超声引导下粗针穿刺活检对甲状腺TI-RADS 4a类结节良恶性的鉴别诊断价值

目的探讨超声引导下粗针穿刺活检(US-CNAB)对甲状腺TI-RADS 4a类结节良恶性的鉴别诊断价值。方法选取2021-02—2023-01于郑州大学第二附属医院行US-CNAB的119例共142个甲状腺TI-RADS 4a类结节患者,以术后常规病理检查为“金标准”,评价US-CNAB鉴别诊断甲状腺TI-RADS 4a类结节良恶性的效能,统计并发症发生率。结果US-CNAB结果与术后常规病理结果的差异无统计学意义(P>0.05)。以术后常规病理检查为“金标准”,US-CNAB鉴别诊断甲状腺TI-RADS 4a类结节良恶性的准确度、灵敏度、特异度、阳性预测值、阴性预测值分别为98.15%、88.24%、95.78%、96.36%93.75%。US-CNAB后出现声音嘶哑1例,血肿6例,并发症发生率为5.88%。结论US-CNAB对鉴别甲状腺TI-RADS 4a类结节的良恶性具有较高的诊断价值。

超声造影成像对TI-RADS 4类甲状腺囊性皱缩结节诊断的临床应用

目的:探讨超声造影成像(CEUS)对TI-RADS 4类甲状腺囊性皱缩结节性质诊断效果.方法:选取2020年1月至2023年1月于本院就诊甲状腺结节患者中,被诊断为TI-RADS 4类甲状腺囊性皱缩结节的106例患者为研究对象,共112处病灶,纳入甲状腺囊性皱缩结节组(n=112).同期被同一家医院诊断的甲状腺乳头状癌(PTC)患者101例作为对照,共105处病灶,纳入PTC组(n=105).采取回顾性分析法比较两组病灶的超声表现及CEUS特点,分析CEUS特异性指标诊断甲状腺囊性皱缩结节的效能.结果:甲状腺囊性皱缩结节组与PTC组整体、壁增强模式的CEUS结果比较,差异有统计学意义(P<0.05);CEUS无增强模式诊断甲状腺囊性皱缩结节的灵敏度为95.11%,准确度为97.12%;壁增强模式诊断甲状腺囊性皱缩结节的灵敏度为55.26%,准确度为78.31%.结论:TI-RADS 4类甲状腺囊性皱缩结节超声表现与PTC存在一定相似度,但是CEUS鉴别诊断TI-RADS 4类甲状腺囊性皱缩结节可减少误诊,降低分类,避免不需要的手术和穿刺活检,值得临床广泛应用.

超声血流参数与乳头状甲状腺癌临床病理及超声TI-RADS分级关系研究

目的:分析超声血流参数与乳头状甲状腺癌临床病理及超声TI-RADS分级关系。方法:以2020年10月至2023年11月于本院确诊的乳头状甲状腺癌患者103例为研究对象,所有研究对象均进行超声检查获取血流参数,并进行B超TI-RADS分级。比较不同病理特征及TI-RADS分级患者的血流参数,分析超声血流参数与乳头状甲状腺癌病理及TI-RADS分级的相关性。结果:Ⅲ~Ⅳ期患者的搏动指数(PI)、阻力指数(RI)、收缩期峰值流速与舒张期峰值流速比值(S/D)高于Ⅰ~Ⅱ期患者(P<0.05);颈部淋巴结转移患者的PI、RI、S/D显著高于未转移患者(P<0.05);TI-RADS分级5~6级患者的PI、RI、S/D高于4a~4c级(P<0.05)。Spearman秩相关性分析结果显示,PI、RI、S/D与乳头状甲状腺癌患者的TNM分期及TI-RADS分级均呈正相关(rTNM分期=0.401,0.453,0.462;rTI-RADS分级=0.308,0.376,0.368,P<0.05)。结论:超声血流参数PI、RI、S/D与乳头状甲状腺癌患者的肿瘤分期及TI-RADS分级密切相关,早期测定超声血流定量参数可为乳头状甲状腺癌的临床诊断、预测提供一定的参考信息。

