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Pooja Chopra

Bio: Pooja Chopra is an academic researcher from Ohio University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

Papers
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Journal ArticleDOI
TL;DR:
Abstract: Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.

39 citations

Pooja Chopra1
01 Jan 2016
TL;DR: It is hypothesized that difference in cell entry time and passage through the channels can be used to distinguish cancer cells from the healthy cells as well as to distinguish differences between cancer cell lines.
Abstract: CHOPRA, POOJA., M.S., August 2016, Physics and Astronomy Fabrication of Multi-Parallel Microfluidic Devices for Investigating Mechanical Properties of Cancer Cells. Director of Thesis: David F. J. Tees There is evidence that mechanical properties and deformability can be used as a biomarker to distinguish between healthy and cancerous cells. A number of biophysical techniques such as atomic force microscopy (AFM), and optical tweezers have been used to measure the mechanical properties of cancer cells. Multi-parallel microfluidic devices can be used as a high throughput method to study the deformation of cells. We hypothesize that difference in cell entry time and passage through the channels can be used to distinguish cancer cells from the healthy cells as well as to distinguish differences between cancer cell lines. We fabricated a multi-parallel microfluidic device using photolithography and channels surfaces were coated with bovine serum albumin (BSA) to minimize non-specific adhesion. The design has a single entry and exit to mimic micropipette experiments. The channel widths can be chosen either to deform the cell membrane or to deform both the cell membrane cortex and the cell nucleus. This work lays the foundation for the development of microfluidic devices which can subsequently be used in the detection of mechanical properties of cancer cells.

5 citations

Journal ArticleDOI
TL;DR: In this paper , an algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells.
Abstract: An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model's ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

2 citations

Journal ArticleDOI
TL;DR: To supervise and manage engineering safety data effectively and display the system construction more intuitively, a method based on computer network technology is proposed and the establishment of the system provides a new research platform for power engineering safety supervision and management.
Abstract: Nowadays, the function of information construction in construction project quality supervision and management is increasingly prominent, and it has become a task that cannot be ignored by administrative departments. To supervise and manage engineering safety data effectively and display the system construction more intuitively, a method based on computer network technology is proposed. K-means clustering, random forest, neural network, and other artificial intelligence algorithms were used for data modelling, and classification model evaluation, regression model evaluation, and other evaluation tools were used to evaluate the quality of the built model, and the power engineering monitoring system was established. The functions of engineering safety supervision and management, data storage and query, deformation graphical display, data analysis and forecast, results report output, and so on are realized. The results showed that the mean square error of K-means was 7.74, the mean square error of random forest was 27.5, and the error of neural network was 4.4. Neural network has the smallest error and the closest data. The establishment of the system provides a new research platform for power engineering safety supervision and management.
Journal ArticleDOI
TL;DR: In this article , a fault detection of computer communication network based on an improved neural network algorithm is proposed, and experiments are carried out, the convolutional neural network structure is designed according to the scale of data features, a series of optimization studies including discarding learning, gradient optimization algorithm, and data enhancement based on this is carried out.
Abstract: In order to meet the new requirements of fault diagnosis response and intelligent degree in the current computer network, a fault detection of computer communication network based on an improved neural network algorithm is proposed. First, from the perspective of deep learning, based on the KDD99 data set, the network fault diagnosis method based on the convolutional neural network model is studied, and the data conversion operation of grayscale matrixed raw data is proposed. And experiments are carried out, the convolutional neural network structure is designed according to the scale of data features, a series of optimization studies including discarding learning, gradient optimization algorithm, and data enhancement based on this is carried out, and the establishment of the entire fault diagnosis model is completed. The experimental results show that, in the diagnostic model designed in this paper, the Tanh activation function is used in the first fully connected layer to achieve the best convergence speed. During the training process, it can start to converge after about 24 iterations, and the accuracy rate of the model training process can reach 98.1%, verifying the correctness and superiority of the algorithm and model.

Cited by
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Journal Article
TL;DR: Research data show that more resistant stem cells than common cancer cells exist in cancer patients, and to identify unrecognized differences between cancer stem cells and cancer cells might be able to develop effective classification, diagnose and treat for cancer.
Abstract: Stem cells are defined as cells able to both extensively self-renew and differentiate into progenitors. Research data show that more resistant stem cells than common cancer cells exist in cancer patients.To identify unrecognized differences between cancer stem cells and cancer cells might be able to develope effective classification,diagnose and treat ment for cancer.

