Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Subject: search.filters.subject.artificial neural networks

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Intelligent Rush Hour Management in Metro Station
    (Institute of Electrical and Electronics Engineers Inc., 2024) Anandu, V.P.; Vinatha Urundady, U.; Bharath, Y.K.; Neethu, V.S.
    Addressing the issue of high crowd density in metro stations during rush hours is indeed a significant challenge, but innovative solutions can help enhance passenger experience and streamline the boarding process. The goal is to implement a Smart Crowd Management System that provides real-time information about congestion levels in metro stations and estimates the time required for passengers to board trains during peak hours. The implementation of a Smart Crowd Management System can significantly improve the passenger experience in metro stations, making the commute more efficient and less stressful during rush hours. This proposal outlines a holistic approach combining sensor technology, machine learning, digital communication, and mobile applications to address the challenges of crowd density in metropolitan cities like Delhi. In this work, an intelligent system is developed with MATLAB/Simulink interface having fuzzy logic and neural network classifier to indicate expected time of departure and degree of congestion in the station. The outputs are displayed in TFT screen, LEDs and ThingSpeak-IoT platform. © 2024 IEEE.
  • No Thumbnail Available
    Item
    Forecasting banking sectors in Indian stock markets using machine intelligence
    (IOS Press BV, 2019) Arjun, R.; Suprabha, K.R.
    The study analyze stock index closing from myriad set of technical and fundamental analysis variables extracted from real market data to assist forecast of market closing. For this, major service sector indices of Bombay stock exchange (BSE) and National stock exchange (NSE) with historical data were taken from banking industry. The predictive model performance of index closing using statistical procedures like automatic linear modeling, time-series based econometric forecasting, vector auto regression as with artificial neural network based models were simulated and analyzed. The results indicate that BSE had higher forecast accuracy using autoregressive models and market volatility factor had major influence. Whereas, NSE was impacted by quarterly performance that can be modeled using neural networks. The empirical results were contrasted with latest state-of-art research theories to provide agenda and future research challenges of market forecast systems. © 2019 - IOS Press and the authors. All rights reserved.