Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: a) Decoding emotions from deep convolutional neural network (DCNN) representations using partial least squares regression. b) fc8 layer in the AlexNet model outperformed fc8 layer in EmoNet model in predicting emotion ratings. Each line and dot represent the result of a cross-validated fold. c) AlexNet consistently outperformed EmoNet in predicting emotion ratings across three subsets of BOLD5000 images. d) EmoNet outperformed AlexNet in predicting emotion ratings for the Cowen17 dataset. In contrast, for the BOLD5000 dataset, AlexNet outperformed EmoNet in predicting emotion ratings. Error bars represent the standard error across cross-validated folds.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques:

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: a) Results of decoding emotions from AlexNet representations. These results show that emotional information is processed hierarchically in a visual object processing system. b) Differences between AlexNet layers in predicting emotion ratings. c) The hierarchical processing of emotional information was consistent across the three subsets of BOLD5000 images. d) The hierarchical processing of emotional information was consistent regardless of whether the BOLD5000 or Cowen17 dataset was used. Error bars represent the standard error across cross-validated folds and each dot represents the result of a cross-validated fold.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques:

Journal: bioRxiv

Article Title: Object representations drive emotion schemas across a large and diverse set of daily-life scenes

doi: 10.1101/2025.02.19.638854

Figure Lengend Snippet: The representational similarity analysis results indicated that both a) the conv1 layer and b) the fc8 layer of AlexNet exhibited greater within-cluster similarity than between-cluster similarity, suggesting that both layers encode emotional information, regardless of the number of K-means clusters. c) The fc8 layer demonstrated a larger difference in pattern similarity between within-cluster and between-cluster comparisons than the conv1 layer, indicating that the fc8 layer encodes more emotional information. Error bars represent the standard error across clusters and each dot represents the result of a cluster.

Article Snippet: The AlexNet DCNN model was implemented via MATLAB’s Deep Learning Toolbox, to extract features from 4913 images.

Techniques:

Journal: Computational and Mathematical Methods in Medicine

Article Title: Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

doi: 10.1155/2022/8330833

Figure Lengend Snippet: AlexNet architecture.

Article Snippet: All images are also diagnosed using deep learning techniques for two models, namely, AlexNet and ResNet-18, through SoftMax and by using machine learning techniques through SVM.

Techniques:

Journal: Computational and Mathematical Methods in Medicine

Article Title: Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

doi: 10.1155/2022/8330833

Figure Lengend Snippet: Hybrid architecture between deep and machine learning: (a) AlexNet+SVM; (b) ResNet-18+SVM.

Article Snippet: All images are also diagnosed using deep learning techniques for two models, namely, AlexNet and ResNet-18, through SoftMax and by using machine learning techniques through SVM.

Techniques:

Journal: Computational and Mathematical Methods in Medicine

Article Title: Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

doi: 10.1155/2022/8330833

Figure Lengend Snippet: Adjusted training parameters of ResNet-18 and AlexNet models.

Article Snippet: All images are also diagnosed using deep learning techniques for two models, namely, AlexNet and ResNet-18, through SoftMax and by using machine learning techniques through SVM.

Techniques: Biomarker Discovery

Journal: Computational and Mathematical Methods in Medicine

Article Title: Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

doi: 10.1155/2022/8330833

Figure Lengend Snippet: (a) Confusion matrix for AlexNet to evaluate MRI brain tumours. (b) Confusion matrix for ResNet-18 to evaluate MRI brain tumours.

Article Snippet: All images are also diagnosed using deep learning techniques for two models, namely, AlexNet and ResNet-18, through SoftMax and by using machine learning techniques through SVM.

Techniques:

Journal: Computational and Mathematical Methods in Medicine

Article Title: Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

doi: 10.1155/2022/8330833

Figure Lengend Snippet: (a) Confusion matrix for AlexNet+SVM to evaluate MRI brain tumours. (b) Confusion matrix for ResNet-18+SVM to evaluate MRI brain tumours.

Article Snippet: All images are also diagnosed using deep learning techniques for two models, namely, AlexNet and ResNet-18, through SoftMax and by using machine learning techniques through SVM.

