Hill-climbing Research Articles

AdaBoost model for rockburst intensity prediction considering class differences and quantitative characterization of misclassification difference.

With the resource development gradually into the deep, rock explosion phenomenon is more and more frequent. The suddenness and harmfulness of rockbursts threaten the safe development of underground resources. In order to more accurately predict the possible intensities of rockbursts in specific rock conditions and stress environments, the rock mechanical and stress parameters between different intensities of rockbursts are further explored, an AdaBoost model considering the differences in the hierarchy is established, and a Flash Hill-Climbing method is proposed to optimize the hyperparameters in the classification model. Finally, a misclassification difference index (MCDI) is defined to quantitatively characterize the severity of misclassification. The results show that the accuracy of the Improved AdaBoost model is about 2.3% higher than that of the Normal AdaBoost model, and the Misclassification Difference Index (MCDI) of the Normal AdaBoost model is 05, while the the Improved AdaBoost model is 00. The model can provide a theoretical reference for rockburst prediction.

The Impact of Using the Hill Climbing Strategy on the Students' Academic Achievement in the College of Educational Sciences at Al-Hussein Bin Talal University in the Subject of the Prophetic Biography and its Teaching Methods

The study aimed to determine the impact of using the Hill Climbing strategy on the students' academic achievement in the College of Educational Sciences at Al-Hussein Bin Talal University in the subject of the Prophetic Biography and its teaching methods. The study sample consisted of 80 students from the College of Educational Sciences studying the Prophetic Biography and its teaching methods during the second semester of the academic year 2021-2022 at Al-Hussein Bin Talal University. The students were divided into two groups: the control group, consisting of 40 students, who learned using traditional methods, and the experimental group, also consisting of 40 students, who learned using the Hill Climbing strategy. The researchers prepared an achievement test to measure the academic achievement of the students in the topic of Prophetic Biography and its teaching methods. The results of the study showed statistically significant differences between the two study groups in the students' level of achievement, in favor of the experimental group that learned using the Hill Climbing strategy. The study recommends the necessity and importance of employing the Hill Climbing strategy in teaching other Islamic education subjects such as Jurisprudence and its teaching methods and Creed and its teaching methods. The study also recommends conducting comparative studies between the Hill Climbing strategy and other modern teaching strategies. Received: 13 August 2024 / Accepted: 29 October 2024 / Published: 05 November 2024

Modeling noisy learning in a dynamic oligopoly experiment

Estimating demand before production poses a significant challenge for many industries, including vaccine manufacturing, newspapers, and perishable foods. Both industry professionals and experimental subjects in laboratory settings often struggle to determine optimal production. This paper sheds light on the cognitive processes that explain individuals' inability to act optimally, using data from Nagel and Vriend (1999a,b). In their experiment, participants, acting as firms, set production levels without prior knowledge of the demand generated by own and competitors' advertising efforts. We first reexamine their two-step learning model, which involves directional learning for production levels and a hill-climbing algorithm for advertising, utilizing exogenous adjustment size distributions. We improve upon this model, inspired by the macroeconomic learning literature, with agents using constant gain learning for production decisions and hill climbing for advertising decisions with endogenous adjustments. We demonstrate that our model provides a better fit to the data than the original model by Nagel and Vriend (1999a) and yields superior out-of-sample forecasts, both for one-period-ahead predictions and simulated paths.

Solving large-scale instances of the urban transit routing problem with a parallel artificial bee colony-hill climbing optimization algorithm

Swarm Intelligence simulates the collective behavior of decentralized and self-organized swarms. One of the main relevant methods is the Artificial Bee Colony (ABC) algorithm which simulates the foraging behavior of bee swarms in a colony to produce efficient solutions to various problems. The Urban Transit Routing Problem (UTRP) involves finding an efficient set of routes in a transit network to satisfy travel demand subject to operational and budget constraints. It is a complex, NP-Hard problem, in which otherwise correct solutions can be rejected because of impracticability. In this study, a hybrid algorithm consisting of a parallel ABC and Hill Climbing was used to find quality solutions to the UTRP. Thorough comparative results on Mandl’s well-known instance and Mumford’s large-scale instances demonstrate that the proposed algorithm outperforms existing techniques, achieving high levels of direct trip coverage in small computational times. Remarkably, when applied to the most extensive benchmark comprising over 6 million trips and 60 bus routes, the proposed algorithm demonstrates an impressive 11 % enhancement in direct coverage over the previously best-reported results, allowing the design of real-world bus networks in under 3 hours.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BackgroundA better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.MethodsThe current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).ResultsOf 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.ConclusionThese findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.Trial registrationTrial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Efficient energy consumption in hybrid cloud environment using adaptive backtracking virtual machine consolidation

