# Application of 5G Combined Wireless Interconnection Technology in the Reform of English Education in Colleges and Universities

Authors: Yuanyuan Chen; Xiaoyan Song
Journal: Mobile Information Systems
 (2022)

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## Abstract

Fifth-generation (5G) network is a digital cellular network communication technology that allows for more data traffic and wireless connectivity. The global demand for wireless communication networks is increasing as the number of wireless users grows and new wireless services are presented. Fifth-generation (5G) wireless networks are expected to provide higher data speeds, expanded coverage, improved cost-effectiveness, resource utilization, security, flexibility, and scalability in the future. To attain higher-order teaching abilities, include cutting-edge technology into the curriculum. Teachers should use network resources, notably 5G network communication tools, to reform English teaching since the rapid advancement of network communication technology provides a new viewpoint for English education in colleges and universities. Researchers are concentrating their efforts on maximizing the benefits of the information technology revolution. It has become a hot subject among academics to use it as an opportunity to innovate in English education. As a result, this study proposes a paradigm for revamping English instruction using 5G integrated wireless connectivity technologies. To preprocess the input data, the Butterworth filter and the Gaussian Mixture Model (GMM) are employed. To extract data features, a Gray Level Co-Occurrence Matrix (GLCM) is employed. For classification, the decision tree approach is used. The File Transport Protocol (FTP) is used for data transfer, which may be enhanced with the Revamped Artificial Bee Colony Optimization Algorithm (RABCOA). Using the MATLAB simulation tool, the proposed system is simulated and its performance is assessed. The outcome of the study’s shows that the scholars are usually unsatisfied with old style English classroom education and are more interested in the instructional innovation of 5G integrated wireless networking technologies.


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## Body

## 1. Introduction

The rise of education for all, customized learning, and quality education as an essential part of the information age’s development of education has significantly enhanced the country’s national quality and innovation. Furthermore, English is an important component of student learning. The study of English may improve students’ ability to comprehend, grow as people, and meet the problems of today’s information globalization. Education for all, individualized learning, and excellent education have had a huge influence on our country’s quality of life and innovation in the information age. Furthermore, English is an important part of student education. Students who learn English are more prepared to have the demands of up-to-date info globalization, which necessitates the ability to communicate in a range of languages [1]. The use of multimedia and the Internet as supplemental teaching techniques in English education is becoming more common. Our school employs a wide range of online multimedia approaches, i.e., video and audio, to teach English. Lines like this may be beneficial to both instructors and students. Students from different grade levels in similar class and their education abilities and understanding levels may be used to develop an “appropriate” learning plan and material. The approaches to online and offline education complement one another. Using a networked learning method, the technology circumvents the geographical and knowledge limits of traditional classroom training [2].A teacher’s teaching method must be developed regularly to remain relevant. Teaching spoken language in today’s classrooms necessitates a different approach than in the past. Both students and teachers benefit from the use of 5G technology in spoken language teaching. On the one hand, students will be more motivated, and teachers will be able to update teaching materials and increase classroom content more quickly [3]. It might make class preparation easier for instructors and relieve them of time-consuming tasks like assignment revision. Individualized counselling and evaluation for students can be provided, enhancing the quality of learning and increasing instructional efficiency. Students no longer have to rely entirely on professors and textbooks to obtain information since artificial intelligence-based English learning solutions have become more readily available. Students now have access to several tailored learning alternatives, which have allowed them to enjoy and excel in their English studies [3]. Due to 5G technologies, educators may now oversee every scholar’s language education condition for the first-time and then offer every learner by a suitable English teaching approach. Furthermore, each scholar was able to contribute in an English communicating discussion at any time and from any location, and they were able to acquire the much-needed language-learning help at the proper time for the first time. It could be said that this new age has made university language learning more useful as it not only provides educators with the methodologies to objectively and thoroughly check the impact of their language teaching but it also provides educators with the tools to accurately and automatically check the impact of their language education [4].In Zhejiang province, an online questionnaire survey of English majors was advised as a means of absorbing the valuable information using SVM algorithm optimization. Questionnaire questions are designed with student happiness, cloud computing, and wireless connectivity in mind, as well as their English competence. According to the report, college students are unsatisfied with traditional English classroom training and are more interested in educational innovations provided by cloud computing and wireless communication technologies that may be employed to improve English learning. This provides us with crucial recommendations for implementing route innovation in college English training at this time [5]. The study discovered that using the mobile Internet to boost engagement in college English classrooms may be done in a variety of ways that are student-centered, interactive, and innovative. As a result, to maximize the potential for student-to-student and student-to-course engagement, instructors must create an ideal interactive teaching pattern (in-classroom + mobile internet-assisted teaching + individual independent study). The success of this hands-on approach to teaching is aided by including a range of strategies, such as issue framing, group problem-solving, statistical analysis, and feedback loops. The use of mobile Internet technology in an interactive pattern increases the value of classroom input while also achieving the pedagogical goal of classroom participation [6–10].Similarly, Artificial Intelligence-Assisted Language Learning (AIALL) is being developed in concert with artificial intelligence technology research and commercialization. As a result, college English classes have been overhauled. AI based on bigData has revolutionized the way education is organized, materials are given, and evaluations are undertaken, among other things. This system is developed on the B/S architectural pattern, using the SSM framework and MYSQL database development. Instructors’ work should be objectively, equitably, and scientifically evaluated in compliance with school-wide teaching quality standards through classroom teaching quality evaluation systems. BOA [11] can also be used to improve the transmission technique. We hope to develop innovative approaches to the oral English teaching of college students majoring in English using virtual reality technology, allowing us to provide a more personalized learning experience while keeping up with the demands of today’s material age, AI era, and 5G era [12]. Managers usually rely on information provided by decision trees while making choices and preserving a company’s budget [13–16].In this study, we suggest a quantitative regression built empirical model of method efficacy in university education reform. The fuzzy set scheduling set of practise effect distribution of the university education reform method is generated under a linear programming model, and the characteristic quantity of practise effect decision of the university education reform method is constructed. We extract the features of the association’s rules of practise and the effect of the university education reform method. The multiorder intrinsic modal function analysis model is built, and the index structure of constraint parameters is constructed. The practice effect of the university education reform technique is studied using quantitative regression analysis. It is determined that the method of effectiveness analysis in college and university educational reform is more dependable, and the quantitative analysis findings are more accurate and reliable, which increases the effectiveness of the empirical analysis of educational reform in colleges and universities.


