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Gupta, Kriti Priya, Preeti Bhaskar, and Swati Singh. "Prioritization of factors influencing employee adoption of e-government using the analytic hierarchy process." Journal of Systems and Information Technology 19, no. 1/2 (March 13, 2017): 116–37. http://dx.doi.org/10.1108/jsit-04-2017-0028.
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Purpose Government employees have various challenges of adopting e-government which include administrative problems, technological challenges, infrastructural problems, lack of trust on computer applications, security concerns and the digital divide. The purpose of this paper is to identify the most salient factors that influence the employee adoption of e-government in India as perceived by government employees involved in e-government service delivery. Design/methodology/approach The paper first identifies different factors influencing the employee adoption of e-government on the basis of literature review and then finds their relative importance by prioritizing them using the analytic hierarchy process (AHP). The AHP is a multi-criteria decision-making (MCDM) tool which combines all the factors into a hierarchical model and quantitatively measures their importance through pair-wise comparisons (Saaty, 1980). Eleven influencing factors of employee adoption of e-government have been identified, which are categorized under four main factors, namely, “employee’s personal characteristics”, “technical factors”, “organizational factors” and “trust”. The data pertaining to pair-wise comparisons of various factors and sub-factors related to the study is collected from ten senior government employees working with different departments and bodies of the Government of National Capital Territory of Delhi. Findings Based on the results obtained, the findings reveal that “organizational factors” and “technical factors” are the two most important factors which influence the intention of government employees to adopt e-government. Moreover, “training”, “technical infrastructure”, “access speed”, “technical support” and “trust” in infrastructure are the top five sub-factors which are considered to be important for the employee adoption of e-government. Research limitations/implications One of the limitations regarding the methodology used in the study is that the rating scale used in the AHP is conceptual. There are chances of biasing while making pair-wise comparisons of different factors. Therefore, due care should be taken while deciding relative scores to different factors. Also, some factors and sub-factors selected, for the model may have interrelationships such as educational level and training; computer skills and trust; etc., and these interrelationships are not considered by the AHP, which is a limitation of the present study. In that case, the analytic network process (ANP) can be a better option. Therefore, this study can be further extended by considering some other factors responsible for e-government adoption by employees and applying the ANP in the revised model. Practical implications The results of the study may help government organizations, to evaluate critical factors of employee adoption of e-government. This may help them in achieving cost-effective implementation of e-government applications by efficiently managing their resources. Briefly, the findings of the study imply that government departments should provide sufficient training and support to their employees for enhancing their technical skills so that they can use the e-government applications comfortably. Moreover, the government departments should also ensure fast access speed of the e-government applications so that the employees can carry out their tasks efficiently. Originality/value Most of the existing literature on e-government is focused on citizens’ point of view, and very few studies have focused on employee adoption of e-government (Alshibly and Chiong, 2015). Moreover, these studies have majorly used generic technology adoption models which are generally applicable to situations where technology adoption is voluntary. As employee adoption of e-government is not voluntary, the present study proposes a hierarchy of influencing factors and sub-factors of employee adoption of e-government, which is more relevant to the situations where technology adoption is mandatory. Also, most of the previous studies have used statistical methods such as multiple regression analysis or structural equation modelling for examining the significant factors influencing the e-government adoption. The present study contributes to this area by formulating the problem as an MCDM problem and by using the AHP as the methodology to determine the weights of various factors influencing adoption of e-government by employees.
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Rugchatjaroen, Krish. "Approach of Electronic Government to Closing the Gap between Public and Citizens." Journal of Social and Development Sciences 5, no. 3 (September 30, 2014): 130–37. http://dx.doi.org/10.22610/jsds.v5i3.813.
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Electronic government uses ICT to improve public activities, bringing also greater organizational efficiency and effectiveness. The aim of this research is to study electronic government in Thailand to move towards success. The questionnaire obtained information on the current status of electronic government in Thailand which intendeds to investigate factors relating to information technology by interviewing state employees in National Electronics and Computer Technology Center: NECTEC. The results reveal 6 conclusion based upon the following area of study; 1) Investment: budget allocations for the integration of ICT in the public sector by considering the national strategic plan and the ASEAN community strategies, to increase the competitiveness and investigate in infrastructure and logistics. 2) Officials’ knowledge and understanding: involving the full cooperation from government agencies workshops on the development of information systems for public sector officials to allow implementation of the projects to restructure more efficiently the form of electronic government. 3) Citizens’ understanding: which investigated the public sectors acceptance of public participation and people-centered government services? There are wide gaps between those used in municipal and non-municipal area. 4) Networking: the form of networking through a collaborative network of TOT and CAT in the core layer, which makes the network redundancy and high availability. 5) Promoting: using website to promote activities and disseminate knowledge about technology in electronics and computer project or training. and 6) Policies: the manner the Ministry of Information and Communication Technology, allocates resources in their expansion of telecommunications infrastructure and communication channels and the way they encourage full access to ICT that will lead to close the gap.
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Patalas-Maliszewska and Halikowski. "A Model for Generating Workplace Procedures Using a CNN-SVM Architecture." Symmetry 11, no. 9 (September 10, 2019): 1151. http://dx.doi.org/10.3390/sym11091151.
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(1) Background: Improving the management and effectiveness of employees’ learning processes within manufacturing companies has attracted a high level of attention in recent years, especially within the context of Industry 4.0. Convolutional Neural Networks with a Support Vector Machine (CNN-SVM) can be applied in this business field, in order to generate workplace procedures. To overcome the problem of usefully acquiring and sharing specialist knowledge, we use CNN-SVM to examine features from video material concerning each work activity for further comparison with the instruction picture’s features. (2) Methods: This paper uses literature studies and a selected workplace procedure: repairing a solid and using a fuel boiler as the benchmark dataset, which contains 20 s of training and a test video, in order to provide a reference model of features for a workplace procedure. In this model, the method used is also known as Convolutional Neural Networks with Support Vector Machine. This method effectively determines features for the further comparison and detection of objects. (3) Results: The innovative model for generating a workplace procedure, using CNN-SVM architecture, once built, can then be used to provide a learning process to the employees of manufacturing companies. The novelty of the proposed methodology is its architecture, which combines the acquisition of specialist knowledge and formalising and recording it in a useful form for new employees in the company. Moreover, three new algorithms were created: an algorithm to match features, an algorithm to detect each activity in the workplace procedure, and an algorithm to generate an activity scenario. (4) Conclusions: The efficiency of the proposed methodology can be demonstrated on a dataset comprising a collection of workplace procedures, such as the repair of the solid fuel boiler. We also highlighted the impracticality for managers of manufacturing companies to support learning processes in a company, resulting from a lack of resources to teach new employees.
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Huda, Asrul, and Noper Ardi. "Predictive Analytic on Human Resource Department Data Based on Uncertain Numeric Features Classification." International Journal of Interactive Mobile Technologies (iJIM) 15, no. 08 (April 23, 2021): 172. http://dx.doi.org/10.3991/ijim.v15i08.20907.
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Business Intelligence is very popular and useful for a better understanding of business progress these days, and there are many different methods or tools being used in Business Intelligence. It uses combination of artificial intelligence, data mining, math, and statistic to gain better understanding and insight on the business process performance. As employees have an important role in business process, the desire to have a tool for classifying and predicting their wages are desirable. In this research, we tried to analyzed dataset from Human Resource Department, and this dataset can be used to analyst the data in order to draw a conclusion about whether any employees would prematurely leave the company, and then, a preventive action based on those parameters can be proposed. This is a kind of predictive analytic system which bases on Naïve Bayes, and it can predict whether an employee would leave or stay according to his or her characteristics. But the Naïve Bayes itself does not enough. So we develop a way to solve the problem using uncertain Numeric features classification on it. The accuracy of the result is depended on the amount and effectiveness of the training sets.
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Liang, Gaoyang, Peng Cao, and Yang Liu. "Optimization and Simulation of Labor Resource Management Information Platform Based on Internet of Things." Wireless Communications and Mobile Computing 2021 (July 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/3031940.