血清25羟维生素D与气阴两虚2型糖尿病合并甲状腺结节TI-RADS分级相关性分析

目的分析血清25羟维生素D[25(OH)D]水平与气阴两虚2型糖尿病合并甲状腺结节TI-RADS(甲状腺影像报告与数据系统)分级之间的关系,为精准筛查甲状腺结节患者提供依据。方法收集2021年3月—2023年3月期间在北部战区总医院内分泌科住院的气阴两虚2型糖尿病患者,根据甲状腺超声结果及TI-RADS分级情况,将单纯气阴两虚2型糖尿病不合并甲状腺结节的设为非结节组,将气阴两虚2型糖尿病合并甲状腺结节的设为结节组,其中TI-RADS 2~3类为良性结节组,TI-RADS 4类及以上为可疑恶性结节组,比较3组血清25(OH)D水平,并分析血清25(OH)D与甲状腺结节分级的相关性。结果结节组与非结节组相比,性别、吸烟、高血压、糖尿病病程、血清镁、TgAb比较,差异有统计学意义(P<0.05)。Logistic回归分析结果显示:高血压、糖尿病病程、血肌酐为糖尿病合并甲状腺结节的影响因素。血清25(OH)D与总胆固醇、甘油三酯、甲状旁腺激素(PTH)、甲状腺TI-RADS分级呈负相关(P<0.05),与高密度脂蛋白胆固醇(HDL-C)呈正相关(P<0.05)。结论25(OH)D与气阴两虚2型糖尿病合并甲状腺结节患者TI-RADS分级呈负相关。

围绝经期女性TI-RADS 4类结节治验1则

近年来随着彩超技术的提升,甲状腺结节的诊断在超声以及TI-RADS分类的辅助下变得更加精确。甲状腺结节属于中医学“瘿病”的范畴,目前中医药在治疗甲状腺结节方面也颇见成效。因围绝经期女性甲状腺结节的发病率较正常经期女性升高,且围绝经期的女性也是甲状腺癌的好发人群,并且现代医学对TI-RADS分类为高级别但未经穿刺确诊为恶性的结节尚未有明确治疗方案,所以中药治疗可以在这一阶段起到整体调理患者症状以及控制结节发展的效果。

Classification of Sailboat Tell Tail Based on Deep Learning

The tell tail is usually placed on the triangular sail to display the running state of the air flow on the sail surface.It is of great significance to make accurate judgement on the drift of the tell tail of the sailboat during sailing for the best sailing effect.Normally it is difficult for sailors to keep an eye for a long time on the tell sail for accurate judging its changes,affected by strong sunlight and visual fatigue.In this case,we adopt computer vision technology in hope of helping the sailors judge the changes of the tell tail in ease with ease.This paper proposes for the first time a method to classify sailboat tell tails based on deep learning and an expert guidance system,supported by a sailboat tell tail classification data set on the expert guidance system of interpreting the tell tails states in different sea wind conditions,including the feature extraction performance.Considering the expression capabilities that vary with the computational features in different visual tasks,the paper focuses on five tell tail computing features,which are recoded by an automatic encoder and classified by a SVM classifier.All experimental samples were randomly divided into five groups,and four groups were selected from each group as the training set to train the classifier.The remaining one group was used as the test set for testing.The highest resolution value of the ResNet network was 80.26%.To achieve better operational results on the basis of deep computing features obtained through the ResNet network in the experiments.The method can be used to assist the sailors in making better judgement about the tell tail changes during sailing.

Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text.This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus.Furthermore,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic criteria.Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus.Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to baselines.Finally,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances.The proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText alone.The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances.

Classification of congenital cataracts based on multidimensional phenotypes and its association with visual outcomes

●AIM:To establish a classification for congenital cataracts that can facilitate individualized treatment and help identify individuals with a high likelihood of different visual outcomes.●METHODS:Consecutive patients diagnosed with congenital cataracts and undergoing surgery between January 2005 and November 2021 were recruited.Data on visual outcomes and the phenotypic characteristics of ocular biometry and the anterior and posterior segments were extracted from the patients’medical records.A hierarchical cluster analysis was performed.The main outcome measure was the identification of distinct clusters of eyes with congenital cataracts.●RESULTS:A total of 164 children(299 eyes)were divided into two clusters based on their ocular features.Cluster 1(96 eyes)had a shorter axial length(mean±SD,19.44±1.68 mm),a low prevalence of macular abnormalities(1.04%),and no retinal abnormalities or posterior cataracts.Cluster 2(203 eyes)had a greater axial length(mean±SD,20.42±2.10 mm)and a higher prevalence of macular abnormalities(8.37%),retinal abnormalities(98.52%),and posterior cataracts(4.93%).Compared with the eyes in Cluster 2(57.14%),those in Cluster 1(71.88%)had a 2.2 times higher chance of good best-corrected visual acuity[<0.7 logMAR;OR(95%CI),2.20(1.25–3.81);P=0.006].●CONCLUSION:This retrospective study categorizes congenital cataracts into two distinct clusters,each associated with a different likelihood of visual outcomes.This innovative classification may enable the personalization and prioritization of early interventions for patients who may gain the greatest benefit,thereby making strides toward precision medicine in the field of congenital cataracts.