## 2. Material and Method

Without the use of traditional teaching, the 5G network communication tool offers an atmosphere and platform for writing. The processes involved in the reformation of the teaching and learning process are given in Figure1.Figure 1
Proposed workflow model.


### 2.1. Benchmark Dataset

Data are sent via 5G connection technologies in this study. The File Transmission Protocol (FTP) is a standard protocol based on the client-server concept that is used in this data transfer procedure. The benefit of utilizing this protocol is that it allows the client and server to have distinct control and data connections. The data gathered from the students are preprocessed before being used as a training and testing dataset [17]. The preprocessed data are then denoised using the Gaussian Mixture Model (GMM), as well as the Butterworth Filter. With the use of the Gray Level Co-Occurrence Matrix, features are retrieved to classify in addition to data filtering and denoising (GLCM). These classified results are stored in the database and transferred for performance analysis through FTP. The performance of the proposed system with the existing models is carried out with the implementation of Revamped Artificial Bee Colony Optimization Algorithm (RABCOA) [18].


### 2.2. Methods

#### 2.2.1. Butterworth Filter

The Butterworth filter is suitable for use as a bandpass filter because it has a smooth frequency response curve in the passband. The system function is chosen as the Butterworth function, whose amplitude is a squared function and is expressed as in the following equation, and the condition for smooth frequency is given in equation (2).(1)Kuw2=11−ε2uΩ/uΩc2N,(2)N≤lnλs.The classical wavelet demonising method also has the disadvantage that a better demonising effect can be obtained by estimating the noise variance for noisy signals, but this is not the case when the noise is small. Because the background noise in sEMG is generally considered to satisfy the Gaussian model. Therefore, in this paper, the estimation of the noise variance of EMG based on the Gaussian mixture model (GMM) is used, and the minimum Gaussian coefficient is used as the estimate of the noise variance which is given as follows:(3)pγ|θ=∑m−1hampγ|θm.Texture analysis aims at finding a distinctive way of representing the essential characteristics of textures and represents them in simpler and unique form, so that they can be used for robust, accurate recognition. A geometric method of examining texture that considers the spatial relationship of pixels is the gray level co-occurrence matrix. The GLCM functions characterize the texture of an image by calculating the pairs of pixel with specific values and specified spatial relationship occur in an image. In a gray co-matrix, pixel is calculated with the intensity valueioccurring in a specific spatial relationship to a pixel with the valuej. Each element (i, j) in GLCM specifies the number of times that the pixel with the value i occurred adjacent to a pixel with value j. GLCM texture indicates the relationship between the reference and neighbour pixel of the gray level image at the various directions as shown in Figure 2.Figure 2
GLCM matrix for four distances and four directions.The adjacency can be specified to occur in each of four directions0°,45°,90°, and135° degrees in a two dimensional square pixel image (directions horizontal, vertical, left, and right diagonal). GLCM matrix stores the instance occurrences between adjacent pixels. Element (1, 2) in the GLCM contains the value 2 because there are two instances in the image as shown in Figure 3. Given an Image I of size NxN, the co-occurrence matrix P can define the gap between the pixel of interest, and its neighbors are specified by the offset (x, y). The intensity levels of the picture are specified by I j, while the spatial coordinates of the image I are specified by x, y. Using the following equation, several texture characteristics are retrieved from the GLCM:(4)pi,j=∑x=1L∑y=1L1,ifIx,y=iandIx+Δx,y+Δy=j,0,otherwise.Figure 3
