# Influence of Ideological and Political Education Strategies on College Students’ Entrepreneurship Based on Wireless Network and Artificial Intelligence Knowledge Map

Authors: Baoyu Zhang; Qian Qi; Linguo Li
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Technological advancements have paved the path for utilizing artificial intelligence and wireless networks in every field. In this study, people will research the ideological and political education (IPE) strategies based on wireless networks and artificial intelligence knowledge maps on college students’ entrepreneurship. IPE deals with a country’s political status, forming a social and economic study. This education utilizes wireless network technology and artificial intelligence for teaching and learning. The proposed system studies IPE strategies that use artificial intelligence and wireless networks in a combined way of intelligent wireless network (IWN) in a knowledge map on college students’ entrepreneurship. The idea is then adapted to the college student’s entrepreneurship and found efficient. Recursive algorithms (RA) call themselves out by lowering input quantities and returning values for the input signal by performing basic processes in a repeated iteration. Artificial intelligence utilizes recursion to develop entrepreneurship skills among college students. The proposed algorithm is compared with the traditional remote supervision algorithm (RSA), and the study results that RA has obtained an accuracy of 98%.


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## Body

## 1. Introduction

Because of the openness and accessibility of the mobile Internet that many of them carry, a new generation of college students may quickly access material that old ideological and political instructors at colleges and universities can no longer tolerate [1]. The teacher’s authority in the classroom will be eroded if they teach content that is not compelling or responsive to fresh ideas. Learning on their own rather than in a classroom is more appealing to them. Due to a lack of sensitivity to embracing new things and other pressures like work and life, educators are slow to adapt to the mobile Internet. As a result, they are unable to keep up with the educational environment’s rapid changes. An inability to use technology effectively may be the result of a person’s lack of technological expertise and knowledge [2]. As a result of the mobile Internet’s availability, college students are better able to learn about the cultures, ideologies, faiths, and ways of life in different nations. This has a profound effect on the already undeveloped morality and worldview of college students. With their deliberate and subsequent goals of cultural colonization, Western countries will further contaminate the values and worldview of college students [3]. Violent, pornographic, superstitious, and other bad content, as well as hype, headlines, and other cues meant to sway people’s emotions and perspectives, can be spread through online virtual games with the use of a value chain. College students’ ideals and outlooks on life are unaffected by putting a stop to emergence and renewal. The term “mobile Internet” refers to the convergence of mobile devices and the Internet. More sources of information are available and can be transmitted more freely. This could be attributed to both mobile phone users and traditional Internet users. This information is more likely to come from information suppliers. Since mobile devices lack IP addresses, it will be far more difficult to maintain control over the internet’s information flow [4]. Mobile internet access is currently not well regulated in the country or society due to a lack of maturity in the regulating technology. There is an everincreasing interdependence between offline and online life on the mobile internet, resulting in an increased requirement for effective network management. The unpredictability of network security and openness is making ideological and political education more challenging in the workplace. To make ideological and political education more three-dimensional, teachers can use the mobile Internet to see and collect multimedia resources related to sound and video on the network at any time and from anywhere. Changing from a static to a dynamic format can improve students’ ideological and political education. College students can utilize mobile networks to locate what they need, including expert lectures and lectures in the subjects of philosophy and politics, and then download them quickly. They can search for what they need whenever and from anywhere they choose. Students’ political understanding and ideological character are the primary goals of traditional ideological and political education [5]. The end results of this situation are a lack of new ideas and political and ideological education with a high stylistic orientation, vagueness, and lack of pertinence. The subject matter does not pique the students’ interests. Ideological and political educators should take advantage of multimedia technologies like the mobile Internet’s sound, video, and video when teaching college students about the issues that matter most. In this study, the psychological aspects of students’ experiences with academics, careers, making friends, therapy, and the legal system are all examined in depth. Political education appeals to college students due to its allure [6]. Because of the mobile characteristics of the mobile Internet, a university education is no longer restricted to a specific time and place, such as a classroom. Mobile teaching can be done at anytime, anywhere, and with any device as long as there is a mobile phone signal. Because it is virtual, mobile teaching avoids the embarrassment of face-to-face contact. It is safe for students to say what they want without fear of repercussions. The openness of the mobile Internet can assist schools as well [7]. The Internet may be an extremely useful resource when used properly. It has made it possible for college and university intellectual and political education to progress