Faculty Publications
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Item Deep Learning for COVID-19(Springer Science and Business Media Deutschland GmbH, 2022) Bs, B.S.; Manoj Kumar, M.V.; Thomas, L.; Ajay Kumar, M.A.; Wu, D.; Annappa, B.; Hebbar, A.; Vishnu Srinivasa Murthy, Y.V.S.Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Machine Learning Approaches for Resource Allocation in the Cloud: Critical Reflections(Institute of Electrical and Electronics Engineers Inc., 2018) Murali, A.; Das, N.N.; Sukumaran, S.S.; Chandrasekaran, K.; Joseph, C.T.; Martin, J.P.Resource Allocation is the effective and efficient use of a Cloud's resources and is a very challenging problem in cloud environments. Many attempts have been made to make Resource Allocation automated and optimal in terms of profit. The best of these methods used Machine Learning, but this comes with an overhead for computation. A lot of research has been done in this domain to find more efficient methods. Distributed Neural Networks (DNN) is the future of computation and will soon be used to make the computation of large-scale data faster and easier. DNN is currently the most researched area. This paper will summarize the major research works in these fields. A new taxonomy is proposed and can be used as a reference for all future research in this domain. The paper also proposes some areas that need more research in the foreseeable future. © 2018 IEEE.Item Speaker Identification and Verification using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2022) Recharla, R.; Jeevan Reddy, C.; Tanguturu, R.; Anand Kumar, A.M.Many voice assistants gained importance across globe in the recent times, for example, Cortana, Siri, Ok Google. These assistants are part of everyone's life these days. The main motive behind the proposed system is to improve recognition assistant system. The speaker prediction model is trained using features MFCC, Chroma, Tonnetz, Mel spectrogram, and Spectral contrast extracted from audio samples. The proposed system has numerous real-world applications, such as meeting transcription, unlocking smart devices using voice, and online viva voice verification. It can replace the existing biometric system for faculty attendance and traditional fingerprint recognition. A Dense Neural Network was created for each audio feature and finally concatenated using a concatenation layer which fetched the best performance output compared to LSTM. Dense Neural Network successfully predicted the speaker with an accuracy of more than 95% most of the times. In the case of LSTM, due to fewer samples, the accuracy of speaker prediction is around 79%. In the case of CNN, the accuracy of speaker prediction is around 86%; this behavior can be attributed to the noise environment. When an unknown speaker tries to speak, the Dense Neural network can manage the task by placing them in an anonymous class. © 2022 IEEE.Item Comparative Assessment of Different Machine Learning Models to Estimate Daily Soil Moisture(Springer Science and Business Media Deutschland GmbH, 2023) Nagashree, G.E.; Nema, M.K.Soil moisture is vital as it is the primary governing factor of agriculture production and natural vegetation growth. It plays an essential role in understanding the hydrological cycle and its effect on weather and climate, and its precise prediction helps to manage the water resources optimally. Prediction of soil moisture is dependent on surface meteorological variables and soil attributes. Existing soil moisture models/prediction methods are inaccurate, and developing an optimum mathematical model for it is difficult. This study evaluates the performance of four machine learning models (deep neural network (DNN) regression, support vector machine (SVM), multiple layer perceptron (MLP), and multi-linear regression (MLR) to estimate the soil moisture conditions. The models were tested for soil moisture at two depths (25 and 50 cm depth) using the meteorological data of two stations located in a Lesser Himalayan catchment. The model outputs were compared with the observed data, and intercomparison was also made. The model performance was evaluated based on MAPE, RMSE, Nash–Sutcliffe efficiency coefficient (EN–S), and R2. The study results indicated that the DNN model outperforms the other prediction models with the highest efficacy for both stations. Therefore, the DNN model can be endorsed to estimate soil moisture when primary meteorological data are available, and it can be promising for water-efficient agriculture applications and draught management. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Utilizing Deep Learning Methods for Cancer Detection through Analysis of MicroRNA Expression Profiles(Institute of Electrical and Electronics Engineers Inc., 2024) Kantamneni, S.; Hegde, P.; Patil, N.Integration of cutting-edge computational methods and genomic data analysis has become crucial in the quest for early cancer diagnosis and enhanced diagnostic accuracy. The genomic sequences of microRNAs (miRNAs), which are important cancer biomarkers, provide important information for this. In this study, we propose a novel deep learning-based framework for cancer detection with a focus on FNNs and a hybrid DNN model with an accuracy of over 90.7%. Our method aims to identify detailed genomic patterns and features that improve the sensitivity and specificity of cancer detection by painstakingly curating and preprocessing large miRNA datasets gathered from various patient cohorts. This research sets the stage for further exploration of deep learning methodologies within the context of miRNA-based cancer detection, promising advancements in personalized diagnosis and prognosis. Our method aims to identify detailed genomic patterns and features that improve the sensitivity and specificity of cancer detection by painstakingly curating and preprocessing large miRNA datasets gathered from various patient cohorts. Our approach seeks to improve sensitivity and specificity by deciphering complex genetic patterns. By utilizing these datasets, we show off the effectiveness of our model and its clinical potential, giving an accuracy of 90.7% for our Hybrid Feedforward and Dense Neural Network model as compared to current state of the art machine learning models. This research promises revolutionary advances in customized oncology, providing a route towards improved diagnostic accuracy and early intervention. It also proves that miRNA expressions values are not sequential in nature. It also lays the groundwork for the development of deep learning in miRNA-based cancer detection. © 2024 IEEE.Item Intraday Stock Prediction Based on Deep Neural Network(Springer, 2020) Naik, N.; Mohan, B.R.Predicting stock price movements is difficult due to the speculative nature of the stock market.Accurate predictions of stock prices allow traders to increase their profits. Stock prices react when receiving new information.During the trading day, it is difficult to understand the up and down movements signaled by stock prices. This paper addresses the problem of fluctuations in stock prices. We proposed the method to identify stock movement trend in data, and this method considered the combination of candlestick data and technical indicator values. The outcome of this method is given as inputs to a deep neural network (DNN) to classify a stock price’s up and down movements. National Stock Exchange, India, datasets are considered for an experiment from the years 2008 to 2018. The work is carried out using H2O deep learning on an RStudio platform. Experimental results are compared with a three-layer artificial neural network (ANN) model. The proposed five-layer DNN model outperforms state-of-the-art methods by 8–11% in predicting up and down movements of a given stock. © 2019, The National Academy of Sciences, India.Item An ensemble learning approach for detecting phishing URLs in encrypted TLS traffic(Springer, 2024) Kondaiah, C.; Pais, A.R.; Rao, R.S.Phishing is a fraudulent method used by hackers to acquire confidential data from victims, including security passwords, bank account details, debit card data, and other sensitive data. Owing to the increase in internet users, the corresponding network attacks have also grown over the last decade. Existing phishing detection methods are implemented for the application layer and are not effectively adapted to the transport layer. In this paper, we propose a novel phishing detection method that extends beyond traditional approaches by utilizing a multi-model ensemble of deep neural networks, long short term memory, and Random Forest classifiers. Our approach is distinguished by its unique feature extraction from transport layer security (TLS) 1.2 and 1.3 network traffic and the application of advanced deep learning algorithms to enhance phishing detection capabilities. To assess the effectiveness of our model, we curated datasets that include both phishing and legitimate websites, using features derived from TLS 1.2 and 1.3 traffic. The experimental results show that our proposed model achieved a classification accuracy of 99.61%, a precision of 99.80%, and a Matthews Correlation Coefficient of 99.22% on an in-house dataset. Our model excels at detecting phishing Uniform Resource Locator at the transport layer without data decryption. It is designed to block phishing attacks at the network gateway or firewall level. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.