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With the rapid development of computer networks and information technology, government departments, financial institutions, and enterprises are increasingly dependent on software, and software security issues have become a focus of attention. In this context, how to effectively evaluate the security of software has become an important issue for research institutions both domestically and internationally. On the basis of exploring software definition, this paper not only analyzes the security and potential threats of software in computer networks, but also introduces Embedded Neural Networks (ENNs) as an evaluation tool, and combines Fuzzy Analytic Hierarchy Process (FAHP) to deeply explore a new method for software security risk assessment. By utilizing the powerful pattern recognition capability of ENNs, software logs, system call sequences, and other data can be classified and analyzed to distinguish between normal and abnormal behavior. This ability is crucial for identifying security incidents such as malicious software and unauthorized access. ENNs are designed with resource constraints in mind, which can reduce energy consumption while ensuring performance. For software systems that require long-term operation, this means higher security and stability. Practice has proven that combining ENNs with FAHP can more scientifically and effectively evaluate software security. This method not only improves the accuracy and efficiency of evaluation, but also provides a more solid theoretical foundation and technical support for software security protection.

In order to realize the quantitative management of higher education quality, an evaluation method of higher education quality management maturity based on OBE concept is proposed. Construct a phase space distribution structure model of higher education quality management maturity, establish a parameter set of higher education quality management maturity distribution index, adopt OBE concept to construct a fuzzy association rule distribution set, extract association regularity features, classify multi-dimensional attribute features, conduct data partition scheduling in the fuzzy clustering center according to the differences of statistical features, construct a feature decomposition model, and reorganize the ontology structure of higher education quality management maturity. The binary structure characteristics are reconstructed in the virtual database, and fuzzy clustering is carried out under the OBE concept according to the reconstruction results, so as to realize the optimal evaluation of the maturity of higher education quality management. The experimental simulation results show that this method has good feature clustering and high reliability in evaluating the maturity of higher education quality management.

Traditional innovation and entrepreneurship education quality evaluation methods often use simple index weighting or cluster analysis, ignoring the correlation and fuzziness between the indicators, resulting in lower credibility of education quality evaluation results. Therefore, an innovation and entrepreneurship education quality evaluation model based on fuzzy clustering is proposed. After analyzing the basic characteristics and influencing factors of innovation and entrepreneurship education, the evaluation index system of innovation and entrepreneurship education is constructed from multiple perspectives according to the principles of scientificity, comprehensiveness, accuracy and operability. In order to improve the consistency of quality data of innovation and entrepreneurship education, it is normalized. The projection pursuit technique is used to reduce dimension and fuzzy classification of multi-dimensional time series education data set, and fuzzy rules are extracted according to classification results and optimal projection values. Three fuzzy membership functions are generated by the trapezoidal distribution method. Finally, the evaluation level of education quality sample data is determined according to the fuzzy approximation degree. The test results show that the construction time of the evaluation model is short, and the reliability of the quality evaluation of innovation and entrepreneurship education is improved.

Oral pronunciation refers to the way words are spoken aloud, encompassing aspects like accent, intonation, and clarity. It plays a crucial role in effective communication, influencing listeners to understand the speaker. The purpose of this research is to construct an English oral pronunciation evaluation model based on a deep learning algorithm. In this research, we propose a novel Dung Beetle Optimization-driven Customized Deep Belief Network (DBO-CDBN) for accurate detection and evaluation of English oral pronunciation. We obtain a dataset that comprises audio recordings of English speech, including both correct and mispronounced samples to train and evaluate the suggested pronunciation evaluation model. Data normalization is used to pre-process the gathered raw audio data, it enhances the overall quality of the data. The Gammatone Frequency Cepstral Coefficients (GFCC) algorithm is employed to extract the crucial features from the obtained audio data. In our proposed model, the DBO optimization algorithm iteratively finetunes the CDBN architecture to enhance the evaluation accuracy. The approach, which is executed in python, is shown to be more successful in pronunciation evaluation and to provide prospective advances in the area when its performance is examined and compared to other approaches in terms of precision (96.78%), recall (93.65%), and $F$-measure (94.53%). The established model is implemented using Python software. During the outcome analysis phase, we evaluate our model’s performance across various parameters. In addition, we also performed a comparative analysis using diverse existing methodologies. The findings demonstrate the excellence and effectiveness of the suggested future economic forecasting model.

