects dataset and recommend appropriate classifier for defective software prediction. This will save the developer the stress and time of searching for the defects all through the program code, which will in turn lead to the software to be free from defects that can cause problems in the future of the use of the software. In this research, six categories of machine learning algorithms (two from each category) were tested in Waikato Environment for Knowledge Analysis (WEKA) which are; Bayes (Naives Bayes and Bayes Net), Functions (Multilayer Perceptron (MLP) and Sequential Minimal Optimization (SMO)), Lazy (IBK and KStar), Meta (Random Committee and Bagging), Rules (Decision Table and JRip) and Trees (J48 and Random Forest). The PROMISE dataset was used and the performance metric recorded were; accuracy, false positive rate, precision, recall, f-measure, Receiver Operating Curve (ROC), Kappa Statistics and Root Mean Square Error (RMSE). It was observed that Random Forest performs better under the 10 folds cross validation than the algorithms tested having an Accuracy of 0.818, a Recall of 0.818, a F-measure of 0.787, a ROC of 0.755 and a RMSE of 0.3669

Development and validation of the elderlies' diabetes risk predictive model using the Chinese data

2020-11-15T14:56:08+00:00

Ageing is closely related to the functional decline and is the predominant causes of the chronic diseases such as cardiovascular disease, stroke and diabetes. Population ageing worldwide accelerates the prevalence of the chronic disease. Ageing China is suffering from the diabetes risk more than other countries according to WHO reports. We adapt a machine learning algorithm Extreme Gradient Boosting to model the incidence rate of diabetes in China using a large amount of individual-level characteristic indexes as predictors. The model performance is guaranteed with a prediction accuracy above 85%, arising from the use of minority class oversampling and a multi-variable grid search technique. We apply the 2000-2002 wave and 2011-2014 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) to investigate how the leading predictors of the diabetes risk change as time pass. The importance of social-economic status, life-style and the access to the medical service rise in the later wave, and the relative importance of isolation and stressful life events which are related to social-psychological health decline in the investigated period, indicating a disparity of the diabetes risk within subgroups of different economic conditions.

External Services and their Integration as a Requirement in Developing a Mobile Framework to Support Farming as a Business via Benchmarking: The Case of NM-AIST

2020-11-15T14:56:09+00:00

Research on agricultural and rural development (ARD) systems in general, and farming as a business (FAAB) in particular, face the limitation of availability of credible and reliable benchmarking data, both for on-farm support for farm management decision making and off-farm support for research, investment and policy decision making. One of the main part of this limitation is to obtain reliable benchmarking data for decision making, both for current conditions and under scenarios of changed bio-physical and socio-economic conditions. This paper presents a framework for mobile application development to support farming as a business via benchmarking (FAABB). This is done with a model that distinguishes between internal and external sources of data and between codified and computed information. Also, the paper demonstrates and emphasizes how integration should be considered as a requirement when developing a typical mobile application for ARD. The paper ends with a description of an ongoing research project at Nelson Mandela African Institution of Technology (NM-AIST) in Tanzania that aims to develop a new framework to facilitate development of mobile applications for FAABB

Monitoring Traffic Behaviors: Lane Detection and Speed Calculation

2020-11-15T14:56:09+00:00

In this work, we propose an image processing-based hybrid technique that provides assistance in detecting certain patterns in traffic infractions committed by drivers on the roads. The proposed technique is based on the speed estimation using video data in conjunction with tracking methods. Our hybrid proposal comprises two parts. First, we propose a method to detect the road lanes using Hough transform. Second, we detect the vehicles in the video datasets using Haar Cascade methodology and then track those vehicles for their speed estimation and for monitoring the driving patterns. In addition, the types of infractions that a driver can commit while driving are also detailed. The most significant cases in which the infractions are determined is when the driver makes a rapid and continuous change of lanes, parking at inappropriate places and prohibited U-turns. The results are provided in terms of vehicle detection and speed estimation. Our results and analysis reveal that the proposed method can assist in monitoring the driving patterns and detecting traffic infractions.

WEB CIBERSECURITY - Danger of PHP Code Injection Vulnerability

2020-11-15T14:58:39+00:00

This work demonstrates that the use of laboratories for the development of curricular work in the area of information technology exclusively supported by cloud computing technology does not decrease the level of learning and assessment objects on the part of students. This scenario arose due to the need to interrupt face-to-face classes in physical laboratories due to the COVID-19 Pandemic. The study had as universe a master's degree in Computer Science.

Artificial intelligence: The technology, challenges and applications

2020-11-15T14:56:09+00:00

Artificial intelligence sometimes called machine intelligence highlights the simulation of intelligence based on human and animal nature. They are programmed in such a way that they can think like living intelligence and behave as like they do. The definition fits for any machine that can behave and think like human being while solving and learning specific problem. The ability of taking specific action while solving problem and achieving the goal is the ideal principle of artificial intelligence. In this work we will provide a thorough technical review of the technology, challenges and its applications.