Enhancing the Efficiency of a Cybersecurity Operations Center Using Biomimetic Algorithms Empowered by Deep Q-Learning.

In the complex and dynamic landscape of cyber threats, organizations require sophisticated strategies for managing Cybersecurity Operations Centers and deploying Security Information and Event Management systems. Our study enhances these strategies by integrating the precision of well-known biomimetic optimization algorithms-namely Particle Swarm Optimization, the Bat Algorithm, the Gray Wolf Optimizer, and the Orca Predator Algorithm-with the adaptability of Deep Q-Learning, a reinforcement learning technique that leverages deep neural networks to teach algorithms optimal actions through trial and error in complex environments. This hybrid methodology targets the efficient allocation and deployment of network intrusion detection sensors while balancing cost-effectiveness with essential network security imperatives. Comprehensive computational tests show that versions enhanced with Deep Q-Learning significantly outperform their native counterparts, especially in complex infrastructures. These results highlight the efficacy of integrating metaheuristics with reinforcement learning to tackle complex optimization challenges, underscoring Deep Q-Learning's potential to boost cybersecurity measures in rapidly evolving threat environments.

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices.

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry.

The immunohistochemical (IHC) evaluation of epidermal growth factor 2 (HER2) for the diagnosis of breast cancer is still qualitative with a high degree of inter-observer variability, and thus requires the incorporation of complementary techniques such as fluorescent in situ hybridization (FISH) to resolve the diagnosis. Implementing automatic algorithms to classify IHC biomarkers is crucial for typifying the tumor and deciding on therapy for each patient with better performance. The present study aims to demonstrate that, using an explainable Machine Learning (ML) model for the classification of HER2 photomicrographs, it is possible to determine criteria to improve the value of IHC analysis. We trained a logistic regression-based supervised ML model with 393 IHC microscopy images from 131 patients, to discriminate between upregulated and normal expression of the HER2 protein. Pathologists' diagnoses (IHC only) vs. the final diagnosis complemented with FISH (IHC + FISH) were used as training outputs. Basic performance metrics and receiver operating characteristic curve analysis were used together with an explainability algorithm based on Shapley Additive exPlanations (SHAP) values to understand training differences. The model could discriminate amplified IHC from normal expression with better performance when the training output was the IHC + FISH final diagnosis (IHC vs. IHC + FISH: area under the curve, 0.94 vs. 0.81). This may be explained by the increased analytical impact of the membrane distribution criteria over the global intensity of the signal, according to SHAP value interpretation. The classification model improved its performance when the training input was the final diagnosis, downplaying the weighting of the intensity of the IHC signal, suggesting that to improve pathological diagnosis before FISH consultation, it is necessary to emphasize subcellular patterns of staining.

Dynamic Population on Bio-Inspired Algorithms Using Machine Learning for Global Optimization.

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term "autonomous" refers to these variants' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential.

Entropy-Based Diversification Approach for Bio-Computing Methods.

Nature-inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well-known population-based bio-inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal's performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances.

Improvement of Patient Classification Using Feature Selection Applied to Bidirectional Axial Transmission.

Osteoporosis is still a worldwide problem, particularly due to associated fragility fractures. Patients at risk of fracture are currently detected using the X-Ray gold standard dual-energy X-ray absorptiometry (DXA), based on a calibrated 2-D image. Different alternatives, such as 3-D X-rays, magnetic resonance imaging (MRI) or ultrasound, have been proposed, the latter having advantages of being portable and sensitive to mechanical and geometrical properties. Bidirectional axial transmission (BDAT) has been used to classify between patients with or without nontraumatic fractures using "classical" ultrasonic parameters, such as velocities, as well as cortical thickness and porosity, obtained from an inverse problems. Recently, complementary parameters acquired with structural and textural analysis of guided wave spectrum images (GWSIs) have been introduced. These parameters are not limited by solution ambiguities, as for inverse problem. The aim of the study is to improve the patient classification using a feature selection strategy for all available ultrasound features completed by clinical parameters. To this end, three classical feature ranking methods were considered: analysis of variance (ANOVA), recursive feature elimination (RFE), and extreme gradient boosting importance feature (XGBI). In order to evaluate the performance of the feature selection techniques, three classical classification methods were used: logistic regression (LR), support vector machine (SVM), and extreme gradient boosting (XGB). The database was obtained from a previous clinical study [Minonzio et al., 2019]. Results indicate that the best accuracy of 71 [66-76]% was achieved by using RFE and SVM with 22 (out of 43) ultrasonic and clinical features. This value outperformed the accuracy of 68 [64-73]% reached with 2 (out of 6) DXA and clinical features. These values open promising perspectives toward improved and generalizable classification of patients at risk of fracture.

Periacetabular osteotomy with concomitant hip arthroscopy: A case series with 24 months minimum follow-up.