Classroom teaching evaluation is one of the important contents of the new round of basic education curriculum reform in China. The new curriculum reform puts forward new requirements for the construction of the teaching evaluation system: promoting the all-round development of students, promoting the continuous improvement of teachers’ level, and promoting the curriculum of continuous development. However, from the current situation of the implementation of the new curriculum, the original teaching evaluation system is far from the requirements of the new curriculum reform, and does not have much practical value, and cannot provide strong support for the new curriculum reform. If it is not reformed, it will inevitably have a negative impact on the overall promotion of curriculum reform. How to improve classroom teaching evaluation under the background of the new curriculum reform, and how to establish a teaching evaluation scale and system suitable for the new curriculum reform, so as to play the role of evaluation in guiding, motivating and promoting, is an urgent problem to be solved at present. By referring to the relevant literature, the concepts of evaluation, teaching evaluation and classroom teaching evaluation are defined and discussed, and the object of classroom teaching evaluation is clarified.

Financial markets have developed formulas and models to derive fair values for bonds, futures, swaps, options and other securities. This model derives a fair value of an exchange rate, which might be used as a benchmark for a long-term equilibrium level to stabilize currency markets. The model is based on the value-added tax adjusted purchasing power parity exchange rate. This rate is then modified by five components: the macro-economic component, the foreign currency reserve component, the debt component, the interest rate component, and the political stability/leadership component. With respect to the American dollar, the model shows that the Euro and the Japanese Yen are overvalued compared to its current exchange rate, while the Brazilian Real, the Russian Ruble, the Chinese Yuan and the Australian dollar are currently undervalued.

This paper considers the evaluation step in a decision-making process that follows decision-making goals setting, feasible alternatives and attributes or criteria that characterize them determination steps. Evaluation step must establish a model or algorithm to evaluate alternatives taking into account their performances with regard to criteria as well as decision makers or stakeholders preferences. Though this problem is rather a classic one, researches related to evaluation model construction continue to be active to find models that cope with more realities or that fit well how human beings behave in group and proceed when facing the problem of choosing, ranking or sorting alternatives or options. The purpose of this paper is to construct an evaluation model that integrate the performances of alternatives with regard to attributes or criteria and decision makers or agents opinions with regard to the importance to assign to each criterion in order to obtain a value function. As any decision problem is almost always a matter of tradeoff, among attributes characterizing alternatives there will be those acting toward the achievement of decision makers goal (benefit) and those that decision makers would like to reduce as much as possible (cost); we will designate the first ones as positive attributes and the later ones as negative attributes. The process of dividing attributes into positive attributes and negative attributes is beyond the scope of this paper and this partition will be considered as a part of the problem specification. The model is constructed in two steps: firstly, satisfiability (selectability and rejectability) measures or functions are obtained for each alternative using attributes values (positive attributes will contribute to selectability measure whereas negative ones are used in the derivation of rejectability measure) and agents opinions in the framework of satisficing game theory and secondly a value function is built on that measures. Agents opinions with regard to attributes will be expressed locally by weighting them by category (positive/negative).

The water eutrophication restricts the development of economy and society in China, which attracts increasing attention. It also affects the health and ecological environment. The evaluation of water eutrophication is very complicated due to the dynamic variability of the water quality data. This paper adopts the hesitant fuzzy set (HFS) to depict the massive data of samples and uncertain preference information of experts, which reduces the complexity of calculation and avoids the loss of information. After that, we construct the projection index function based on the main factors of water eutrophication. The particle swarm optimization (PSO) algorithm is applied to determine the global optimal projection direction by optimizing the projection index function. Therefore, we construct an improved projection pursuit regression (PPR) model. Finally, the water eutrophication evaluation of several lakes in China is used to demonstrate the improved PPR model. Also, the comparative analysis and contribution rate analysis are conducted to validate its rationality and advantages.