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Pathological and Clinical Correlation European Union-Thyroid Imaging Reporting and Data System (EU-TIRADS) Classification of Thyroid Nodules in Two University Hospitals in Cotonou

Introduction: Since its creation in 2017 by the European community, the EU-TIRADS classification has enjoyed an excellent reputation in several countries around the world. Indeed, several studies conducted in these countries testify to the effectiveness of this tool for the management of nodular thyroid pathology. However, in Benin, the contribution of this classification has not yet been evaluated. It is therefore to overcome this inadequacy that we undertook this study. Objective: Participate in improving the diagnostic and therapeutic management of thyroid nodules at the CNHU HKM in Cotonou and at the CHUZ in Suru-Léré. Methods: This is a cross-sectional study with retrospective data collection spread over a period of 3 years 5 months, from January 2019 to May 2022 and carried out jointly in the Endocrinology Metabolism Nutrition and ORL-CCF departments of the CNHU HKM of Cotonou and in the ORL-CCF department of the CHUZ of Suru-Léré. The study population consisted of patients who consulted the University Clinic of Endocrinology Metabolism Nutrition, the University Clinic of ORL-CCF of the CNHU-HKM and the University Clinic of ORL-CCF of the CHUZ of Suru-Léré for thyroid nodule and who have had surgery. The study data was collected from patients hospitalization records using a survey form. Results: On ultrasound, according to the EU-TIRADS classification, 56.8% of nodules presented a low risk of malignancy (EU-TIRADS 3) compared to respectively 19.8%;23% and 2.5% of nodules with zero (EU-TIRADS 2), intermediate (EU-TIRADS 4) and high (EU-TIRADS 5) risk of malignancy. Regarding the performance of this classification, it is sensitive in 37.5% of cases and has a specificity of 78.5% with a PPV (Positive Predictive Value) and a NPV (Negative Predictive Value) respectively of 6.6 % and 91.6%. Furthermore, the bivariate correlations revealed that the size of the nodule was significantly associated with the malignancy of the nodule (p = 0.014) and the calculated value of the Yule’s Q coefficient (0.375) reflects a moderate intensity of the connection between the EU-TIRADS and histology. Conclusion: the EU-TIRADS classification, due to its excellent NPV, is of great interest for the management of thyroid nodules at the CNHU-HKM of Cotonou and at the CHUZ of Suru-Léré. In view of this, particular emphasis must be placed on its regular and rigorous use.

Orchard Sports Injury and Illness Classification System (OSIICS) Version 15

Background:Sports medicine(injury and illnesses)requires distinct coding systems because the International Classification of Diseases is insuf-ficient for sports medicine coding.The Orchard Sports Injury and Illness Classification System(OSIICS)is one of two sports medicine coding systems recommended by the International Olympic Committee.Regular updates of coding systems are required.Methods:For Version 15,updates for mental health conditions in athletes,sports cardiology,concussion sub-types,infectious diseases,and skin and eye conditions were considered particularly important.Results:Recommended codes were added from a recent International Olympic Committee consensus statement on mental health conditions in athletes.Two landmark sports cardiology papers were used to update a more comprehensive list of sports cardiology codes.Rugby union protocols on head injury assessment were used to create additional concussion codes.Conclusion:It is planned that OSIICS Version 15 will be translated into multiple new languages in a timely fashion to facilitate international accessibility.The large number of recently published sport-specific and discipline-specific consensus statements on athlete surveillance warrant regular updating of OSIICS.

Monitoring Surface Water Change in Northeast China in 1999–2020:Evidence from Satellite Observation and Refined Classification

As a typical region with high water demand for agricultural production,understanding the spatiotemporal surface water changes in Northeast China is critical for water resources management and sustainable development.However,the long-term variation characteristics of surface water of different water body types in Northeast China remain rarely explored.This study investigated how surface water bodies of different types(e.g.,lake,reservoir,river,coastal aquaculture,marsh wetland,ephemeral water) changed during1999–2020 in Northeast China based on various remote sensing-based datasets.The results showed that surface water in Northeast China grew dramatically in the past two decades,with an equivalent area increasing from 24 394 km^(2) in 1999 to 34 595 km^(2) in 2020.The surge of ephemeral water is the primary driver of surface water expansion,which could ascribe to shifted precipitation pattern.Marsh wetlands,rivers,and reservoirs experienced a similar trend,with an approximate 20% increase at the interdecadal scale.By contrast,coastal aquacultures and natural lakes remain relatively stable.This study is expected to provide a more comprehensive investigation of the surface water variability in Northeast China and has important practical significance for the scientific management of different types of surface water.

Diffraction deep neural network-based classification for vector vortex beams

The vector vortex beam(VVB)has attracted significant attention due to its intrinsic diversity of information and has found great applications in both classical and quantum communications.However,a VVB is unavoidably affected by atmospheric turbulence(AT)when it propagates through the free-space optical communication environment,which results in detection errors at the receiver.In this paper,we propose a VVB classification scheme to detect VVBs with continuously changing polarization states under AT,where a diffractive deep neural network(DDNN)is designed and trained to classify the intensity distribution of the input distorted VVBs,and the horizontal direction of polarization of the input distorted beam is adopted as the feature for the classification through the DDNN.The numerical simulations and experimental results demonstrate that the proposed scheme has high accuracy in classification tasks.The energy distribution percentage remains above 95%from weak to medium AT,and the classification accuracy can remain above 95%for various strengths of turbulence.It has a faster convergence and better accuracy than that based on a convolutional neural network.

Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

TEAM:Transformer Encoder Attention Module for Video Classification

Much like humans focus solely on object movement to understand actions,directing a deep learning model’s attention to the core contexts within videos is crucial for improving video comprehension.In the recent study,Video Masked Auto-Encoder(VideoMAE)employs a pre-training approach with a high ratio of tube masking and reconstruction,effectively mitigating spatial bias due to temporal redundancy in full video frames.This steers the model’s focus toward detailed temporal contexts.However,as the VideoMAE still relies on full video frames during the action recognition stage,it may exhibit a progressive shift in attention towards spatial contexts,deteriorating its ability to capture the main spatio-temporal contexts.To address this issue,we propose an attention-directing module named Transformer Encoder Attention Module(TEAM).This proposed module effectively directs the model’s attention to the core characteristics within each video,inherently mitigating spatial bias.The TEAM first figures out the core features among the overall extracted features from each video.After that,it discerns the specific parts of the video where those features are located,encouraging the model to focus more on these informative parts.Consequently,during the action recognition stage,the proposed TEAM effectively shifts the VideoMAE’s attention from spatial contexts towards the core spatio-temporal contexts.This attention-shift manner alleviates the spatial bias in the model and simultaneously enhances its ability to capture precise video contexts.We conduct extensive experiments to explore the optimal configuration that enables the TEAM to fulfill its intended design purpose and facilitates its seamless integration with the VideoMAE framework.The integrated model,i.e.,VideoMAE+TEAM,outperforms the existing VideoMAE by a significant margin on Something-Something-V2(71.3%vs.70.3%).Moreover,the qualitative comparisons demonstrate that the TEAM encourages the model to disregard insignificant features and focus more on the essential video features,capturing more detailed spatio-temporal contexts within the video.

Development of a novel staging classification for Siewert Ⅱ adenocarcinoma of the esophagogastric junction after neoadjuvant chemotherapy

BACKGROUND Stage classification for Siewert Ⅱ adenocarcinoma of the esophagogastric junction(AEG)treated with neoadjuvant chemotherapy(NAC)has not been established.AIM To investigate the optimal stage classification for Siewert Ⅱ AEG with NAC.METHODS A nomogram was established based on Cox regression model that analyzed variables associated with overall survival(OS)and disease-specific survival(DSS).The nomogram performance in terms of discrimination and calibration ability was evaluated using the likelihood-ratio test,Akaike information criterion,Harrell concordance index,time-receiver operating characteristic curve,and decision curve analysis.RESULTS Data from 725 patients with Siewert type Ⅱ AEG who underwent neoadjuvant therapy and gastrectomy were obtained from the Surveillance,Epidemiology,and End Results database.Univariate and multivariate analyses revealed that sex,marital status,race,ypT stage,and ypN stage were independent prognostic factors of OS,whereas sex,race,ypT stage,and ypN stage were independent prognostic factors for DSS.These factors were incorporated into the OS and DSS nomograms.Our novel nomogram model performed better in terms of OS and DSS prediction compared to the 8th American Joint Committee of Cancer pathological staging system for esophageal and gastric cancer.Finally,a user-friendly web application was developed for clinical use.CONCLUSION The nomogram established specifically for patients with Siewert type Ⅱ AEG receiving NAC demonstrated good prognostic performance.Validation using external data is warranted before its widespread clinical application.

Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes,which is vital for India’s socio-economic growth.Due to natural and artificial factors,frequency of slope instabilities along the networks has been increasing over last few decades.Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year.Several rock mass classification methods are generally used to assess the strength and deformability of rock mass.This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas.Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes.For evaluating the stability of these slopes,kinematic analysis was performed along with geological strength index(GSI),rock mass rating(RMR),continuous slope mass rating(CoSMR),slope mass rating(SMR),and Q-slope in the present study.The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable.The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating(SSR)for the factor of safety(FoS)of 1.2 and 1 respectively.Q-slope with probability of failure(PoF)1%gives two slopes as stable slopes.Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS.The value ranges given by different empirical classifications were RMR(37-74),GSI(27.3-58.5),SMR(11-59),and CoSMR(3.39-74.56).Good relationship was found among RMR&SSR and RMR&GSI with correlation coefficient(R 2)value of 0.815 and 0.6866,respectively.Lastly,a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Design of a novel hybrid quantum deep neural network in INEQR images classification

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.