Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to individual source. This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing. A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly. Multispectral data is obtained using a multispectral radiometer, while digital camera is used for image dataset. It has been observed that each image contained 229 texture features, while 30 optimized texture features data for each image has been obtained by joining together three features selection techniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information. This 30-optimized-texture-feature dataset is merged with five-spectral-feature dataset to build the fused dataset. A comparison is performed among texture, multispectral, and fused dataset using machine vision classifiers. It has been observed that fused dataset outperformed individually both datasets. The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was 96.67%, 97.60%, and 99.60%, respectively.

The purpose of this study is to enhance the performance of Multistream Fast Transmission Control Protocol (TCP) keeping in view the recent web-based applications that are being deployed on long-range, high-speed, and high-bandwidth networks. To achieve the objective of the research study, a congestion control after fast-recovery module for congestion control scheme of Multistream Fast TCP is proposed. The module optimized the performance of the protocol by reducing the time that is required to consume the available bandwidth after a fast-recovery phase. The module is designed after studying additive-increase, multiplicative-decrease and rate-based congestion window management schemes of related transport protocols. The module adjusts the congestion window on receipt of each individual acknowledgment instead of each round trip time after the fast-recovery phase until it consumes vacant bandwidth of the network link. The module is implemented by using Network Simulator 2. Convergence time, throughput, fairness index, and goodput are the parameters used to assess the performance of proposed module. The results indicate that Enhanced Multistream Fast TCP with congestion control after fast recovery recovers its congestion window in a shorter time period as compared to multistream Fast TCP, Fast TCP, TCP New Reno, and Stream Control Transmission Protocol. Consequently, Enhanced Multistream Fast TCP consumes the available network bandwidth in lesser time and increases the throughput and goodput. The proposed module enhanced the performance of the transport layer protocol. Our findings demonstrate the performance impact in the form of a decrease in the convergence time to consume the available network bandwidth and the increase in the throughput and the goodput.