Conference Papers
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Item Application of Neural Networks in coastal engineering - An overview(2008) Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output system. In addition, ANNs have a very high degree of freedom and are very simple to train the system for any number of input values, which makes the network attractive and reliable. ANNs are ideally suited to find many solutions like pattern reorganization, data classification, forecasting future events and time series analysis. This paper gives an overview of application of ANN in the field of coastal engineering.Item A Healthcare management using clinical decision support system(Institute of Electrical and Electronics Engineers Inc., 2018) Thomas, L.; Manoj Kumar, M.V.; Annappa, B.From the literature it is studied that, most of the medical error is due to faulty healthcare system. Due to this, there is treatment delay, that leads to complications in later stages of disease progression. Medical error caused due to the failure in healthcare system can be reduced by employing an appropriate clinical decision support system (CDSS). CDSS helps in identifying the severity of disease by predicting its progression. The treatment management of gallstone disease is considered as a case study in this paper.This paper presents a CDSS with the help of machine learning for improving the treatment management. CDSS with the help of a statistical comparator, identifies an efficient tool for finding the associated risk factors. These risk factors are then used to predict the disease progression and identify the cases that may need Endoscopic Retrograde Cholangio-Pancreatography (ERCP) as the treatment progresses. The model that learns and predicts accurately is selected, using the concept of Area Under Curve (AUC). For this purpose, a Modified Cascade Neural Network (ModCNN) built upon the architecture of Cascade-Correlation Neural Network (CCNN) is proposed and tested using an ADAptive LInear NEuron (ADALINE) circuit. It's performance is evaluated and compared with Artificial Neural Network (ANN) and CCNN.Using this prediction information, disease progression is analysed and proper treatment is initiated, thereby reducing the medical error. ModCNN showed better accuracy (96.42%) for predicting the disease progression when compared with CCNN (93.24%) and ANN (89.65%). Thus, CDSS presented here, assisted in reducing the medical error and providing better healthcare management. © 2018 IEEE.Item Machine Learning Based Framework to Predict Performance Evaluation of On-Chip Networks(Institute of Electrical and Electronics Engineers Inc., 2018) Kumar, A.; Talawar, B.Chip Multiprocessors(CMPs) and Multiprocessor System-on-Chips(MPSoCs) are meeting the ever increasing demand for high performance in processing large scale data and applications. There is a corresponding increase in the volume and frequency of traffic in the Network-on-Chip(NoC) architectures like CMPs and SoCs. NoC performance parameters like network latency, flit latency and hop count are critical measures which directly influence the overall performance of the architecture and execution time of the application. Unfortunately, cycle-accurate software simulators become slow for interactive use with an increase in architectural size of NoC. In order to provide the chip designer with an efficient framework for accurate measurements of NoC performance parameters, we propose a Machine Learning(ML) framework. Which is designed using different ML regression algorithms like Support Vector Regression(SVR) with different kernels and Artificial Neural Networks(ANN) with different activation functions. The proposed learning framework can be used to analyze the performance parameters of Mesh and Torus based NoC architectures. Results obtained are compared against the widely used cycle-accurate Booksim simulator. Experiments were conducted by variables like topology size from 2\times 2 to 30\times 30 with different virtual channels, traffic patterns and injection rates. The framework showed an approximate prediction error of 5% to 8% and overall minimum speedup of 1500\times to 2000\times. © 2018 IEEE.Item Gender Identification from Children's Speech(Institute of Electrical and Electronics Engineers Inc., 2018) Ramteke, P.B.; Dixit, A.A.; Supanekar, S.; Dharwadkar, N.V.; Koolagudi, S.G.Children's speech can be characterized by higher pitch and format frequencies compared to the adult speech. Gender identification task from children's speech is difficult as there is no significant difference in the acoustic properties of male