Distance direction chart using the GLCM matrix.Mathematical modelling of the texture descriptions is given in the following points.(1) Contrast. Measure of contrast or local intensity variation will favour contributions from p (i, j) away from the diagonal, i.e., i 6 = j is given in the following equation:(5)constrast=∑i,j=0L−1i−j2pi,j.(2) Dissimilarity. (6)Dissmilarity=∑i,j=0L−1i=jpi,j.(3) Entropy. This measure of the randomness of intensity distribution is given in the following equation:(7)entropy=∑i,j=0L−1pi,j−lnpi,j.(4) Sum of Square Variance. The calculation of sum of square variance is given in the following equation:(8)variance=∑i=0L−1∑j=0L−1i−μ2pi,j.(5) Sum Average. The following equation represents the average calculation of the pixel value:(9)average=∑i=02L−2iPx+yi.(6) Sum Variances. Sum variances of the pixels in the image are calculated using the following equation:(10)sum_variance=∑i=02L−2i−average2Px+yi.(7) Sum Entropy. (11)SENT=−∑i=02L−2Px+yilogpx+yi.(8) Difference Variance. (12)DV=∑i=0L−2i−average2Px+yi.(9) Difference Entropy. (13)DENT=−∑i=0L−2Px+yilogpx+yi.


#### 2.2.2. Revamped Artificial Bee Colony Optimization Algorithm

The revised Artificial Bee Colony Optimization Algorithm categorizes bees into three types: employed workers, observer bees, and scouts. Employed bees are used to explore the search space and approximate a likely solution. Onlooker bees are meant to make additional exploitation surrounding the region of employed bees with greater performance, helping to increase solution accuracy. Scout bees are meant to keep the population’s worldwide search capability. These three organizations work together to analyse English instruction.(1) Initialization. Given the upper U and lower L bounds of the search space, the position for the jth variable of the ith food source is generated as in the following equation:(14)xij=Lij+Uij−Lij∗rand0,1,where 1, 2, ..., N = i, and 1, 2, ...,. Here, = jDNdenotes the number of English teaching involved in the optimization process, D represents the dimension of the search space, and rand (0, 1) is a random value generated uniformly within the interval [0, 1].(2) Division of Employed Bees. The search equation for employed bees is given as follows:(15)vijt+1=xijt+ϕ∗xijt−xkjt,where k is a randomly selected neighbour within the range of [1, N] which is different from the current individual; ϕi is a randomly generated value within, and (1, 1) t represents the current iteration. After generating a candidate solution, its corresponding objective value i and fitness value Fi are calculated. i is computed by the target optimization problem, and ABC employs a greedy selection strategy to determine whether a candidate solution Vi should survive in the next iteration. If fi is betterthan the current objective value of Xi,, Vi is preserved and Xi is discarded. Otherwise, Vi is abandoned. Fi is calculated as in the following equation:(16)Fi=11+fi,iffi≥0,1+fi,iffi<0.Division of Onlooker Bees. Onlooker bees are selected to make further search around English teaching with better performance provided by employed bees. During this stage, the selection probability of each food source is calculated as shown in equation (17). Then, the onlooker bees select a food source to exploit by using the classical roulette wheel selection strategy. It means the better English teaching is, the greater chances it is selected as shown in the following equation:(17)pi=Fi∑1NFi.


