The increasing prevalence of type 2 diabetes mellitus (T2DM) has led to an urgent need for predictive models capable of early detection and timely management. Key factors in forecasting T2DM risks are commonly characterized by their intricate connections between biological markers — features of these existing frameworks, such as the boosting ensemble, XGBoost and AdaBoost, have faced difficulty in tackling accurately. This research introduces an innovative decision tree-based T2DM detection and classification ensemble model (DDCEM). It combines the Pima Indians Diabetes Dataset (PIDD) and the Early-Stage Diabetes Risk Prediction Dataset (ESDRPD), in which decision trees (DT) with a random forest (RF) classifier are applied to achieve better predictive performance. Compared with the other traditional frameworks, the DDCEM model achieved an accuracy of 90%, which was appropriate and less accurate than the proposed methods, namely, the boosting ensemble (85%), XGBoost (82%) and AdaBoost (86%) methods. It was even better in precision (0.85), recall (0.80) and F1 score (0.82), demonstrating that these alterations efficiently minimized misclassified instances. Moreover, the model had a rapid response with low latency (0.15 s), suggesting its suitability for real-time healthcare settings. This paper effectively illustrates the establishment and validation of the DDCEM model, highlighting its efficiency in identifying and managing T2DM at an early stage. The proposed DDCEM model represents a notable advancement in T2DM prediction, but was not specifically introduced to replace decision trees or become a new addition to the random forest classifier techniques. There is potential for future work that involves leveraging different datasets across populations, applying advanced machine learning (ML) methodologies to account for the identified data imbalance issue and expanding our approach in predicting other related health conditions. This approach can improve the reliability and adaptability of models, leading to better healthcare results for patients.
The integration of artificial intelligence in healthcare represents a transformative opportunity to enhance clinical decision-making and patient engagement. This research presents a comprehensive qualitative investigation into the design, development, and implementation of an intelligent virtual health assistant (VHA) specifically engineered to address critical gaps in contemporary healthcare delivery. The study employed a multi-faceted methodological approach, incorporating expert interviews with healthcare professionals, comprehensive user surveys, and controlled pilot testing conducted across diverse healthcare environments including both public institutions and private medical facilities. The developed VHA leverages advanced natural language processing capabilities and sophisticated semantic analysis algorithms to provide three core functionalities: automated symptom assessment and triage, comprehensive chronic disease management support, and clinical second-opinion analysis. Through iterative development and rigorous testing protocols, the system demonstrated significant potential in augmenting healthcare professionals' diagnostic capabilities while simultaneously empowering patients with accessible, evidence-based health information and personalized care recommendations. Key findings reveal that AI-driven health assistants can effectively bridge communication gaps between patients and providers, streamline clinical workflows, and enhance the accuracy of preliminary health assessments. The research identifies specific areas where NLP technologies excel in medical contexts, including patient history documentation, symptom pattern recognition, and treatment adherence monitoring. However, the study also acknowledges important limitations, including the need for continuous model training, ethical considerations regarding patient data privacy, and the necessity for human oversight in complex medical scenarios.
Infant cry analysis is often put in the context of traditional audio classification. However, the reductionist formulation often hides structural issues that abound in the real world such as a high level of class imbalance, overlapping features of acoustics and a low level of ecological validity of the datasets involved. In neonatal care facilities that are operating (e.g. smart incubators or intelligent monitoring systems) cry signals do not adhere to a controlled lab setting. To counter such deficits, the current paper proposes a transformer-based sequence of modeling of multi-class infant cry recognition with a set of eight behavioral states. The methodology clearly presents the imbalance of datasets through a synthesis of data augmentation based on controlled data, class-weighted optimisation, and termination, thus stabilising the training of models instead of artificially inflating the performance measures.The system uses a hybrid acoustic model which combines timefrequency structures with complementary spectral and prosodic features. This collection of composite features enables the transformer encoder to obtain short-term acoustics as well as long-term dependencies that characterize infant crying behavior. The imbalance-conscious metrics (Macro-F1 score and balanced accuracy) are made the priority of the model evaluation to avoid interpretive bias introduced by the dominant classes. The experimental findings indicate that the suggested structure can achieve the overall accuracy of 96.27% and maintain the steady performance across the minority cry types. Despite these positive observations, the study presents limitations that are related to the size of datasets and possible subject-level overlap of data. Under these limitations, the results suggest that transformer based cry state inference can serve a reliable decision support component of intelligent infant monitoring system, but it cannot be considered an autonomous diagnostic resource.
Academic anxiety is an emerging concern for students in demanding engineering fields such as Telematics, which impacts both their academic performance and mental well-being. This study aims to develop an AI-based system to continuously monitor and, in real time, detect symptoms of anxiety. Using a qualitative-quantitative mixed method approach, a sample of 103 students was analyzed through validated surveys and the Zung test. The findings showed that 91% of the subjects have some level of anxiety. This was associated with academic workload (94% considered it high), intense pressure to meet deadlines (94%), and insufficient time for personal activities (54%). In response to these findings, SerenIA, a mobile application incorporating GPT-4, was designed to assess conversations via text/voice and generate reports that can be reviewed by mental health specialists. The tool demonstrated accurate detection of anxiety monitoring algorithms based on the Zung test, an accessible interface with multimodal interaction, and automatic generation of clinical report texts. The findings highlight the potential of AI for proactive risk management in early intervention care, ultimately mitigating the risks of student dropout or underachievement. This model is the first of its kind, integrating technology and psychology, providing a scalable solution for educational institutions
Solar-powered electric vehicles have become a viable way to decrease carbon emissions and improve energy efficiency in the current era of concern for the environment and the push for sustainable mobility. By skilfully incorporating cutting-edge solar panel systems into their design, this initiative seeks to transform the performance of solar-powered electric vehicles. The main objective is to effectively use solar energy to increase the overall efficiency of electric vehicles, which will increase their range and lessen their dependency on traditional grid electricity. In order to do this, the study uses Artificial Neural Networks (ANNs) to precisely forecast solar power generation and load demand. By aligning energy demand with the supply of renewable solar energy, these forecasts play a critical role in power supply system optimization. In order to achieve the best possible balance between energy demand and the use of renewable energy sources, the project also investigates an innovative Hybrid Artificial Bee Colony-Gravitational Optimization Algorithm (ABC-GOA). In accordance with the current global trend towards greener mobility options, this study represents a critical step towards sustainable and sustainable transportation solutions.