Virtualization is the process of sharing physical machine resources into multiple Virtual Machines (VMs) for the effective utilization of resources. The major issues are in handling physical resources such as high demand of resources, infrastructure issues and operational issues. Also violating Service Level Agreement (SLA) is another problem to lead the violations. In this case, VM consolidation method is used to migrate the VMs and optimize the efficient way to use the resources. So we get minimized energy consumptions. Existing methods has increased energy consumption and overhead issues. In this paper we propose adaptive backtracking methods to provide VM consolidation based on less energy consumptions. We use Adaptive Hill Climbing and Pursuit algorithms to consolidate the VMs and use the VMs with efficient energy consumption. We implement this simulations using Matlab in Hybrid Cloud Environment. The performance factors are obtained with less energy consumption and compare the results with existing methods. Overall our proposed system is achieving 95% of accuracy index with lower energy consumption such as 28%, 30% and 32% with multiple VMs consolidations.

Implementation of the MPPT Particle Swarm Optimization Algorithm on a Hybrid PV-TEG Solar Panel with a DC-DC Buck Converter

Abstract Solar panels necessitate power control to locate the optimal working point amidst quickly changing voltages and diverse weather circumstances. This enables the system to adapt and sustain optimal performance in real-time. In order to address this issue, the optimization methodology known as Maximum Power Point Tracking (MPPT) is employed using an algorithmic artificial intelligence (AI) method. Particle Swarm Optimization (PSO) is a swarm intelligence technique that has effectively tackled diverse optimization issues in intricate systems. The DC-DC Buck Converter, which incorporates Maximum Power Point Tracking (MPPT), serves as an interface between the load and the photovoltaic (PV) system to control the output voltage of the system. A comparison is made between the performance of the Maximum Power Point Tracking (MPPT) technique using the Particle Swarm Optimization (PSO) algorithm and the hill climbing (HC) algorithm developed through MATLAB/SIMULINK simulations. The findings indicate that the PSO algorithm exhibits superior performance in terms of tracking time, output power, and stability, with little fluctuations or noise, as compared to the HC method. The tracking time is 0.02 seconds and 2.52 seconds, respectively. The power at constant irradiation is 14.49 Watts and 14.43 Watts. The power at irradiation variation is 6.98 Watt and 6.96 Watt. The PSO algorithm achieved a remarkable accuracy of 99.76% in this investigation, surpassing that of HC. This enhancement makes the system more effective in acquiring maximum power values.

Online controller optimization for a voltage-doubler boost rectifier with high power factor

ABSTRACT The design of converters is essential for the proper use of electrical energy. Half-bridge topology represents a suitable conversion scheme to implement a boost rectifier converter. Consequently, this article proposes the parameter optimization of a proportional-integral-derivative (with filter) controller for a half-bridge boost rectifier with a high power factor. Regarding the high simulation time for the converter, stochastic hill-climbing and simulated annealing algorithms are employed for the optimization process. The results display that the optimized controller allows suitable values for the power factor and output waveform. The controller parameters obtained through optimization algorithms result in a high power factor of 0.9934 when the half-bridge boost rectifier is operating at full load.

Integration of MPPT algorithms with spacecraft applications: Review, classification and future development outlook

The electric power system is one of the key subsystems of a spacecraft and is responsible for the acquisition, conversion, storage and control of energy during the spacecraft mission. Therefore, ensuring the adequacy and stability of energy sources has become an important part of the spacecraft design process. Currently, most spacecraft acquire energy through the solar cell arrays they carry, but the actual output power of the photovoltaic system is difficult to reach the peak power due to the influence of the on-orbit space environment such as alternating temperature changes and radiation. In order to improve the output efficiency of PV systems, researchers have proposed the maximum power point tracking method based on the operating characteristics of the PV cells, and the classical methods include the incremental conductance method, hill-climbing method, constant voltage method, and perturbation observation method. On its basis, in order to further improve the control accuracy and adaptability of MPPT under complex environment, related scholars have proposed several new MPPT algorithms, such as fuzzy algorithm and neural network algorithm based on the principle of intelligence, particle swarm optimization algorithm based on the principle of optimization, gray wolf algorithm and hybrid optimization algorithm based on multiple algorithms.This paper provides a comprehensive review of the topology, hardware architecture, mathematical models, and multiple DC-DC circuits of maximum power tracking PV systems; and reviews and summarizes the classical, intelligent, optimization, and hybrid types of maximum power point tracking methods, describing the principles, advantages, disadvantages, and applications of the different methods, respectively. Finally, an outlook on the future development and novel applications of maximum power point tracking methods for photovoltaic systems in aerospace is presented.