## 2.1. Benchmark Dataset

Data are sent via 5G connection technologies in this study. The File Transmission Protocol (FTP) is a standard protocol based on the client-server concept that is used in this data transfer procedure. The benefit of utilizing this protocol is that it allows the client and server to have distinct control and data connections. The data gathered from the students are preprocessed before being used as a training and testing dataset [17]. The preprocessed data are then denoised using the Gaussian Mixture Model (GMM), as well as the Butterworth Filter. With the use of the Gray Level Co-Occurrence Matrix, features are retrieved to classify in addition to data filtering and denoising (GLCM). These classified results are stored in the database and transferred for performance analysis through FTP. The performance of the proposed system with the existing models is carried out with the implementation of Revamped Artificial Bee Colony Optimization Algorithm (RABCOA) [18].


## 2.2. Methods

### 2.2.1. Butterworth Filter

The Butterworth filter is suitable for use as a bandpass filter because it has a smooth frequency response curve in the passband. The system function is chosen as the Butterworth function, whose amplitude is a squared function and is expressed as in the following equation, and the condition for smooth frequency is given in equation (2).(1)Kuw2=11−ε2uΩ/uΩc2N,(2)N≤lnλs.The classical wavelet demonising method also has the disadvantage that a better demonising effect can be obtained by estimating the noise variance for noisy signals, but this is not the case when the noise is small. Because the background noise in sEMG is generally considered to satisfy the Gaussian model. Therefore, in this paper, the estimation of the noise variance of EMG based on the Gaussian mixture model (GMM) is used, and the minimum Gaussian coefficient is used as the estimate of the noise variance which is given as follows:(3)pγ|θ=∑m−1hampγ|θm.Texture analysis aims at finding a distinctive way of representing the essential characteristics of textures and represents them in simpler and unique form, so that they can be used for robust, accurate recognition. A geometric method of examining texture that considers the spatial relationship of pixels is the gray level co-occurrence matrix. The GLCM functions characterize the texture of an image by calculating the pairs of pixel with specific values and specified spatial relationship occur in an image. In a gray co-matrix, pixel is calculated with the intensity valueioccurring in a specific spatial relationship to a pixel with the valuej. Each element (i, j) in GLCM specifies the number of times that the pixel with the value i occurred adjacent to a pixel with value j. GLCM texture indicates the relationship between the reference and neighbour pixel of the gray level image at the various directions as shown in Figure 2.Figure 2
GLCM matrix for four distances and four directions.The adjacency can be specified to occur in each of four directions0°,45°,90°, and135° degrees in a two dimensional square pixel image (directions horizontal, vertical, left, and right diagonal). GLCM matrix stores the instance occurrences between adjacent pixels. Element (1, 2) in the GLCM contains the value 2 because there are two instances in the image as shown in Figure 3. Given an Image I of size NxN, the co-occurrence matrix P can define the gap between the pixel of interest, and its neighbors are specified by the offset (x, y). The intensity levels of the picture are specified by I j, while the spatial coordinates of the image I are specified by x, y. Using the following equation, several texture characteristics are retrieved from the GLCM:(4)pi,j=∑x=1L∑y=1L1,ifIx,y=iandIx+Δx,y+Δy=j,0,otherwise.Figure 3
Distance direction chart using the GLCM matrix.Mathematical modelling of the texture descriptions is given in the following points.(1) Contrast. Measure of contrast or local intensity variation will favour contributions from p (i, j) away from the diagonal, i.e., i 6 = j is given in the following equation:(5)constrast=∑i,j=0L−1i−j2pi,j.(2) Dissimilarity. (6)Dissmilarity=∑i,j=0L−1i=jpi,j.(3) Entropy. This measure of the randomness of intensity distribution is given in the following equation:(7)entropy=∑i,j=0L−1pi,j−lnpi,j.(4) Sum of Square Variance. The calculation of sum of square variance is given in the following equation:(8)variance=∑i=0L−1∑j=0L−1i−μ2pi,j.(5) Sum Average. The following equation represents the average calculation of the pixel value:(9)average=∑i=02L−2iPx+yi.(6) Sum Variances. Sum variances of the pixels in the image are calculated using the following equation:(10)sum_variance=∑i=02L−2i−average2Px+yi.(7) Sum Entropy. (11)SENT=−∑i=02L−2Px+yilogpx+yi.(8) Difference Variance. (12)DV=∑i=0L−2i−average2Px+yi.(9) Difference Entropy. (13)DENT=−∑i=0L−2Px+yilogpx+yi.


### 2.2.2. Revamped Artificial Bee Colony Optimization Algorithm

The revised Artificial Bee Colony Optimization Algorithm categorizes bees into three types: employed workers, observer bees, and scouts. Employed bees are used to explore the search space and approximate a likely solution. Onlooker bees are meant to make additional exploitation surrounding the region of employed bees with greater performance, helping to increase solution accuracy. Scout bees are meant to keep the population’s worldwide search capability. These three organizations work together to analyse English instruction.(1) Initialization. Given the upper U and lower L bounds of the search space, the position for the jth variable of the ith food source is generated as in the following equation:(14)xij=Lij+Uij−Lij∗rand0,1,where 1, 2, ..., N = i, and 1, 2, ...,. Here, = jDNdenotes the number of English teaching involved in the optimization process, D represents the dimension of the search space, and rand (0, 1) is a random value generated uniformly within the interval [0, 1].(2) Division of Employed Bees. The search equation for employed bees is given as follows:(15)vijt+1=xijt+ϕ∗xijt−xkjt,where k is a randomly selected neighbour within the range of [1, N] which is different from the current individual; ϕi is a randomly generated value within, and (1, 1) t represents the current iteration. After generating a candidate solution, its corresponding objective value i and fitness value Fi are calculated. i is computed by the target optimization problem, and ABC employs a greedy selection strategy to determine whether a candidate solution Vi should survive in the next iteration. If fi is betterthan the current objective value of Xi,, Vi is preserved and Xi is discarded. Otherwise, Vi is abandoned. Fi is calculated as in the following equation:(16)Fi=11+fi,iffi≥0,1+fi,iffi<0.Division of Onlooker Bees. Onlooker bees are selected to make further search around English teaching with better performance provided by employed bees. During this stage, the selection probability of each food source is calculated as shown in equation (17). Then, the onlooker bees select a food source to exploit by using the classical roulette wheel selection strategy. It means the better English teaching is, the greater chances it is selected as shown in the following equation:(17)pi=Fi∑1NFi.