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This paper conducts an in-depth analysis and research on the optimization of the labor resource management information platform through the Internet of Things (IoT) technology; through the collection, classification, and data search functions of this application system, it meets the supply and demand of professional talents within a certain enterprise. At the same time, it also realizes the curriculum training application on improving the skills and literacy of the employees of a certain enterprise, and it can learn the enterprise curriculum training from the comments of the employees on the enterprise curriculum. The effect of the enterprise course training can be learned from the comments of the employees on the enterprise course, providing an important reference basis for the future revision of the enterprise course training content. The performance of the participants in the training also has objective data for reference, so that the situation will not be disconnected from reality, and the interaction between enterprise management and employees can achieve a balanced effect. The goal of this workforce resource management system is to create a systematic workforce resource management platform for professional talents and help enterprises achieve the goal of speeding up and increasing efficiency. The system interface provided by the third party is used for horizontal data expansion to realize the sharing of basic information or video data as well as system expansion to realize real-time monitoring and management of project works. The cloud platform realizes efficient management and scientific application of construction site projects by construction management departments, which better solves the current problem of lack of supervision at construction sites.
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Hidayatulloh, Syarif, and Wahyudin Wahyudin. "Perancangan Wide Area Network (WAN) Dengan Teknologi Virtual Private Network (VPN)." Jurnal Teknik Komputer 5, no. 1 (February 6, 2019): 7–14. http://dx.doi.org/10.31294/jtk.v5i1.4552.
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The use of information technology and its use in collecting and processing data into information that is useful in decision making will play a role in determining the success of an organization or company in the future. This happened to PT. Jasa Cendekia Indonesia, the computer network that is owned has not met the needs of its employees. A good computer network is one that can serve sharing resources, data security, resources more efficiently and up-to-date information. Basically if a company can hold a computer network that serves the above for employees, of course it will make it easier for employees to do the work and improve the standards of the company itself. The proposed network built by the author for PT. Indonesian Scholar Services is a computer network built with Virtual Private Network technology. Because companies that have communication between the head office and branches that are good and safe, will be the capital for their companies in facing challenges in the era of globalization. Communication that is connected to a fast and secure computer network will make it easier for a company to supervise the activities of its company.
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Alhawary, Faleh Abdelgader, and Hanadi Al-Zegaier. "The Successful Implementation of Knowledge Management Processes: The Role of Human Resource Systems "An Empirical Study in the Jordanian Mobile Telecommunication Companies"." Journal of Information & Knowledge Management 08, no. 02 (June 2009): 159–73. http://dx.doi.org/10.1142/s0219649209002300.
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Knowledge is a resource that is valuable to an organisation's ability to innovate and compete. It exists within the individual employees, and also in a composite sense within the organisation. Therefore, it is important that organisations find a way to tap into this knowledge and effectively manage knowledge processes in order to preserve and expand its core competencies to maintain a sustainable competitive advantage. The purpose of this study is to examine the impact of human resource systems (HR planning System, HR Training System, HR Reward System, HR recruitment System) on the successful implementation of knowledge management processes (acquisition, application, sharing). Respondents consist of top/senior managers, human resource managers, and heads of departments in the Jordanian mobile telecommunication companies. A questionnaire survey was developed to collect data from respondents, a sample of 150 were selected according to a simple random sampling technique. Different statistical tools were used to test study hypotheses. The study findings shows that overall human resource systems have a significant impact on knowledge management processes. Based on the findings, the study suggest future research which can seek an enhanced understanding of the relationship of HRS with knowledge management processes in other sectors and other countries as well, since cultural differences exist among organisations, which influence employee perceptions regarding knowledge management processes. This study has implications for HR managers or decision-makers to create an organisational environment that encourages employee empowerment, integration and socialisation by eliminating all forms of barriers and red tapes, which can allow people to participate for new opportunities and foster a positive social interaction culture before introducing knowledge management initiatives.
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Turygina, L. "NETWORK INTERACTION AS A MECHANISM FOR DEVELOPING THE HUMAN RESOURCES OF CULTURAL INSTITUTIONS." EurasianUnionScientists 1, no. 11(80) (December 14, 2020): 38–39. http://dx.doi.org/10.31618/esu.2413-9335.2020.1.80.1095.
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The article presents the results of the analysis of the possibilities of using network interaction in solving the problems of developing the personnel potential of cultural institutions. There is a weak development of the topic andthe need to extrapolate its content from pedagogical practice. The possibilities of using network forms of advanced training in relation to employees of cultural institutions are described.
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Makeri, Yakubu Ajiji. "The Effectiveness of Cybersecurity Compliance in a Corporate Organization in Nigeria." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 6 (June 11, 2019): 16–19. http://dx.doi.org/10.17762/ijritcc.v7i6.5312.
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The complexity and growth also create asymmetries between attackers and their targets, and incentives that drive underinvestment in cybersecurity The Digital technologies have transformed how people socialize, shop, interact with government and do business. The World Wide Web is of made amounts of information instantly available. The smartphones have put our fingertips everywhere we go it an improvement on effectiveness cybersecurity training for end users of systems and offers suggestions about and how topManagement leaders can improve on trainingto effectively combat cybersecurity threats at the organizations. Is imperative to achieve higher end-user cybersecurity compliance; practice is accepted, as a means to increase compliance behavior in any organization. The Training can influence compliance by one or more of three causal pathways: by increasing cybersecurity awareness, by increasing cybersecurity proficiency (i.e., improve cybersecurity skills) and by raising cybersecurity self-efficacy. This includes an extensive review of the cybersecurity policies and competencies that are the basis for training needs analysis, setting learning goals, and practical training. This paper discusses opportunities for human resource (HR) practitioners and industrial and organizational (I-O) psychologists, and informationtechnology (IT) specialists, and to integrate their skills and enhance the capabilities of organizations to counteract cybersecurity threats. AnyOrganizations cannot achieve their cybersecurity goalson workers alone, so all employees who use computer networks must be trained on the skill and policies related to cybersecurity.
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Hasgall, Alon, and Snunith Shoham. "Digital social network technology and the complex organizational systems." VINE 37, no. 2 (June 26, 2007): 180–91. http://dx.doi.org/10.1108/03055720710759955.
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PurposeIn a competitive business environment, organizations must leverage their resources efficiently in order to provide system‐wide solutions and maintain the standards all customers expect. To do so, the resources must be integrated; however, the integration of information and resources within organizations has thus far not produced satisfactory results. In contrast, it has been found that efficient, ongoing and timely transfer of information is conducted over the internet. This research seeks to examine whether the use of internet technology within organizations can indeed enhance and streamline the ability of employees to function as fractals in complex organizations.Design/methodology/approachThe research is a qualitative study, allowing for the examination of behavior in the organizational reality as is, by analyzing interviews and observations of over 60 employees in different organizations.FindingsIt is found that the ability of a digital social network to create immediate system‐wide solutions, together with a management approach that transforms the organization into a complex adaptive system, allows employees to behave as fractals – i.e. to share applied‐knowledge, to take responsibility for performance and management of the processes, to update their superiors, and to develop self‐management abilities at the local level.Originality/valueSocial networks in organizations should be viewed as a shared “knowledge” system. Use of the network is “natural” and less rational and synchronized up front. However, it must be backed by a relevant management culture that enables all employees to serve as fractals in a complex adaptive system. In this manner, employees can contribute personally to work processes, determine their needs, and receive credit.
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Wei, Dan Dan, and Le Xing Qiu. "Personalized Resources Recommendation System Design for Teachers Training." Applied Mechanics and Materials 644-650 (September 2014): 5765–68. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.5765.
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With the integration of computer technology, network technology and mobile communication technology continuously, the formation of personalized resource recommendation has become possible. Based on teachers' demand investigation and analysis, this paper analyzes the teacher system architecture, personalized resource recommendation and service safeguard mechanism.
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Gara Bach Ouerdian, Emna, and Nizar Mansour. "The relationship of social capital with objective career success: the case of Tunisian bankers." Journal of Management Development 38, no. 2 (March 4, 2019): 74–86. http://dx.doi.org/10.1108/jmd-09-2018-0257.
