A dynamic hybrid model for efficient tuberculosis incidence rate prediction.

Tuberculosis remains a global health challenge, predicting its incidences is crucial for effective planning and intervention strategies. This study combines AutoRegressive Integrated Moving Average (ARIMA) and Nonlinear AutoRegressive with exogenous input (NARX) models as an innovative approach for TB incidence rate prediction. The performance of the proposed model (ARIMA-NARX) was evaluated using standard metrics (MSE, RMSE, MAE, and MAPE), and it was refined to achieve the best average predictive accuracies with an MSE: 0.0622, RMSE: 0.0851, MAE: 0.07520, and MAPE: 0.05535 followed by NARX 0.1597, 0.3189, 0.2724, and 0.3366, and ARIMA (2,0,0) 0.7781, 0.5959, 0.6524, and 0.6080 Models. These findings are expected to shed light on the TB incidence rate, providing valuable information to policymakers such as the World Health Organization (WHO) and health professionals. The developed model can potentially serve as a predictive tool for proactive TB control and intervention strategies in the region and the world at large.

Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.

Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions. This led researchers to consider Electroencephalography (EEG) signals for more accurate emotion detection. However, the complexity of EEG recordings and data analysis using conventional machine learning algorithms caused inconsistent emotion recognition. Therefore, utilizing hybrid deep learning models and other techniques has become common due to their ability to analyze complicated data and achieve higher performance by integrating diverse features of the models. However, researchers prioritize models with fewer parameters to achieve the highest average accuracy. This study improves the Convolutional Fuzzy Neural Network (CFNN) for emotion recognition using EEG signals to achieve a reliable detection system. Initially, the pre-processing and feature extraction phases are implemented to obtain noiseless and informative data. Then, the CFNN with modified architecture is trained to classify emotions. Several parametric and comparative experiments are performed. The proposed model achieved reliable performance for emotion recognition with an average accuracy of 98.21% and 98.08% for valence (pleasantness) and arousal (intensity), respectively, and outperformed state-of-the-art methods.

Determining information system end-user satisfaction and continuance intension with a unified modeling approach.

With the different characters of datatypes and large amount of data going to be managed in open-source database, localization to the specific linguistics is the major concern in Ethiopia, as the nation used different datatypes compared to the Gregorian systems. In this regard Amharic localization in open-source database can handle the difficulties in managing data for governmental and non-governmental organizations. Amharic Extension Module was introduced to governmental organizations for the data management capabilities. But, there is no research that can explore the system's quality, the users' satisfaction and intension of continuance of Amharic Extension Module from the perspective of both computer literates and illiterates. Therefore, this research work attempt or try to empirically examine and analyze the system quality, the users' satisfaction and intension of continuance of Amharic Extension Module from the perspective of all users in POESSA The major purpose/aim of this study/research is to brand or make up the research break/gap in the area of localization specific to the Amharic locals, and to show the implication of the practical and theoretical way based on the results of the research. For this purpose, questionnaires were used for the collection of the research data. A total of 395 copies of the questionnaires were distributed and 385 of them are collected without any problem from the organization indicated herewith. The statistical analysis tools such as SPSS and AMOS, and methods such as Structural equation model were used for the analysis of the research data. The results of the research recommended and suggested that system quality can significantly influence confirmation. Meanwhile, confirmation can directly and significantly influence perceived usefulness, performance expectations, and satisfaction. Additionally, performance expectation, perceived usefulness and confirmation can significantly impact/influence satisfaction. The satisfaction directly and most importantly and significantly influences the continuance intension. Finally, the research delivers/provides a concert indication for the legitimacy and validity of the integrated and combined models of UTUAT, ECTM, and D&M ISS in the field of localizations which can be a hypothetical and theoretical foundation for Amharic Extension Module-AEM users' and services of it.

A Systematic Literature Review on the Application of Machine-Learning Models in Behavioral Assessment of Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is associated with significant social, communication, and behavioral challenges. The insufficient number of trained clinicians coupled with limited accessibility to quick and accurate diagnostic tools resulted in overlooking early symptoms of ASD in children around the world. Several studies have utilized behavioral data in developing and evaluating the performance of machine learning (ML) models toward quick and intelligent ASD assessment systems. However, despite the good evaluation metrics achieved by the ML models, there is not enough evidence on the readiness of the models for clinical use. Specifically, none of the existing studies reported the real-life application of the ML-based models. This might be related to numerous challenges associated with the data-centric techniques utilized and their misalignment with the conceptual basis upon which professionals diagnose ASD. The present work systematically reviewed recent articles on the application of ML in the behavioral assessment of ASD, and highlighted common challenges in the studies, and proposed vital considerations for real-life implementation of ML-based ASD screening and diagnostic systems. This review will serve as a guide for researchers, neuropsychiatrists, psychologists, and relevant stakeholders on the advances in ASD screening and diagnosis using ML.