Optimizing solar energy efficiency with an improved hill-climbing maximum power point tracking control approach: hardware implementation

Abstract This article implements maximum power point tracking (MPPT) based on the improved hill-climbing algorithm for photovoltaic (PV) systems feeding resistive loads. A direct current-to-direct current boost converter is inserted between the PV system and the load to achieve matching. The converter is managed using MPPT based on the hill-climbing algorithm. The objective of this paper is to optimize the code program to achieve the best compromise between accuracy and rapidity by implementing this algorithm using a microcontroller. Two PV systems are tested under identical meteorological conditions. In the first, an improved hill-climbing MPPT controller is used whereas, in the second, the conventional version is employed. The experimental results obtained show a significant enhancement in terms of speed for the improved algorithm with a value of 0.4 s for the response time and 3% for the oscillation power; those values remain satisfactory in terms of precision of the algorithm compared with the conventional system studied and the compared algorithm from the literature.

Enhancing logic-based testing with EvoDomain: A search-based domain-oriented test suite generation approach

ContextEffective software testing requires test adequacy criteria. MC/DC, a widely used logic-based testing criterion, struggles to detect domain errors caused by incorrect arithmetic operations. Domain errors occur when test requirement boundaries shift or tilt, causing unpredictable behavior and system crashes. ObjectiveTo address the inadequacy of MC/DC in detecting domain errors, we present EvoDomain, a search-based testing technique. MethodEvoDomain uses a memetic algorithm combining genetic and hill-climbing algorithms, along with the DBSCAN clustering algorithm to select diversified boundary test data. The memetic algorithm is designed to efficiently enhance the search process for covering boundary test data. We compared EvoDomain with two logic-based testing approaches, a domain-oriented test suite generation approach, and random testing. ResultsEvaluations on 30 case studies show EvoDomain increases fault detection by 74.44% over MC/DC and 65.06% over RoRG. Additionally, EvoDomain improves support for different fault types by up to 68.89% for MC/DC and 66.33% for RoRG. Compared to COSMOS, which uses static analysis, EvoDomain improves the convergence effectiveness of identifying feasible subdomains by 32%. It offers high accuracy (0.99-1) and F1-score (0.99-1). EvoDomain finds the subdomains in less than 1/3 the time of Random search. ConclusionEvoDomain effectively generates domain-oriented test suites, enhancing the accuracy and effectiveness of fault detection.

A Classical Approach for MPPT Extraction in Hybrid Energy Systems

A novel approach for Maximum Power Point Tracking (MPPT) extraction using the Hill Climbing method in hybrid solar and wind energy systems. MPPT is essential for optimizing the energy harvesting efficiency of sustainable energy sources, the integration of multiple sources poses unique challenges. The proposed Hill Climbing algorithm is applied to both solar photovoltaic (PV) panels and wind turbines, enabling efficient tracking of the Maximum Power Points (MPPs) under varying environmental circumstances. This article investigates the performance of the Hill Climbing MPPT method through simulation and experimental validation in a hybrid energy system. The algorithm's adaptability to the dynamic nature of solar irradiance and wind speed is analyzed, demonstrating its capability to rapidly converge to the MPPs for both solar and wind components. The integration of Hill Climbing MPPT for both sources enhances the overall energy harvesting efficiency of the hybrid system. The Hill Climbing MPPT method offers a robust and unified solution for hybrid solar and wind energy systems, providing improved performance and simplicity of implementation. The findings contribute to advancing the optimization of renewable energy systems by addressing the challenges associated with the simultaneous utilization of solar and wind resources.