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PurposeAlthough much research has investigated the impact of social capital on objective career success, the process through which this relation is established remains under-explored. In addition, studies conducted in the Middle East and North Africa region are scarce. The purpose of this paper is to examine and potentially bridge these gaps.Design/methodology/approachData were collected via survey from 348 Tunisian bankers. Path analysis using AMOS was used to explore the relationships between mentoring received, network resources training and development and objective career success. For testing the mediating hypotheses, the authors employed bootstrapping.FindingsResults support the conjecture that social capital is useful for career success. The authors found that when the employees receive mentoring, they seem to develop more instrumental network resources, and consequently they have wider access to training and development, which, in turn, will be related to better promotion outcomes. However, expressive network resources were not related to objective career success, and training and development did not mediate the relationship between these network resources and career success.Originality/valueTo the authors’ knowledge, this is one of the first studies to explore the relationship between social capital and objective career success in the Tunisian context. This paper also reveals the mediating role of training and development in the above relationship. These findings add to the cross-cultural literature on careers.
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Chen, Liying. "The Application of Computer Management System in Physical Education Teaching." E3S Web of Conferences 275 (2021): 03016. http://dx.doi.org/10.1051/e3sconf/202127503016.
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School physical education(PE) is an indispensable part of school education, which plays an important and irreplaceable role in training builders of socialist cause with all-round development of morality, intelligence and sports. Sports network teaching management greatly improves the efficiency and efficiency of school sports teaching, which is a great change of the traditional mode., it provides a solid foundation for the establishment of Sports Network Teaching in Colleges and universities(CAU). This paper mainly studies the application of computer management system(CMS) in physical education teaching(PET). This paper studies and analyzes the method of university teaching computer management resources integration, studies the architecture of sports teaching CMS from four aspects of computing resources, storage resources, backup resources and network system, and uses ant colony algorithm to design and use sports teaching CMS. This paper also uses charts to analyze students’ attitude towards the use of CMS in PET, and the proportion of CMS in PET. The experimental results show that in the CMS of PET, the computing resources account for 38.33%, the storage resources account for 31.76%, the backup resources account for 14.62%, and the network system account for 15.29%.
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Li Gaofen. "Based on Artificial Neural Network in the Training of Human Resources Performance Evaluation Analysis." International Journal of Digital Content Technology and its Applications 7, no. 6 (March 31, 2013): 319–27. http://dx.doi.org/10.4156/jdcta.vol7.issue6.36.
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Thien, Duy Dang Pham, Karlheinz Kautz, Siddhi Pittayachawan, and Vince Bruno. "A Canonical Action Research Approach to the Effective Diffusion of Information Security with Social Network Analysis." International Journal of Systems and Society 4, no. 2 (July 2017): 22–43. http://dx.doi.org/10.4018/ijss.2017070103.
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As modern organisations are using strategic information systems as their competitive advantage, the management of information security (IS) is regarded as a top priority. However, technical measures are no longer sufficient for protecting IS, and the prevalence of centralised IS controls and top-down approach in IS management are challenged by the dynamic socio-organisational environment. In this article, a canonical action research (CAR) project discusses the use of social network analysis (SNA) methods to design and implement a cascading IS training/diffusion, which leveraged the social dynamics in the workplace to enhance the IS-related interactions between the employees in a large construction organisation in Southeast Asia. Through the enhanced IS interactions, which involved the employees' provisions of IS resources and IS influence, results indicated an improvement in the employees' attitudes towards IS. The research outcomes advocated the effective use of SNA methods, in combination with the CAR approach, which included the network metrics and means to select the suitable champions for the diffusion of IS, as well as to measure the diffusion effectiveness. Future directions to develop new IS-related network theories and apply SNA methods to study other IS concepts are also discussed.
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Kalawilapathirage, Hansani, Olufemi Omisakin, and Susan Zeidan. "A Data Analytic Approach of Job Satisfaction: A Case Study on Airline Industry." Journal of Information & Knowledge Management 18, no. 01 (March 2019): 1950003. http://dx.doi.org/10.1142/s0219649219500035.
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Intense competition has made it critical for airlines to retain its highly capable staff by ensuring the highest job satisfaction of its employees. This competition has resulted from the emergence of budget airlines focussed on a niche market. To provide a differentiated passenger experience whilst flying with airlines, the management should ensure that all the staff, including ground level and cabin crew, who are the initial contact point with customers are highly satisfied in terms of their job roles. The study evaluates human resource (HR) factors affecting job satisfaction with a given (anonymous) airline. A detailed study and analysis of major factors contributing to job satisfaction in the said airline was carried out. In analysing the relationship and current level of job satisfaction, the study uses a quantitative approach, with primary data obtained from questionnaires completed by employees in one of the airlines. Further, the study has identified independent variables as being financial rewards and recognition, training and development, and work environment. Statistical tools, such as correlation and regression analysis, are used to evaluate the responses from questionnaires and to provide significance of the independent variables contributing to job satisfaction.
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Zhao, Juan. "English Grammar Discrimination Training Network Model and Search Filtering." Complexity 2021 (May 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/5528682.
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The statistics-based method ignores the semantic constraints in the English grammar area branch training model and is unable to identify the orientation information effectively. This paper systematically discusses the close relationship between English grammar area branch training model filtering, English grammar area branch training model retrieval, and machine learning. By analyzing the role of the situation in the understanding of the English grammar area branch training model, the relationship between the English grammar area branch training model and situation model and the correlation between the features of the English grammar area branch training model and situation model are determined, and then, a set of filtering methods for the English grammar area branch training model are proposed. At present, there are few research studies on bias filtering, and the method of thematic filtering is generally used, which has poor effect. This paper makes full use of the domain knowledge and adopts the semantic pattern analysis technology to establish a wealth of semantic analysis resources, including various dictionaries, rules, and weight representation, so as to effectively filter the inclined English grammar area branch training model. The introduction of semantic data sources solves the problem of data sparsity and cold start in the traditional collaborative filtering system. In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. This paper proposes a search and filter scheme based on the orientation of the training model in English grammar area, elaborates on the details, constructs a whole set of function structure from representation to weight, and gives the experimental results, which prove that the system has a good filtering effect and is fast. Compared with the traditional statistical methods, the results are satisfactory.
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Divini, Aikaterini, and Nikos Schiniotakis. "Performance and profile: a twofold bank profitability riddle." Team Performance Management 21, no. 1/2 (March 9, 2015): 51–64. http://dx.doi.org/10.1108/tpm-02-2014-0009.
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Purpose – The purpose of this research is to examine whether a relation between employees’ profile and bank branch profitability holds in the Greek banking sector. Employees’ profile may include education, training, work experience, age and place of origin/living. In addition, it is examined whether high employee performance is related to profitable bank branches. Design/methodology/approach – The case of a Greek cooperative bank is selected with a network of 49 branches and a sample of 258 bank branch employees. Secondary data are collected from the bank’s human resources department database and electronic archives in reference to the year 2011. The methodology used in the research includes descriptive analysis, discriminant analysis and binominal logistic regression analysis. Findings – There are specific employees’ profile features relating to efficient performance that affect bank branch profitability. The findings highlight the importance of recruiting in accordance to a bank’s skills requirements and the significant role of alternative training programs, motivation and performance evaluation systems in augmenting a bank’s overall profitability. Originality/value – This research is the first attempt to combine and connect particular employee characteristics with efficient performance. It is also the first time that this particular bank, sample and data are examined and analyzed to serve the purpose of this research.
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Dai, Weihuang, Yi Hu, Zijiang Zhu, and Xiaofang Liao. "Human Resource Petri Net Allocation Model Based on Artificial Intelligence and Neural Network." Mobile Information Systems 2021 (August 27, 2021): 1–13. http://dx.doi.org/10.1155/2021/5988742.
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The reasonable allocation and use of human resources is an important content in the process of complex system analysis and design. This paper studies the human resource allocation model of Petri net based on artificial intelligence and neural network. In this paper, combined with the characteristics of human resource scheduling, human resource mobility, concurrency, and obvious classification characteristics, the human resource allocation model based on Petri net is implemented. In this paper, the model is trained with the python version of human resource analysis data set. The training parameters are 100, the error coefficient is 0.001, and the learning speed is 0.01. First, the coding rules of human resource data are established. Then, the parameters are input into the model, and the human resource data are trained in the model. Finally, the results of the model output layer are analyzed. The research study shows that the average prediction accuracy of this model is 78.85%. Model training requires the addition of 25 neurons for every 0.01 increase to improve the accuracy of predicting dynamic data of human resources. If the accuracy rate exceeds 75%, the increase in the number of neurons cannot be compensated for by the increase in the accuracy rate, but it is most efficient when the amount of data for human resource scheduling is 2000 to 4000. Therefore, this system can effectively allocate small- and medium-sized human resources and has a high accuracy.
