Chandigarh University

About: Chandigarh University is a education organization based out in Mohali, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1358 authors who have published 2104 publications receiving 10050 citations.

A novel technique for LED dot-matrix text detection and recognition for non-uniform color system

Abstract: Nowadays, LED dot matrix has increasingly role in many application areas to showing messages and contents. These messages contain various characters to display the message. A single character is displayed by a matrix containing a particular number of rows and columns. By combining a number of characters we displayed any message on LED display board. The message shown with the help of LED is the LED text. The LED text is very hard to detect because it shows discontinuity. This paper proposed a method to solve the problem of LED text detection and recognition. To perform the method we need to extract the board region from natural image. The work is developed by firstly inputting an image. The input image is then processed to convert the colors to gray-scale. Then, the image is segmented to extract the rectangular region. From this paper we can detect even a single character with single character extraction method. This paper recognized the characters with 88.57% accuracy.

Gene Mutation Classification through Text Evidence Facilitating Cancer Tumour Detection.

Abstract: A cancer tumour consists of thousands of genetic mutations. Even after advancement in technology, the task of distinguishing genetic mutations, which act as driver for the growth of tumour with passengers (Neutral Genetic Mutations), is still being done manually. This is a time-consuming process where pathologists interpret every genetic mutation from the clinical evidence manually. These clinical shreds of evidence belong to a total of nine classes, but the criterion of classification is still unknown. The main aim of this research is to propose a multiclass classifier to classify the genetic mutations based on clinical evidence (i.e., the text description of these genetic mutations) using Natural Language Processing (NLP) techniques. The dataset for this research is taken from Kaggle and is provided by the Memorial Sloan Kettering Cancer Center (MSKCC). The world-class researchers and oncologists contribute the dataset. Three text transformation models, namely, CountVectorizer, TfidfVectorizer, and Word2Vec, are utilized for the conversion of text to a matrix of token counts. Three machine learning classification models, namely, Logistic Regression (LR), Random Forest (RF), and XGBoost (XGB), along with the Recurrent Neural Network (RNN) model of deep learning, are applied to the sparse matrix (keywords count representation) of text descriptions. The accuracy score of all the proposed classifiers is evaluated by using the confusion matrix. Finally, the empirical results show that the RNN model of deep learning has performed better than other proposed classifiers with the highest accuracy of 70%.

An Experimental Study of Nanofluids Operated Shell and Tube Heat Exchanger with Air Bubble Injection

Abstract: Shell and Tube heat exchangers are the heat exchangers that are most widely used in industries and for other commercial purposes. There are many techniques that have been utilized to enhance the heat transfer performance of the shell and tube heat exchangers. Air bubble injection is one of the promising and inexpensive techniques that can create turbulence in the fluids resulting in to enhancement of heat transfer characteristics of the shell and tube heat exchangers. In this paper, experimental study of heat transfer characteristics have been done by injecting air bubbles at tube inlet and throughout the tube for 0.1%v/v and 0.2%v/v Al2O3 nanoparticle concentration. Results obtained at two different injection points for both concentrations are compared with the case when no air bubble injection is done. The results showed the enhancement in the heat transfer characteristics with air bubble injection and volumetric concentration of nanoparticles. The maximum enhancement was found to be in the case where air bubbles are injected throughout the tube which is followed by the air bubble injection at the tube inlet and without air bubble injection. As the bubbles were injected throughout the tube, approximately 22-33% enhancement was observed. The overall heat transfer coefficient with injecting air bubbles throughout the tube showed an enhancement of about 12-23% and 14-25% for 0.1% and 0.2% of nanofluids.

Electrical and Methanol Sensing Characteristics of RF Sputtered n-ZnO/p-Si Heterojunction Diodes

Abstract: This paper reports structural, electrical, and gas sensing characteristics of n-ZnO/p-Si heterojunction diodes fabricated using RF sputtering technique. The microstructural and surface morphological properties have been studied using X-ray diffraction, atomic force microscopy, and scanning electron microscopy, respectively. The electrical properties of the fabricated diodes have been investigated using current-voltage ( $I$ – $V$ ) and capacitance-voltage ( $C$ – $V$ ) measurements. The estimated values for rectification ratio, ideality factor, and barrier height were found to be ~100, 3.27, and 0.72eV, respectively, at room temperature. The values recorded for carrier concentration and barrier height using $C$ – $V$ measurement were $3.82\times 10^{14}$ cm $^{-3}$ and 0.79 eV, respectively. The value of series resistance using Chueng’s function was found to be 1710 $\Omega $ . After detailed structural and electrical characterization, methanol sensing response (3–100 ppm) of n-ZnO/p-Si heterojunction diodes for the temperature range of 27 °C–150 °C have also been investigated. The optimum operating temperature for methanol sensing was found to be considerably low, i.e., 100 °C. The values of response magnitude, response time and recovery time at 100 °C were estimated as 82%, ~4 and ~7 s, respectively. The cross sensitivity study for the nearest interfering species, such as ethanol, 1-propanol, butanol, butanone, and benzene, has confirmed the high selectivity of n-ZnO/p-Si heterojunction diodes for methanol sensing.

Estimation and prediction based on type-I hybrid censored data from the Poisson-Exponential distribution

Abstract: This paper considers the problems of estimation and prediction when lifetime data following Poisson-exponential distribution are observed under type-I hybrid censoring. For both the problems, we co...