Sachin Bhat

Bio: Sachin Bhat is an academic researcher from Reva Institute of Technology and Management. The author has contributed to research in topics: Phase congruency & Convolutional neural network. The author has an hindex of 1, co-authored 11 publications receiving 7 citations.

Convolutional Neural Network approach for the Classification and Recognition of Lung Nodules

Abstract: Lung cancer is remaining as a major cause of cancer death, where it makes up almost 25% of cancer deaths worldwide. As per the report of World Health Organization (WHO), around 18 million people die due to lung cancer every year. Though there are many standard detecting techniques available, most of the patients die in a year of diagnosis. Hence, it is essential to find an alternative approach for the early recognition of lung cancer, which altogether improves the odds of endurance. This paper addresses the classification and recognition task to detect lung tumor in the early stages of Computerized Tomography (CT) scans for lungs. Lung Image Database Consortium and Infectious Disease Research Institute (LIDC/IDRI) lung image dataset is used for this purpose. Convolutional-Neural-Network (CNN) based deep learning (DL) approach is proposed for the recognition and classification from the CT scan images and the results are compared with the classical machine learning algorithms in the literature with respect to standard evaluation metrics. It is additionally observed that, the Deep CNN classifier has performed better than all the other three traditional classification algorithm in the literature and viewed as the viable classifier.

Preprocessing and Binarization of Inscription Images using Phase Based Features

Abstract: Epigraphs are important sources for reshaping our culture and history. They have a remarkable importance to mankind. But modern epigraphists find it difficult to interpret the information in scripts. It is mainly because inscriptions are eroded over a period of time due to natural calamities. Scripts of ancient times are largely unknown. Character sets used have changed from one form to another over the centuries. Therefore, for reading ancient scripts the characters have to be extracted. In this paper, a model for enhancement and binarization of historical epigraphs is proposed. This model consists phase congruency and of Gaussian model based background elimination using expectation maximization(EM) algorithm, preprocessing and binarization. In binarization, phase based features are used with specialised filters. Adaptive Gaussian filters are used to smoothen the output images. Weighted mean angle is calculated to differentiate the foreground from the background. EM algorithm removes the background noise completely where foreground characters are untouched. Proposed method is tested on different datasets of inscriptions and epigraphs. Obtained results are compared with the existing classical algorithms.

Character Recognition of Tulu Script Using Convolutional Neural Network

Abstract: Handwriting classification and identification is one of the most interesting issues in the current research because of its variety of applications. It has leveraged its potential in reducing the manual work of converting the documents containing handwritten characters to machine-readable texts. The deep convolutional neural networks (DCNNs) are successfully implemented for the recognition of characters in various languages. This paper proposes a DCNN-based architecture for the classification of Tulu language characters. Tulu is one of the five Dravidian groups of languages used by around 50 Lakh people in the states of Karnataka and Kerala. This model is mainly developed to assist the character recognition of Tulu documents. A total of 90,000 characters including both vowels and consonants have been included in the dataset. This architecture is showing a satisfactory test accuracy of 92.41% for the classification of 45 handwritten characters.

ARECA-COPT: Pesticide Sprayer Quad Copter

Abstract: In the recent year UAV( Unmanned Aerial Vehicle) having quadcopter helicopter i.e., quadcopter configuration has been receiving increasing attention amongst the global researchers due its wide range of applications such as surveillance in military, civilian, and disaster management application. Here we try to implement its application to agriculture field. In agricultural fields, the use of pesticides is necessary for better crop yields. The use of aircrafts and drones has become common for carrying out this operation mainly because of its speed and effectiveness in the spraying operation. The problem with manual spraying is the lack labour. Also manual spraying cause direct health problem.The WHO (World Health Organization) estimates there are more than 1 millionpesticide cases in every year. In that more than one lakh deaths in each year, especially indeveloping countries due to the pesticides sprayed by human being.This project is to mainly overcome the ill-effects of pesticides on human beings(manual pesticide sprayers) and also to cover larger areas while spraying pesticides in a short span of time when compared to a manual sprayer.

Fabrication of Portable Foot Operated Agricultural Fertilizers and Pesticides Spraying Pump

Abstract: There are different types of cultivators in India namely small, marginal, medium and rich. Small scale farmer’s use conventional manually lever operated knapsack sprayers because of the three reasons; it is user friendly equipment, ease of design and cost effective machine. But it cannot maintain required pressure; it also leads to lumbar pain. However this equipment can also lead to misapplication of chemicals and ineffective control of target pest. It leads to the loss of pesticides due to dribbling or drift during application. This process not only adds to cost but also hazardous to the environment and causes imbalance in natural eco system This paper suggests a proto type of foot operated pesticide spraying pump. It helps spraying at maximum rate in less time. Key Words—Spray pump, Lumbar pain, foot operated, constant flow valves, medium farmer.

Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction.

