1. Introduction

This paper is an extension of work originally presented in 2019 International Conference on Information Management and Technology (ICIMTech) [1]. The development of the information system used by the client has an impact on the expertise that must be mastered by the auditor who was originally approached manually, so with these changes the auditor is required to master the information system process used by the client and computerized audit technique for adjusting the audit process and the procedures used when carrying out field work such as changes in the manual accounting system environment into a computer-based accounting information system causes the auditor to study a system. Purpose auditing for carried out effectively and efficiently, the auditor should adjust his audit techniques to the client’s information system [2].

General Audit Software or commonly abbreviated as GAS is an auditing with computer technique, or better known as Computer Assisted Audit Techniques. In Western countries, this GAS has been widely applied by audit companies. [3] Revealed that “Adoption of General Audit Software (GAS) can improve audit quality, and this is the reason why its use is applied by US Auditing Standards”.

In terms of regulation (environment), the Indonesian Audit Standards suggest GAS in conducting works [4], but its use is not mandated / charged. Therefore, the use of technology in audit companies in Indonesia is not fully regulated. One of the Public Accounting Firm’s Big Four states they use Generalized Audit Software because they have the software and own the resources.

In terms technology, information technology proficiency including English as foreign language is very influential for public accounting firms medium and small. In terms of technological skills, the Big Four is not in doubt. Obviously, become problem in domain of public accounting firms that smaller. On the contrary, is nor serious problem; the elite office believe language matters won’t hamper their work, it used in operation every day. In addition, auditors must understand compatible software that is compatible to check/audit clients for accuracy in testing data integrity.

Associate terms of audit companies (organizations), IT Capital Budget is very influential on the implementation of General Audit Software. Big companies tend having customized audit tools, while medium and small audit companies usually rely on commercially available and cheaper software. Besides that, the IT skills of auditors in the company are also very influential.

On the other hand, the attitudes and intentions of auditors are also very influential in adopting General Audit Software (GAS). One senior auditor from the intermediate audit commented “Software is to help auditor work, nor to make it more complicated”. Difficult to operate software make hamper usage. As a result, the auditor will need even more effort to be able to use it. Interestingly, the intermediate audit office found young employee pay more interest in Generalized Audit Software (GAS) [5]. These premises also supported by research conducted by [6] which resulted that people willing to adopt, when it simplify audit process times and do their jobs more efficiently. Auditors who are young and have IT insights will usually be more interested if given the opportunity to enhance their skills.

2. Theoretical Framework and Hypothesis

2.1.  Technology Acceptance Model

The Technology Acceptance Model (TAM) developed by [7]  a successful and highly acceptable model for predicting willingness to adopt current information approach. Until now this theory was a pioneer and the foremost in uncovering the reasons people want to change to keep abreast of technological developments. Many studies have re-examined, expanded, and used TAM.

The TAM model originated from theory of reason action [8], that is grounded from study of reaction perception for something, effect on person’s will and behavior. Reaction and perception affect in acceptance of technology. One factor that can influence is the user’s will, so that the reason someone sees the actions / behavior as a benchmark in the acceptance of a technology. The TAM model that is grounded with a premise reaction or perception for something which outcome in result behavior.  Reaction and perception affect in acceptance of technology. One factor that can influence it is the user’s will his or herself.

According to [9] people willing to leave old method and change to new modern one, because they think it was useful and easy. Both of these components when associated with TRA are part of belief.

      Basically it will be very strong if someone already has a perception, perception will be a suggestion, it will make someone want to do it [10]. The main perception here is the perception of usefulness, namely that this technology is very useful. But in addition to usability, it must also be supported by perceptions of ease, because we find many useful but difficult, it will also be left by people. Based on this premises and previous study by [11], [12] we formulated our premise as follows:

Hypothesis 1: PU has positive influence on System Usage (SU).

Hypothesis 2: PEU has positive influence on System Usage (SU).

Hypothesis3: PEU has positive influence on Perceived Usefulness (PU).

2.2.  Auditing Software

CAATs according to [13] software designed to enable auditors with less sophisticated computer skills to carry out audits related to data processing functions. These packages can carry out certain analytical calculations, thus detect anomalies. Audit software is also interpreted as a computer program that allows automatic decision. Conventional tools such as system use programs, information reappearance programs, or high-level programming languages ​​can be used for this audit. Real data processing goes through an audit program. Outputs are simulated and compared with regular outputs for monitoring purposes. Parallel simulation, redundant processing of all input data by conducting a separate program test, allows comprehensive and very precise validation to be carried out on important transactions that require 100% audit [14]. The audit program used in parallel simulations is usually a type of general audit program that processes data and produces output that is identical to the program being audited. Audit software is one of the software that adapted in educational work, along with other software used in college/university [15].

2.3.  Social, Individual, and Organizational

Reflecting on the preliminary research, we find that there are recognizable 3 factors that are triggers for the perception of ease and perception of use. these three factors are individual, social and organizational [16]. The organization in our research context is the office where the auditor works. The policies taken by partners, which are the highest leaders in the public accounting firm, determine whether software is used or not. In accordance with [16] organization impact both Perceived Usefulness, Perceived Ease of Use (PEU).