without interruption [8]. Another advantage is that it demonstrates numerous design possibilities. For this initiative, a few of the most extreme and erroneous remarks will be removed from the college website, along with better governance of network comments [9].The teaching aid is neatly wrapped in its own box. The teaching unit is wonderful in terms of encapsulation, but it does not come close to the front-to-back relationship. In the meantime, people can move on to learn the next lesson. If the student’s encapsulation is poor, they will frequently be interrupted while studying a unit. If that is the case, people can just jump right in the next time they study and waste a lot of time learning the unit [10]. Because of the Internet and mobile technology, students in China’s institutions are becoming more entrepreneurial. It is only amplified by the government’s assistance. More college graduates are starting their own businesses than ever before, with a 1.9 percentage point increase over the past decade, according to a report. This compares to 1.0 percent of college graduates who started their own businesses in 2012 [11]. Because of this, Chinese universities have begun to provide innovative courses and services aimed at fostering entrepreneurial spirit among students and increasing their chances of success. These include entrepreneurial programs, contests, and simulated business environments.The most crucial indicator of an individual’s entrepreneurial behavior is whether or not they have an entrepreneurial mindset. More and more studies have been conducted on the motivations behind people starting their own enterprises [12]. In research on entrepreneurship characteristics, age, gender, education level, personality traits, entrepreneurial knowledge and ability, drive for achievement, spirit of adventure, and value orientation have all been found to be relevant. In addition to the antithesis of a proactive mindset, such as narcissism, psychopathy, and Machiavellianism, these traits have a substantial impact on the entrepreneurial goal. According to recent research, family education and market accessibility, as well as government rules and training programs, are critical to the entrepreneurial climate [13]. It is possible to understand an individual’s entrepreneurial objectives and actions in light of the interplay between internal and external forces using a variety of psychological models. The most significant of these theories is the TPB or Theory of Planned Behavior. According to the Theory of Planned Behavior, entrepreneurial purpose is determined by an individual’s attitude and perception of behavioral control [14]. A lack of attention has been paid to university-based entrepreneurship education, which is crucial to the development of an individual’s entrepreneurial quality and a key factor in company success in the above psychological models. The Theory of Planned Behavior was utilized in this study to analyze the impact of entrepreneurial education and self-efficacy on college students’ entrepreneurial intents [15].According to the author, entrepreneurship may be taught. Another example of how education and training may aid entrepreneurs in acquiring the personality traits, abilities, and abilities they need to succeed in their ventures is described below. According to the author of this research, starting a firm and taking entrepreneurship classes are two ways to learn about entrepreneurship [16]. Entrepreneurial education, which aims to assist students prepare for a certain career or business plan, is all about creating and increasing the quality of entrepreneurship. As an entrepreneur, people want to help them uncover and seize new business opportunities. Therefore, strategic resources and abilities are also important. In recent years, universities and affiliated external organizations have sponsored a variety of entrepreneurial training programs, and these programs have steadily gained reputation. Entrepreneurs who are just starting out or who believe they lack the requisite skills and knowledge are the typical participants in such a program. Those that engage in these programs seek to learn how to recognize, identify, and investigate opportunities [17]. Social psychologists use the term “attitude” to describe how people view themselves and others, as well as how they view the rest of the world. A person’s reactions and behavior are greatly influenced by this. Entrepreneurial education is claimed to increase a person’s entrepreneurial spirit and enthuse them even more about starting and running their own business. It is said to have a positive impact on entrepreneurial mindsets and abilities, as well as entrepreneurship education and training. Education and training in entrepreneurship to help students develop the mindset, knowledge, and skills. As a result, students can benefit from both self-directed and externally provided entrepreneurship training in order to better understand the entrepreneurial process and develop a more proactive attitude toward it [18, 19].Entrepreneurs feel that beginning a new business without a clear goal is impossible. When someone has an entrepreneurial purpose, they have faith in their ability to start a new business and plan to do so [20]. It is the behavior that potential entrepreneurs see as necessary for their startup to succeed. It has been found that teaching students about starting their own business can provide them with the motivation and skills they need to succeed. One theory says that entrepreneurship education should be used to nurture an individual’s desire to start a business. Training in entrepreneurship has been found to improve an individual’s desire to become an entrepreneur. Using a similar research design, entrepreneurial training has been found to increase entrepreneurs’ entrepreneurial intent and behavior, as well as their entrepreneurial performance [21]. Some people think entrepreneurial education might help college students who want to start a business develop their entrepreneurial skills and knowledge and so improve their chances of success.


