Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks

This paper constructs a forecasting model using the PAM-LSTM time series model based on historical human resource data. The status quo, structure, efficiency and talent of human resource management of teachers in Chinese institutions of higher education are analysed in depth so as to discuss more concisely and systematically the methods of improving the efficiency of human resource management of teachers in colleges and universities and the path of talent development. The study shows that the student-teacher ratio of higher education institutions is expected to reach 10:1 in 2026, the doctoral degree increases to 34.24%, and the number of full-time teachers with full senior titles is expected to be 2.5% higher in 2026 than in 2020. The comprehensive efficiency value of human resource management in higher education institutions is expected to reach 96.7% in 2026. Therefore, strategies for improving HRM efficiency and developing talent, such as combining rigid and flexible management of human resources with an optimal organizational structure for resource allocation, are proposed.

An analysis of the peculiarities of human resources management process implementation at domestic enterprises under conditions of the russian-Ukrainian war is presented in the article. A study of the main factors influencing on the enterprises’ personnel functioning and development is carried out. It is determined that the most negative one is the military and political situation in the country. A significant number of challenges and problems, affecting the efficiency of human resources management is identified, in particular: the necessity to ensure the personnel's physical safety, mobilization of qualified workers for the purpose of country defense, the gradual release and optimization of human resources, an insufficient number of active qualified labor force, a significant deterioration of social and psychological climate within collectives, deformation of communication and reduction in the efficiency of the management system, low (absent) level of readiness for staff inclusion, etc. In addition, it is determined that despite the current situation in the country, it is important and necessary to adjust the personnel management approach to the social norms of Western countries in the process of European integration processes. To increase the efficiency of human resources management and improve the psychological climate at the enterprise, some measures are suggested, aimed at providing psychological support and rehabilitation of employees: introducing flexible work schedules, programs for professional development and increasing the personnel qualifications, strengthening the motivation system through new approaches, using modern information technologies to optimize personnel management, active involvement of external experts to provide advisory assistance on issues of personnel management in wartime, etc. Emphasis is put on the necessity to develop and implement existing professional programs for veterans of war readaptation and resocialization, including the ones at workplaces. It is noted that only a comprehensive approach to the implementation of the above-mentioned measures makes it possible to ensure an efficiency increase of the enterprises’ human resources management systems, which as a result, will have a positive impact on the economic situation in the country.

In this paper, an evaluation of various discriminant neural networks classifiers for text- independent speaker verification problem is presented. Each person to be verified has a personalized neural network model. A new classifier called neural tree network (NTN) is also examined for this application. The memoryless feedforward neural network architecture makes decisions based on static features. Time delay neural network (TDNNs) have proved to be an efficient way to handle the dynamic nature of speech. Furthermore, a model called recurrent time delay neural networks (RTDNNs), obtained through a local feedback connection at the first hidden layer level of TDNNs is investigated. The training is carried out by backpropagation for sequence algorithm. The database used is a subset of the TIMIT database consisting of 38 speakers from the same dialect region. The NTN is compared with the MLP, TDNN, and RTDNN. It is shown that NTN is found to perform better than the other neural networks classifiers. Also, a little bit performance improvement was achieved due to the addition of temporal information for text-independent speaker verification problem using TDNNs and RTDNNs. Finally, we described the experimental results obtained using different neural network models.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents a hybrid autoregressive moving average (ARMA) and a nonlinear functional link neural network for electricity price forecasting in an Energy market. The functional neural block helps to introduce nonlinearity by expanding the input space to higher dimensional space through a basis function without using any hidden layers like MLP structure. Unlike the conventional functional link artificial neural network (FLANN), the input layer comprises the inputs and tangent hyperbolic functions of the linear combination of the inputs known as the basis functions. The proposed hybrid neural network is trained by an unscented H-infinity filter to provide an accurate forecasting of day ahead electricity prices. The noise covariance parameters of the unscented H-infinity filter are further optimised with an adaptive differential evolution strategy. The studies on PJM, Spanish and Australian energy markets exhibit excellent forecasting results over different seasonal horizons for one day ahead of time.

Computer-aided design (CAD) technology is widely used in modern design businesses. However, the management of these organizations' human resources is critical to their success. As a result, guaranteeing the effectiveness of human resource management is critical to the success of CAD businesses. The purpose of this research is to identify the most effective ways for managing human resources in CAD businesses. The specific aims of this study are to examine the impact of efficient human resource management on the performance of CAD companies, identify the elements that influence this efficiency, and make recommendations for efficient human resource management in CAD businesses. Human resource management must be done successfully for CAD firms to prosper. By using best practices in human resource management, CAD businesses may achieve long-term growth. As part of the study's mixed-methods research strategy, which also included a worker survey, in-depth interviews with management of CAD businesses were undertaken. The information received from the survey and interviews was evaluated using both quantitative and qualitative methodologies. According to the survey, effective human resource management increases the performance of CAD organizations. Human resource management in CAD businesses is influenced by factors like leadership, training and development, performance management, and employee engagement. Based on these findings, the study presents recommendations for effective human resource management in CAD businesses, such as the importance of strong leadership, the implementation of training and development efforts, and the use of effective performance management systems. These recommendations can help CAD firms improve their human resource management operations and achieve long-term success.