The second factor to be investigated is social. Socials in our study are peers or peers of the auditor. When working of course the auditor works in a team, where in the team consists of several people. This is where social interactions were influenced [16]. [11] said people tend to be the same and not too different from their peers or social community. If one uses software easily, the others don’t want to be left behind to use it. this opinion is reinforced by the results of the study [17], [18].

The third factor is individual. These factors originated from within the auditor himself. Where they have their own intentions, have their own perceptions before being influenced by the surrounding environment [16]. Individual factors are suspected to have strong potential, because it involves beliefs from within [13], Based on this premises, we form these several hypotheses related to the social, individual and organizational factors to PU and PEU.

H4: Organizational factor has positive influence on Perceived Usefulness

H5: Social factor has positive influence on Perceived Usefulness

H6: Individual factor has positive influence on Perceived Usefulness

H7: Organizational factor has positive influence on PEU

H8: Social factor has positive influence on PEU

H9: Individual factor has positive influence on PEU

3. Research Methodology

Our research was an associative research using path analysis test. We use a total of six factors. The six variables contain three independents, namely: individual, social, organizational, also two intervening variables namely perceived usefulness and perceived ease of use and one dependent variable, namely system usage. Hypothesis testing performed using statistical software. Hypothesis testing is done after the precondition namely validity and reliability.

3.1.  Population and Samples

Respondents in our study were people who worked as independent auditors or financial auditors in public accounting firms. We do not limit the accounting firm, whether the big four, ten or others. The population size is difficult to know, because the number of auditors is very large and changing, based on the approach taken by Chassan [19]. it was concluded that the respondents were at least 30, so we decided to use 100 respondents similar like [20]. .

3.2.  Measurements of Factors

Factors used within our research are adjectives, which are basically abstract. For this reason, in order to be concrete and can be explored with certainty, then we made the operation of variables to make measurements. Variable measurements are made to make abstract variables (derived from adjectives) more tangible and can be calculated quantitatively. Variable measurement is based on preliminary research and grand theory. Here in table 1 presented the operation of variables.

Table 1: Table Operation of Variables

4. Research Result

We use path analysis to examine whether there is influence and how strong is the influence between variables in our study. However, before we conduct hypothesis testing, the data must first be tested for validity, reliability and classic assumptions. This is done to ensure that the data is valid, reliable and worth testing.

4.1.  Reliability and Validity Testing

A variable is said to be reliable when the Cronbach alpha value is above 0.7 [22], reliable means that even if tested over and over again, the results will remain consistent.

Table 2: Cronbach’s Alpha

Different from reliability, validity is intended to know whether if a question is asked to many parties then the answer remains the same. Validity can be seen from the r count greater than r table.

Table 3: Validity Testing

4.2.  Classical Assumption

This must be performed as a data quality test in testing the linear regression model. Quality multiple regression analysis in this study are free from deviations of assumptions. The classical assumption test, there are several tests including normality, multicollinearity, heteroscedasticity, and autocorrelation.

4.2.1. Normality Test

In this study normality testing is performed using graph analysis and statistical analysis.

The statistical test used in the study is Kolmogorov-Smirnov (K-S) non-parametric statistical test. Data requirements are normal if the probability or p> 0.05 in the Kolmogorov-Smirnov normality test. Kolmogorov-smirnov test value > 0.05 means that the data is normally distributed [22]. If the Kolmogorov-Smirnov test value <0.05 then the data is not normally distributed. In Table 4 the K-S test value is 0.200 which means it is greater than 0.05, which means that the data are normally distributed.

Table 4: Normality Test

The criteria used in chart analysis are, if the data spreads around a diagonal line and follows the direction of the diagonal line or the histogram graph shows a normal distribution pattern, then the regression model meets the normality assumption.

Based on Figure 1 it can be concluded that the data is spread around the diagonal line, so it can be concluded that the regression model meets the normality assumption

Figure 1: Graphic Normality

4.2.2. Multicollinearity Test

Multicollinearity test aims to test whether the regression model found a correlation between independent variables. A good regression model should not accept a correlation between independent variables. To find out the presence / absence of multicollinearity is to use Variance Inflation Factor (VIF) and tolerance. A low tolerance value is the same as a high VIF value (because VIF = 1 / Tolerance). If the tolerance value ≤ 0.10 or VIF value ≥ 10, it means that there is multicollinearity, while the tolerance value ≥ 0.10 or VIF value ≤ 10, it means there is no multicollinearity.

