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Advancing times and rapidly developing technology put pressure and responsibility on the management of organizations. Organizational ambidexterity is a concept for an organization that can balance profitability with innovation and development. This study examined the relationship between the triple helix and quality dimensions on organizational ambidexterity mediated by technology readiness and user satisfaction to give management an advantage in addressing this problem. Quantitative analysis methods using PLS-SEM (Partial Least Square-Structural Equation Modeling) were employed in this study. This study was conducted in Indonesia with 425 respondents participating in the data collection, 411 of which were declared valid after filtering. The results of this study demonstrate that the role of the triple helix in developing organizational ambidexterity is very significant and that other variables, such as quality dimensions and technology readiness, also play an essential role. The framework for organizational ambidexterity presented in this study may be helpful for future research in this field. This study can be further developed for future research, especially by adding new external variables that change over time and focusing more on a specific organization. At the very least, this study is relevant for researchers and practitioners to improve business quality using the concept of the triple helix, quality dimensions, and technology readiness.

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Lung cancer is the most critical disease because it affects both men and women. Most of the time, lung cancer leads to death due to less health care and medical attention. In addition, lung cancer is difficult to identify in earlier stages due to the low‐level symptoms and risk factors. To overcome the complexity, effective techniques must predict lung cancer earlier. To attain the problem statement, an lung cancer identification system is developed with the help of a meta‐heuristic algorithm. The CT imageries obtained from the CIA database are analyzed step by step. The gathered image noise is removed by applying the mean filter, and the affected regions are segmented with the help of the Butterfly Optimization Algorithm‐based K‐Means Clustering (BOAKMC) algorithm. Afterward, various statistical features are derived, and the Supervised Jaya Optimized Rough Set related Feature Selection (SJORSFS) process is used to select the lung features. Finally, the lung cancer is identified using Autoencoder based Recurrent Neural Network (ARNN) classification algorithm, successfully recognizing the lung cancer features. Then the system's efficiency is evaluated using a MATLAB setup; here, 3000 are treated as training images and 2043 for testing images. The effective training enhances overall lung cancer prediction accuracy by up to 99.15%.