To address evolving security challenges in cloud computing, this study proposes a hybrid deep learning architecture integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) for cloud intrusion detection. The BiLSTM-BiGRU model synergizes BiLSTM's long-term dependency modeling with BiGRU's efficient gating mechanisms, achieving a detection accuracy of 96.7% on the CIC-IDS 2018 dataset. It outperforms CNN-LSTM baselines by 2.2% accuracy, 3.3% precision, 3.6% recall, and 3.6% F1-score while maintaining 0.03% false positive rate. The architecture demonstrates operational efficiency through 20% reduced computational latency and 15% lower memory foo... More >

This study aims to computationally investigate the role of WS2 as an electron transport layer (ETL) and CuI as a hole transport layer (HTL) in optimizing the performance of perovskite solar cells (PSCs). The novelty lies in systematic optimization of band gaps, layer thicknesses, and temperatures to achieve enhanced performance metrics, utilizing the unique properties of WS2 and CuI, which are less explored compared to conventional materials like TiO_2 and Spiro-OMeTAD. The optimized PSC configuration achieves a power conversion efficiency (PCE) of 20.71% with a fill factor (FF) of 71.80%, an open-circuit voltage (Voc) of 0.8469 V, and short-circuit current density (Jsc) of 34.06894 mA/cm^2... More >

3D holographic displays on vehicles are a new out-of-home (OOH) advertisement technology offering an interactive and dynamic means of engaging consumers. They enhance advertisement visibility and viewer interaction through the projection of colorful, three-dimensional images in public areas. An Internet of Things (IoT) based holographic advertisement system is presented, integrating remote content management and personalized advertising control. The system includes an Android application that allows users to select and schedule ads displayed on moving vehicles. A GPS module provides real-time vehicle tracking, supporting security and targeted advertisements. Cloud-based storage ensures remot... More >

The Tuned Liquid Damper (TLD) method offers a practical and cost-effective solution for seismic design. Accurate modeling of the TLD system’s dynamic behavior is crucial for optimizing its performance. In this study, the nonlinear dynamics of the TLD system are characterized using the Housner model, with parameters estimated via a nonlinear state estimation approach. To address challenges associated with model discretization and unknown noise processes, we introduce a Robust Extended Kalman Filter (REKF) that incrementally incorporates uncertainties to more accurately capture system dynamics. The proposed method is evaluated through real-time hybrid simulation, employing seismic input sign... More >

This study presents an intelligent automated system for real-time detection and classification of tomato diseases using a Convolutional Neural Network (CNN) integrated within an Internet of Things (IoT) based unmanned ground vehicle (UGV). The CNN was trained and evaluated using a dataset comprising over 20,000 images of tomato leaves categorized into ten distinct diseases—Late Blight, Early Blight, Septoria Leaf Spot, Tomato Yellow Leaf Curl Virus, Bacterial Spot, Target Spot, Tomato Mosaic Virus, Leaf Mold, Spider Mites Two-Spotted Spider Mite, Powdery Mildew—and healthy leaves. The developed CNN architecture, optimized for lightweight deployment on edge devices like Raspberry Pi 4, ac... More >