An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection

The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex.Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks.Recently,machine learning has beenwidely applied to network traffic recognition.Still,high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms.Taking advantage of the faster optimizationseeking capability of the jumping spider optimization algorithm(JSOA),this paper proposes a jumping spider optimization algorithmthat incorporates the harris hawk optimization(HHO)and small hole imaging(HHJSOA).We use it in network traffic identification feature selection.First,the method incorporates the HHO escape energy factor and the hard siege strategy to forma newsearch strategy for HHJSOA.This location update strategy enhances the search range of the optimal solution of HHJSOA.We use small hole imaging to update the inferior individual.Next,the feature selection problem is coded to propose a jumping spiders individual coding scheme.Multiple iterations of the HHJSOA algorithmfind the optimal individual used as the selected feature for KNN classification.Finally,we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 dataset.Experimental results show that compared with other algorithms for the UNSW-NB15 dataset,the improvement is at least 0.0705,0.00147,and 1 on the accuracy,fitness value,and the number of features.In addition,compared with other feature selectionmethods for the same datasets,the proposed algorithmhas faster convergence,better merit-seeking,and robustness.Therefore,HHJSOAcan improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local optimum due to high-dimensional features.

Clinicopathological Features and Prognosis of Small Cell Carcinoma of the Cervix

Small cell carcinoma of cervix (SCCC) is a rare disease with highly aggressive behaviour and is pathologically hard to diagnose.In this study, the clinicopathological features, diagnosis, treatment and prognosis of the condition were examined.Clinical records and follow-up data of 7 cases of SCCC were retrospectively studied.Our results showed that five non-recurrent cases initially presented irregular vaginal bleeding or increased apocenosis of varying degrees.Pathological examination revealed that the stroma was diffusely infiltrated with small monomorphous cells ranging from round to oval shape.Three cases were immunohistochemically confirmed.One case was accompanied with squamous cell cancer.Of the 7 cases, one case was classified as stage Ⅰb 1, two stageⅠ b2, one stage Ⅱ a, one stage Ⅱb , and one stage Ⅲ b.On the basis of their stages of condition, one subject with stage III b underwent chemotherapy, and one with stage Ib2 received extensive hysterectomy plus pelvic lymphadenectomy, while the other 5 cases were treated by extensive hysterectomy and pelvic lymphadenectomy in combination with pre-and/or post-operative adjuvant chemotherapy and radiotherapy.Of the 7 patients, 4 had relapse-free survival of 14, 14, 16 and 28 months respectively.It is concluded that SCCC is an aggressive tumor with propensity for early pelvis lymph node metastases.Early-stage patients should be treated by extensive hysterectomy and pelvic lymphadenectomy in combination with pre-and/or post-operative adjuvant chemotherapy and radiotherapy.

Construction Features of Small Roadside Parks in Tropical Area——A Case Study of Zhanjiang City

Accessibility and flexibility of small roadside parks make them significant transitional spaces in urban landscape environment. Three representative small parks in Zhanjiang City, a typical tropical city in south China, were selected to analyze their location features, spatial processing, demonstration of regional landscapes and recreational characteristics. It was proposed that construction of small roadside parks in tropical area should put human needs on the priority, present regional features of tropical garden landscapes, and focus on inheritance and innovation of regional cultures.

Clinical and computed tomography features of adult abdominopelvic desmoplastic small round cell tumor

To investigate the clinical and computed tomography(CT)features of desmoplastic small round cell tumor(DSRCT),we retrospectively analyzed the clinical presentations,treatment and outcome,as well as CT manifestations of four cases of DSRCT confirmed by surgery and pathology.The CT manifestations of DSRCT were as follows:(1)multiple soft-tissue masses or diffuse peritoneal thickening in the abdomen and pelvis,with the dominant mass usually located in the pelvic cavity;(2)masses without an apparent organbased primary site;(3)mild to moderate homogeneous or heterogeneous enhancement in solid area on enhanced CT;and(4)secondary manifestations,such as ascites,hepatic metastases,lymphadenopathy,hydronephrosis and hydroureter.The prognosis and overall survival rates were generally poor.Commonly used treatment strategies including aggressive tumor resection,polychemotherapy,and radiotherapy,showed various therapeutic effects.CT of DSRCT shows characteristic features that are helpful in diagnosis.Early discovery and complete resection,coupled with postoperative adjuvant chemotherapy,are important for prognosis of DSRCT.Whole abdominopelvic rather than locoregional radiotherapy is more effective for unresectable DSRCT.

Feature-Enhanced RefineDet: Fast Detection of Small Objects

Object detection has been studied for many years.The convolutional neural network has made great progress in the accuracy and speed of object detection.However,due to the low resolution of small objects and the representation of fuzzy features,one of the challenges now is how to effectively detect small objects in images.Existing target detectors for small objects:one is to use high-resolution images as input,the other is to increase the depth of the CNN network,but these two methods will undoubtedly increase the cost of calculation and time-consuming.In this paper,based on the RefineDet network framework,we propose our network structure RF2Det by introducing Receptive Field Block to solve the problem of small object detection,so as to achieve the balance of speed and accuracy.At the same time,we propose a Medium-level Feature Pyramid Networks,which combines appropriate high-level context features with low-level features,so that the network can use the features of both the low-level and the high-level for multi-scale target detection,and the accuracy of the small target detection task based on the low-level features is improved.Extensive experiments on the MS COCO dataset demonstrate that compared to other most advanced methods,our proposed method shows significant performance improvement in the detection of small objects.

Relevance of EGFR gene mutation with pathological features and prognosis in patients with non-small-cell lung carcinoma

Objective:To study the relevance of EGFR gene mutation with pathological features and prognosis in patients with non-small-cell lung carcinoma.Methods:A total of 297 patients from July 2009 to May 2013 were chosen as objects.EGFR gene mutation were detected with fluorescence quantitative PCR.Relevance of EGFR gene mutation with clinical and pathological features was analyzed,and the prognosis of EGFR- mutant-patients and that of EGFR- wide type-patients was compared.Results:In 297 patients.136(45.79%) showed EGFR gene mutation.EGFR gene mutation had no significant relevance with age.gender,smoking history,family history of cancer and clinical stage(P>0.05);there was significant relevance between EGFR gene mutation and blood type,pathologic types,differentiation and diameter of cancer(P<0.05).The difference between prognosis of EGFR- mutant-patients and that of EGFR- wide type-patients was statistical significance(P<0.05).Conclusions:EGFR gene mutation has significant relevance with pathological features,the prognosis of EGFRmutant-paticnts is better than that of EGFR- wide type-patients.

Accurate machine learning models based on small dataset of energetic materials through spatial matrix featurization methods

A large database is desired for machine learning(ML) technology to make accurate predictions of materials physicochemical properties based on their molecular structure.When a large database is not available,the development of proper featurization method based on physicochemical nature of target proprieties can improve the predictive power of ML models with a smaller database.In this work,we show that two new featurization methods,volume occupation spatial matrix and heat contribution spatial matrix,can improve the accuracy in predicting energetic materials' crystal density(ρ_(crystal)) and solid phase enthalpy of formation(H_(f,solid)) using a database containing 451 energetic molecules.Their mean absolute errors are reduced from 0.048 g/cm~3 and 24.67 kcal/mol to 0.035 g/cm~3 and 9.66 kcal/mol,respectively.By leave-one-out-cross-validation,the newly developed ML models can be used to determine the performance of most kinds of energetic materials except cubanes.Our ML models are applied to predict ρ_(crystal) and H_(f,solid) of CHON-based molecules of the 150 million sized PubChem database,and screened out 56 candidates with competitive detonation performance and reasonable chemical structures.With further improvement in future,spatial matrices have the potential of becoming multifunctional ML simulation tools that could provide even better predictions in wider fields of materials science.

Structural features of the nucleotide sequences of virus and organelle genomes

The four nucleotides (bases), A, T (U), G and C in small genomes, virus DNA/RNA, organelle and plastid genomes were also arranged sophisticatedly in the structural features in a single-strand with 1) reverse-complement symmetry of base or base sequences, 2) bias of four bases, 3) multiple fractality of the distribution of each four bases depending on the distance in double logarithmic plot (power spectrum) of L (the distance of a base to the next base) vs. P (L) (the probability of the base-distribution at L), although their genomes were composed of low numbers of the four bases, and the base-symmetry was rather lower than the prokaryotic-and the eukaryotic cells. In the case of the genomic DNA composed of less than 10,000 nt, it was better than to be partitioned at 10 of the L-value, and the structural features for the biologically active genomic DNA were observed as the large genomes. As the results, the base sequences of the genomic DNA including the genomic-RNA might be universal in all genomes. In addition, the relationship between the structural features of the genome and the biological complexity was discussed.

Small intestinal angiosarcoma on clinical presentation, diagnosis, management and prognosis: A case report and review of the literature

BACKGROUND Angiosarcoma is a highly malignant soft-tissue sarcoma derived from vascular endothelial cells that mainly occurs in the skin and subcutaneous tissues.Smallintestinal angiosarcomas are rare,and the prognosis is poor.CASE SUMMARY We reported a case of primary multifocal ileal angiosarcoma and analyze previously reported cases to improve our understanding of small intestinal angiosarcoma.Small intestinal angiosarcoma is more common in elderly and male patients.Gastrointestinal bleeding,anemia,abdominal pain,weakness,and weight loss were the common symptoms.CD31,CD34,factor VIII-related antigen,ETS-related gene,friend leukemia integration 1,and von Willebrand factor are valuable immunohistochemical markers for the diagnosis of small-intestinal angiosarcoma.Small-intestinal angiosarcoma most commonly occurs in the jejunum,followed by the ileum and duodenum.Radiation and toxicant exposure are risk factors for angiosarcoma.After a definite diagnosis,the mean and median survival time was 8 mo and 3 mo,respectively.Kaplan-Meier survival analysis showed that age,infiltration depth,chemotherapy,and the number of small intestinal segments invaded by tumor lesions were prognostic factors for small intestinal angiosarcoma.Multivariate Cox regression analysis showed that chemotherapy and surgery significantly improved patient prognosis.CONCLUSION Angiosarcoma should be considered for unexplained melena and abdominal pain,especially in older men and patients with a history of radiation exposure.Prompt treatment,including surgery and adjuvant chemotherapy,is essential to prolonging patient survival.

Infrared Small Target Detection Algorithm Based on ISTD-CenterNet

This paper proposes a real-time detection method to improve the Infrared small target detection CenterNet(ISTD-CenterNet)network for detecting small infrared targets in complex environments.The method eliminates the need for an anchor frame,addressing the issues of low accuracy and slow speed.HRNet is used as the framework for feature extraction,and an ECBAM attention module is added to each stage branch for intelligent identification of the positions of small targets and significant objects.A scale enhancement module is also added to obtain a high-level semantic representation and fine-resolution prediction map for the entire infrared image.Besides,an improved sensory field enhancement module is designed to leverage semantic information in low-resolution feature maps,and a convolutional attention mechanism module is used to increase network stability and convergence speed.Comparison experiments conducted on the infrared small target data set ESIRST.The experiments show that compared to the benchmark network CenterNet-HRNet,the proposed ISTD-CenterNet improves the recall by 22.85%and the detection accuracy by 13.36%.Compared to the state-of-the-art YOLOv5small,the ISTD-CenterNet recall is improved by 5.88%,the detection precision is improved by 2.33%,and the detection frame rate is 48.94 frames/sec,which realizes the accurate real-time detection of small infrared targets.

