LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

Harshavardhan, A.; Boyapati, Prasanthi; Neelakandan, S.; Akeji, Alhassan Alolo Abdul-Rasheed; Pundir, Aditya Kumar Singh; Walia, Ranjan

doi:10.1155/2022/2170839

Cited by 22 publications

(16 citation statements)

References 33 publications

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“…The results are tested using the KDDCup99 dataset [ 23 ] comprising different classes and 41 features. The results show that the DNN model has gained lower outcomes with the accu y of 91.64%, whereas the LSTM-RNN and GRU-RNN techniques have resulted in moderately reasonable accu y of 93.39% and 92.63%, respectively [ 35 – 38 ]. Moreover, the DBN and CNID models have accomplished considerable accu y values of 95.22% and 98.54%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

Lakshmanna

Kavitha

Geetha

et al. 2022

Computational Intelligence and Neuroscience

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The Industrial Internet of Things (IIoT) has received significant attention from several leading industries like agriculture, mining, transport, energy, and healthcare. IIoT acts as a vital part of Industry 4.0 that mainly employs machine learning (ML) to investigate the interconnection and massive quantity of the IIoT data. As the data are generally saved at the cloud server, security and privacy of the collected data from numerous distributed and heterogeneous devices remain a challenging issue. This article develops a novel multi-agent system (MAS) with deep learning-based privacy preserving data transmission (BDL-PPDT) scheme for clustered IIoT environment. The goal of the BDL-PPDT technique is to accomplish secure data transmission in clustered IIoT environment. The BDL-PPDT technique involves a two-stage process. Initially, an enhanced moth swarm algorithm-based clustering (EMSA-C) technique is derived to choose a proper set of clusters in the IIoT system and construct clusters. Besides, multi-agent system is used to enable secure inter-cluster communication. Moreover, multi-head attention with bidirectional long short-term memory (MHA-BLSTM) model is applied for intrusion detection process. Furthermore, the hyperparameter tuning process of the MHA-BLSTM model can be carried out by the stochastic gradient descent with momentum (SGDM) model to improve the detection rate. For examining the promising performance of the BDL-PPDT technique, an extensive comparison study takes place and the results are assessed under varying measures. A significant amount of capital is required. It goes without saying that one of the most obvious industrial IoT concerns is the high cost of adoption. Secure data storage and management connectivity failures are common among IoT devices due to the massive amount of data they create. The simulation results demonstrate the enhanced outcomes of the BDL-PPDT technique over the recent methods. Despite the fact that the offered BDL-PPDT technique has an accuracy of just 98.15 percent, it produces the best feasible outcome. Because of the data analysis conducted as detailed above, it was determined that the BDL-PPDT technique outperformed the other current techniques on a range of different criteria and was thus recommended.

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Section: Resultsmentioning

confidence: 99%

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

Lakshmanna

Kavitha

Geetha

et al. 2022

Computational Intelligence and Neuroscience

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“…Figure 4 depicts the global sensitivity, specificity, and precision analysis of the FSHDL-HDDC method after 10 iterations. e results revealed that the FSHDL-HDDC algorithm had reached improved outcomes under every iteration [28][29][30][31][32]. For instance, with iteration-1, the FSHDL-HDDC method has offered sens y , spec y , and prec n of 98.17%, 97.12%, and 97.58%, respectively.…”

Section: Experimental Validationmentioning

confidence: 97%

A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment

Latha

Annamalai

Kallimani³

et al. 2022

Computational Intelligence and Neuroscience

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With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset of features. Moreover, an attention-based convolutional neural network (ACNN) with long short-term memory (LSTM), called (ACNN-LSTM) model, is utilized for the detection of HD by using medical data. An extensive experimental study is performed to ensure the improved classification performance of the FSHDL-HDDC technique. A detailed comparison study reported the betterment of the FSHDL-HDDC method on existing techniques interms of different performance measures. The suggested system, the FSHDL-HDDC, has reached its maximum level of accuracy, which is 0.9772.

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“…Furthermore, the Euclidean distance is executed for defining the distance in a CH to BS. Using minimum distance, the energy consumption is kept significantly low [38,[54][55][56][57]. When the distance is increased, additional energy is expended.…”

Section: Algorithm 1: Tlbo Algorithmmentioning

confidence: 99%

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Lakshmanna

Neelakandan²,

Alotaibi

et al. 2022

Sustainability

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The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

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LSGDM with Biogeography-Based Optimization (BBO) Model for Healthcare Applications

Cited by 22 publications

References 33 publications

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

Deep Learning-Based Privacy-Preserving Data Transmission Scheme for Clustered IIoT Environment

A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

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