iosr-JECE   Volume-21 ~ Issue-5 ~ Sep. - Oct. 2026

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

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,.

Abstract: The study titled "Analysis, diagnosis, and performance optimization of printed antennas for the telecommunications network: Airtel Boma in the DRC" focuses on examining and improving printed antennas used in mobile telecommunication systems, specifically within the context of the Airtel network in Boma, Democratic Republic of the Congo.
The primary objective.....

Keywords: Printed antenna, Bandwidth, Directivity, Optimization, 4G/5G network

[1] Ahmed Kishk, Avancement des antennes Micro ruban avec des Applications récentes, 2013[2] Hasna Djaidja et Khouloud Bechelaleg, Conception et amélioration des caractéristiques d’un réseau d’antennes microstrips à FSS, 2024
[3] Ahlem Chenouf et Basma Bouguettaya, Amélioration des performances de l’antenne patch mocrostrip, 2020
[4] S.O. zakariyya et al, design of an improved microstrip antenna operating at 28 GHz, 2024
[5] Din et al, High-gain microstrip antenna with improved radiation characteristics for microwave 5G and IoT, 2025
[6] Jasmin Khudhair Salih Turfa, Métamaterial-Enhanced Microstrip Antenna with Integrated Channel Performance Evaluation, 2025
[7] Magnus PETERSSON. Microstrip Solutions for Innovative Microwave Feed Systems Norrköping den 24 octobre, 2001.
[8] Christian aichele, Déploiement des compteurs intelligents ,2013.

Abstract: Worldwide, more than 460 million metric tons of plastic are produced annually; less than 10% is recycled, which demands the development of industrial-scale automated sorting platforms. This paper presents a taxonomy of plastic classification.....

Keywords: waste management, Chemical and spectroscopic sorting, automated sorting systems, Deep learning, Computer Vision, plastic classification

[1] OECD, "Global Plastics Outlook: Economic Drivers, Environmental Impacts And Policy Options," OECD Publishing Pp 1–150, 2022. Https://Doi.Org/10.1787/de747aef-en
[2] R. Geyer, J. R. Jambeck And K. L. Law, "Production, Use, And Fate Of All Plastics Ever Made," Science Advances Vol.3(7), Pp e1700782, 2017. Https://Doi.Org/10.1126/sciadv.1700782
[3] J. R. Jambeck, R. Geyer, C. Wilcox, T. R. Siegler, M. Perryman, A. Andrady, R. Narayan And K. L. Law, "Plastic Waste Inputs From Land Into The Ocean," Science Vol.347(6223), Pp 768–771, 2015. Https://Doi.Org/10.1126/science.1260352
[4] M. H. Milne, S. R. Jones And K. L. Davies, "Ubiquitous Presence Of Microplastics In Global Protein Sources And Food Webs," Environmental Pollution Vol.342, Pp 123045, 2024. Https://Doi.Org/10.1016/j.envpol.2023.123045
[5] W. O. Meneses Quelal, B. Velázquez-Martí And A. Ferrer Gisbert, "Separation Of Plastic Mixtures By Sink-Float Combined With Froth Flotation Techniques," Waste Management Vol.141, Pp 198–207, 2022. Https://Doi.Org/10.1016/j.wasman.2022.01.03.

Abstract: Mobile handoffs are a process very sensitive where measures the strengths of the signal and compare with the threshold to make decision of handover the mobile to new cell while the mobile is moves between cells where it measure signal strengths and quality for nearby base station where assist the network in deciding the optimal moment to handover to new cell where in this paper we use two methods, RSS with Hysteress and with threshold.

Keywords: cell size,RSS Hysteress, threshold, Handover

[1]. Hakki cavdar, O. akcat "the optimization of cell stations power level in cell planning" in proc. vehicular. Tech. Vol. 42.No.2.may2001
[2]. Fabio Graziosi, Marco pratesi,Mariha ruggieri, Fortunato santuce "A multi cell model of hand over Initiation in mobile cellular networks" in transaction on tech.voL48.no.3 may 1999.
[3]. How Cellular Phone Technologies Comper”
www.ee.washington.edu/classs/498/sp98/final/marsha/final.html.4/3/2002
[4]. Introduction to Mobile phoneslf,www.hailydoy.co.uk/ce1l2.html. 15/7/2002.
[5]. Wang, L.,Stuber, G.,and lea,C.,“Architecture design, frequency planning and performance analysis for a Microcell/Macroceli overlaying system" IEEE transaction on vehicular technology. Vol

Abstract: Cassava diseases, particularly cassava mosaic disease (CMD) and cassava brown streak disease (CBSD), threaten food production in the Democratic Republic of the Congo (DRC). This paper presents Eagle AgriP, a proposed plant-health information system combining deep-learning analysis of leaf images, agroclimatic variables, geolocation and geographic information systems. The visual branch uses MobileNetV2 with transfer learning, while a multilayer perceptron encodes tabular environmental data; the representations are fused to support preliminary.....

Keywords : phytopathologie numérique ; manioc ; CMD ; CBSD ; MobileNetV2 ; apprentissage multimodal ; SIG ; télédétection ; RDC

[1]. Dean, R., Van Kan, J. A. L., Pretorius, Z. A., Hammond-Kosack, K. E., Di Pietro, A., Spanu, P. D., Rudd, J. J., Dickman, M., Kahmann, R., Ellis, J., & Foster, G. D. (2012). The Top 10 fungal pathogens in molecular plant pathology. Molecular Plant Pathology, 13(4), 414–430.
[2]. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
[3]. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90.
[4]. Legg, J. P., et al. (2014). Cassava virus diseases: biology, epidemiology, and management. Advances in Virus Research, 91, 85–142.
[5]. Madden, L. V., Hughes, G., & van den Bosch, F. (2007). The Study of Plant Disease Epidemics. APS Press.