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Liu, Yuanyang, Gautam Pant, and Olivia R. L. Sheng. "Predicting Labor Market Competition: Leveraging Interfirm Network and Employee Skills." Information Systems Research 31, no. 4 (December 2020): 1443–66. http://dx.doi.org/10.1287/isre.2020.0954.
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Human capital is a key component of the knowledge economy. Firms compete not only for consumers in the product market but also for human capital in the labor market. In this study, we perform an interfirm labor market competitor analysis using the online profiles of more than 89,000 employees and their career histories that span more than 3,000 public firms. Using these profiles, we characterize firms through the granular skill distribution of their employees. Also, using employee migrations across firms, we derive and analyze a human capital flow (HCF) network. Such information allows us to measure the interfirm human capital overlap in terms of similarity in their employees’ skills and HCF network structure. We show that our proposed human capital overlap metrics have superior predictive power over conventional firm-level measures in predicting future labor market competitors. We further demonstrate how our proposed metrics and the prediction framework can be incorporated into a comprehensive two-dimensional competitor analysis that includes both product and labor overlap between firms. By evaluating interfirm relationships in the product and labor markets simultaneously, this two-dimensional competitor analysis framework can help managers make strategic decisions beyond human resources, such as product development and customer relationship management.
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Zheng, Jiafeng, and Ruijun Ma. "Analysis of Enterprise Human Resources Demand Forecast Model Based on SOM Neural Network." Computational Intelligence and Neuroscience 2021 (June 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/6596548.
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Human resource planning is the prerequisite of human resource management, and the basic work of human resource planning is to predict human resource demand. Scientific and reasonable human resource demand forecasting results can provide important data support for enterprise human resource planning and strategic decision-making so that human resources management can play a better role in the realization of corporate goals. Because human resource demand is affected by many factors, there is a high degree of nonlinearity and uncertainty between each factor and personnel demand, as well as the incompleteness and inaccuracy of corporate human resource data. In this paper, the self-organizing feature mapping (SOM) artificial neural network prediction model is selected as the prediction model, and the input and output process of sample data is converted into the optimal solution process of the nonlinear function. In the application of the model, the human resource demand prediction index system is used as the input of the SOM neural network and the total number of employees in the enterprise is used as the output so that the problem of nonlinear fitting between human resource demand-influencing factors and human resource demand can be solved. Finally, through the empirical analysis of the enterprise, the model forecasting process is explained and the human resource demand forecast is realized.
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Wang, Qingjun, and Peng Lu. "Research on Application of Artificial Intelligence in Computer Network Technology." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1959015. http://dx.doi.org/10.1142/s0218001419590158.
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With the continuous expansion of the application scope of computer network technology, various malicious attacks that exist in the Internet range have caused serious harm to computer users and network resources. This paper attempts to apply artificial intelligence (AI) to computer network technology and research on the application of AI in computing network technology. Designing an intrusion detection model based on improved back propagation (BP) neural network. By studying the attack principle, analyzing the characteristics of the attack method, extracting feature data, establishing feature sets, and using the agent technology as the supporting technology, the simulation experiment is used to prove the improvement effect of the system in terms of false alarm rate, convergence speed, and false negative rate, the rate reached 86.7%. The results show that this fast algorithm reduces the training time of the network, reduces the network size, improves the classification performance, and improves the intrusion detection rate.
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Qutub, Aseel, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani, and Hanan S. Alghamdi. "Prediction of Employee Attrition Using Machine Learning and Ensemble Methods." International Journal of Machine Learning and Computing 11, no. 2 (March 2021): 110–14. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1022.
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Employees are the most valuable resources for any organization. The cost associated with professional training, the developed loyalty over the years and the sensitivity of some organizational positions, all make it very essential to identify who might leave the organization. Many reasons can lead to employee attrition. In this paper, several machine learning models are developed to automatically and accurately predict employee attrition. IBM attrition dataset is used in this work to train and evaluate machine learning models; namely Decision Tree, Random Forest Regressor, Logistic Regressor, Adaboost Model, and Gradient Boosting Classifier models. The ultimate goal is to accurately detect attrition to help any company to improve different retention strategies on crucial employees and boost those employee satisfactions.
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Valenzuela-Fernández, Leslier M., Francisco-Javier Arroyo-Cañada, and Francisco Javier Villegas Pinuer. "How would the managementof human behavior variables influence customer-oriented management?" Kybernetes 49, no. 3 (May 30, 2019): 797–818. http://dx.doi.org/10.1108/k-07-2018-0376.
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Purpose Salesperson’s actions are critical in helping the firm develop customer value orientation and long-term relationship with profitable customers to achieve sustainable sales growth and profitability over time. The purpose of this paper is to examine the salespeople and service executives’ perceptions about the relevance of some human resource management variables and employees’ attitudes as key factors to develop a company’s customer value orientation. The authors tested whether the perceptions of role ambiguity, incentives policy and provided training (PT) had an impact on job involvement (JI), job satisfaction (JS), and consequently, on customer value orientation. Design/methodology/approach Research design was nested with data from 327 executives from medium and upper positions in Chilean companies. Findings The results show that while the perception of role ambiguity had an indirect negative impact on customer value orientation through JI, perception of PT level had a direct impact over and above the other variables. Research limitations/implications JS and JI are attitudinal variables, which companies try to encourage in their employees through different human resources, practices. Incentives and training are ways to develop favorable employees’ attitudes and improve their customer value orientation. With the research, companies could invest their resources in better and more effective practices to generate favorable attitudes toward customer value orientation. Originality/value Through structural equation modeling, the model shows the relevance in the perception of sales executives about the relationship of employees’ JI and customer value orientation. This commands to open the view of the customer value orientation management to include other attitudinal variables as JI.
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Romashkina, T. A. "General tendencies and priorities in the staff potential development in public libraries of Khabarovsk region and Republic of Belarus: the international cooperation experience." Bibliosphere, no. 2 (June 30, 2019): 52–58. http://dx.doi.org/10.20913/1815-3186-2019-2-52-58.
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Sociological research “Human resources of public libraries: modern requirements for professional activity and opportunities for its implementation” was carried out by the Far Eastern State Scientific Library. It was made on the base public libraries in Khabarovsk region within the framework of the partnership and cooperation agreement with the National Library of Belarus. A comparative analysis of the results of the survey of employees in both libraries allowed to identify the characteristic features of personnel problems of public libraries of the Khabarovsk region, to assess the existing the library system of training, to show the level of professional selfassessment of employees, their willingness to accept new knowledge, to determine the professional competence that the managers and ordinary employees of libraries that would like to improve. Despite the geographical remoteness of the Khabarovsk region and Belarus, the peculiarities of social and historical development of the territories, the differences in the structure and density of municipal libraries network, the number of staff, the analysis of the results of the survey of library specialists showed the presence of common personnel problems in the library industry, regardless of the region and its social and economic well-being: the aging of the main staff in libraries, the shortage of specialized human resources and a significant number of specialists in related fields in library industry.
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Romashkina, T. A. "General tendencies and priorities in the staff potential development in public libraries of Khabarovsk region and Republic of Belarus: the international cooperation experience." Bibliosphere, no. 2 (June 30, 2019): 59–64. http://dx.doi.org/10.20913/1815-3186-2019-2-59-64.
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Sociological research “Human resources of public libraries: modern requirements for professional activity and opportunities for its implementation” was carried out by the Far Eastern State Scientific Library. It was made on the base public libraries in Khabarovsk region within the framework of the partnership and cooperation agreement with the National Library of Belarus. A comparative analysis of the results of the survey of employees in both libraries allowed to identify the characteristic features of personnel problems of public libraries of the Khabarovsk region, to assess the existing the library system of training, to show the level of professional selfassessment of employees, their willingness to accept new knowledge, to determine the professional competence that the managers and ordinary employees of libraries that would like to improve. Despite the geographical remoteness of the Khabarovsk region and Belarus, the peculiarities of social and historical development of the territories, the differences in the structure and density of municipal libraries network, the number of staff, the analysis of the results of the survey of library specialists showed the presence of common personnel problems in the library industry, regardless of the region and its social and economic well-being: the aging of the main staff in libraries, the shortage of specialized human resources and a significant number of specialists in related fields in library industry.
