Performance overview of an artificial intelligence in biomedics: a systematic approach

Patil, Shashikant; Patil, Kiran; Patil, Chandragouda R.; Patil, Smit S.

doi:10.1007/s41870-018-0243-8

Cited by 18 publications

(9 citation statements)

References 56 publications

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“…The phishing URL detection system proposed by Ozgur et al [14] makes improvements on the basis of previous research, they used the NLP-based features to considered more word information. The malicious URLs detection system proposed by Cho et al [15], Ripon et al [16], Kamel et al [17], Yogendra et al [18], Patil et al [19], Ammara et al [20], Ferhat et al [21], Mohammad et al [22] are typical detection systems based on Feature Engineering. They used the number of characters '.…”

Section: The Url Embedding Methods Of the Malicious Urls Detection Re...mentioning

confidence: 99%

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An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

Chen,

Omote

2024

Preprint

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Nowadays, machine learning is used in many fields. Not only in fields such as image recognition, machine learning is also used for malicious detection. Especially in recent years, there have been many studies using machine learning for malicious URL detection to replace traditional blacklists. In order to compare the performance of the malicious URLs detection method, researches used the F-score or other detection accuracy to evaluate, but there are some difficulties in evaluating the URL embedding method used in malicious URLs detection because the detection accuracy is also effect by machine learning or deep learning models and data sets. An evaluation method of URL embedding method that is not affected by other factors is particularly important. In this paper, we proposed an intrinsic evaluation method for URL embedding method that is not affected by machine learning models or deep learning models and data sets. Besides, We analyse some URL embedding methods according to intrinsic and extrinsic methods and offer a guidance in selecting suitable embedding methods in URL by analysing the results.

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Section: The Url Embedding Methods Of the Malicious Urls Detection Re...mentioning

confidence: 99%

“…Secondly, the Token Pairs used in this study for the Intrinsic Evaluation Method are split by Token segmentation method and can be detected by the Intrinsic Evaluation Method. [15], [16], [17], [18], [19], [20], [21], [22] Numerical Random Forest 83 0.95208…”

Section: The Evaluation Of Related Workmentioning

confidence: 99%

An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

Chen,

Omote

2024

Preprint

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“…The primary function of dynamic capabilities is to refresh and reorganize static ones. Traders rely on them more frequently in unstable markets [81,82]. Previous empirical research demonstrates that firms operating in highly chaotic environments benefit more strongly from the favorable benefits of dynamic skills on ordinary capabilities [83].…”

Section: The Moderating Role Of Market Turbulencementioning

confidence: 99%

Boosting Innovation Performance through Big Data Analytics Powered by Artificial Intelligence Use: An Empirical Exploration of the Role of Strategic Agility and Market Turbulence

Alghamdi,

Agag

2023

Sustainability

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Scholars and practitioners have paid attention to the critical role of big data analytics driven by AI in enhancing business performance. However, firms investing in big data analytics often fail to achieve those advantages. Our research explores the critical role played by strategic agility and market turbulence on the link between big data analytics driven by AI and innovation performance. Based on dynamic capabilities view, we developed an integrated model to examine the relationship between our study variables. We utilized a quantitative approach to collect data from manufacturing companies in Saudi Arabia. We employed structural equation modelling (SEM) through AMOS 26.0 to analyze and test the study hypotheses. Our findings indicated that big data analytics driven by AI have a significant impact on strategic agility and innovation performance. It also revealed that strategic agility mediates the relationship between big data analytics driven by AI and innovation performance. The results also showed that higher levels of market turbulence are associated with more robust connections between big data analytics, strategic agility, and innovation performance. Our work provides managers with meaningful implications on the value that organizations can create through the use of big data analytics and strategic agility.

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“…This field focuses on the definition and application of algorithms that can be trained on data to model underlying patterns [11,73,77,88]. Machine learning approaches can be applied to many different research fields, including biomedical science [59,84,95,126], natural language understanding [22,83], [97] anomaly detection [17], image classification [71], database knowledge discovery [32], robot learning [3], online advertising [86], time series forecasting [13], brain computer interfacing [78] and many more [98]. To train these algorithms, it is necessary to define an objective function, which gives a scalar measure of the algorithm's performance [77,116].…”

Section: Introductionmentioning

confidence: 99%

A survey and taxonomy of loss functions in machine learning

Ciampiconi¹,

Elwood²,

Leonardi³

et al. 2023

Preprint

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lastminute.com group, Switzerland Most state-of-the-art machine learning techniques revolve around the optimisation of loss functions. Defining appropriate loss functions is therefore critical to successfully solving problems in this field. We present a survey of the most commonly used loss functions for a wide range of different applications, divided into classification, regression, ranking, sample generation and energy based modelling. Overall, we introduce 33 different loss functions and we organise them into an intuitive taxonomy. Each loss function is given a theoretical backing and we describe where it is best used. This survey aims to provide a reference of the most essential loss functions for both beginner and advanced machine learning practitioners.

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Performance overview of an artificial intelligence in biomedics: a systematic approach

Cited by 18 publications

References 56 publications

An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

An Intrinsic Evaluator for Embedding Methods in Malicious URL Detection

Boosting Innovation Performance through Big Data Analytics Powered by Artificial Intelligence Use: An Empirical Exploration of the Role of Strategic Agility and Market Turbulence

A survey and taxonomy of loss functions in machine learning

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