Leonid Alemán Gonzales

Most approaches use interactive priors to find tumours and then segment them based on tumour-centric candidates. A fully convolutional network is demonstrated for end-to-end breast tumour segmentation. When confronted with such a variety of options, to enhance tumour detection in digital mammograms, one uses multiscale picture information. Enhanced segmentation precision. The sampling of convolution layers are carefully chosen without adding parameters to prevent overfitting. The loss function is tuned to the tumor pixel fraction during training. Several studies have shown that the recommended method is effective. Tumour segmentation is automated for a variety of tumour sizes and forms postprocessing. Due to an increase in malignant cases, fundamental IoT malignant detection and family categorisation methodologies have been put to the test. In this paper, a novel malignant detection and family categorisation model based on the improved stochastic channel attention of convolutional neural networks (CNNs) is presented. The lightweight deep learning model complies with tougher execution, training, and energy limits in practice. The improved stochastic channel attention and DenseNet models are employed to identify malignant cells, followed by family classification. On our datasets, the proposed model detects malignant cells with 99.3 percent accuracy and family categorisation with 98.5 percent accuracy. The model can detect and classify malignancy.

Los desastres naturales y tecnológicos representan una amenaza importante para la vida y la propiedad de las personas, así como para el medio ambiente en general. La gestión adecuada de desastres y pandemias es una tarea difícil, que implica procesar y distribuir información para tomar decisiones efectivas y brindar la ayuda necesaria. Sin embargo, muchas agencias y planes de socorro en casos de desastre no funcionan correctamente debido a varios desafíos, como la falta de coordinación, la corrupción y la falta de seguridad de la información. En este contexto, la tecnología blockchain ofrece una solución prometedora al proporcionar una forma segura y eficiente de almacenar y compartir información de manera transparente y descentralizada. Blockchain es una base de datos compartida y distribuida que garantiza la facilitación y protección de un sistema de intercambio de datos verdaderamente eficiente. Con características fundamentales como el cifrado y la transparencia, la tecnología utiliza claves privadas y públicas para brindar una mejor seguridad y proteger la identidad y la información asociada con el desastre al emitir certificados de víctimas. Este capítulo propuesto evalúa críticamente los sistemas existentes de gestión de desastres orientados a blockchain y su futuro alcance de investigación. El uso de la tecnología blockchain en la gestión de desastres puede reducir la corrupción, facilitar y acelerar la formación de asociaciones entre las agencias de socorro en casos de desastre, entregar comunicaciones de desastres verificadas y oportunas, mejorar la asignación de recursos vitales y permitir el acceso seguro a datos valiosos. que se produce durante las operaciones de respuesta y recuperación.

Quadratic Unconstrained Binary Optimization (QUBO) provides a principled framework for feature selection by encoding relevance–redundancy trade-offs and explicit constraints directly in a combinatorial objective. This study presents a stability-aware QUBO pipeline for tabular binary classification, evaluated on two standard benchmarks, namely Breast Cancer Wisconsin Diagnostic (569 samples, 30 features) and Pima Indians Diabetes (768 samples, 8 features; clinically invalid zeros treated as missing and imputed within folds). We study four QUBO variants spanning a base relevance–redundancy formulation, an exact-cardinality formulation enforcing a fixed budget k, a stability-regularized formulation that incorporates bootstrap uncertainty estimates of relevance and redundancy directly into the QUBO objective, and a performance-weighted relevance variant based on inner-CV univariate utility. All methods are assessed under repeated nested stratified cross-validation (5 outer folds × 3 repeats, n = 15 outer test evaluations), reporting AUC-ROC, AUC-PR, MCC, and Brier score with 95% confidence intervals, alongside selection stability via mean Jaccard similarity across outer-fold selected subsets. Results show that QUBO-based selection is competitive with strong classical baselines (RFECV, L1-logistic, permutation-importance ranking, and mutual information) while enabling strict budget control and transparent stability diagnostics. On the near-ceiling Breast Cancer benchmark, predictive differences are marginal and the main differentiators become subset-size control and stability; on Pima, QUBO-k remains competitive while enforcing strict cardinality constraints. These findings support QUBO as a practical framework when budgeted, interpretable, and reproducible feature selection is required, though evaluation is limited to low-dimensional tabular settings.

The increasing prevalence and sophistication of ransomware pose a significant challenge to cybersecurity. Traditional mitigation methods often fail to detect threats during early stages, especially when malware presents limited behavioral signatures. This study introduces a hybrid feature selection framework that integrates classical machine learning models with a Quantum Unconstrained Binary Optimization (QUBO)-based formulation to identify early-stage ransomware indicators. Our approach evaluates the relevance and redundancy of static features extracted from Windows Portable Executable (PE) files. By solving the QUBO problem using a simulated annealing algorithm, we achieve a compact feature subset that preserves predictive performance. Comparative experiments involving XGBoost, Random Forest, and other classifiers demonstrate that the QUBO-selected subset, consisting of only six features, performs comparably to full feature sets, with an F1 score exceeding 0.99. These findings highlight the potential of quantum-inspired techniques for lightweight malware detection on resource-constrained systems.

From smart home devices to wearable devices, electronics have become an indispensable part of modern life. Vast volumes of data have been collected by these electronic devices, revealing precise information about device communications, user behaviours, and more. Improvements to device features, insights into the user experience, and the detection of security risks are just some of the many uses for this information. However, advanced analytical methods are required to make sense of this plethora of data successfully. The K-means clustering algorithm is used in the present research to analyse the data sent and received by different types of electronics. The first step of the research is collecting data, intending to create a representative sample of people using various devices and communication methods. After collecting data, preprocessing is necessary to ensure it can be analysed successfully. In the next step, the K-means algorithm classifies the information into subsets that stand for distinct modes of interaction. The primary objective of the research is to gain an improved understanding of these groups by demonstrating how users communicate, device communication, and possibilities for enhancing functionality and security.