The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.

UAV Frequency-based Crowdsensing Using Grouping Multi-agent Deep Reinforcement Learning

Mobile CrowdSensing(MCS)is a promising sensing paradigm that recruits users to cooperatively perform sensing tasks.Recently,unmanned aerial vehicles(UAVs)as the powerful sensing devices are used to replace user participation and carry out some special tasks,such as epidemic monitoring and earthquakes rescue.In this paper,we focus on scheduling UAVs to sense the task Point-of-Interests(PoIs)with different frequency coverage requirements.To accomplish the sensing task,the scheduling strategy needs to consider the coverage requirement,geographic fairness and energy charging simultaneously.We consider the complex interaction among UAVs and propose a grouping multi-agent deep reinforcement learning approach(G-MADDPG)to schedule UAVs distributively.G-MADDPG groups all UAVs into some teams by a distance-based clustering algorithm(DCA),then it regards each team as an agent.In this way,G-MADDPG solves the problem that the training time of traditional MADDPG is too long to converge when the number of UAVs is large,and the trade-off between training time and result accuracy could be controlled flexibly by adjusting the number of teams.Extensive simulation results show that our scheduling strategy has better performance compared with three baselines and is flexible in balancing training time and result accuracy.

ASL Recognition by the Layered Learning Model Using Clustered Groups

American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.

Group Work Learning In English Learning And Teaching

Group work learning is one of the hot topics in English learning and teaching today. This discourse will probe the meaning and the advantages of group work learning, as well as its implementation. Also, the discourse discusses the proper time for group work learning. In addition to that, problems of group work learning are enclosed.

Geometry Algorisms of Dynkin Diagrams in Lie Group Machine Learning

This paper uses the geometric method to describe Lie group machine learning(LML)based on the theoretical framework of LML,which gives the geometric algorithms of Dynkin diagrams in LML.It includes the basic conceptions of Dynkin diagrams in LML,the classification theorems of Dynkin diagrams in LML,the classification algorithm of Dynkin diagrams in LML and the verification of the classification algorithm with experimental results.

Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision loss.Traditional methods of diagnosing and treating the disease are time-consuming and expensive.However,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic retinopathy.These advanced techniques offer higher accuracy and performance.ComputerAided Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease diagnoses.Overall,these technological advancements hold great potential for improving the management of diabetic retinopathy.The study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these classes.The robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its robustness.The outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance understanding.This study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of DR.The manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

Extra-curriculum Activity Groups in English Learning

Among the various extra-curriculum activities,which are indispensable for English teaching,Extra-curriculum Activity(ECA) group studies are of great value in English learning.

Recognition of Group Activities Using Complex Wavelet Domain Based Cayley-Klein Metric Learning

A group activity recognition algorithm is proposed to improve the recognition accuracy in video surveillance by using complex wavelet domain based Cayley-Klein metric learning.Non-sampled dual-tree complex wavelet packet transform(NS-DTCWPT)is used to decompose the human images in videos into multi-scale and multi-resolution.An improved local binary pattern(ILBP)and an inner-distance shape context(IDSC)combined with bag-of-words model is adopted to extract the decomposed high and low frequency coefficient features.The extracted coefficient features of the training samples are used to optimize Cayley-Klein metric matrix by solving a nonlinear optimization problem.The group activities in videos are recognized by using the method of feature extraction and Cayley-Klein metric learning.Experimental results on behave video set,group activity video set,and self-built video set show that the proposed algorithm has higher recognition accuracy than the existing algorithms.

Deep Learning Based User Grouping for FD-MIMO Systems Exploiting Statistical Channel State Information

The joint spatial division and multiplexing(JSDM)is a two-phase precoding scheme for massive multiple-input-multiple-output(MIMO)system under frequency division duplex(FDD)mode to reduce the amount of channel state information(CSI)feedback.To apply this scheme,users need to be partitioned into groups so that users in the same group have similar channel covariance eigenvectors while users in different groups have almost orthogonal eigenvectors.In this paper,taking the clustered user model into account,we consider the user grouping of JSDM for the downlink massive MIMO system with uniform planar antenna array(UPA)at base station(BS).A deep learning based user grouping algorithm is proposed to improve the efficiency of the user grouping process.The proposed grouping algorithm transfers the statistical CSI of all users into a picture,and utilizes the deep learning enabled objective detection model you look only once(YOLO)to divide the users into different groups rapidly.Simulation results show that the proposed user grouping scheme can achieve higher sum rate with less time delay.

