556 results on '"Batch normalization"'
Search Results
2. BCN: Batch Channel Normalization for Image Classification
- Author
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Khaled, Afifa, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Antonacopoulos, Apostolos, editor, Chaudhuri, Subhasis, editor, Chellappa, Rama, editor, Liu, Cheng-Lin, editor, Bhattacharya, Saumik, editor, and Pal, Umapada, editor
- Published
- 2025
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- View/download PDF
3. Impact of Batch Normalization on Convolutional Network Representations
- Author
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Potgieter, Hermanus L., Mouton, Coenraad, Davel, Marelie H., Li, Gang, Series Editor, Filipe, Joaquim, Series Editor, Xu, Zhiwei, Series Editor, Gerber, Aurona, editor, Maritz, Jacques, editor, and Pillay, Anban W., editor
- Published
- 2025
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4. Target detection algorithm for basketball robot based on IBN-YOLOv5s algorithm.
- Author
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Li, Yuan-hui and Yu, Hong-bo
- Subjects
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DETECTION algorithms , *TARGETS (Sports) , *BASKETBALL games , *SPORTS competitions , *DEEP learning - Abstract
Being able to quickly and accurately detect and recognize target balls is a key task for basketball robots in automated and intelligent sports competitions. This study aims to propose an effective target detection method for basketball robots, which is based on the improved YOLOv5s model and introduces spatial pyramid pooling and instance-batch normalization structure. The study first pre-trained the model and compared the migration training with the random initialization approach. The experimental outcomes denote that the migration-trained model obtains a mean average precision value of 0.918 on the target detection task, which is significantly better than the model trained from scratch. Then, the study applies different improvement schemes to the YOLOv5s model and compares the improvement effects of the various schemes. The experimental outcomes denote that scheme 2 has the best improvement effect on the YOLOv5s model, and its detection accuracy on dataset 1 is 94.5%. The experiment proves that the target detection algorithm designed in the study is effective and accurate, and can help the basketball robot successfully accomplish the target detection task. This research helps to advance the development of basketball robotics and provides theoretical support and technical basis for efficient automated basketball games in the future. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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5. A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification.
- Author
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Krishnamoorthy, Umapathi, Jagan, Shanmugam, Zakariah, Mohammed, Almazyad, Abdulaziz S., and Gurunathan, K.
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CONVOLUTIONAL neural networks ,EPILEPSY ,ARTIFICIAL intelligence ,EVOLUTIONARY algorithms ,GENETIC algorithms ,ELECTROENCEPHALOGRAPHY - Abstract
Brain signal analysis from electroencephalogram (EEG) recordings is the gold standard for diagnosing various neural disorders especially epileptic seizure. Seizure signals are highly chaotic compared to normal brain signals and thus can be identified from EEG recordings. In the current seizure detection and classification landscape, most models primarily focus on binary classification—distinguishing between seizure and non-seizure states. While effective for basic detection, these models fail to address the nuanced stages of seizures and the intervals between them. Accurate identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert system. This granularity is essential for improving patient-specific interventions and developing proactive seizure management strategies. This study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network (DCNN). The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes, thus providing a more detailed analysis of seizure stages. To enhance the model's performance, we have optimized the DCNN using two advanced techniques: the Stochastic Gradient Algorithm (SGA) and the evolutionary Genetic Algorithm (GA). These optimization strategies are designed to fine-tune the model's accuracy and robustness. Moreover, k-fold cross-validation ensures the model's reliability and generalizability across different data sets. Trained and validated on the Bonn EEG data sets, the proposed optimized DCNN model achieved a test accuracy of 93.2%, demonstrating its ability to accurately classify EEG signals. In summary, the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system, thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinical settings. With its inherent classification performance, the proposed approach represents a significant step forward in improving patient outcomes through advanced AI techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Automated Structural Bolt Micro Looseness Monitoring Method Using Deep Learning.
- Author
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Qin, Min, Xie, Zhenbo, Xie, Jing, Yu, Xiaolin, Ma, Zhongyuan, and Wang, Jinrui
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DATA structures , *BOLTED joints , *AIRPLANE motors , *FLANGES , *COST - Abstract
The detection of bolt loosening in key components of aircraft engines faces problems such as complex and difficult-to-establish bolt loosening mechanism models, difficulty in identifying early loosening, and difficulty in extracting signal features with nonlinear and non-stationary characteristics. Therefore, the automated structural bolt micro looseness monitoring method using deep learning was proposed. Specifically, the addition of batch normalization methods enables the established Batch Normalized Stacked Autoencoders (BNSAEs) model to converge quickly and effectively, making the model easy to build and effective. Additionally, using characterization functions preprocess the original response signal not only simplifies the data structure but also ensures the integrity of features, which is beneficial for network training and reduces time costs. Finally, the effectiveness of the proposed method was verified by taking the bolted connection structures of two key components of aircraft engines, namely bolt connection structures and flange connection structures, as examples. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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7. Improved transferability of self-supervised learning models through batch normalization finetuning.
