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The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been referred to as the "New Great Game." This research centres on the power struggle, considering geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil politics, and conventional and nontraditional security are all explored and explained by the researcher. Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role in Central Asia. This study adheres to the empirical epistemological method and has taken care of objectivity. This study analyze primary and secondary research documents critically to elaborate role of china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade, pipeline politics, and winning states, according to this study, thanks to important instruments like the Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study, China is seeing significant success in commerce, pipeline politics, and gaining influence on other governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai Cooperation Organisation and the Belt and Road Economic Initiative.

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Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to precisely delineate tumor boundaries from magnetic resonance imaging (MRI) scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The model is rigorously trained and evaluated, exhibiting remarkable performance metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical image analysis and enhance healthcare outcomes. This research paves the way for future exploration and optimization of advanced CNN models in medical imaging, emphasizing addressing false positives and resource efficiency.

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