Browsing by Author "Mondal, R."

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    A study on solubility of bismuth cations in nickel cobalt ferrite nanoparticles and their influence on dielectric and magnetic properties
    (Elsevier Ltd, 2023) Patil, S.; Meti, S.; Kanavi, P.S.; Bhajantri, R.F.; Anandalli, M.; Mondal, R.; Karmakar, S.; Muhiuddin, M.; Rahman, M.R.; Kumar, B.C.; Hegde, B.G.
    In this work, a low temperature (∼600 °C) solution combustion technique is employed for the synthesis of Ni0.5Co0.5BixFe2-xO4 (NCBFO, where x = 0.0, 0.05, 0.1, 0.15, & 0.2) nanoparticles with crystallite size variation of 17–22 nm. The X-ray diffraction (XRD) technique is used to confirm the formation of cubic spinel phase of Bi3+ doped (for x ≤ 0.05 samples) nickel–cobalt ferrite (NCFO) nanoparticles. The increase in bismuth substitution (x > 0.05) results in the formation of the Bi2O3 along with the NCFO structure, which results in the reduction of binding energy and is confirmed by the XRD and X-ray photoelectron spectroscopy (XPS) techniques. From the Raman spectra, the change in the intensities of the peaks is observed due to the variation of Bi3+ in NCFO matrix. Due to increasing cation concentration and electronegativity, the FTIR absorption band shifts toward the lower wave numbers. Dielectric measurements were carried out to examine the charge transport behavior and electric conduction mechanism. The FESEM images shows the non-magnetic bismuth atoms are diffused into the NCFO nanoparticles. From the vibrating sample magnetometer (VSM) analysis, it is observed that saturation magnetization, remanent magnetization, coercivity and squareness ratio are found to be maximum for x = 0.15 NCBFO sample. The high coercivity (Hc = 916.8 Oe) for the x = 0.15 sample indicates the hard ferromagnetic behaviour of the samples. © 2023 Elsevier B.V.
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    DeCS: A Deep Neural Network Framework for Cold Start Problem in Recommender Systems
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mondal, R.; Bhowmik, B.R.
    With the exponential growth of e-commerce platforms, recommendation systems are widely used in predicting user interests, improving user experience, and increasing the number of sales. However, recommendation performance degrades for users who have very little interaction or new users who have never opted for the service. Consequently, the recommender systems cannot suggest items and services to these users due to the cold start issue. Naturally, a compelling demand for an efficient recommender system is essentially needed to guide users toward items of their interests. This paper proposes a deep neural network (DNN) framework that addresses the cold start problem in recommendation systems. The proposed framework named 'DeCS' works primarily in stages that involve creating embeddings and vectors followed by training and prediction of three fundamental metrics-mean square error (MSE), mean absolute error (MAE), and root MSE (RMSE) by the framework. Several experiments evaluate the DeCS framework for different recommender metrics at various datasets. Predictions show that the proposed DeCS model achieves the MSE, RMSE, and MAE metrics in the range of 0.4338-1.2911, 0.6883-1.1362, and 0.4691-0.8745, respectively. Further, the result shows that the proposed approach improves these metrics by 15.81% compared to many state-of-the-art methods. © 2022 IEEE.