Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.

Deep Learning techniques (DL) significantly improved the accuracy of predictions and classifications of deoxyribonucleic acid (DNA). On the other hand, identifying and predicting splice sites in eukaryotes is difficult due to many erroneous discoveries. To address this issue, we propose a deep learning model for recognizing and anticipating splice sites in eukaryotic DNA sequences based on a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) and Gated recurrent unit (GRU). The non-coding introns of the gene are spliced out, and the coding exons are joined during the splicing of the original mRNA transcript. This bidirectional LSTM-RNN-GRU model incorporates intron features in order of their length constraints, beginning with splice site donor (GT) and ending with splice site acceptor (AG). The performance of the model improves as the number of training epochs grows. The best level of accuracy for this model is 96.1 percent.

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The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events.

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP.