This study presents an AI-based framework that unifies civil and mechanical engineering principles to optimize the structural performance of steel frameworks. Unlike traditional methods that analyze material behavior, load-bearing capacity, and dynamic response separately, the proposed model integrates these factors into a single hybrid feature space combining material properties, geometric descriptors, and load-response characteristics. A deep learning model enhanced with physics-informed reliability constraints is developed to predict both safety states and optimal design configurations. Using AISC steel datasets and experimental records, the framework achieves 99.91% accuracy in distinguishing safe from unsafe designs, with mean absolute errors below 0.05 and percentage errors under 2% for reliability and load-bearing predictions. The system also demonstrates high computational efficiency, achieving inference latency below 3 ms, which supports real-time deployment in design and monitoring environments. the proposed framework provides a scalable, interpretable, and code-compliant approach for optimizing steel structures, advancing data-driven reliability assessment in both civil and mechanical engineering.

Wearable devices and deep learning methods for Human Activity Recognition (HAR) have attracted a lot of interest because they could change healthcare monitoring. This study presents a CNN-LSTM model to accurately and reliably detect human movements from smartphone sensor data. The proposed model takes advantage of both the strengths of Long Short-Term Memory (LSTM) networks for modeling time and Convolutional Neural Networks (CNNs) for extracting features from space. This enables determining how the input data change over time and space. This study examines whether this method can work and is practical in real-life healthcare settings, focused on uses such as watching patients from distance, caring for the elderly, and therapy. The proposed model was evaluated on publicly accessible standard datasets. Various architectural configurations and hyperparameters were examined to determine their performance. The proposed CNN-LSTM model performed well and has great potential for practical use in activity tracking and environment understanding systems.

Skin-lesion recognition from clinical photographs is clinically valuable yet computationally challenging due to large intra-class variation, subtle inter-class boundaries, class imbalance, and heterogeneous acquisition conditions. To address these constraints under realistic compute budgets, we investigate Inception-family convolutional baselines and propose GISLC—a Gated-Inception model that augments a GoogLeNet/Inception-V1 backbone with a lightweight, spatial gating head inspired by ConvLSTM. Unlike static fusion (concatenation/summation) of multi-branch features, the proposed gated head performs per-location, learnable regulation of feature flow across branches, prioritizing diagnostically salient patterns while suppressing redundant activations. Experiments were conducted on the clinical-images subset of the Multimodal Augmented Skin Lesion Dataset (MASLD), an augmented derivative of HAM10000, using stratified train/validation/test splits, clinically motivated augmentation, and class-weighted optimization to mitigate skewed label frequencies. A controlled ablation study evaluates backbone choices and optimization settings and isolates the contribution of gated fusion relative to standard Inception heads. Across runs, the gated fusion strategy improves discriminative performance while remaining parameter-efficient, supporting the view that spatially adaptive regulation can enhance robustness on non-dermatoscopic clinical imagery. We further outline practical steps for calibration analysis and compression-aware deployment in clinical and edge settings.

Driver drowsiness is a major cause of road accidents worldwide, leading to thousands of injuries and fatalities each year. Detecting early signs of fatigue remains a critical challenge in road safety and intelligent transportation systems. This paper proposes a novel deep learning-based framework named SAFE-DRIVE-AI, designed to detect drowsiness in real-time by analyzing visual cues from the driver's eyes. The proposed framework integrates three components: A Convolutional Neural Network (CNN) to extract spatial features from eye images, a Long Short-Term Memory (LSTM) layer for capturing temporal patterns such as blink duration and frequency, and an Attention mechanism to enhance the model’s focus on the most relevant time steps. To validate the effectiveness of Deep Learning (DL) techniques, a comparative analysis was conducted using both traditional Machine Learning (ML) models (Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines) and DL models (CNN, LSTM). The experimental results showed that the DL models significantly outperformed the ML ones in all metrics. The hybrid CNN+LSTM model achieved an accuracy of 97.3% and an F1-score of 96.8%, being the most accurate approach. SAFE-DRIVE-AI is designed for real-world deployment, with potential integration into edge devices, 5G-enabled systems, and real-time alert mechanisms. The proposed method offers a practical and intelligent solution for enhancing driver safety through proactive fatigue monitoring.

The potholes in the road cause substantial monetary and physical damage to the ongoing traffic. Also, it requires extensive maintenance, especially in areas where the temperature may go down below freezing point. One of the major causes of potholes is the rain or running water that is accumulated in the cracks and later on due to low temperature, changes into ice. Then, ice forms a larger volume for the same amount of water after expanding and causes cracks to expand and at some point become a pothole. They are also the cause of traffic congestion. Therefore, pothole repair needs urgent attention and is one of the routine road maintenance tasks. Kansas City is one of the cities with established social data networks for residents to request road services to mitigate the problem. Although the policymakers have not ignored the issue and rudimentary patching policies are in place, unfortunately, that does not provide efficient road maintenance routes. This paper proposes a Hierarchical Clustering Algorithm for Road Service Routing Enhancement (HiCARE). HiCARE is a practical framework, that makes use of open data in order to optimize the route for maintenance. The optimization considers the time and date of reported potholes, locations, traffic situations, weather conditions, type of patch or other repairs needed, and crew availability, to mention some. This research has characterized spatiotemporal pothole density by analyzing the past 16 years of pothole data from the Open Data KC 311 in Kansas City. HiCARE enhances the NP-hard Traveling Salesperson Problem (TSP) by classifying potholes into layers of clusters. HiCARE finds a cluster’s shortest possible pothole route by identifying each cluster group’s entrance and exit pothole points. Moreover, it modifies the final routes to skip any local minima. The empirical research and analysis indicate that HiCARE significantly reduces the traversing distance and is faster in computation time when compared to typical TSP heuristic algorithms for daily resolution scheduling.

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles.