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Five Level Inverter for Renewable Energy Power Generation Systems

A five level inverter is developed and applied for to reduce the switching loss, harmonic distortion and electromagnetic interference caused by the switching operation of power semiconductor devices.

Published by: M. Divya, Golagani Rama Harini, Bowribilli Mounika, Shaik Karishma, Vesapogu Jyothsna, Bhimavarapu Yuva Madhuri

Author: M. Divya

Paper ID: V7I6-1143

Paper Status: published

Published: November 6, 2021

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Thesis

Method Development and Validation for the estimation of Azilsartan in Bulk and Pharmaceutical Dosage Form by using HPLC

A simple, sensitive and rapid stability indicating HPLC method was developed and validated for the determination of Azilsartan in bulk and pharmaceutical dosage form. The method was developed by HPLC using a Inertsil C18 (250x4.5mm ID) 5µm column in a isocratic mode with mobile phase constituted by buffer: Methanol and water, pH 3.5 flow rate was 1.0ml/min, column temperature at 20-25°C, UV detection wavelength 240nm and 20µL of injection volume. The retention time of Azilsartan was 3.084min. The validation parameters were in accordance with ICH specifications, assay exhibited a linear range of 50-250µg/ml with regression coefficient 0.998. The limit of detection and quantification were 0.46 µg/ml and 1.42 µg/ml. Accuracy was between 98-102%. The drug was subjected to various stress conditions like peroxide, photolytic, acidic, alkaline, thermal degradations. Stress study of Azilsartan was found susceptible to degrade under hydrolytic (acid and base) conditions. The proposed method has stability indicating the resolution of the main peak from their degradation peak

Published by: P. Satish Kumar, Ravi Harsha, Prathiba

Author: P. Satish Kumar

Paper ID: V7I6-1137

Paper Status: published

Published: October 30, 2021

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Research Paper

The relationship between generational differences and work motivation of executive employees in the glass industry in Sri Lanka

Employee motivation at work is one of the important factors which significantly impact job satisfaction, commitment, quality of work, and job performance. Organizations need to determine the best way to motivate their employees who are part of different generational groups (Baby boomers, Generation X, and Generation Y). Hens, this study was conducted to investigate the relationship between generational differences and work motivation of executive-level employees in the glass industry in Sri Lanka. Using Deci’s intrinsic and extrinsic motivation theory as the foundation, we evaluate the relationship between generational differences and employee motivation in the glass industry in Sri Lanka. At present, only three generations, the Baby Boomers, Generation X, and generation Y, are available within the organizations’ workforce. Therefore, only these three generations were used to conduct this study. The data were collected from a randomly selected sample of 70 executive-level employees who work in a leading company in the glass industry in Sri Lanka, by administering a structured questionnaire with 23 questions/statements on a five-point Likert scale. the method was used as the method of data collection. Data were analyzed using univariate analysis and bivariate analysis including correlation analysis with the SPSS 23.0 version and derived the results. According to the results of the study, there is a significant positive correlation between baby boomers and extrinsic motivation. The Pearson correlation between the main two variables of Generation "X" and intrinsic motivation, and also Generation "Y" and intrinsic motivation, was positive in executive employees in the glass industry in Sri Lanka. Positive relationships with extrinsic motivation and baby boomers were also discovered. Generation "X" and "Y" positively related to the intrinsic motivation at work in executive employees in the glass industry in Sri Lanka. In the near future, there will be a new generation in the workplace, i.e., Generation Z. As a result, management must now start understanding and developing new strategies to better prepare for Generation Z employees, as well as consider how to best integrate this next generation with their current employee workforce.

Published by: Bhashini Paranagama, Dr. Rasika Aponsu, H. K. T. Dilan, J. V. Karunarathna

Author: Bhashini Paranagama

Paper ID: V7I5-1383

Paper Status: published

Published: October 29, 2021

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Research Paper

Video Shot Segmentation: Hybrid Approach using YOLOv4 and Deep Sort Algorithm

A shot is a sequence frame in an edited video taken by a single camera. Shot Segmentation is the process of splitting video and finding the boundaries of video data. In this paper, we study the method in content-based video retrieval which uses object detection and tracking for video segmentation. Data collected via segmenting can be categorized in a hierarchy manner as scene layer, camera shot layer, and the frame in their accordance. The data collected is used for segmentation. YOLOv4 is used to enhance the accuracy and the process of tracking and detection much faster.

Published by: Shakthi T.

Author: Shakthi T.

Paper ID: V7I5-1381

Paper Status: published

Published: October 29, 2021

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Research Paper

Car logo detection and classification by Deep Learning base Transfer Learning

Vehicle identification systems rely on logo recognition to identify vehicles (VLRS). Convolutional Neural Networks are used to automatically learn characteristics for car logo recognition (CNNs). However, CNN struggles with rotated or noisy pictures. CNN's Random Forest classification technique is used to create an image recognition system. Random forest decision tree ensemble and train. This work's primary contribution is a multiclass logo using convolution mapping in nonlinear space and random forest ensemble learning. In the experiment, 400 pictures with 10 classes were analyzed to increase accuracy by about 20%.

Published by: Sushil Kumar, Ms. Bhuvneshwari

Author: Sushil Kumar

Paper ID: V7I5-1392

Paper Status: published

Published: October 28, 2021

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Research Paper

An overview on application areas of soft computing

We know that Soft Computing refers to the science of reasoning, thinking, and deduction that recognizes and uses the real-world phenomena of grouping, memberships, and classification of various quantities under study. As such, it is an extension of natural heuristics and capable of dealing with complex systems because it does not require strict mathematical definitions and distinctions for the system components. It differs from hard computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, and partial truth. In effect, the role model for soft computing is the human mind. The guiding principle of soft computing is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, and low solution cost. The applications of soft computing have proved two main advantages. First, it made solving nonlinear problems, in which mathematical models are not available, possible. Second, it introduced human knowledge such as cognition, recognition, understanding, learning, and others into the fields of computing. This resulted in the possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed systems. This paper highlights various Application areas of soft computing.

Published by: Dr. Shailendra Kumar Srivastava

Author: Dr. Shailendra Kumar Srivastava

Paper ID: V7I5-1393

Paper Status: published

Published: October 28, 2021

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