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Sirotkin, Kirill, Escudero-Viñolo, Marcos, Carballeira, Pablo, and García-Martín, Álvaro
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TRANSFER of training ,TASK performance ,CLASSIFICATION ,PATHOLOGY ,COST - Abstract
Abundance of unlabelled data and advances in Self-Supervised Learning (SSL) have made it the preferred choice in many transfer learning scenarios. Due to the rapid and ongoing development of SSL approaches, practitioners are now faced with an overwhelming amount of models trained for a specific task/domain, calling for a method to estimate transfer performance on novel tasks/domains. Typically, the role of such estimator is played by linear probing which trains a linear classifier on top of the frozen feature extractor. In this work we address a shortcoming of linear probing — it is not very strongly correlated with the performance of the models finetuned end-to-end— the latter often being the final objective in transfer learning— and, in some cases, catastrophically misestimates a model's potential. We propose a way to obtain a significantly better proxy task by unfreezing and jointly finetuning batch normalization layers together with the classification head. At a cost of extra training of only 0.16% model parameters, in case of ResNet-50, we acquire a proxy task that (i) has a stronger correlation with end-to-end finetuned performance, (ii) improves the linear probing performance in the many- and few-shot learning regimes and (iii) in some cases, outperforms both linear probing and end-to-end finetuning, reaching the state-of-the-art performance on a pathology dataset. Finally, we analyze and discuss the changes batch normalization training introduces in the feature distributions that may be the reason for the improved performance. The code is available at https://github.com/vpulab/bn_finetuning. [ABSTRACT FROM AUTHOR]
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- 2024
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8. GDnet-IP: Grouped Dropout-Based Convolutional Neural Network for Insect Pest Recognition.
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Li, Dongcheng, Xu, Yongqi, Yuan, Zheming, and Dai, Zhijun
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CONVOLUTIONAL neural networks ,RECOGNITION (Psychology) ,INSECT pests ,IMAGE recognition (Computer vision) ,SMART structures - Abstract
Lightweight convolutional neural network (CNN) models have proven effective in recognizing common pest species, yet challenges remain in enhancing their nonlinear learning capacity and reducing overfitting. This study introduces a grouped dropout strategy and modifies the CNN architecture to improve the accuracy of multi-class insect recognition. Specifically, we optimized the base model by selecting appropriate optimizers, fine-tuning the dropout probability, and adjusting the learning rate decay strategy. Additionally, we replaced ReLU with PReLU and added BatchNorm layers after each Inception layer, enhancing the model's nonlinear expression and training stability. Leveraging the Inception module's branching structure and the adaptive grouping properties of the WeDIV clustering algorithm, we developed two grouped dropout models, the iGDnet-IP and GDnet-IP. Experimental results on a dataset containing 20 insect species (15 pests and five beneficial insects) demonstrated an increase in cross-validation accuracy from 84.68% to 92.12%, with notable improvements in the recognition rates for difficult-to-classify species, such as Parnara guttatus Bremer and Grey (PGBG) and Papilio xuthus Linnaeus (PXLL), increasing from 38% and 47% to 62% and 93%, respectively. Furthermore, these models showed significant accuracy advantages over standard dropout methods on test sets, with faster training times compared to four conventional CNN models, highlighting their suitability for mobile applications. Theoretical analyses of model gradients and Fisher information provide further insight into the grouped dropout strategy's role in improving CNN interpretability for insect recognition tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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9. Intelligent evaluation method for identifying favorable shale oil areas based on improved stacked sparse autoencoder
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Rui Xu, Tie Yan, Shihui Sun, Jingyu Qu, and Zhaokai Hou
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shale oil ,favorable area ,autoencoder ,batch normalization ,semi-supervised learning ,Technology ,Science (General) ,Q1-390 - Abstract
Identifying and evaluating favorable areas is crucial for shale oil exploration and development, well-location deployment, and fracturing design. Traditional machine learning methods struggle to accurately extract the characteristics of favorable shale oil areas with limited labeled data, affecting accuracy and generalization. This study proposes an intelligent method for identifying favorable shale oil areas under semi-supervised learning (SSAE-plus) to identify and evaluate favorable shale oil areas of the Qingshankou Formation in the Songliao Basin. The experimental results show that this method can effectively overcome the favorable area identification modelâs reliance on labeled data and can adaptively extract the characteristics of favorable shale oil areas without supervision. The accuracy of model identification is as high as 98.82 percent. Compared with other methods, the SSAE-plus yields higher accuracy and efficiency, while being more stable and generalizable. The SSAE-plus achieved over 95 percent accuracy in identifying favorable shale oil areas across six datasets. It has broad application prospects in identifying and evaluating favorable areas, and provides valuable theoretical insights for shale oil development and exploration well layout.
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- 2025
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10. Identifying radiogenomic associations of breast cancer based on DCE‐MRI by using Siamese Neural Network with manufacturer bias normalization.
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Chen, Junhua, Zeng, Haiyan, Cheng, Yanyan, and Yang, Banghua
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EPIDERMAL growth factor receptors , *MAGNETIC resonance imaging , *HORMONE receptors , *BREAST cancer , *RADIOMICS , *BREAST - Abstract
Background and Purpose: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides prognostic information and guides treatment for patients with invasive breast cancer. The objective of this paper is to establish a non‐invasive system for identifying HER2 and HR in breast cancer using dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI). Methods: In light of the absence of high‐performance algorithms and external validation in previously published methods, this study utilizes 3D deep features and radiomics features to represent the information of the Region of Interest (ROI). A Siamese Neural Network was employed as the classifier, with 3D deep features and radiomics features serving as the network input. To neutralize manufacturer bias, a batch effect normalization method, ComBat, was introduced. To enhance the reliability of the study, two datasets, Predict Your Therapeutic Response with Imaging and moLecular Analysis (I‐SPY 1) and I‐SPY 2, were incorporated. I‐SPY 2 was utilized for model training and validation, while I‐SPY 1 was exclusively employed for external validation. Additionally, a breast tumor segmentation network was trained to improve radiomic feature extraction. Results: The results indicate that our approach achieved an average Area Under the Curve (AUC) of 0.632, with a Standard Error of the Mean (SEM) of 0.042 for HER2 prediction in the I‐SPY 2 dataset. For HR prediction, our method attained an AUC of 0.635 (SEM 0.041), surpassing other published methods in the AUC metric. Moreover, the proposed method yielded competitive results in other metrics. In external validation using the I‐SPY 1 dataset, our approach achieved an AUC of 0.567 (SEM 0.032) for HR prediction and 0.563 (SEM 0.033) for HER2 prediction. Conclusion: This study proposes a non‐invasive system for identifying HER2 and HR in breast cancer. Although the results do not conclusively demonstrate superiority in both tasks, they indicate that the proposed method achieved good performance and is a competitive classifier compared to other reference methods. Ablation studies demonstrate that both radiomics features and deep features for the Siamese Neural Network are beneficial for the model. The introduced manufacturer bias normalization method has been shown to enhance the method's performance. Furthermore, the external validation of the method enhances the reliability of this research. Source code, pre‐trained segmentation network, Radiomics and deep features, data for statistical analysis, and Supporting Information of this article are online at: https://github.com/FORRESTHUACHEN/Siamese_Neural_Network_based_Brest_cancer_Radiogenomic. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
11. Enhanced Convolutional Neural Network for Fashion Classification.
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Haji, Lailan M., Mustafa, Omar M., Abdullah, Sherwan A., and Ahmed, Omar M.
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CONVOLUTIONAL neural networks ,IMAGE recognition (Computer vision) ,FASHION ,GENERALIZATION ,ROTATIONAL motion - Abstract
Fashion items are hard to classify since there are a million variations in style, texture, and pattern. Image classification is among the noted strengths of convolutional neural networks. This research introduces an improved CNN architecture for fashion classification, utilizing image augmentation and batch normalization to improve model performance and generalization. To make the model more robust, image augmentation techniques like rotation, width and height shift, zoom, and flips were employed. In addition, a Batch Normalization layer is added in the middle, which can help on stabilizing the learning process and accelerating convergence. The proposed model was trained on an augmented dataset, achieving a satisfactory improvement in test accuracy of 91.97% compared to a baseline CNN model, which obtained 88.5% accuracy. According to the results, the image augmentation with the application of Batch Normalization improves the CNN architecture for better effectiveness in fashion classification tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
12. A Faster Privacy-Preserving Medical Image Diagnosis Scheme with Machine Learning
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Ran, Jiuhong and Li, Dong
- Published
- 2025
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13. Dhivehi Speech Recognition: A Multimodal Approach for Dhivehi Language in Resource-Constrained Settings
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Mehra, Sunakshi, Ranga, Virender, and Agarwal, Ritu
- Published
- 2024
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14. Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome
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Yulu Huang, Haomiao Sun, Qinchang Chen, Junjun Shen, Jin Han, Shiguang Shan, and Shushui Wang
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Noonan syndrome ,Genetic syndrome ,Convolution neural network ,Facial recognition ,Batch normalization ,Pediatrics ,RJ1-570 - Abstract
Abstract Background Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. Objectives This study develops advanced models to enhance the accuracy of diagnosis of NS. Methods A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. Results All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. Conclusion Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.
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- 2024
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15. SPOC learner’s final grade prediction based on a novel sampling batch normalization embedded deep neural network method
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Liang, Zhuonan, Liu, Ziheng, Shi, Huaze, Chen, Yunlong, Cai, Yanbing, Hong, Hong, Liang, Yating, Feng, Yafan, Yang, Yuqing, Zhang, Jing, and Fu, Peng
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Information and Computing Sciences ,Machine Learning ,Clinical Research ,Grade prediction ,Class balance ,SPOC ,Deep neural network ,Batch normalization ,Artificial Intelligence and Image Processing ,Computer Software ,Distributed Computing ,Information Systems ,Artificial Intelligence & Image Processing ,Software Engineering ,Electronics ,sensors and digital hardware ,Computer vision and multimedia computation ,Data management and data science ,Distributed computing and systems software - Published
- 2023
16. Intelligent Beta-Based Polynomial Approximation of Activation Functions for a Robust Data Encryption System.
- Author
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Issaoui, Hanen, ElAdel, Asma, and Zaied, Mourad
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CONVOLUTIONAL neural networks ,MACHINE learning ,POLYNOMIAL approximation ,BETA functions ,DATA encryption - Abstract
Deep neural network-based machine learning algorithms are widely used within different sectors and produce excellent results. However, their use requires access to private, often confidential, and sensitive information (financial, medical, etc). This requires precise measures and particular attention to data security and confidentiality. In this paper, we propose a new solution to this problem by integrating a proposed Convolutional Neural Network (CNN) model on encrypted data within the constraints of homomorphic encryption techniques. Specifically, we focus on the approximate activation functions ReLU, Sigmoid, and Tanh, which appear to be the key functions of CNNs. We start by developing new low-degree polynomials, which are essential for successful Homomorphic Encryption (HE). The activation functions will be replaced by these polynomials, which are based on the Beta function and its primitive. To make certain that the data is contained within a given range, the next step is to build a new CNN model using batch normalization. Finally, our methodology and the effectiveness of the proposed strategy are evaluated using Mnist and Cifar10. The experimental results support the proposed approach's efficiency. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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17. Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks.
