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This study introduces the CP-EODE algorithm, a novel hybrid of the Equilibrium Optimizer (EO), and the Differential Evolution (DE) algorithm. It addresses EO’s tendency toward premature convergence by enhancing its exploration capabilities. The motivation for this research stems from the need for more efficient and economically viable designs in engineering, particularly in the optimization of shell-and-tube heat exchangers (STHEs) and photovoltaic module parameters. The CP-EODE algorithm leverages DE's exploration strengths to enhance EO’s performance adopting a cooperative parallel approach that divides the population into EO and DE sub-populations for a more effective search space exploration. The validation of CP-EODE’s effectiveness commenced with its application to 29 benchmark functions, where a Friedman test yielded an Fr\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${F}_{r}$$\end{document} value of 22.19, significantly surpassing the critical value of 7.81, and a Wilcoxon signed rank test confirmed statistical improvements with a z-score beyond − 1.96. Four quality metrics were discussed to provide a detailed assessment of CP-EODE’s performance highlighting its robust exploration and exploitation capabilities. In practical applications, CP-EODE demonstrated significant advancements in STHE design achieving up to 19.706% reduction in heat exchanger surface area compared to traditional and other algorithmic designs, showcasing its potential to enhance engineering design efficiency and cost-effectiveness. A critical examination of the algorithm's convergence rate revealed its swift optimization capability, with rapid attainment of optimal solutions in various test cases, further establishing CP-EODE’s advantage in handling complex optimization challenges. Through rigorous statistical analysis and performance metrics, CP-EODE emerges as a superior solution for complex optimization challenges, underscoring the value of hybrid algorithmic strategies in engineering optimizations. [ABSTRACT FROM AUTHOR]

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substantially improve the independence and safety of visually impaired individuals in various environments. [ABSTRACT FROM AUTHOR]

The need for non-interactive human recognition systems to ensure safe isolation between users and biometric equipment has been exposed by the COVID-19 pandemic. This study introduces a novel Multi-Scaled Deep Convolutional Structure for Punctilious Human Gait Authentication (MSDCS-PHGA). The proposed MSDCS-PHGA involves segmenting, preprocessing, and resizing silhouette images into three scales. Gait features are extracted from these multi-scale images using custom convolutional layers and fused to form an integrated feature set. This multi-scaled deep convolutional approach demonstrates its efficacy in gait recognition by significantly enhancing accuracy. The proposed convolutional neural network (CNN) architecture is assessed using three benchmark datasets: CASIA, OU-ISIR, and OU-MVLP. Moreover, the proposed model is evaluated against other pre-trained models using key performance metrics such as precision, accuracy, sensitivity, specificity, and training time. The results indicate that the proposed deep CNN model outperforms existing models focused on human gait. Notably, it achieves an accuracy of approximately 99.9% for both the CASIA and OU-ISIR datasets and 99.8% for the OU-MVLP dataset while maintaining a minimal training time of around 3 min. [ABSTRACT FROM AUTHOR]

In recent years, the increasing prevalence of credit card usage has raised concerns about accurately predicting and managing credit card defaults. While machine learning and deep learning methods have shown promising results in default prediction, the black-box nature of these models often limits their interpretability and practical adoption. This study presents a new method for predicting credit card default using a combination of deep learning and explainable artificial intelligence (XAI) techniques. Integrating these methods aims to improve the interpretability of the decision-making process involved in credit card default prediction. The proposed approach is evaluated using a real-world dataset and compared to existing state-of-the-art models. Results show that the proposed approach achieves competitive prediction accuracy while providing meaningful insights into the factors driving credit card default risk. The present investigation adds to the increasing body of literature on explainable artificial intelligence (AI) in the realm of finance. Besides, it provides a pragmatic approach to assessing credit risk, balancing precision and comprehensibility. In conclusion, the model demonstrates strong potential as a credit risk assessment tool, with an accuracy of 0.8350, sensitivity of 0.8823, and specificity of 0.9879. Among the most important features identified by the model are payment delays and outstanding bill amounts. This study is a step toward more interpretable and transparent credit scoring models. [ABSTRACT FROM AUTHOR]

The Particle Swarm Optimization technique (PSO) is widely used in practical applications due to its flexibility and strong optimization performance. However, like other metaheuristic algorithms, PSO has limitations, such as a propensity to become trapped in local minima and an uneven distribution of effort between exploration and exploitation stages. A novel local search technique called QPSOL, based on PSO is the proposed solution to mitigate these issues. QPSOL aims to increase diversity and achieve a closer balance between the exploration and exploitation phases. The QPSOL incorporates a dynamic optimization strategy to enhance the method's efficiency. Unlike the novel local search strategy, which uses a new local search approach (LSA) to break out of local optima, QPSOL employs quadratic interpolation around the optimal search agent to enhance its exploitation capability and solution accuracy. These strategies complement each other and contribute to boosting PSO's convergence efficiency while seeking to balance exploration and exploitation. The proposed method is assessed using the IEEE CEC'2021 test suite, and its efficacy is evaluated against other metaheuristics and cutting-edge algorithms to determine its trustworthiness. The optimal parameters of three PV models are determined using the proposed technique and compared to different well-established algorithms. Systematic comparisons show that QPSOL is competitive with, and often outperforms, commonly used methods in research for predicting model parameters. [ABSTRACT FROM AUTHOR]