Neurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector.

Water quality in Peru is an increasing concern, particularly in the upper Huarmey watershed, which is affected by heavy metal contamination and untreated wastewater. This study proposes an automated classification approach using three supervised machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF)—to assess the water quality based on the Water Quality Index (WQI) of Peru. The experimental results show that KNN outperforms other methods, reaching an accuracy of 95.2%. The proposed system automates and improves the classification accuracy compared with manual methods based on Microsoft Excel. The methodology, performance metrics, dataset characteristics, and geographical context are detailed to ensure replicability. This algorithm assists decision-makers with environmental monitoring and public health protection.

Water quality in the city of Huarmey-Lima-Peru is assessed through monitoring processes conducted by the National Water Authority (ANA). Traditional formula-based calculations and Excel macros, along with manual data validation, significantly extend the analysis and documentation time for evaluating water quality parameters. This study focuses on classifying water samples into Human Consumption (1-A2) and Animal Drinking (3-D2) categories by developing a machine learning model, specifically utilizing the K-Nearest Neighbors (KNN) algorithm. The primary objective is to enhance accuracy and efficiency in classifying water quality for human and animal consumption, aligning with Peru's Water Quality Index standards (WQI). The implementation of the KNN model demonstrated superior classification performance compared to traditional spreadsheet-based methods, achieving an accuracy of 75% with Excel-based classification and 90% using KNN. Additionally, the research evaluates the performance of KNN against Random Forest (RF) and Support Vector Machine (SVM). This approach significantly improved the speed and accuracy of water sample categorization, benefiting decision-making in water resource management for the Huarmey basin.

Two assumptions dominate deep learning research on pneumonia detection from chest radiographs: that newer architectures outperform older ones, and that intra-dataset evaluation suffices to establish clinical utility. This study tested both. Nine architectures, spanning classical, efficient-scaling, and modern convolutional networks and Vision Transformers, plus a weighted soft-voting ensemble, were compared under a unified protocol on the pediatric Chest X-Ray Images dataset. None of the post-2020 architectures outperformed InceptionV3 (2016), which achieved the best individual accuracy (98.18%). The validation-weighted ensemble reached 98.52% accuracy and 99.22% pneumonia sensitivity on the internal test set; because its components were selected using test-set rankings, these values are upper-bound estimates. Bonferroni-corrected McNemar tests found no significant differences among the top-performing models. A cross-population external evaluation on an adult RSNA subset, which combines age, institutional, acquisition, and label shifts, revealed markedly different behavior: InceptionV3 retained 85.30% accuracy, whereas VGG16 fell to 59.60%, and the ensemble underperformed InceptionV3 alone. Ablation, Grad-CAM, and efficiency analyses complemented these findings. Architectural recency is not a proxy for performance, and external evaluation should be an architecture-selection criterion rather than an optional addendum.

Conventional selfie sticks, typically limited to a maximum reach of 1.5 m, are unable to effectively capture large group photographs or wide-background scenes, frequently resulting in restricted framing and facial cropping. This work presents the design, implementation, and experimental validation of a Bluetooth-controlled mobile robot with five degrees of freedom capable of capturing photographs at distances of up to 10 m using a dual-smartphone master–slave architecture. The proposed system integrates an Arduino Nano microcontroller, an L298N motor driver, three SG90 servo motors, an HC-05 Bluetooth module, and a rechargeable Li-Ion battery mounted on a four-wheel chassis. A master smartphone remotely controls both the mobile platform and the articulated camera arm, while a slave smartphone mounted on the arm captures images through its high-resolution rear camera with real-time video streaming. Experimental evaluations demonstrated 100% command transmission success at distances up to 10 m, automatic emergency stopping within 100–110 ms after Bluetooth disconnection, accurate face centering with minimal servo adjustments, and approximately 45 min of battery autonomy. The proposed open-source, low-cost teleoperated platform extends the effective photographic range from 1.5 m to 10 m while providing potentially improved photographic quality due to rear-camera imaging characteristics and full remote framing control compared with conventional selfie accessories.