@krce.ac.in
ASSISTANT PROFESSOR/ECE
K.RAMAKRISHNAN COLLEGE OF ENGINEERING
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
G. V. Krishna Kumar, B. Natarajan, Venkatraman K, S. Gayathri Devi, Prabu Selvam, and N. R. Nagarajan
IEEE
Analyzing finances has become increasingly challenging in today’s investment landscape, where making valuable and informed investment decisions is crucial. The fluctuation of share prices plays a pivotal role in determining investors’ profits or losses. Current forecasting techniques encompass both linear and non-linear algorithms. However, these methods primarily emphasize forecasting changes in the stock index or forecasting prices for individual companies based on their daily concluding rates. The proposed methodology introduces a model-agnostic method. Rather than conforming data to a specific model, this approach aims to identify unseen trends inherent in the data through deep learning models. In this study, three distinct deep learning models—HMM, RNN, and LSTM—are employed to predict prices using the dataset from ICICI Bank. Their performances are compared, revealing that the LSTM model adeptly discerns evolving trends. The LSTM model displays the lowest error percentage at 2.36%, outperforming other models such as HMM (7.32%) and RNN (3.94%).
Diponkar Kundu, Sakhawat Hossain, N. R. Nagarajan, K. V. Karthikeyan, Nalini Neelamegam, Srinivasan Mallan, A. H. M. Iftekharul Ferdous, Maruf Billah, and Ahmed Nabih Zaki Rashed
Springer Science and Business Media LLC
M. Anuradha, G. Mani, T. Shanthi, N. R. Nagarajan, P. Suresh, and C. Bharatiraja
Computers, Materials and Continua (Tech Science Press)
N R Nagarajan and M Maheswari
IOP Publishing
Abstract As of now, the 5G attempts to address the current OFDM-based LTE issues, for example, Bit Error Rate (BER), Spectral Loss, Signal to Noise Ratio (SNR), Symbol Error Rate (SER) and high peak-to-average power ratio (PAPR). Universal-filtered multi-carrier (UFMC) method can be measured as an up-and-comer waveform for 5G correspondences since it gives advantages by minimizing the Inter Symbol Interference (ISI) and Inter Carrier Interference (ICI) reasonable for low-idleness situations. There are various filters for designing an UFMC which suits for a 5G application. This survey is carried out because of the Industrial 4.0 revolutionary is going on at a higher phase for which Machine to Machine (M2M) communication is widely required for suitability and reliability leading to the 5G requirement for high speed data transfer with appropriate filter design.
K. Kiruthiga and N.R. Nagarajan
IEEE
UFMC is a suitable waveform for 5G requirements. It has the ability to overcome the drawbacks of the UFMC, FBMC, GFDM and OFDM techniques .IDFT and filtering part generates the UFMC waveform. This paper discusses about the IDFT part and filtering part. In IDFT part for reducing the hardware complexity and improves the performance of the system. IDFT part using IFFT radix-2 Decimation in Time technique (DIT) is used for reducing the complexity. The filtering part is dedicated mainly to achieve the flexibility and scalability requirements. It transmits the large input data by using small number of multipliers and registers.