Cosmetics consumers need to be aware of their skin type before purchasing products. Identifying skin types can be challenging, especially when they vary from oily to dry in different areas, with skin specialist providing more accurate results. In recent years, artificial intelligence and machine learning have been utilized across various fields, including medicine, to assist in identifying and predicting situations. This study developed a skin type classification model using a Convolutional Neural Networks (CNN) deep learning algorithms. The dataset consisted of normal, oily, and dry skin images, with 112 images for normal skin, 120 images for oily skin, and 97 images for dry skin. Image quality was enhanced using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, with data augmentation by rotation applied to increase dataset variety, resulting in a total of 1,316 images. CNN architectures including MobileNet-V2, EfficientNet-V2, InceptionV2, and ResNet-V1 were optimized and evaluated. Findings showed that the EfficientNet-V2 architecture performed the best, achieving an accuracy of 91.55% with average loss of 22.74%. To further improve the model, hyperparameter tuning was conducted, resulting in an accuracy of 94.57% and a loss of 13.77%. The Model performance was validated using 10-fold cross-validation and tested on unseen data, achieving an accuracy of 89.70% with a loss of 21.68%.

Cardiovascular diseases, specifically heart failure and aortic stenosis, are considered common and deadly, with the additional risk of developing dementia in the elderly population. Early diagnosis can help prevent or alleviate these diseases and potentially reduce mortality rates. Machine learning algorithms, especially gradient boosting (GB), can effectively predict the presence of these diseases through binary classification using demographic and medical data. However, research has yet to combine data from all three diseases for multiclass classification, which is the purpose of the present study. Using a dataset collected from Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand, a GB-based model is proposed for the multiclass classification of elderly people with heart failure, aortic stenosis, and dementia, with the inclusion of feature engineering techniques for maximum accuracy. Other existing methods, including decision tree, support vector machine, k-nearest neighbors, random forest, and extra trees were applied for comparison. The Optuna framework was used with the tree-structured Parzen estimator for hyperparameter optimization. The results produced by each classifier were compared using various performance metrics, namely precision, recall, F1 score, accuracy, the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Matthews correlation coefficient. The results are presented separately for each machine learning algorithm for comparison. Based on these metrics, it can be concluded that our proposed GB-based model outperformed other comparative models after applying feature engineering techniques.

The intra-hospital transfer of critically ill patients are associated with complications at up to 70%. Numerous issues can be avoided with optimal pre-transport planning and communication. Simulation models have been demonstrated to be an effective method for modeling processes and enhancing on-time service and queue management. Discrete-event simulation (DES) models are acceptable for general hospital systems with increased variability. Herein, they are used to improve service effectiveness. A prospective observational study was conducted on 13 official day patient transfers, resulting in a total of 827 active patient transfers. Patient flow was simulated using discrete-event simulation (DES) to accurately and precisely represent real-world systems and act accordingly. Several patient transfer criteria were examined to create a more realistic simulation of patient flow. Waiting times were also measured to assess the efficiency of the patient transfer process. A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. The most effective approach for decreasing waiting times involved prioritizing patients with the most severe symptoms. After a transfer process was completed, staff attended to the next transfer process without returning to base. Results show that the average waiting time was reduced by 21.78% which is significantly important for emergency cases. A significant difference was recorded between typical and recommended patient transfer processes when the number of requests increased. To decrease waiting times, the patient transfer procedure should be modified according to our proposed DES model, which can be used to analyze and design queue management systems that achieve optimal waiting times.

Temperature and humidity excursions during transport accelerate quality loss in fresh produce. This study develops and validates a self-contained Internet of Things (IoT) platform for in-transit monitoring and energy-aware operation. The battery-powered device operates independently of vehicle power and continuously logs temperature, relative humidity, GPS position, and onboard power draw. Power budgeting combines firmware-level deep-sleep scheduling with a LiFePO₄ battery pack, enabling uninterrupted operation for up to four days. Using ∼10,000 time-stamped observations collected over four consecutive days in a standard dry truck (non-commercial validation), we trained and compared Gradient Boosting Machine (GBM), Random Forest (RF), and Linear Regression (LR) models to predict energy consumption under varying environmental and routing conditions. GBM and LR achieved high explanatory power ($R^2\approx 0.88$) with a mean absolute error of 0.77 A·h, while RF provided interpretable feature importance data, identifying temperature as the dominant driver, followed by trip duration and humidity. The end-to-end system integrates an EMQX MQTT broker, a Laravel web application, MongoDB storage, and Node-RED flows for real-time dashboards and multi-day historical analytics. The proposed platform supports proactive decision-making in perishable logistics, with the AI analysis validating that the collected time-aligned on-route data can configure sampling/transmit cadence to preserve autonomy under stressful conditions.

The early diagnosis of dementia, a progressive condition impairing memory, cognition, and functional ability in older adults, is essential for timely intervention and improved patient outcomes. This study proposes a novel multiclass classification that differentiates dementia from other comorbid conditions, specifically cardiovascular diseases, including heart failure and aortic valve disorder, by leveraging the “blessing of dimensionality” to enhance predictive performance while ensuring feature accessibility. Using a dataset of 26,474 electronic health records from two hospitals in Chiang Rai, Thailand, the proposed framework introduced clinically informed feature augmentation to enhance model generalizability. Furthermore, the borderline synthetic minority oversampling technique was employed to address class imbalance, enhancing the model’s performance for minority classes. This study systematically evaluated a suite of machine learning models, including extreme gradient boosting, gradient boosting, random forest, support vector machine, decision trees, k-nearest neighbors, extra trees, and TabNet, across both the original and enriched datasets, with the latter integrating augmented features and synthetic data. Predictive performance was assessed using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve, and area under the precision–recall curve. The results revealed that all the models exhibited consistent performance improvements with the enriched dataset, affirming the value of dimensionality when guided by domain expertise.