Abstract: With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amount of publicly available lung image data, it has become possible to use convolutional neural networks for benign and malignant classification of lung nodules. However, as the network depth increases, network training methods based on gradient descent usually lead to gradient dispersion. Therefore, we propose a novel deep convolutional network approach to classify the benignity and malignancy of lung nodules. Firstly, we segmented, extracted, and performed zero-phase component analysis whitening on images of lung nodules. Then, a multilayer perceptron was introduced into the structure to construct a deep convolutional network. Finally, the minibatch stochastic gradient descent method with a momentum coefficient is used to fine-tune the deep convolutional network to avoid the gradient dispersion. The 750 lung nodules in the lung image database are used for experimental verification. Classification accuracy of the proposed method can reach 96.0%. The experimental results show that the proposed method can provide an objective and efficient aid to solve the problem of classifying benign and malignant lung nodules in medical images.

Metal nanoparticles decorated two-dimensional nanosheets as heterogeneous catalysts for coupling reactions

Abstract: This review presents a brief discussion on the numerous synthetic techniques used to prepare metal nanoparticles supported on various two-dimensional (2D) materials. Special emphasis has been given to graphene and other 2D analogues such as g-C3N4, h-BN, MoS2, WS2 etc. supported metal nanoparticles. In addition to these, this review outlines the applications of the developed metal nanoparticles–2D composite materials for catalytic coupling reactions, which have recently emerged as a promising strategy in carbon–heteroatom or carbon–carbon bond formation. The effect of size and morphology of metal nanoparticles–2D composite materials on their catalytic performance toward different coupling reactions such as Suzuki, Heck, Sonogashira, etc. have been discussed in detail.

Transformasi Lontar Babad Lombok Menuju Digitalisasi Berbasis Natural Gradient Flexible (NGF)

Abstract: Suku Sasak, yang tinggal di pulau Lombok Nusa Tenggara Barat, memiliki tradisi penulisan di daun lontar ( Borassus Flabellifer ) kering, salah satunya adalah naskah Lontar Babad Lombok. Naskah Lontar Babad Lombok seiring berlalunya waktu, menjadi rapuh dan mudah patah sehingga memerlukan perawatan. Keadaan ini mendorongnya perlu dilakukan digitalisasi naskah lontar babad lombok sebagai bentuk pelestarian sehingga para generasi Milenial, khususnya di Lombok, dapat menikmati lontar babad lombok. Digitalisasi citra tersebut tantangan utama adalah tepi kabur teks dan perbedaan minimum antara teks dan bagian non-tekssebagai akibat dari proses perawatan. Oleh karena itu, dibutuhkan proses peningkatan kualitas citra hasil digitalisasi agar tulisan dapat lebih jelas terbaca. Salah satu metode yang terbukti mampu untuk memisahkan teks dari latar belakang yang sangat berkorelasi adalah Natural Gradient Flexibel (NGF) berbasiskan Independent Component Analysis (ICA), NGF-ICA. Penelitian ini bertujuan untuk melakukan peningkatan kualitas citra digitalisasi sebelum diumpankan pada database dan sistem informasi yang telah dibangun. Kualitas citra yang telah ditingkatkan diukur menggunakan metode MSE dan PSNR untuk tingkat kemiripannya, dan metode Entropi dan SSIM untuk informasi dan perspektif visual. Hasil penelitian menunjukkan bahwa penerapan algoritma NGF-ICA dapat memberikan citra keluaran dengan kualitas yang tinggi dengan nilai rata-rata MSE, PSNR, SSIM dan peningkatan Entropi sebesar 708, 19.95 db, 0.87 dan 0.45, secara berturut-turut. Abstract Sasak tribe, who lives on Lombok Island, West Nusa Tenggara, has been writing manuscripts on dry palm leaves (Borassus Flabellifer) as a tradition, one of the manuscripts is Lontar Babad Lombok. As time pass by, the manuscript becomes brittle and breaks easily, therefore maintenances are required. this situation force the need to digitalize the manuscript as an act of preservation, hence the millennial generation, especially on Lombok Island, can enjoy the manuscript. the main challenge is the blurry edge of the text and the slight difference between the text and non-text part caused by the treatment process. Hence, it is needed to enhance the quality of the digitalize image to make the manuscript can be more clearly read. One of the proven methods that able to separate text from highly correlated backgrounds is Natural Gradient Flexibel (NGF) based on Independent Component Analysis (ICA), NGF-ICA. The aim of this study is to improve the quality of the digitized images before they fed into the database and information system that has been built. The enhanced image quality was measured, MSE and PSNR methods were used to measure the similarity level, and the Entropy and SSIM method were used to measure the information and visual perspective. The results show that the application of the NGF-ICA algorithm can generate high-quality output images with average values of MSE, PSNR, SSIM, and increasing Entropy by 708, 19.95 dB, 0.87, and 0.45, respectively.

Character Recognition of Tulu Script Using Convolutional Neural Network

Abstract: Handwriting classification and identification is one of the most interesting issues in the current research because of its variety of applications. It has leveraged its potential in reducing the manual work of converting the documents containing handwritten characters to machine-readable texts. The deep convolutional neural networks (DCNNs) are successfully implemented for the recognition of characters in various languages. This paper proposes a DCNN-based architecture for the classification of Tulu language characters. Tulu is one of the five Dravidian groups of languages used by around 50 Lakh people in the states of Karnataka and Kerala. This model is mainly developed to assist the character recognition of Tulu documents. A total of 90,000 characters including both vowels and consonants have been included in the dataset. This architecture is showing a satisfactory test accuracy of 92.41% for the classification of 45 handwritten characters.