Search Results (152)

Rapid non-destructive evaluation of the moisture content of freshly harvested paddy rice in the field is essential for determining the optimal harvest timing, ensuring high-quality rice production and energy savings. This study developed a non-destructive prediction model for the moisture content of paddy rice using near-infrared (NIR) spectroscopy combined with machine learning and deep learning techniques. Rice samples were collected weekly during the ripening period after heading, and NIR reflectance spectra were acquired in the range of 950–2200 nm. Seven spectral preprocessing techniques were applied; and the prediction models developed, using partial least squares regression, support vector regression, deep neural network, and one-dimensional convolutional neural networks (1D-CNNs) based on VGGNet and EfficientNet architectures. Among these, the EfficientNet-based 1D-CNN combined with Savitzky–Golay 1st order derivative preprocessing showed the highest performance, achieving an ${\mathrm{R}}_{\mathrm{p}}^2$ of 0.999 and an RMSE_P of 0.001 (Friedman test, p < 0.001; Kendall’s W = 0.97), significantly outperforming previous traditional machine learning models. The results demonstrate that the proposed prediction model enables highly accurate estimation of moisture content in freshly harvested paddy rice without requiring drying or milling. The proposed approach can be implemented across various agricultural operations, enabling optimal harvest timing, quality control during storage, energy efficient drying, and real-time monitoring via on-combine sensor systems. Full article

Brain age has been widely recognized as an important biomarker for monitoring adolescent brain development and assessing dementia risk. However, existing model visualization methods primarily highlight brain regions associated with aging, making it difficult to comprehensively reveal broader brain changes. In this study, we developed a VGGNet-based brain age prediction model and proposed the Softmax-Derived Brain Age Mapping algorithm to simultaneously identify brain regions associated with both youthful and aging features. The resulting saliency maps provide explicit representations of developmental and degenerative processes across different brain regions. Brain Age Map analysis revealed that aging features in the healthy group were primarily confined to the frontal cortex, aligning with findings that the frontal lobe is the earliest region to undergo natural senescence. In contrast, the dementia group exhibited widespread aging across the frontal, temporal, parietal, and occipital lobes, as well as the ventricular regions. These results suggest that the spatial distribution of brain aging can serve as a critical biomarker for distinguishing normal aging trajectories from pathological degeneration. From an application perspective, we further explored the potential of the proposed framework in neurodegenerative diseases. The analysis reveals that dementia patients generally exhibit an advanced brain age, with cortical aging being markedly more pronounced than in age-matched healthy samples. Notably, although dementia cases were not included in the training set, the model was still able to localize abnormalities in relevant brain regions, underscoring its potential value as an assistive tool for early dementia diagnosis. Full article

Activation functions are fundamental to the representational capacity and optimization dynamics of deep neural networks. Although numerous nonlinearities have been proposed, ranging from classical sigmoid and tanh to modern smooth and trainable functions, no single activation is universally optimal, as each involves trade-offs among gradient flow, stability, computational cost, and expressiveness. This study introduces TRex, a novel activation function that combines the efficiency and linear growth of rectified units with the smooth gradient propagation of saturating functions. TRex features a non-zero, smoothed negative region inspired by tanh while maintaining near-linear behavior for positive inputs, preserving gradients and reducing neuron inactivation. We evaluate TRex against five widely used activation functions (ReLU, ELU, Swish, Mish, and GELU) across eight convolutional architectures (AlexNet, DenseNet-121, EfficientNet-B0, GoogLeNet, LeNet, MobileNet-V2, ResNet-18, and VGGNet) on two benchmark datasets (MNIST and Fashion-MNIST) and a real-world medical imaging dataset (SkinCancer). The results show that TRex achieves competitive accuracy, AUC, and convergence stability across most deep, connectivity-rich architectures while maintaining computational efficiency comparable to those of other smooth activations. These findings highlight TRex as a contextually efficient activation function that enhances gradient flow, generalization, and training stability, particularly in deeper or densely connected architectures, while offering comparable performance in lightweight and mobile-optimized models. Full article