The work is devoted to resolving the contradiction between the accuracy of modelling nonlinear dynamics and the speed of model construction under conditions of limited computing resources. The purposeof the work is to reduce the time for building time delay neural networks while ensuring a given accuracy in the tasks of identifying nonlinear dynamic objects with continuous characteristics. This goal is achieved by developing a method for pre-training neural networks that reflect the basic characteristics of the subject area. The scientific novelty of the work is the development of a method for identifying nonlinear dynamic objectsin the form of time delay neural networks based on the use of a set of basic pre-trained neural networks that reflect the typical properties of the subject area. In contrast to the traditional approach to pre-training, the developed method allows building models of lower complexity.A formal criterion is proposed for determining the moment of termination of the neural network pre-training, the use of which allows avoiding retraining of the base model and ensuring a significant reduction in the model training time on the target data set. The practical utility of the work lies in the development of an algorithm for the method of pre-training time delay neural networks in the tasks of identifying nonlinear dynamic objects with continuous characteristics, which allows to significantlyreduce the training time of neural networks without losing model accuracy. The value of this study is todetermine the area of effective use of the proposed method, namely, when the general and target datasets do not have significant differences and the target dataset is of sufficient size to reflect the properties of the research object.

The efficient management of human resources has emerged as a crucial factor in determining the performance of organizations in the knowledge-driven economy of today. However, different organizations have different levels of success with these strategies. The difference lies in the paradigm adopted in the organization: personnel management or human resource management. The purpose and objectives of the article are to analyze the theoretical, methodological, organizational, structural, and value foundations of the role of the HR department in implementing HR management as a social management practice aimed at forming and developing the organization's human resources potential and transferring the organization to a development mode. The study is based on a theoretical analysis of scientific works related to personnel management in organizational management in the context of socio-economic transformation in Ukraine. The main focus is on the analysis of holistic and strategic approaches to human resources management, emphasizing talent management, organizational development, and a proactive role in shaping the workforce to solve business problems promptly. The general theoretical basis and methodological principles are determined taking into account the interdisciplinary nature of research in the field of sociology of management, management theory, and personnel management theory. The article, based on the application of elements of qualitative content analysis and narrative review, explores the features and vectors of transformation of the role and functions of the HR department in modern conditions. It is shown that in today’s fast-paced, talent-centric market, organizations must regularly evaluate and enhance their HR policies and best practices to stay competitive, and the points of attention today should include providing the role of personnel departments in value creation and knowledge sharing, which ultimately leads to the company’s high performance and competitiveness. It is found that human resource management has undergone transformational change, reflecting changing organizational priorities and the recognition of employees as vital assets that contribute to business success. It is noted that human resource management recognizes the strategic importance of effective workforce management. Such changes have become a transitional step in understanding the evolution of personnel management from personnel management to strategic human capital management. The prospects of organizations depend on the actualization of internal resources and are not limited by financial and material capabilities. The foundation of modern economic knowledge is intellectual capital, the ability to form a management team, create a system of individual personnel management, and openness and innovation in management. Modern human resources management primarily involves the need to implement a holistic approach to employees with their needs and wishes, as well as a timely response to them by management.

In a rapidly changing competitive environment, human resources are one of the important sources of competitive advantage. Human resources tools can contribute to achieving a sustainable competitive advantage by facilitating the development of competencies for the organization. Strategic human resources management tools are concerned with finding a link between the overall strategic objectives of the business and the human resources strategy. And implemented in order to enhance the competencies of employees in the organization. The current research aims to demonstrate the impact of strategic human resources tools (strategic map, human resources management card, dashboard) in enhancing human competencies (knowledge, relationship management, work intelligence, capacity assessment) in the Salah al-Din Education Directorate. It includes variables and dimensions, according to the exploratory curriculum in the Salah al-Din Education Directorate, then formulating research hypotheses and testing them using appropriate statistical methods and using the statistical package (SPSS) program. In general, the descriptive approach was adopted in explaining the theoretical and analytical frameworks to test and analyze the research hypotheses, while the study community consisted of workers in the Salah al-Din Education Directorate at the three administrative levels, and the sample size was (85) individuals. The research reached several conclusions, the most important of which was the significant effect of the strategic human resources tools (strategic map, human resources management card, dashboard) in enhancing human competencies in the Salah al-Din Education Directorate. In conclusion, the researcher recommended the necessity of identifying the appropriate tools for strategic human resource management, by relying on academic research regarding the impact of strategic human resource management practices on the efficiency of human resource management in the directorate.

