Series-1 (Sep. - Oct. 2026)Sep. - Oct. 2026 Issue Statistics
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| Paper Type | : | Research Paper |
| Title | : | Evolution of Wind Power Electronics and a Converter-Demand Forecast in WECS |
| Country | : | México |
| Authors | : | J.G. González-Hernández || M. Castillo-Juárez || L.F. Cisneros-Sinencio || G. Hernández-Martínez || R. Castillo-Gutiérrez |
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: | 10.9790/2834-2105010108  |
Abstract: The rapid expansion of wind generation and the increasing scale of individual turbines are shifting power-electronic converters from auxiliary interfaces to the central components of wind energy conversion systems (WECS). This article examines the progression from fixed-speed and partially rated doubly fed induction generator systems to full-scale converter architectures, medium-voltage multilevel converters, modular offshore collection....
Keywords: wind energy conversion system; power electronics; multilevel converter; modular multilevel converter; grid-forming control; wide-bandgap semiconductor; neural network; market forecasting
[1] Global Wind Energy Council, Global Wind Report 2024, Brussels, Belgium, 2024. [Online]. Available: https://www.gwec.net/reports/globalwindreport
[2] Global Wind Energy Council, Global Wind Report 2025, Brussels, Belgium, 2025. [Online]. Available: https://www.gwec.net/reports/globalwindreport
[3] Global Wind Energy Council, Global Wind Report 2026, Brussels, Belgium, 2026. [Online]. Available: https://www.gwec.net/reports/globalwindreport
[4] International Energy Agency, Renewables 2025: Analysis and Forecast to 2030, Paris, France, 2025. [Online]. Available: https://www.iea.org/reports/renewables-2025
[5] F. Blaabjerg et al., “Power electronics technology for large-scale renewable energy generation,” Proc. IEEE, 2023. [Online]. Available: https://ieeexplore.ieee.org/document/10070105
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| Paper Type | : | Research Paper |
| Title | : | An Intelligent Deep Learning Framework for Early Breast Cancer Detection Using Medical Imaging |
| Country | : | India |
| Authors | : | Abhinav Pratap Singh || Dr. Reeta Pawar || Dr. Ashwala Mohan |
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: | 10.9790/2834-2105010920 |
Abstract: The early and correct diagnosis of breast cancer is important for better clinical results and prompt treatment planning. The analysis of medical images, however, is complex due to complex tissue structures, variations in image quality and subtle malignant patterns. In this paper, an intelligent deep learning framework is proposed for automated.....
Keywords: Breast Cancer Detection, Deep Learning, Vision Transformer, GWO
[1].
S. Gao, J. Liu, L. Li, D. Yang, Y. Miao, X. Zhang, Q. Han, Y. Shi, J. Wu, and K. Zhang, “Application of deep learning technology in breast cancer: A systematic review of segmentation, detection, and classification approaches,” BMC Biomed. Eng., vol. 25, no. 1, p. 19, 2026
[2].
“Recent advancements in machine learning and deep learning for early detection of breast cancer: A comprehensive review,” Meta-Radiology, 2026[3].
“AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability,” Cancers, vol. 18, no. 8, p. 1305, 2026.
[4].
Amin, D. Acharya U, P. Koteshwara, S. P. Siddalingaswamy, et al., “A systematic literature review on mammography: Deep learning techniques for breast cancer detection with global and Asian perspectives,” BMC Cancer, vol. 25, p. 1627, 2025.
[5].
Y. Chen, X. Shao, K. Shi, A. Rominger, F. Caobelli, and others, “AI in breast cancer imaging: An update and future trends,” Semin. Nucl. Med., vol. 55, no. 3, pp. 358–370, 2025,.