Epilepsy is a prevalent neurological disorder affecting 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a novel and comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms' performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.
Probabilistic self-organizing maps and Gaussian mixture models represent flexible and interpretable probabilistic models that address various machine learning needs, such as speech processing and compression. In this context, the probabilistic self-organization map (PRSOM), as an extension of the classical Kohonen self-organization map (SOM), estimates the density distributions of the data using a combination of normal distributions. However, the likelihood function in the normal mixture may exhibit unbounded features and spurious local maxima (degeneracy). To tackle this problem, we introduce specific constraints to the PRSOM model, based on Ingrassia’s approach. Finally, we provide an implementation of the proposed method and give a comparative evaluation of its performance through numerical experiments.
Artificial intelligence (AI) has the potential to have an impact on every aspect of a business, including the purchasing division. This article analyzes how AI is used in the fulfillment of the purchasing function in enterprise resource planning (ERP) systems, especially SAP editor (Systems, Applications, and Products in Data Processing), and its impact on the system's performance. First, a review is undertaken of how AI is used in different fields in companies. Thereafter, the paper follows a deep research to collect the information on SAP editor from any sources such as industry reports, article ad information systems site webs. The results indicate that the application of AI can be integrated with the purchasing function of ERP systems almost throughout their entire lifecycle, helping overcome many problems that ERP systems are not able to automate and streamline.
As online learning evolves rapidly and discussion forums become increasingly important in educational environments, this study analyzes the impact of participation in discussion forums on the learning performance of future computer science teachers. Data were collected to assess their level of engagement and academic outcomes. The methodology included the use of natural language processing techniques, such as the Bidirectional Encoder Representations from Transformers (BERT) model, and Latent Semantic Analysis (LSA) to analyze the emotional state and relevance of their contributions. Preliminary results indicate a positive correlation between active participation and their learning performance. Additionally, this study makes a significant contribution by analyzing the correlation between various participation factors and learner performance. These findings highlight the importance of discussion forums as interactive educational tools to enhance learning and promote learner engagement. Important implications are also presented in relation to the obtained results.
In this paper, we will focus on how highly variable characteristics impact assembly line balance. Mass customization makes balancing the production line more difficult. Typical assembly lines are designed to produce a single product or a narrow range of comparable items in high quantities, the goal of this research is to determine the essential components that have a substantial influence on the balance of the assembly line. To do this, the research proposes a 3D matrix interpretation and statistical methods using the correlation study with the MATLAB tool. The objective is to discover the highly changeable parameters and the interaction between each parameter that impact the line's balance. The results of this approach show that altering the piloting parameters of the assembly line has an influence on its balancing, and hence the line must be dynamically balanced. The proposed approach has been used in the automotive sector, notably in the wire harness manufacturing line.