Two recent papers by Petruccelli and Woolford (1984) and Chan et al. (1985) showed that the key element governing ergodicity of a threshold AR(1) model is the joint behavior of the two linear AR(1) pieces falling in the two boundary threshold regimes. They used essentially the necessary and sufficient conditions for ergodicity of a general Markov chain of Tweedie (1974), (1975) in a rather clever manner. However, it is difficult to extend the results to the more general threshold ARMA models. Besides, irreducibility is also required to apply Tweedie's results. In this paper, instead of pursuing the ideas in Tweedie's results, we shall develop a criterion similar in spirit to the technique used by Beneš (1967) in the context of continuous-time Markov chains. Consequently, we derive a necessary and sufficient condition for existence of a strictly stationary solution of a general non-linear ARMA model to be introduced in Section 2 of this paper. This condition is then applied to the threshold ARMA(1, q) model to yield a sufficient condition for strict stationarity which is identical to the condition given by Petruccelli and Woolford (1984) for the threshold AR(1). Hence, the conjecture that the moving average component does not affect stationarity is partially verified. Furthermore, under an additional irreducibility assumption, ergodicity of a non-linear ARMA model is established. The paper then concludes with a necessary condition for stationarity of the threshold ARMA(1, q) model.

Recently, x-vector [1] has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from variable-length utterances. Improvement upon the x-vector has been an active research area, and enormous neural networks have been elaborately designed based on the x-vector, e.g., extended TDNN (E-TDNN) [2], factorized TDNN (F-TDNN) [3], and densely connected TDNN (D-TDNN) [4]. In this work, we try to identify the optimal architectures from a TDNN based search space employing neural architecture search (NAS), named SpeechNAS. Leveraging the recent advances in the speaker recognition, such as high-order statistics pooling, multi-branch mechanism, D-TDNN and angular additive margin softmax (AAM) loss with a minimum hyper-spherical energy (MHE), SpeechNAS automatically discovers five network architectures, from SpeechNAS-1 to SpeechNAS-5, of various numbers of parameters and GFLOPs on the large-scale text-independent speaker recognition dataset VoxCelebl. Our derived best neural network achieves an equal error rate (EER) of 1.02% on the standard test set of VoxCelebl, which surpasses previous TDNN based state-of-the-art approaches by a large margin.

With the development of information technology and the arrival of the era of big data, the traditional employee management can no longer meet the needs of modern enterprises, and the informatization, automation and intellectualization of enterprise human resource management has been the trend.Therefore, we developed a human resource management system based on SSM (i.e.Spring, SpringMVC and MyBatis) framework for small and medium-sized enterprises (SMEs).By setting multiple roles, the human resource information can be efficiently and safely managed.After comprehensive testing, the system has been able to meet the basic needs of enterprises, and has a good user experience that can improve the efficiency of human resource management, and has a good reference and guiding significance for the research and development of human resource management system in SMEs.

Improving the efficiency of human resource management and enhancing organizational competitiveness is one of the effective ways to improve efficiency in public administration. An important way to improve human resource management is information-based human resource management. Informatized HRM is a new comprehensive and open HRM model that uses advanced electronic information technology as a means and software systems as a platform to achieve low cost, high efficiency, and joint participation of all employees in the management process to realize the strategic position of human resources. In this paper, we study the basis of HR-related theories and develop and design an HR informational management system based on B/S architecture, MVC model, which improves the efficiency of HR integration and improves the operational efficiency of public administration.

Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir

The article considers the potential for the widespread use of artificial intelligence (AI) to address current challenges in the labor market and improve the efficiency of human resource management (HRM). It analyzes modern approaches to integrating relevant methods and tools into HRM practices. The study explores how AI can complement existing HR processes, creating a more data-driven and adaptive framework for decision-making. Companies are actively seeking ways to incorporate innovative solutions, so the results of this study can be seen as a step towards combining current HRM practices with AI's transformative potential to support effective management decisions. The paper presents powerful AI capabilities: mechanical AI, thinking AI, and feeling AI. Mechanical AI is best suited for automating tasks such as job creation, enrolment in training courses, reporting, monitoring career development, and performance evaluation. This type of AI significantly reduces administrative burdens, allowing HR professionals to focus on strategic decision-making and employee engagement. Thinking AI excels in analyzing CVs, learning outcomes, productivity, and career forecasting. Feeling AI focuses on analyzing applicant behavior, personalizing training programs, providing psychological support, and monitoring the workplace's psychological climate. To address challenges in emotional analysis, the article presents a conceptual model for sentiment analysis of data on the emotional state and satisfaction of employees. The paper includes specific recommendations for implementation and demonstrates the model's application on Glassdoor Job Reviews data using Python programming language. This approach illustrates how AI can enhance employee well-being while aligning HR practices with organizational goals.

This paper proposes two hybrid grey-based short-term wind power prediction techniques: GM(1,1)-ARMA and GM(1,1)-NARnet. These techniques are combined with ARMA models and nonlinear autoregressive neural network (NARnet) models, respectively. The efficiency of these algorithms is examined using a recorded wind power dataset. The performance of these predictors is compared with classical ARMA models as well as the traditional grey model GM(1,1). Unlike the classical predictors, the proposed hybrid algorithms are not affected by the inherent uncertainty in the wind power. Therefore, the results obtained using the proposed hybrid algorithms outperform those obtained using classical predictors. In contrast to the GM(1,1)-ARMA model, the GM(1,1)-NARnet model utilises the nonlinear components of wind power in the forecasting procedure. Consequently, the obtained results from the GM(1,1)-NARnet outperform those obtained by the GM(1,1)-ARMA.