- Author
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BUM JUN KIM, HYEYEON CHOI, HYEONAH JANG, and SANG WOO KIM
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TRANSFORMER models , *DEEP learning - Abstract
L2 regularization for weights in neural networks is widely used as a standard training trick. In addition to weights, the use of batch normalization involves an additional trainable parameter γ, which acts as a scaling factor. However, L2 regularization for γ remains an undiscussed mystery and is applied in different ways depending on the library and practitioner. In this article, we study whether L2 regularization for γ is valid. To explore this issue, we consider two approaches: (1) variance control to make the residual network behave like an identity mapping and (2) stable optimization through the improvement of effective learning rate. Through two analyses, we specify the desirable and undesirable γ to apply L2 regularization and propose four guidelines for managing them. In several experiments, we observed that applying L2 regularization to applicable γ increased 1% to 4% classification accuracy, whereas applying L2 regularization to inapplicable γ decreased 1% to 3% classification accuracy, which is consistent with our four guidelines. Our proposed guidelines were further validated through various tasks and architectures, including variants of residual networks and transformers. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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18. Towards Defending Multiple ℓp-Norm Bounded Adversarial Perturbations via Gated Batch Normalization.
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Liu, Aishan, Tang, Shiyu, Chen, Xinyun, Huang, Lei, Qin, Haotong, Liu, Xianglong, and Tao, Dacheng
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ARTIFICIAL neural networks - Abstract
There has been extensive evidence demonstrating that deep neural networks are vulnerable to adversarial examples, which motivates the development of defenses against adversarial attacks. Existing adversarial defenses typically improve model robustness against individual specific perturbation types (e.g., ℓ ∞ -norm bounded adversarial examples). However, adversaries are likely to generate multiple types of perturbations in practice (e.g., ℓ 1 , ℓ 2 , and ℓ ∞ perturbations). Some recent methods improve model robustness against adversarial attacks in multiple ℓ p balls, but their performance against each perturbation type is still far from satisfactory. In this paper, we observe that different ℓ p bounded adversarial perturbations induce different statistical properties that can be separated and characterized by the statistics of Batch Normalization (BN). We thus propose Gated Batch Normalization (GBN) to adversarially train a perturbation-invariant predictor for defending multiple ℓ p bounded adversarial perturbations. GBN consists of a multi-branch BN layer and a gated sub-network. Each BN branch in GBN is in charge of one perturbation type to ensure that the normalized output is aligned towards learning perturbation-invariant representation. Meanwhile, the gated sub-network is designed to separate inputs added with different perturbation types. We perform an extensive evaluation of our approach on commonly-used dataset including MNIST, CIFAR-10, and Tiny-ImageNet, and demonstrate that GBN outperforms previous defense proposals against multiple perturbation types (i.e., ℓ 1 , ℓ 2 , and ℓ ∞ perturbations) by large margins. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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19. Computer-based facial recognition as an assisting diagnostic tool to identify children with Noonan syndrome.
- Author
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Huang, Yulu, Sun, Haomiao, Chen, Qinchang, Shen, Junjun, Han, Jin, Shan, Shiguang, and Wang, Shushui
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FACE perception ,NOONAN syndrome ,CONVOLUTIONAL neural networks ,SYNDROMES in children ,PHYSICIANS - Abstract
Background: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects. Objectives: This study develops advanced models to enhance the accuracy of diagnosis of NS. Methods: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians. Results: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics. Conclusion: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
20. Improving Pneumonia Detection with Deep Learning Models: Insights from Chest X-Rays
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Dey, Puja, Mahmud, Tanjim, Hossain, Mohammad Shahadat, Andersson, Karl, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Panchenko, Vladimir, editor, Munapo, Elias, editor, Weber, Gerhard-Wilhelm, editor, Thomas, J. Joshua, editor, Intan, Rolly, editor, and Shamsul Arefin, Mohammad, editor
- Published
- 2024
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21. An Optimized Eight-Layer Convolutional Neural Network Based on Blocks for Chinese Fingerspelling Sign Language Recognition
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Chu, Huiwen, Jiang, Chenlei, Xu, Jingwen, Ye, Qisheng, Jiang, Xianwei, Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Wang, Bing, editor, Hu, Zuojin, editor, Jiang, Xianwei, editor, and Zhang, Yu-Dong, editor
- Published
- 2024
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- View/download PDF
22. Dealing with Training Deficiencies
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Toennies, Klaus D. and Toennies, Klaus D.
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- 2024
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23. Voltage Transformer Fault Diagnosis Based on Improved ResNet50
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Wang, Zezhou, Chen, Lun, Gan, Yucheng, Chen, Gang, Lu, Yanfeng, Zhou, Hongyi, Xu, Shuiqing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, and Cai, Li Cai, editor
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- 2024
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24. A Comparative Analysis of Pneumonia Detection Using Chest X-rays with DNN
- Author
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Jha, Prateek, Rohilla, Mohit, Goyal, Avantika, Arora, Siddharth, Sharma, Ruchi, Kumar, Jitender, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Swaroop, Abhishek, editor, Polkowski, Zdzislaw, editor, Correia, Sérgio Duarte, editor, and Virdee, Bal, editor
- Published
- 2024
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25. Human Age and Gender Prediction from Facial Images Using Deep Learning Methods.
