Person:
Anand, Pritam

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Pritam Anand

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Faculty

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079-68261657

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Support Vector Machines, Loss Functions, Regression, Extreme Learning Machine, Quantile Regression

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Pritam Anand is currently Assistant Professor at DAIICT. His research interest moves around the Support Vector Machine Algorithms. He has obtained his Master degree in Computer Science from South Asian University, New Delhi (An international university established by SAARC countries).� He has obtained his Ph.D. degree in Computer Science from South Asian University, New Delhi.� He was the recipient of the prestigious Visvesvaraya Ph.D. fellowship during his doctoral degree.

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Now showing 1 - 3 of 3
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    New Improved Wave Hybrid Models for Short-Term Significant Wave Height Forecasting
    (IEEE, 29-08-2023) Anand, Pritam; Jain, Shantanu; Savaliya, Harsh; DA-IICT, Gandhinagar; Jain, Shantanu (202011026); Savaliya, Harsh (202111052)
    n this paper, we have developed a series of wave hybrid models for significant wave height prediction. Our developed hybrid models uses a triplet of signal decomposition method, regression model and meta-heuristic algorithm. We have used the ?-Support Vector Regression (?-SVR) , Least Squares Support Vector Regression (LS-SVR) , Long Short-Term Memory (LSTM) and Large-margin Distribution Machine based Regression (LDMR) model for the regression task. For signal decomposition methods, we have considered theWavelet Decomposition (WD), Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) method. Apart from this, we have also used the Particle Swarm Optimization (PSO) method to tune the parameters of the used regression model in our wave hybrid models. Till now, the VMD method and LDMR model have not been used in any wave hybrid model. We have evaluated the performance of our developed wave hybrid models on time-series significant wave heights, collected from four different buoys using the different evaluation criteria. After the detailed statistical analysis of the obtained numerical results, we conclude that the VMD-PSO-LDMR based wave hybrid model obtain best performance on six datasets out of seven considered datasets. Also, the VMD based wave hybrid models can obtain better performance than other decomposition based hybrid models. Further, we also conclude from our numerical results that the LSTM model outperforms the SVR, LS-SVR and LDMR based hybrid models if we do not decompose the significant wave height signals apriori. But, when we decompose the SWH time-series signals using a particular decomposition method, then SVR, LS-SVR and LDMR based hybrid models tend to improve their prediction ability significantly.
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    Time efficient variants of Twin Extreme Learning Machine
    (Elsevier, 01-02-2023) Anand, Pritam; Bharti, Amisha; Rastogi, Reshma; DA-IICT, Gandhinagar
    Twin Extreme Learning Machine models can obtain better generalization ability than the standard Extreme Learning Machine model. But, they require to solve a pair of quadratic programming problems for this. It makes them more complex and computationally expensive than the standard Extreme Learning Machine model. In this paper, we propose two novel time-efficient formulations of the Twin Extreme Learning Machine, which only require the solution of systems of linear equations for obtaining the final classifier. In this sense, they can combine the benefits of the Twin Support Vector Machine and standard Extreme Learning Machine in the true sense. We term our first formulation as �Least Squared Twin Extreme Learning Machine�. It minimizes the L2-norm of error variables in its optimization problem. Our second formulation �Weighted Linear loss Twin Extreme Learning Machine� uses the weighted linear loss function for calculating the empirical error, which makes it insensitive towards outliers. Numerical results obtained with multiple benchmark datasets show that proposed formulations are time efficient with better generalization ability. Further, we have used the proposed formulations in the detection of phishing websites and shown that they are much more effective in the detection of phishing websites than other Extreme Learning Machine models.
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    Huber SVR-Based Hybrid Models for Significant Wave Height Forecasting Using Buoy Sensors
    (IEEE, 17-11-2023) Anand, Pritam; Jain, Shantanu; Mishra, Rahul; DA-IICT, Gandhinagar; Jain, Shantanu (202011026)
    Ocean buoy sensors stand out as the most reliable means of recording Significant Wave Height (SWH). However, accurate forecasting of SWH is still challenging in energy production. To circumvent such a challenge, this work introduces a Huber Support Vector Regression (SVR) based wave hybrid model for short-term Significant Wave Height (SWH) forecasting using data collected from the buoy sensors. The model employs three distinct signal techniques, including wavelet decomposition, empirical mode decomposition, and variational mode decomposition, along with particle swarm optimization to fine-tune the parameters of the Huber SVR model. Our extensive experimental analysis encompassed six cases from buoys located at four different geographical regions. We compared the Huber SVR-based wave hybrid models with twelve alternative models incorporating�?�-SVR, LS-SVR, or LSTM models with different decomposition methods. The results of our numerical experiments demonstrate that the Huber SVR-based wave hybrid models yield superior SWH predictions compared to the other existing models.