Techniques:

Journal: Computational and Mathematical Methods in Medicine

Article Title: Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

doi: 10.1155/2022/8330833

Figure Lengend Snippet: Results of diagnosing brain tumours using deep learning models and hybrid deep and machine learning techniques.

Article Snippet: All images are also diagnosed using deep learning techniques for two models, namely, AlexNet and ResNet-18, through SoftMax and by using machine learning techniques through SVM.

Techniques:

Journal: Computational and Mathematical Methods in Medicine

Article Title: Early Diagnosis of Brain Tumour MRI Images Using Hybrid Techniques between Deep and Machine Learning

doi: 10.1155/2022/8330833

Figure Lengend Snippet: Diagnostic accuracy of the four models for diagnosing each tumour class.

Article Snippet: All images are also diagnosed using deep learning techniques for two models, namely, AlexNet and ResNet-18, through SoftMax and by using machine learning techniques through SVM.

Techniques: Diagnostic Assay

Journal: Applied Soft Computing

Article Title: The ensemble deep learning model for novel COVID-19 on CT images

doi: 10.1016/j.asoc.2020.106885

Figure Lengend Snippet: AlexNet network structure diagram.

Article Snippet: In first experiment, deep learning model uses AlexNet model, classification model uses Softmax, named AlexNet-Softmax.

Techniques:

Journal: Applied Soft Computing

Article Title: The ensemble deep learning model for novel COVID-19 on CT images

doi: 10.1016/j.asoc.2020.106885

Figure Lengend Snippet: AlexNet_Softmax classification results.

Article Snippet: In first experiment, deep learning model uses AlexNet model, classification model uses Softmax, named AlexNet-Softmax.

Techniques:

Journal: Applied Soft Computing

Article Title: The ensemble deep learning model for novel COVID-19 on CT images

doi: 10.1016/j.asoc.2020.106885

Figure Lengend Snippet: AlexNet_Softmax classification evaluation index.

Article Snippet: In first experiment, deep learning model uses AlexNet model, classification model uses Softmax, named AlexNet-Softmax.

Techniques:

Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Review of existing leaf disease methodologies with limitations.

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Extraction, Modification

Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Comparison of the proposed approach with the latest approaches (tomato leaf 10 classes).

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Comparison, Modification

Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Comparison of the suggested approach with recently established models for various crops.

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Comparison

Journal: Scientific Reports

Article Title: Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

doi: 10.1038/s41598-024-72237-x

Figure Lengend Snippet: Comparison of the proposed model's training parameters with state-of-the-art models.

Article Snippet: Da Rocha et al. , BO DL , AlexNet, ResNet50, SqueezeNet , Lack of extensive hyperparameter optimization.

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection †

doi: 10.3390/s21113883

Figure Lengend Snippet: Summary of related work.

Article Snippet: 2017, Mansour et al. [ ] , AlexNet with multiple optimization techniques , Accuracy of 95.26% with principal component analysis and 97.93% with FC7 features , EyePACS.

Techniques: Biomarker Discovery, Activation Assay

Journal: Scientific Reports

Article Title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

doi: 10.1038/srep20410

Figure Lengend Snippet: The errors were computed over 3 epochs, of which each has 20000 iterations. Both learning processes used the same local receptive fields and drop ratio. The two loss functions were associated with a large L2-norm weight decay constant 0.005 (larger than that used in AlexNet ), which has proved to be useful for improving generalization of neural networks . Under these settings, softmax and hinge respectively achieved 0.932 and 0.891 in validation accuracy.

Article Snippet: AlexNet , Softmax , 0.834.

Techniques: Biomarker Discovery

Journal: Scientific Reports

Article Title: Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network

doi: 10.1038/srep20410

Figure Lengend Snippet: Comparison of DCNNs with other methods on the same dataset.

Article Snippet: AlexNet , Softmax , 0.834.

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection †

doi: 10.3390/s21113883

Figure Lengend Snippet: Summary of related work.

Article Snippet: 2018, Wang et al. [ ] , AlexNet, VGG16, and Inception-V3 , 37.43%, 50.03%, 63.23% accuracy, respectively , EyePACS.

Techniques: Biomarker Discovery, Activation Assay