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In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.
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Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model's inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model's ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling-Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.
RESUMEN
While convolutional operation effectively extracts local features, their limited receptive fields make it challenging to capture global dependencies. Transformer, on the other hand, excels at global modeling and effectively captures global dependencies. However, the self-attention mechanism used in Transformers lacks a local mechanism for information exchange within specific regions. This article attempts to leverage the strengths of both Transformers and convolutional neural networks (CNNs) to enhance the Swin Transformer V2 model. By incorporating both convolutional operation and self-attention mechanism, the enhanced model combines the local information-capturing capability of CNNs and the long-range dependency-capturing ability of Transformers. The improved model enhances the extraction of local information through the introduction of the Swin Transformer Stem, inverted residual feed-forward network, and Dual-Branch Downsampling structure. Subsequently, it models global dependencies using the improved self-attention mechanism. Additionally, downsampling is applied to the attention mechanism's Q and K to reduce computational and memory overhead. Under identical training conditions, the proposed method significantly improves classification accuracy on multiple image classification datasets, showcasing more robust generalization capabilities.
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Small object detection is one of the difficulties in the development of computer vision, especially in the case of complex image backgrounds, and the accuracy of small object detection still needs to be improved. In this article, we present a small object detection network based on YOLOv4, which solves some obstacles that hinder the performance of traditional methods in small object detection tasks in complex road environments, such as few effective features, the influence of image noise, and occlusion by large objects, and improves the detection of small objects in complex background situations such as drone aerial survey images. The improved network architecture reduces the computation and GPU memory consumption of the network by including the cross-stage partial network (CSPNet) structure into the spatial pyramid pool (SPP) structure in the YOLOv4 network and convolutional layers after concatenation operation. Secondly, the accuracy of the model on the small object detection task is improved by adding a more suitable small object detection head and removing one used for large object detection. Then, a new branch is added to extract feature information at a shallow location in the backbone part, and the feature information extracted from this branch is fused in the neck part to enrich the small object location information extracted by the model; when fusing feature information from different levels in the backbone, the fusion weight of useful information is increased by adding a weighting mechanism to improve detection performance at each scale. Finally, a coordinated attention (CA) module is embedded at a suitable location in the neck part, which enables the model to focus on spatial location relationships and inter-channel relationships and enhances feature representation capability. The proposed model has been tested to detect 10 different target objects in aerial images from drones and five different road traffic signal signs in images taken from vehicles in a complex road environment. The detection speed of the model meets the criteria of real-time detection, the model has better performance in terms of accuracy compared to the existing state-of-the-art detection models, and the model has only 44M parameters. On the drone aerial photography dataset, the average accuracy of YOLOv4 and YOLOv5L is 42.79% and 42.10%, respectively, while our model achieves an average accuracy (mAP) of 52.76%; on the urban road traffic light dataset, the proposed model achieves an average accuracy of 96.98%, which is also better than YOLOv4 (95.32%), YOLOv5L (94.79%) and other advanced models. The current work provides an efficient method for small object detection in complex road environments, which can be extended to scenarios involving small object detection, such as drone cruising and autonomous driving.
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The Transformer has achieved great success in many computer vision tasks. With the in-depth exploration of it, researchers have found that Transformers can better obtain long-range features than convolutional neural networks (CNN). However, there will be a deterioration of local feature details when the Transformer extracts local features. Although CNN is adept at capturing the local feature details, it cannot easily obtain the global representation of features. In order to solve the above problems effectively, this paper proposes a hybrid model consisting of CNN and Transformer inspired by Visual Attention Net (VAN) and CoAtNet. This model optimizes its shortcomings in the difficulty of capturing the global representation of features by introducing Large Kernel Attention (LKA) in CNN while using the Transformer blocks with relative position self-attention variant to alleviate the problem of detail deterioration in local features of the Transformer. Our model effectively combines the advantages of the above two structures to obtain the details of local features more accurately and capture the relationship between features far apart more efficiently on a large receptive field. Our experiments show that in the image classification task without additional training data, the proposed model in this paper can achieve excellent results on the cifar10 dataset, the cifar100 dataset, and the birds400 dataset (a public dataset on the Kaggle platform) with fewer model parameters. Among them, SE_LKACAT achieved a Top-1 accuracy of 98.01% on the cifar10 dataset with only 7.5M parameters.
RESUMEN
Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have used traditional methods to estimate cultivated land quality, and there is little research on using deep learning for this purpose. Using Ya'an cultivated land as the research object, this study constructs an evaluation system for cultivated land quality based on seven aspects, including soil organic matter and soil texture. An attention mechanism (AM) is introduced into a back propagation (BP) neural network model. Therefore, an AM-BP neural network that is suitable for Ya'an cultivated land is designed. The sample is divided into training and test sets by a ratio of 7:3. We can output the evaluation results of cultivated land quality through experiments. Furthermore, they can be visualized through a pie chart. The experimental results indicate that the model effect of the AM-BP neural network is better than that of the BP neural network. That is, the mean square error is reduced by approximately 0.0019 and the determination coefficient is increased by approximately 0.005. In addition, this study obtains better results via the ensemble model. The quality of cultivated land in Yucheng District is generally good, i.e.,mostly third and fourth grades. It conforms to the normal distribution. Lastly, the method has certain to evaluate cultivated land quality, providing a reference for future cultivated land quality evaluation.