Transactions on Machine Learning and Artificial Intelligence 2022-01-13T00:00:00+00:00 Thomas Harvey Open Journal Systems <p>Transactions on Machine Learning and Artificial Intelligence is peer-reviewed open access online journal that provides a medium of the rapid publication of original research papers, review articles, book reviews and short communications covering all areas of machine learning and artificial Intelligence. The journal publishes state-of-the-art research reports and critical evaluations of applications, techniques and algorithms in machine learning, artificial intelligence, cognitive science, software engineering, database systems, soft computing, optimization and modelling and related application areas.</p> Kullback-Leibler Divergence of Mixture Autoregressive Random Processes via Extreme-Value-Distributions (EVDs) Noise with Application of the Processes to Climate Change 2022-01-03T10:37:04+00:00 Rasaki Olawale Olanrewaju Anthony Gichuhi Waititu <p>This paper designs inter-switch autoregressive random processes in a mixture manner with Extreme-Value-Distributions (EVDs) random noises to give EVDs-MAR model. The EVDs-MAR model comprises of Fr├ęchet, Gumbel, and Weibull distributional error terms to form FMA, GMA, and WMA models with their embedded inter-switching transitional weights (w<sub>k</sub>) , distributional parameters, and autoregressive coefficients . The Kullback-Leibler divergence was used to measure the proximity (D) between finite/ delimited mixture density &nbsp;and infinite mixture density of the EVDs-MAR model with Expectation-Maximization (EM) algorithm adopted as the parameter estimation technique for the extreme mixture model. The FMA, GMA, and WMA models were subjected to monthly temperature in Celsius (<sup>o</sup>C) from 1900 to 2020 and annual rainfall in Millimeter (mm) from 1960 to 2020 datasets in Nigeria context.</p> 2022-01-21T00:00:00+00:00 Copyright (c) 2022 Rasaki Olawale Olanrewaju, Anthony Gichuhi Waititu A Smartphone-based Plant Disease Detection and Treatment Recommendation System using Machine Learning Techniques 2021-11-20T12:35:14+00:00 Folasade Isinkaye <p>Plant diseases cause major crop production losses worldwide, and a lot of significant research effort has been directed toward making plant disease identification and treatment procedures more effective. It would be of great benefit to farmers to be able to utilize the current technology in order to leverage the challenges facing agricultural production and hence improve crop production and operation profitability. In this work, we designed and implemented a user-friendly smartphone-based plant disease detection and treatment recommendation system using machine learning (ML) techniques. CNN was used for feature extraction while the ANN and KNN were used to classify the plant diseases; a content-based filtering recommendation algorithm was used to suggest relevant treatments for the detected plant diseases after classification. The result of the implementation shows that the system correctly detected and recommended treatment for plant diseases</p> 2022-01-13T00:00:00+00:00 Copyright (c) 2022 Folasade Isinkaye