## 2.2.1. Butterworth Filter

The Butterworth filter is suitable for use as a bandpass filter because it has a smooth frequency response curve in the passband. The system function is chosen as the Butterworth function, whose amplitude is a squared function and is expressed as in the following equation, and the condition for smooth frequency is given in equation (2).(1)Kuw2=11−ε2uΩ/uΩc2N,(2)N≤lnλs.The classical wavelet demonising method also has the disadvantage that a better demonising effect can be obtained by estimating the noise variance for noisy signals, but this is not the case when the noise is small. Because the background noise in sEMG is generally considered to satisfy the Gaussian model. Therefore, in this paper, the estimation of the noise variance of EMG based on the Gaussian mixture model (GMM) is used, and the minimum Gaussian coefficient is used as the estimate of the noise variance which is given as follows:(3)pγ|θ=∑m−1hampγ|θm.Texture analysis aims at finding a distinctive way of representing the essential characteristics of textures and represents them in simpler and unique form, so that they can be used for robust, accurate recognition. A geometric method of examining texture that considers the spatial relationship of pixels is the gray level co-occurrence matrix. The GLCM functions characterize the texture of an image by calculating the pairs of pixel with specific values and specified spatial relationship occur in an image. In a gray co-matrix, pixel is calculated with the intensity valueioccurring in a specific spatial relationship to a pixel with the valuej. Each element (i, j) in GLCM specifies the number of times that the pixel with the value i occurred adjacent to a pixel with value j. GLCM texture indicates the relationship between the reference and neighbour pixel of the gray level image at the various directions as shown in Figure 2.Figure 2
GLCM matrix for four distances and four directions.The adjacency can be specified to occur in each of four directions0°,45°,90°, and135° degrees in a two dimensional square pixel image (directions horizontal, vertical, left, and right diagonal). GLCM matrix stores the instance occurrences between adjacent pixels. Element (1, 2) in the GLCM contains the value 2 because there are two instances in the image as shown in Figure 3. Given an Image I of size NxN, the co-occurrence matrix P can define the gap between the pixel of interest, and its neighbors are specified by the offset (x, y). The intensity levels of the picture are specified by I j, while the spatial coordinates of the image I are specified by x, y. Using the following equation, several texture characteristics are retrieved from the GLCM:(4)pi,j=∑x=1L∑y=1L1,ifIx,y=iandIx+Δx,y+Δy=j,0,otherwise.Figure 3
Distance direction chart using the GLCM matrix.Mathematical modelling of the texture descriptions is given in the following points.(1) Contrast. Measure of contrast or local intensity variation will favour contributions from p (i, j) away from the diagonal, i.e., i 6 = j is given in the following equation:(5)constrast=∑i,j=0L−1i−j2pi,j.(2) Dissimilarity. (6)Dissmilarity=∑i,j=0L−1i=jpi,j.(3) Entropy. This measure of the randomness of intensity distribution is given in the following equation:(7)entropy=∑i,j=0L−1pi,j−lnpi,j.(4) Sum of Square Variance. The calculation of sum of square variance is given in the following equation:(8)variance=∑i=0L−1∑j=0L−1i−μ2pi,j.(5) Sum Average. The following equation represents the average calculation of the pixel value:(9)average=∑i=02L−2iPx+yi.(6) Sum Variances. Sum variances of the pixels in the image are calculated using the following equation:(10)sum_variance=∑i=02L−2i−average2Px+yi.(7) Sum Entropy. (11)SENT=−∑i=02L−2Px+yilogpx+yi.(8) Difference Variance. (12)DV=∑i=0L−2i−average2Px+yi.(9) Difference Entropy. (13)DENT=−∑i=0L−2Px+yilogpx+yi.


## 2.2.2. Revamped Artificial Bee Colony Optimization Algorithm

The revised Artificial Bee Colony Optimization Algorithm categorizes bees into three types: employed workers, observer bees, and scouts. Employed bees are used to explore the search space and approximate a likely solution. Onlooker bees are meant to make additional exploitation surrounding the region of employed bees with greater performance, helping to increase solution accuracy. Scout bees are meant to keep the population’s worldwide search capability. These three organizations work together to analyse English instruction.(1) Initialization. Given the upper U and lower L bounds of the search space, the position for the jth variable of the ith food source is generated as in the following equation:(14)xij=Lij+Uij−Lij∗rand0,1,where 1, 2, ..., N = i, and 1, 2, ...,. Here, = jDNdenotes the number of English teaching involved in the optimization process, D represents the dimension of the search space, and rand (0, 1) is a random value generated uniformly within the interval [0, 1].(2) Division of Employed Bees. The search equation for employed bees is given as follows:(15)vijt+1=xijt+ϕ∗xijt−xkjt,where k is a randomly selected neighbour within the range of [1, N] which is different from the current individual; ϕi is a randomly generated value within, and (1, 1) t represents the current iteration. After generating a candidate solution, its corresponding objective value i and fitness value Fi are calculated. i is computed by the target optimization problem, and ABC employs a greedy selection strategy to determine whether a candidate solution Vi should survive in the next iteration. If fi is betterthan the current objective value of Xi,, Vi is preserved and Xi is discarded. Otherwise, Vi is abandoned. Fi is calculated as in the following equation:(16)Fi=11+fi,iffi≥0,1+fi,iffi<0.Division of Onlooker Bees. Onlooker bees are selected to make further search around English teaching with better performance provided by employed bees. During this stage, the selection probability of each food source is calculated as shown in equation (17). Then, the onlooker bees select a food source to exploit by using the classical roulette wheel selection strategy. It means the better English teaching is, the greater chances it is selected as shown in the following equation:(17)pi=Fi∑1NFi.