### 1.1. Motivation for the Study

The investigation into IPE strategies based on wireless networks and artificial intelligence knowledge maps on college students’ entrepreneurship focuses on determining how academic institutions continue providing knowledge during the academic year. They would have to match the teaching experience to solely online teaching and learning relatively quickly. This research investigates students’ perceptions of online learning, their ability to assimilate information, and a simple recursive algorithm to analyze the educational learning platforms. An online survey based on a moderately structured questionnaire was conducted. Students from academic institutions provided the data. According to the study’s findings, Romanian higher learning institutions cannot solely prepare for online learning.


## 1.1. Motivation for the Study

The investigation into IPE strategies based on wireless networks and artificial intelligence knowledge maps on college students’ entrepreneurship focuses on determining how academic institutions continue providing knowledge during the academic year. They would have to match the teaching experience to solely online teaching and learning relatively quickly. This research investigates students’ perceptions of online learning, their ability to assimilate information, and a simple recursive algorithm to analyze the educational learning platforms. An online survey based on a moderately structured questionnaire was conducted. Students from academic institutions provided the data. According to the study’s findings, Romanian higher learning institutions cannot solely prepare for online learning.


## 2. Materials and Methods

The influence of the deployment of advanced technology such as wireless networks and artificial intelligence in IPE is applied in entrepreneurship for college students. Wireless networks are referred to as computer networks that use wireless data. The 4G and 5G mobile networks, wireless sensor networks WSN, and satellite communication systems are wireless networks. The data is transferred between the network nodes with this wireless technology. The IPE utilized these advanced techniques in the teaching process through online classes. Artificial intelligence is an advanced technology with which we can make machines controlled by computer systems to work as human beings or to carry out tasks that require human intelligence. Machine learning (ML) and deep learning (DL) are some technologies that come under artificial intelligence. Artificially intelligent techniques such as machine learning and deep learning are employed in IPE education. The knowledge map is a visual aid that shows the organization’s knowledge. The information is shown in this visual map. Entrepreneurship among the students is a good practice that has to be nurtured.The application of wireless network systems and artificial intelligence in the ideological and political education (IPE) systems as a model or inspiration in the student’s entrepreneurship is represented in Figure1. The wireless networks systems such as 4G and 5G technologies and wireless sensor networks (WSNs) are deployed in the student’s entrepreneurship and the application of artificial intelligence technology such as DL. Thus, these are the influence of IPE strategies based on wireless networks and artificial intelligence on the student’s entrepreneurship.Figure 1
IPE for college student’s entrepreneurship development.A basic recursive method is investigated for analyzing educational learning systems. A moderately structured questionnaire was used to conduct an online survey. The information was submitted by students from academic institutions. According to the conclusions of the study, Romanian higher education institutions are unable to equip students only for online learning.a=a1,a2,.⋯an represents the simple recursive algorithm. The resemblance between both the a because student information is shown in the solution below the Equation (1).
(1)a=∑a=1na12+a22+⋯+an2.Theδ functionality derivative without respect to a service presumed being a constant and also the independence is specified for this, whereas other variables are left unmodified with in Equation (2).