PURPOSE: To describe patient-reported outcomes, radiological results, and revision to total hip replacement in patients with hip dysplasia that underwent periacetabular osteotomy as isolated treatment or concomitant with hip arthroscopy. METHODS: Case series study. Between 2014 and 2017, patients were included if they complained of hip pain and had a lateral center-edge angle ≤ of 20°. Exclusion criteria included an in-maturate skeleton, age of 40 or older, previous hip surgery, concomitant connective tissue related disease, and Tönnis osteoarthritis grade ≥ 1. All patients were studied before surgery with an anteroposterior pelvis radiograph, false-profile radiograph, and magnetic resonance imaging. Magnetic resonance imaging was used to assess intraarticular lesions, and if a labral or chondral injury was found, concomitant hip arthroscopy was performed. The non-parametric median test for paired data was used to compare radiological measures (anterior and lateral center-edge angle, Tönnis angle, and extrusion index) after and before surgery. Survival analysis was performed using revision to total hip arthroplasty as a failure. Kaplan Meier curve was estimated. The data were processed using Stata. RESULTS: A total of 15 consecutive patients were included; 14 (93%) were female patients. The median follow-up was 3.5 years (range, 2 to 8 years). The median age was 20 (range 13 to 32). Lateral center-edge angle, Tönnis angle, and extrusion index correction achieved statistical significance. Seven patients (47%) underwent concomitant hip arthroscopy; three of them (47%) were bilateral (10 hips). The labrum was repaired in six cases (60%). Three patients (15%) required revision with hip arthroplasty, and no hip arthroscopy-related complications are reported in this series. CONCLUSION: To perform a hip arthroscopy concomitant with periacetabular osteotomy did not affect the acetabular correction. Nowadays, due to a lack of conclusive evidence, a case by case decision seems more appropriate to design a comprehensive treatment.

OBJETIVO: Describir los resultados funcionales, radiológicos y la tasa revisión a artroplastia total de cadera en una cohorte de pacientes con displasia de cadera que requirió osteotomía periacetabular como tratamiento aislado o concomitante con artroscopia de cadera. MÉTODO: Estudio de series de casos. Fueron incluidos pacientes intervenidos quirúrgicamente entre 2014 y 2017. Los criterios de inclusión fueron dolor en la cadera y un ángulo lateral de centro borde ≤ 20°. Los criterios de exclusión fueron esqueleto maduro, edad de 40 años o más, cirugía previa de cadera, enfermedad concomitante relacionada con el tejido conectivo y coxartrosis grado ≥ 1 de Tönnis. Todos los pacientes fueron estudiados antes de la cirugía con una radiografía anteroposterior de pelvis, radiografía de falso perfil y resonancia magnética. En caso de pesquisar una lesión intrarticular en resonancia magnética se realizó una artroscopia concomitante a osteotomía periacetabular. La prueba mediana no paramétrica para datos no pareados se utilizó para comparar medidas radiológicas (ángulo del borde central anterior y lateral, ángulo de Tönnis e índice de extrusión) antes y después de la cirugía. El análisis de sobrevida se realizó utilizando la revisión de la artroplastia total de cadera como fracaso. Se estimó la curva de Kaplan Meier. Los datos se procesaron con Stata. RESULTADOS: Fueron incluidos un total de 15 pacientes consecutivos; Siete pacientes (47%) se sometieron a artroscopia de cadera concomitante, tres de ellos (47%) fueron bilaterales (10 caderas). La mediana de seguimiento fue de 3.5 años (rango, 2 a 8 años). La mediana de edad fue de 20 (rango 13 a 32). El ángulo de cobertura lateral preoperatorio era de 12° (rango, -29° a 19°) y posterior a la cirugía fue de 25° (rango, 8° a 34°), logrando una mediana de corrección de 16° (rango, 7° a 53°). El labrum fue reparado en seis casos (60%). Tres pacientes (15%) requirieron revisión con artroplastia de cadera, y no se informan complicaciones relacionadas con la artroscopia en esta serie. CONCLUSIÓN: Realizar una artroscopia concomitante con osteotomía periacetabular no afectó la corrección acetabular, ni el resultado clínico. Hoy en día, debido a la falta de evidencia concluyente, el realizar una artroscopía es una decisión que debe ser tomada caso a caso para un tratamiento integral.

Periacetabular osteotomy with concomitant hip arthroscopy: a case series with 24 months minimum follow-up

