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Omdev Dahiya

Bio: Omdev Dahiya is an academic researcher from University Institute of Engineering and Technology, Panjab University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 2, co-authored 6 publications receiving 12 citations.

Papers
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Journal ArticleDOI
TL;DR: The results are presented of the study conducted on different prioritization approaches showing mostly stressed areas by researchers and the areas where there is a future scope for the researchers to work upon.
Abstract: Regression testing is about running the entire test ensemble again to ensure that amendments do not negatively affect the system. A popular approach in regression testing is test case prioritization which reorders test cases in such a way that those with higher priorities are run earlier than those with lower priorities based on some criterion. Numerous researchers have worked on different aspects of prioritization approaches. This paper presents the result of the study conducted on different prioritization approaches showing mostly stressed areas by researchers and the areas where there is a future scope. For it, studies related to test case prioritization in regression testing from the year 2004 to 2018 are analyzed by dividing this time period into three slots of five years each. 36 studies were selected from 948 studies to answer the research questions framed for this study. The trends followed in TCP along with the approaches evolved are thus documented to find the current trends and future scope for the researchers to work upon.

32 citations

Proceedings ArticleDOI
13 Oct 2022
TL;DR: In this article , the authors investigated the capabilities of a convolutional neural network observed using the VGG16 method, often known as a CNN community model, and evaluated the entire performance with the extraction of capabilities from segmented and normalised iris images.
Abstract: The human iris is a magnificent asset that can be used reliably for identifying purposes. It can eventually recognize humans with a serve degree of assertiveness. The extraction of enormous highlights is an essential component of the iris popularity framework. Previously, a variety of factors were used to run the iris popularity framework. The application of the capabilities acquired via the use of convolutional neural networks (CNNs) to iris recognition has attracted substantial interest due to the accomplishment of a high level of expertise in iris recognition. In this article, we investigate the capabilities of a convolutional neural network observed using the VGG16 method, often known as a convolutional community model. The entire performance of the advising device is evaluated with the extraction of capabilities from segmented and normalised iris images. The proposed iris popularity device is analysed using the CASIA-1000 dataset. The device provides incredibly effective effects at an exceedingly high rate of efficiency. On well-known iris datasets, the suggested method has been assessed and shown to achieve an accuracy rate of 96%, which surpasses the previous result.

22 citations

Book ChapterDOI
01 Jan 2021
TL;DR: In this article, the current state of the art of requirements-based test case prioritization techniques in the field of software testing is found, which can help the testers in identifying the source of the defects more quickly and validating the product corresponding to its requirements.
Abstract: Testing the software is a time-consuming and costly process. Testing teams are often constrained to end their testing endeavors soon, owing to time and budget constraints. This can lead to issues with the satisfaction of the customer and the quality of the product. Test case prioritization (TCP) techniques have shown to improve the viability of regression-testing activities. Due to these, the fault detection rate is increased, which allows testers to discover faults early in the software product. In the testing phase, the necessity of requirements information has been very widely documented by the requirements engineering community. Most of the techniques for regression testing rely upon the code information of the software. Including the requirements information to the existing testing, techniques will help the testers in identifying the source of the defects more quickly and validating the product corresponding to its requirements. This paper has focused on finding the current state of the art of requirements-based test case prioritization techniques in the field of software testing.

20 citations

Journal ArticleDOI
TL;DR: In this article , a new methodology is planned based on an improved FCN (fully connected network) to regulate the assessment of the quality of students in Higher Education HE, which is composed of different phases: the first phase is data acquisition, in which the data are gathered from various sources for training and testing of the proposed method.
Abstract: EDM and LA are two fields that study how to use facts to get more academic learning and enhance the students’ entire performance. Both areas are concerned with a broad range of issues such as curriculum strategies, coaching, mental well-being of students, learning motivation, and academic achievement. The COVID-19 pandemic highly disrupted the higher education sector and shifted the old, chalk-talk teaching-learning model to an online learning format. This meant that the structure and nature of teaching, learning, assessment, and feedback methodologies also changes. With the empowerment in technology, timely and effective feedback is provided by the teachers to achieve greater learning. Through these studies, it is noted that negative feedback discourages the effort and achievement of learners, so it should be carefully crafted and delivered. In this work, a new methodology is planned based on an improved FCN (fully connected network). The key impartial of the proposed method is to regulate the assessment of the quality of students in Higher Education HE. The proposed methodology is composed of different phases: The first phase is data acquisition, in which the data are gathered from various sources for training and testing of the proposed method. The second phase is data orientation, in which the information is oriented in a specific file format. After that, data are cleaned, and preprocessing methods are applied. In the fourth phase, a machine learning-based model is developed to predict student’s academic performance. The fully connected neural network is enhanced with LA to better assess student quality in higher education. The proposed work is evaluated with the OULAD database, which was gathered from the students of Open University. The proposed methodology has attained an accuracy of 84%, more significant than the conventional ANN model accuracy rate. The proposed methodology’s Recall, F1-score, and precision rates are 0.88, 0.91, and 0.93, respectively.

20 citations

Book ChapterDOI
01 Jan 2021
TL;DR: In this article, the authors have discussed various issues that arise during the software testing process and laid points that should be taken care of so that delivery of the right quality product to its intended users is ensured.
Abstract: Comprehensive studies play an essential role in the field of engineering and technology. They aim to make us understand why and how things work. This enables budding practitioners and researchers to use this understanding to bring innovations that will shape the modern world. Software testing plays a fundamental role in the development of the software. Software is subjected to testing to discover faults so that necessary amendments can be made in it. This overall increases its quality, reliability, and robustness. Researchers have even used many techniques in an integrated form to increase the effectiveness of testing. This study aims to discuss various issues that arise during the software testing process. Addressing these issues will result in the development of a fault-free, reliable, and robust software product. For this process, this study has laid points that should be taken care of so that delivery of the right quality product to its intended users is ensured. The objective of this study is to bring the attention of emerging practitioners and testers toward the areas where there is a need for inspection and investigation.

