Recommender systems are crucial in managing information overload by providing personalized suggestions based on user preferences. Traditional collaborative filtering methods like K-Nearest Neighbors (KNN) and Singular Value Decomposition (SVD) are effective but face sparse datasets and scalability challenges. This research introduces a hybrid collaborative filtering recommendation a lgorithm that integrates KNN, SVD Decomposition, and XGBoost, a gradient-boosting framework. This approach aims to enhance recommendation quality by combining KNN's neighborhood-based filtering, SVD's latent factor modeling, and XGBoost's predictive power. This research investigates the impact of different KNN similarity measurements on the accuracy of the XGBoost model within the hybrid framework. The goal is to identify the optimal similarity measurement that, when combined with SVD and XGBoost, results in accurate and personalized recommendations across various scenarios. Detailed analysis and experiments on benchmark datasets evaluate the model's performance in terms of accuracy, scalability, and computational efficiency. The results demonstrate that the hybrid model, enhanced with diverse KNN similarity measures, consistently outperforms standalone techniques and conventional ensemble methods. This research underscores the potential of the proposed model to develop more effective and scalable recommendation systems across diverse domains.

Skin cancer is a widespread and fatal disease in which early and accurate detection is an important aspect for effective treatment. The issues that arise when performing automated analysis of dermatoscopic images include artifacts such as hair, low contrast, and irregular edges of lesions that interfere with segmentation and classification. This study proposes an automated image preprocessing pipeline designed to remove artifacts while saving lesion texture and boundary. The method combines various computer vision methods and processes to produce a hairless dermatoscopic image of the sample, and lesion segmentation is subsequently performed using the HSV color space and binary masking. The effectiveness of the proposed preprocessing approach is evaluated using five state-of-the-art models: VGG16, ResNet50, InceptionV3, EfficientNet-B4, and DenseNet121.

Image-to-image translation is one of the major image processing tasks in the computer vision field that can be utilized in many types of applications such as style transfer, image enhancement, and more. This study introduces a novel approach for image-to-image translation based on a conditional generator adversarial network with a new hybrid generator architecture that combines the U-Net and ResNet architectures. This combination allows the model to benefit from both of their advantages due to their high compatibility. The discriminator uses the PatchGAN architecture for patch-wise discrimination. The model was evaluated by using the SSIM and PSNR which are standard metrics for image quality evaluation. The results are also compared to previous work that uses the same evaluation criteria and datasets. Furthermore, a public survey was conducted in which the participants were asked to choose the image that most closely resembled the target image between the proposed model and another study. The outcome of both the evaluation metrics and the public survey successfully demonstrated that the proposed image-to-image translation method is superior to that of previous studies.