Based on Table 5, the tolerance values ​​for the OF, SF, and IF variables are 0.597, 0.338 and 0.486, also for the PU and PEU variables, each at 0.532, which is greater than 0.100. In addition, the VIF values ​​of the OF, SF, and IF variables of 1,674, 2,956 and 2,059, also for the PU and PEU variables, respectively 1,880, which are all smaller than 10. Based on these results, we can conclude that the research data free from multicollinearity

Table 5: Multicollinearity Test

4.2.3. Heteroscedasticity Test

Heteroscedasticity test aims to determine whether in the residual regression model there is an imbalance of variance from one observation to another. If the variance from one observation to another is the same, it can be called homoscedasticity and if it is different it can be called heteroscedasticity. Testing heteroscedasticity in this study is using statistical tests and plot graph tests.

The statistics used in this study to determine the presence or absence of heteroscedasticity is the Goldfeld-Quandt Test. The testing steps are as follows:

1. Sort the independent variable X from the smallest largest
2. Then make two separate regressions, first for the smallest X value. Second for large X values ​​and omit some data in the middle.
3. Make the ratio of RSS (Residual Sum of Square = error sum if square) from the second regression to the first regression (RSS2 / RSS1) to get the calculated F value.
4. Perform the F test using degrees of freedom of (n-d-2k) / 2, where

n = number of observations,

d = the amount of data or observation values ​​lost

k = estimated number of parameters.

F test criteria if:

F arithmetic > F table, then there is heteroskedasticity

F arithmetic < F table, then there is no heteroskedaticity

Results of group I regression with RSS1 = 381.974. The results of group II regression with RSS2 = 814.614. F-stat = RSS2 / RSS1 = 814.614 / 381.974 = 2.132. F-table is 4.85. It can be concluded that F-statistic < F-table it means that no heteroscedasticity

Heteroscedasticity test plot graph to detect the presence or absence of heteroscedasticity by looking at the plot graph, if there are certain patterns such as points that form certain patterns and orderly, then heteroscedasticity has occurred. If the pattern image spreads above and below the number 0 on the Y axis indicates the absence of heteroscedasticity.

Figure 2 illustrates the spread of scattered data so that it can be concluded that the data in this study is free from heteroscedasticity.

Figure 2: Graphic Heteroscedasticity

4.2.4. Autocorrelation Test

Autocorrelation test was performed using Durbin Watson. If the Durbin Watson value ranges between the upper limit values (dU), then an autocorrelation violation is not expected. The following is a table of autocorrelation test results:

Table 6: Autocorrelation Test

Based on Table 6 it is known that the Durbin Watson (DW) value is 1,740. In the table, we see DW to obtain dL value of 1.61 and dU of 1.74. So, that in the regression equation, the DW value is in the dU <d <4-dU region. Then H₀ is accepted meaning that the DW value is in the criteria of no autocorrelation. Thus, the assumptions on autocorrelation in the regression equation model have been fulfilled.

We also used Breusch-Godfrey or often called the LM test. In order to detect the presence of autocorrelation, the following are things that can be done:

1. Pay attention to the t-statistic value, R², F test, and Durbin Watson (DW) statistics.
2. Perform LM test (Breusch Godfrey method). This method is based on the values ​​of F and Obs*R-squared, where if the probability value of Obs*R-squared exceeds the level of confidence, then H₀ is accepted. This means that there is no autocorrelation problem.

Testing the autocorrelation hypothesis:

1. H₀: autocorrelation does not occur
2. H_a: autocorrelation occurs
3. If the p-value Obs*R-square < α, H₀ is rejected.

Following are the results of autocorrelation testing with the Breusch-Godfrey test:

Table 7: Breusch Godfrey Test

Based on the results of calculations using the Breusch-Godfrey test, Obtained probability value Obs*R-square is equal to 0.657. This matter means probability > α = 0.01, then the conclusion is the level of confidence 99% of the regression models are free from autocorrelation problems.

4.3.  Hypothesis Testing

The data has passed the prerequisite test, which means it is time to test the initial assumption / hypothesis. The test is presented in Tables 8 and 9. If the p-value is significant below 0.05 we find that the variable has a significant effect. After having an effect, we also see whether the effect is positive or negative. The direction is seen from the value of the beta coefficient, whether positive or negative. If positive means positive and vice versa. When referring to tables 8 and 9 we can conclude that hypotheses 1, 2 and 3 are all accepted, have a significant effect and are in a positive direction, this result supported previous result in preliminary study by [23].

Table 8: Hypothesis Testing 1

Table 9: Hypothesis Testing 2

 H4 can’t be accepted and H5 and H6 can’t be rejected, this resulted in line with [24], in other word not supported [16] and [25].

Table 10: Hypothesis Testing 3

While H7 is can’ be accepted, H8 also can’t be accepted and H9 positively influenced. We have result that support [26],[25], [27].

Table 11: Hypothesis Testing 4

The research path coefficient is presented below.

Figure 3: Research Path Analysis

5. Conclusion and Suggestion

After getting the test results statistically, we followed up. The follow up that we do is to conduct interviews and observations of respondents. Our results have found that audit judgment expectancy and expectancy incentives can influence auditors’ interest in using GAS. This is because the auditor’s performance will increase, when performance increases, then incentives such as salary increases, benefits, bonuses, which are material and also praise/acknowledgement, non-material recognition will accompany their careers. Defers case with age similarity influence; It is true that auditors in their daily life prefer to associate with the same age. However, this association only affects behavior outside of work obligations. For example, it only affects hobbies, games and favorites foods. Meanwhile, to use audit software, it still has to be mandatory from the lead leader.