2,194 citations

Journal Article
TL;DR: The phase-shifting mask as mentioned in this paper consists of a normal transmission mask that has been coated with a transparent layer patterned to ensure that the optical phases of nearest apertures are opposite.
Abstract: The phase-shifting mask consists of a normal transmission mask that has been coated with a transparent layer patterned to ensure that the optical phases of nearest apertures are opposite. Destructive interference between waves from adjacent apertures cancels some diffraction effects and increases the spatial resolution with which such patterns can be projected. A simple theory predicts a near doubling of resolution for illumination with partial incoherence σ < 0.3, and substantial improvements in resolution for σ < 0.7. Initial results obtained with a phase-shifting mask patterned with typical device structures by electron-beam lithography and exposed using a Mann 4800 10× tool reveals a 40-percent increase in usuable resolution with some structures printed at a resolution of 1000 lines/mm. Phase-shifting mask structures can be used to facilitate proximity printing with larger gaps between mask and wafer. Theory indicates that the increase in resolution is accompanied by a minimal decrease in depth of focus. Thus the phase-shifting mask may be the most desirable device for enhancing optical lithography resolution in the VLSI/VHSIC era.

705 citations

Journal Article
TL;DR: This book cuts through the mountain of details flooding into this topic and presents in a simple way the techniques currently in use and some cutting-edge research.
Abstract: • Over 70 new Data Analysis Problems, with answers and explanations FIND OUT • New online videos, referenced throughout the book MORE INSIDE... , “I have enjoyed using this book because it cuts through the mountain of details flooding into this topic and presents in a simple way the techniques currently in use and some cutting-edge research. When I chose this book, it was after looking at several other books on the market —this one thoroughly covered the topics, but in a simple, easy-to-understand way for the student.”

68 citations

Journal ArticleDOI
01 Jul 2022-2
TL;DR: In this article , the authors provide guidelines for guaranteed allocation and distribution of distributed generation (DG) in distribution systems for an acceptable reliability level and voltage profile, which involves the use of GA techniques and is solved by combining systems to estimate system reliability, losses and dg impacts on the voltage profile.
Abstract: The definition of a restricted power supply area for a distribution network disqualifies this scheme as a distributed product even though it is a very common dg scheme. Power system quality is a key issue for low and medium voltage power companies and consumers, to minimize power network losses; this paper provides guidelines for guaranteed allocation and distribution of distributed generation (DG) in distribution systems for an acceptable reliability level and voltage profile. The optimization process involves the use of genetic algorithms (GA) techniques and is solved by combining systems to estimate system reliability, losses and dg impacts on the voltage profile. The fitness evaluation process leads to the determination of the ga's relationship between investment and operating costs as a benefit of setting numerical units. Estimation based on current flow method for radial networks reconciliation of scattered generator losses with profile of voltage profile

5 citations

Journal ArticleDOI
01 Jul 2022
TL;DR: In this article , a model of workplace learning is presented to examine the sequence of opportunities available to learners as they rebuild their roles as learners through relationships with their immediate social and physical contexts.
Abstract: In recent years, technology has required workplace learning, continuous professional development, and general training of employees. However, little research has been done in this area to evaluate the methods and effectiveness of these teaching styles and is welcomed by learners. Learning collection designers often use identical policy collections, targeting students or testifying to the success of younger learning students. The word used to describe the education of a very mature learner (mostly in the workplace) of androgyny (according to the knowledge of androgyny knowledge) should have a variety of experiences, while students have only a limited amount so should be basic. The concept of opportunities for learning is borrowed from classroom discourses and expands its understanding of "socially identifiable and identifiable event", including cultural and environmental aspects such as content, time and participant. In this study, teachers are used to examine the sequence of opportunities available to them as they rebuild their roles as learners through relationships with their immediate social and physical contexts. Development, environment and conditions required for workplace learning many ideas aimed at clarifying the factors The following discussion introduces. The workplace learning model is presented in fostering the need for focus change. Outlined in this paper the proposed model of workplace learning on various ideas put forward by accredited academics and incorporates elements relevant to most workplaces.

4 citations