## 3. Result and Discussion

A simulated experiment was carried out, utilizing the technique to assess the efficiency of the new strategy for experimental data and analysis. MATLAB is the programme utilised for the analysis. Figure4 depicts the absolute values acquired for each node during data transfer. It is clear from this graph that the measured value is greater than the variance. In the context of absolute value, there is a minimum difference of 0.1 between the measured and variance level.Figure 4
Absolute value vs. node number.A total of 3149 students are considered for the assessment procedure in this study. Ten assessments are considered and assessed for the given course utilizing the suggested RABCOA algorithm. This assessment focuses on the number of students who attended or submitted the assessment after attending the online sessions, as well as the marks assigned to them based on their scores. If your score is more than 90, you will receive an “A” grade; similarly, if your score is between 80 and 90, you will receive a “B” grade. Between 70 and 80 points, a “C” grade is given; between 60 and 70 points, a “D” grade is given. Additionally, the letter “E” is assigned to pupils with a grade range of 50 to 60. If your score is less than 50, you have got a problem.The total number of students attended the assessment 1 and assessment 2 of the course is 359, and 342 are evaluated with the implementation of RABCOA. The evaluation results for the assessment 1 and assessment 2 are given in Figure5. From the graph, it is observed that the number of students attend the second assessment is less in count of 17 than the assessment 1. However, it is seen that the number of students achieved A Grade is higher in assessment 2 than the assessment 1 with the count of 18. Also, the number of students unable to get a minimum score of 50% is lesser in assessment 2.Figure 5
Assessment 1 and assessment 2 evaluation using RABCOA.The assessment evaluation for assessments 3 and 4 using the RABCOA is shown in Figure6. Assessment 3 had 331 pupils in attendance, whereas assessment 4 had just 303. It can be seen from these two assessments that the number of pupils receiving “B,” “C,” and “D” grades is higher in assessment 3 than in assessment 4. However, in assessment 4, the number of fails is lowered by three counts compared with assessment 3.Figure 6
Assessment 3 and assessment 4 evaluation using RABCOA.Assessment 5 is represented by the inner ring in Figure7, whereas assessment 6 is represented by the outside ring.Figure 7
Assessment 5 and assessment 6 evaluation using RABCOA.The proportion of grades differs between the two exams, indicating that there are little variances in the pupils’ grades. The exception is the “D” grade, which increased by 5% in assessment 6 over assessment 5. Table1 also shows the student’s performance evaluations for assessments 7 through 10. Near the conclusion of the total number of exams, the number of applicants who participated in the assessment submission drops dramatically, to a minimum of 278 students, compared with the remaining set of assessments.Table 1
Evaluation of assessments 7 to 10 by implementing RABCOA.
GradesAssess7Assess8Assess9Assess10Grand totalA grade473923B grade56626853239C grade85898193348D grade80797560294E grade56393936170FAIL36281427105Grand total3173042802781179Figure8 shows the proposed RABCOA performance comparison with the current SVM and KNN. The study is based on teacher and student happiness, comprehensive ability, instructor ability, and management effectiveness. It has also been discovered that instructor happiness with the teaching process is higher than student satisfaction with the learning process. This disparity in satisfaction levels might be related to a lack of knowledge of ideas while using the online method.Figure 8
Comparative analysis for various optimizations.


## 4. Conclusion

The implementation of 5G networks will not only alter business but also academic institutions. Traditional English classrooms have changed as wireless communication, and cloud computing technologies have been more integrated into college English education by revealing numerous new aspects. The passion for English study among other mains in universities and colleges has dwindled as a result of system changes. As a significant issue in college English instruction, the challenges must be handled scientifically by the united forces, taking into account different elements. According to a preliminary experimental study, students’ views about English learning in the setting of wireless communication varied according to gender and grade. We must actively adjust and begin the process of self-transformation. Integrate English instruction with students’ professional demands while also daring to break through teaching techniques’ constraints and choosing teaching approaches that can inspire students’ motivation to serve as teachers. It improves the teaching effect. Numerous critical elements can be discovered with the help of the bee optimization method.



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*Source: 5774132-2022-06-22.xml*