While other results state that the availability of software and the desire to adopt influence people to actually use it. This is reasonable, because how is it possible for an auditor to use, if the software is not provided by the relevant accounting firm where he works. It is impossible for the auditor to install himself on a personal laptop and use it without the company’s approval. For the influence between intention and behavior clearly follows psychological rules, that someone who already wants, will usually continue to use.

Future studies can examine the actual process, on internal auditors and small accounting firms outside the big four and big ten. Future studies can test the extent to which the ability of the software. Future studies examine affect partners or owners can increase utilization by providing facilities and infrastructure as well as conducting training and socialization of its use.

1. B. L. Handoko, Meiryani, S. Sabrina, and N. Ayuanda, “Admission of Information Technology in External Audit Profession: Impact of Organizational, Social and Individual Factors,” in Proceedings of 2019 International Conference on Information Management and Technology, ICIMTech 2019, 2019, pp. 36–41.
2.  N. Mahzan and A. Lymer, “Examining the adoption of computer-assisted audit tools and techniques,” Manag. Audit. J., vol. 29, no. 4, pp. 327–349, 2014.
3. J. J. Schultz, J. L. Bierstaker, and E. O’Donnell, “Integrating business risk into auditor judgment about the risk of material misstatement: The influence of a strategic-systems-audit approach,” Accounting, Organ. Soc., 2010.
4. S. M. Glover, M. H. Taylor, and C. Western, “Mind the Gap: Why Do Experts Have Differences of Opinion Regarding the Sufficiency of Audit Evidence Supporting Complex Fair Value Measurements” Contemp. Account. Res., 2019.
5. N. K.-G. J. of and U. 2017, “’Attitudes and Perceptions Towards Incorporating Computer Assisted Audit Techniques in an Undergraduate Auditing …,” Theibfr.Com, vol. 11, no. 3, pp. 55–71, 2017.
6. M. Mustapha and S. J. Lai, “Information Technology in Audit Processes: An Empirical Evidence from Malaysian Audit Firms,” Int. Rev. Manag. Mark., vol. 7, no. 2, pp. 53–59, 2017.
7. F. D. Davis, “Perceived Usefulness , Perceived Ease Of Use , And User Acceptance,” MIS Q., vol. 13, no. 3, pp. 319–339, 1989.
8. M. Fishbein and I. Ajzen, “Belief, Attitude, Intention and Behaviour: An Introduction to Theory and Research,” Read. MA AddisonWesley, no. August, p. 480, 1975.
9. V. Venkatesh, F. D. Davis, and S. M. W. College, “Theoretical Acceptance Extension Model: Field Four Studies of the Technology Longitudinal,” vol. 46, no. 2, pp. 186–204, 2012.
10. A. Rogers, “Examining Small Business Adoption of Computerized Accounting Systems Using the Technology Acceptance Model.,” Walden Diss. Dr. Stud., p. 126, 2016.
11. Y. L. Chen, H. T. Chih, and C. C. Wan, “The relationship between attitude toward using and customer satisfaction with mobile application services: An empirical study from the life insurance industry,” J. Enterp. Inf. Manag., vol. 28, no. 5, pp. 680–697, 2015.
12. H.-J. Kim, A. Kotb, and M. K. Eldaly, “The use of generalized audit software by Egyptian external auditors,” J. Appl. Account. Res., vol. 17, no. 4, pp. 456–478, 2016.
13. R. Widuri, B. L. Handoko, and I. E. Riantono, “Perception of Accounting Student on Learning of Generalized Audit Software,” in Proceedings of 2019 International Conference on Information Management and Technology, ICIMTech 2019, 2019.
14. H. J. Kim, A. Kotb, and M. K. Eldaly, “The use of generalized audit software by Egyptian external auditors: The effect of audit software features,” J. Appl. Account. Res., 2016.
15. E. Symeonaki, M. Papoutsidakis, D. Tseles, and M. Sigala, “Post-Implementation Evaluation of a University Management Information System (UMIS),” in Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI), 2016, pp. 14–19.
16. K. Rosli, P. H. P. Yeow, and E.-G. Siew, “Adoption of Audit Technology in Audit Firms,” Proc. 24th Australas. Conf. Inf. Syst., pp. 1–12, 2013.
17. A. S. Shammari, “An Examination Factors Influencing The Intention To Use Instructional Technologies: An Extention Technlogy Acceptance Model (TAM),” Int. J. Inf. Res. Rev., vol. 04, no. 02, pp. 3637–3641, 2017.
18.  A. Ahmi and S. Kent, “The utilisation of generalized audit software (GAS) by external auditors,” Manag. Audit. J., vol. 28, no. 2, pp. 88–113, 2013.
19.  J. B. Chassan, Research Design in Clinical Psychology and Psychiatry. New York: Irvington Publishers Inc, 1979.
20.  J. B. Chassan, “Intensive design in medical research,” Pharmacol. Ther. Part B Gen. Syst., vol. 1, no. 1, pp. 139–148, 1975.
21.  H. J. Kim, A. Kotb, and M. K. Eldaly, “The use of generalized audit software by Egyptian external auditors: The effect of audit software features,” J. Appl. Account. Res., vol. 17, no. 4, pp. 456–478, 2016.
22. U. Sekaran and R. Bougie, “Research Methods For Business. A Skill Builing Approch. 7th Edition,” Book, 2016.
23.  A. Tarhini, K. Hone, and X. Liu, “A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students,” Br. J. Educ. Technol., vol. 46, no. 4, pp. 739–755, 2015.
24. M. Sharif Abbasi, A. Tarhini, M. Hassouna, and F. Shah, “Social, Organizational, Demography and Individuals’ Technology Acceptance Behaviour: a Conceptual Model,” Eur. Sci. J., vol. 11, no. 9, pp. 1857–7881, 2015.
25. K. Rosli, P. Yeow, and E.-G. Siew, “Factors Influencing Audit Technology Acceptance by Audit Firms: A New I-TOE Adoption Framework,” J. Account. Audit. Res. Pract., vol. 2012, pp. 1–11, 2012.
26. C. Chiu, S. Chen, and C. Chen, “An Integrated Perspective of TOE Framework and Innovation Diffusion in Broadband Mobile Applications Adoption by Enterprises,” Int. J. Manag. Econ. Soc. Sci., vol. 6, no. 1, pp. 14–39, 2017.
27. R. Widuri, B. L. Handoko, and I. C. Prabowo, “Adoption of Information Technology in Public Accounting Firm,” 2019.