PURPOSE To describe patient-reported outcomes, radiological results, and revision to total hip replacement in patients with hip dysplasia that underwent periacetabular osteotomy as isolated treatment or concomitant with hip arthroscopy. METHODS Case series study. Between 2014 and 2017, patients were included if they complained of hip pain and had a lateral center-edge angle ≤ of 20°. Exclusion criteria included an in-maturate skeleton, age of 40 or older, previous hip surgery, concomitant connective tissue related disease, and Tönnis osteoarthritis grade ≥ 1. All patients were studied before surgery with an anteroposterior pelvis radiograph, false-profile radiograph, and magnetic resonance imaging. Magnetic resonance imaging was used to assess intraarticular lesions, and if a labral or chondral injury was found, concomitant hip arthroscopy was performed. The non-parametric median test for paired data was used to compare radiological measures (anterior and lateral center-edge angle, Tönnis angle, and extrusion index) after and before surgery. Survival analysis was performed using revision to total hip arthroplasty as a failure. Kaplan Meier curve was estimated. The data were processed using Stata. RESULTS A total of 15 consecutive patients were included; 14 (93%) were female patients. The median follow-up was 3.5 years (range, 2 to 8 years). The median age was 20 (range 13 to 32). Lateral center-edge angle, Tönnis angle, and extrusion index correction achieved statistical significance. Seven patients (47%) underwent concomitant hip arthroscopy; three of them (47%) were bilateral (10 hips). The labrum was repaired in six cases (60%). Three patients (15%) required revision with hip arthroplasty, and no hip arthroscopy-related complications are reported in this series. CONCLUSION To perform a hip arthroscopy concomitant with periacetabular osteotomy did not affect the acetabular correction. Nowadays, due to a lack of conclusive evidence, a case by case decision seems more appropriate to design a comprehensive treatment.

SIGICAM: A New Software to Improve the Patient Care Supported by a Constraint-Based Model.

Health facilities are care centers that receive patients with different requirements. The management of patients falls to the clinical staff trained for this activity. However, given the demands of the population, the task of managing beds is sometimes too complicated when carried out manually. In this work, we propose the design and implementation of a technological platform that provides an improved optimization approach. It manages the patient-bed allocation efficiently, by considering hospital resources given the number of units and patient diagnosis. This tool was deployed in hospitals of the Atacama regional health service in Chile, boosting the work of the clinical staff of the health facility.

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios.

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

Ant Colony Clustering for ROI Identification in Functional Magnetic Resonance Imaging.

Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.

Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition.

Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.

Managing an Acute and Chronic Periprosthetic Infection.

A case report of a 65-year-old female with a history of right total hip arthroplasty (THA) in 2007 and left THA in 2009 was presented. She consulted with our institution for the first time, on December 2013, for right hip pain and fistula on the THA incision. It was managed as a chronic infection, so a two-stage revision was performed. First-time intraoperative cultures were positive for Staphylococcus aureus (3/5) and Proteus mirabilis (2/5). Three weeks after the second half of the review, it evolved with acute fever and pain in relation to right hip. No antibiotics were used, arthrocentesis was performed, and a coagulase-negative staphylococci multisensible was isolated at the 5th day. Since the germ was different from the first revision, it was decided to perform a one-stage revision. One year after the first review, the patient has no local signs of infection and presents ESV and RPC in normal limits. The indication and management of periprosthetic infections are discussed.

Escala de medición del aprendizaje organizacional en centros escolares / Scale of organizational learning in schools

El aprendizaje organizacional es un elemento clave para el desarrollo de las organizaciones. Las organizaciones escolares no están ajenas a este desafío y actualmente enfrentan un contexto altamente dinámico y demandante de políticas educativas que ponen énfasis en la capacidad de aprendizaje de los centros escolares. Así, la investigación sobre aprendizaje organizacional en contextos educativos requiere de instrumentos validados que permitan dar cuenta de las especificidades de los centros escolares como organizaciones. En este estudio se adaptó y validó una escala de aprendizaje organizacional para centros escolares en una muestra de 119 escuelas de administración municipal en Chile (N= 1.545). Los resultados indican un modelo estructural de tres factores: cultura de aprendizaje, claridad estratégica y aprendizaje grupal. Estos factores mostraron ser predictores de ciertas dimensiones del desempeño educativo, medido según el Sistema Nacional de Evaluación del Desempeño Educativo (SNED). Se discuten estos resultados a la luz de la literatura sobre mejoramiento escolar (AU)

Organizational learning is a key element for the development of organizations. School organizations are not exempt from this challenge and they currently face a highly dynamic and demanding context of education policies that emphasize the school’s ability to learn. Thus, research on organizational learning in educational contexts requires valid instruments that are sensitive to the specifics of schools as organizations. In this study, we adapted and validated a scale of organizational learning in a sample of 119 Chilean municipal schools (N= 1,545). The results suggest a structural model made up of three factors: culture of learning, strategic clarity, and group learning. These factors predicted dimensions of educational achievement, as measured through the National Assessment System of Educational Achievement (SNED). Results are discussed in view of the literature on school improvement (AU)

[Scale of organizational learning in schools. Organizational learning is a key element for the development of organizations]. / Escala de medición del aprendizaje organizacional en centros escolares.

Organizational learning is a key element for the development of organizations. School organizations are not exempt from this challenge and they currently face a highly dynamic and demanding context of education policies that emphasize the school's ability to learn. Thus, research on organizational learning in educational contexts requires valid instruments that are sensitive to the specifics of schools as organizations. In this study, we adapted and validated a scale of organizational learning in a sample of 119 Chilean municipal schools (N= 1,545). The results suggest a structural model made up of three factors: culture of learning, strategic clarity, and group learning. These factors predicted dimensions of educational achievement, as measured through the National Assessment System of Educational Achievement (SNED). Results are discussed in view of the literature on school improvement.