(2)a=∑a=1TTa−δ/ω+∫a12+a22+⋯+an2.ω represents the limited ordinal and identifiable to organize a differential environmental percentages. In Equation (3),a=a1,a2 represents the tasks involved in the education systems that involves, and IPE is disguised.
(3)a=∑a∈sTTa−T¯saF+∫Ta−δ/ω.T¯srepresents as mean is direction of vector, and aF is represent as standard deviation example. The composed of random example represented the Equation (4). The function w blurs the human desire.
(4)w=∑a=1na1a,a2a+∑a∈sTTa−T¯saF.The following Equation (5) can alternatively be represented as a presence of a small amount method to analyze the result of a variable of directionT¯s.
(5)ai=∑a=1Tβ0+β1Tai+εoi+∫a1a,a2a.We can observe thatcosθ=a1/aandcosα=a2/a a result, the directional vector w could also be expressed as the Equations (6) and (7):
(6)w=∫a1a,a2a+∫cosθ,cosα,(7)a.b=∑a=1babcosθ.It is observed thatθ=β−α, and hence after substation of values, Equation (8) is obtained.
(8)cosθ=∑a=1b=1cosβ−α=cosβcosα=∑aba1ab1b+a2ab2b=∑a=1ba1b1+a2b2ab.The political and ideological learning centralized repository integrated framework is an essential part of the curriculum that relates to recognizing the inappropriate evolution of such a knowledge-based economy, that have misplaced its vital place in ongoing learning, as represented inside the Equation (9) below.
(9)a.b=∑a=1baba1b1+a2b2ab=∑a=1ba1b1+a2b2.The political and ideological learning centralized database integrated framework is an important part of the curriculum that relates with recognizing the inappropriate evolution of such a knowledge-based economy, that have misplaced its vital place in ongoing learning, as represented inside the Equation (10) below.
(10)a.b=∑a=1ba1b1+a2b2+∑i=1naibi.According to students, education plays a vital role in their life. Equation (11) represents that education involves goal of life and provides advancement, gratitude, recognizing, service, and support enable students to enhance their skills.
(11)sa1=∑aba1ab1b+a2ab2b=∑a=1ba1b1+a2b2aba2+b=0.Thea1b1+a2b2/ab current ideological but rather methodological allows organizations is incapable of meeting the a1b1+a2b2 needs of higher education institution, ideological, but also political student economic expansion is illustrated in the Equation (12); schooling should be centered on communist ideology but also methodology, also with goal of liberating people from system that was developed manacles but also establishing a set of regulations as the primary objective of intellectuals learning.Whena=a1,a2andw=a,−1 the following Equation (12) is derived, and hai represents the classroom analysis.
(12)hai=∑aba1ab1b+a2ab2b=∑a=1ba1b1+a2b2ab+∑+1ifw.a+b≥0,=1ifw.a+b<0.The recursive algorithm (RA) will be executed into wireless communication technology and analyses of classroom training optimization methods. Researchers woulda1b1+a2b2 specifically describe the graph’s method of construction and also the relating optimization method based on supervised multiprocessing learning, and hence Equation (13) is obtained.
(13)∂1k2≤∫n0n0+N0UNτk2abτ≤∑k=1n∂2k2,∀n0≥0,n∈Cp.The simple recursive algorithm use to computation grows as∂1k2, and it defines the structure length that is utilized for probability computation and is implemented throughout the method. The parameters as UNτk2abτ≤∂2k2,∀n0≥0 that quantifies the IPE strategies fpn are determined by the Equation (14) word preceding it.
(14)fpn=limg⟶01gp∑q=0p−1q+∑pnpqfn−qg.The sentence is determined solely∇aa/∇aa+t with the two or more words preceding it, the following Equation (15).
(15)fpn=∫∇aa∇aa+t+∑a=1eλea−a0=0.qigwhich is the student’s language quality objective represents the difference between the student’s language status that finds the difficulty in learning resources. The learner’s progress is represented by Einn the distinction between of helps the audience understand enclosed with in learning resource and the knowledge notes the learner wants to acquire. When the difference is smaller, learning resources are closer to the expertise points that are defined as EjEijnn; the learner’s knowledge points are specified in the Equation (16). Einr represents the knowledge notes that a learner is willing to acquire, and i and j denote the learner’s knowledge points.
(16)Einr=∑j∈LEjEijnr+∑a=1t∇aa∇aa+t.