基于YOLOv5s的改进实时红外小目标检测

针对红外图像分辨率低、背景复杂、目标细节特征缺失等问题,提出了一种基于YOLOv5s的改进实时红外小目标检测模型Infrared-YOLOv5s。在特征提取阶段,采用SPD-Conv进行下采样,将特征图切分为特征子图并按通道拼接,避免了多尺度特征提取过程中下采样导致的特征丢失情况,设计了一种基于空洞卷积的改进空间金字塔池化模块,通过对具有不同感受野的特征进行融合来提高特征提取能力;在特征融合阶段,引入由深到浅的注意力模块,将深层特征语义特征嵌入到浅层空间特征中,增强浅层特征的表达能力;在预测阶段,裁减了网络中针对大目标检测的特征提取层、融合层及预测层,降低模型大小的同时提高了实时性。首先通过消融实验验证了提出各模块的有效性,实验结果表明,改进模型在SIRST数据集上平均精度均值达到了95.4%,较原始YOLOv5s提高了2.3%,且模型大小降低了72.9%,仅为4.5 M,在Nvidia Xavier上推理速度达到28 f/s,利于实际的部署和应用。在Infrared-PV数据集上的迁移实验进一步验证了改进算法的有效性。提出的改进模型在提高红外图像小目标检测性能的同时,能够满足实时性要求,因而适用于红外图像小目标实时检测任务。

基于ATO-YOLO的小目标检测算法

小目标检测在计算机视觉领域具有重要意义,但现有方法在应对小目标的尺度变化、目标密集和无规则排列等挑战时经常出现漏检和误检的问题。为解决这些问题,提出基于改进YOLOv5算法的ATO-YOLO。为提升检测模型的特征表达能力,提出一种结合注意力机制的自适应特征提取模块(adaptive feature extraction,AFE),通过动态调整权重分配突出关键目标的特征表示,提高目标检测任务在不同场景下的准确性和鲁棒性。设计一种三重特征融合机制(triple feature fusion,TFF),能够在不同尺度下充分利用多尺度信息,将多个尺度的特征图融合,以获取更全面的目标特征,提升对小目标的检测效果。引入一种输出重构模块(output reconstruction,ORS),通过去除大目标检测层并增加小目标检测层,实现精确定位和识别小目标,并且相对于原模型复杂度更低,检测速度更快。实验结果表明,ATO-YOLO算法在VisDrone数据集上的mAP@0.5达到了38.2%,较原YOLOv5提升了6.1个百分点,且FPS较改进前提升了4.4%,能够快速准确地对小目标进行检测。

基于深度学习的油田在线视频目标检测

油田背景复杂多变,摄像头悬挂较高,导致物体在监控画面中的比例较小,加大了检测难度。从油田实际场景出发,深入研究了SSD算法检测小目标准确率比较低的问题并对其改进,提出了RP-SSD算法,通过在特征金子塔中增加上采样模块和预测模块,更好地融合前后卷积层产生的特征图,并使用空洞卷积扩大了前面卷积层的感受野,提高了对小目标检测的准确率。采用Pascal VOC验证改进算法的有效性,同时选取了faster R-CNN、SSD300、DSSD321作为对照试验。试验结果表明,RP-SSD在小目标检测方面性能显著提高,可以达到实时检测的要求。

无人机高空航拍视角下小尺度车辆精确检测方法

无人机高空航拍图像中车辆像素占比极低,目标可视化信息较少,在目标检测任务中容易漏检和误检。因此,本文提出一种基于改进YOLOX(You Only Look Once X)的无人机高空航拍视角下小尺度车辆精确检测方法。首先,为增强网络对低级特征的提取能力,在原始YOLOX预测头部增加一个160 pixel×160 pixel的浅层特征提取网络;其次,在骨干网络后端嵌入基于归一化的注意力机制模块(Normalization-based Attention Module,NAM),以抑制冗余的非显著特征表达;最后,为了增大小尺度车辆的相对像素比,提升网络捕捉有效特征信息的能力,提出一种基于滑动窗口的图像切分检测方法。试验结果表明,改进YOLOX网络表现出良好的检测效能,检测精度达到了84.58%,优于典型的目标检测网络Faster R-CNN(79.95%)、YOLOv3(83.69%)、YOLOv5(84.31%)及YOLOX(83.10%)。此外,改进YOLOX能够有效解决无人机高空航拍图像中小尺度车辆的漏检和误检问题,且预测框更贴合车辆的实际轮廓;同时,在不同航拍高度的目标检测任务中具有较高的鲁棒性。