and female child. Here, an attempt has been made to explore the features efficient in discriminating the gender from children's speech. Different combinations of spectral features such as Mel-frequency cepstral coefficients (MFCCs), ΔMFCCs and ΔΔMFCCs, Formants, Linear predictive cepstral coefficients (LPCCs); Shimmer and Jitter; Prosodic features like pitch and its statistical variations along with Δpitch related features are explored. Features are evaluated using non linear classifiers namely Artificial Neural Network (ANNs), Deep Neural Network (DNNs) and Random Forest (RF). From the results it is observed that the RF achieves an highest accuracy of 84.79% amongst the other classifiers. © 2018 IEEE.Item Prediction of Compressive Strength and Workability Characteristics of Self-compacting Concrete Containing Fly Ash Using Artificial Neural Network(Springer Science and Business Media Deutschland GmbH, 2023) Netam, N.; Palanisamy, T.This study aims to propose an artificial neural network (ANN) model for predicting the properties of self-compacting concrete (SCC). SCC has enhanced properties such as very high workability and it can go through very tight spaces between reinforcements without any application of vibration. To get the desired strength and workability, it is necessary to understand the parameters determining the nature and properties of SCC and the relationships involved among those parameters. In this study binder content, water to binder ratio, fly ash percentage, coarse aggregate, fine aggregate, and superplasticizer content are chosen as input parameters, and output results from the model are slump flow value, L-box ratio, V-funnel time, and compressive strength. An ANN model is constructed and its architecture is selected by evaluating the performance of a network with a different number of neurons for the optimum results. Then this model is trained, tested, and validated through a database of experimental test results gathered from various literature. The accuracy of this model is evaluated by evaluation matrices such as R and MSE. To check the efficiency, the current model comparison was made with an existing data envelopment analysis model (DEA). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item IOT Devices Using Supervised Machine Learning Models for Anomaly Based Intrusion Detection(Institute of Electrical and Electronics Engineers Inc., 2023) Divakarla, U.; Chandrasekaran, K.Identifying dangers and irregularities in any infrastructure is a growing problem in the Internet of Things (IoT) industry. IoT infrastructure is utilised more frequently across a wide spectrum of organisations, which increases the risks and attack methods. Attacks and anomalies that could lead an IoT system to malfunction include denial of service attacks, data type probing, malicious control, malicious operation, scans, surveillance, and improper configuration. This article studies the ability of several machine learning models to predict attacks and abnormalities on IoT devices. The f1 score, area under the receiver operating characteristic curve, accuracy, precision, recall, and precision are among the metrics used to assess performance. ANNs, decision trees, and random forests all shown performance with a 99.4% accuracy rate in the system's tests. © 2023 IEEE.Item Portable Executable Header Based Ransomware Detection using Power Iteration and Artificial Neural Network(Institute of Electrical and Electronics Engineers Inc., 2023) Singh, M.P.; Karkhur, Y.In the present world, the dependency on different devices connected to the internet is increasing at a rapid rate day by day. Devices like smart watches, mobile phones, personal computers, etc., are part of our day-to-day life. We rely on them for most of our daily tasks. Since these devices are frequently used, they contain users' personal information and other essential data. More and more people use the internet due to emerging technology and intelligent devices, increasing the risk of misusing confidential information and other user-specific crucial data. With the development of cryptocurrency, Ransomware is one of the emerging attacks that prevent authentic users from accessing systems, resources, or data and enables adversaries to control access to such information. This paper presents an Artificial Neural Network (ANN) based model that uses the 'Power Iteration' method and Portable Executable (PE) Headers to detect various types of Ransomware. We analyze the performance of the proposed model by experimenting with a dataset created using the PE files collected from multiple sources and demonstrate