Chanki Pandey
@nitk.ac.in
Teaching Assistant / M. Tech. Scholar at Department of Electronics and Communication Engineering
National Institute of Technology Karnataka
Chanki Pandey is a highly motivated and dedicated engineer with a passion for exploring cutting-edge technologies and finding solutions to real-world problems. A recent B.Tech graduate from GEC Jagdalpur, he is currently pursuing his M.Tech at National Institute of Technology Surathkal, where he is diving deeper into his research interests in electronics, VLSI design, machine learning, deep learning, and image processing.
With 17 research papers published in reputed journals and conferences and a patent to his name, Chanki is already making a significant impact in his field. He is particularly proud of receiving the "Best Research Paper Presentation Award" at the 4th ICCE-2020 organized by KIET Group of Institutions Delhi-NCR Ghaziabad, India. This recognition has further fueled his motivation to continue his research journey and strive for even greater achievements.
Chanki's technical expertise, combined with his passion for finding innovative solutions, makes him a valuable asset t
EDUCATION
National Institute of Technology Karnataka Surathkal
Master of Technology- MTech (VLSI Design)
Department of Electronics and Communications Engineering
2021-2023
Government Engineering College, Jadalpur, CG, India
Bachelor of Technology - BTech
Department of Electronics and Telecommunications Engineering
2017-2021
Chavara Hr Sec School Kondagaon ,Bastar,CG-India
CBSE- 12th
Mathematics
2016- 2017
RESEARCH INTERESTS
My interest of research areas are Electronics, VLSI, Bio-photonics, Signal Processing, Machine Learning, Image Processing, and Deep Learning.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Chanki Pandey and Kalpana G Bhat
IEEE
Wafer maps used to display defect patterns in the integrated circuits industry include crucial information that quality engineers may utilize to identify the cause of a defect and increase yield. In this paper, we put forth a framework for accurately and quickly categorizing semiconductor wafer faults utilizing particularly CNN-based models. This paper seeks to provide a scalable, adaptive, and user-friendly implementation of convolutional neural networks for applications classifying semiconductor defects. In categorizing the defects found on semiconductor wafers, the suggested CNN model obtained an accuracy of 90.50% & 92.28% and losses of 0.39 & 0.29 while performing the training and validation, respectively, along with the misclassification rate of 0.0772. The suggested model also learns rapidly on the validation set at a rate of 1e-03 per second. The proposed custom CNN model architecture incorporates only two convolution layers, resulting in a greatly reduced number of parameter weights and biases. Specifically, the number of parameters is only 44000, which makes the model more compact, cost-effective, and robust against random noise. Moreover, this model can function well under low power and processing limits.
Ankita Patra, Chanki Pandey, Karthikeyan Palaniappan, and Prabira Kumar Sethy
Springer Nature Singapore
Chanki Pandey, Yogesh Kumar Sahu, Nithiyananthan Kannan, Md Rashid Mahmood, Prabira Kumar Sethy, and Santi Kumari Behera
Wiley
Chanki Pandey, Prabira Kumar Sethy, Santi Kumari Behera, Jaya Vishwakarma, and Vishal Tande
Elsevier
Prabira Kumar Sethy, Chanki Pandey, Yogesh Kumar Sahu, and Santi Kumari Behera
Springer Science and Business Media LLC
Sandeep Kumar, Sneha Singh, Prabhakar Agarwal, Upendra Kumar Acharya, Prabira Kumar Sethy, and Chanki Pandey
Springer Science and Business Media LLC
Sharad Chandra Rajpoot, Chanki Pandey, Prashant Singh Rajpoot, Sanjay Kumar Singhai, and Prabira Kumar Sethy
Springer Science and Business Media LLC
Prabira Kumar Sethy, Santi Kumari Behera, Nithiyakanthan Kannan, Sridevi Narayanan, and Chanki Pandey
SAGE Publications
Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.
