Method: In this study, we propose a novel deep learning (DL) framework that integrates a Bidirectional Long Short-Term Memory (BiLSTM)-Attention model with Principal Component Analysis (PCA) and Bayesian Optimization (BO) for feature selection. This approach enhances GNSS signal reliability by accurately detecting anomalies, especially in environments prone to interference. The PCA-BO feature selection process optimizes relevant features like signal strength and Doppler shifts, improving model performance while reducing overfitting.

Results and Discussion: Our results demonstrate that the proposed model significantly outperforms conventional methods, enhancing the precision of pharmaceutical deliveries in critical healthcare settings.

Conclusion: This work represents a key advancement in using DL to ensure GNSS signal integrity for SMSCL, contributing to more efficient and secure logistics operations.

Methods: The AISD and Kaggle datasets containing patients’ brain CT scan images (acute ischemic stroke, hemorrhage, and normal cases) were taken. Initially, adaptive histogram equalization (AHE) techniques were applied as preprocessing operations to enhance the quality of the CT images. Furthermore, features were extracted from the preprocessed data using several feature extraction techniques, including our proposed LMGP feature descriptor. The features were then scaled using the standard scaling technique. Subsequently, preprocessed images were fed into different classifiers to build models for classifying brain CT scan images.

Results: The effectiveness of our methodology LMGP was determined using different metrics, such as recall, precision, F1 score, logarithmic loss, accuracy (ACC), and area under the curve (ROC). Conclusively, LMGP performed best when the RBF-SVM classifier was used for the classification and gave an accuracy of 94% and 96% in the case of five-fold and ten-fold cross-validation, respectively.

Conclusion: LMGP offers a distinctive and robust method of feature extraction from CT scan images by combining local information along with gradient change in pixels of the image. The efficacy of our proposed methodology (LMGP) was evaluated by using distinct classifiers, and the results were compared with eight different feature extraction methods. Overall, LMGP effectively outperformed all other feature descriptor methods in this study.

Methods: The proposed solution involves constructing a hierarchical system of pattern recognition models. The lower level processes the outcomes of recognition and classification performed by ML methods or algorithmic procedures. The middle levels represent the static aspect of a scene, captured through object properties and logical connections, which are expressed as syntactic patterns. The higher levels describe an information flow of events derived from the low-level model data.

Results: We propose the application of the logical language Logtalk for constructing syntactic Pattern Recognition (PR) models and for transformational interpretation. Two use cases are presented: 1) the automatic generation of Java source code for objects representing modules of a Big Data analysis library, and 2) the recognition of PDF structure in documents generated according to a common template.

Discussion: A technique is proposed to justify the correctness of ML results by evaluating their correspondence to a structure with predefined relations. This is achieved by utilizing these results within a first-order logic inference procedure of an applied theory. A successful inference indicates that the ML results are consistent with the logical theory, thereby providing a higher degree of confidence in their accuracy. The integration of syntactic PR with neural networks and analysis techniques based on large linguistic models allows for the automated validation of results against the possibility of such logical inference.

Conclusion: This technique enables software developers to utilize ML results as input data for syntactic pattern recognition, thereby facilitating the description of dynamic processes. Significant emphasis is placed on modeling the interpretation of recognition results as a transformation of an information model.