Citations by Dimensions

Citations by PlumX

Google Scholar

Crossref Citations

1. Kento Yasuda, Hiromitsu Shimakawa, Fumiko Harada, "Identifying Comprehension Faults Through Word Embedding and Multimodal Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 6, pp. 42–54, 2025. doi: 10.25046/aj100604
2. Diego Costa, Gabriel Matos, Anderson Lins, Leon Barroso, Carlos Aguiar, Erick Bezerra, "Web Application Interface Data Collector for Issue Reporting", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 5, pp. 01–08, 2024. doi: 10.25046/aj090501
3. Afrodite Papagiannopoulou, Chrissanthi Angeli, "Social Media Text Summarization: A Survey Towards a Transformer-based System Design", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 26–36, 2023. doi: 10.25046/aj080604
4. Mohamed Nayef Zareer, Rastko Selmic, "Modeling Control Agents in Social Media Networks Using Reinforcement Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 5, pp. 62–69, 2023. doi: 10.25046/aj080507
5. Joana Braguez, "SEVEN ReImagined: A Transmedia Storytelling Evolution Proposal", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 122–130, 2023. doi: 10.25046/aj080414
6. Evgeniy Kostyrin, Evgeniy Sokolov, "Social Financial Technologies for the Development of Enterprises and the Russian Economy", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 118–135, 2023. doi: 10.25046/aj080314
7. Clemens Gnauer, Andrea Prochazka, Elke Szalai, Sebastian Chlup, Anton Fraunschiel, "Technical Aspects and Social Science Expertise to Support Safe and Secure Handling of Autonomous Railway Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 283–294, 2022. doi: 10.25046/aj070632
8. Ahmed Abdelaziz Elsayed, Mohamed Ahmed Abdellah, Mansour Ahmed Mohamed, Mohamed Abd Elazim Nayel, "µPMU Hardware and Software Design Consideration and Implementation for Distribution Grid Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 59–71, 2022. doi: 10.25046/aj070409
9. Bougar Marieme, Ziyati El Houssaine, "Analysis Methods and Classification Algorithms with a Novel Sentiment Classification for Arabic Text using the Lexicon-Based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 12–18, 2022. doi: 10.25046/aj070302
10. Edgard Musafiri Mimo, Troy McDaniel, Jeremie Biringanine Ruvunangiza, "COVIDFREE App: The User-Enabling Contact Prevention Application: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 149–155, 2022. doi: 10.25046/aj070215
11. Mohammad M. Qabajeh, "Ethical Implications and Challenges in using Social Media: A Comprehensive Study", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 1, pp. 47–52, 2022. doi: 10.25046/aj070105
12. Lucie Böhmová, Antonín Pavlíček, "Innovations in Recruitment—Social Media", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 6, pp. 88–97, 2021. doi: 10.25046/aj060613
13. Boris Kontsevoi, Sergei Terekhov, "TETRA™ Techniques to Assess and Manage the Software Technical Debt", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 303–309, 2021. doi: 10.25046/aj060534
14. Mykoniati Maria, Lambrinoudakis Costas, "Software Development Lifecycle for Survivable Mobile Telecommunication Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 259–277, 2021. doi: 10.25046/aj060430
15. Le Anh Tuan, Mai Thi Quynh Nhu, Nguyen Le Nhan, "Factors Affecting the Decision of Selecting Banking to Save Money of Individual Customers – Experimental in Da Nang City", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 409–417, 2021. doi: 10.25046/aj060345
16. Rotimi Adediran Ibitomi, Tefo Gordan Sekgweleo, Tiko Iyamu, "Decision Support System for Testing and Evaluating Software in Organizations", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 303–310, 2021. doi: 10.25046/aj060334
17. Abderrahmane Ouddasser, Anass Mellouki, Yassine Belyagou, Kamal Yazzif, "The health Sector Between Innovation and Organizational Performance: Applied Research in Moroccan Hospitals", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 277–285, 2021. doi: 10.25046/aj060331
18. Yanshuo Wang, Jim (Jinming) Yang, Ngandu M. Mbiye, "Effectiveness and Suitability of the Automotive EHPS Software Reliability and Testing", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 205–212, 2021. doi: 10.25046/aj060323
19. Carla Blank, Matthew McBurney, Maria Morgan, Raed Seetan, "A Survey of Big Data Techniques for Extracting Information from Social Media Data", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 189–204, 2021. doi: 10.25046/aj060322
