Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.

Machine learning offers significant potential for lung cancer detection, enabling early diagnosis and potentially improving patient outcomes. Feature extraction remains a crucial challenge in this domain. Combining the most relevant features can further enhance detection accuracy. This study employed a hybrid feature extraction approach, which integrates both Gray-level co-occurrence matrix (GLCM) with Haralick and autoencoder features with an autoencoder. These features were subsequently fed into supervised machine learning methods. Support Vector Machine (SVM) Radial Base Function (RBF) and SVM Gaussian achieved perfect performance measures, while SVM polynomial produced an accuracy of 99.89% when utilizing GLCM with an autoencoder, Haralick, and autoencoder features. SVM Gaussian achieved an accuracy of 99.56%, while SVM RBF achieved an accuracy of 99.35% when utilizing GLCM with Haralick features. These results demonstrate the potential of the proposed approach for developing improved diagnostic and prognostic lung cancer treatment planning and decision-making systems.

Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features.

Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of lung cancer.

A corpus-based analysis of the stylistic features of Chinese and American diplomatic discourse.

The study investigates the linguistic aspects of Chinese and American diplomatic discourse using Biber's theoretical underpinnings of multi-dimensional (MD) analysis. The corpus of the study comprises texts taken from the official websites of the Chinese and US governments from 2011 to 2020. The study results show that China's diplomatic discourse falls into the text type of learned exposition which includes informational expositions focused on conveying information. In contrast, the United States diplomatic discourse falls into the text type of "involved persuasion," which is persuasive and argumentative. Furthermore, the two-way ANOVA test reveals few distinctions between spoken and written diplomatic discourse from the same country. Furthermore, T-tests demonstrate that the diplomatic discourse of the two countries differs significantly in three dimensions. In addition, the study highlights that China's diplomatic discourse is informationally dense and context independent. In contrast, the United States diplomatic discourse is emotive and interactional, strongly dependent on context, and created within time restrictions. Finally, the study's findings contribute to a systematic knowledge of the genre aspects of diplomatic discourse and are helpful for more effective diplomatic discourse system creation.

Can second language metaphorical competence be taught through instructional intervention? A meta-analysis.

Background: For a long time, the traditional view regarded metaphor as merely a rhetorical device that served to enrich linguistic expression. With the continuous development of cognitive linguistics, foreign language educators began to realize the vital role of metaphor in foreign language education. Objectives: This study looked at how well pedagogical interventions improve metaphorical competence by looking at how well teachers teach metaphors. Methods: After a rigorous literature search and selection process from the Chinese and English databases, 13 Chinese and 7 international studies with 51 effect sizes were included in this meta-analysis. With the help of the meta-analysis 3.0 software, the literature and heterogeneity tests were performed to ensure that the meta-analysis results were as accurate and valid as possible. Results: The effect size tests revealed that the metaphorical instructional intervention was significantly effective in general and produced a large effect size (d = 0.888) on improving learners' metaphorical abilities. Meta-regression analyses were also conducted to examine how other factors might change the effects of the interventions. Findings: Research has shown that instructional interventions that combine prolonged input of metaphorical concepts with reinforcement of metaphorical practice can help second language learners develop metaphorical competence. Teaching puts more pressure on teachers and the learning environment, and the results of this study could help teachers teach metaphors in the future.

Mobile assisted language learning in learning English through social networking tools: An account of Instagram feed-based tasks on learning grammar and attitude among English as a foreign language learners.

Advancement of social media in the modern era provides a good incentive for researchers to unleash the potential of social networking (SN) tools in order to improve education. Despite the significant role of social media in affecting second/foreign language (L2) learning processes, few empirical studies have tried to find out how Instagram feed-based tasks affect learning grammar structure. To fill this lacuna of research, the current study set forth to delve into the influence of Instagram feed-based tasks on learning grammar among English as a foreign language (EFL) learners. In so doing, a sample of 84 intermediate EFL learners were randomly divided into experimental and control groups. The learners in the control group received regular online instruction via webinar platforms. In contrast, the learners in the experimental group were exposed to Instagram feed-based tasks. Data inspection applying one-way ANCOVA indicated that the learners in the experimental group outperformed their counterparts in the control group. The results highlighted the significant contributions of Instagram feed-based tasks in fostering learning grammar. Furthermore, EFL learners' positive attitudes toward using Instagram Feed-based Tasks in Learning Grammar was concluded. The implications of this study may redound to the benefits of language learners, teachers, curriculum designers, as well as policy makers in providing opportunities for further practice of Instagram feed-based tasks in language learning and teaching.