Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

The remote sensing ships’fine-grained classification technology makes it possible to identify certain ship types in remote sensing images,and it has broad application prospects in civil and military fields.However,the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop.There is still an opportunity for future enhancement of the classification impact.To solve the challenges brought by the above characteristics,this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network(VAN-MR)for fine-grained classification tasks.For the complex background of remote sensing images,the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model’s feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets.For the problem of multi-grained feature mixing in remote sensing images,the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features.The parallel network has different depths,considering both high-level and lowlevel semantic information.The model achieves better classification performance in remote sensing ship images with multi-granularity mixing.Finally,the model achieves 88.73%and 94.56%accuracy on the public fine-grained ship collection-23(FGSC-23)and FGSCR-42 datasets,respectively,while the parameter size is only 53.47 M,the floating point operations is 9.9 G.The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns.Emails stay in the leading positions for business as well as personal use.This popularity grabs the interest of individuals with malevolent inten-tions—phishing and spam email assaults.Email filtering mechanisms were developed incessantly to follow unwanted,malicious content advancement to protect the end-users.But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced.Thus,this study provides a solution related to email message body text automatic classification into phishing and email spam.This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network(IFFO-SRRNN)based on Applied Linguistics for Email Classification.The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails.At the preliminary level,the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation.Next,the SRRNN method can be useful in recognizing and classifying spam emails.As hyperparameters of the SRRNN model need to be effectually tuned,the IFFO algorithm can be utilized as a hyperparameter optimizer.To investigate the effectual email classification results of the IFFO-SRDL technique,a series of simulations were taken placed on public datasets,and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.

Discussion on classification and naming scheme of fine-grained sedimentary rocks

Based on reviews and summaries of the naming schemes of fine-grained sedimentary rocks, and analysis of characteristics of fine-grained sedimentary rocks, the problems existing in the classification and naming of fine-grained sedimentary rocks are discussed. On this basis, following the principle of three-level nomenclature, a new scheme of rock classification and naming for fine-grained sedimentary rocks is determined from two perspectives: First, fine-grained sedimentary rocks are divided into 12 types in two major categories, mudstone and siltstone, according to particle size(sand, silt and mud). Second,fine-grained sedimentary rocks are divided into 18 types in four categories, carbonate rock, fine-grained felsic sedimentary rock,clay rock and mixed fine-grained sedimentary rock according to mineral composition(carbonate minerals, felsic detrital minerals and clay minerals as three end elements). Considering the importance of organic matter in unconventional oil and gas generation and evaluation, organic matter is taken as the fourth element in the scheme. Taking the organic matter contents of 0.5% and 2% as dividing points, fine grained sedimentary rocks are divided into three categories, organic-poor, organic-bearing,and organic-rich ones. The new scheme meets the requirement of unconventional oil and gas exploration and development today and solves the problem of conceptual confusion in fine-grained sedimentary rocks, providing a unified basic term system for the research of fine-grained sedimentology.

A Preliminary Study on the Problems and Improvement of the Latest Land Use Classification System in China

The establishment of a unified land use classification system is the basis for realizing the unified management of land and sea,urban and rural areas,and aboveground and underground space.In November 2020,the Ministry of Natural Resources of the People's Republic of China issued the Classification Guide for Land and Space Survey,Planning and Use Control of Land and Sea(for Trial Implementation),which aims to establish a national unified land and sea use classification system,lay an important foundation for scientific planning and unified management of natural resources,rational use and protection of natural resources,and speed up the construction of a new pattern of land and space development and protection.However,there are still some obvious shortcomings in the Classification Guide.This paper analyzes some problems existing in this classification standard from three aspects of logicality,rigorousness and comprehensiveness,and puts forward some suggestions for further improvement.This has important practical significance to better guiding the practice of land use and land resources management,and then to achieving the goal of unified management of natural resources.

Multi-Branch Deepfake Detection Algorithm Based on Fine-Grained Features

With the rapid development of deepfake technology,the authenticity of various types of fake synthetic content is increasing rapidly,which brings potential security threats to people’s daily life and social stability.Currently,most algorithms define deepfake detection as a binary classification problem,i.e.,global features are first extracted using a backbone network and then fed into a binary classifier to discriminate true or false.However,the differences between real and fake samples are often subtle and local,and such global feature-based detection algorithms are not optimal in efficiency and accuracy.To this end,to enhance the extraction of forgery details in deep forgery samples,we propose a multi-branch deepfake detection algorithm based on fine-grained features from the perspective of fine-grained classification.First,to address the critical problem in locating discriminative feature regions in fine-grained classification tasks,we investigate a method for locating multiple different discriminative regions and design a lightweight feature localization module to obtain crucial feature representations by augmenting the most significant parts of the feature map.Second,using information complementation,we introduce a correlation-guided fusion module to enhance the discriminative feature information of different branches.Finally,we use the global attention module in the multi-branch model to improve the cross-dimensional interaction of spatial domain and channel domain information and increase the weights of crucial feature regions and feature channels.We conduct sufficient ablation experiments and comparative experiments.The experimental results show that the algorithm outperforms the detection accuracy and effectiveness on the FaceForensics++and Celeb-DF-v2 datasets compared with the representative detection algorithms in recent years,which can achieve better detection results.

Feature selection algorithm for text classification based on improved mutual information

In order to solve the poor performance in text classification when using traditional formula of mutual information （MI） , a feature selection algorithm were proposed based on improved mutual information. The improved mutual information algorithm, which is on the basis of traditional improved mutual information methods that enbance the MI value of negative characteristics and feature＇ s frequency, supports the concept of concentration degree and dispersion degree. In accordance with the concept of concentration degree and dispersion degree, formulas which embody concentration degree and dispersion degree were constructed and the improved mutual information was implemented based on these. In this paper, the feature selection algorithm was applied based on improved mutual information to a text classifier based on Biomimetic Pattern Recognition and it was compared with several other feature selection methods. The experimental results showed that the improved mutu- al information feature selection method greatly enhances the performance compared with traditional mutual information feature selection methods and the performance is better than that of information gain. Through the introduction of the concept of concentration degree and dispersion degree, the improved mutual information feature selection method greatly improves the performance of text classification system.

Classification for Glass Bottles Based on Improved Selective Search Algorithm

The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection.At present,the classification of recycled glass bottles is difficult due to the many differences in specifications and models.This paper proposes a classification algorithm for glass bottles that is divided into two stages,namely the extraction of candidate regions and the classification of classifiers.In the candidate region extraction stage,aiming at the problem of the large time overhead caused by the use of the SIFT(scale-invariant feature transform)descriptor in SS(selective search),an improved feature of HLSN(Haar-like based on SPP-Net)is proposed.An integral graph is introduced to accelerate the process of forming an HBSN vector,which overcomes the problem of repeated texture feature calculation in overlapping regions by SS.In the classification stage,the improved SS algorithm is used to extract target regions.The target regions are merged using a non-maximum suppression algorithm according to the classification scores of the respective regions,and the merged regions are classified using the trained classifier.Experiments demonstrate that,compared with the original SS,the improved SS algorithm increases the calculation speed by 13.8%,and its classification accuracy is 89.4%.Additionally,the classification algorithm for glass bottles has a certain resistance to noise.

Application of improved back-propagation algorithms in classification and detection of scars defects on rails surfaces

An experimental platform with bracket structures,cables,parallel computer and imaging system is designed for defects detecting on steel rails. Meanwhile,an improved gradient descent algorithm based on a self-adaptive learning rate and a fixed momentum factor is developed to train back-propagation neural network for accurate and efficient defects classifications. Detection results of rolling scar defects show that such detection system can achieve accurate positioning to defects edges for its improved noise suppression. More precise characteristic parameters of defects can also be extracted.Furthermore,defects classification is adopted to remedy the limitations of low convergence rate and local minimum. It can also attain the optimal training precision of 0. 00926 with the least 96 iterations. Finally,an enhanced identification rate of 95% has been confirmed for defects by using the detection system. It will also be positive in producing high-quality steel rails and guaranteeing the national transport safety.

A Multi-feature Fusion Apple Classification Method Based on Image Processing and Improved SVM

In order to achieve accurate classification of apple, a multi-feature fusion classification method based on image processing and improved SVM was proposed in this paper. The method was mainly divided into four parts, including image preprocessing, background segmentation, feature extraction and multi-feature fusion classification with improved SVM. Firstly, the homomorphic filtering algorithm was used to improve the quality of apple images. Secondly, the images were converted to HLS space. The background was segmented by the QTSU algorithm. Morphological processing was employed to remove fruit stem and surface defect areas. And apple contours were extracted with the Canny algorithm. Then, apples’ size, shape, color, defect and texture features were extracted. Finally, the cross verification method was used to optimize the penalty factor in SVM. A multi-feature fusion classification model was established. And the weight of each index was calculated by Fisher. In this study, 146 apple samples were selected for training and 61 apple samples were selected for testing. The test results showed that the accuracy of the classification method proposed in this paper was 96.72%, which can provide a reference for apple automatic classification.

Chinese News Text Classification Based on Convolutional Neural Network

With the explosive growth of Internet text information,the task of text classification is more important.As a part of text classification,Chinese news text classification also plays an important role.In public security work,public opinion news classification is an important topic.Effective and accurate classification of public opinion news is a necessary prerequisite for relevant departments to grasp the situation of public opinion and control the trend of public opinion in time.This paper introduces a combinedconvolutional neural network text classification model based on word2vec and improved TF-IDF:firstly,the word vector is trained through word2vec model,then the weight of each word is calculated by using the improved TFIDF algorithm based on class frequency variance,and the word vector and weight are combined to construct the text vector representation.Finally,the combined-convolutional neural network is used to train and test the Thucnews data set.The results show that the classification effect of this model is better than the traditional Text-RNN model,the traditional Text-CNN model and word2vec-CNN model.The test accuracy is 97.56%,the accuracy rate is 97%,the recall rate is 97%,and the F1-score is 97%.

A New Childhood Pneumonia Diagnosis Method Based on Fine-Grained Convolutional Neural Network

Pneumonia is part of the main diseases causing the death of children.It is generally diagnosed through chest Xray images.With the development of Deep Learning(DL),the diagnosis of pneumonia based on DL has received extensive attention.However,due to the small difference between pneumonia and normal images,the performance of DL methods could be improved.This research proposes a new fine-grained Convolutional Neural Network(CNN)for children’s pneumonia diagnosis(FG-CPD).Firstly,the fine-grainedCNNclassificationwhich can handle the slight difference in images is investigated.To obtain the raw images from the real-world chest X-ray data,the YOLOv4 algorithm is trained to detect and position the chest part in the raw images.Secondly,a novel attention network is proposed,named SGNet,which integrates the spatial information and channel information of the images to locate the discriminative parts in the chest image for expanding the difference between pneumonia and normal images.Thirdly,the automatic data augmentation method is adopted to increase the diversity of the images and avoid the overfitting of FG-CPD.The FG-CPD has been tested on the public Chest X-ray 2017 dataset,and the results show that it has achieved great effect.Then,the FG-CPD is tested on the real chest X-ray images from children aged 3–12 years ago from Tongji Hospital.The results show that FG-CPD has achieved up to 96.91%accuracy,which can validate the potential of the FG-CPD.

基于人工智能技术的智媒体就业推荐平台构建研究

为了解决目前就业推荐方法资源覆盖率低的问题,设计一种基于人工智能技术的智媒体就业推荐平台。通过人工智能技术设计智媒体就业推荐平台的整体框架,包括基础数据模块、用户界面模块、后台管理模块及个性化推荐模块,并设计学生和管理者在智媒体就业推荐平台中的业务流程。采用基于就业意向的个性化推荐算法,在智媒体就业推荐平台中为用户推荐就业资源,完成智媒体就业推荐平台的构建。实验结果表明,该平台运行时间随着请求数量的增加缓慢上升,抗压性较好,资源覆盖率均高于80%。

基于改进级联算法的不平衡数据集分类检测算法

以提升不平衡数据集分类检测为研究目标,提出基于改进级联算法的不平衡数据集分类检测算法.首先,采用卡尔曼滤波法进行数据去噪预处理,利用小波阈值去噪算法二次消除噪声数据,并对去噪结果进行归一化预处理;利用DPC算法提取数据的局部密度特征,利用时间编码挖掘数据的时序性特征,采用Apriori算法的强关联规则提取数据集特征;利用模糊层次聚类算法对支持向量机进行优化,实现数据类型的划分;利用改进的级联算法联合布谷鸟算法实现不平衡数据集分类检测.实验结果表明本方法的分类协方差低于0.15,检测准确率高于95%,检测时间低于2.2 ms,有效提升了不平衡数据集分类检测效果.

基于改进CART算法的退役动力电池等级筛选方法

针对退役动力电池存在一致性差、等级筛选效率低的难题,提出了一种基于改进CART算法的退役动力电池等级筛选方法。首先,分析了传统CART算法的基本原理,为克服算法计算量大的缺陷,将Fayyad边界点判定定理与CART算法相结合,通过选取属性最优阈值点来减少计算量,提高分类效率;其次,基于代价复杂度后剪枝算法,采用交叉验证法对算法进行进一步优化;最后,将改进CART算法用于退役动力电池筛选分类,实验结果表明改进CART算法在保持较高准确率的情况下,可以有效提高退役动力电池的等级筛选效率。

多联融合优化模板匹配的纱纸管分类方法

圆锥纱纸管的自动分类识别一直是该部件智能制造方面的技术难题,针对传统图像分类方法无法兼顾速度与精度,以及深度学习成本大、部署难、硬件要求高等问题,提出了一种基于多联融合优化模板匹配的纱纸管分类方法。采用多个改进算法及策略并使用三次数据降维加快模板匹配速度。将用于运动估计的优化算法SEA(successive elimination algorithm)用于模板匹配中,并把该算法的阈值改进为自适应阈值,用于加强算法鲁棒性;采用小波金字塔进行数据降维,减少运算量并提高运算速度;最后采用十字灰度特征模板代替传统SAD(sum of absolute differences)算法及其模板计算性能指标,并采用提前停止迭代搜索的策略进一步滤除数据,设置累计误差阈值来提前停止搜索。匹配实验表明,本文的改进算法保证了精度,并且匹配速度达到了0.126 s左右;对比、消融实验表明,本文算法在保证了精度的前提下,速度比传统SAD算法提升了近11倍,相比于一些其他经典的方法在速度上也均有提升,证明了该方法的有效性。

基于信誉分类的拜占庭容错共识算法

针对许可区块链场景下实用拜占庭容错(Practical Byzantine Fault Tolerance,PBFT)共识算法通信开销大、主节点选取随意以及吞吐量低等问题,通过引入并优化信誉评分模型(Reputation Scoring Model,RSM)。提出了一种基于信誉分类的拜占庭容错(Byzantine Fault Tolerance Based on Reputation Classification,RCBFT)共识算法。定义RSM,依据节点的历史共识行为所获得的信誉评分排序对参与节点进行动态分类以及分级管理,提出基于信誉分类的多层次节点架构;在可信节点层中随机选取节点来担任主节点,优化主节点选取机制;设计了缓冲节点层类型转换策略(Type Conversion Strategy for Nodes,TCSN),兼顾了环境等非主观因素导致低信誉评分的诚实节点不能参与共识的问题,使得诚实节点尽可能多地参与共识,而拜占庭节点快速下降到最差类型中限制共识权限;RCBFT共识算法还对传统三阶段共识协议进行优化,减少通信开销,在确保容错性的同时能够提高算法性能。实验分析表明,相较于PBFT共识算法,RCBFT共识算法能够提升交易吞吐量,降低通信开销与共识时延。