Nowadays, valuation and measurement of knowledge assets has become a challenging problem for many organisations. This paper first introduces the concept of knowledge assets and presents some evaluation models of knowledge assets. Then, based on the methodology of the balanced scorecard, this paper proposes an index system which comprises quantitative indices and qualitative indices and financial indices and non-financial indices which are weighted by the analytical hierarchy process method to evaluate organisational knowledge assets. Thus, the status of organisational knowledge assets management can be estimated by a final score calculated by efficacy coefficient method and professional evaluation method in the model. At last, the paper gives a case study for this model's application. It is hoped that the information accrued from the case study, together with the evaluation model, will help to pave the way for organisations to evaluate their knowledge assets.

The evaluation system of entrepreneurial practice and curriculum teaching mostly depends on the evaluation of people. Qualitative evaluation lacking specific performance and quantitative measurement is carried out through small groups. This evaluation method lacks specific quantitative and weight basis. Therefore, the evaluation results often depend on the emotional bias and cognitive bias of individuals, and the subjectivity in the evaluation process is too strong. To solve this problem, BP neural network (BP-net) is introduced into the evaluation system of entrepreneurial practice and course teaching. The model has higher adaptability and nonlinear processing ability, so the evaluation results with higher objectivity and lower deviation rate can be obtained in the evaluation. According to the evaluation characteristics of entrepreneurial practice and course teaching, a combined update design is carried out, and a new momentum term is introduced to enhance the overall operation effect of the model. Finally, the model is tested by means of performance comparison and empirical analysis. The results show that the performance score of the model reaches 4.93 points of the highest value, and the R value of the regression fitting process is stable at more than 0.90. It can conduct a comprehensive and objective analysis for different index dimensions, and split out the advantages and disadvantages of entrepreneurial practice and course teaching. This model can evaluate the quality of entrepreneurial practice and course teaching, promote the self-improvement of colleges, and create a better innovation and entrepreneurship environment for students.

This paper discusses the evaluation method of mobile internet social apps through a humanist perspective. We analyzed classic humanlogy theories such as Marxist practical human thought and demand theory, Freud’s sub consciousness theory, Sartre’s freedom to pursue ideas, Fromm’s interpersonal and social demand, Marcuse creative lust theory and Maslow’s hierarchy of needs in the growth of the existing research achievements, etc. By integrating the interaction design theory with related digital art design theory, we carried out a series of experiments to explore this new evaluation method for social applications. Research conducted in this paper based on the study of Social applications DPCF (Demand Production Consume Feeling) model and the SFT (Social Feeling Tools) Social application evaluation model, provides new methods to optimize the design of social applications.

In the information war, the electromagnetic environment of the battlefield is becoming more and more complex. In order to study the interference effect caused by random noise on co-mmunication equipment in complex electromagnetic environment, an experiment that randomnoise disturb a certain communication station are designed. using the continuous wave to irradiate the radio, the radiation sensitivity curve of the radio station in the bandwidth is obtained. In the radio sensitive bandwidth, doing a curvilinear integral with a spectral density of the noise power, the paper presents an evaluation model of the jamming effect caused by random noise on the communication station based on the analysis of the application mechanism and the statistical analysis of the experiment data.

Modern electronic warfare equipment presents a development trend which is highly integrated, smart and intelligent. They can produce a variety of high strength and targeted electronic jamming in the whole airspace, frequency domain and time domain, which affects the air defense intelligence radar detection ability seriously. In order to ensure operational effectiveness in a complex interference environment, it is the necessary to evaluate radar anti-jamming system. Some have complex technical factors, but also contain a lot of uncertain factors and fuzzy factors or human factors. To evaluate the effect and performance is difficult, hence, how to objectively and comprehensively evaluate the anti-interference ability of modern radar systems has become a radar department of design, production and use of important topics of common interest.

The analysis of system reliability is methods of finding and analyzing problems in reliability engineering, what's more, it is also an important means of have a definite object in view to solve the problems. The dynamic reliability evaluation model uses the distribution of the residual strength replaces intensity distribution of parts. Based on the statistical meaning of random load action and the order statistic theory, the system reliability model is established under the action of loading.