Prabira Kumar Sethy, Chanki Pandey, Mohammad Rafique Khan, Santi Kumari Behera, K. Vijaykumar, and Sibarama Panigrahi
IOS Press
In the last decade, there have been extensive reports of world health organization (WHO) on breast cancer. About 2.1 million women are affected every year and it is the second most leading cause of cancer death in women. Initial detection and diagnosis of cancer appreciably increase the chance of saving lives and reduce treatment costs. In this paper, we perform a survey of the techniques utilized in breast cancer detection and diagnosis in image processing, machine learning (ML), and deep learning (DL). We also proposed a novel computer-vision based cost-effective method for breast cancer detection and diagnosis. Along with the detection and diagnosis of breast cancer, our proposed method is capable of finding the exact position of the abnormality present in the breast that will help in breast-conserving surgery or partial mastectomy. The proposed method is the simplest and cost-effective approach that has produced highly accurate and useful outcomes when compared with the existing approach.
Chanki Pandey, Prabira Kumar Sethy, Santi Kumari Behera, Sharad Chandra Rajpoot, Bitti Pandey, Preesat Biswas, and Millee Panigrahi
Springer Singapore
Prabira Kumar Sethy, Santi Kumari Behera, Komma Anitha, Chanki Pandey, and M.R. Khan
IOS Press
The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.
Preesat Biswas, Chanki Pandey, Ashish Kumar Thakur, M.R. Khan, and Shanti Rathore
IEEE
GMSK (Gaussian Minimum Shift Keying) has become the system whose importance increases as the world getting the age of electronics and associating with cellular technologies. GMSK is the most preferred Modulation format for Mobile communication such as GSM, CDPD, DECT and Digital communications system in the 900Hz to 1800 MHz band. GMSK used for GSM because of its spectral efficiency and for radio power amplifiers it delegates’ high efficiency. Power consumption or using the low battery is the crucial factor in cellular technology which can be attained by using a nonlinear amplifier to give a better response. Modulation Scheme becomes an essential factor for better performance of cellular technology. As, ISI degrades the performance of the GMSK system with various types of MIMO. Here in the research article we proposed an approach for reducing the ISI for different m values i.e. 4, 8, 16, 32 and 64 along with the different power i.e. 10dB, 20dB 30dB. As well as GMSK performance is improved by using optimum filter as cost and noise is the major factor we must reduce in our system or different modulation scheme so that we can have better cellular technology. Also we made a comparison for PSK and QAM by computing the probability of BER for different modulating techniques
Chanki Pandey, Prabira Kumar Sethy, Preesat Biswas, Santi Kumari Behera, and M.R. Khan
IEEE
With the advancement of livelihood of human society, the more attention brings towards the quality of products, especially foods. Nutritional value is an unknown characteristic that affects our bodies in ways that we cannot perceive. Still, this quality attribute becomes a necessary factor for consumers, scientists, and the medical profession. In this paper, we propose a novel approach to evaluate the quality of pomegranate using image processing techniques.
Yogesh Kumar Sahu, Chanki Pandey, Preesat Biswas, M.R. Khan, and Shanti Rathore
IEEE
Brain tumor detection is one of the intriguing task in the medical field still now. Earlier they uses pneumoencephalography and cerebral angiography had the drawback, CT and MRI scan techniques with the help of surgeons to providing a higher quality result in image processing. It is difficulty in distinguishing between brain tumor tissue and normal tissue because it was similar in color. Hence Brain tumor must be analyzed more precisely in order to cure it. In this paper Tumor Detection with help of MATLAB image processing catches three stages Pre-processing, Processing and Post-processing in morphological detection. After the getting MRI report first stage is pre-processing which is converting the original RGB image to gray-scale image and then Gaussian high pass filter for noise reduction In the second stage processing system for pixel enhancement we uses Median filter and in third stage is the post-processing which Entropy Filter., Standard Deviation Filter(SDF), Weiner Filter, Gradient Magnitude, Regional Maxima for various different-different results. In this post processing which is followed by algorithm not only automatically create report but also very less delay time and get better brain tumor detection more efficient.