Accurate house price forecasts are very important for formulating national economic policies. In this paper, we offer an effective method to predict houses’ sale prices. Our algorithm includes one-hot encoding to convert text data into numeric data, feature correlation to select only the most correlated variables, and a technique to overcome the missing data. Our approach is an effective way to handle missing data in large datasets with the K-nearest neighbor algorithm based on the most correlated features (KNN–MCF). As far as we are concerned, there has been no previous research that has focused on important features dealing with missing observations. Compared to the typical machine learning prediction algorithms, the prediction accuracy of the proposed method is 92.01% with the random forest algorithm, which is more efficient than the other methods.

The early and accurate diagnosis of skin cancer is crucial for providing patients with advanced treatment by focusing medical personnel on specific parts of the skin. Networks based on encoder–decoder architectures have been effectively implemented for numerous computer-vision applications. U-Net, one of CNN architectures based on the encoder–decoder network, has achieved successful performance for skin-lesion segmentation. However, this network has several drawbacks caused by its upsampling method and activation function. In this paper, a fully convolutional network and its architecture are proposed with a modified U-Net, in which a bilinear interpolation method is used for upsampling with a block of convolution layers followed by parametric rectified linear-unit non-linearity. To avoid overfitting, a dropout is applied after each convolution block. The results demonstrate that our recommended technique achieves state-of-the-art performance for skin-lesion segmentation with 94% pixel accuracy and a 88% dice coefficient, respectively.

Introduction: Students in non-English higher education are increasingly using large language models (LLMs) as informal study aids; however, the factual reliability of these systems in lower-resource languages - and its consequences for learning - remain largely unmeasured. Methods: We report a controlled, fully reproducible evaluation of factual hallucination in eight open-weight LLMs answering 100 verifiable factual questions in English, Russian, and Uzbek. Uzbek, an agglutinative and comparatively low-resource Turkic language, is the primary language of instruction at our institution. Responses were scored with a deterministic answer-key procedure. Results: The factual error rate increased monotonically with the resource level of the query language: 3.5% in English, 10.9% in Russian, and 15.9% in Uzbek (pooled over models; chi-square = 68.5, df = 2, p < 10 _-14 ). The gap was present in nearly every model, but its size varied widely: a 12B general-purpose model answered reliably across all three languages, whereas the smaller 1-4B models had an error rate of 24%-27% in Uzbek. Language-adapted models - open checkpoints continued-pretrained or fine-tuned on Uzbek - closed the gap almost entirely, reaching an error rate of 2%-8% in Uzbek, comparable to their reliability in English. Discussion: A student querying an off-the-shelf model in Uzbek is several times more likely to receive an incorrect fact than an English-speaking peer, which is both an equity concern and a hazard for academic integrity when unverified output enters student work. The risk, however, is not intrinsic to the language: it can be substantially reduced by preferring language-adapted models and by auditing per-language reliability before classroom deployment. All questions, code, and model outputs are released to support replication.