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Amin, Muhammad, and Muhammad Sabir Ramadhan. "Pelatihan Perakitan Komputer Pada CV. Rifanta Tanjungbalai." Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal 4, no. 3 (September 16, 2021): 307–12. http://dx.doi.org/10.33330/jurdimas.v4i3.1252.
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Abstrack : The training carried out at CV. Rifanta Tanjungbalai is a tridharma of community service in understanding computer assembly for employees of CV. Rifanta Tanjungbalai in installing computer hardware and software, where the quality of employees is very influential on human resources that can be used as a benchmark in mastery of assembly. As is known CV. Rifanta Tanjungbalai is engaged in trade and services and this is a benchmark for employees in knowing or understanding the main parts of a computer. The computer assembly training activities are presented in the form of explanations, video screenings, discussions and practice in the field. The benefits derived from this activity include being able to assemble computers and computer parts, so that employees work according to their field of expertise and can master proper assembly techniques. It shows the employees CV. Rifanta Tanjungbalai is very proficient in recognizing hardware and software, so that consumers are satisfied with the performance of employees.Keyword : computer assembly; hardware; softwareAbstrak : Pelatihan yang dilaksanakan di CV. Rifanta Tanjungbalai merupakan tridharma pengabdian kepada masyarakat dalam pemahaman perakitan komputer untuk karyawan CV. Rifanta Tanjungbalai dalam melakukan instalasi perangkat keras dan perangkat lunak komputer, dimana kualitas karyawan sangat berpengaruh pada sumber daya manusia yang dapat dijadikan sebagai tolak ukur dalam penguasaan perakitan. Sebagaimana diketahui CV. Rifanta Tanjungbalai bergerak dalam perdagangan dan jasa dan ini merupakan tolak ukur karyawan dalam mengetahui atau pun memahami tentang bagian-bagian utama komputer. Kegiatan pelatihan perakitan komputer tersebut disajikan dalam bentuk penjelasan, pemuataran video, diskusi dan praktek dilapangan. Manfaat yang diperoleh dari kegiatan ini anatara lain dapat melakukan perakitan komputer dan bagian-bagian komputer, sehingga karyawan bekerja sesuai dengan bidang keahlian dan dapat menguasai teknik merakit secara tepat. Ini menunjukkan para karyawan CV. Rifanta Tanjungbalai sangat mahir dalam mengenal perangkat keras dan perangkat lunak, sehingga konsumen merasa puas dengan kinerja para karyawan.Keyword : perakitan komputer; perangkat keras; perangkat lunak
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Strader, Troy J., J. Royce Fichtner, Suzanne R. Clayton, and Lou Ann Simpson. "The Impact of Context on Employee Perceptions of Acceptable Non-Work Related Computing." International Journal of Technoethics 2, no. 2 (April 2011): 30–44. http://dx.doi.org/10.4018/jte.2011040103.
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Employees have access to a wide range of computer-related resources at work, and often these resources are used for non-work related personal activities. In this study, the authors address the relationship between employee’s utilitarian ethical orientation, the factors that create the context that influences their ethical perceptions, and their overall perceptions regarding the level of acceptability for 14 different non-work related computing activities. The authors find that time and monetary cost associated with an activity has a negative relationship to perceived acceptability. Results indicate that contextual variables, such as an employee’s supervisory or non-supervisory role, opportunity, computer self-efficacy, and whether or not an organization has computer use policies, training, and monitoring, influence individual ethical perceptions. Implications and conclusions are discussed for organizations and future research.
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Agustin, Rina Dian, Muhammad Firdaus, and Nanda Widaninggar. "Determinants of Accounting Information System in PT. Indomarco Adi Prima, Jember Branch." International Journal of Environmental, Sustainability, and Social Science 1, no. 1 (April 1, 2020): 70–76. http://dx.doi.org/10.38142/ijesss.v1i1.48.
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This study aims to analyze the impact of Education and Training Programs, Involvement of System Users, and Human Resources (HR) Competence on the Quality of Accounting Information Systems at PT. Indomarco Adi Prima Jember Branch, since there were ineffective process in selling application, by the late of manager approval and the network problem. The population in this study are all employees who use Information Systems at PT. Indomarco Adi Prima Jember Branch. The analytical method in this study uses the validity and reliability test, the Classic Assumption Test is a normality test, a multicollinearity test, and the heteroscedasticity test. Multiple Linear Regression Analysis, hypothesis test, using t test and coefficient of determination (R2). The results showed that Education and Training Programs, System Users Involvement, and HR Competencies significantly impact the Quality of Accounting Information Systems, and the coefficient of determination (R2) of all independent variables strongly explained the dependent variable.
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Patil, Sachin K., and R. Kant. "A Fuzzy DEMATEL Method to Identify Critical Success Factors of Knowledge Management Adoption in Supply Chain." Journal of Information & Knowledge Management 12, no. 03 (August 25, 2013): 1350019. http://dx.doi.org/10.1142/s0219649213500196.
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In globalisation of business, Knowledge Management (KM) plays an important role in Supply Chain (SC) to create, build and maintain competitive advantage through utilisation of knowledge and through collaborative practices. Literature review have suggested the performance of KM adoption in SC may be affected by various influencing factors but it is always difficult for the practitioners to improve all aspects at the same time. The aim of this study is to identify Critical Success Factors (CSFs) of KM adoption in SC. This study presents a favourable method combining fuzzy set theory and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to segment the critical factors for successful KM adoption in SC. The empirical case study analysis of an Indian hydraulic valve manufacturing organisation is conducted to illustrate the use of the proposed framework for identifying the CSFs of KM adoption in SC. According to the results of the empirical study, six CSFs of KM adoption in SC are identified out of 25 influencing factors, these are top management support, communication and collaboration techniques, employee involvement, employee training and education, communication among the SC members and trustworthy teamwork to exchange knowledge within SC which will help to improve effectiveness and efficiency of KM adoption in SC. The decision makers can apply a phased implementation of these CSFs to ensure the effective KM adoption in SC under the constraints of available resources. This proposed method provides a more accurate, effective and systematic decision support tool for identifying CSFs of KM adoption in SC.
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Hocine, Nadia. "Agent-based access control framework for enterprise content management." Multiagent and Grid Systems 17, no. 2 (August 23, 2021): 129–43. http://dx.doi.org/10.3233/mgs-210346.
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Telework is an important alternative to work that seeks to enhance employees’ safety and well-being while reducing the company costs. Employees can work anytime, any where and under high mobility conditions using new devices. Therefore, the access control of remote exchanges of Enterprise Content Management systems (ECM) have to take into consideration the diversity of users’ devices and context conditions in a telework open network. Different access control models were proposed in the literature to deal with the dynamic nature of users’ context and devices. However, most access control models rely on a centralized management of permissions by an authorization entity which can reduce its performance with the increase of number of users and requests in an open network. Moreover, they often depend on the administrator’s intervention to add new devices’ authorization and to set permissions on resources. In this paper, we suggest a distributed management of access control for telework open networks that focuses on an agent-based access control framework. The framework uses a multi-level rule engine to dynamically generate policies. We conducted a usability test and an experiment to evaluate the security performance of the proposed framework. The result of the experiment shows that the ability to resist deny of service attacks over time increased in the proposed distributed access control management compared with the centralized approach.
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Stonehill, Robert M., and Lynn Smarte. "ERIC in Cyberspace: Expanding Access and Services." Education Libraries 18, no. 3 (September 5, 2017): 12. http://dx.doi.org/10.26443/el.v18i3.71.
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In recent years, the Educational Resources Information Center (ERIC) system has undergone tremendous changes in the kinds of products and services it offers and the methods by which users can access them. AskERIC, a computer network-based question-answering service and virtual library, exemplifies these changes. This article describes AskERIC, other ERIC gopher sites, the National Parent Information Network, ERIC listserv activity on the Internet, and ERIC 's offerings on commercial online services. It also lists resources for librarians who do training sessions on ERIC and sketches ERIC's future direction.
