Comparison of Bioinspired Computation and Optimization Techniques

Hanif, Muhammad Kashif; Talib, Ramzan; Awais, Muhammad; Saeed, Muhammad Yahya; Sarwar, Umer

doi:10.18520/cs/v115/i3/450-453

Cited by 3 publications

(4 citation statements)

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“…BIC solution can be obtained by selecting appropriate dimensions of the problem, as sessing the significance of the comparative solutions and describing the operators. Research is being carried out on BIC processes to resolve complicated computational problems [186].…”

Section: Discussionmentioning

confidence: 99%

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

et al. 2022

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Every year, phishing results in losses of billions of dollars and is a major threat to the Internet economy. Phishing attacks are now most often carried out by email. To better comprehend the existing research trend of phishing email detection, several review studies have been performed. However, it is important to assess this is sue from different perspectives . None of the surveys have ever comprehensively studied the use of Natural Language Processing (NLP) techniques for detection of phishing except one that shed light on the use of NLP techniques for classification and training purposes , while exploring a few alternatives. To bridge the gap, this study aims to systematically review and synthesise research on the use of NLP for detecting phishing emails. Based on specific predefined criteria, a total of 100 research articles published between 2006 and 2022 were identified and analysed. We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. The findings include that the main research area in phishing detection studies is feature extraction and selection, followed by methods for classifying and optimizing the detection of phishing emails. Amongst the range of classification algorithms, support vector machines (SVMs) are heavily utilised for detecting phishing emails . The most frequently used NLP techniques are found to be TF-IDF and word embeddings. Furthermore, the most commonly used datasets for benchmarking phishing email detection methods is the Nazario phishing corpus. Also, Python is the most commonly used one for phishing email detection. It is expected that the findings of this paper can be helpful for the scientific community, especially in the field of NLP application in cybersecurity problems. This survey also is unique in the sense that it relates works to their openly available tools and resources. The analysis of the presented works revealed that not much work had been performed on Arabic language phishing emails using NLP techniques. Therefore, many open issues are associated with Arabic phishing email detection.INDEX TERMS Phishing email detection; systematic literature review; natural language processing; machine learning.

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Section: Discussionmentioning

confidence: 99%

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

et al. 2022

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“…The challenges enumerated by Hanif et al [92] were corroborated by Sarkar et al [48]. Beyond the critical observations made by Sarkar et al [48] and Hanif et al [92], the review was influenced by contrasting evidence on the benefits of robotic and IoT systems operated using algorithms. For example, there is widespread support for robot bees, given they are more resilient than natural bees, manual pollination, and flowers [83].…”

Section: Background On Bio-inspired Innovations In Agriculturementioning

confidence: 99%

“…Despite the immense potential of biomimetic innovations, widespread commercial adoption was impeded by limited models of biological phenomena (not all biological phenomena can be modeled) [92] and the need to integrate architecture into the biological processes, simulate the point's fitness, and develop models that achieve the desired biological function. The challenges enumerated by Hanif et al [92] were corroborated by Sarkar et al [48]. Beyond the critical observations made by Sarkar et al [48] and Hanif et al [92], the review was influenced by contrasting evidence on the benefits of robotic and IoT systems operated using algorithms.…”

Section: Background On Bio-inspired Innovations In Agriculturementioning

confidence: 99%

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Maraveas

Arvanitis

Bartzanas

et al. 2022

Arch Computat Methods Eng

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The article reviewed the four major Bioinspired intelligent algorithms for agricultural applications, namely ecological, swarm-intelligence-based, ecology-based, and multi-objective algorithms. The key emphasis was placed on the variants of the swarm intelligence algorithms, namely the artificial bee colony (ABC), genetic algorithm, flower pollination algorithm (FPA), particle swarm, the ant colony, firefly algorithm, artificial fish swarm, and Krill herd algorithm because they had been widely employed in the agricultural sector. There was a broad consensus among scholars that certain BIAs' variants were more effective than others. For example, the Ant Colony Optimization Algorithm and genetic algorithm were best suited for farm machinery path optimization and pest detection, among other applications. On the contrary, the particle swarm algorithm was useful in determining the plant evapotranspiration rates, which predicted the water requirements and optimization of the irrigation process. Despite the promising applications, the adoption of hyper-heuristic algorithms in agriculture remained low. No universal algorithm could perform multiple functions in farms; different algorithms were designed to perform specific functions. Secondary concerns relate to data integrity and cyber security, considering the history of cyber-attacks on smart farms. Despite the concerns, the benefits associated with the BIAs outweighed the risks. On average, farmers can save 647–1866 L on fuel which is equivalent to US$734-851, with the use of GPS-guided systems. The accuracy of the BIAs mitigated the risk of errors in applying pesticides, fertilizers, irrigation, and crop monitoring for better yields.

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“…Bio-inspired algorithms are an actively developing area of heuristic optimization and decision-making methods [1]. Meta-heuristic optimization methods motivated by examples in nature are becoming more common in real-life applications.…”

Section: Introductionmentioning

confidence: 99%

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

Damaševičius

Maskeliūnas

2021

Algorithms

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This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

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Comparison of Bioinspired Computation and Optimization Techniques

Cited by 3 publications

References 0 publications

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

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