College-level entrepreneurship education seeks to provide students with the knowledge, abilities, and mindset they need to launch their businesses or exercise entrepreneurialism inside already-established enterprises. It's critical to remember that entrepreneurship education frequently involves hands-on activities, case studies, guest lecturers from businesspeople, and chances for students to work on real startup initiatives. Students may use the knowledge and skills they learn in a practical setting due to this hands-on approach.
A. Clustering algorithm using K-Means for Entrepreneurship Education
Cluster analysis is a fundamental data science approach for identifying distributions and trends. Data points must be grouped into clusters where they are as similar as possible inside each cluster while being as dissimilar as possible between clusters to solve the clustering problem. K-means clustering is distinct in several ways, including being a classical approach to clustering questions; being quick and simple to implement, and producing dense data distributions within each subcategory and large differences between them in the final clustering result. The algorithm's system architectural arrangement is depicted in Fig. 2.
Cluster analysis integrates multivariate analysis methodologies with quantitative research and is an unsupervised learning tool. When the dataset has no foundation or reference point, reasonable classification of a huge amount of preprocessed data is done based on the data's features. It groups information into categories based on shared characteristics and connections among data items. Data clustered together share many similarities and data clustered with other data sets share few similarities or differences. The efficiency of data grouping increases as both the likelihood inside each cluster and the variation across clusters grows. Each row in the matrix represents an item, and each column represents a characteristic value for an attribute associated with that object. The clustering approach requires the following procedures: “data preprocessing, feature selection and extraction, clustering, and performance evaluation”. Missing values, noise, and incomplete or erroneous data are common outcomes of data collection and preparation, rendering analysis of the raw data unfeasible. The reliability of one's data is the bedrock of any analytical endeavor. The initial stage in any data analysis process should be data cleaning. In this procedure, you must address issues of data consistency, invalidity, and missing values. Methods of verifying and checking and correcting are used extensively in the processing of consistency, whereas data-cleaning techniques such as estimation and attribute deletion are used in dealing with errors and missing data.The number of outcomes and feature values contributes to the difficulty of clustering in feature selection and extraction since the characteristics and noise included in actual objects make studying them difficult. Therefore, dimensional reduction processing can be applied to the data points to simplify the data analysis. Selecting functions, also known as function separation, examines a set of functions and then chooses a subset of the influencing functions to minimize the size of the output. In feature extraction, a high-latitude feature vector is reduced in dimension by mapping it onto a lower-dimensional space. The best results can be obtained in real applications by combining the two approaches. When comparing two things, it's helpful to choose neighboring terms that emphasize their similarities and contrasts. The greater the degree of similarity between two things, the greater the value placed on them. The distance between the objects is used as a metric to evaluate their degree of resemblance; a smaller distance denotes a higher degree of similarity.
$$K=\sum _{w=1}^{r}\sum _{fϵ{N}_{w}}{‖f-{e}_{w}‖}^{2}$$
1
$$G\left(N,M\right)=\left\{\sum _{w=1}^{r}\left|{N}_{w}-{M}_{w}\right|\right\}$$
2
where\(G\) is the definite spatial separation.
$$G\left(N,M\right)={\left\{\sum _{w=1}^{r}{\left|{N}_{w}-{M}_{w}\right|}^{2}\right\}}^{1/2}$$
3
Using the classic Euclidean distance measure\(G\left(N, M\right)\)
$$G\left(n,M\right)={\left\{\sum _{w=1}^{r}{\left|{N}_{w}-{M}_{w}\right|}^{\infty }\right\}}^{1/\infty }$$
4
Chebyshev distance \(G\left(n, M\right)\)is used here.
$${Z}_{V}=\sum _{v=1}^{v}\sum _{r=1}^{p}{‖{n}_{r}^{\left(c\right)}-{e}_{c}‖}^{2}$$
5
$${e}_{c}=\frac{1}{{p}_{c}\left({\sum }_{c=1}^{p}{n}_{c}\right)}$$
6
Here \({e}_{c }\)is the sample median and \({p}_{c}\) is the sample size distribution.
$${Z}_{J}=\sum _{c=1}^{V}{Q}_{c}{T}_{c}$$
7
$${T}_{c}^{*}=\frac{2}{{p}_{c}\left({p}_{c}-1\right)}\sum _{nϵ{n}_{w}}\sum _{nϵ{n}_{c}}{‖n-n{\prime }‖}^{2}$$
8
Where\({T}_{c}^{*}\)are the sample distances between categories as measured by the mean squared deviation.
