Automatic Atrial Fibrillation Detection Using Modified Moth Flame Optimization Algorithm

Trust based routing with energy efficiency in mobile ad hoc networks (MANET) is a challenging task due to the complex nature of the network. Based on the MANET infrastructure, the interoperability of temporary communication is assured; however, there is no supervisory control process for MANET routing in terms of trust and security. As a result, stability routing is avoided in MANETs which can damage the network's dynamic nature. In this paper, a trust based multi-objective battle royale optimization (T-MOBRO) algorithm is proposed based on the conventional battle royale optimization (BRO) algorithm. To address the limitation of trust based energy efficiency routing in conventional BRO, a novel method T-MOBRO is suggested. The T-MOBRO algorithm establishes trust based routing along with energy efficiency by considering trust, energy, distance as its fitness function. The multiobjectives of the proposed model are said to be distance, energy consumption, packet forwarding rate (PFR), end to end delay (EED), direct trust (Trust DT ), and indirect trust (Trust IDT ). The BRO algorithm is based on gameplay that is inspired by battle royale video games. The proposed approach is evaluated using throughput, average energy consumption, packet delivery ratio, end to end delay (EED). The PDR for the developed T-MOBRO method for 10 nodes is 99.58%, throughput is 4032.29 bits/second, end to end delay is less with 0.0070 seconds, and energy consumption of the MANET is less with 1.7Joules for the T-MOBRO method.

Section: Literature Reviewmentioning

confidence: 99%

“…The recent optimization techniques such as grey wolf optimization's (GWO) [17], modified moth flame optimization algorithm (MMFO) [19], and enhanced whale optimization (EWO) [20]. GWO reduces time consuming tasks and reduces operational time for high dimensional data.…”

Section: Literature Reviewmentioning

confidence: 99%

Metaheuristic Based Energy Efficient Routing Protocol in MANET Using Battle Royale Optimization

2023

“…Accuracy: Accuracy is defined as the ratio of precisely classified samples to the total number of samples that depicts the overall performance of the proposed model and is mathematically represented as, 𝐴 𝑐 = 𝑡 𝑝 +𝑡 𝑛 𝑡 𝑝 +𝑡 𝑛 +𝑓 𝑝 +𝑓 𝑛 (37) Here, true positive is denoted as𝑡 𝑝 , true negative is denoted as𝑡 𝑛 false positive as𝑓 𝑝 and false negative as𝑓 𝑛 .…”

Section: Performance Metricsmentioning

confidence: 99%

“…Fig. 11 shows the convergence curve with the proposed and existing optimization algorithms such as Aquila optimization algorithm (AOA) [34], guided pelican algorithm (GPA) [35], stochastic komodo algorithm (SKA) [36], modified moth flame optimization algorithm (NMOA) [37], artificial rabbits optimization algorithm (AROA) [38], northern goshawk optimization (NGO) [39], enhanced whale optimization algorithm (EWOA) [40], fixed step average and subtraction (FSAS) [41] and puzzle optimization algorithm (POA) [42]. Fig.…”

Section: Performance Analysis Of Feature Selection Techniquementioning

confidence: 99%

OptiBiNet_GRU: Robust Network Intrusion Detection System Using Optimum Bi-Directional Gated Recurrent Unit

2023

Security is considered one of the major challenges in the network due to the increase in network services. Many systems linked to the network play a major role in business and other applications that provide network services. Therefore, it is necessary to identify effective ways to secure the system. One of the significant information security technologies is the intrusion detection system (IDS) that utilizes various deep learning (DL) and machine learning (ML) algorithms to identify network issues. As a result, a novel DL technique is used to classify and detect attacks in the network. To perform this approach, pre-processing is initially done for the collected data using Z-score normalization (ZsN). Then, feature selection is performed using the hybrid pattern search whale optimization algorithm (HPS-WOA). Followed by, network attacks are detected and classified using the optimum bi-directional long shortterm memory with the gated recurrent unit (OptiBiNet_GRU). Finally, the hyper parameters such as loss and weight of the proposed DL model are optimized using the pelican optimization algorithm (POA). The effectiveness of the proposed method is validated using two datasets on various performance metrics with some existing baselines. The results show that the proposed method outperforms other methods in terms of accuracy of 99%, precision of 95%, f1score of 97%, specificity of 99%, and recall of 98% for UNSW-NB15 dataset, whereas accuracy of 99%, precision of 95%, f1-score by 99%, specificity of 99%, and recall of 99% for CICIDS 2017 dataset, respectively.

“…Many authors had applied the optimization features in electrical, health care domains and even for environmental assessments like identifying groundwater quality. A slew of researchers had incorporated the preying and mating features of animals and had mapped their behaviours in attaining optimal solutions adopting multiheuristic approaches [16][17][18][19][20][21][22][23][24].…”

Section: Related Workmentioning

confidence: 99%

Task Scheduling Based on Cost and Execution Time Using Ameliorate Grey Wolf Optimizer Algorithm in Cloud Computing

2023

An enriched computational platform has been unfolded with the introduction of cloud technology offering ensemble services to users that includes storage, database and processing power. More recently, the cloud technology has been upgraded to a federated environment that offers even more features where various service providers could interconnect for providing an integrated service in a transparent way to cloud users. Applications that demand enormous computing resources like for instance bioinformatics workflow applications could very well make use of the abundant cloud resources for effective execution. Fine tuning the task scheduling activity in cloud could further boost the overall cloud performance. In this paper, we had designed an optimal and ideal task scheduling algorithm that primarily focuses on reducing the cost and makespan QoS parameters, eventually leading to enhanced cloud performance. The proposed algorithm, which is a meta-heuristic enhanced hybrid version named, grey wolf optimizer cuckoo (GWOC) is formally designed from the existing grey wolf optimizer and cuckoo search algorithms. results obtained clearly justify the goal accomplishment of the proposed GWOC algorithm and its swiftness in achieving convergence, thereby clearly outperforming existing contemporaries like gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm. The proposed GWOC technique had produced an improvement of 2.11%, 3.5% and 5.17% for makespan and had reduced the cost to the tune of 7.71%, 11.3%, and 15.4% when compared with gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm respectively when used with 100 VMs. Detailed results have been presented in section 5.