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.
Mental Health (MH) is a fundamental and indispensable component of general well-being that permits an individual to perform their activities to the fullest, in harmony with themselves and their social and physical surroundings. It enables an individual to manage life’s challenges (literacy, attitudes toward disorders, and cognitive abilities) effectively. MH comorbidity commonly denotes the coexistence of several different MH problems, a matter of considerable importance in the realms of clinical care and the overall well-being of society. However, the precise prediction of comorbidity in mental health (MH) enables the implementation of earlier treatment strategies and improves overall treatment outcomes. In this study, we used Linear Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision trees (DT), and AdaBoost with DT for the modeling of MH comorbidities. The aforementioned models were evaluated for accuracy using these metrics: Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Rsquared ($\mathrm{R}^{\mathbf{2}}$), and Mean Absolute Error (MAE). The study found that KNN outperformed other models by achieving the highest scores of $0.0187,0.0109,0.00$, and 1.00 for RMSE, MAE, MSE, and $R^{2}$ respectively. In conclusion, ML can enhance the prediction of MH comorbidities. Therefore, with accurate and earlier predictions, patient satisfaction and effective medical therapies will be achieved.
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
