Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
COVID-19, one of the most highly transmissible diseases in the twenty-first century, has had a profound impact on global lifestyles. Recently, the medical industry has increasingly relied on machine learning, which shows promise in anticipating the presence of COVID-19. By using machine learning techniques, test result turnaround time can be accelerated, and medical personnel can promptly attend to patients' needs. These algorithms analyze various attributes to classify COVID patients and predict their likelihood of contracting the disease. This study aims to utilize X-ray images processed by machine learning algorithms to predict the occurrence of COVID-19 and enhance its detection rate. The paper outlines two strategies employing machine learning techniques: one for predicting the likelihood of infection and the other for identifying positive cases. Different machine learning algorithms, such as decision trees, logistic regression, support vector machines, naive Bayes, and artificial neural networks, were employed. The simulation results reveal that the artificial neural networks model outperforms other methods in terms of accuracy rate.
Read MoreDoi: https://doi.org/10.54216/JAIM.090101
Vol. 9 Issue. 1 PP. 01-10, (2025)
The lack of water is one of the most crucial problems of our day; therefore, optimized water resource management and predictions gathered by patrons are of utmost importance. In the turmoil of a country like India, which lives a variety of lifestyles and has a complicated network of rivers, the urgent need for an active point of view to take care of water shortages becomes exceptionally vital. In this study, India’s groundwater, available at the district level for the year 2017, was the area of focus, with this analysis utilizing a dataset of 689 rows, each representing a district, and 16 columns describing the various groundwater extraction and recharge metrics. The study involves five regression models adapting RandomForestRegressor, DecisionTreeRegressor, MLPRegressor, KNeighborsRegressor, and SupportVectorRegression for water resource evaluation and prediction. Every model is appraised by using a thorough metrics set where we incorporate Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Explained Variance Score (EVS), Max Error, Median Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R-squared (R2), among others. Our results put the spotlight on RandomForestRegressor, making MSE measures the same as 0.000206624, endorsing its better performance versus the criteria considered. The approach used in this model provides us with an ensemble effect that makes it more robust in the sense that we can capture the interrelationships within the dataset in a comprehensive way. DecisionTreeRegressor also provides nice options for precision and transparency. The use of such models depicts the potential value of predictive analytics, which has the role of improving resource management and planning because we can all agree that the impending water crisis is also a fact. These research outcomes provide us with important data for well-informed decisionmaking and strategic management of water reserves through all avenues and most affected areas to air most of the impact of water scarcity.
Read MoreDoi: https://doi.org/10.54216/JAIM.090102
Vol. 9 Issue. 1 PP. 11-19, (2025)
The utilization of artificial intelligence (AI) algorithms has significantly transformed the field of blood disease diagnosis, enabling enhanced capabilities in prediction, categorization, and optimization. However, there is still a lack of research exploring the advancement of hybrid machine learning models that combine qualitative and quantitative datasets to address issues associated with blood diseases. To tackle this gap, we evaluate algorithmic combinations using datasets that include key characteristics from complete blood count (CBC) examinations. This manuscript presents an evaluation of prominent deep learning models, such as CNN, RNN, and RCNN, as part of our methodology. The assessment identified XGBoost as the optimal machine learning algorithm, and RCNN as the best deep learning model. Consequently, we propose a hybrid model named ‘RCNNX,’ which integrates Robust Scaler, SelectKBest feature selection, RCNN, and the XGBoost algorithm. The hybrid model, ‘RCNNX,’ achieves exceptional testing accuracy levels of 100% and 95.12% on the Anemia Diagnosis Dataset and a second dataset, respectively. Additionally, it demonstrates recall rates of 100% and 94.64% for the same datasets. These findings highlight the superiority of the proposed model, as it effectively utilizes feature selection to reduce the number of input variables, minimizing the risk of overfitting. Moreover, XGBoost enhances the predictive accuracy of RCNN.
Read MoreDoi: https://doi.org/10.54216/JAIM.090103
Vol. 9 Issue. 1 PP. 20-33, (2025)
The study considers the community of ”urban air quality improvement in modern cities” using an extensive dataset obtained from ”Air quality data of Delhi, India” for the period between 25 November 2020 and 24 January 2023. Research aims to significantly reduce air pollutants, including particulate matter, including PM2.5 and PM10, NO2, SO2, CO2, O3, and others. Different machine learning models are being used for airquality level forecasts. Among the models assessed, the Nearest Neighbors algorithm comes out on top and exhibits a very low Mean Squared Error (MSE) of 0.0002. The model’s superb precision is further supported by very low statistics in other key metrics, which confirm the Nearest Neighbors approach to forecasting the quality of air in urban zones. The Nearest Neighbors algorithm is shown to have its place in the application as a tool in the hands of researchers and decision-makers pursuing the fight against air pollution is also a signal of its efficiency and broad applicability. This modeling approach has thus the potential to first identify and later pinpoint localized empirical patterns and statistical dependencies from the data set. Its high predictive precision makes it a very valuable assistant to public health and environmental management, especially so in regions like Delhi.
Read MoreDoi: https://doi.org/10.54216/JAIM.090104
Vol. 9 Issue. 1 PP. 34-43, (2025)
The incidence of lung cancer varies in males and females, which occurs due to the abnormal and uncontrolled growth of cells in the lungs. It has a greater predilection in males as compared to females. Smoking is the most important risk factor for lung cancer. It causes serious breathing issues and also affects other organs. It increases the mortality rate both in young adults as well as in the older age group. Therefore, there is improvement in medical technologies to facilitate specialized diagnosis and treatment, but the mortality has not been controlled to a satisfactory extent. It is important to take preventive measures and precautions at the initial stages. Machine learning brings various advancements to the medical sector due to which various diseases can be detected at an early stage. In this paper, we presented different machine learning classifier techniques used for the classification of the present lung cancer data in the UCI machine learning repository as benign and malignant. The dataset is divided into cancerous and non-cancerous by converting the input data into binary form and using the classifier technique in theWeka tool. This specifically includes classifiers used: Logistic Regression, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Trees, and Na¨Ä±ve Bayes. In addition, we study the effect of data preprocessing methods on our prediction accuracy, such as data normalization and feature selection. The study seeks to help develop various reliable resources for lung cancer identification, which are critical for diagnosing and treating patients in a timely manner and improving their outcomes.
Read MoreDoi: https://doi.org/10.54216/JAIM.090105
Vol. 9 Issue. 1 PP. 44-52, (2025)
This paper proposes to evaluate how different machine learning techniques can be used to predict daytime power generation based on the ”Daily Power Generation Data” dataset. As a result of six models, which contain Random Forest Regressor, Decision Tree Regressor, Nearest Neighbors, Linear Regression, MLP Regressor, and SVR, a clear understanding has been accomplished by assessing the performance using multiple metrics. First of all, the Random Forest Regressor turned out to be the best in terms of the Mean Squared Error (MSE) of 3.57×10−6, which was the lowest among the three ML models. The introduction of the paper highlights the role of precise planning of the power market and the consecutive sections describing the topic mathematically. The table below, with a total list of performance issues, explains why the Random Forest Regressor is the superior full-proof model using the lowest MSE, highest explained variance, and great resistance to outlying samples. The paper thus gave various useful approval criteria that, to a great extent, we can choose the best model out of them because the Random Forest Regressor was in a position to get the highest performance metrics.
Read MoreDoi: https://doi.org/10.54216/JAIM.090106
Vol. 9 Issue. 1 PP. 53-71, (2025)