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Single-phase Thirteen-level Dual-boost Inverter Based Shunt Active Power Filter Control Using Resonant and Fuzzy Logic Controllers

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CSEE Journal of Power and Energy Systems 2022年第3期8卷 849-863页

作者： shaik gouse basha Venkatesan Mani Subbarao Mopidevi Vignan's Foundation for Science Technology and Research Andhra Pradesh522213India Vignan's Lara Institute of Technology and Science Vadlamudi522213India

This paper addresses the power quality improvement of a single-phase utility grid using a thirteen-level dualboost inverter(TLDBI)-shunt active filter(SAF).The TLDBI uses a single DC source of low voltage magnitude and five switched capacitor arrangement to achieve self-balanced thirteen level output ***,the charging and voltage balancing of the capacitor is achieved by proper switching sequence control in a series,through which boosted inverter output voltage levels are *** with the tradional H-bridge multilevel inverters(MLIs),all the elements in the proposed TLDBI are able to withstand a voltage stress which is equal to the input DC *** feature ensures the performance of the proposed TLDBI in high-frequency *** power at the electrical grid is highly affected by a wide range of non-linear *** proposed SAF is used for measuring and controlling the current flow from source to *** difference between the targeted and actual currents from the utility grid is measured by using the modified synchronous reference frame(SRF)*** estimated error current is used by the controllers to predict the optimum suitable switching angle and modulation index(MI)to the *** this paper,the traditional proportional-integral(PI)controller,fuzzy logic controller(FLC)and proportional resonant controller(PRC)are compared and the results are presented to validate the performance of *** stability and robustness of the proposed controller is evaluated using Bode,Root locus,and Nyquist *** modeling and analysis of the proposed system are done using MATLAB/Simulink *** simulation results are presented in the various MI of TLDBI and also subjected to non-linear load *** results are also compared to the claim novelty of the proposed study.

关键词： FLC power quality PRC thirteen-level DBI shunt active filter single-phase utility grid SRF

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Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1981-2004页

作者： shaik Mahaboob basha Victor Hugo Cde Albuquerque Samia Allaoua Chelloug Mohamed Abd Elaziz shaik Hashmitha Mohisin Suhail Parvaze Pathan Department of Electronics and Communication Engineering N.B.K.R.Institute of Science&TechnologyVidyanagarAndhra Pradesh524413India Department of Teleinformatics Engineering Federal University of CearáFortaleza60455-970Brazil Information Technology Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Department of Mathematics Faculty of ScienceZagazig UniversityZagazig44519Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos13-5053Lebanon Department of Avionics Indian Institute of Space Science and TechnologyThiruvananthapuramKerala695547India Department of Veterinary Clinical Sciences University of MinnesotaSt.PaulMinnesota55108USA

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 ***,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning *** study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal *** information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering *** learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding *** ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different ***,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased *** analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered ***,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

关键词： Chest radiography(CXR)image COVID-19 classifier machine learning random forest texture analysis

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In-vitro pharmacological evaluation of Sulforaphane from Brassica oleracea

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TMR Integrative Medicine 2019年第12期3卷 1-6页

作者： Sodum Nalini shaik Chand basha TS Mohamed Saleem Annamacharya College of Pharmacy New Boyanapalli Rajampet India Department of Pharmaceutical Chemistry Annamacharya College of Pharmacy New Boyanapalli Rajampet India Department of Pharmacology Annamacharya College of Pharmacy New Boyanapalli Rajampet India

Objectives: Sulforaphane has numerous Pharmacological and therapeutic effects like anti-oxidant, anti-cancer, anti-diabetic, anti-arthritic, anti-ulcer, anti-viral. The present study was designed to evaluate the anti-arthritic activity of Sulforaphane by in-vitro screening methods. Methods: The anti-arthritic activity of Sulforaphane was investigated by protein denaturation assay by using Bovine serum albumin (BSA) and egg albumin. Sulforaphane used in various concentration (10, 50, 100, 250, 500 μg/ml) against both the methods and Diclofenac sodium was used as reference standard. Results: The anti-arthiritic activity sulforaphane was directly proportional to inhibition of denaturation of albumin. Sulforaphane shows concentration dependent inhibition activity in both bovine serum albumin and egg albumin assay. The maximum inhibition was observed in the concentration of 500 μg/ml with 92.8% inhibition for BSA and 96.3% for egg albumin respectively. Conclusion: Sulforaphane have significant anti-arthritic activity accessed by BSA denaturation and egg albumin denaturation assay. Based on present finding future direction also planned to conform the activity by using well established in-vivo methods.

关键词： Sulforaphane Anti-arthritic activity Protein denaturation methods

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