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Dey, Puja, Mahmud, Tanjim, Chowdhury, Mohammad Sanaullah, Hossain, Mohammad Shahadat, and Andersson, Karl
- Subjects
DEEP learning ,CONVOLUTIONAL neural networks ,DATA augmentation ,FEATURE extraction ,GENDER ,FORECASTING - Abstract
Human age and gender prediction from facial images has garnered significant attention due to its importance in various applications. Traditional models struggle with large-scale variations in unfiltered images. Convolutional Neural Networks (CNNs) have emerged as effective tools for facial analysis due to their robust performance. This paper presents a novel CNN approach for robust age and gender classification using unconstrained real-world images. The CNN architecture includes convolution, pooling, and fully connected layers for feature extraction, dimension reduction, and mapping to output classes. Adience and UTKFace datasets were utilized, with the best training and testing accuracies achieved using an 80% training and 20% testing data split. Robust image pre-processing and data augmentation techniques were applied to handle dataset variations. The proposed approach outperformed existing methods, achieving age prediction accuracies of 86.42% and 81.96%, and gender prediction accuracies of 97.65% and 96.32% on the Adience and UTKFace datasets, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Stronger Heterogeneous Feature Learning for Visible-Infrared Person Re-Identification.
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Wang, Hao, Bi, Xiaojun, and Yu, Changdong
- Abstract
Visible-Infrared person re-identification (VI-ReID) is of great importance in the field of intelligent surveillance. It enables re-identification of pedestrians between daytime and dark scenarios, which can help police find escaped criminals at night. Currently, existing methods suffer from inadequate utilisation of cross-modality information, missing modality-specific discriminative information and weaknesses in perceiving differences between different modalities. To solve the above problems, we innovatively propose a stronger heterogeneous feature learning (SHFL) method for VI-ReID. First, we innovatively propose a Cross-Modality Group-wise constraint to solve the problem of inadequate utilization of cross-modality information. Secondly, we innovatively propose a Second-Order Homogeneous Invariant Regularizer to address the problem that missing modality-specific discriminative information. Finally, we innovatively propose a Modality-Aware Batch Normalization to address the problem of weaknesses in perceiving differences between different modalities. Extensive experimental results on two generic VI-ReID datasets demonstrate that the proposed final method outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. Classification of multi-spectral data with fine-tuning variants of representative models.
- Author
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Lakshmi, T. R. Vijaya, Reddy, Ch. Venkata Krishna, Kora, Padmavathi, Swaraja, K., Meenakshi, K., Kumari, Ch. Usha, and Reddy, L. Pratap
- Abstract
Due to rapid urbanization, agriculture drought, and environmental pollution, significant efforts have been focused on land use and land cover (LULC) multi-spectral scene classification. Identifying the changes in land use and land cover can facilitate updating the geographical maps. Besides, the technical challenges in multi-spectral images with implicit deep learning models due to the nature of multi-modal, it tackles real-life issues such as the collection of large-scale high-resolution data. The limited training samples are considered a crucial challenge in LULC deep learning classification as requiring a huge number of training samples to ensure the optimal learning procedure. The present work has focused on considering the fraction of multi-spectral data (EuroSAT data) and evaluated the exemplary CNN architectures such as shallow network (VGG16) and deep network (ResNet152V2) with different tuning variants along with the additional layers prior to classification layer to improve the optimal training of the networks to classify the multi-spectral data. The performance of the thirteen spectral bands of EuroSAT dataset that contain ten scene classes of land use and land cover were analyzed band-wise and combination of spectral bands. For the scene class 'Sea & lake' the best accuracy obtained was 96.17% with individual band B08A and 95.7% with Color Infra Red (CIR) band combination. The analysis provided in this work enables the remote sensing research community to boost performance even if the multi-spectral dataset size is small. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
28. Tooth and Supporting Tissue Anomalies Detection from Panoramic Radiography Using Integrating Convolution Neural Network with Batch Normalization.
- Author
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Fariza, Arna, Asmara, Rengga, Astuti, Eha Renwi, and Putra, Ramadhan Hardani
- Subjects
CONVOLUTIONAL neural networks ,PANORAMIC radiography ,ANOMALY detection (Computer security) ,TEETH ,THIRD molars - Abstract
Abnormalities commonly encountered in dental practice include tooth and supporting tissue issues such as caries, periapical abnormalities, resorption, and impacted third molars. Panoramic radiographs are frequently used for image scanning in dentistry and oral surgery. Diagnosing dental anomalies can be time-consuming due to the complexity of the orthodontic area, potentially leading to inaccuracies. This research proposes an end-to-end automated detection of dental and supporting tissue anomalies in patients, encompassing cavities, periapical lesions, resorption, and impacted third molars. This study evaluated the effectiveness of employing various pre-trained Convolutional Neural Network architectures, including ResNet-50, ResNeXt-50 32×4d, Inception-V3, and EfficientNet-V2. To enhance model performance, a batch normalization technique was integrated into the classification layer of these pre-trained models. Data pre-processing techniques, including horizontal and vertical flips, as well as random affine transformations, were applied to augment the dataset. Additionally, an image normalization procedure was implemented before the training and prediction phases. In the evaluation on 202 images, the integrated ResNeXt-50 32x4d model with batch normalization achieved the highest accuracy, precision, recall, and F1-score of 83.663%, 81.615%, 81.271%, and 81.066%, respectively. Based on the F1-score, this model demonstrates promising predictions of tooth and supporting tissue anomalies in an imbalanced dataset. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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- View/download PDF
29. 基于多尺度特征融合和多头自注意力机制的 非侵入式负荷监测.