Human Perception of Group Synchronization Error in Remote Learning: Dependencies of Voice and Video Contents in One-Way Communication

This paper examines dependencies of voice and video contents on human perception of group (or inter-destination) synchronization error in remote learning by Quality of Experience (QoE) assessment. In our assessment, we use two videos and three voices (two voices for one video and one voice for the other video). We also investigate influences of silence periods in the voices and temporal relations between the voices and videos (called the tightly-coupled and loosely-coupled contents here). The voices are spoken by a teacher according to the videos. Each subject as a student assesses the group synchronization quality by watching each lecture video and the corresponding explanation voice, and then the subject answers whether he/she perceives the group synchronization error or not. As a result, assessment results illustrate that silence periods mitigate the perception rate of the error, and we can also find that we can more easily perceive the error for tightly-coupled contents than loosely-coupled ones.

Solidarity in interactive groups in a Learning Community of Spain

The Learning Communities are an educational project.Its aim is to achieve quality education for children,youth and adults.The method to complete this goal is educational activities as interactive groups.In this paper,we present the solidarity of the students in interactive groups.For this reason,we use the critical communicative method.The research techniques are communicative observation and document analysis.The data has been obtained a learning community in Spain.The results of the analysis show that in interactive groups,the students help each other.Likewise,they cheer up and wait for each other.

Group Cooperative Learning—Making Learning a Deeper Process

The paper is to explore whether or not group cooperative learning in author’s university can make students learning deeply.In 2004,the Chinese Ministry of Education constituted"College English Teaching Syllabus"(College English Teaching Syllabus,2004,showed in appendix),in which it makes it clear that the properties and objectives of College English teaching are:College English teaching is a teaching system which has the content of English language knowledge,English applied skills,learning strategies,intercultural communication.According to the syllabus,lots of Chinese universities will aim to explore new and effective teaching modes,which will stimulate college English teachers to reflect their traditional teaching methods and make the corresponding improvement inevitably.

Comparison of the Perception of English Learning Between Ethnic Group Students and Han Students

This paper has analyzed the discrepancies of the perception of English learning between ethnic students and Han studens in a trilingual language context.The research results will be expected to broaden our understanding of the ethnic group students in the minority regions,and to provide some empirical references and implications for teachers.

Experimental Study of The Group Cooperative Learning in Oral English Class of English Majors

College students have better English foundations for them to develop English speaking abilities.However, most of them are not good at it compared with reading and listening.Therefore, teachers need to find out some methods to develop students' ability of Group Cooperative Learning to know their errors in time and make sure that they can benefit from it so that students have the enthusiasm to improve their oral English.The thesis is mainly about all empirical study of Group Cooperative Leaning in college English oral course.The major experiment was done with some English majors at North University of China.Though many subjects supported Cooperative Learning and believed that it did effectively reduce some anxiety in students' English oral learning and enhanced their interests, the improvement of their English oral ability is far from satisfaction.This thesis employs questionnaire survey and contrastive analysis methods.This research paper means to investigate the Group Cooperative Learning of oral English learning for college students.The thesis includes five parts.The first part illustrates the purpose, significance of the research and the definition of Group Cooperative Learning. The second part shows the research background from abroad and home, and the factors of poor oral English.The third part presents the research design of the author.The fourth part states the results and discussions about the research.The conclusion part summarizes the major findings of research and also explains the limitations of the author's research and further study about Group Cooperative Learning in oral class for English Majors.

Importance of a Good Grasp of Sentence Sense Groups in English Learning

In the past twenty years, many good ways of learning English have been put forward. In this paper, the author comes up with a new approacha good grasp of sentence sense groups which he thinks is very helpful in improving the student English in many aspects. The author believes that a good grasp of sentence groups in English sentences is the basis of learning English well and that many students will benefit much from it when applying it to their English learning.

高校E-learning的现状调查与思考

本文是关于高校e-learning应用现状的调查研究。笔者以广东省16所高校为研究对象,采用焦点团体访谈法对来自上述高校的18位教师或教育技术从业人员进行了访谈,然后使用内容分析和现象解释学方法对访谈数据进行分析与讨论。研究结果表明,我国高校教师对e-learning的现状、概念、原理、方法和实践等还存在着片面或错误的认识,高校在实施e-learning的过程中也存在诸多误区。针对这些错误认识和应用误区,本文分析了其原因并提出了切实可行的解决方案。