Identification and prediction of frailty among community-dwelling older Japanese adults based on Bayesian network analysis: a cross-sectional and longitudinal study

BackgroundFrailty is a multifactorial syndrome; through this study, we aimed to investigate the physiological, psychological, and social factors associated with frailty and frailty worsening in community-dwelling older adults.MethodsWe conducted a cross-sectional and longitudinal study using data from the “Community Empowerment and Well-Being and Healthy Long-term Care: Evidence from a Cohort Study (CEC),” which focuses on community dwellers aged 65 and above in Japan. The sample of the cross-sectional study was drawn from a CEC study conducted in 2014 with a total of 673 participants. After excluding those who were frail during the baseline assessment (2014) and at the 3-year follow-up (2017), the study included 373 participants. Frailty assessment was extracted from the Kihon Checklist, while social relationships were assessed using the Social Interaction Index (ISI). Variable selection was performed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and their predictive abilities were tested. Factors associated with frailty status and worsening were identified through the Maximum-min Hillclimb algorithm applied to Bayesian networks (BNs).ResultsAt baseline, 14.1% (95 out of 673) participants were frail, and 24.1% (90 out of 373) participants experienced frailty worsening at the 3-years follow up. LASSO regression identified key variables for frailty. For frailty identification (cross-sectional), the LASSO model’s AUC was 0.943 (95%CI 0.913–0.974), indicating good discrimination, with Hosmer–Lemeshow (H–L) test p = 0.395. For frailty worsening (longitudinal), the LASSO model’s AUC was 0.722 (95%CI 0.656–0.788), indicating moderate discrimination, with H–L test p = 0.26. The BNs found that age, multimorbidity, function status, and social relationships were parent nodes directly related to frailty. It revealed an 85% probability of frailty in individuals aged 75 or older with physical dysfunction, polypharmacy, and low ISI scores; however, if their social relationships and polypharmacy status improve, the probability reduces to 50.0%. In the longitudinal-level frailty worsening model, a 75% probability of frailty worsening in individuals aged 75 or older with declined physical function and ISI scores was noted; however, if physical function and ISI improve, the probability decreases to 25.0%.ConclusionFrailty and its progression are prevalent among community-dwelling older adults and are influenced by various factors, including age, physical function, and social relationships. BNs facilitate the identification of interrelationships among these variables, quantify the influence of key factors. However, further research is required to validate the proposed model.

Cancer classification using RNA sequencing gene expression data based on Game Shapley local search embedded binary social ski-driver optimization algorithms

Cancer remains a significant health concern due to its high mortality rates. Recent decades have witnessed substantial progress in cancer research, driven by advancements in high throughput sequencing technology and the application of diverse machine learning (ML) methods, particularly in the analysis of gene expression data. However, the proliferation of high-dimensional datasets, such as RNA-seq data, underscores the need for more robust ML techniques capable of efficiently handling large volumes of data to enable accurate treatment decisions. This paper introduces a novel hybrid feature selection (FS) algorithm, termed Game kernel SHapley Additive exPlanations (kSHAP), which combines with binary Social Ski Driver (bSSD), Adaptive Beta Hill Climbing (ABHC) and Late Acceptance Hill Climbing (LAHC) algorithms. The study comprehensively investigates three novel FS algorithms—kSHAP-bSSD, kSHAP-ABHC, and kSHAP-LAHC for cancer classification tasks using RNA sequencing (RNA-seq) datasets. An experiment conducted on five well-established RNA-seq cancer datasets: Lung Adenocarcinoma (LUAD), Stomach Adenocarcinoma (STAD), Breast Invasive Carcinoma (BRCA), lung squamous cell carcinoma (LUSC) and uterine corpus endometrial carcinoma (UCEC). The objective is to enhance cancer classification accuracy, robustness, and scalability using RNA-seq datasets. Additionally, the study evaluates six classifiers—Autoencoder (AE), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB), Neural Network (NN), and Random Forest (RF) with AE consistently out performing others. Evaluation metrics include accuracy, recall, precision, box plot, F1-score, radar plot, confusion matrix, ROC and statistical analysis. Our approach is compared against recent state-of-the-art FS algorithms, showing improvements in gene selection and classification accuracy. The kSHAP-bSSD demonstrates superior performance across all datasets compared to traditional methods, achieving an accuracy rate of 99.9% in LUAD and exhibiting higher accuracy rates and robustness in STAD, BRCA, LUSC, and UCEC datasets. Assessment across multiple metrics affirms the superiority of kSHAP-bSSD and kSHAP-ABHC combinations, underscoring their effectiveness in cancer classification tasks.