Accurate differentiation between microseismic signals induced by hydraulic fracturing and those from roof fracturing is vital for optimizing fracturing efficiency, assessing roof stability, and mitigating mining-induced hazards in coal mining operations. We propose an automatic identification method for microseismic signals generated by hydraulic fracturing in coal seam roofs. This method first transforms the microseismic signals induced by hydraulic fracturing and roof fracturing into time-frequency feature images using the Frequency Slice Wavelet Transform (FSWT) technique, and then employs a sliding window (Swin) Transformer network to automatically identify and classify these two types of time-frequency feature maps. A comparative analysis is conducted on the performance of three methods—including the signal energy distribution method, Residual Network (ResNet) model, and VGG Network (VGGNet) model—in identifying microseismic signals from hydraulic fracturing in coal seam roofs. The results demonstrate that the Swin Transformer recognition model combined with FSWT achieves an accuracy of 92.49% and an F1-score of 92.96% on the test set of field-acquired microseismic signals from hydraulic fracturing and roof fracturing. These performance metrics are significantly superior to those of the signal energy distribution method (accuracy: 64.70%, F1-score: 64.70%), ResNet model (accuracy: 88.04%, F1-score: 89.24%), and VGGNet model (accuracy: 88.47%, F1-score: 89.52%). This advancement provides a reliable technical approach for monitoring hydraulic fracturing effects and ensuring roof safety in coal mines. Full article

Identifying concealed explosives in X-ray backscatter (XRBS) imagery remains a critical challenge, primarily due to low image contrasts, cluttered backgrounds, small object sizes, and limited structural details. To address these limitations, we propose YOLOv11-XRBS, an enhanced detection framework tailored to the characteristics of XRBS images. A dedicated dataset (SBCXray) comprising over 10,000 annotated images of simulated explosive scenarios under varied concealment conditions was constructed to support training and evaluation. The proposed framework introduces three targeted improvements: (1) adaptive architectural refinement to enhance multi-scale feature representation and suppress background interference, (2) a Size-Aware Focal Loss (SaFL) strategy to improve the detection of small and weak-feature objects, and (3) a recomposed loss function with scale-adaptive weighting to achieve more accurate bounding box localization. The experiments demonstrated that YOLOv11-XRBS achieves better performance compared to both existing YOLO variants and classical detection models such as Faster R-CNN, SSD512, RetinaNet, DETR, and VGGNet, achieving a mean average precision (mAP) of 94.8%. These results confirm the robustness and practicality of the proposed framework, highlighting its potential deployment in XRBS-based security inspection systems. Full article

To address the limitations of traditional cotton leaf nitrogen content estimation methods, which include low efficiency, high cost, poor portability, and challenges in vegetation index acquisition owing to environmental interference, this study focused on emerging non-destructive nutrient estimation technologies. This study proposed an innovative method that integrates multi-color space fusion with deep and machine learning to estimate cotton leaf nitrogen content using smartphone-captured digital images. A dataset comprising smartphone-acquired cotton leaf images was processed through threshold segmentation and preprocessing, then converted into RGB, HSV, and Lab color spaces. The models were developed using deep-learning architectures including AlexNet, VGGNet-11, and ResNet-50. The conclusions of this study are as follows: (1) The optimal single-color-space nitrogen estimation model achieved a validation set R² of 0.776. (2) Feature-level fusion by concatenation of multidimensional feature vectors extracted from three color spaces using the optimal model, combined with an attention learning mechanism, improved the validation R² to 0.827. (3) Decision-level fusion by concatenating nitrogen estimation values from optimal models of different color spaces into a multi-source decision dataset, followed by machine learning regression modeling, increased the final validation R² to 0.830. The dual fusion method effectively enabled rapid and accurate nitrogen estimation in cotton crops using smartphone images, achieving an accuracy 5–7% higher than that of single-color-space models. The proposed method provides scientific support for efficient cotton production and promotes sustainable development in the cotton industry. Full article

Magnetic resonance imaging (MRI), with its high resolution and radiation-free characteristics, has become a crucial tool for brain tumor diagnosis. We classified brain tumors into non-tumors, glioma, meningioma, and pituitary tumors by integrating public image datasets with preprocessing and data augmentation techniques and employing four deep learning models, such as a convolutional neural network (CNN), visual geometry group network 19 (VGGNet 9), residual network 101 version 2 (ResNet101V2), and efficient network version 2 b2 (EfficientNetV2B2). VGGNet19 and CNNs excelled in accuracy and stability, while EfficientNetV2B2 was efficient yet required refinement for specific categories, and ResNet101V2 benefited from further optimization. Deep learning significantly enhances diagnostic efficiency and accuracy, assisting clinical decision-making and improving patient survival rates. Full article