19 citations


Cited by
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Proceedings ArticleDOI
13 Oct 2022
TL;DR: In this article , the authors investigated the capabilities of a convolutional neural network observed using the VGG16 method, often known as a CNN community model, and evaluated the entire performance with the extraction of capabilities from segmented and normalised iris images.
Abstract: The human iris is a magnificent asset that can be used reliably for identifying purposes. It can eventually recognize humans with a serve degree of assertiveness. The extraction of enormous highlights is an essential component of the iris popularity framework. Previously, a variety of factors were used to run the iris popularity framework. The application of the capabilities acquired via the use of convolutional neural networks (CNNs) to iris recognition has attracted substantial interest due to the accomplishment of a high level of expertise in iris recognition. In this article, we investigate the capabilities of a convolutional neural network observed using the VGG16 method, often known as a convolutional community model. The entire performance of the advising device is evaluated with the extraction of capabilities from segmented and normalised iris images. The proposed iris popularity device is analysed using the CASIA-1000 dataset. The device provides incredibly effective effects at an exceedingly high rate of efficiency. On well-known iris datasets, the suggested method has been assessed and shown to achieve an accuracy rate of 96%, which surpasses the previous result.

22 citations

Proceedings ArticleDOI
13 Oct 2022
TL;DR: In this article , a framework for secure face recognition (SoF) is proposed, which allows verified customers to enter a private residence using a TensorFlow-based classifier.
Abstract: Internet-based innovation has advanced significantly during the past decade. As a result, advances in security technology have become a crucial resource for protecting our daily lives. In this research, we suggest a framework for secure face recognition (SoF). In particular, we tailor this infrastructure to allowing verified customers entry into a private residence. In order to get the classifier ready, another flexible learning technique is used. For starters, we get our preliminary data from social groups and institutions. As the client continues to make use of the framework, the classifier's accuracy increases. The classifier model has been improved by employing an epic technique that makes use of human interaction and online community. Using the powerful learning framework TensorFlow, the system may be easily repurposed to work with a wide variety of devices and applications.

22 citations

Proceedings ArticleDOI
13 Oct 2022
TL;DR: In this paper , the authors present a robot that can monitor the distance between seeds to prevent unnecessary water loss and use a soil PH sensor to ascertain when plants need to be fertilized.
Abstract: Agriculture is one of man's earliest known occupations. Because of its pervasiveness in our life and our reliance on it, many engineers search for ways to improve agricultural robots. Despite this, the vast majority of farmers still use obsolete methods and implements, such as wooden ploughs and sickles, to cultivate their land. Random sowing is one approach, but it requires more resources (seeds, water, fertiliser) and hence increases the expense of addressing the issues in agriculture. In this study, we present a robot that can address this issue by monitoring the distance between seeds to prevent unnecessary water loss and by using a soil PH sensor to ascertain when plants need to be fertilised. The HC-05 Bluetooth module is linked to an Android device, allowing for remote control of the robot's movement. The simulation analysis and temperature/humidity graph were generated with the help of Proteus software. Proteus software also simulated the soil's water content and the ph value of items in the soil.

21 citations

Proceedings ArticleDOI
15 Nov 2022
TL;DR: In this article , a convolutional neural network (CNN) was used to detect tumors in brain MRI images and achieved 97.8% score for the accuracy, 98.5% specificity, 96.2% recall and 97.3% precision.
Abstract: Recently, the rate at which people die from cancer disease is very alarming. The major reason is because the cancer cells were not detected at the early stages. It may be difficult for medical professionals to evaluate the results of medical imaging techniques such as magnetic resonance imaging (MRI) because cancer cells may not be adequately represented for detection. This is why deep learning techniques are needed for earlier detection of cancer cells so as to reduce the death rate from such.In this paper, we have applied the convolutional neural network on some MRI image dataset downloaded from Kaggle. The model was trained to be able to detect tumors in brain MRI images. The performance of the model was evaluated and it achieved 97.8% score for the accuracy, 98.5% specificity, 96.2% recall, 98.5% F1-score and 97.3% precision. A graph was drawn to compare the training loss to the validation loss, as well as the training accuracy to the validation accuracy.

21 citations

Book ChapterDOI
01 Jan 2021
TL;DR: In this article, the current state of the art of requirements-based test case prioritization techniques in the field of software testing is found, which can help the testers in identifying the source of the defects more quickly and validating the product corresponding to its requirements.
Abstract: Testing the software is a time-consuming and costly process. Testing teams are often constrained to end their testing endeavors soon, owing to time and budget constraints. This can lead to issues with the satisfaction of the customer and the quality of the product. Test case prioritization (TCP) techniques have shown to improve the viability of regression-testing activities. Due to these, the fault detection rate is increased, which allows testers to discover faults early in the software product. In the testing phase, the necessity of requirements information has been very widely documented by the requirements engineering community. Most of the techniques for regression testing rely upon the code information of the software. Including the requirements information to the existing testing, techniques will help the testers in identifying the source of the defects more quickly and validating the product corresponding to its requirements. This paper has focused on finding the current state of the art of requirements-based test case prioritization techniques in the field of software testing.

20 citations