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Journal : International Journal of Reconfigurable and Embedded Systems (IJRES)

Accurate plant species analysis for plant classification using convolutional neural network architecture Patil, Savitha; Sasikala, Mungamuri
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i1.pp160-170

Abstract

Recently, plant identification has become an active trend due to encouraging results achieved in plant species detection and plant classification fields among numerous available plants using deep learning methods. Therefore, plant classification analysis is performed in this work to address the problem of accurate plant species detection in the presence of multiple leaves together, flowers, and noise. Thus, a convolutional neural network based deep feature learning and classification (CNN-DFLC) model is designed to analyze patterns of plant leaves and perform classification using generated fine-grained feature weights. The proposed CNN-DFLC model precisely estimates which the given image belongs to which plant species. Several layers and blocks are utilized to design the proposed CNN-DFLC model. Fine-grained feature weights are obtained using convolutional and pooling layers. The obtained feature maps in training are utilized to predict labels and model performance is tested on the Vietnam plant image (VPN-200) dataset. This dataset consists of a total number of 20,000 images and testing results are achieved in terms of classification accuracy, precision, recall, and other performance metrics. The mean classification accuracy obtained using the proposed CNN-DFLC model is 96.42% considering all 200 classes from the VPN-200 dataset.
An efficient novel dual deep network architecture for video forgery detection Chandrakala, Chandrakala; Sasikala, Mungamuri
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 2: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i2.pp458-471

Abstract

The technique of video copy-move forgery (CMF) is commonly employed in various industries; digital videography is regularly used as the foundation for vital graphic evidence that may be modified using the aforementioned method. Recently in the past few decades, forgery in digital images is detected via machine intellect. The second issue includes continuous allocation of parallel frames having relevant backgrounds erroneously results in false implications, detected as CMF regions third include as the CMF is divided into inter-frame or intra-frame forgeries to detect video copy is not possible by most of the existing methods. Thus, this research presents the dual deep network (DDN) for efficient and effective video copy-move forgery detection (VCMFD); DDN comprises two networks; the first detection network (DetNet1) extracts the general deep features and second detection network (DetNet2) extracts the custom deep features; both the network are interconnected as the output of DetNet1 is given to DetNet2. Furthermore, a novel algorithm is introduced for forged frame detection and optimization of the falsely detected frame. DDN is evaluated considering the two benchmark datasets REWIND and video tampering dataset (VTD) considering different metrics; furthermore, evaluation is carried through comparing the recent existing model. DDN outperforms the existing model in terms of various metrics.