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徐瑞琪 and 刘丹丹
- Abstract
In order to address the current issues of insufficient extraction of deep load features, low decomposition accuracy, and high training costs in the load decomposition model, a multi-scale feature fusion model was proposed. The model was composed of two parts: the load decomposition subnetwork and the load recognition subnetwork, both of which were employed with convolutional blocks composed of one-dimensional convolution and batch normalization for the initial extraction of load features. Subsequently, a pyramid pooling module was incorporated to precisely extract deep load features from multiple dimensions and fused them with the initial feature extraction part. Network parameters and training costs were significantly reduced by the pyramid pooling module. At the same time, in contrast to previous models with attention mechanisms, a multi-head self-attention mechanism was incorporated by the network. Different segments of load features were focused on by each attention head, achieving the selection of crucial load characteristics from multiple perspectives and further enhancing the performance of load disaggregation. Finally, experiments on the UK-DALE and REDD datasets show that the proposed model outperforms four benchmark models in both load disaggregation performance and appliance operation state recognition ability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
30. A robust inversion of logging-while-drilling responses based on deep neural network.
- Author
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Zhu, Gaoyang, Gao, Muzhi, and Wang, Bin
- Subjects
- *
ARTIFICIAL neural networks , *CHROMOSOME inversions , *NOISE measurement , *DATA logging - Abstract
Resistivity inversion plays a significant role in recent geological exploration, which can obtain formation information through logging data. However, resistivity inversion faces various challenges in practice. Conventional inversion approaches are always time-consuming, nonlinear, non-uniqueness, and ill-posed, which can result in an inaccurate and inefficient description of subsurface structure in terms of resistivity estimation and boundary location. In this paper, a robust inversion approach is proposed to improve the efficiency of resistivity inversion. Specifically, inspired by deep neural networks (DNN) remarkable nonlinear mapping ability, the proposed inversion scheme adopts DNN architecture. Besides, the batch normalization algorithm is utilized to solve the problem of gradient disappearing in the training process, as well as the k-fold cross-validation approach is utilized to suppress overfitting. Several groups of experiments are considered to demonstrate the feasibility and efficiency of the proposed inversion scheme. In addition, the robustness of the DNN-based inversion scheme is validated by adding different levels of noise to the synthetic measurements. Experimental results show that the proposed scheme can achieve faster convergence and higher resolution than the conventional inversion approach in the same scenario. It is very significant for geological exploration in layered formations. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
31. An In-Memory-Computing Binary Neural Network Architecture With In-Memory Batch Normalization
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Prathamesh Prashant Rege, Ming Yin, Sanjay Parihar, Joseph Versaggi, and Shashank Nemawarkar
- Subjects
Batch normalization ,binary neural network ,edge device ,in-memory computing ,process variation ,SRAM ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
This paper describes an in-memory computing architecture that combines full-precision computation for the first and last layers of a neural network while employing binary weights and input activations for the intermediate layers. This unique approach presents an efficient and effective solution for optimizing neural-network computations, reducing complexity, and enhancing energy efficiency. Notably, multiple architecture-level optimization methods are developed to ensure the binary operations thereby eliminating the need for intricate “digital logic” components external to the memory units. One of the key contributions of this study is in-memory batch normalization, which is implemented to provide good accuracy for CIFAR10 classification applications. Despite the inherent challenges posed by the process variations, the proposed design demonstrated an accuracy of 78%. Furthermore, the SRAM layer in the architecture showed an energy efficiency of 1086 TOPS/W and throughput of 23 TOPS, all packed efficiently within an area of 60 TOPS/mm2. This novel in-memory computing architecture offers a promising solution for next-generation efficient and high-performance deep learning applications.
- Published
- 2024
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32. Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios
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Bruno Casella, Roberto Esposito, Antonio Sciarappa, Carlo Cavazzoni, and Marco Aldinucci
- Subjects
Batch normalization ,epochs per round ,federated averaging ,federated learning ,neural networks ,non-IID data ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Training Deep Learning (DL) models require large, high-quality datasets, often assembled with data from different institutions. Federated Learning (FL) has been emerging as a method for privacy-preserving pooling of datasets employing collaborative training from different institutions by iteratively globally aggregating locally trained models. One critical performance challenge of FL is operating on datasets not independently and identically distributed (non-IID) among the federation participants. Even though this fragility cannot be eliminated, it can be debunked by a suitable optimization of two hyper-parameters: layer normalization methods and collaboration frequency selection. In this work, we benchmark five different normalization layers for training Neural Networks (NNs), two families of non-IID data skew, and two datasets. Results show that Batch Normalization, widely employed for centralized DL, is not the best choice for FL, whereas Group and Layer Normalization consistently outperform Batch Normalization, with a performance gain of up to about 15 % in the most challenging non-IID scenario. Similarly, frequent model aggregation decreases convergence speed and mode quality.
- Published
- 2024
- Full Text
- View/download PDF
33. Deep Learning Multi-User Detection for PD-SCMA
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Simon Chege and Tom Walingo
- Subjects
PD-SCMA ,deep learning ,multi-user detection ,batch normalization ,SER ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The performance of hybrid multi-radio access technologies depends on the sufficiency of the multi-user detection (MUD) at the receiver. For optimal performance of the hybrid power-domain sparse code multiple access (PD-SCMA), robust detection strategies are necessary to alleviate MUD complexity and reduces computational time. Deep learning (DL) based MUD techniques are the most promising as they can detect all symbols of an overloaded PD-SCMA without requiring additional operations of channel estimation and interference cancellation. This work proposes a deep neural network (DNN) aided MUD scheme (DNN-MUD) for an uplink PD-SCMA system supporting near users (NUs) and far users (FUs) multiplexed in power-, and code-domain, respectively. The proposed DNN-MUD features a unified framework that jointly performs successive interference cancellation (SIC) and message passing algorithm (MPA)/expectation propagation algorithm (EPA) operations to overcome interference propagation of SIC and computational complexity of MPA/EPA. The DNN training is enhanced by batch normalization to reduce the internal covariant shifts, thus enhancing the efficiency of detection. Performance results show that the average symbol error rate (SER), complexity and computational time of the proposed DNN-MUD significantly outperforms the conventional joint SIC-MPA/EPA schemes.
