scispace - formally typeset
Search or ask a question
Author

Amar Partap Singh Pharwaha

Bio: Amar Partap Singh Pharwaha is an academic researcher from Sant Longowal Institute of Engineering and Technology. The author has contributed to research in topics: Fractal antenna & Radiation pattern. The author has an hindex of 7, co-authored 28 publications receiving 134 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: An Artiflcial Neural Network (ANN) based simple approach is proposed as forward side for the design of a Circular Fractal Antenna (CFA) and analysis as reverse side of problem.
Abstract: A Neural Network is a simplifled mathematical model based on Biological Neural Network, which can be considered as an extension of conventional data processing technique. In this paper, an Artiflcial Neural Network (ANN) based simple approach is proposed as forward side for the design of a Circular Fractal Antenna (CFA) and analysis as reverse side of problem. Proposed antenna is simulated up to 2nd iteration using method of moment based IE3D software. Antenna is fabricated on Roger RT 5880 Duroid substrate (High frequency material) for validation of simulated, measured and ANN results. The main advantage of using ANN is that a properly trained neural network completely bypasses the complex iterative process for the design and analysis of this antenna. Results obtained by using artiflcial neural networks are in accordance with the simulated and measured results.

41 citations

Journal ArticleDOI
TL;DR: A comparative evaluation of the relative performance of LM-MLFFBP-ANN and SMO-SVM classifiers is investigated to classify MCCs as benign or malignant.

28 citations

Journal ArticleDOI
TL;DR: From experimental results, it is observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.
Abstract: Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.

21 citations

Journal ArticleDOI
TL;DR: Feed-Forward Back-Propagation Artificial Neural Network trained with Levenberg-Marquardt algorithm is used for estimation of different performance parameters of CMPA and the results of NC estimation are in very agreement with simulated, measured and theoretical results.

13 citations

Journal ArticleDOI
TL;DR: In this paper, a modified Hilbert curve fractal (MHCF) antenna with parasitic patches for multiband applications is presented, where the fractal antenna has been designed on a dual-layer substructure.
Abstract: This paper presents the development of a Modified Hilbert Curve Fractal (MHCF) antenna with parasitic patches for multiband applications. The fractal antenna has been designed on a dual-layer subst...

12 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: The quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis by investigating patient hyperspectral images.
Abstract: Oral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment. In this research work, we have developed a deep learning algorithm for automated, computer-aided oral cancer detecting system by investigating patient hyperspectral images. To validate the proposed regression-based partitioned deep learning algorithm, we compare the performance with other techniques by its classification accuracy, specificity, and sensitivity. For the accurate medical image classification objective, we demonstrate a new structure of partitioned deep Convolution Neural Network (CNN) with two partitioned layers for labeling and classify by labeling region of interest in multidimensional hyperspectral image. The performance of the partitioned deep CNN was verified by classification accuracy. We have obtained classification accuracy of 91.4% with sensitivity 0.94 and a specificity of 0.91 for 100 image data sets training for task classification of cancerous tumor with benign and for task classification of cancerous tumor with normal tissue accuracy of 94.5% for 500 training patterns was obtained. We compared the obtained results from another traditional medical image classification algorithm. From the obtained result, we identify that the quality of diagnosis is increased by proposed regression-based partitioned CNN learning algorithm for a complex medical image of oral cancer diagnosis.

173 citations

Journal ArticleDOI
TL;DR: This research embeds a particle swarm optimization as feature selection into three renowned classifiers, namely, naive Bayes, K-nearest neighbor, and fast decision tree learner, with the objective of increasing the accuracy level of the prediction model.
Abstract: Women who have recovered from breast cancer (BC) always fear its recurrence. The fact that they have endured the painstaking treatment makes recurrence their greatest fear. However, with current advancements in technology, early recurrence prediction can help patients receive treatment earlier. The availability of extensive data and advanced methods make accurate and fast prediction possible. This research aims to compare the accuracy of a few existing data mining algorithms in predicting BC recurrence. It embeds a particle swarm optimization as feature selection into three renowned classifiers, namely, naive Bayes, K-nearest neighbor, and fast decision tree learner, with the objective of increasing the accuracy level of the prediction model.

130 citations

Journal ArticleDOI
12 Nov 2018
TL;DR: This paper investigates the use of machine learning strategies to the classification and identification problem in RF waveforms, and evaluates four different strategies: conventional deep neural nets, convolutional neuralnets, support vector machines, and deep neuralnets with multi-stage training.
Abstract: With the increasing domain and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. To counter security threats posed by rogue or unknown transmitters, we must identify RF transmitters not only by the data content of the transmissions but also based on the intrinsic physical characteristics of the transmitters. RF waveforms represent a particular challenge because of the extremely high data rates involved and the potentially large number of transmitters sharing a channel in a given location. These factors outline the need for rapid fingerprinting and identification methods that go beyond the traditional hand-engineered approaches. In this paper, we investigate the use of machine learning strategies to the classification and identification problem. We evaluate four different strategies: conventional deep neural nets, convolutional neural nets, support vector machines, and deep neural nets with multi-stage training. The latter was by far the most accurate, achieving 100% classification accuracy of 12 transmitters, and showing remarkable potential for scalability to large transmitter populations.

97 citations

Journal ArticleDOI
TL;DR: A novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information is proposed.
Abstract: The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps-playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well-known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

62 citations