The health of crops is a major challenge for productivity growth in agriculture, with plant diseases playing a key role in limiting crop yield. Identifying and understanding these diseases is crucial to preventing their spread. In particular, greenhouse pepper leaves are susceptible to diseases such as mildew, mites, caterpillars, aphids, and blight, which leave distinctive marks that can be used for disease classification. The study proposes a seven-class classifier for the rapid and accurate diagnosis of pepper diseases, with a primary focus on pre-processing techniques to enhance colour differentiation between green and yellow shades, thereby facilitating easier classification among the classes. A novel algorithm is introduced to improve image vibrancy, contrast, and colour properties. The diagnosis is performed using a modified VGG16Net model, which includes three additional layers for fine-tuning. After initialising on the ImageNet dataset, some layers are frozen to prevent redundant learning. The classification is additionally accelerated by introducing flattened, dense, and dropout layers. The proposed model is tested on a private dataset collected specifically for this study. Notably, this work is the first to focus on diagnosing aphid and caterpillar diseases in peppers. The model achieves an average accuracy of 92.00%, showing promising potential for seven-class deep learning-based disease diagnostics. Misclassifications in the aphid class are primarily due to the limited number of samples available. Full article

Tongue cancer, the most aggressive subtype of oral cancer, presents critical challenges due to the limited number of specialists available and the time-consuming nature of conventional histopathological diagnosis. To address these issues, we developed an intelligent diagnostic system integrating Mueller matrix microscopy with deep learning to enhance diagnostic accuracy and efficiency. Through Mueller matrix polar decomposition and transformation, micro-polarization feature parameter images were extracted from tongue cancer tissues, and purity parameter images were generated by calculating the purity of the Mueller matrices. A multi-stage feature dataset of Mueller matrix parameter images was constructed using histopathological samples of tongue cancer tissues with varying stages. Based on this dataset, the clinical potential of Mueller matrix microscopy was preliminarily validated for histopathological diagnosis of tongue cancer. Four mainstream medical image classification networks—AlexNet, ResNet50, DenseNet121 and VGGNet16—were employed to quantitatively evaluate the classification performance for tongue cancer stages. DenseNet121 achieved the highest classification accuracy of 98.48%, demonstrating its potential as a robust framework for rapid and accurate intelligent diagnosis of tongue cancer. Full article

The rapid advancements in generative artificial intelligence (AI), particularly through models like Generative Adversarial Networks (GANs) and diffusion-based architectures, have made it increasingly difficult to distinguish between real and synthetically generated images. While these technologies offer benefits in creative domains, they also pose serious risks in terms of misinformation, digital forgery, and identity manipulation. This paper presents a novel hybrid deep learning model for detecting AI-generated images by integrating the ResNet-50 architecture with Squeeze-and-Excitation (SE) attention blocks. The proposed SE-ResNet50 model enhances channel-wise feature recalibration and interpretability by integrating Squeeze-and-Excitation (SE) blocks into the ResNet-50 backbone, enabling dynamic emphasis on subtle generative artifacts such as unnatural textures and semantic inconsistencies, thereby improving classification fidelity. Experimental evaluation on the CIFAKE dataset demonstrates the model’s effectiveness, achieving a test accuracy of 96.12%, precision of 97.04%, recall of 88.94%, F1-score of 92.82%, and an AUC score of 0.9862. The model shows strong generalization, minimal overfitting, and superior performance compared with transformer-based models and standard architectures like ResNet-50, VGGNet, and DenseNet. These results confirm the hybrid model’s suitability for real-time and resource-constrained applications in media forensics, content authentication, and ethical AI governance. Full article

Background: Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI)-based brain scans. Methods: In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Results: The number of participants in both ASD and normal control (CN) subject groups was not statistically different (p = 0.23). The mean age of the CN subject group was 14.62 years (standard deviation: 4.34), and the ASD group had mean age of 14.89 years (standard deviation: 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. Conclusions: To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD using VGG-Net. Full article

