Classification of Chest X-ray Images using Convolutional Neural Nework

The current worldwide Covid-19 epidemic is linked to a respiratory lung infection caused by a novel corona virus disease (SARSCoV- 2), the evolution of which is still not known. More than 100,000 cases were confirmed worldwide using the current case definition of Covid-19 infection, based on pneumonia diagnosis, with a death rate ranging between 2% and 3%. Since the expanding sick population might not have simple access to current laboratory testing, new screening techniques are necessary. The Computed tomography of chest is an important technique for the former detection and treatment of Covid-19 pulmonary symptoms, even though its utility as a screening tool has not yetbeen established. Even though it lacked specificity, it exhibited excellent sensitivity. We demonstrate a neural network based on pneumonia and covid classification in Tensor Flow and Keras. The suggested method is based on the CNN uses images and the CNN model to categorize Covid-19 or pneumonia. It is anticipated that discoveries will become more successful. If the covid-19 or pneumonia classification algorithms and other feature extraction methods are added, the CNN approach will be successfully supported

Improved control strategy of DFIG-based wind turbines using direct torque and direct power control techniques

This paper presents different control strategies for a variable-speed wind energy conversion system (WECS), based on a doubly fed induction generator. Direct Torque Control (DTC) with Space-Vector Modulation is used on the rotor side converter. This control method is known to reduce the fluctuations of the torque and flux at low speeds in contrast to the classical DTC, where the frequency of switching is uncontrollable. The reference for torque is obtained from the maximum power point tracking technique of the wind turbine. For the grid-side converter, a fuzzy direct power control is proposed for the control of the instantaneous active and reactive power. Simulation results of the WECS are presented to compare the performance of the proposed and classical control approaches.Peer reviewedFinal Accepted Versio

Not Available

Not AvailableThe aim of the study was to determine the postharvest fruit quality of mango cv. Alphonso treated with the laboratory grade (LG) and commercial grade (CG) Calcium Carbide (CaC 2 ) at the reported highest acceptable dose, and elemental composition analysis (EDX) results to support the statements for traceability of hazardous trace elements in CaC 2, which can serve as a basis towards developing sensors for identifying CaC 2 treated mangoes through detection of trace elements. Physical, physiological, biochemical and EDX of mango cv. Alphonso harvested from farmers’ field of Santur village in Krishnagiri district of Tamil Nadu, India were used for the study. All studied physical characteristics except fruit firmness of CG CaC 2 treated fruits did not correlate to desirable fruit characteristics like total soluble solids (TSS), pH, titrable acidity, total sugars and ascorbic acid. Besides, these parameters were desirable only in control fruits, though a number of days taken to reach fruit consumption stage was relatively more compared to CaC 2 treatment. In vitro, free radical scavenging potential of DPPH was comparatively higher in control fruits than CaC 2 treated fruits of both grades. Lab grade (LG) CaC 2 treated fruits were non-significant in modifying physical, physiological and biochemical properties of mango cv. Alphonso except for TSS. However, at the end of the experimental period, CG CaC 2 treated fruits recorded higher TSS than LG CaC 2 treated fruits. Energy Dispersive X-Ray (EDX) results confirmed traceability of health hazardous chemical substances of arsenic (As) and phosphorous (P) in both LG and CG CaC 2 lumps. Calcium carbide when used as an artificial ripening agent was not in contact with the fruit surface, the presence of arsenic and phosphorus were not detected in the EDX spectrum, a novel finding of our study.Not Availabl

Classification of Chest X-ray Images using Convolutional Neural Nework

The current worldwide Covid-19 epidemic is linked to a respiratory lung infection caused by a novel corona virus disease (SARSCoV- 2), the evolution of which is still not known. More than 100,000 cases were confirmed worldwide using the current case definition of Covid-19 infection, based on pneumonia diagnosis, with a death rate ranging between 2% and 3%. Since the expanding sick population might not have simple access to current laboratory testing, new screening techniques are necessary. The Computed tomography of chest is an important technique for the former detection and treatment of Covid-19 pulmonary symptoms, even though its utility as a screening tool has not yetbeen established. Even though it lacked specificity, it exhibited excellent sensitivity. We demonstrate a neural network based on pneumonia and covid classification in Tensor Flow and Keras. The suggested method is based on the CNN uses images and the CNN model to categorize Covid-19 or pneumonia. It is anticipated that discoveries will become more successful. If the covid-19 or pneumonia classification algorithms and other feature extraction methods are added, the CNN approach will be successfully supported

Abstracts of the International Conference on Recent Trends in Mathematics and Computer Science 2023

This book presents the abstracts of the selected contributions to the International Conference on Recent Trends in Mathematics and Computer Science 2023 (ICRTMCS-2023), held on 19-21 October 2023 by the Auxilium College of Arts and Science for Women, Regunathapuram, Tamil Nadu, India. ICRTMCS-2023 was a multidisciplinary conference organized with the objective of bringing together eminent academicians, research scholars, and students to exchange ideas, communicate, to discuss research findings and new advances on recent and emerging trends in the field of Mathematics and Computer Science. Moreover, the conference would also enable the participants to explore new fields and gain immense knowledge. Conference Title: International Conference on Recent Trends in Mathematics and Computer Science 2023Conference Acronym: ICRTMCS-2023Conference Date: 19-21 October 2023Conference Venue: Hybrid (Online and Auxilium College of Arts and Science for Women, Regunathapuram, India)Conference Organizer: Departments of Mathematics and Computer Science, Auxilium College of Arts and Science for Women, Regunathapuram, Tamil Nadu, Indi