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Priyanka Yadlapalli

Bio: Priyanka Yadlapalli is an academic researcher from Gokaraju Rangaraju Institute of Engineering and Technology. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 3, co-authored 4 publications receiving 126 citations.

Papers
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Journal Article
TL;DR: This paper discusses different crossover operators that help in solving the problem that involves large population size, which is travelling sales man problem.
Abstract: Genetic Algorithms are the population based search and optimization technique that mimic the process of natural evolution. Genetic algorithms are very effective way of finding a very effective way of quickly finding a reasonable solution to a complex problem. Performance of genetic algorithms mainly depends on type of genetic operators which involve crossover and mutation operators. Different crossover and mutation operators exist to solve the problem that involves large population size. Example of such a problem is travelling sales man problem, which is having a large set of solution. In this paper we will discuss different crossover operators that help in solving the problem.

164 citations

Journal ArticleDOI
TL;DR: Fast Conjugate Symmetric Sequency Ordered Complex Hadamard Transform (CS-SCHT) for extracting relevant features from the ECG signal is presented and its computational performance is examined and compared to that of the HT and NCHT.

21 citations

Journal ArticleDOI
01 Sep 2021
TL;DR: In this paper, Gabor filters are used to extract the textural characteristics of the left and right breasts, which are then classified as normal or malignant based on textural asymmetry between the breasts (SVM).
Abstract: Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

5 citations

Journal ArticleDOI
TL;DR: This paper considers Sequency Ordered Complex Hadamard Transform (SCHT) as a feature extraction technique for sleep apnea affected patients based on sensitivity, specificity and accuracy.
Abstract: Sleep apnea is a potentially serious breath disorder. This can be detected using a test called as Polysomnography (PSG). But this method is very inconvenient because of its time consuming and expensive nature. This can be overcome by using other methods like Respiratory rate interval, ECG – derived respiration and heart rate variability analysis using Electrocardiography (ECG). These methods are used to differentiate sleep apnea affected patients and normal persons. But the major drawback of these is in performance. Hence, in this paper this disadvantage is overcome by considering Sequency Ordered Complex Hadamard Transform (SCHT) as a feature extraction technique. A minute to minute classification of thirty – five patients based on sensitivity, specificity and accuracy are 93.74%, 96.15% and 95.6%. General Terms Preprocessing, Feature Extraction, Classifier

3 citations

Proceedings ArticleDOI
25 Jan 2022
TL;DR: This paper has classified MRI into four types (glioma, no tumor, meningioma, pituitary, and according to the findings, transfer learning works well when the dataset is limited.
Abstract: A benign or malignant brain tumour is a development of cells within the brain or skull that is abnormal. A primary tumour is one that grows directly from the brain's tissue, while a secondary tumour is one that spreads from another part of the body to the brain (metastasis). Depending on the tumor's kind, size, and location, there are a variety of treatment options. Radiologist use MRI (magnetic resonance images) to classify them. Due to the complexity of brain tumours and their qualities, a manual examination might be error-prone. To aid physicians throughout the globe, we've proposed a solution that uses Deep Learning Algorithms like ConvolutionNeural Networks and TransferLearning (TL). In this paper we have classified MRI into four types (glioma, no tumor, meningioma, pituitary). We have trained our architecture using EfficientNet. From According to our findings, transfer learning works well when the dataset is limited. An accuracy of 99 percent is achieved by the suggested approach.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: Various computer-aided diagnosis (CADx) methods have been proposed to remedy shortcomings of electrocardiogram (ECG) feature analysis, and different CADx systems developed by researchers are discussed.

126 citations

Journal ArticleDOI
TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

92 citations

Journal ArticleDOI
TL;DR: Easy and effective algorithms for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning are proposed in this paper.
Abstract: Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.

80 citations

Journal ArticleDOI
TL;DR: This work proposes a hybrid GA which combines the classical genetic mechanisms with the gradient-descent technique for local searching and constraints management and confers to GAs the capability of escaping from the discovered local optima, by progressively moving towards the global solution.

76 citations

Journal ArticleDOI
TL;DR: The automatic detection of QRS complex has been proposed which is useful in early diagnosis of cardiac diseases and essential feature of detection stage is to build feature selection approach for having a minimal feature set which includes ample information about data for the planned application.
Abstract: The early detection of heart abnormalities through electrocardiography (ECG) is essential for reducing the prevalence of cardiac arrest worldwide. Often, subjects are unaware of the condition of their hearts until detected at the last stage. In this study, various records in real-time and PhysioNet databases were examined using chaos analysis. Chaos analysis was implemented by plotting different attractors against various time-delay dimensions. The main advantages of chaos analysis approach include: (1) a preprocessing stage is not demanded to the recorded ECG signal, and (2) it helps to estimate the reliable and robust thresholds for QRS detection using time-delay dimension (embedding), correlation dimension, Lyapunov exponent, and entropy. ECG may be a useful candidate to classify heart diseases; however, visualization through ECG may not be sufficient because of the minute differences that exist in the ECG recordings. Therefore, the effective automatic detection of ECG signals is essential. Further, ECG datasets should be analyzed using time–frequency representations for getting frequency contents of the signal at each time point. ECG signals are nonstationary in nature; the assumption of stationarity is valid on a short-time basis. For this purpose, a short-time spectrum is computed using the short-time Fourier transform (STFT) as a feature extraction tool in this paper. Noise and baseline wander are filtered before the STFT operation to ensure correct frequency components of the QRS complex. For filtering, a digital band-pass filter has been used since its filtering characteristics are invariant with drift and temperature. The automatic detection of QRS complex has been proposed which is useful in early diagnosis of cardiac diseases. The essential feature of detection stage is to build feature selection approach for having a minimal feature set which includes ample information about data for the planned application. In this paper, the QRS complex is detected by applying principal component analysis (PCA) on the fused results of individual features extracted using chaos analysis and STFT. Using PCA, the estimated principal components show the degree of morphological beat-to-beat variability. The detection performance is evaluated in terms of sensitivity (Se), positive predictivity (PP), detection error rate (DER), and accuracy (Acc). The proposed technique yields encouraging performance parameter values such as 99.93% Se, 99.97% PP, 0.0895% DER, and 99.91% Acc in the analysis of data from the PhysioNet database and 99.93% Se, 99.96% PP, 0.097% DER, and 99.90% Acc in the analysis of data from the real-time database. Suitable comparisons have been presented with the existing techniques.

67 citations