Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach

Kariri, Elham; Louati, Hassen; Louati, Ali; Masmoudi, Fatma

doi:10.3390/app13053186

Cited by 15 publications

(4 citation statements)

References 26 publications

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“…Several studies have been conducted on proactive manufacturing sites. Paul et al proposed a series AC arc fault detection method based on Random Forests, achieving simplicity and high accuracy compared to conventional ANN-and DNN-based algorithms [7][8][9]. This method employed grid search algorithms for hyperparameter tuning and precision-recall trade-off analysis to find the optimal classification threshold.…”

Section: Preliminary Research On Predicting Anomaly Data On the Facto...mentioning

confidence: 99%

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Kim,

Seo,

Lee

2024

Electronics

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In the realm of manufacturing processes, equipment failures can result in substantial financial losses and pose significant safety hazards. Consequently, prior research has primarily been focused on preemptively detecting anomalies before they manifest. However, within industrial contexts, the precise interpretation of predictive outcomes holds paramount importance. This has spurred the development of research in Explainable Artificial Intelligence (XAI) to elucidate the inner workings of predictive models. Previous studies have endeavored to furnish explanations for anomaly detection within these models. Nonetheless, rectifying these anomalies typically necessitates the expertise of seasoned professionals. Therefore, our study extends beyond the mere identification of anomaly causes; we also ascertain the specific adjustments required to normalize these deviations. In this paper, we present novel research avenues and introduce three methods to tackle this challenge. Each method has exhibited a remarkable success rate in normalizing detected errors, scoring 97.30%, 97.30%, and 100.0%, respectively. This research not only contributes to the field of anomaly detection but also amplifies the practical applicability of these models in industrial environments. It furnishes actionable insights for error correction, thereby enhancing their utility and efficacy in real-world scenarios.

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Section: Preliminary Research On Predicting Anomaly Data On the Facto...mentioning

confidence: 99%

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Kim,

Seo,

Lee

2024

Electronics

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“…Artificial neural networks are a specific type of machine learning algorithm that are inspired by the structure and function of the human brain [36][37][38][39]. The human nervous system can learn from the past, and, in a similar way, ANNs are able to learn from the data and provide responses in the form of predictions or classifications.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra

Pokhrel

Sirisomboon

Khurnpoon

et al. 2023

Sensors

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The aim of this study was to evaluate and compare the performance of multivariate classification algorithms, specifically Partial Least Squares Discriminant Analysis (PLS-DA) and machine learning algorithms, in the classification of Monthong durian pulp based on its dry matter content (DMC) and soluble solid content (SSC), using the inline acquisition of near-infrared (NIR) spectra. A total of 415 durian pulp samples were collected and analyzed. Raw spectra were preprocessed using five different combinations of spectral preprocessing techniques: Moving Average with Standard Normal Variate (MA+SNV), Savitzky–Golay Smoothing with Standard Normal Variate (SG+SNV), Mean Normalization (SG+MN), Baseline Correction (SG+BC), and Multiplicative Scatter Correction (SG+MSC). The results revealed that the SG+SNV preprocessing technique produced the best performance with both the PLS-DA and machine learning algorithms. The optimized wide neural network algorithm of machine learning achieved the highest overall classification accuracy of 85.3%, outperforming the PLS-DA model, with overall classification accuracy of 81.4%. Additionally, evaluation metrics such as recall, precision, specificity, F1-score, AUC ROC, and kappa were calculated and compared between the two models. The findings of this study demonstrate the potential of machine learning algorithms to provide similar or better performance compared to PLS-DA in classifying Monthong durian pulp based on DMC and SSC using NIR spectroscopy, and they can be applied in the quality control and management of durian pulp production and storage.

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“…The scientific community's fascination with CNNs possessing numerous layers reached new heights in 2006, owing to the research conducted by a group of scholars [4,5], and they have been increasingly employed for difficult classification problems or specific purposes requiring high accuracy. The design of CNNs is affected by a large number of hyperparameters [6,7], which need to be fine-tuned for optimal performance [8]. Previously, studies have concentrated on refining architectures such as VGGNet [9] and ResNet [10], which are either manually crafted by experts or automatically generated through greedy induction methods [6,11].…”

Section: Introductionmentioning

confidence: 99%

Embedding channel pruning within the CNN architecture design using a bi-level evolutionary approach

Louati

Bechikh

et al. 2023

J Supercomput

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Remarkable advancements have been achieved in machine learning and computer vision through the utilization of deep neural networks. Among the most advantageous of these networks is the convolutional neural network (CNN). It has been used in pattern recognition, medical diagnosis, and signal processing, among other things. Actually, for these networks, the challenge of choosing hyperparameters is of utmost importance. The reason behind this is that as the number of layers rises, the search space grows exponentially. In addition, all known classical and evolutionary pruning algorithms require a trained or built architecture as input. During the design phase, none of them consider the process of pruning. In order to assess the effectiveness and efficiency of any architecture created, pruning of channels must be carried out before transmitting the dataset and computing classification errors. For instance, following pruning, an architecture of medium quality in terms of classification may transform into an architecture that is both highly light and accurate, and vice versa. There exist countless potential scenarios that could occur, which prompted us to develop a bi-level optimization approach for the entire process. The upper level involves generating the architecture while the lower level optimizes channel pruning. Evolutionary algorithms (EAs) have proven effective in bi-level optimization, leading us to adopt the co-evolutionary migration-based algorithm as a search engine for our bi-level architectural optimization problem in this research. Our proposed method, CNN-D-P (bi-level CNN design and pruning), was tested on the widely used image classification benchmark datasets, CIFAR-10, CIFAR-100 and ImageNet. Our suggested technique is validated by means of a set of comparison tests with regard to relevant state-of-the-art architectures.

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Exploring the Advancements and Future Research Directions of Artificial Neural Networks: A Text Mining Approach

Cited by 15 publications

References 26 publications

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Advanced Anomaly Detection in Manufacturing Processes: Leveraging Feature Value Analysis for Normalizing Anomalous Data

Comparing Machine Learning and PLSDA Algorithms for Durian Pulp Classification Using Inline NIR Spectra

Embedding channel pruning within the CNN architecture design using a bi-level evolutionary approach

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