Journal of Applied Artificial Intelligence 2023-09-06T06:56:49+00:00 Open Journal Systems <p>Journal of Applied Artificial Intelligence (JAAI) is an international and interdisciplinary scholarly peer reviewed journal on artificial intelligence published by Saba Publishing.<br />JAAI devoted entirely to Artificial Intelligence and welcomes papers in the overall field including, but not limited to, machine learning and cognition, deep learning, supervised learning, unsupervised learning, classification, regression, clustering, big and streaming data, optimization algorithms, feature selection and extraction, pattern recognition, bio-informatics, uncertain information processes, recommender systems, E-service personalization, distributed and parallel processing, computer vision, neural networks, natural language processing, heuristic search, multi-objective optimization, multi-agent systems, advances in social network systems, reasoning under uncertainty, forecasting and predication models as well as other hot topics.</p> <p><strong>Editor in Chief: <a href="" target="_blank" rel="noopener">Dr Nibras Abdullah</a></strong><br /><strong>ISSN (online)</strong>: <a href="" target="_blank" rel="noopener">2709-5908</a><br /><strong>Frequency:</strong> Semiannual</p> FEATURE SUBSET GENERATION FOR ENSEMBLE LEARNING USING FEATURE CLUSTERING AND MUTUAL INFORMATION 2023-02-06T16:10:20+00:00 Hana Amar <p>Ensemble learning is a powerful technique for constructing accurate predictive models. Feature subset generation is an important step for ensemble learning. This paper proposes feature clustering and mutual information as a new feature subset generation method for ensemble learning. The proposed feature subset generation technique clusters the features using a hierarchical clustering algorithm. Mutual information is used to compute the similarity between the features within each cluster. Feature subset generation is then performed by selecting the most informative features from each cluster. Experiments are conducted on a real-world dataset to compare the proposed feature subset generation technique to other existing feature subset generation techniques. The experimental results show that the proposed technique outperforms existing feature subset generation techniques.</p> <p>&nbsp;</p> 2023-04-20T00:00:00+00:00 Copyright (c) 2023 Journal of Applied Artificial Intelligence Seasonal Crop Yield Prediction in Nigeria Using Machine Learning Technique 2023-03-11T17:50:02+00:00 Abdulbasit Ahmed Sunday Eric Adewumi Victoria Yemi-peters <p>The old methods adopted in the past by were very slow, undependable and sizable quantity of crops are damaged in fields because bacterial attacks and lack of adequate information. automating agriculture processes may likely be the solution to feed the nation in the future. Though there is still debate on its application to agriculture. The importance of food security in any society cannot be over emphasized, therefore balancing the inputs and outputs on a farm is fundamental to its success and profitability. With the increase in population index food production need to meet population growth, creating a wide gap between demand and supply of food. Data Mining is emerging research field in crop yield analysis. In the past farmers make use past yield to predict what they may likely have when farming in the current season The yield prediction is a major issue that remains to be solved based on available data. Data mining are the better choice for this purpose. Three (3) Different Data Mining techniques will be used for predicting crop yields during rainy and dry season. This research proposes and implements a system to predict crop yield from previous data. This can be done by using association rule mining on agriculture data. This research focuses on creation of a prediction model which may be used to future prediction of crop yield. It also shows that South East has the best in terms of accuracy for rainy season farming with model performance evaluation 138.9 using Decision Tree Classifier.</p> 2023-06-30T00:00:00+00:00 Copyright (c) 2023 Journal of Applied Artificial Intelligence Hybrid Neural Network Models for the Optimization of Induction Hardening Processes 2023-09-06T06:56:49+00:00 Salman Lari Jong-Moon Kim Hyock Ju Kwon <p>We describe a simple hybrid methodology to simulate an induction heating process that combines observational (black-box) and physics-based (white-box) methodologies. This method uses a neural network to predict the process' physical characteristics, which were previously unknown. A primary emphasis is placed on monitoring temperature variations within a subsurface layer of a bolt sample. The hybrid model incorporates an ordinary differential equation for the heating rate, leading to improved data accuracy compared to a standalone black-box model. This innovative approach not only improves predictive precision but also simplifies interpretability, ultimately serving as a pivotal instrument for the effective management and advancement of induction heating operations.</p> 2023-10-07T00:00:00+00:00 Copyright (c) 2023 Journal of Applied Artificial Intelligence Development of a Lightning Prediction Model Using Machine Learning Algorithm: Survey. 2023-04-12T09:23:37+00:00 BABATUNDE RAHEEM Emeka Ogbuju Francisca Oladipo <p>This research is aimed at preventing broadcast equipment from lightning damage. Inview of the location in which my broadcast outfit is located (located in a valley; some few meters above sea level in the Confluence of Lokoja Kogi State Nigeria). Several improvement of earthing and installation of lightning arresting facilities, there has not been significant change protecting broadcast equipment from lightning. The solution I proffered is to isolate all electrical connection from equipment. Lightning as a natural phenomenon is very unpredictive and destructive which can occur during transmission. How do we know the day and time distructive lightning will come? The answer is to develop a lightning prediction system that is accurate. When lightning lead time is known, personnel on duty will be alerted to isolate all broadcast equipment from the mains and central earth connection. Since the lightning prediction system has to be localized. Deployment of machine learning algorithm is most appropriate. The use of ten(10) years Weather Numerical Values(2012-2022) such as rainfall, atmospheric pressure, relative humidity, temperature and lightning records which are gotten from Nigeria Meteorological Agency (NIMET), Lokoja Area Office, Kogi State. This parametization are factors that are used to work on the lightning forecast system as lightning will occur at their certain threshold values. The model is intended to be deployed on web application. Prediction model can be localized to support Numerical Weather Prediction in any environment in Nigeria.</p> 2023-04-20T00:00:00+00:00 Copyright (c) 2023 Journal of Applied Artificial Intelligence