20. Anh Nguyen-Duc, Manh-Viet Do, Quan Luong-Hong, Kiem Nguyen-Khac, Hoang Truong-Anh, "On the Combination of Static Analysis for Software Security Assessment – A Case Study of an Open-Source e-Government Project", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 921–932, 2021. doi: 10.25046/aj0602105
21. Kakoma Chilala Bowa, Mabvuto Mwanza, Mbuyu Sumbwanyambe, Kolay Ulgen, Jan-Harm Pretorius, "Assessment of Electricity Industries in SADC Region Energy Diversification and Sustainability", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 894–906, 2021. doi: 10.25046/aj0602102
22. Ahmad AA Alkhatib, Abeer Alsabbagh, Randa Maraqa, Shadi Alzubi, "Load Balancing Techniques in Cloud Computing: Extensive Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 860–870, 2021. doi: 10.25046/aj060299
23. Abdulla M. Alsharhan, "Survey of Agent-Based Simulations for Modelling COVID-19 Pandemic", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 439–447, 2021. doi: 10.25046/aj060250
24. Celia Chen, Michael Shoga, "A Large Empirical Study on Automatically Classifying Software Maintainability Concerns from Issue Summaries", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 161–174, 2021. doi: 10.25046/aj060219
25. Yousra Karim, Abdelghani Cherkaoui, "Fuzzy Analytical Hierarchy Process and Fuzzy Comprehensive Evaluation Method Applied to Assess and Improve Human and Organizational Factors Maturity in Mining Industry", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 75–84, 2021. doi: 10.25046/aj060210
26. Akram Ajouli, "SEA: An UML Profile for Software Evolution Analysis in Design Phase", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1334–1342, 2021. doi: 10.25046/aj0601153
27. Mandlenkosi Shezi, Abejide Ade-Ibijola, "Deaf Chat: A Speech-to-Text Communication Aid for Hearing Deficiency", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 826–833, 2020. doi: 10.25046/aj0505100
28. Abdulla Alsharhan, Said Salloum, Khaled Shaalan, "The Impact of eLearning as a Knowledge Management Tool in Organizational Performance", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 928–936, 2021. doi: 10.25046/aj0601102
29. Lesia Marushchak, Olha Pavlykivska, Galyna Liakhovych, Oksana Vakun, Nataliia Shveda, "Accounting Software in Modern Business", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 862–870, 2021. doi: 10.25046/aj060195
30. Sonia Souabi, Asmaâ Retbi, Mohammed Khalidi Idrissi, Samir Bennani, "A Recommendation Approach in Social Learning Based on K-Means Clustering", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 719–725, 2021. doi: 10.25046/aj060178
31. Arman Mirmanov, Aidar Alimbayev, Sanat Baiguanysh, Nabi Nabiev, Askar Sharipov, Azamat Kokcholokov, Diego Caratelli, "Development of an IoT Platform for Stress-Free Monitoring of Cattle Productivity in Precision Animal Husbandry", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 501–508, 2021. doi: 10.25046/aj060155
32. Diena Rauda Ramdania, Dian Sa’adillah Maylawati, Yana Aditia Gerhana, Novian Anggis Suwastika, Muhammad Ali Ramdhani, "Octalysis Audit to Analyze gamification on Kahoot!", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 457–463, 2021. doi: 10.25046/aj060149
33. Edy Budiman, Unmul Hairah, Masna Wati, Haviluddin, "Sensitivity Analysis of Data Normalization Techniques in Social Assistance Program Decision Making for Online Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 49–56, 2021. doi: 10.25046/aj060106
34. Angelina Ervina Jeanette Egeten, Harjanto Prabowo, Ford Lumban Gaol, Meyliana, "Features Preference using Conjoint Analysis Method for E-marketplace Social Care System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1593–1597, 2020. doi: 10.25046/aj0506190
35. Bilal Babayigit, Eda Nur Hascokadar, "A Software-Defined Network Approach for The Best Hospital Localization Against Coronavirus (COVID-19)", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1537–1544, 2020. doi: 10.25046/aj0506184
36. Abba Suganda Girsang, Sani Muhamad Isa, Natasya, Megga Eunike Cristilia Ginzel, "Implementation of a Journalist Business Intelligence in Social Media Monitoring System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1517–1528, 2020. doi: 10.25046/aj0506182
37. Omar Khaled Barakat, Ahmed El-Biomey Mansour, Mahmoud Mohamed Abd Elrazik, Ashraf Aboshosha, Amir Yassin Hassan, "Simulation Based Energy Consumption Optimization for Buildings by Using Various Energy Saving Methods", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1480–1487, 2020. doi: 10.25046/aj0506178
