This article explores the potential for addressing issues related to improving the sustainability and safety of a city's transportation system. The study demonstrates the need for a comprehensive approach that considers not only the direct but also the indirect effects of potential solutions, selecting those that enhance the overall sustainability and safety of the system. The article presents a conceptual model of such a system and illustrates implementation methods using a large city as an example. Furthermore, it demonstrates how simulation modeling can address urban transportation network issues and quantifies the impact of implementing the proposed solutions.
With the advancement and widespread use of digital devices, capturing images has become effortless in any location. Images serve as evidence, making the authenticity of digital images increasingly critical. Some individuals alter images by adding or removing elements, rendering the images unreliable. Consequently, detecting image forgery has become essential. The evolution of image editing software has intensified this issue within the realm of computer vision. Recently, a variety of algorithms have been developed to identify image forgery. However, with the progress in digital technology, the ease of image manipulation has led to a surge in forgery cases, presenting significant obstacles when verifying their authenticity. Thus, there is a pressing requirement for effective forgery detection methods. This paper introduces a novel copy-move forgery detection approach based on the fusion of a handcrafted forgery detection system utiliszing the scale-invariant feature transform and the support vector machine with a deep-learning-based forgery detection system using VGG16. The aim is to address the challenges of subtle forgery detection and blending and seamless Integration. We conduct the experiments on the MICC_F2000 image manipulation dataset and assess the efficacy of the proposed approach, achieving $\mathbf{9 6 . 7 5 \%}$ accuracy, $\mathbf{1 0 0 \%}$ precision and $95.5 \%$ F1-score. This research demonstrates superior performance compared to state-of-the-art methods.
MobileNet is a lightweight convolutional neural network optimized for resource-limited environments. However, its use of depthwise separable convolutions to minimize parameters and computation can reduce accuracy due to oversimplified channel interactions. Principal Component Analysis (PCA) can address this issue by reducing the dimensionality of weight matrices while preserving key features. Applying PCA can help maintain accuracy and compress the model simultaneously. Based on this, we propose a novel copy-move forgery detection approach based on MobilNetV2 and PCA for feature extraction, and a random forest for classification. This allows us to enhance MobileNet accuracy while keeping its model size compact. The results of our experiments conducted on the MICC-F2000 dataset reveal that the proposed hybrid lightweight model outperforms the individual transfer learning structures and the existing literature, achieving 96.37% accuracy.
This study explores the classification of date fruit varieties in the Kingdom of Saudi Arabia using advanced image processing techniques and genotype analysis. Utilizing a unified model with 30 epochs and 640x640 pixel resolution, we achieved a classification accuracy of $99.382 \%$ with both median and Gaussian filters. Notably, misclassifications were observed between Sugaey and Sokari varieties. The training loss exhibited a sharp initial decrease, stabilizing at a low value, while the validation loss showed fluctuations, particularly around the 20th epoch. Top-1 accuracy increased rapidly, reaching near $100 \%$ by epoch 10, with Top- 5 accuracy consistently higher. The low pass and high pass filters demonstrated similar trends, with Top-1 accuracy stabilizing near $90 \%$ and misclassifications predominantly involving the Sugaey variety. These findings underscore the potential of refined image processing techniques to enhance fruit classification, improve crop management, and support genetic enhancement efforts in the date industry, ultimately contributing to economic growth and product quality in Saudi Arabia. From the analysis the following results were obtained. In the median filter, there is a notable misclassification between Sugaey and Sokari. The validation loss decreases but shows some fluctuations, especially around the 20th epoch
Non-destructive identification of cosmetics is essential to confirm their identity and purity. Near-infrared (NIR) spectroscopy provides a fast and non-destructive method for cosmetic identification. For this study, 99 raw materials and 30 products were measured using a palm sized NIR spectrometer. Spectral pre-treatment and analysis involved techniques such as multiplicative scatter correction-first derivative (MSC-D1), correlation in wavelength space (CWS), and Principal Component Analysis (PCA) respectively. Among the raw materials, 66 exhibited strong NIR activity, 25 showed medium NIR activity, and eight had weak NIR activity, while all 30 samples were found to be NIR active. The CWS method revealed a high frequency of Type II errors, with 81 out of 99 raw materials mismatching. All 30 of the products also resulted in mismatches. PCA proved to be the more accurate of the data analysis techniques with creams $\mathbf{9 7 \%}$ of variance was accounted for in PC1, and PC scores demonstrating strong differentiation among seven of the eight cream products. In contrast, the perfume products displayed $\mathbf{8 4 \%}$ variance in PC1 and $13 \%$ in PC2, with two of the seven products showing significant discrimination from the others. This study confirmed that NIR spectroscopy is well-suited for this application as it provides a quick analysis time, no sample preparation and allows for sample preservation.
