RETRACTION: B. Yang, “,” Expert Systems 40, no. (2023): , https://doi.org/10.1111/exsy.12948. The above article, published online on 11 February 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. Following publication, it has come to our attention that the article was not reviewed in line with the journal's peer review standards. Moreover, multiple inconsistencies and flaws were identified in this article that affect the validity of the conclusions. Relevant information is missing so that the research described is not comprehensible.

RETRACTION: A. Samanta, A. Saha, S. C. Satapathy, H. Lin, “,” Expert Systems (Early View): , https://doi.org/10.1111/exsy.12709. The above article, published online on 06 May 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. The retraction has been agreed on as the article was not reviewed in line with the journal's peer review standards. Furthermore, the methodology and model in this manuscript are insufficiently described. Accordingly, the results are not considered reliable. The authors disagree with the retraction.

Retraction: W. Wei, D. Xiao, and W. Liu, “,” Expert Systems 41, no. (2024): . https://doi.org/10.1111/exsy.12985. The above article, published online on 04 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, the investigation found significant flaws and logical inconsistencies in the article. Therefore, the article must be retracted. The authors did not respond to the notice regarding the retraction.

Retraction: X. Yu, Y. Zhu, C. Jia, W. Lu, and H. Xu, “,” Expert Systems 41, no. (2024): . https://doi.org/10.1111/exsy.13258. The above article, published online on 22 February 2023, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, the investigation found logical inconsistencies between the topic of the article and the conclusions reached in this article. Therefore, the article must be retracted. The authors did not respond to the notice regarding the retraction.

Retraction: M. M. Kamruzzaman, O. Alruwaili, and D. Aldaghmani, “,” Expert Systems 41, no. (2024): . https://doi.org/10.1111/exsy.12971. The above article, published online on 10 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, the investigation found there was significant unattributed textual overlap between this article and online course documents available on an online learning platform. Therefore, the article must be retracted. The authors did not respond to the notice regarding the retraction.

Retraction: J. Liu, “,” Expert Systems 41, no. (2024): . https://doi.org/10.1111/exsy.13266. The above article, published online on 01 March 2023, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor‐in‐Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, the investigation found there was significant unattributed overlap between this article and a previously‐published article (Dunnan et al. 2021 [https://doi.org/10.1088/1755‐1315/687/1/012140]), including the re‐use of the charts between Figure 5 in this article and Figures 2 and 3 in the previously‐published article. Therefore, the article must be retracted. The authors did not respond to the notice regarding the retraction.

ABSTRACTTo fulfil the demands of the industry in autonomous intelligent inspection, innovative frameworks that allow Convolutional Neural Networks to run at the edge in real‐time are required. This paper proposes an end‐to‐end approach and system to enable crack detection onboard a customised embedded system. In order to make possible the deployment and execution on edge, this work develops a dataset by combining new and existing images, it introduces a quantization approach that includes inference optimization, memory reuse, and freezing layers. Real‐time, onsite results from aerial and hand‐held setup images of industrial environments show that the system is capable of identifying and localiszing cracks within the field of view of the camera with a mean average precision (mAP) of 98.44% and at ~2.5 frames per second with real‐time inference. Therefore, it is evidenced that, despite using a full model, the introduced model customization improved the mAP by ~8% with respect to lighter state‐of‐the‐art models, and the quantization technique led to a model inference two times faster. The proposed intelligent and autonomous approach advances common offline inspection techniques to enable on‐site, artificial intelligence‐based inspection systems, which also aid in reducing human errors and enhance safety conditions by automatically performing defect‐recognition in tight and difficult‐to‐reach spots.

ABSTRACT Multi‐objective evolutionary optimisation algorithms (MOEAs) have become a widely adopted way of solving the multi‐objective optimisation problems (MOPs). The decomposition‐based MOEAs demonstrate a promising performance for solving regular MOPs. However, when handling the irregular MOPs, the decomposition‐based MOEAs cannot offer a convincing performance because no intersection between weight vector and the Pareto Front (PF) may lead to the same optimal solution assigned to the different weight vectors. To solve this problem, this paper proposes an MOEA based on decomposition with the orthogonal experimental design (MOEA/D‐OED) that involves the selection operation, Orthogonal Experimental Design (OED) operation, and adjustment operation. The selection operation is to judge the unpromising weight vectors based on the history data of relative reduction values and convergence degree. The OED method based on the relative reduction function could make an explicit guidance for removing the worthless weight vectors. The adjustment operation brings in an estimation indicator of both diversity and convergence for adding new weight vectors into the interesting regions. To verify the versatility of the proposed MOEA/D‐OED, 26 test problems with various PFs are evaluated in this paper. Empirical results have demonstrated that the proposed MOEA/D‐OED outperforms eight representative MOEAs on MOPs with various types of PFs, showing promising versatility. The proposed algorithm shows highly competitive performance on all the various MOPs.

ABSTRACT Heart disease continues to be a primary cause of mortality globally, highlighting the critical necessity for efficient early prediction and classification techniques. This study presents a new hybrid model attention‐based CNN‐Bi‐LSTM that integrates the SMOTE with an attention‐driven improved convolutional neural network‐recurrent neural network architecture to improve the classification of heart sounds, especially from imbalanced datasets. Heart sounds are difficult to classify because of their complex acoustic properties and the variability of their characteristics across frequency and temporal domains. The proposed model utilises an advanced CNN to effectively extract global and local features, in conjunction with a bidirectional long short‐term memory network to improve the architecture by capturing contextual information from both preceding and subsequent time sequences. The incorporation of spatial attention within the CNN and temporal attention in the RNN enables the model to concentrate on the most pertinent audio segments. To address the challenges presented by imbalanced and noisy datasets that may impede the efficacy of deep learning algorithms, our model employs SMOTE to improve data representation. The hybrid model outperformed popular models such as CNN, LSTM and CNN‐LSTM, achieving a classification accuracy of more than 97% on the PCG and PASCAL heart sound datasets. The findings demonstrate the model's reliability as an initial evaluation tool in clinical settings, thereby improving support for cardiovascular disease diagnosis.