The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients’ health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person’s physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed “Stress-Track”. The device is intended to track a person’s stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement.

Remote sensing plays a vital role in various industries, including smart cities, agriculture, and environmental monitoring, by capturing multispectral images of the Earth’s surface and analyzing its features. Accurate classification of these images is essential for extracting valuable information and making informed decisions. However, traditional image classification models, such as Convolutional Neural Networks (CNNs), often struggle to fully capture the complex spectral and spatial information inherent in multispectral images. Addressing this research gap, we propose FusionNet-Remote, a novel hybrid deep learning ensemble model that integrates CNNs with Random Forests (RF) to enhance the accuracy and robustness of remote image classification. The primary objective of this study is to develop a model that combines the strengths of CNNs for spatial feature extraction with the robust classification capabilities of RFs. Through comprehensive evaluations using LANDSAT data, FusionNet-Remote demonstrates superior performance compared to existing models, achieving an outstanding training accuracy of 99.8% and a testing accuracy of 98.7%. The model also exhibits the lowest training loss (0.0001) and testing loss (0.0043), significantly outperforming traditional models such as standalone CNNs, VGG16, and Inceptionv3. These results highlight the effectiveness of the hybrid ensemble approach in overcoming the limitations of conventional methods. This research contributes to the field by introducing an innovative ensemble technique that significantly improves remote image classification accuracy and reliability. The model’s high performance underscores its potential for critical applications such as land cover mapping, vegetation analysis, and disaster monitoring. Future work will explore the integration of more advanced CNN architectures and alternative fusion techniques to further enhance the model's performance and adaptability across different remote sensing scenarios.

In the realm of software project management, predicting and mitigating risks are pivotal for successful project execution. Traditional risk assessment methods have limitations in handling complex and dynamic software projects. This study presents a novel approach that leverages artificial neural networks (ANNs) to enhance risk prediction accuracy. We utilize historical project data, encompassing project complexity, financial factors, performance metrics, schedule adherence, and user‐related variables, to train the ANN model. Our approach involves optimizing the ANN architecture, with various configurations tested to identify the most effective setup. We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. Furthermore, we demonstrate the effectiveness of our model through comprehensive risk assessment. We predict both the overall project risk and individual risk factors, providing project managers with a valuable tool for risk mitigation. Validation results confirm the robustness of our approach when applied to previously unseen data. The achieved accuracy of 97.12% (or 99.12% with uncertainty consideration) underscores the potential of ANNs in risk management. This research contributes to the software project management field by offering an innovative and highly accurate risk assessment model. It empowers project managers to make informed decisions and proactively address potential risks, ultimately enhancing project success.

This study introduces NemoNet, a novel deep-learning framework designed for the automated detection and staging of Renal Cell Carcinoma (RCC) in 3D CT images. Leveraging the comprehensive HubMAP RCC dataset, NemoNet integrates a 3D encoder-decoder architecture with advanced radiomic feature analysis to enhance tumour segmentation and staging accuracy. The model employs a multi-objective loss function to balance segmentation precision and staging prediction, outperforming traditional architectures like U-Net and ResNet. Evaluation metrics, including Dice Coefficient, sensitivity, and specificity, indicate superior performance, achieving an accuracy of 92% and a Dice score of 0.88. While the model demonstrates robust results, challenges remain in handling variability in imaging quality and achieving full interpretability. The findings suggest that NemoNet offers significant advancements in RCC detection and staging, with potential applications in personalized oncology treatment planning.

Chronic Kidney Disease (CKD) affects over 850 million people globally, with early detection critical for effective intervention. We present DeepCKD-Net, a hybrid deep learning framework that synergistically integrates transformer architectures with gradient-boosting ensembles for multi-stage CKD prediction. Using a clinical dataset of 400 patients with 26 biomarker features from the UCI repository, our framework introduces three key innovations: (1) a hierarchical attention mechanism capturing complex inter-dependencies among clinical parameters, (2) an adaptive feature fusion module combining transformer-learned patterns with gradient-boosting decision boundaries, and (3) a confidence-aware ensemble strategy providing uncertainty quantification for clinical decision support. DeepCKD-Net achieves 98.7% accuracy and 0.993 AUC, surpassing state-of-the-art methods by 4.2% while maintaining 16.8 ms inference time suitable for real-time clinical deployment. Integrated SHAP analysis provides interpretable predictions, with serum creatinine (SHAP value: 0.342) and blood urea (0.287) identified as top predictive biomarkers, aligning with established clinical knowledge. The framework demonstrates robust performance under realistic clinical conditions, maintaining >90% accuracy with 20% missing data. Our contributions advance AI-driven nephrology diagnostics by providing a deployable, interpretable, and clinically validated solution for early CKD detection.