$${Z}_{y}=\sum _{c=1}^{V}\left({e}_{w}-e\right)\left({e}_{c}-e\right)$$
9
Where\({e}_{c}\)indicates the average of the vector of interest, while \({e}_{w}\) and is the average of all vectors.
$$Intra\left(r\right)=\frac{1}{P}\sum _{w=1}^{r}\sum _{nϵ{V}_{w}}{‖n-{H}_{w}‖}^{2}$$
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$$Intra\left(r\right)=min{\left(‖\begin{array}{c}{H}_{w}-{H}_{c}\\ w,c\end{array}‖\right)}^{2}$$
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Where \(P\)the total is number of observations and \({H}_{c}\) is the center of mass of the cluster
$${V}_{c}\left(r+1\right)=\frac{1}{Pc}\sum _{nϵ{V}_{c}\left(r\right)}N$$
12
Where \(Pc\) represents the total number of things
$${O}_{w}=\left(\frac{1}{{p}_{w}}\sum {w}_{w1}\frac{1}{{p}_{w}}\sum {w}_{w2}\dots \dots \frac{1}{{p}_{w}}\sum {w}_{up}\right)$$
13
Where
\({O}_{w}\) – a group of centroids,
\({p}_{w}\) – the quantity of data points,
\({w}_{up}\) – the data items in a cluster.
$$cos\theta =\frac{{n}_{1}{n}_{2}+{m}_{1}{m}_{2}}{\sqrt{{n}_{1}^{2}+{m}_{1}^{2}}\sqrt{{n}_{2}^{2}+{m}_{2}^{2}}}$$
14
When eigenvectors are separated by a distance of \(cos\theta\),
$$\frac{cre}{num}={f}^{-1}{\left(\frac{\theta }{K}\right)}^{2}={l}^{-1}{\lambda }^{2}$$
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Where \(f\) represents the grouped nature class divergence
$$K=\sum _{w=1}^{neigh}\sum _{nϵf}{\left({n}_{w}-\stackrel{-}{n}\right)}^{2}$$
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$$sim\left({V}_{1},{V}_{1}\right)=\frac{{\sum }_{{g}_{1}ϵ{V}_{1},{g}_{2}ϵ{V}_{2}}sim\left({g}_{1},{g}_{2}\right)}{se\left({V}_{1}\right)\times se\left({V}_{2}\right)}$$
17
Where\({g}_{1},{g}_{2}\), and \(we\)represent the data objects and the overall number of data objects respectively.
$$g=st\left(\sum _{r=1}^{e}{\left({N}_{wr}-{N}_{cr}\right)}^{2}\right)$$
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Where \({N}_{w }\)and \({N}_{c}\)are data items from the dataset that are being analyzed.
$$H=\frac{1}{p}\sum _{c=1}^{p}{N}_{c}$$
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$$K\left(p\right)=\frac{1}{2}\sum _{f=1}^{F}{k}_{rf}^{2}$$
20
Where \({N}_{c}\) is subclasses data object and \(p\) is the total number of data objects that have been subclasses.
Despite the long history of clustering research, there are several new difficulties that clustering applications must overcome in the age of databases, the Internet, and multimedia. Convergence of the k-means to a local minimum is guaranteed. The cluster centers serve as initial guesses for the local minimum. It is NP-complete to find the global minimum. Cluster centers are iterated by the k-means algorithm until a minimum is reached. Algorithm1 displays a pseudo-code of K-means clustering.
B. Reliability
Traditional clustering approaches can only discover convex and spherical groups, even though many groups are both convex and complex. One of the most popular study areas is still the accurate identification of complicated clusters. The database's clusters are no longer uniform as it gets bigger. Because the database contains clusters of various shapes, densities, and sizes, and because it can occasionally be difficult to distinguish between clusters and groups, it is also important to research how to discover these clusters. Since the purpose of group testing is to establish validity, this is done in nearly every instance. With the advent of large-scale databases and the development of the largest databases ever, the classification of huge and complex data has become a serious challenge. This problem will be addressed if techniques are developed for precise measurement of numerical and statistical data. The new program is concentrated on clustering with databases that don't have identical data types. Large groups of things can form in clusters, and each group has its structure. The quantity or scale of the data is no longer a factor in many clustering techniques.