38. Tedi Priatna, Dian Sa’adillah Maylawati, Hamdan Sugilar, Muhammad Ali Ramdhani, "Social Engineering to Establish Digital Culture in Higher Education", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1474–1479, 2020. doi: 10.25046/aj0506177
39. Lili Sifuentes-Gomez, Doris Vega-Davila, Betty Flores-Paz, Brian Meneses-Claudio, Hernan Matta-Solis, Eduardo Matta-Solis, "Social Skills and Resilience in Adolescents of an Educational Institution in North Lima, 2019", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1350–1355, 2020. doi: 10.25046/aj0506162
40. Evelyn Roncal-Cespedes, Gloria Castillo-Laban, Brian Meneses-Claudio, Hernan Matta-Solis, Lourdes Matta-Zamudio, Eduardo Matta-Solis, "Social skills and Resilience in Adolescents of Secondary Education of the Kumamoto I 3092 Educational Institution, of the Puente Piedra District – Lima 2019", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1345–1349, 2020. doi: 10.25046/aj0506161
41. Jared Zavala-Izaguirre, Fanny Mego-Llanos, Sarita Cornejo-Quispitongo, Brian Meneses-Claudio, Hernan Solis-Matta, Lourdes Matta-Zamudio, "Quality of Life in Patients with Type 2 Diabetes of the Central Hospital of the Peruvian Air Force, 2019", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1340–1344, 2020. doi: 10.25046/aj0506160
42. Clara Silveira, Leonilde Reis, Vitor Santos, Henrique S. Mamede, "Creativity in Prototypes Design and Sustainability – The case of Social Organizations", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1237–1243, 2020. doi: 10.25046/aj0506147
43. Issam Jebreen, "An Analysts’ Skills: Bespoke Software vs Packaged Software at Small Software Vendors", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1021–1026, 2020. doi: 10.25046/aj0506123
44. Rosmery Ramos-Sandoval, Jano Ramos-Diaz, "Barriers and Supports in Engineering Career Development: An Exploration of First-Year Students", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 920–925, 2020. doi: 10.25046/aj0506109
45. Nelson Russo, Leonilde Reis, "Updated Analysis of Business Continuity Issues Underlying the Certification of Invoicing Software, Considering a Pandemic Scenario", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 845–852, 2020. doi: 10.25046/aj0506101
46. Sara Abas, Malika Addou, Zineb Rachik, "Polarity Switch within Social Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 817–820, 2020. doi: 10.25046/aj050697
47. Kristianus Oktriono, Surjandy Surjandy, Meyliana Meyliana, Michele Carolina, Stephanie Stephanie, "Social Influence Factor of e-Tourism Application Case Study University Student", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 684–688, 2020. doi: 10.25046/aj050682
48. Arooba Shahoor, Rida Shaukat, Sumaira Sultan Minhas, Hina Awan, Kashif Saghar, "SharpniZer: A C# Static Code Analysis Tool for Mission Critical Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 561–570, 2020. doi: 10.25046/aj050668
49. Andre Figliuolo da Cruz, Cristiano Pereira Godoy, Lanier Menezes dos Santos, Lucas Frota Marinho, Marco Santarelle Jardim, Elisangela Paiva da Silva, C ́ıcero Augusto Pahins, Paulo Fonseca, Felipe Taliar Giuntini, "Blueprint Model: An Agile-Oriented Methodology for Tackling Global Software Development Challenges", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 353–362, 2020. doi: 10.25046/aj050643
50. Jim Scheibmeir, Yashwant Malaiya, "Multi-Model Security and Social Media Analytics of the Digital Twin", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 323–330, 2020. doi: 10.25046/aj050639
51. Oswaldo René Banda-Sayco, "Infrared Uplink Implementation for Software Defined Visible Light Communication Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 127–132, 2020. doi: 10.25046/aj050614
52. Sergiy Kostrikov, Rostyslav Pudlo, Dmytro Bubnov, Vladimir Vasiliev, Yury Fedyay, "Automated Extraction of Heavyweight and Lightweight Models of Urban Features from LiDAR Point Clouds by Specialized Web-Software", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 72–95, 2020. doi: 10.25046/aj050609
53. Toto Ruhimat, Deni Darmawan, "Development of Group-Based Differentiated Learning (GBDL) Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 52–62, 2020. doi: 10.25046/aj050607
54. Ihsan Mizher Baht, Petre Marian Nicolae, Ileana Diana, Nameer Baht, "Analysis of Green Building Effect on Micro grid Based on Potential Energy Savings and BIM", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 30–35, 2020. doi: 10.25046/aj050604