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Kim, Kwihoon, Joo-Hyung Lee, Hyun-Kyo Lim, Se-Won Oh, and Youn-Hee Han. "Deep RNN-based network traffic classification scheme in edge computing system." Computer Science and Information Systems, no. 00 (2021): 38. http://dx.doi.org/10.2298/csis200424038k.
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This paper proposes a deep recurrent neural network (RNN)-based traffic classification scheme (deep RNN-TCS) for classifying applications from traffic patterns in a hybrid edge computing and cloud computing architecture.We can also classify traffic from a cloud server, but there will be a time delay when packets transfer to the server. Therefore, the traffic classification is possible almost in realtime when it performed on edge computing nodes. However, training takes a lot of time and needs a lot of computing resources to learn traffic patterns. Therefore, it is efficient to perform training on cloud server and to perform serving on edge computing node. Here, a cloud server collects and stores output labels corresponding to the application packets. Then, it trains those data and generates inferred functions. An edge computation node receives the inferred functions and executes classification. Compared to deep packet inspection (DPI), which requires the periodic verification of existing signatures and updated application information (e.g., versions adding new features), the proposed scheme can classify the applications in an automated manner. Also, deep learning can automatically make classifiers for traffic classification when there is enough data. Specifically, input features and output labels are defined for classification as traffic packets and target applications, respectively, which are created as two-dimensional images. As our training data, traffic packets measured at Universitat Politecnica de Catalunya Barcelonatech were utilized. Accordingly, the proposed deep RNN-TCS is implemented using a deep long short-term memory system. Through extensive simulation-based experiments, it is verified that the proposed deep RNN-TCS achieves almost 5% improvement in accuracy (96% accuracy) while operating 500 times faster (elapsed time) compared to the conventional scheme.
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Zhang, Yaming, Yaya Hamadou Koura, and Yanyuan Su. "MLP Modeling and Prediction of IP Subnet Packets Forwarding Performance." International Journal of Computational Intelligence and Applications 18, no. 01 (March 2019): 1950006. http://dx.doi.org/10.1142/s1469026819500068.
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In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.
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Putranto, Agus. "Perancangan Training dengan E-Learning pada Perusahaan Manufacture." ComTech: Computer, Mathematics and Engineering Applications 2, no. 1 (June 1, 2011): 317. http://dx.doi.org/10.21512/comtech.v2i1.2758.
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The use of information technology has been very widely in many fields including industry. Along with that, the need for a concept and mechanism of IT-based learning becomes inevitable. The purpose of this paper is to analyze the training process and identify any related problems occurred at PT.Suzuki Indomobil Motor. The problems are about the limited training time, lack of material distribution media and consultations out of training time. E-Learning is a concept of electronic application use to support learning using the internet and computer network. This concept influences the process of conventional education transformation to digital form, both in content and system. The learning system will be replaced with a web-based training media. The method used is the Object Oriented Analysis Design, which begins with a depiction of rich pictures to the Deployment diagram. This system is expected to meet the needs of employees while joining the training process, so that they will obtain excellent learning and achieve the company objectives.
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Seong, Yongho, Changhyup Park, Jinho Choi, and Ilsik Jang. "Surrogate Model with a Deep Neural Network to Evaluate Gas–Liquid Flow in a Horizontal Pipe." Energies 13, no. 4 (February 21, 2020): 968. http://dx.doi.org/10.3390/en13040968.
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This study developed a data-driven surrogate model based on a deep neural network (DNN) to evaluate gas–liquid multiphase flow occurring in horizontal pipes. It estimated the liquid holdup and pressure gradient under a slip condition and different flow patterns, i.e., slug, annular, stratified flow, etc. The inputs of the surrogate modelling were related to the fluid properties and the dynamic data, e.g., superficial velocities at the inlet, while the outputs were the liquid holdup and pressure gradient observed at the outlet. The case study determined the optimal number of hidden neurons by considering the processing time and the validation error. A total of 350 experimental data were used: 279 for supervised training, 31 for validating the training performance, and 40 unknown data, not used in training and validation, were examined to forecast the liquid holdup and pressure gradient. The liquid holdups were estimated within less than 8.08% of the mean absolute percentage error, while the error of the pressure gradient was 23.76%. The R2 values confirmed the reliability of the developed model, showing 0.89 for liquid holdups and 0.98 for pressure gradients. The DNN-based surrogate model can be applicable to estimate liquid holdup and pressure gradients in a more realistic manner with a small amount of computating resources.
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Rahmanidoust, Mohammad, and Jianguo Zheng. "Evaluation of Factors Affecting Employees' Performance Using Artificial Neural Networks Algorithm: The Case Study of Fajr Jam." International Business Research 12, no. 10 (September 26, 2019): 86. http://dx.doi.org/10.5539/ibr.v12n10p86.
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Human resources are the most valuable assets of any organization. Therefore, human resources performance has the greatest impact on the organization's performance and its ability to operate. Many factors affect the performance of employees in organizations. In this research, we seek to evaluate the factors affecting the performance of Fajr Jam refinery employees. For this purpose, firstly, the literature of the research, the indicators affecting the performance of employees were identified and the conceptual model of the problem was formed. Then, the required data were collected using a standard questionnaire based on the conceptual model of the problem among employees of FJG Company. After assessing the validity and reliability of the collected data, it is time to evaluate the performance of the indicators. For this purpose, an artificial neural network algorithm was used to estimate the efficiency boundary values. After calculating the efficiency values in the presence of all the indices, each indices were eliminated from the conceptual model and again the efficiency values were estimated. Now, by comparing the performance statistics in the state before and after the removal of each indicator from the conceptual model, the degree and the mode of its effect are determined. The results of this study indicate that the "payroll" indicators, "environmental conditions" and "reporting culture" are the strengths of the system under review, and are now at an appropriate level. Also, the results indicate a negative impact on the indicators of "awareness", "system planning and preparation for critical situations," "amenities," "training," and "job security" in the system under review.
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Muhammad, Arif Wirawan, Cik Feresa Mohd Foozy, and Kamaruddin Malik bin Mohammed. "Multischeme feedforward artificial neural network architecture for DDoS attack detection." Bulletin of Electrical Engineering and Informatics 10, no. 1 (February 1, 2021): 458–65. http://dx.doi.org/10.11591/eei.v10i1.2383.
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Distributed denial of service attack classified as a structured attack to deplete server, sourced from various bot computers to form a massive data flow. Distributed denial of service (DDoS) data flows behave as regular data packet flows, so it is challenging to distinguish between the two. Data packet classification to detect DDoS attacks is one solution to prevent DDoS attacks and to maintain server resources maintained. The machine learning method especially artificial neural network (ANN), is one of the effective ways to detect the flow of data packets in a computer network. Based on the research that has carried out, it concluded that ANN with hidden layer architecture that contains neuron twice as neuron on the input layer (2n) produces a stable detection accuracy value on Quasi-Newton, Scaled-Conjugate and Resilient-Propagation training functions. Based on the studies conducted, it concluded that ANN Architecture sufficiently affected the Scaled-Conjugate and Resilient-Propagation training functions, otherwise the Quasi-Newton training function. The best detection accuracy achieved from the experiment is 99.60%, 1.000 recall, 0.988 precision, and 0.993 f-measure using the Quasi-Newton training function with 6-(12)-2 neural network architecture.
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Cui, Shuqin, Mingyou Gao, Yang Xun, Sai-Fu Fung, Yujiao Tan, Yu Zhang, Chenghao Wang, Huanqing Wang, and You Xiong. "Research on the Structure and Characteristics of the Overall Social Network of Professional Athletes." Complexity 2021 (May 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/6484098.
