Computational Intelligence最新文献

RETRACTION: P. Kirubanantham, G. Vijayakumar, “ Novel Recommendation System Based on Long-term Composition for Adaptive Web Services,” Computational Intelligence 36 no. 3 (2020): 1063–1077, https://doi.org/10.1111/coin.12309.

The above article, published online on 17 March 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

RETRACTION: N. Dhanachandra, Y.J. Chanu, and K.M. Singh, “ A New Hybrid Image Segmentation Approach Using Clustering and Black Hole Algorithm,” Computational Intelligence 39 no. 2 (2023): 194–213, https://doi.org/10.1111/coin.12297.

The above article, published online on 01 March 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

RETRACTION: P. Deepika, R.M. Suresh, and P. Pabitha, “ Defending Against Child Death: Deep Learning-based Diagnosis Method for Abnormal Identification of Fetus Ultrasound Images,” Computational Intelligence 37 no. 1 (2021): 128–154, https://doi.org/10.1111/coin.12394.

The above article, published online on 07 October 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

RETRACTION: A. A. Babu, V. M. A. Rajam, “ Water-body Segmentation from Satellite Images using Kapur's Entropy-based Thresholding Method,” Computational Intelligence 36 no. 3 (2020): 1242–1260, https://doi.org/10.1111/coin.12339.

The above article, published online on 14 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors do not agree with the retraction.

RETRACTION: J.D. Kharibam, T. Khelchandra, “ Automatic Speaker Recognition from Speech Signal Using Bidirectional Long Short-term Memory Recurrent Neural Network,” Computational Intelligence 39 no. 2 (2023): 170–193, https://doi.org/10.1111/coin.12278.

The above article, published online on 23 January 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

RETRACTION: G. Premalatha, P. V. Chandramani, “ Improved Gait Recognition through Gait Energy mage Partitioning,” Computational Intelligence 36 no. 3 (2020): 1261–1274, https://doi.org/10.1111/coin.12340.

The above article, published online on 22 June 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors have been informed of the retraction.

RETRACTION: S. Narasimhan, M. Arunachalam, “ Bio-PUF-MAC Authenticated Encryption for Iris Biometrics,” Computational Intelligence 36 no. 3 (2020): 1221–124, https://doi.org/10.1111/coin.12332.

The above article, published online on 27 May 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors do not agree with the retraction.

Alzheimer's disease (AD) diagnosis using MRI scans must be very accurate since the subtle differences throughout the course of the disease are difficult to identify. Traditional approaches are not effective, and new computational techniques are required that can provide fast and accurate diagnosis. In this paper, a novel deep learning methodology that greatly enhances the sensitivity and specificity of AD stage identification by analyzing in-depth MRI scans is proposed. The model applies a novel Sequential Convolutional Neural Network (CNN) architecture, which has been deeply trained on the “Augmented Alzheimer MRI Dataset” made available by Kaggle, to integrate various layers of depth and complexity to identify and scan in-depth features on MRI images. Major enhancements include the use of learning rate schedulers and dropout regularization to fine-tune training as well as avoid overfitting, with a diagnosis accuracy of 94.2%. This level of accuracy not only makes diagnostic processes easier but also allows for early detection of Alzheimer's phases, which is crucial for timely interventions and effective management of the condition. The model is rigorously trained on a large set of augmented data with varying levels of AD to guarantee robustness and generalizability in various demographic and clinical settings. Batch normalization and higher-order activation functions allow faster and stable convergence of training, and thus the model is more efficient and scalable. Application of this model to the clinic has the potential to sharply reduce time to diagnosis, lessen dependence on radiological expertise, and offer a high-accuracy, scalable imaging device enabling early and accurate treatment in Alzheimer's care. This innovation represents a significant next phase in medical imaging with artificial intelligence, and it offers a highly effective tool for fine detection and staging of Alzheimer's disease.