NM23基因与非小细胞肺癌临床病理学特征及^(18)F-FDG PET/CT影像特征的相关性

目的 比较非小细胞肺癌中NM23基因表达情况不同在其临床病理学特征、^(18)F-FDG PET/CT影像特征、生存时间的差异。方法 选择2018年1月~2022年12月于新疆医科大学附属医院病理确诊为非小细胞癌的107例患者的术后标本,运用免疫组化方法检测NM23表达情况,分为NM23低表达组(≤++)(n=64)与NM23高表达组(>++)(n=43),比较两组的临床病理学特征、^(18)FFDG FDG PET/CT图像上影像特征、生存期方面的差异。结果 NM23低表达组与高表达组在性别、临床分期、组织类型、分化程度、吸烟、PET/CT图像上生长部位、生存时间的差异无统计学意义(P>0.05),但在原发灶分期、PET/CT图像上淋巴结转移情况的差异有统计学意义(P<0.05)。结论 NM23基因在非小细胞癌患者T分期、淋巴结转移方面支持其为抑癌基因。

基于改进YOLOv5s的小目标检测算法

针对当前主流目标检测算法对图像中远距离小目标产生的漏检、误检等问题,提出一种改进YOLOv5s的小目标检测算法。在模型训练过程中,通过引入Focal-EIOU定位损失函数,加强边界框的定位精度;在骨干网络中,通过添加小目标检测层,提高小目标的检测精度;在Neck结构中,通过优化上采样算子和添加注意力机制,加强小目标的特征信息。实验结果表明,改进后的算法在VisDrone数据集上与YOLOv5s算法相比,mAP@small提高了3.2%,且检测速度满足实时性的要求,能够很好地应用于小目标检测任务中。

基于双线性插值的单目标检测算法

针对已有算法对于空中小目标检测效果差、精度低、漏检等问题,提出一种基于双线性插值的单目标检测算法。对主干网络进行设计,再根据特征图的特点,使用不同的方法进行特征融合。在特征融合的过程中使用双线性插值算法进行上采样。对算法的先验框进行设计,使算法的先验框尺寸与数据集目标框尺寸更加契合。实验结果表明,提出算法的平均检测精度对比SSD提升了10.29%,算法计算量对比Faster-RCNN减少了82.18%。

MFE-YOLOX:无人机航拍下密集小目标检测算法

针对无人机航拍时物体尺度变化大,检测目标大多较小且物体较密集的问题,提出一种混合特征增强结构(mix feature enhancement, MFE)方法。通过在超分辨率方法中加入注意力机制以增强小目标信息提取,利用一种新的特征层融合计算方法,加强不同特征层间的融合效率,提高了中小型目标的检测精度;设计了尾端感受野扩大层以扩大尾端特征层感受野,使检测头可接收丰富的物体信息来定位并区分密集物体。实验在数据集VisDrone2021的测试集上进行测试,MFE-YOLOX网络的AP50结果为47.78%,在参数量、计算量与原网络相近的情况下精度提高了9.43个百分点。

基于上下文信息与特征细化的无人机小目标检测算法

无人机航拍图像中的目标检测是近年来研究的热点,针对无人机视角下目标小而密集、背景复杂导致检测精度低的问题,提出一种基于上下文信息与特征细化的无人机小目标检测算法。通过上下文特征增强模块,利用多尺度扩张卷积捕获与周围区域像素点的潜在关系,为网络补充上下文信息,并根据不同尺度的特征层自适应生成各层级特征图的输出权重,动态优化特征图表达能力;由于不同特征图细粒度不同,使用特征细化模块来抑制特征融合中冲突信息的干扰,防止小目标特征淹没在冲突信息中;设计了一种带权重的损失函数,加快模型收敛速度,进一步提高小目标检测精度。在VisDrone2021数据集进行大量实验表明,改进后的模型较基准模型mAP50提高8.4个百分点,mAP50:95提高5.9个百分点,FPS为42,有效提高了无人机航拍图像中小目标的检测精度。