An efficient spider wasp optimizer-based tracker for enhancing the harvested power from thermoelectric generation sources

The distribution of heat at the cold and hot sides of a thermoelectric generator (TEG) is essential to its operation. Due to the inhomogeneous pattern, the TEG power-voltage (P–V) curve exhibits multiple peaks, which pose significant consequences for TEG generation. In order to increase the system conversion efficiency at this point, tracking global peak power (GPP) using a maximum power point tracker (MPPT) is crucial. The peak generation from TEG devices cannot be harvested by traditional algorithms such as hill climbing (HC) because they fall in local peaks. Therefore, the spider wasp optimizer (SWO), a new metaheristic-based tracker, is proposed in this paper to monitor the global power from TEG system operating at inhomogeneous heat distribution. In SWO, the iterative process of changing the population length aids in avoiding local solutions. The suggested SWO-based tracker is responsible for regulating the dc-dc boost converter linked to the TEG array terminals by giving its MOSFET the proper duty cycle. Seven inhomogeneous heat distributions are examined for two TEG arrays: symmetric 9 × 9 and asymmetric 10 × 15. These heat distributions are uneven row, uneven column, diagonal, outer, center, and random. The proposed approach is compared to artificial gorilla troops optimizer (GTO), gold rush optimizer (GRO), walrus optimization algorithm (WaOA), particle swarm optimizer (PSO), chef-based optimization approach (CBOA), cuckoo search (CS), Jellyfish search approach (JS), seagull optimization approach (SOA), and sine cosine approach (SCA). With regard to 9 × 9 array, the suggested SWO-tracker performed better than the published GTO method since it increased the generated power in the following scenarios: uneven row, uneven column, SN, diagonal, outer, center, and random patterns by 1.225 W, 0.4083 W, 19.5838 W, 2.4596 W, 1.394 W, and 2.4463 W, respectively. In asymmetric array, the suggested SWO performed better than all others, obtaining the least errors in relation to the Simulink GPPs of 0.016 W, 0.0822 W, 0.3615 W, 0.0603 W, 0.0569 W, 0.0081 W, and 0.0089 W. The obtained outcomes validated the proficiency and dependability of the suggested SWO-tracker in all examined cases.

FORMULATION AND IN VITRO CHARACTERIZATION OF METOCLOPRAMIDE LOZENGES BY USING THE QUALITY BY DESIGN (QBD) APPROACH

Objective: Conventional dosage forms may not be suitable for all patients, especially those facing difficulty with swallowing, which results in reduced adherence to medication, impacting treatment outcomes. This study aimed to formulate and characterize metoclopramide lozenges using the quality-by-design approach to provide a patient-friendly alternative. Materials and Method: Formulations were optimized using response surface methodology and a Design Expert, focusing on Metoclopramide, Citric Acid, and Carboxymethyl Cellulose, with hardness and dissolution as primary response variables. The experimental design involved 20 runs, six replications of the central point, and a quadratic model for improved model fitting, with adjustments made through transformation and numerical hill-climbing algorithms. Results and Discussion: Formulations of metoclopramide lozenges were obtained through a systematic quality-by-design approach. Using numerical optimization, the formulation with the highest desirability score of 1.00 was selected, yielding a hardness of 14.55 kg and a dissolution time of 1.25 minutes. The optimal coded values were 1.53223 grams for Metoclopramide (A), 4.84302 grams for Citric acid (B), and 18.5048 grams for Carboxymethyl cellulose (C). The optimized formulation was subjected to in vitro testing to validate its performance, yielding a hardness ranging from 13.98 to 15.00 kg/cm2 and a dissolution time ranging from 1.11 to 2.23 minutes. The effectiveness of the QbD approach was confirmed as outcomes closely aligned with the targeted responses. Conclusions: The Quality by Design approach was found effective in optimizing the formulation of metoclopramide lozenges. Using the Design of Expert and Response Surface Methodology, an optimized formulation was obtained with the desired hardness and dissolution rate. Peer Review History: Received 26 April 2024; Revised 4 May 2024; Accepted 28 June; Available online 15 July 2024 Academic Editor: Dr. DANIYAN Oluwatoyin Michael, Obafemi Awolowo University, ILE-IFE, Nigeria, toyinpharm@gmail.com Average Peer review marks at initial stage: 5.5/10 Average Peer review marks at publication stage: 7.0/10 Reviewers: Dr. A.A. Mgbahurike, University of Port Harcourt, Nigeria, amaka_mgbahurike@yahoo.com Dr. Hatem Sameir Abbas, Al-Azhar University, Egypt, hsam8406@yahoo.com