NRS分型及椎旁肌的MRI影像学与腰椎管狭窄症术后改善率的关系

目的 探讨数字评分量表(NRS)分型及椎旁肌的MRI影像学与腰椎管狭窄症术后改善率的关系。方法 选取本院收治的220例腰椎管狭窄患者作为观察组,选取同期健康体检者100例作为对照组;对受试者NRS分型进行判断,并分析患者竖脊肌横截面积、多裂肌横截面积、多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度;采用日本脊柱外科学会(JOA)下腰痛评分系统对腰椎管狭窄症术后改善率进行分析。结果 对照组NRS分型明显优于观察组,差异有统计学意义(P <0.05);观察组患者竖脊肌横截面积、多裂肌横截面积均明显低于对照组,多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度均明显高于对照组,且差异有统计学意义(P <0.05);术后改善率良好组患者NRS分型、多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度明显低于不良组,术后改善率良好组患者竖脊肌横截面积、多裂肌横截面积均明显高于不良组,差异有统计学意义(P <0.05);腰椎管狭窄症术后改善率与NRS分型、竖脊肌横截面积、多裂肌横截面积呈明显正相关关系,与多裂肌脂肪浸润程度和竖脊肌脂肪浸润程度呈明显负相关关系,差异有统计学意义(P <0.05);腰椎管狭窄症患者NRS分型、竖脊肌横截面积、多裂肌横截面积、多裂肌脂肪浸润程度、竖脊肌脂肪浸润程度均是影响其术后改善率的独立性因素,且差异有统计学意义(P <0.05)。结论 NRS分型及椎旁肌的MRI表现与腰椎管狭窄症术后改善率呈显著相关性关系。

基于决策树分类思想的线上思政教学课程推荐方法设计

随着教育形式的快速发展,线上思政教学已经逐渐成为一种常见的教育模式。为了提高线上思政教育的效率,此次研究通过引入信息增益率和重要度参数,对决策树分类算法中的迭代二叉树三代算法进行改进。再采用改进后的迭代二叉树三代算法构建思政教育资源分类模型,并根据该模型构建了思政教育课程推荐系统。实验结果表明,改进迭代二叉树三代算法下的思政教育分类模型可以达到99.5%的分类准确率和0.4%的损失率。在系统应用中,该系统的功能模块满意度均在90%以上。故此次研究提出的思政教育课程推荐系统可以良好地应用于线上教学课程推荐。

新形势下耕地资源质量分类与等别转换的应用研究——以济南市为例

以济南市为研究区域,在2021年度耕地资源质量分类成果的基础上,运用改进的灰靶模型和指标赋分求和法2种等别转换方式将济南市耕地资源质量分类成果转换为等别数据。结果表明:改进的灰靶模型兼顾了耕地的自然本底特征和耕地的粮食产能,在赋权和评价过程更为客观、科学,转换后的等别成果更为合理,济南市质量较好的耕地主要位于中部和北部平原地区以及沿黄地区,等别主要为3等和4等地,占总耕地面积的77%,符合济南市实际情况;采用指标赋分求和法转换的等别整体较为合理,但主观性较强,评价结果区域内部差异性较小。因此,改进的灰靶模型在耕地资源质量分类与等别衔接研究中应用性更强,转换成果可为耕地占补平衡与进出平衡等实践应用提供参考。