Informatics Publishing Limited

R. Neelamegam ¹, P. Murugan ²

Affiliations
1 Indian Council of Social Science Research, Department of Management Studies, V.H.N.S.N. College (Autonomous), Virudhunagar – 626 00, IN
2 Department of Management Studies, V.H.N.S.N. College (Autonomous), Virudhunagar – 626 001, IN

Abstract

Now a days, there is a stiff competition in surface transport among government buses, private buses, vans, taxies, etc. It is very tough to Indian railways to be a robust competitor in the surface transport. Passengers need quality services from the rail transport. Internet plays a vital role in providing a speedy and an easy access to the services of ticket booking, ticket cancellation and the time of the availability of services. But it is beset with some problems while accessing the services through online. The present paper, focuses on the passengers' kind of problem in online ticket booking in Indian railways with reference to Virudhunagar junction, Madurai division of Southern Railway zone.

Keywords

Indian Railways, Online Booking, Discriminant Analysis.

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P. Murugan ¹, R. Sivakumar ¹, R. Pandiyan ², M. Annadurai ²

Affiliations
1 Department of Civil Engineering, SRM University, Kattankulathur - 603203, Tamil Nadu, IN
2 ISRO Satellite Centre, Bangalore - 560017, Karnataka, IN

Abstract

Background/Objectives: Water body management and food-grains supply will be challenging tasks. Selecting spectral bands for accurate area estimation of water body and selected vegetation (crop) is the objective of this study. Methods/ Statistical Analysis: Indices such as Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI) are used for extracting water and vegetation respectively from other features. Generally bands providing high index values for the target are utilized for extraction. In this study the bands giving high index value for selected target and low index for surrounding are selected. Bands selected in this method provide better extraction and accurate area estimation. Findings: The NDWI and NDVI are based on multispectral data and have less number of combinations for band sets selection. Though the proposed method is derived from NDWI, it utilizes the Hyperspectral data that has narrow and hundreds of bands. Another advantage of this method is it utilizes the index value of target and surrounding features. It selects suitable band set by iteration method and provides accurate extraction of the targets and area estimation. The performance of the bands selected in this method was tested with coastal and inland water bodies. Area estimated with this method matches with NDVI and MNDWI values. Applications/Improvements: This method selects suitable bands to estimate area of water body and vegetation. This estimation will be useful for water body management and food production forecasting.

Keywords

Algorithm, Hyperspectral, Index, Vegetation, Water Body.

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Vidyabharathi Dakshinamurthi ¹, G. P. Suja ², P. Murugan ³, Sk. Riyaz Hussain ⁴

Affiliations
1 Department of Computer Science and Engineering, Sona College of Technology, IN
2 Research Center of Computer Science, Muslim Arts College, IN
3 Department of BBA, School of Management Studies, Vels Institute of Science Technology and Advanced Studies, IN
4 Department of Electronics and Communication Engineering, Rajiv Gandhi University of Knowledge Technologies, IN

Abstract

In recent years, image dehazing has gained significant attention in the field of computer vision and image processing due to its crucial role in enhancing visibility and improving visual perception. The presence of haze in images captured under adverse weather conditions or polluted environments poses a challenge to various computer vision applications, such as autonomous driving, surveillance, and satellite imagery. Traditional image dehazing methods often struggle to achieve optimal results, particularly in complex scenes with varying degrees of haze and intricate details. The need for a robust and efficient dehazing approach has become imperative for addressing real-world challenges in computer vision applications. Despite the advancements in traditional methods, a research gap exists in developing a comprehensive solution that can handle diverse atmospheric conditions and complex scenes effectively. The integration of deep learning techniques presents an opportunity to bridge this gap, leveraging the power of neural networks to learn and adapt to intricate patterns in hazy images. This research proposes a novel deep learning-based approach for image dehazing and visibility enhancement. A Convolutional Neural Network (CNN) architecture is designed to learn complex relationships between hazy and clear images, allowing the model to effectively remove haze and enhance visibility. The network is trained on a diverse dataset encompassing various atmospheric conditions and scene complexities to ensure generalization. Experimental results demonstrate the superior performance of the proposed deep learning approach compared to traditional methods. The model exhibits robustness in handling challenging scenarios, achieving significant improvements in image clarity, contrast, and overall visibility. The findings highlight the potential of deep learning in addressing the limitations of existing dehazing techniques.

Keywords

Deep Learning, Image Dehazing, Visibility Enhancement, Convolutional Neural Network, Computer Vision

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