Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance evaluation analysis is conducted on our proposed hybrid model and the existing models based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Sum of Squared Error (SSE), and Mean Absolute Error (MAPE). The result demonstrates the capability of EA in terms of accuracy and effectiveness as the learning algorithm in HNN for RANkSAT with a different number of neurons compared to ES and GA.

Accurate parameter estimation is fundamental in financial modeling, especially in investment analysis, where the Modified Internal Rate of Return (MIRR) plays a key role in evaluating investment performance. This study aims to enhance risk and return predictions in Sharia-compliant property investments by exploring the efficacy of various optimization techniques for estimating Weibull distribution parameters within the MIRR framework. To achieve this, we employed a comparative analysis of optimization methods, including Simulated Annealing (SA), Differential Evolution (DE), Genetic Algorithm (GA), and traditional Numerical Methods (NM). Performance was assessed through metrics such as Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC), R-squared (R2) values, and Kolmogorov-Smirnov (KS) statistics. The results reveal that metaheuristic algorithms (SA, DE, GA) significantly outperform traditional numerical methods in terms of parameter estimation accuracy. Specifically, SA achieved the lowest RMSE of 0.042, with a Weibull shape parameter estimate of 1.254 and variance of 0.004, followed closely by DE with an RMSE of 0.048, and GA with 0.046. In contrast, NM exhibited a higher RMSE of 0.067, with a shape parameter estimate of 1.310 and a variance of 0.006. The AIC values for metaheuristic methods ranged from 14.25 to 14.68, compared to 15.12 for NM, and R2 values for metaheuristic methods ranged from 0.932 to 0.945, compared to 0.910 for NM. KS statistics further underscored the superior model fit of metaheuristics, with SA showing the lowest KS value of 0.045. The study underscores the critical role of metaheuristic optimization in improving the accuracy of parameter estimation based on MIRR models. This enhancement provides more reliable risk assessments and returns predictions, offering valuable insights for informed investment decision-making and contributing to optimized financial outcomes in the property sector.

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.

The Weibull distribution is one of the most popular statistical models extensively applied to lifetime data analysis such as survival data, reliability data, wind speed, and recently in financial data, due to itsts flexibility to adaptably imitate different families of statistical distributions. This study proposed a modified version of the two-parameter Weibull distribution by incorporating additional parameters in the internal rate of return and insurance claims data. The objective is to examine the behaviour of investment return on the assumption of the proposed model. The proposed and the existing Weibull distribution parameters have been estimated via a simulated annealing algorithm. Experimental simulations have been conducted mimicking the internal rate of return (IRR) data for both short time (small sample) and long-term investment periods (large samples). The performance of the proposed model has been compared with the existing two-parameter Weibull distribution model in terms of their R-square (R2), mean absolute error (MAE), root mean squared error (RMSE), Akaike’s information criterion (AIC), and the Kolmogorov-Smirnov test (KS). The numerical simulation revealed that the proposed model outperformed the existing two-parameter Weibull distribution model in terms of accuracy, robustness, and sensitivity. Therefore, it can be concluded that the proposed model is entirely suitable for the long-term investment period. The study will be extended using the internal rate of return real data set. Furthermore, a comparison of the various Weibull distribution parameter estimators such as metaheuristics or evolutionary algorithms based on the proposed model will be carried out.

In this era, the agile mindset has innovated the traditional software engineering (SE) process through the integration of DevOps flow engines, scrum iterations, and automation of continuous integration (CI) and continuous deployment (CD) cycles. However, the CI/CD integration requires manual code-revisions and refactoring at large scales. Recently, machine-learning (ML) is employed in SE that allows legacy codes to be highly dynamic, less coupling in related modules, automatic code versioning, and refactoring, with less coupling among related modules. However, over time, ML models tend to become bulky, with increasing monotonic losses during model training. To address this, SE techniques like code revisions are employed over ML codes to allow low-order training losses, that enables seamless and precise workflow structures. Motivated from the aforementioned discussions, the paper presents a systematic review of the close interplay of SE and ML and possible interactions in different applications. Suitable research questions and case studies are presented for possible adoption scenarios that depict the close ML-SE interplay share with each other, with the concluding remarks. The paper forms useful insights for ML engineers, data science practitioners, and SE quality estimators towards the building of efficient and scalable software solutions.