## 3. Result and Discussion

Uniform layer classification was performed to categorize the dataset into male and female home environments for cities in Figure2. On college students’ entrepreneurship, political and ideological teaching tactic mobile wireless networks are with artificial intelligence knowledge maps. It achieves results for male accuracy (64 percent) and women accuracy (64.34) in rural areas (57.12%). Another receiving result for urban area is men accuracy (75.21%) and women accuracy (75.64%) (70.92%). The inquiry of political and ideological ways to address deep learning methods to determine how higher learning institutions succeeded in providing information during in the academic. This symbol denotes the first limitation ordinal and represents it. It is denoted and distinguishable by the organization of a variety of ecological percentages. The a1b1+a2b2/ab current ideological but rather methodological allows organizations is incapable of meeting the a1b1+a2b2 needs of higher education institution, ideological, but also political student economic expansion is mentioned in the Equation (12); schooling should be centered on communist ideology but also methodology, also with goal of liberating people from system that was developed.Figure 2
Analysis for performance IPE strategies based on rural and urban using RSA with artificial intelligence.To categorize the dataset into men and women home environments for cities and suburbs, established based on the residential environments. IPE techniques for cities and suburbs for mobile wireless networks with AI information maps for university students’ entrepreneurship training is considered. It is also obtaining results for rural region men accuracy (63.54%) and women accuracy (63.78%) (56.67%). Another receiving results for urban location men accuracy (79.58) and women accuracy (79.23) (72.90%) are as follows. It is observed thatcosθ=a1/aandcosα=a2/a. As a result, the directional vector w could also be expressed as shown in Figure 3. The investigational IPE focuses on determining. It compares the performance of previous material but also RSA to DL to obtain the precise outcome for RSA to DL. It needs to be compared the existing results analysis again for RSA to ML (refer Table 1).Figure 3
Analysis for existing method residential environment IPE strategies based on WSN and AI knowledge maps on college students (rural and urban) entrepreneurship.Table 1
Recursive algorithm (RA) with artificial intelligence to classify respondents’ residential characteristics.
ParametersClassificationEnvironment countPercentage (%)GenderMen16985.15Women18689.42Residential of IPE environmentRural area12946Urban area18582EducationEducation of college/school36295.7515–23 years9517.73ActivitiesExtracurricular activities31665.4In a nonprobabilistic fashion, the report’s population was composed of students representing two of the most prominent schools and colleges. Majority of the population are women, between both the years of 15 to 22, from regions, and are enrolled in elementary school (Table1). Nonetheless, 382 (89.43%) of students were men while 169 (85.15%) were women, 184 (89.42%) were of a rural area, and 129 (46%) have been from a more urban location, while 185 (82%) have been in education, with the rest 316 (65.4%) in professional education (refer Table 1).The average, standard error, mean difference, standard deviation, and standard error difference values are specified in the performance analysis again for impartial measurement of school tests, and the assessment for that achievement is dependent on these data. The IPE of the investigation focuses on selecting the best option, which would be represented either by RSA. Depending on it to retrieve in Figure4, the a1b1+a2b2/ab existing ideological and methodological detailed integrated is unable of providing the a1b1+a2b2 2 needs of educational, ideological, and political student growth.Figure 4
Performance analysis for IPE strategies based on wireless networks and artificial intelligence measurements of impartiality school education assessment.On the entrepreneurship of young adults, political and ideological teaching tactic mobile wireless networks are with artificially intelligent knowledge maps. Despite the difficulties, students believe that the traditional head technique is the ideal way of carrying out the complete classroom instruction but that the platforms would be utilized as a supplement to aid the learning system. Thus, 68.12% of respondents feel face expression instructional, 59.31% prefer a blending of offline and online courses, and 18.83% choose web-based training (refer Table2).Table 2