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This study chooses Chinese athletes as the research object and constructs the overall network of its social support network and discussion network. From the micro-, meso-, and macrolevels of the social network structure, the structure and characteristics of the athlete’s overall social network are analyzed. Through research, we found that there is embeddedness, that is, the relevance, between society support networks, between society discussion networks, and between society support networks and society discussion networks. At the same time, in the athletes’ social support network and social discussion network, some athletes have no contact with other players; they have no “power” in the group as well, so it is difficult to obtain network resources. We also found that there are small-world characteristics in the social network of Chinese professional athletes. The above findings will provide a deeper understanding of the peculiarities of athlete groups and have certain practical significance for improving athletes’ daily training and life management conditions.
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Shinkevich, Alexey I., Tatiana V. Malysheva, Yulia V. Vertakova, and Vladimir A. Plotnikov. "Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling." Mathematics 9, no. 4 (February 6, 2021): 322. http://dx.doi.org/10.3390/math9040322.
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Improving the energy efficiency of chemical industries and increasing their environmental friendliness requires an assessment of the parameters of consumption and losses of energy resources. The aim of the study is to develop and test a method for solving the problem of optimizing the use of energy resources in chemical production based on the methodology of descriptive statistics and training of neural networks. Research methods: graphic and tabular tools for descriptive data analysis to study the dynamics of the structure of energy carriers and determine possible reserves for reducing their consumption; correlation analysis with the construction of scatter diagrams to identify the dependences of the range of limit values of electricity consumption on the average rate of energy consumption; a method for training neural networks to predict the optimal values of energy consumption; methods of mathematical optimization and standardization. The authors analyzed the trends in the energy intensity of chemical industries with an assessment of the degree of transformation of the structure of the energy portfolio and possible reserves for reducing the specific weight of electrical and thermal energy; determined the dynamics of energy losses at Russian industrial enterprises; established the correlation dependence of the range of limiting values of power consumption on the average rate of power consumption; determined the optimal limiting limits of the norms for the loss of electrical energy by the example of rubbers of solution polymerization. The results of the study can be used in the development of software complexes for intelligent energy systems that allow tracking the dynamics of consumption and losses of energy resources. Using the results allows you to determine the optimal parameters of energy consumption and identify reserves for improving energy efficiency.
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Zhang, Zuopeng (Justin), Wu He, Wenzhuo Li, and M'Hammed Abdous. "Cybersecurity awareness training programs: a cost–benefit analysis framework." Industrial Management & Data Systems 121, no. 3 (January 27, 2021): 613–36. http://dx.doi.org/10.1108/imds-08-2020-0462.
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PurposeEmployees must receive proper cybersecurity training so that they can recognize the threats to their organizations and take the appropriate actions to reduce cyber risks. However, many cybersecurity awareness training (CSAT) programs fall short due to their misaligned training focuses.Design/methodology/approachTo help organizations develop effective CSAT programs, we have developed a theoretical framework for conducting a cost–benefit analysis of those CSAT programs. We differentiate them into three types of CSAT programs (constant, complementary and compensatory) by their costs and into four types of CSAT programs (negligible, consistent, increasing and diminishing) by their benefits. Also, we investigate the impact of CSAT programs with different costs and the benefits on a company's optimal degree of security.FindingsOur findings indicate that the benefit of a CSAT program with different types of cost plays a disparate role in keeping, upgrading or lowering a company's existing security level. Ideally, a CSAT program should spend more of its expenses on training employees to deal with the security threats at a lower security level and to reduce more losses at a higher security level.Originality/valueOur model serves as a benchmark that will help organizations allocate resources toward the development of successful CSAT programs.
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Андрій Шуляк. "THE MODEL OF FORMATION OF FUTURE IT TEACHERS’ PREPARATION FOR THE USE OF EDUCATIONAL WEB-RESOURCES." Collection of Scientific Papers of Uman State Pedagogical University, no. 4 (September 4, 2020): 67–77. http://dx.doi.org/10.31499/2307-4906.4.2020.224100.
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The article reveals the structure of the model of forming future IT teachers’ preparation to use educational WEB-resources. Its component composition (blocks) is established. The methodological target block includes the purpose: formation of future IT teachers’ preparation to use educational WEB-resources, and the following approaches: informational, personality-centered, systemic, competence, technological, activity; also such principles: general pedagogical (accessibility, continuity, clarity, systematicity, sequence, scientific) and specific (effectiveness, dialogue, professional mobility, creativity, interactivity, multimedia), organizational and instrumental (stages: motivational-propaedeutic, technological-productive, organizational-methodical); content: traditional basic computer science courses; elective courses, special seminars with remote support, master classes, methodical seminars; professional courses and practices of methodical preparation of students; forms (traditional: lecture-press conference, lecture-conversation, seminar-discussion, colloquium, etc.; innovative: online-lecture, streaming video, slide lecture, video lecture, multimedia lectures, e-mail consultation, e-seminars, webinars, group projects on wiki technology, case technologies, forum, network interaction, network chat, joint blogging, local and network tutorials, educational portals, directories), methods: (projects, cooperative learning), case-study, game methods (business game); “E-portfolio”, round table, associative method; method of “falsification”, information resources, “reification”, demonstration examples, precedent, expediently selected tasks; training (using teleconferences; educational modeling), tools: modeling, educational, testing software; virtual labs software; reference information (legal) systems; automated training systems; electronic educational and methodical materials; expert training systems; intelligent educational systems; means of automation of professional activity, technologies (problem-based learning, individualized learning, developmental learning, differentiated learning, active learning, game learning), evaluation-effective (components and their indicators), levels (high, medium, low), result (preparation to use educational WEB-resources in professional activities).
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Cao, Jianfang, Chenyan Wu, Lichao Chen, Hongyan Cui, and Guoqing Feng. "An Improved Convolutional Neural Network Algorithm and Its Application in Multilabel Image Labeling." Computational Intelligence and Neuroscience 2019 (July 4, 2019): 1–12. http://dx.doi.org/10.1155/2019/2060796.
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In today’s society, image resources are everywhere, and the number of available images can be overwhelming. Determining how to rapidly and effectively query, retrieve, and organize image information has become a popular research topic, and automatic image annotation is the key to text-based image retrieval. If the semantic images with annotations are not balanced among the training samples, the low-frequency labeling accuracy can be poor. In this study, a dual-channel convolution neural network (DCCNN) was designed to improve the accuracy of automatic labeling. The model integrates two convolutional neural network (CNN) channels with different structures. One channel is used for training based on the low-frequency samples and increases the proportion of low-frequency samples in the model, and the other is used for training based on all training sets. In the labeling process, the outputs of the two channels are fused to obtain a labeling decision. We verified the proposed model on the Caltech-256, Pascal VOC 2007, and Pascal VOC 2012 standard datasets. On the Pascal VOC 2012 dataset, the proposed DCCNN model achieves an overall labeling accuracy of up to 93.4% after 100 training iterations: 8.9% higher than the CNN and 15% higher than the traditional method. A similar accuracy can be achieved by the CNN only after 2,500 training iterations. On the 50,000-image dataset from Caltech-256 and Pascal VOC 2012, the performance of the DCCNN is relatively stable; it achieves an average labeling accuracy above 93%. In contrast, the CNN reaches an accuracy of only 91% even after extended training. Furthermore, the proposed DCCNN achieves a labeling accuracy for low-frequency words approximately 10% higher than that of the CNN, which further verifies the reliability of the proposed model in this study.
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Sonang Sitohang, Sonang Sitohang. "FAKTOR-FAKTOR YANG MEMPENGARUHI INTERAKSI MANUSIA DENGAN KOMPUTER PADA SISTEM INFORMASI BERBASIS JARINGAN DI KANTOR BALAI DIKLAT INDAG SURABAYA." EKUITAS (Jurnal Ekonomi dan Keuangan) 9, no. 2 (January 1, 2007): 243. http://dx.doi.org/10.24034/j25485024.y2005.v9.i2.2380.
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This study examines the impact of interaction human factors with computer used by Local Area Network (LAN) at Regional Office Surabaya Industrial and Trade Training Service. The object of study are employees which worked by LAN Service office and used primary datas non probability sampling with analysis factor throught SPSS 10,0 windows program.The corelation matrix result shows of variable population; Barlets test of Sphericity (BTS) 0,000 proved, and Kayser Meyer Oklin (KMO) 0,671 > 0,50 means sample strength enough. By rotation from 35 variables become 22 variables with cumulative percentation 64,67. Tools is the dominant factor impact of human computer interaction by Local Area Network (LAN). The contribution of variable 10,187%, with eigen values 3,573 consist of; tools by loading variable 0,813, language with loading variable 0,745 and on line by 0,661 variable loading.
