Početkom Domovinskog rata na području Osijeka vodile su se najžešće borbe. Postojala je intenzivna ratna dinamika hrvatskih oružanih snaga protiv dijela pobunjenih Srba i Jugoslavenske narodne ...armije, što je rezultiralo najvećim brojem vojnih i civilnih žrtava. Zbog položaja Osijeka u istočnoj Hrvatskoj, gdje Srbija izravno graniči s Republikom Hrvatskom, pobunjeni Srbi i Jugoslavenska narodna armija imali su neograničenu logističku podršku. Nadalje, nacionalna struktura stanovništva u istočnoj Hrvatskoj, gdje su postojale srpske enklave (Tenja, Bobota, Vera, Pačetin, Trpinja, Bijelo Brdo i druga sela), olakšavala je velikosrpsku agresiju i ometala obrambene aktivnosti hrvatskih vlasti. Grad Osijek bio je stožerni grad koji je pridonio obrani istočne Hrvatske. Gradsko gospodarstvo kontinuirano je funkcioniralo tijekom Domovinskog rata, snažno podržavajući njegovu obranu, unatoč brojnim ratnim štetama i demografskim gubicima.
The dark web has been confronted with a significant increase in the number and variety of onion services of illegitimate and criminal intent. Anonymity, encryption, and the technical complexity of ...the Tor network are key challenges in detecting, disabling, and regulating such services. Instead of tracking an operational location, cyber threat intelligence can become more proactive by utilizing recent advances in Artificial Intelligence (AI) to detect and classify onion services based on the content, as well as provide an interpretation of the classification outcome. In this paper, we propose a novel multimodal classification approach based on explainable deep learning that classifies onion services based on the image and text content of each site. A Convolutional Neural Network with Gradient-weighted Class Activation Mapping (Grad-CAM) and a pre-trained word embedding with Bahdanau additive attention are the core capabilities of this approach that classify and contextualize the representative features of an onion service. We demonstrate the superior classification accuracy of this approach as well as the role of explainability in decision-making that collectively enables proactive cyber threat intelligence in the dark web.
In response to the current lack of annotations for flower images and insufficient focus on key image features in traditional fine-grained flower image classification based on deep learning, this ...study proposes the SA-ConvNeXt flower image classification model. Initially, in the image preprocessing stage, a padding algorithm was used to prevent image deformation and loss of detail caused by scaling. Subsequently, the model was integrated using multi-level feature extraction within the Efficient Channel Attention (ECA) mechanism, forming an M-ECA structure to capture channel features at different levels; a pixel attention mechanism was also introduced to filter out irrelevant or noisy information in the images. Following this, a parameter-free attention module (SimAM) was introduced after deep convolution in the ConvNeXt Block to reweight the input features. SANet, which combines M-ECA and pixel attention mechanisms, was employed at the end of the module to further enhance the model’s dynamic extraction capability of channel and pixel features. Considering the model’s generalization capability, transfer learning was utilized to migrate the pretrained weights of ConvNeXt on the ImageNet dataset to the SA-ConvNeXt model. During training, the Focal Loss function and the Adam optimizer were used to address sample imbalance and reduce gradient fluctuations, thereby enhancing training stability. Finally, the Grad-CAM++ technique was used to generate heatmaps of classification predictions, facilitating the visualization of effective features and deepening the understanding of the model’s focus areas. Comparative experiments were conducted on the Oxford Flowers102 flower image dataset. Compared to existing flower image classification technologies, SA-ConvNeXt performed excellently, achieving a high accuracy of 96.7% and a recall rate of 98.2%, with improvements of 4.0% and 3.7%, respectively, compared to the original ConvNeXt. The results demonstrate that SA-ConvNeXt can effectively capture more accurate key features of flower images, providing an effective technical means for flower recognition and classification.