C. Science and Technology for college students
An effort to reproduce the external world's physics and evolution, as well as to forecast and discover new ones, is the goal of scientific computing. When it comes to solving mathematical difficulties in the fields of science and technology, scientists and engineers increasingly turn to computers to do numerical calculations. When it comes to boosting computer speed, researchers might go in one of two paths. One way to improve processing performance is to raise the clock frequency of a single processor. Increasing the number of processing cores in a particular device is one technique to improve performance. The power consumption wall brought on by the increase in core frequency rendered the computational performance boost brought on by the higher clock speed obsolete. A single-core processor's power consumption scales roughly as the cube of its primary frequency. Power consumption grows linearly with the number of cores used to improve computing performance. Therefore, multicore platforms have become the standard recommendation for boosting computational efficiency. Research into the parallelization of scientific computing methods is required to fully utilize the multicore platform's processing power. Due to the long-standing prevalence of mainframes and large-scale computer clusters in scientific computing, studies into the parallelization of scientific computing techniques on these platforms are well-established. The computational performance of embedded processing systems, however, has grown considerably as our understanding of processor design has expanded and on-chip multicore architectures have spread. The benefits of reduced energy consumption and simple chopping are also becoming more and more obvious. Thus, more and more focus is being placed on practical applications in industry and daily life. There has been a shift from the strictly theoretical to the practically applicable use of computers in science. Matrix calculations and other scientific computing algorithms see widespread use in practical picture processing. It is essential to develop and employ parallelization methods for scientific computing algorithms on a multicore integrated platform. To begin the process of data visualization, the raw input data is filtered and then the visual data of the application is collected using a mapping approach. It takes in geometry information for visualization, together with picture information, and then sends the whole package off to the final visualization tool. Microprocessor architectural specifications are displayed in Table 1.
Table 1
Microprocessor architectural specifications
Processor | PR4 | PA8 | UII | IL4 | IM2 | R140 |
---|
Pipeline (stage) | 16 | 9 | 16 | 26 | 9 | 5/6/7 |
Number of transistors (M) | 171 | 301 | 31 | 46 | 216 | 101 |
Command firing rate | 9 | 7 | 6 | 4 | 7 | 5 |
Features | 13 | 13 | 7 | 7 | 23 | 7 |
D. Student Creativity and Enterprise Initiatives
Entrepreneurship and innovative problem-solving contribute to the growth of the common good. It raises the standard of living for everyone by boosting the economy and elevating scientific knowledge across the board. The essence of this concept, as it relates to innovation and entrepreneurship, is that it describes the activities of persons involved in social production as manifested in their imaginative business conduct. It's a strategy for making new things in the physical world through teamwork and mass production. Entrepreneurs that embrace innovation have the opportunity to educate themselves on the topic, hone their craft, expand their horizons, foster personal development, and launch original businesses. The development of entrepreneurship and an appreciation for innovation requires grounding in scientific theories informed by scientific notions. Innovation and business startup courses are not only accessible but actively taught nowadays.
It has its theoretical grounding and character that must be respected. Marx's idea of human integral development rests on three distinct theoretical pillars: subjective education theory, Marxist innovation theory, and Marx's theory of human integral development. Innovation and entrepreneurship can be interpreted on several different levels and in different contexts. It's about always adapting and bettering oneself through the destruction of static structures and rigid thought patterns. The developmental expansion entails the introduction of novel development mechanisms and ways of thinking, as well as the production of novel developments, the formulation of novel rules, and the investigation of novel domains. Rather than focusing on destroying existing connections and forging brand-new ones, innovators and entrepreneurs should aim to transcend and recreate all existing connections at a deeper, broader, and more complicated level. Innovation and entrepreneurship in the context of a society are those that increase that society's ability to generate wealth. A nation's fundamental competitiveness rests on the shoulders of its innovators and entrepreneurs. When it comes to a country's fundamental competitiveness, innovation, and entrepreneurship are crucial pillars. Innovation and entrepreneurship are not complete without a focus on practical applications. Education that encourages innovation and entrepreneurship differs from the standard classroom experience. It's a fresh approach to schooling that better fits the needs of the modern world.Their efforts to better higher education help students develop personally and academically. However, the following features are representative of an educational system that fosters innovation and entrepreneurship.
To start, there is originality. Teachers who take the time to acquire new concepts and approaches to the classroom can help their students gain a more holistic knowledge of the need of adapting to change and thinking creatively. This gives students more confidence in confronting the gaps between what they know today and what they need to know. Students acquire new information and technologies through imaginative visualizations and scientific inquiry made possible by education's emphasis on innovation and entrepreneurship. Synthesis is number two. Recognizing and nurturing each student's unique qualities is essential to providing them with the most beneficial creative and entrepreneurial education. They take into account a variety of student characteristics and implement new instructional strategies accordingly. Efficiency is the third factor. Practical experience is emphasized more in programs that teach students about innovation and entrepreneurship than in conventional classrooms.