IPE strategies based on wireless networks and artificial intelligence knowledge maps on college students’ entrepreneurship impartial measurements.
ObjectivesIPE groupIPE meanMean differenceS.D.Std. error differenceAverageIts internet environmentalSchool2.902.902.560.65-0.75Established online learning/teachingSchool3.653.651.970.34-0.29Practice on the platformSchool2.212.211.840.230.07Platform efficiencySchool3.233.232.520.25-0.43The overall performance of a teaching and learning process is employed, with the E-learning framework functioning as a complement to such learning system. Thus, a 93 percent overall quality analysis of students would propose employing RSA for the first, second, and through final grade instructional on the datasets. Another factor for the existing performance analysis of 82.89% would recommend a mix of offline and online classes, and RSA is performance quality, which would recommend available on the internet educational (refer Figure5). Wireless communication technologies and classroom training optimization approaches will both benefit from the recursive algorithm (RA). Researchers would describe the graph’s building approach as well as the corresponding optimization method based on supervised multiprocessing learning.Figure 5
Performance analysis influence of IPE strategies based on WSN and RSA with AI.RSA and optimization approach is being used not only to analyze supervised neural data but also to extract the most important information and knowledge by evaluating and altering essential information. DL techniques struggle to characterize patterns from data, even though data processing is generally highly advanced, and the level of available information is relatively high. Students know that education seems to be the primary goal, but that education also includes direction, evolution, recognition, comprehension, distribution, and teamwork, all of which assist students to achieve (refer to Figure6). The attainments of students’ effectiveness frameworks are dependent on behavioral product performance—the performance analysis’s average score and the political and social teaching data variations. The results indicate the optimum for RSA employing optimization using DL methods (refer to Table 3). Its comparison result analysis for the statistics using RSA vs. RA is the best performance result to analysis for IPE teaching statistics. The structure length is used for probability computation and is defined by the simple recursive algorithm, which grows as ∂1k2 and is implemented throughout the process. The IPE strategies fpn are quantified by the parameters |UNτk2abτ≤∂2k2,∀n0≥0, which are defined by the Equation (14) word preceding it.Figure 6
Performance evaluation IPE in differences in median scores and differential statistics on IPE teaching using RSA with AI.Table 3
Comparison result analysis IPE teaching statistics using RSA with AI.
StudentRemote supervised algorithmRecursive algorithmsPerformance analysis (%)Performance analysis (%)Time (s)FrequencyAccuracy (%)Time(s)FrequencyAccuracy (%)Teaching recognition4.70.923983.50.93499Training recognition40.8458530.91289Learning context3.40.967792.40.99581Effect of classroom3.80.861712.80.88074


## 4. Conclusions

With the development of technological breakthroughs, artificial intelligence and wireless networks may now be used in any industry. As part of this research project involving college students’ entrepreneurial endeavours, IPE techniques based on wireless networks and artificial intelligence knowledge maps will be studied as part of this research project. An IPE is a social and economic analysis of a country’s political situation. The teaching and learning processes in this program are supported by wireless network technology and artificial intelligence. The proposed system uses artificial intelligence and wireless networks in a knowledge map to study IPE techniques’ impacts on college students’ entrepreneurialism. This is the focus of the proposed system. The college student then adapts the theory to his or her own entrepreneurial endeavours and finds it to be effective. These algorithms reveal themselves by decreasing input quantities and returning values for a signal by completing simple actions. Recursion is used by AI to teach college students entrepreneurship skills.



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