Identifying influencing factors of metabolic syndrome in patients with major depressive disorder: A real-world study with Bayesian network modeling

BackgroundThe bidirectional relationships between metabolic syndrome (MetS) and major depressive disorder (MDD) were discovered, but the influencing factors of the comorbidity were barely investigated. We aimed to fully explore the factors and their associations with MetS in MDD patients. MethodsThe data were retrieved from the electronic medical records of a tertiary psychiatric hospital in Beijing from 2016 to 2021. The influencing factors were firstly explored by univariate analysis and multivariate logistic regressions. The propensity score matching was used to reduce the selection bias of participants. Then, the Bayesian networks (BNs) with hill-climbing algorithm and maximum likelihood estimation were preformed to explore the relationships between influencing factors with MetS in MDD patients. ResultsTotally, 4126 eligible subjects were included in the data analysis. The proportion rate of MetS was 32.6 % (95 % CI: 31.2 %–34.1 %). The multivariate logistic regression suggested that recurrent depression, uric acid, duration of depression, marriage, education, number of hospitalizations were significantly associated with MetS. In the BNs, number of hospitalizations and uric acid were directly connected with MetS. Recurrent depression and family history psychiatric diseases were indirectly connected with MetS. The conditional probability of MetS in MDD patients with family history of psychiatric diseases, recurrent depression and two or more times of hospitalizations was 37.6 %. ConclusionUsing the BNs, we found that number of hospitalizations, recurrent depression and family history of psychiatric diseases contributed to the probability of MetS, which could help to make health strategies for specific MDD patients.

Improved brain effective connectivity modelling by dynamic Bayesian networks

Background:If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning. New method:This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modelling. Results:The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed. Comparison with existing methods:The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature. Conclusions:Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modelling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.

Infusing Weighted Average Ensemble Diversity for Advanced Breast Cancer Detection

ABSTRACTBreast cancer is a widespread health threat for women globally, often difficult to detect early due to its asymptomatic nature. As the disease advances, treatment becomes intricate and costly, ultimately resulting in elevated fatality rates. Currently, despite the widespread use of advanced machine learning (ML) and deep learning (DL) techniques, a comprehensive diagnosis of breast cancer remains elusive. Most of the existing methods primarily utilize either attention‐based deep models or models based on handcrafted features to capture and gather local details. However, both of these approaches lack the capability to offer essential local information for precise tumor detection. Additionally, the available breast cancer datasets suffer from class imbalance issue. Hence, this paper presents a novel weighted average ensemble network (WA‐ENet) designed for early‐stage breast cancer detection that leverages the ability of ensemble technique over single classifier‐based models for more robust and accurate prediction. The proposed model employs a weighted average‐based ensemble technique, combining predictions from three diverse classifiers. The optimal combination of weights is determined using the hill climbing (HC) algorithm. Moreover, the proposed model enhances overall system performance by integrating deep features and handcrafted features through the use of HOG, thereby providing precise local information. Additionally, the proposed work addresses class imbalance by incorporating borderline synthetic minority over‐sampling technique (BSMOTE). It achieves 99.65% accuracy on BUSI and 97.48% on UDIAT datasets.

SBGen: A high-performance library for rapid generation of cryptographic S-boxes

In the realm of cryptographic research, the generation of S-boxes with high nonlinearity and optimal cryptographic properties remains a critical challenge. This paper presents a novel approach to S-box generation, leveraging the strengths of heuristic optimization methods. Through a meticulous integration of Simulated Annealing (SA) and Hill Climbing (HC) algorithms with sophisticated cost functions, we introduce an innovative software tool that significantly advances the efficiency of generating highly nonlinear S-boxes. Our methodology is distinguished by its ability to consistently produce S-boxes that meet stringent security criteria, with a remarkable 100 % success rate and minimized computational overhead. A comparative analysis reveals that our approach outperforms existing methods in terms of the probability of generating target S-boxes and the average number of iterations required. The software's practical implications extend beyond theoretical advancements, offering a valuable resource for cryptographic system designers in their quest to fortify cipher systems against linear and differential cryptanalysis. By setting new benchmarks for nonlinearity and search efficiency, our work paves the way for future research in cryptographic S-box generation, highlighting the potential of combining heuristic techniques with domain-specific cost functions to achieve superior security outcomes.