GP-ELM-RNN: Garson-pruned extreme learning machine based replicator neural network for anomaly detection

Hashmi, Adeel; Ahmad, Tanvir

doi:10.1016/j.jksuci.2019.09.007

Cited by 13 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The minimum norm least square of (2) is

where H + is the Moore–Penrose generalized inverse of matrix H . H + is evaluated by singular value decomposition (SVD), QR approach, orthogonal projection model [ 22 ], and orthogonalization method.…”

Section: The Proposed Modelmentioning

confidence: 99%

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Krishnaraj

Sivakumar

Kuppusamy³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Due to the exponential growth of high-quality fake photos on social media and the Internet, it is critical to develop robust forgery detection tools. Traditional picture- and video-editing techniques include copying areas of the image, referred to as the copy-move approach. The standard image processing methods physically search for patterns relevant to the duplicated material, restricting the usage in enormous data categorization. On the contrary, while deep learning (DL) models have exhibited improved performance, they have significant generalization concerns because of their high reliance on training datasets and the requirement for good hyperparameter selection. With this in mind, this article provides an automated deep learning-based fusion model for detecting and localizing copy-move forgeries (DLFM-CMDFC). The proposed DLFM-CMDFC technique combines models of generative adversarial networks (GANs) and densely connected networks (DenseNets). The two outputs are combined in the DLFM-CMDFC technique to create a layer for encoding the input vectors with the initial layer of an extreme learning machine (ELM) classifier. Additionally, the ELM model’s weight and bias values are optimally adjusted using the artificial fish swarm algorithm (AFSA). The networks’ outputs are supplied into the merger unit as input. Finally, a faked image is used to identify the difference between the input and target areas. Two benchmark datasets are used to validate the proposed model’s performance. The experimental results established the proposed model’s superiority over recently developed approaches.

show abstract

“…The minimum norm least square of (2) is

Section: The Proposed Modelmentioning

confidence: 99%

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Krishnaraj

Sivakumar

Kuppusamy³

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The group of stable nodes is utilized to create the multipath among the pair of source to destination. 26 The major objective of the stable node is to reduce the link faults and minimize the packet-drop rate. The Garson-pruned-based RNN with a modified seagull optimization approach is utilized to choose the stable node for efficient routing.…”

Section: Rmsg For Stable Node Estimationmentioning

confidence: 99%

“…In routing, the parallel path is established from source to destination using additional stable nodes and forwards the packets through the parallel path rather than the single path. The group of stable nodes is utilized to create the multipath among the pair of source to destination 26 . The major objective of the stable node is to reduce the link faults and minimize the packet‐drop rate.…”

Section: Proposed Approachmentioning

confidence: 99%

An efficient stable node selection based on Garson's pruned recurrent neural network and MSO model for multipath routing in MANET

Hemalatha

Umamaheswari²,

Jothi

2022

Concurrency and Computation

View full text Add to dashboard Cite

In mobile ad hoc network, the routing task is considered to be more complicated issue.In order to find efficient route in MANET, the positions of each stable node in the network is identified initially. The main contribution of this paper is to identify the locations of neighboring nodes in MANET for the establishment of multi path routing in diverse mobility patterns. It also handles packet scheduling to balance the load as well as data forwarding with less communication time. The proposed work explains four phases such as stable node prediction, stability determination, route exploration, and packet dissemination. At first, the stable nodes are identified using recurrent neural network along with modified seagull optimization approach. By means of Garson's pruning based recurrent neural network accompanied with modified sea gull optimization (RMSG) algorithm, the stable neighbors are chosen. The network routing is formed by interconnecting the stable nodes from the source to the destination. When a routing link failure happens, the route recovery process will be initiated. Thus, the data packets are broadcasted in multi-paths without any intervention. The measures namely packet delivery ratio, throughput, end-to-end delay, routing overhead, optimal path selection, and energy consumption are utilized to evaluate the performance of proposed approach. The experimental analysis proved that the proposed approach well performed than other compared existing approaches.

show abstract

“…In routing, the parallel path is established from source to destination using additional stable nodes and forwards the packets through the parallel path rather than the single path. The group of stable nodes is utilized to create the multipath among the pair of source to destination (Hashmi and Ahmad, 2019). The major objective of the stable node is to reduce the link faults and minimizes the packet-drop rate.…”

Section: Rmsg For Stable Node Estimationmentioning

confidence: 99%

Load Balanced Multipath Routing using Seagull Optimizer Based Garson’s Pruned Recurrent Neural Network for Multipath Routing in MANET

Hemalatha

Umamaheswari

Jothi

2021

Preprint

View full text Add to dashboard Cite

In recent years, routing is considered one of the most challenging issues in MANET. The location of the stable node and the routing is based on the predicted locations that assists in establishing a routing path in MANET. The major intention of this paper is to detect the stable neighbor node in the MANET and also to establish stable multi-path routing for the various mobility patterns. Also, this paper deals with the data packet scheduling over multi-paths for balancing the load and forward the entire packets in the least broadcast time. The proposed approach elucidates four significant phases: stable node prediction, determination of stability measure, route exploration and packet dissemination. Initially, the stable node is predicted using the RMSG approach. Here, the stable neighbors are selected via Garson’s pruning based Recurrent neural network with a Modified seagull optimization algorithm (RMSG). In the route exploration phase, the path is created among the source and destination by the stable node. If any of the links fails, the route recovery process is established. Finally, the structure is formed for data packet distribution across the multipath. The proposed approach is evaluated by few performance measures such as throughput, packet delivery ratio, end-to-end delay, routing overhead energy consumption, and optimal path. This result describes that the proposed approach outperforms other state-of-art approaches.

show abstract

GP-ELM-RNN: Garson-pruned extreme learning machine based replicator neural network for anomaly detection

Cited by 13 publications

References 10 publications

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

Design of Automated Deep Learning-Based Fusion Model for Copy-Move Image Forgery Detection

An efficient stable node selection based on Garson's pruned recurrent neural network and MSO model for multipath routing in MANET

Load Balanced Multipath Routing using Seagull Optimizer Based Garson’s Pruned Recurrent Neural Network for Multipath Routing in MANET

Contact Info

Product

Resources

About