The guava plant is widely cultivated in various regions of the Sub-Continent and Asian countries, including Bangladesh, due to its adaptability to different soil conditions and climate environments. ...The fruit plays a crucial role in providing food security and nutrition for the human body. However, guava plants are susceptible to various infectious leaf diseases, leading to significant crop losses. To address this issue, several heavyweight deep learning models have been developed in precision agriculture. This research proposes a transfer learning-based model named GLD-Det, which is designed to be both lightweight and robust, enabling real-time detection of guava leaf disease using two benchmark datasets. GLD-Det is a modified version of MobileNet, featuring additional components with two pooling layers such as max and global average, three batch normalisation layers, three dropout layers, ReLU as an activation function with four dense layers, and SoftMax as a classification layer with the last lighter dense layer. The proposed GLD-Det model outperforms all existing models with impressive accuracy, precision, recall, and AUC score with values of 0.98, 0.98, 0.97, and 0.99 on one dataset, and with values of 0.97, 0.97, 0.96, and 0.99 for the other dataset, respectively. Furthermore, to enhance trust and transparency, the proposed model has been explained using the Grad-CAM technique, a class-discriminative localisation approach.
Breast cancer is a significant health concern among women. Prompt diagnosis can diminish the mortality rate and direct patients to take steps for cancer treatment. Recently, deep learning has been ...employed to diagnose breast cancer in the context of digital pathology. To help in this area, a transfer learning-based model called 'HE-HER2Net' has been proposed to diagnose multiple stages of HER2 breast cancer (HER2-0, HER2-1+, HER2-2+, HER2-3+) on H&E (hematoxylin & eosin) images from the BCI dataset. HE-HER2Net is the modified version of the Xception model, which is additionally comprised of global average pooling, several batch normalization layers, dropout layers, and dense layers with a swish activation function. This proposed model exceeds all existing models in terms of accuracy (0.87), precision (0.88), recall (0.86), and AUC score (0.98) immensely. In addition, our proposed model has been explained through a class-discriminative localization technique using Grad-CAM to build trust and to make the model more transparent. Finally, nuclei segmentation has been performed through the StarDist method.
Several training methods have been developed to acquire motion information during real-time walking; these methods also feed the information back to the trainee. Trainees adjust their gait to ensure ...that the measured value approaches the target value, which may not always be suitable for each trainee. Therefore, we aim to develop a gait feedback training system that considers individual differences, classifies the gait of the trainee, and identifies adjustments for body parts and timing. A convolutional neural network (CNN) has a feature extraction function and is robust in terms of each feature position; therefore, it can be used to classify a gait as ideal or non-ideal. Additionally, when the gradient-weighted class activation mapping (Grad-CAM) is applied to the gait classification model, the output measures the influence degree contributed by the trainee’s each body part to the classification results. Thus, the trainee can visually determine the body parts that need to be adjusted through the use of the output. In this study, we focused on gaits related to stumbling. We measured the kinematics and kinetics data for participants and generated multivariate gait data, which were labeled as “gait rarely associated with stumbling” class or “gait frequently associated with stumbling” class using clustering with dynamic time warping. Next, the multichannel deep CNN (MC-DCNN) was used to learn the gait using the multivariate gait data and the corresponding classes. Finally, the data for verification were input into the MC-DCNN model, and we visualized the influence degrees of each place of the multivariate gait data for classification using Grad-CAM. The MC-DCNN model classified gaits with a high accuracy of 97.64±0.40%, and it learned the features that determine the thumb-to-ground distance. The output of the Grad-CAM indicated body parts, timing, and the relative strength of features that have an important effect on the thumb-to-ground distance.