Timely identification of crop conditions is relevant for informed decision-making in precision agriculture. The initial step in determining the conditions that crops require involves isolating the components that constitute them, including the leaves and fruits of the plants. An alternative method for conducting this separation is to utilize intelligent digital image processing, wherein plant elements are labeled for subsequent analysis. The application of Deep Learning algorithms offers an alternative approach for conducting segmentation tasks on images obtained from complex environments with intricate patterns that pose challenges for separation. One such application is semantic segmentation, which involves assigning a label to each pixel in the processed image. This task is accomplished through training various models of Convolutional Neural Networks. This paper presents a comparative analysis of semantic segmentation performance using a convolutional neural network model with different backbone architectures. The task focuses on pixel-wise classification into three categories: leaves, fruits, and background, based on images of semi-hydroponic tomato crops captured in greenhouse settings. The main contribution lies in identifying the most efficient backbone-UNet combination for segmenting tomato plant leaves and fruits under uncontrolled conditions of lighting and background during image acquisition. The Convolutional Neural Network model UNet is is implemented with different backbones to use transfer learning to take advantage of the knowledge acquired by other models such as MobileNet, VanillaNet, MVanillaNet, ResNet, VGGNet trained with the ImageNet dataset, in order to segment the leaves and fruits of tomato plants. Highest percentage performance across five metrics for tomato plant fruit and leaves segmentation is the MVanillaNet-UNet and VGGNet-UNet combination with $0.88089$ and $0.89078$ respectively. A comparison of the best results of semantic segmentation versus those obtained with a color-dominant segmentation method optimized with a greedy algorithm is presented. Full article

Background/Objectives: Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area. Methods: Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU. Results: According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model’s high accuracy in brain tumor classification. Conclusions: The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors. Full article

The leaf water content (LWC) of citrus is a pivotal indicator for assessing citrus water status. Addressing the limitations of traditional hyperspectral modelling methods, which rely on single preprocessing techniques and struggle to fully exploit the complex information within spectra, this study proposes a novel strategy for estimating citrus LWC by integrating spectral preprocessing combinations with an enhanced deep learning architecture. Utilizing a citrus plantation in Guangxi as the experimental site, 240 leaf samples were collected. Seven preprocessing combinations were constructed based on multiplicative scatter correction (MSC), continuous wavelet transform (CWT), and first derivative (1st D), and a new multichannel network, EDPNet (Ensemble Data Preprocessing Network), was designed for modelling. Furthermore, this study incorporated an attention mechanism within EDPNet, comparing the applicability of SE Block, SAM, and CBAM in integrating spectral combination information. The experiments demonstrated that (1) the triple preprocessing combination (MSC + CWT + 1st D) significantly enhanced model performance, with the prediction set R² reaching 0.8036, a 13.86% improvement over single preprocessing methods, and the RMSE reduced to 2.3835; (2) EDPNet, through its multichannel parallel convolution and shallow structure design, avoids excessive network depth while effectively enhancing predictive performance, achieving a prediction accuracy (R² = 0.8036) that was 5.58–9.21% higher than that of AlexNet, VGGNet, and LeNet-5, with the RMSE reduced by 9.35–14.65%; and (3) the introduction of the hybrid attention mechanism CBAM further optimized feature weight allocation, increasing the model’s R² to 0.8430 and reducing the RMSE to 2.1311, with accuracy improvements of 2.08–2.36% over other attention modules (SE, SAM). This study provides a highly efficient and accurate new method for monitoring citrus water content, offering practical significance for intelligent orchard management and optimal resource allocation. Full article

Eye illness detection is important, yet it can be difficult and error-prone. In order to effectively and promptly diagnose eye problems, doctors must use cutting-edge technologies. The goal of this research paper is to develop a sophisticated model that will help physicians detect different eye conditions early on. These conditions include age-related macular degeneration (AMD), diabetic retinopathy, cataracts, myopia, and glaucoma. Common eye conditions include cataracts, which cloud the lens and cause blurred vision, and glaucoma, which can cause vision loss due to damage to the optic nerve. The two conditions that could cause blindness if treatment is not received are age-related macular degeneration (AMD) and diabetic retinopathy, a side effect of diabetes that destroys the blood vessels in the retina. Problems include myopic macular degeneration, glaucoma, and retinal detachment—severe types of nearsightedness that are typically defined as having a refractive error of –5 diopters or higher—are also more likely to occur in people with high myopia. We intend to apply a user-friendly approach that will allow for faster and more efficient examinations. Our research attempts to streamline the eye examination procedure, making it simpler and more accessible than traditional hospital approaches. Our goal is to use deep learning and machine learning to develop an extremely accurate model that can assess medical images, such as eye retinal scans. This was accomplished by using a huge dataset to train the machine learning and deep learning model, as well as sophisticated image processing techniques to assist the algorithm in identifying patterns of various eye illnesses. Following training, we discovered that the CNN, VggNet, MobileNet, and hybrid Deep Learning models outperformed the SVM and Random Forest machine learning models in terms of accuracy, achieving above 98%. Therefore, our model could assist physicians in enhancing patient outcomes, raising survival rates, and creating more effective treatment plans for patients with these illnesses. Full article