The rising prevalence of retinal diseases is a significant concern, as certain untreated conditions can lead to severe vision impairment or even blindness. Deep learning algorithms have emerged as a powerful tool for the diagnosis and analysis of medical images. The automated detection of retinal diseases not only aids ophthalmologists in making accurate clinical decisions but also enhances efficiency by saving time. This study proposes a deep learning-based approach for the automated classification of multiple retinal diseases using fundus images. For this research, a balanced dataset was compiled by integrating data from various sources. Artificial Neural Networks (ANN) and transfer learning techniques were utilized to differentiate between healthy eyes and those affected by diabetic retinopathy, cataracts, or glaucoma. Multiple feature extraction methods were employed in conjunction with ANN for the multi-classification of retinal diseases. The results demonstrate that the model combining Artificial Neural Networks (ANN) with MobileNetV2 and DenseNet121 architectures, along with Principal Component Analysis (PCA) for feature extraction and dimensionality reduction, as well as the Discrete Wavelet Transform (DWT) algorithm, achieves highly satisfactory performance, attaining a peak accuracy of 98.2%.

Technology is always a double-edged sword, and with the astonishing advancements in technology, it is expected that the DeepFake problem will become more common and serious. DeepFake has recently caused a lot of trouble because its flaws outweigh its advantages. Since DeepFake has such a significant influence on individuals deception, instability of principles and falsification of evidence. Instead of just affecting people, it led to multiple incidents that affected the image of entire nations. In this work, a model that has been built to mitigate the negative effects of deepFake and maintain an individual's reputation by detecting the alteration of people's photographs and videos. A model with integrated vision transformer architectures Deep-ViT and Cross-ViT is designed to process pre-extracted faces from FF++ dataset. The model distinguishes between the real and fake faces in two different perspectives, subclass detection on each manipulation method and overall detection of all types. The proposed model achieves an outstanding results and the highest accuracy in FaceSwap manipulation method with 98%.

The pressures of daily life result in a proliferation of terms such as stress, anxiety, and mood swings. These feelings may be developed to depression and more complicated mental problems. Unfortunately, the mood and emotional changes are difficult to notice and considered a disease that must be treated until late. The late diagnosis appears in suicidal intensions and harmful behaviors. In this work, main human observable facial behaviors are detected and classified by a model that has developed to assess a person’s mental health. Haar feature-based cascade is used to extract the features from the detected faces from FER+ dataset. VGG model classifies if the user is normal or abnormal. Then in the case of abnormal, the model predicts if he has depression, anxiety, or other disorder according to the detected facial expression. The required assistance and support can be provided in a timely manner with this prediction. The system has achieved a 95% of overall prediction accuracy.

Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes.

Small pulmonary nodules are radiologic findings that represent an important challenge in diagnosis systems. While these nodules are the major indicator for lung cancer and metastasis, their properties like size and location play an important role in classifying the benign one from the malignant. Estimating the growth rate of the nodule size states the degree of malignancy. This paper presents a computer-aided diagnosis (CAD) system to detect small-size pulmonary nodules from the chest computed tomography (CT) images through two dimensional (2-D) and three-dimensional (3-D) methods. Also, a computed volumetric growth is a promising way to distinguish malignant from nonmalignant pulmonary nodules. It was applied to lung nodules (2 to 7 mm in diameter) and achieved sensitivity 94.6% with an average; it is expected to aid radiologists in the detection of small nodules on thin-section multi–detector row CT images.

Instance segmentation in heterogeneous waste scenes remains challenging due to object variability, deformable shapes, partial occlusion, and large appearance differences across packaging types. This study presents a YOLOv11-based deep learning framework for mixed plastic waste instance segmentation, developed to connect visual perception with reliable material quantification. The framework integrates curated instance-level annotations, strict split isolation, multi-stage optimization, training strategy ablation, and seed-robustness analysis to support reproducible model selection. Experimental results on a held-out test set show that the optimized model achieves a mask mAP@50:95 of 0.9337, indicating strong segmentation performance under heterogeneous waste-scene conditions. To extend the analysis beyond standard vision metrics, the framework incorporates a physics-informed mask-to-mass module that converts predicted masks into class-specific mass estimates using geometric calibration and material priors. Applied to a representative stream of 1253 detected objects, the system estimated a total plastic mass of 15.48 ± 1.08 kg, corresponding to a theoretical H₂ potential of 0.41 ± 0.04 kg and a greenhouse-gas avoidance of 34.57 ± 4.15 kg CO₂e. Overall, the proposed framework extends waste-scene understanding beyond vision-level assessment toward physically grounded, data-driven decision support for smart material recovery systems.