Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences

Naser, M.Z.; Alavi, Amir H.

doi:10.1007/s44150-021-00015-8

Cited by 169 publications

(66 citation statements)

References 115 publications

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“…Likewise, another possible application would be to use bibliometric analyzes [28] about the books that have been recovered or that have been most searched so that trend lists can be created on books and offered to recommend books to users. Finally, comment that no measures have been taken about the performance of the application in the sense that is discussed in [29,30] since the recognition functionality is a component external to the application that is simply used to combine it with others and therefore it is not possible to modify their behavior or improve it. However, as future work, it is proposed to use machine learning algorithms to create recommendations for users.…”

Section: Discussionmentioning

confidence: 99%

Development of a Mobile Application to Buy Books through Visual Recognition

Cabezuelo

2022

Knowledge

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Mobile devices have become the most used tool for a large number of tasks that we regularly perform such as relating them, searching for information, and in particular for making purchases. A situation that is frequently repeated in many areas is discovering an object that belongs to another person but we would be interested in being able to acquire it. However, the problem arises of knowing where to buy it. For example, this happens with the clothes that other people are wearing. Today, technology offers recognition mechanisms that can help solve this problem. This article presents an Android app that can recognize a book based on an image and offer places where it can be purchased. For this, Google technology was used to recognize objects from images and it has been combined with the information provided by Google Books to find stores that sell recognized books. In this way, a system has been created that makes it easier for any user to identify and purchase books that they discover at any given time.

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Section: Discussionmentioning

confidence: 99%

Development of a Mobile Application to Buy Books through Visual Recognition

Cabezuelo

2022

Knowledge

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“…Since this work explores the spalling phenomenon (as if a concrete mixture spalls or not), classification metrics will be adopted. Given the unique derivation of metrics, these constructs often have advantages and disadvantages; thus, it is best to utilize a series of independent metrics [ 73 ]. Three metrics are used: Area under the ROC curve (AUC), Log Loss Error (LLE), and the confusion matrix.…”

Section: Data Collection and Methodologymentioning

confidence: 99%

“…On the other hand, the ACC presents a ratio of the correct predictions to the total number of samples, respectively. All the metrics formulas are described in Table 2 [ 73 ].…”

Section: Data Collection and Methodologymentioning

confidence: 99%

Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

Tapeh

Naser

2022

Fire Technol

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Fire-induced spalling of concrete continues to be an intriguing and intricate research problem. A deep dive into the open literature highlights the alarming discrepancy and inconsistency of existing theories, as well as the complexity of predicting spalling. This brings new challenges to creating fire-safe concretes and primes an opportunity to explore modern methods of investigation to tackle the spalling phenomenon. Thus, this paper leverages the latest advancements in explainable Artificial Intelligence (XAI) to vet existing theories on fire-induced spalling and to discover solutions/heuristics to predict spalling of concrete mixtures. The developed heuristics are in the form of graphs and nomograms . The proposed solutions allow interested researchers and engineers to graphically identify the propensity of a given concrete mixture to spalling directly and with ease. For example, we report that concrete mixtures with a combination of moderate water/binder ratio (of about 0.3), low heating rate (less than 2.5°C/min), moderate rise in temperature (less than 500°C), and have moisture content (less than 3%) are expected to be less prone to spalling. Further, findings from this research showcase the potential for developing simple (i.e., one-shot) and graphical (coding-free and formula-free) XAI-based solutions and web applications to address decades-long problems in the area of concrete research.

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“…ese include 52 specimens from Lu [36], 37 specimens from Ludwig and Nunes [37], 25 specimens from Hameed et al [38], 53 specimens from Hameed et al [39], 12 specimens from Naser and Alavi [40], 12 specimens from Ludwig et al [41], 6 specimens by Nguyen et al [42], 12 specimens by Yaseen et al [43], 19 specimens by Zhang et al [44], 39 specimens by Gong, and 4 specimens by Gandomi et al [45]. It is worth noting that the database contains a wide range of RC deep beams to improve the generativity of the model.…”

Section: Shear Strength Of Rc Deep Beams and Data Collectionmentioning

confidence: 99%

Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams

et al. 2022

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The deep beam in load transfer is very important as well as difficult to design due to its shear stress problems. Accurate estimation of shear stress would help engineers to get a safer design. One of the major obstacles in building an accurate prediction model is optimising the input variables. Therefore, developing an efficient algorithm to select the optimal input parameters that have the highest information content to represent the target and minimise redundant data is very important. The feature-section algorithm based on the combination of genetic algorithm and information theory (GAITH) was used to select the most important input combinations and introduce them into the prediction models. Four models were used in this study: locally weighted linear regression (LWLR) based on the radial basis kernel function, multiple linear regression (MLR), extreme learning machine (ELM), and random forest (RF). The study found that all applied models were significantly improved by the presence of the GAITH algorithm, except for the MLR model. The LWLR-GAITH model showed 29.15% to 47.88% higher performance accuracy in terms of root mean square error (RMSE) than the other hybrid models during the test phase. Moreover, the results of the standard models (without using the GAITH algorithm) proved the superiority of the LWLR model in reducing the RMSE by 34.51%, 55.17%, and 35.35% compared to RF, MLR, and ELM, respectively. Thus, the inclusion of the LWLR model with GAITH has demonstrated a reliable and applicable computer aid for modelling shear strength in deep beams.

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Error Metrics and Performance Fitness Indicators for Artificial Intelligence and Machine Learning in Engineering and Sciences

Cited by 169 publications

References 115 publications

Development of a Mobile Application to Buy Books through Visual Recognition

Development of a Mobile Application to Buy Books through Visual Recognition

Discovering Graphical Heuristics on Fire-Induced Spalling of Concrete Through Explainable Artificial Intelligence

Optimising the Selection of Input Variables to Increase the Predicting Accuracy of Shear Strength for Deep Beams

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