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.
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%.
In the agricultural sector, identifying plant diseases is crucial as they hamper the plant's robustness and health, which play a vital role in agricultural productivity. Early detection allows farmers to take proper measurements and save crops from complete failure. Biosensor applications in agricultural production and plant monitoring improve the yield through definitive recommendations and improved practices. Fluorescence-based assays, colorimetric biosensors, and surface plasmon resonance-based biosensors are the most commonly used for plant pathogen detection. Plant disease detection is a prime biosensor application for preventing seasonal and cultural defects in raising crops. The sensor data analysis ensures reliable processing for distinguishable features for identifying the disease, wherein discrete information is handled with error. This article introduces a Preemptive Classification using Discrete Data (PC-DD) technique to resolve this issue. This technique requires partial series through probabilistic data substitution to improve the analysis rate. In the analysis process, the classification is performed using random forest based on two combinations: series, difference, and probability. This probability is based on identical data observed through series probability classification in the previous iteration. The unidentical data is classified under difference that is used for individual classification. This process is progressive until the detection is performed, wherein the alterations are adaptable for different biosensor input data. Therefore, the proposed technique's performance is validated using the metrics detection accuracy, analysis ratio, analysis time, classifications, and difference factor.
Cloud-based enterprise resource planning (ERP) and cloud computing are critical requirements for all SMEs since they can be used to facilitate the SMEs’ growth by creating competitive and personalized innovations considering their required business scope. To date, the growth of cloud technologies has led to the development of new systems and applications in many fields and areas including businesses. Our previous study proposed an adoption model to investigate the main determinants and logistical factors that influence decision-makers of SMEs to adopt cloud-based ERP systems. The aim of this research is to enhance the previous work by evaluating and validating the new model in real life to determine whether it has achieved what it was developed for and determine the reliability of the research results. The methodology and results of the evaluation and validation process of the proposed model are presented in this research. Considering there is little documentation in the literature specifically relevant to how proposed models have been evaluated and validated, hence providing this insight will assist both the academic researchers and decision-makers. The evaluation and validation methodology and the model itself contribute toward a better understanding of adoption processes. Furthermore, the evaluation and validation procedure in future work can be used to measure, enhance and determine whether the proposed models can be used in real life.
Lung tumor detection using computer-aided modeling improves the accuracy of detection and clinical recommendation precision. An optimal tumor detection requires noise reduced computed tomography (CT) images for pixel classification. In this paper, the butterfly optimization algorithm-based [Formula: see text]-means clustering (BOAKMC) method is introduced for reducing CT image segmentation uncertainty. The introduced method detects the overlapping features for optimal edge classification. The best-fit features are first trained and verified for their similarity. The clustering process recurrently groups the feature matched pixels into clusters and updates the centroid based on further classifications. In this classification process, the uncertain pixels are identified and mitigated in the tumor detection analysis. The best-fit features are used to train local search instances in the BOA process, which influences the similar pixel grouping in the uncertainty detection process. The proposed BOAKMC improves accuracy and precision by 10.2% and 13.39% and reduces classification failure and time by 11.29% and 11.52%, respectively.
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