- Published
- 2024
- Full Text
- View/download PDF
34. Classification of Paediatric Pneumonia Using Modified DenseNet-121 Deep-Learning Model
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T. S. Arulananth, S. Wilson Prakash, Ramesh Kumar Ayyasamy, V. P. Kavitha, P. G. Kuppusamy, and P. Chinnasamy
- Subjects
Batch normalization ,chest X-ray ,DenseNet ,Drouput ,Maxpooling ,pediatric pneumonia ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
There is a substantial worldwide effect, both in terms of disease and death, that is caused by pediatric pneumonia, which is a disorder that affects children under the age of five. Even while Streptococcus pneumoniae is the most prevalent agent responsible for this sickness, it may also be brought on by other bacteria, viruses, or fungi. An efficient approach utilizing deep-learning methods to forecast pediatric pneumonia reliably using chest X-ray images has been developed. The current study presents an updated version of the DenseNet-121 deep-learning model developed for identifying scans of pediatric pneumonia. The batch normalization, maximum pooling, and dropout layers introduced into the standard model were done so to improve its accuracy. The activations of the preceding layers are scaled and normalized using batch normalization, leading to a mean value of zero and a variance of one. This helps decrease internal variability during training, which speeds up the training process, promotes model stability, and improves the model’s overall capacity to generalize. Max pooling is a beneficial technique for reducing the number of model parameters, making the model more computationally effective. Meanwhile, dropout is a preventative measure against overfitting by decreasing the co-dependence of neurons. As a result, the network acquires more durable and adaptive features. Classifying instances of pediatric pneumonia with the help of the proposed model resulted in an exceptional accuracy rate of 97.03%.
- Published
- 2024
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- View/download PDF
35. GDnet-IP: Grouped Dropout-Based Convolutional Neural Network for Insect Pest Recognition
- Author
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Dongcheng Li, Yongqi Xu, Zheming Yuan, and Zhijun Dai
- Subjects
common pests ,lightweight CNN ,insect image recognition ,grouped dropout ,activation function ,batch normalization ,Agriculture (General) ,S1-972 - Abstract
Lightweight convolutional neural network (CNN) models have proven effective in recognizing common pest species, yet challenges remain in enhancing their nonlinear learning capacity and reducing overfitting. This study introduces a grouped dropout strategy and modifies the CNN architecture to improve the accuracy of multi-class insect recognition. Specifically, we optimized the base model by selecting appropriate optimizers, fine-tuning the dropout probability, and adjusting the learning rate decay strategy. Additionally, we replaced ReLU with PReLU and added BatchNorm layers after each Inception layer, enhancing the model’s nonlinear expression and training stability. Leveraging the Inception module’s branching structure and the adaptive grouping properties of the WeDIV clustering algorithm, we developed two grouped dropout models, the iGDnet-IP and GDnet-IP. Experimental results on a dataset containing 20 insect species (15 pests and five beneficial insects) demonstrated an increase in cross-validation accuracy from 84.68% to 92.12%, with notable improvements in the recognition rates for difficult-to-classify species, such as Parnara guttatus Bremer and Grey (PGBG) and Papilio xuthus Linnaeus (PXLL), increasing from 38% and 47% to 62% and 93%, respectively. Furthermore, these models showed significant accuracy advantages over standard dropout methods on test sets, with faster training times compared to four conventional CNN models, highlighting their suitability for mobile applications. Theoretical analyses of model gradients and Fisher information provide further insight into the grouped dropout strategy’s role in improving CNN interpretability for insect recognition tasks.
- Published
- 2024
- Full Text
- View/download PDF
36. Retinal lesions classification for diabetic retinopathy using custom ResNet-based classifier.
- Author
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Kumar, Silpa Ajith and Kumar, James Satheesh
- Subjects
DEEP learning ,DIABETIC retinopathy ,RETINAL diseases ,CONVOLUTIONAL neural networks ,RETINAL imaging ,MEDICAL personnel - Abstract
Failure to diagnose and treat retinal illnesses on time might lead to irreversible blindness. The focus is on three common retinal lesions associated with diabetic retinopathy (DR): microaneurysms (MAs), haemorrhages, and exudates. The proposed solution leverages deep learning, employing a customized residual network (ResNet) based classifier trained on real-time retinal images meticulously annotated and graded by ophthalmologists. Annotation noise was a significant obstacle addressed by downsampling and augmenting the data. Compared to cutting-edge techniques, this one performs better with test-set accuracy of 93.34% across all classes. This approach holds great promise for enhancing early detection and treatment of DR by automating the recognition of these vital retinal abnormalities. The ability to automatically classify these symptoms can aid clinicians in making more precise diagnosis and starting treatments sooner. This research shows that deep learning-based approaches are highly effective, especially when combined with a customised ResNet-based classifier and thorough preprocessing steps. We observed that this method provides the ability to better the lives of patients and lower the rate of permanent blindness resulting from retinal disorders. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
37. Learnable Leakage and Onset-Spiking Self-Attention in SNNs with Local Error Signals.
- Author
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Shi, Cong, Wang, Li, Gao, Haoran, and Tian, Min
- Subjects
- *
ARTIFICIAL neural networks , *LEAKAGE , *CONVOLUTIONAL neural networks - Abstract
Spiking neural networks (SNNs) have garnered significant attention due to their computational patterns resembling biological neural networks. However, when it comes to deep SNNs, how to focus on critical information effectively and achieve a balanced feature transformation both temporally and spatially becomes a critical challenge. To address these challenges, our research is centered around two aspects: structure and strategy. Structurally, we optimize the leaky integrate-and-fire (LIF) neuron to enable the leakage coefficient to be learnable, thus making it better suited for contemporary applications. Furthermore, the self-attention mechanism is introduced at the initial time step to ensure improved focus and processing. Strategically, we propose a new normalization method anchored on the learnable leakage coefficient (LLC) and introduce a local loss signal strategy to enhance the SNN's training efficiency and adaptability. The effectiveness and performance of our proposed methods are validated on the MNIST, FashionMNIST, and CIFAR-10 datasets. Experimental results show that our model presents a superior, high-accuracy performance in just eight time steps. In summary, our research provides fresh insights into the structure and strategy of SNNs, paving the way for their efficient and robust application in practical scenarios. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
38. 多分支图像去噪算法研究.