Facial expression recognition from images is a challenging problem in computer vision applications. Convolutional neural network (CNN), the state-of-the-art method for various computer vision tasks, ...has had limited success in predicting expressions from faces having extreme poses, illumination, and occlusion conditions. To mitigate this issue, CNNs are often accompanied by techniques like transfer, multitask, or ensemble learning that provide high accuracy at the cost of increased computational complexity. In this article, the authors propose a part-based ensemble transfer learning network that models how humans recognize facial expressions by correlating visual patterns emanating from facial muscles' motor movements with a specific expression. The proposed network performs transfer learning from facial landmark localization to facial expression recognition. It consists of five subnetworks, and each subnetwork performs transfer learning from one of the five subsets of facial landmarks: eyebrows, eyes, nose, mouth, or jaw to expression classification. The network's performance is evaluated using the Cohn-Kanade (CK+), Japanese female facial expression (JAFFE), and static facial expressions in the wild datasets, and it outperforms the benchmark for CK+ and JAFFE datasets by 0.51% and 5.34%, respectively. Additionally, the proposed ensemble network consists of only 1.65 M model parameters, ensuring computational efficiency during training and real-time deployment. Gradient-weighted class activation mapping visualizations of the network reveal the complementary nature of its subnetworks, a key design parameter of an effective ensemble network. Lastly, cross-dataset evaluation results show that the the proposed ensemble has a high generalization capacity, making it suitable for real-world usage.
Objective: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate ...recognition of the typical morphological characteristics. A deep learning-based model to automatically detect, segment, and quantify the region of cavity lesion on CT scans has potential in clinical diagnosis, monitoring, and treatment efficacy assessment. Methods: A weakly-supervised deep learning-based method named CSA2-ResNet was proposed to quantitatively characterize cavity lesions in this paper. The lung parenchyma was firstly segmented using a pretrained 2D segmentation model, and then the output with or without cavity lesions was fed into the developed deep neural network containing hybrid attention modules. Next, the visualized lesion was generated from the activation region of the classification network using gradient-weighted class activation mapping, and image processing was applied for post-processing to obtain the expected segmentation results of cavity lesions. Finally, the automatic characteristic measurement of cavity lesions (e.g., area and thickness) was developed and verified. Results: the proposed weakly-supervised segmentation method achieved an accuracy, precision, specificity, recall, and F1-score of 98.48%, 96.80%, 97.20%, 100%, and 98.36%, respectively. There is a significant improvement (P < 0.05) compared to other methods. Quantitative characterization of morphology also obtained good analysis effects. Conclusions: The proposed easily-trained and high-performance deep learning model provides a fast and effective way for the diagnosis and dynamic monitoring of pulmonary cavity lesions in clinic. Clinical and Translational Impact Statement: This model used artificial intelligence to achieve the detection and quantitative analysis of pulmonary cavity lesions in CT scans. The morphological features revealed in experiments can be utilized as potential indicators for diagnosis and dynamic monitoring of patients with cavity lesions
The opacity of deep learning makes its application challenging in the medical field. Therefore, there is a need to enable explainable artificial intelligence (XAI) in the medical field to ensure that ...models and their results can be explained in a manner that humans can understand. This study uses a high-accuracy computer vision algorithm model to transfer learning to medical text tasks and uses the explanatory visualization method known as gradient-weighted class activation mapping (Grad-CAM) to generate heat maps to ensure that the basis for decision-making can be provided intuitively or via the model. The system comprises four modules: pre-processing, word embedding, classifier, and visualization. We used Word2Vec and BERT to compare word embeddings and use ResNet and 1Dimension convolutional neural networks (CNN) to compare classifiers. Finally, the Bi-LSTM was used to perform text classification for direct comparison. With 25 epochs, the model that used pre-trained ResNet on the formalized text presented the best performance (recall of 90.9%, precision of 91.1%, and an F1 score of 90.2% weighted). This study uses ResNet to process medical texts through Grad-CAM-based explainable artificial intelligence and obtains a high-accuracy classification effect; at the same time, through Grad-CAM visualization, it intuitively shows the words to which the model pays attention when making predictions.
Matoševa fantastična proza ima antologijsku važnost unutar korpusa hrvatske fantastične književnosti. Napisana je u dosluhu s promjenom paradigme na području fantastike krajem 19. stoljeća. Namjera ...ovog rada analizirati je reprezentaciju velegrada u Matoševim novelama Miš, Ubio! i Duševni čovjek i teorijskim osloncem na radove G. Simmela, M. Bahtina, J. Lotmana i G. Bachelarda utvrditi u kojoj je mjeri pojava neobičnih, neobjašnjivih i bizarnih događaja vezana uz urbane prostore u kojima se radnja odvija.
PDF