its better detection capability. . © 2023 IEEE.Item Helical Gearbox Fault Diagnosis Using Adaptive Artificial Neural Network and Adaptive Coyote Optimization(Institute of Electrical and Electronics Engineers Inc., 2023) Bokil, P.P.; Joladarashi, S.; Kadoli, R.; Chavan, P.; Bhangale, R.The Helical gearboxes (HG) are considered a significant part of providing power transmission of manufacturing administrations and are exposed to numerous failures because of their extended and intensive situation of acceleration. Therefore, to enhance the security and dependency of the HGs, monitoring the health condition and detecting different types of failures is essential. The estimation of HG failure detection majorly includes electric signals, the noise produced by airborne, lubricant examination, thermal images, and so on. Therefore, this research proposes an Adaptive Coyote Optimization-Adaptive Artificial Neural Network (A2CO-ANN) Gearbox fault diagnosis and missing data imputation for preventing the loss of significant data values. Moreover, the comparative analysis of the A2CO-ANN technique is examined using the available datasets DTS1 and DTS2 with the existing classifiers like Random Forest (RF), K-Nearest Neighbors (KNN), Decision tree (DT), Fuzzy, as well as Adaptive ANN is examined in terms of the performance metrics. Thus, the accuracy of the A2CO-ANN method on training percentage 80 for DTS1 and DTS2 is 91.54% and 90.05%, whereas the sensitivity rate is estimated as 98.26% and 98.35%, as well as the specificity rate, is valued as 84.08% and 81.09% respectively, which is increased than the traditional methods. © 2023 IEEE.Item Outlier Detection in Streaming Data Using Deep Learning Models(Institute of Electrical and Electronics Engineers Inc., 2024) Dudipala, S.; Gangavarapu, S.; Girish, K.K.; Bhowmik, B.In the realm of the Internet of Things (IoT), devices continuously generate a vast and relentless stream of data, providing a real-time representation of digital landscape. The continuous and high-velocity nature of this streaming data poses significant challenges for real-time analysis. Accurate outlier detection within this data is essential, as such anomalies may indicate critical issues, attacks, or errors. Nevertheless, the dynamic and rapidly evolving characteristics of streaming data render traditional outlier detection methods inadequate. This paper investigates the application of Artificial Neural Networks (ANNs), specifically a Multi-Layer Perceptron (MLP), for outlier detection in streaming IoT data. The selection of the MLP from a range of Deep Neural Networks (DNNs) is based on its optimal balance between computational efficiency and model complexity. The model's efficacy is confirmed through rigorous experimentation, demonstrating strong performance across diverse scenarios and data classes. The MLP achieved an accuracy of 99.4%, underscoring its ability to detect even minor deviations from expected patterns. This high level of accuracy establishes the MLP as a robust tool for outlier detection in dynamic IoT environments. © 2024 IEEE.Item A Survey of Hyperparameter Selection Methods for Weather Forecasting Using State-of-the-Art Machine Learning Algorithms(Springer Science and Business Media Deutschland GmbH, 2025) Sen, A.; Sen, U.; Paul, M.; Sutradhar, A.; Vankala, T.N.; Mallick, C.; Mallik, A.; Roy, A.; Sai, S.; Roy, S.Weather forecasting is an important aspect across various sectors, but the intricate dynamics of weather systems pose a challenge for conventional statistical models to forecast accurately. Besides auto-regressive time forecasting models like ARIMA, deep learning architectures like ANNs, LSTMs, and GRU networks have been shown to enhance the accuracy of forecasts by considering temporal dependencies. This paper studies various machine learning models like XGBoost, SVR, KNN Regressor, Random Forest Regressor and the application of metaheuristic algorithms, like Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), on some deep learning model architectures like ANNs, LSTMs and GRUs, to automate the process of finding the best hyperparameters for the models. Furthermore, this paper explores the Quantum LSTM (QLSTM) network and novel QLSTM Ensemble models. We conduct a comparative study of these model structures, evaluating their effectiveness in weather prediction using measures such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The findings underscore the capabilities of metaheuristic algorithms and innovative quantum methods in enhancing the precision of weather forecasts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.