- Author
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耿俊, 李文海, 吴子豪, and 孙鑫杰
- Abstract
Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
39. 全连接残差网络武器站决策方法.
- Author
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张健 and 李强
- Abstract
Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2023
- Full Text
- View/download PDF
40. Audio verification in forensic investigation using light deep neural network
- Author
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AL-Shakarchy, Noor D., Abdullah, Zahraa Najm, Alameen, Zainab M., and Harjan, Zahraa A.
- Published
- 2024
- Full Text
- View/download PDF
41. FairAdaBN: Mitigating Unfairness with Adaptive Batch Normalization and Its Application to Dermatological Disease Classification
- Author
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Xu, Zikang, Zhao, Shang, Quan, Quan, Yao, Qingsong, Zhou, S. Kevin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Greenspan, Hayit, editor, Madabhushi, Anant, editor, Mousavi, Parvin, editor, Salcudean, Septimiu, editor, Duncan, James, editor, Syeda-Mahmood, Tanveer, editor, and Taylor, Russell, editor
- Published
- 2023
- Full Text
- View/download PDF
42. Classification of Alzheimer’s Disease Subjects from MRI Using Deep Convolutional Neural Networks
- Author
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Papadimitriou, Orestis, Kanavos, Athanasios, Mylonas, Phivos, Maragoudakis, Manolis, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabassi, Katerina, editor, Mylonas, Phivos, editor, and Caro, Jaime, editor
- Published
- 2023
- Full Text
- View/download PDF
43. Comparative Study of Regularization Techniques for VGG16, VGG19 and ResNet-50 for Plant Disease Detection
- Author
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Suryawanshi, Vaishali, Adivarekar, Sahil, Bajaj, Krish, Badami, Reem, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Kumar, Sandeep, editor, Hiranwal, Saroj, editor, Purohit, S.D., editor, and Prasad, Mukesh, editor
- Published
- 2023
- Full Text
- View/download PDF
44. The Prediction of Protein Structure Using Neural Network
- Author
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Shifana Rayesha, S. M., Aisha Banu, W., Priya, Sharon, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Sharma, Neha, editor, Goje, Amol, editor, Chakrabarti, Amlan, editor, and Bruckstein, Alfred M., editor
- Published
- 2023
- Full Text
- View/download PDF
45. A Framework with IOAHT for Heat Stress Detection and Haemoprotozoan Disease Classification Using Multimodal Approach Combining LSTM and CNN
- Author
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Reddy, Shiva Sumanth, Nandini, C., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahapatra, Rajendra Prasad, editor, Peddoju, Sateesh K., editor, Roy, Sudip, editor, and Parwekar, Pritee, editor
- Published
- 2023
- Full Text
- View/download PDF
46. Improvement Deep Leaning Network Performance by Increasing Its Complexity
- Author
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El Hadi, Moulay Lhabib, Nasri, M’barek, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Ben Ahmed, Mohamed, editor, Boudhir, Anouar Abdelhakim, editor, Santos, Domingos, editor, Dionisio, Rogerio, editor, and Benaya, Nabil, editor
- Published
- 2023
- Full Text
- View/download PDF
47. Performance Assessment of Normalization in CNN with Retinal Image Segmentation
- Author
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Kundalakkaadan, Junaciya, Rawat, Akhilesh, Kumar, Rajeev, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Thakur, Manoj, editor, Agnihotri, Samar, editor, Rajpurohit, Bharat Singh, editor, Pant, Millie, editor, Deep, Kusum, editor, and Nagar, Atulya K., editor
- Published
- 2023
- Full Text
- View/download PDF
48. Domain-Conditioned Normalization for Test-Time Domain Generalization
- Author
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Jiang, Yuxuan, Wang, Yanfeng, Zhang, Ruipeng, Xu, Qinwei, Zhang, Ya, Chen, Xin, Tian, Qi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Karlinsky, Leonid, editor, Michaeli, Tomer, editor, and Nishino, Ko, editor
- Published
- 2023
- Full Text
- View/download PDF
49. An Improved Conv-LSTM Method for Gear Fault Detection
- Author
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Zhang, Yang, Zhang, Jianwu, Zhang, Guanhong, Li, Hong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Xu, Yuan, editor, Yan, Hongyang, editor, Teng, Huang, editor, Cai, Jun, editor, and Li, Jin, editor
- Published
- 2023
- Full Text
- View/download PDF
50. Intelligent Recognition of Waterline Value Based on Neural Network
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Zhang, Kun, Kong, Chaoran, Sun, Fuquan, Cong, Chenglong, Shen, Yue, Jiang, Yushan, Ceccarelli, Marco, Series Editor, Agrawal, Sunil K., Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, and Dai, Honghua, editor
- Published
- 2023
- Full Text
- View/download PDF
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