In the field of L2 （second language） reading, scholars generally agree that ER （extensive reading） improves L2 learners＇ reading speed and comprehension and enriches their L2 vocabulary （Grabe ＆ Stoller, 1997）...In the field of L2 （second language） reading, scholars generally agree that ER （extensive reading） improves L2 learners＇ reading speed and comprehension and enriches their L2 vocabulary （Grabe ＆ Stoller, 1997）. This teacher-inquiry type of paper presents a practical suggestion for supplementing ER activity with the element of CL （cooperative learning）. ER, theoretically speaking, focuses on the solitary task of silent reading. The CL technique used in this study was a book-talk activity. Forty-five freshmen from a course of children and young adult literatures were required to read at least 20 English books throughout a semester. CL was added to facilitate students＇ ER in young adult literature. After a semester, short-answer questions were asked regarding students＇ comments on ER as well as CL. Students overall agreed that when ER is supplemented with CL, reading in an L2 seems to be less intimidating.展开更多
The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving ...The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient care.Developing a novel ML algorithm specific to Diabetic Retinopathy(DR)is a chal-lenge and need of the hour.Biomedical images include several challenges,including relevant feature selection,class variations,and robust classification.Although the cur-rent research in DR has yielded favourable results,several research issues need to be explored.There is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-fication.This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier(SKPD-PSC)method.The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in har-mony with the Platt Scale Classifier(PSC)to improve the accuracy of DR detection.First,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background noise.Finally,a Platt Scale Classifier(PSC)is applied to the localized features for robust classification.For the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classifiers.Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to the state-of-the-art meth-ods.The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation.Compared to state-of-the-art methods,the method increases DR detection accuracy by 24%and DR detection time by 37.展开更多
Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including we...Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including weather con-ditions,soil qualities,water levels and the location of the farm.A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting.The combination of data mining and deep learning creates a whole crop yield pre-diction system that is able to connect raw data to predicted crop yields.The sug-gested study uses a Discrete Deep belief network with Visual Geometry Group(VGG)Net classification method over the tweak chick swarm optimization approach to estimate agricultural production.The Network’s successively stacked layers were fed the data parameters.Based on the input parameters,a crop produc-tion prediction environment is constructed using the network architecture.Using the tweak chick swarm optimization technique,the best characteristics of input data are preprocessed,and the optimal output is used as input for the classification process.Discrete Deep belief network with the Visual Geometry Group Net clas-sifier is used to classify the data and forecast agricultural production.The sug-gested model correctly predicts crop output with 97 percent accuracy,exceeding existing models by maintaining the baseline data distribution.展开更多
In recent years,the integration of deep learning with computational imaging has fundamentally transformed optical imaging paradigms.Traditional methods encounter significant challenges when reconstructing high-dimensi...In recent years,the integration of deep learning with computational imaging has fundamentally transformed optical imaging paradigms.Traditional methods encounter significant challenges when reconstructing high-dimensional information in complex scenarios[1].By leveraging the powerful nonlinear modeling and advanced feature extraction capabilities of deep learning,these barriers have been effectively overcome,enabling end-to-end optimization—from optical system design to image reconstruction[2].This shift transforms optoelectronic imaging from a conventional“what you see is what you get”model toward a more adaptive“what you see is what you need”approach,catalyzing breakthroughs across diverse applications including optical imaging,medical diagnostics,remote sensing,and beyond.展开更多
知识推理是补全知识图谱的重要方法,旨在根据图谱中已有的知识,推断出未知的事实或关系.针对多数推理方法仍存在没有充分考虑实体对之间的路径信息,且推理效率偏低、可解释性差的问题,提出了将TuckER嵌入和强化学习相结合的知识推理方法...知识推理是补全知识图谱的重要方法,旨在根据图谱中已有的知识,推断出未知的事实或关系.针对多数推理方法仍存在没有充分考虑实体对之间的路径信息,且推理效率偏低、可解释性差的问题,提出了将TuckER嵌入和强化学习相结合的知识推理方法TuckRL(TuckER embedding with reinforcement learning).首先,通过TuckER嵌入将实体和关系映射到低维向量空间,在知识图谱环境中采用策略引导的强化学习算法对路径推理过程进行建模,然后在路径游走进行动作选择时引入动作修剪机制减少无效动作的干扰,并将LSTM作为记忆组件保存智能体历史动作轨迹,促使智能体更准确地选择有效动作,通过与知识图谱的交互完成知识推理.在3个主流大规模数据集上进行了实验,结果表明TuckRL优于现有的大多数推理方法,说明将嵌入和强化学习相结合的方法用于知识推理的有效性.展开更多
The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Net...The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.展开更多
文摘In the field of L2 （second language） reading, scholars generally agree that ER （extensive reading） improves L2 learners＇ reading speed and comprehension and enriches their L2 vocabulary （Grabe ＆ Stoller, 1997）. This teacher-inquiry type of paper presents a practical suggestion for supplementing ER activity with the element of CL （cooperative learning）. ER, theoretically speaking, focuses on the solitary task of silent reading. The CL technique used in this study was a book-talk activity. Forty-five freshmen from a course of children and young adult literatures were required to read at least 20 English books throughout a semester. CL was added to facilitate students＇ ER in young adult literature. After a semester, short-answer questions were asked regarding students＇ comments on ER as well as CL. Students overall agreed that when ER is supplemented with CL, reading in an L2 seems to be less intimidating.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R195),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The study aimed to apply to Machine Learning(ML)researchers working in image processing and biomedical analysis who play an extensive role in compre-hending and performing on complex medical data,eventually improving patient care.Developing a novel ML algorithm specific to Diabetic Retinopathy(DR)is a chal-lenge and need of the hour.Biomedical images include several challenges,including relevant feature selection,class variations,and robust classification.Although the cur-rent research in DR has yielded favourable results,several research issues need to be explored.There is a requirement to look at novel pre-processing methods to discard irrelevant features,balance the obtained relevant features,and obtain a robust classi-fication.This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier(SKPD-PSC)method.The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in har-mony with the Platt Scale Classifier(PSC)to improve the accuracy of DR detection.First,a Steerable Filter Kernel Pre-processing(SFKP)model is applied to the Retinal Images(RI)to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians(DDG).Next,the Partial Derivative Image Localization(PDIL)model is applied to the extracted fea-tures to localize candidate features and suppress the background noise.Finally,a Platt Scale Classifier(PSC)is applied to the localized features for robust classification.For the experiments,we used the publicly available DR detection database provided by Standard Diabetic Retinopathy(SDR),called DIARETDB0.A database of 130 image samples has been collected to train and test the ML-based classifiers.Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accu-racy rate with minimum time and complexity compared to the state-of-the-art meth-ods.The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation.Compared to state-of-the-art methods,the method increases DR detection accuracy by 24%and DR detection time by 37.
文摘Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including weather con-ditions,soil qualities,water levels and the location of the farm.A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting.The combination of data mining and deep learning creates a whole crop yield pre-diction system that is able to connect raw data to predicted crop yields.The sug-gested study uses a Discrete Deep belief network with Visual Geometry Group(VGG)Net classification method over the tweak chick swarm optimization approach to estimate agricultural production.The Network’s successively stacked layers were fed the data parameters.Based on the input parameters,a crop produc-tion prediction environment is constructed using the network architecture.Using the tweak chick swarm optimization technique,the best characteristics of input data are preprocessed,and the optimal output is used as input for the classification process.Discrete Deep belief network with the Visual Geometry Group Net clas-sifier is used to classify the data and forecast agricultural production.The sug-gested model correctly predicts crop output with 97 percent accuracy,exceeding existing models by maintaining the baseline data distribution.
文摘In recent years,the integration of deep learning with computational imaging has fundamentally transformed optical imaging paradigms.Traditional methods encounter significant challenges when reconstructing high-dimensional information in complex scenarios[1].By leveraging the powerful nonlinear modeling and advanced feature extraction capabilities of deep learning,these barriers have been effectively overcome,enabling end-to-end optimization—from optical system design to image reconstruction[2].This shift transforms optoelectronic imaging from a conventional“what you see is what you get”model toward a more adaptive“what you see is what you need”approach,catalyzing breakthroughs across diverse applications including optical imaging,medical diagnostics,remote sensing,and beyond.
文摘知识推理是补全知识图谱的重要方法,旨在根据图谱中已有的知识,推断出未知的事实或关系.针对多数推理方法仍存在没有充分考虑实体对之间的路径信息,且推理效率偏低、可解释性差的问题,提出了将TuckER嵌入和强化学习相结合的知识推理方法TuckRL(TuckER embedding with reinforcement learning).首先,通过TuckER嵌入将实体和关系映射到低维向量空间,在知识图谱环境中采用策略引导的强化学习算法对路径推理过程进行建模,然后在路径游走进行动作选择时引入动作修剪机制减少无效动作的干扰,并将LSTM作为记忆组件保存智能体历史动作轨迹,促使智能体更准确地选择有效动作,通过与知识图谱的交互完成知识推理.在3个主流大规模数据集上进行了实验,结果表明TuckRL优于现有的大多数推理方法,说明将嵌入和强化学习相结合的方法用于知识推理的有效性.
文摘The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns.In this work,an Auxiliary Classifier(AC)-Generative Adversarial Network(GAN)based Lung Cancer Classification(LCC)system is developed.The pro-posed AC-GAN-LCC system consists of three modules;preprocessing,Lungs Region Detection(LRD),and AC-GAN classification.A Wienerfilter is employed in the preprocessing module to remove the Gaussian noise.In the LRD module,only the lung regions(left and right lungs)are detected using itera-tive thresholding and morphological operations.In order to extract the lung region only,floodfilling and background subtraction.The detected lung regions are fed to the AC-GAN classifier to detect the nodules.It classifies the nodules into one of the two classes,i.e.,binary classification(such as nodules or non-nodules).The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image.Three different optimization techniques,adaptive gradient optimi-zation,root mean square propagation optimization,and Adam optimization are employed for optimizing the AC-GAN architecture.The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium(LIDC)data-base Computed Tomography(CT)scan images.The proposed AC-GAN-LCC system classifies∼15000 CT slices(7310 non-nodules and 7685 nodules).It pro-vides an overall accuracy of 98.8%on the LIDC database using Adam optimiza-tion by a 10-fold cross-validation approach.
文摘基于蛋白质组表达的差异性探讨二仙汤改善去卵巢大鼠学习记忆能力的作用机制。实验动物按体质量随机分为3组,即假手术组、模型组、二仙汤组,其中假手术组和模型组灌服生理盐水,二仙汤组则按生药量12 g·kg^-1灌服二仙汤,连续给药90 d后,采用Morris水迷宫考察大鼠的学习记忆能力,以Nano-LC-LTQ-Orbitrap系统检测海马蛋白,Protein Discovery软件进行蛋白质鉴定,SIEVE软件对蛋白进行相对定量分析,DIVAD数据库对差异蛋白进行生物学功能分析。结果显示,与模型组相比,二仙汤组逃避潜伏期明显缩短,穿越平台次数、平台象限的停留时间（TP）和TP与总游泳时间（T）的百分比（TP/T）均增加。与模型组相比,二仙汤组共发现216个与假手术组趋势一致的差异蛋白,主要富集于Ca^2＋信号通路,Rab3A与Rab14为functional classification富集分值最高的基因组中的关键基因。结果表明,二仙汤能提高去卵巢大鼠的学习记忆能力,Ca^2＋信号通路以及Ras-related protein Rab-3A,Ras-related protein Rab-14等相关差异蛋白质可能与二仙汤的干预效应有关。