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Sitohang, Sonang. "FAKTOR-FAKTOR YANG MEMPENGARUHI INTERAKSI MANUSIA DENGAN KOMPUTER PADA SISTEM INFORMASI BERBASIS JARINGAN DI KANTOR BALAI DIKLAT INDAG SURABAYA." EKUITAS (Jurnal Ekonomi dan Keuangan) 9, no. 2 (September 18, 2018): 243–64. http://dx.doi.org/10.24034/j25485024.y2005.v9.i2.329.
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This study examines the impact of interaction human factors with computer used by Local Area Network (LAN) at Regional Office Surabaya Industrial and Trade Training Service. The object of study are employees which worked by LAN Service office and used primary datas non probability sampling with analysis factor throught SPSS 10,0 windows program.The corelation matrix result shows of variable population; Barlets test of Sphericity (BTS) 0,000 proved, and Kayser Meyer Oklin (KMO) 0,671 > 0,50 means sample strength enough. By rotation from 35 variables become 22 variables with cumulative percentation 64,67. Tools is the dominant factor impact of human computer interaction by Local Area Network (LAN). The contribution of variable 10,187%, with eigen values 3,573 consist of; tools by loading variable 0,813, language with loading variable 0,745 and on line by 0,661 variable loading.
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Jia, Yuan. "Research on Behavior Prediction Based on Deep Learning – Take Chengdu Economic Innovation Enterprise as an Example." E3S Web of Conferences 275 (2021): 03060. http://dx.doi.org/10.1051/e3sconf/202127503060.
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As the company’s workforce continues to expand, finding key features related to employee performance, quickly identifying high-potential employees, and predicting a rise in turnover are hot spots for research. This paper first analyzes the key characteristics of dataset performance and applies deep learning to identify high-potential employees and predicts the rise of separation. Compared with traditional machine learning methods, it can be seen that deep learning applications have a greater improvement. The aim is to provide a new idea for the intersection of human resources and computer AI. In the preparation of this article, a large number of companies’ desensitized employee data were collected in the real industry, including job, performance, education, and data communication between employees. Firstly, an interactive network-based employee topology map was established. According to the large amount of data collected from the real industry, the key characteristics of employee performance were analyzed, and a series of models were compared to traditional machine learning methods and deep learning calculation indicators, including accuracy, AUC and other indicators.
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Casciaro, Tiziana, and Miguel Sousa Lobo. "When Competence Is Irrelevant: The Role of Interpersonal Affect in Task-Related Ties." Administrative Science Quarterly 53, no. 4 (December 2008): 655–84. http://dx.doi.org/10.2189/asqu.53.4.655.
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This paper examines the role of a person's generalized positive or negative feelings toward someone (interpersonal affect) in task-related networks in organizations. We theorize that negative interpersonal affect renders task competence virtually irrelevant in a person's choice of a partner for task interactions but that positive interpersonal affect increases a person's reliance on competence as a criterion for choosing task partners, facilitating access to organizational resources relevant to the task. Using social psychological models of interpersonal perception and hierarchical Bayesian models, we find support for this theory in social network data from employees in three organizations: an entrepreneurial computer technology company, staff personnel at an academic institution, and employees in a large information technology corporation. The results suggest that competence may be irrelevant not just when outright dislike colors a relationship. Across organizational contexts and types of task-related interaction, people appear to need active liking to seek out the task resources of potential work partners and fully tap into the knowledge that resides in organizations. We discuss contributions of our study to research on the interplay of psychological and structural dimensions of organizational life.
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Cabrol-Bass, D., C. Cachet, C. Cleva, A. Eghbaldar, and T. P. Forrest. "Application pratique des réseaux neuro mimétiques aux données spectroscopiques (infrarouge et masse) en vue de l'élucidation structurale." Canadian Journal of Chemistry 73, no. 9 (September 1, 1995): 1412–26. http://dx.doi.org/10.1139/v95-176.
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In the last few years, intensive research by several groups has shown that neural networks can be used to analyse spectral data for structural elucidation, and that their performance approaches that of an expert in the field. The construction of such networks, their training and evaluation, requires large structural and spectral databases and significant computational resources and time. However, once the network has been completed it can be used very effectively for practical applications on an ordinary desktop computer. In this article we describe the methodology for creating such a network for infrared and mass spectra, and present a program for use on a personal computer, either connected to a spectrometer or independently. The program accepts data in ASCII format, both for the network description and for the spectral information. This approach permits the use of neural networks in an analytical laboratory with limited computational resources. Keywords: neural networks, infrared spectroscopy, mass spectroscopy, structure determination.
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Endale, Bedada, Abera Tullu, Hayoung Shi, and Beom-Soo Kang. "Robust Approach to Supervised Deep Neural Network Training for Real-Time Object Classification in Cluttered Indoor Environment." Applied Sciences 11, no. 15 (August 2, 2021): 7148. http://dx.doi.org/10.3390/app11157148.
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Unmanned aerial vehicles (UAVs) are being widely utilized for various missions: in both civilian and military sectors. Many of these missions demand UAVs to acquire artificial intelligence about the environments they are navigating in. This perception can be realized by training a computing machine to classify objects in the environment. One of the well known machine training approaches is supervised deep learning, which enables a machine to classify objects. However, supervised deep learning comes with huge sacrifice in terms of time and computational resources. Collecting big input data, pre-training processes, such as labeling training data, and the need for a high performance computer for training are some of the challenges that supervised deep learning poses. To address these setbacks, this study proposes mission specific input data augmentation techniques and the design of light-weight deep neural network architecture that is capable of real-time object classification. Semi-direct visual odometry (SVO) data of augmented images are used to train the network for object classification. Ten classes of 10,000 different images in each class were used as input data where 80% were for training the network and the remaining 20% were used for network validation. For the optimization of the designed deep neural network, a sequential gradient descent algorithm was implemented. This algorithm has the advantage of handling redundancy in the data more efficiently than other algorithms.
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Arnab, Sylvester, Ludmila Walaszczyk, Mark Lewis, and Sarah Kernaghan-Andrews. "Designing Mini-Games as Micro-Learning Resources for Professional Development in Multi-Cultural Organisations." Electronic Journal of e-Learning 19, no. 2 (April 21, 2021): 44–58. http://dx.doi.org/10.34190/ejel.19.2.2141.
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The need for self-directed learning for professional development drives an increase in the delivery of easy to use ‘just-in-time’ resources that respond to the often-dynamic workplace and work culture. This is especially important in the era of globalisation, when the number of employees, who are culturally diverse, increases each year. Most medium and large companies operate in an international environment, and this is due to the expansion of international enterprises with branches in various countries that requires cooperation with foreign clients, and the employment of foreign nationals in their companies. In order to guarantee the effectiveness of workings in companies, there is a need for continuous education in the aspect of the cultural diversity. This paper explores micro-learning, which focuses on delivering brevity through bite-sized learning units or short-term learning activities. Learning content in this case can take many forms, from text to interactive multimedia. These contents are often created on demand, which can sometimes be less contextualised and pedagogically informed. Based on a case study of the need for training on cultural risks in multi-cultural organisations, this paper focuses on the design of mini-games as playful learning resources for supporting an online learning platform that has been developed as a response to this training need. Fifteen mini-games have been developed to complement eight main topics related to cultural risks and to promote reflection, practice and the self-assessment of knowledge acquired through the platform. The main eight topics represent the risk areas identified that include cultural awareness, understanding different cultures, communication, learning styles, hierarchy, team-working, qualities in the working place, and stereotypes through a survey carried out with personnel (n=154) from multi-cultural organisations across five countries - Cyprus, Italy, Latvia, Poland, and the UK. The discussions include unpacking the mapping of pedagogical and gameful design considerations based on Arnab et al.‘s (2015) Learning Mechanics-Game Mechanics Mapping (LMGM) model. The paper also discusses the findings from the testing of the online platform across 5 countries including 166 participants (two-step testing). The insights provided will be valuable to researchers, practitioners, designers, and developers of micro-learning resources.