Artificial intelligence based quality of transmission predictive model for cognitive optical networks

Singh, Harinder P.; Ramya, D.; Saravanakumar, R.; Sateesh, Nayani; Anand, Rohit; Singh, Swarnjit; Neelakandan, S.

doi:10.1016/j.ijleo.2022.168789

Cited by 50 publications

(11 citation statements)

References 22 publications

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“…At last, a hybrid NN using group teaching algorithm was presented for determining the optimal route. Vaiyapuri et al [14] presented an IoT-assisted cluster-based routing (CBR) method for information-centric WSNs (ICWSN), named CBR-ICWSN. The proposed method undergoes a black widow optimization (BWO)-founded gathering approach for efficiently choosing an optimal set of CHs.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Neelakandan²,

et al. 2022

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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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Section: Literature Reviewmentioning

confidence: 99%

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

Neelakandan²,

et al. 2022

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“…In addition, an expanded Muirhead mean operator was developed to determine the amounts of reliance between the activities of consecutive operators. Finally, empirical medical dependency analysis is used to demonstrate the applicability and effectiveness of the presented LGDA model [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

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

Harshavardhan

Boyapati²,

Neelakandan

et al. 2022

Journal of Healthcare Engineering

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Several studies aimed at improving healthcare management have shown that the importance of healthcare has grown in recent years. In the healthcare industry, effective decision-making requires multicriteria group decision-making. Simultaneously, big data analytics could be used to help with disease detection and healthcare delivery. Only a few previous studies on large-scale group decision-making (LSDGM) in the big data-driven healthcare Industry 4.0 have focused on this topic. The goal of this work is to improve healthcare management decision-making by developing a new MapReduce-based LSDGM model (MR-LSDGM) for the healthcare Industry 4.0 context. Clustering decision-makers (DM), modelling DM preferences, and classification are the three stages of the MR-LSDGM technique. Furthermore, the DMs are subdivided using a novel biogeography-based optimization (BBO) technique combined with fuzzy C-means (FCM). The subgroup preferences are then modelled using the two-tuple fuzzy linguistic representation (2TFLR) technique. The final classification method also includes a feature extractor based on long short-term memory (LSTM) and a classifier based on an ideal extreme learning machine (ELM). MapReduce is a data management platform used to handle massive amounts of data. A thorough set of experimental analyses is carried out, and the results are analysed using a variety of metrics.

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“…In this section, we validate the proposed model performance under several aspects. Table 1 investigates the performance of the feature selection techniques in terms of classification accuracy under different sets of training data and varying number of residuals [ 34 , 35 ]. Figure 4 examines the result analysis of different feature selection techniques in terms of classification accuracy on 60% of training data.…”

Section: Performance Evaluationmentioning

confidence: 99%

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media

Neelakandan¹,

Sridevi

Chandrasekaran

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the ease with which the internet and cell phones can be accessed, online social networks (OSN) and social media have seen a significant increase in popularity in recent years. Security and privacy, on the other hand, are the key concerns in online social networks and other social media platforms. On the other hand, cyberbullying (CB) is a serious problem that needs to be addressed on social media platforms. Known as cyberbullying (CB), it is defined as a repetitive, purposeful, and aggressive reaction performed by individuals through the use of information and communication technology (ICT) platforms such as social media platforms, the internet, and cell phones. It is made up of hate messages that are sent by e-mail, chat rooms, and social media platforms, which are accessed through computers and mobile phones. The detection and categorization of CB using deep learning (DL) models in social networks are, therefore, crucial in order to combat this trend. Feature subset selection with deep learning-based CB detection and categorization (FSSDL-CBDC) is a novel approach for social networks that combines deep learning with feature subset selection. The suggested FSSDL-CBDC technique consists of a number of phases, including preprocessing, feature selection, and classification, among others. Additionally, a binary coyote optimization (BCO)-based feature subset selection (BCO-FSS) technique is employed to select a subset of features that will increase classification performance by using the BCO algorithm. Additionally, the salp swarm algorithm (SSA) is used in conjunction with a deep belief network (DBN), which is known to as the SSA-DBN model, to detect and characterize cyberbullying in social media networks and other online environments. The development of the BCO-FSS and SSA-DBN models for the detection and classification of cyberbullying highlights the originality of the research. A large number of simulations were carried out to illustrate the superior classification performance of the proposed FSSDL-CBDC technique. The SSA-DBN model has exhibited superior accuracy to the other algorithms, with a 99.983 % accuracy rate. Overall, the experimental results revealed that the FSSDL-CBDC technique beats the other strategies in a number of different aspects.

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Artificial intelligence based quality of transmission predictive model for cognitive optical networks

Cited by 50 publications

References 22 publications

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

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

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

Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media

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