Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Advanced methods are needed for fast and reliable detection of cardiovascular illnesses, which continue to be a primary source of morbidity and death globally. Using deep learning, this research presents a new method, dubbed "DeepLearnCardia," for analyzing electrophysiological data in cardiac bioengineering. To improve the analysis of cardiac electrophysiological data and provide a complete solution for arrhythmia prediction, the proposed technique combines wavelet transformations, attention processes, and multimodal fusion. Data preprocessing, feature extraction using wavelets, temporal encoding using Long Short-Term Memory (LSTM) networks, an attention mechanism, multimodal fusion, and spatial analysis with Convolutional Neural Networks (CNNs) are all components of this technique. In order to train the model, we use an adaptive optimizer and binary cross entropy as the loss function. Key performance metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the ROC curve (AUC-ROC) are used to compare the proposed method's performance to that of six established methods: Signal Pro Analyzer, Electro Cardio Suite, Bio Signal Master, Cardio Wave Analyzer, EKG Precision Pro, and Heart Stat Analyzer. The results suggest that the proposed technique is superior to the state-of-the-art in cardiac signal analysis across all criteria. The suggested technique not only requires less resources, but also trains and infers more quickly and uses less of them.
Read MoreDoi: https://doi.org/10.54216/JAIM.070101
Vol. 7 Issue. 1 PP. 08-18, (2024)
This paper deals with a pivotal part of educational data analytics, aiming to increase the accuracy and interpretability of student performance prediction models. The cornerstone of our method is the innovative application of binary waterwheel plant algorithm bWWPA in the feature selection. As we can see, an essential part of any model is the predicted values, which correctly define all the characteristics of this model. Practically, we begin with solid data pre-processing, which incorporates data cleaning and missing values, duplicate removal, and data transformation in order to get model input as optimally as possible. Preceding the application of bWWPA, we employ an ensemble of regression machine learning models. Set up a baseline for predictive capability, getting initial outcomes with an average Mean Squared Error (MSE) of 0.064. The following feature selection phase proceeds, showing the algorithm. Ability to recognize important elements and, as a result, improve model effectiveness and explain power. The comparative analyses after feature selection point to refined gains in the model, and the performance is reporting a lower MSE of 0.032 with the refined models. These findings, methodologically, add to student performance prediction. Accordingly, it emphasizes the decisive status of feature selection in improving models. The paper's significance extends to teachers, institutions, and researchers, giving insights into more precise and relevant student success-supporting interventions.
Read MoreDoi: https://doi.org/10.54216/JAIM.070102
Vol. 7 Issue. 1 PP. 19-37, (2024)
Student-centered analysis of academic performance is also the most important aspect in improving education by being able to determine what measures work best, individualized learning approaches, and intervention programs. In this study, we performed a detailed analysis based on the "Students Performance in Exams" dataset and different regression methods to estimate students' grades. We sought to assess the functioning of numerous metrics and determine an optimal model for this task. Our descriptive analysis identified meaningful trends within this dataset, as it includes central factors like 'gender," race/ethnic diversity-based status of a student,' and parental education level based on which the children are catered to by informing them about important lunches and test preparation courses alongside scores in "Math," Readings," Writing" etc. We used a wide range of regression models: XGBoost, CatBoost, GradientBoostingRegressor, etc. Metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Average Marginal Loss were used to assess each model rigorously. Importantly, the XGBoost model gave out an MSE value of 0.028, which was the best among all values obtained from various other models. The superiority of the XGBoost model is supported by the excellent performance that was reported across many metrics. This work can be important for informing educational practitioners and policymakers regarding the best possible accurate and realistic model that would predict the students' outcome results. Educational data analytics incorporating the XGBoost model can be used for the customization of interventions and mapping resource allocation while promoting a results-oriented approach based on data in education. This study is a step towards the accumulation of knowledge on educational data analytics. It can serve as a background for further research aimed at improving predictive models regarding student performance.
Read MoreDoi: https://doi.org/10.54216/JAIM.070103
Vol. 7 Issue. 1 PP. 38-52, (2024)
Digital transformation is steadily changing states and organizations, and making them more cutthroat, as well as it offers a few valuable open doors for monetary development and success, as it empowers nations to including, more expanded instructive open doors, widespread admittance to the web and an exhaustive and supportive climate to the improvement interaction. The Communication and Information Technology industry's role in achieving sustainable development is highlighted in this paper, which focuses on the concept of sustainable development. hence, this paper audits the 17 SDGs (supportable advancement objectives) and makes sense of what data innovation has mean for every objective. In addition, we track progress toward the SDGs, existing e-governance initiatives, and big data initiatives in four distinct nations in the world. While in the next part, we significantly review the digital transformation in Egypt and its contribution in achieving sustainable development in Egypt.
Read MoreDoi: https://doi.org/10.54216/JAIM.070104
Vol. 7 Issue. 1 PP. 53-66, (2024)
The feature selection area in data analytics is explored through a comprehensive literature review, and the increasing areas that have a data dependency problem and are being resolved with feature selection are highlighted. Review topics of this course cover the foundations to present use cases, for example, cybersecurity, healthcare, and finance. Particularly crucially for the healthcare domain, it reduces the dimensionality and elucidates complex causal links. The further investigation overlaps contemporary techniques, including optimization-based methods, swarm intelligence and algorithms for the diagnosis of heart diseases. The conclusion builds on the practical assessment and underlines research gaps, serving as a basis to set a diversified technological review. This also exhibits new techniques that have released their efficiency in classification environments, for example, hybrid Ant Colony Optimization and the Gray Wolf Optimizer. The ISSA algorithm stands out as a swarm intelligence technique that is best among others. The paper concludes by demonstrating that feature selection goes beyond the preprocessing stage, but it instead stands as a vital part of the fields of machine learning and data science and thus aids the researchers in both retrospective analysis and forthcoming projects.
Read MoreDoi: https://doi.org/10.54216/JAIM.070105
Vol. 7 Issue. 1 PP. 67-77, (2024)