Madan Mohan Malaviya University of Technology

About: Madan Mohan Malaviya University of Technology is a education organization based out in Gorakhpur, Uttar Pradesh, India. It is known for research contribution in the topics: Wireless sensor network & Photovoltaic system. The organization has 603 authors who have published 924 publications receiving 3619 citations. The organization is also known as: Madan Mohan Malaviya Engineering College.

Grid-integrated permanent magnet synchronous generator based wind energy conversion systems: A technology review

Abstract: The growing trends in wind energy technology are motivating the researchers to work in this area with the aim towards the optimization of the energy extraction from the wind and the injection of the quality power into the grid. Over the last few years, wind generators based on permanent magnet synchronous machines (PMSMs) are becoming the most popular solution for the modern wind energy conversion systems (WECSs). This paper presents a concise review of the grid-integrated WECSs employing permanent magnet synchronous generators (PMSGs). It reviews the trends in converter topologies, control methodologies, and methods for maximum energy extraction in PMSG based WECSs, which have been reported in various research literatures primarily in reputed research journals and transactions during last few years. It also presents an overview to the grid interconnection issues related to output power smoothing and reactive power control in addition to fault-ride-through (FRT) and grid support capabilities of PMSG based WECSs. This review article will serve the researchers working in the area of grid-integrated PMSG based WECSs in the exploration of trends, developments and challenges in the past research works and in finding out the relevant references for their research work.

Tensile and flexural properties of natural fiber reinforced polymer composites: A review:

Abstract: In recent years, researchers and scientists are facing problems in terms of environmental imbalance and global warming owing to numerous use of composite materials prepared by synthetic fibers and

Gold-Immobilized Photonic Crystal Fiber-Based SPR Biosensor for Detection of Malaria Disease in Human Body

Abstract: This article presents the photonic crystal fiber (PCF) based surface plasmon resonance (SPR) biosensor for early detection of malaria disease in humans by measurement of the variation of red blood cells (RBCs). In the proposed PCF, two layers of air holes are arranged in a hexagonal lattice structure and a thin film of gold-coating is used over PCF for the occurrence of SPR phenomena. It occurs when surface plasmon polariton (SPP)-mode coupled with the core-mode during phase-matching conditions. Malaria infected RBCs samples are filled into the PCF, which have own refractive index (RI) that shift the SPR resonance wavelength during confinement loss measurement. The resonance wavelength of malaria-infected RBCs samples is different from their normal RBCs samples due to the difference in RI of infected and normal RBCs samples. The proposed work is helpful in the detection of different stages of malaria-infected RBCs such as ring phase, trophozoite phase and Schizont phase by measuring the shift in resonance wavelength. The calculated wavelength sensitivities of the proposed sensor for the ring phase, trophozoite phase and Schizont phase RBCs are 13714.29 nm/RIU, 9789.47 nm/RIU, and 8068.97 nm/RIU, respectively in x-polarized direction and 14285.71 nm/RIU, 10000 nm/RIU, and 8206.9 nm/RIU, respectively in y-polarized direction with the maximum detection limit of 0.029. The proposed PCF-based SPR biosensor is suitable for the early diagnosis of malaria disease due to its enhanced sensing performance (low detection limit and high sensitivity).

Heart Disease Prediction Using Machine Learning Algorithms

Abstract: Heart plays significant role in living organisms Diagnosis and prediction of heart related diseases requires more precision, perfection and correctness because a little mistake can cause fatigue problem or death of the person, there are numerous death cases related to heart and their counting is increasing exponentially day by day To deal with the problem there is essential need of prediction system for awareness about diseases Machine learning is the branch of Artificial Intelligence(AI), it provides prestigious support in predicting any kind of event which take training from natural events In this paper, we calculate accuracy of machine learning algorithms for predicting heart disease, for this algorithms are k-nearest neighbor, decision tree, linear regression and support vector machine(SVM) by using UCI repository dataset for training and testing For implementation of Python programming Anaconda(jupytor) notebook is best tool, which have many type of library, header file, that make the work more accurate and precise