This study introduces the LPBSA, an advanced optimization algorithm that combines Learner Performance-based Behavior (LPB) and Simulated Annealing (SA) in a hybrid approach. Emphasizing metaheuristics, the LPBSA addresses and mitigates the challenges associated with traditional LPB methodologies, enhancing convergence, robustness, and adaptability in solving complex optimization problems. Through extensive evaluations using benchmark test functions, the LPBSA demonstrates superior performance compared to LPB and competes favorably with established algorithms such as PSO, FDO, LEO, and GA. Real-world applications underscore the algorithm's promise, with LPBSA outperforming the LEO algorithm in two tested scenarios. Based on the study results many test function results such as TF5 by recording (4.76762333) and some other test functions provided in the result section prove that LPBSA outperforms popular algorithms. This research highlights the efficacy of a hybrid approach in the ongoing evolution of optimization algorithms, showcasing the LPBSA's capacity to navigate diverse optimization landscapes and contribute significantly to addressing intricate optimization challenges.

Heart disease caused of death worldwide, efforts are continued to treat this hard disease, one of the treatments could powerfully infected for a large number of patients is stent placement. However, accurately determining the necessity for a stent based on coronary angiogram data is challenging due to the complexity of the decision-making process. There are a large number of metaheuristic and optimization algorithms so far could serve of these kinds of determining. However, the results of necessity of stent using optimization algorithms are not promising a happiness results. The current study attempted to discover a promising result for necessity of a stent for infected patients of heart disease. An image dataset has been used including stent and notstent images. This study also aiming to improve accuracy in assessing stent necessity for heart disease infects by leveraging ResNet-50 for feature extraction, combining with six optimization algorithms utilized to obtain and present the promising results using five comparative metrics like accuracy, sensitivity, specificity, precision and confusion matrix. Among those algorithms LPBSA overwhelming the other proposed algorithms by scoring 100% for all the five metrics, however PSO competed comprehensively for the majority of the results with the score of 100% for all the metrics excepting sensitivity metric obtaining 75%.

In today's digital age, information systems (IS) are indispensable tools for organizations of all sizes. The quality of these systems, encompassing system, information, and service dimensions, significantly impacts organizational performance. This study investigates the intricate relationships between these three quality dimensions and their collective influence on key performance indicators such as customer satisfaction and operational efficiency. By conducting a comparative analysis of various quality metrics, we aim to identify the most effective indicators for assessing IS quality. Our research contributes to the field by providing actionable insights for researchers or practitioners to develop the implementation, evaluation and design of information systems. Also, a quantitative study employing a structured questionnaire survey was conducted to achieve primary data from respondents across various sectors. Statistical analysis, including Cronbach's Alpha (0.953) and factor analysis (KMO = 0.965, Bartlett's Test p < 0.000), revealed strong interdependencies among System Quality (SQ), Information Quality (IQ), and Service Quality (SerQ). The results demonstrate that high SQ leads to improved IQ, which in turn contributes to enhanced SerQ and user satisfaction. While all three qualities are crucial, SerQ emerges as the most relevant indicator of overall system performance due to its broader representation of quality dimensions

Cancer diseases are remaining a leading cause of death in all over the world, this is encouraging the development of advanced diagnostic systems for reliable and accurate detection. The current study investigates the models of traditional machine learning and nature-inspired algorithms for classifying liver, breast and lung cancer cases utilizing structured datasets. More focuses of this research will be on the performance evaluation of five nature-inspired optimizer algorithms like Learner Performance-Based Behavior with Simulated Annealing (LPBSA), Learner Performance-Based Behavior (LPB), FOX Algorithm (FOX), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT). Among these models LPBSA performing the best, consistently obtained the highest accuracy of 92%, sensitivity 94% and specificity 90%, with confusion matrices reflecting minimal false negative and positives. This work contributes to growing adoption of popular algorithms for enhancing healthcare diagnosis and LPBSA can be utilized in other field investigations.