55. Niurka Jacome-Olacua, Joselyne Rodríguez-Paucar, Prhitty Marin-Garcia, Brian Meneses-Claudio, Hernan Solis-Matta, Eduardo Matta-Solis, "Social Skills and Resilience in Adolescent of Secondary Level of a Public Educational Institution in Puente Piedra Lima – 2020", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1036–1041, 2020. doi: 10.25046/aj0505127
56. Surjandy, Meyliana, Kristianus Oktriono, Mika Milenia Catherine, Chutiporn Anutariya, Erick Fernando, "Smartphone Influence Factor of University Student’s Academic Achievement", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 692–697, 2020. doi: 10.25046/aj050585
57. Lambe Mutalub Adesina, Ademola Abdulkareem, James Katende, Olaosebikan Fakolujo, "Newton-Raphson Algorithm as a Power Utility Tool for Network Stability", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 444–451, 2020. doi: 10.25046/aj050555
58. Matthew Oluwole Arowolo, Adefemi Adeyemi Adekunle, Martins Oluwaseun Opeyemi, "Design and Implementation of a PLC Trainer Workstation", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 755–761, 2020. doi: 10.25046/aj050489
59. Bambang Leo Handoko, Ang Swat Lin Lindawati, "The Importance of Sustainability Audit Report in Go Public Companies Sector, in Indonesia", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 217–222, 2020. doi: 10.25046/aj050427
60. Yogi Udjaja, "Entertainment Technology: Dynamic Game Production", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 203–206, 2020. doi: 10.25046/aj050425
61. Jim Scheibmeir, Yashwant Malaiya, "Contextualization of the Augmented Reality Quality Model through Social Media Analytics", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 184–191, 2020. doi: 10.25046/aj050422
62. Nia Rohayati, Deni Darmawan, "Adapting to Individual Differences (ATID) For Inductive Thinking and Learning Purpose", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 35–39, 2020. doi: 10.25046/aj050405
63. Erick Fernando, Meyliana, Achmad Nizar Hidayanto, Harjanto Prabowo, "Factors Influencing Social Knowledge Management in Social Society: A Systematic Literature Review", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 198–206, 2020. doi: 10.25046/aj050326
64. Andry Alamsyah, Sri Widiyanesti, Rizqy Dwi Putra, Puspita Kencana Sari, "Personality Measurement Design for Ontology Based Platform using Social Media Text", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 100–107, 2020. doi: 10.25046/aj050313
65. Ishaq Sider, Khaled Sider, "Evaluation of Vapor Jet Refrigeration Cycle Driven by Solar Thermal Energy for Air Conditioning Applications: Case Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 646–652, 2020. doi: 10.25046/aj050280
66. Emmanuel Peters, George Kwamina Aggrey, "An ISO 25010 Based Quality Model for ERP Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 578–583, 2020. doi: 10.25046/aj050272
67. Ranyelson Neres Carvalho, Lucas R. Costa, Jacir Luiz Bordim, Eduardo Adilo Pelinson Alchieri, "Enhancing an SDN Architecture with DoS Attack Detection Mechanisms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 215–224, 2020. doi: 10.25046/aj050228
68. Mohamed Saleh Al Breiki, Suiping Zhou, Yuan Roger Luo, "Design and Validation of a Meter Band Rate in OpenFlow and OpenDaylight for Optimizing QoS", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 35–43, 2020. doi: 10.25046/aj050205
69. Woochun Jun, "A Study on Development of Information and Communication Ethics Sensitivity Measurement for Elementary School Students", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 169–173, 2020. doi: 10.25046/aj050122
70. Elfadil Abdalla Mohameds, Nazar Zakis, Mohammad Marjans, "Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 244–254, 2019. doi: 10.25046/aj040631
71. Issar Arab, Bouchaib Falah, Kenneth Magel, "SCMS: Tool for Assessing a Novel Taxonomy of Complexity Metrics for any Java Project at the Class and Method Levels based on Statement Level Metrics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 220–228, 2019. doi: 10.25046/aj040629
72. Isaac Onyeyirichukwu Chukwuma, Emmanuel Kalu Agbaeze, Nkiru Peace Nwakoby, Gertrude Chinelo Ugwuja, Fidelis Odinakachukwu Alaefule, Ifeanyi Leo Madu, "The Relationship of Coalition on Employee Spiritual Engagement: Interplay of Organisational Politics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 45–52, 2019. doi: 10.25046/aj040606
73. Woochun Jun, "A Study on Development of Evaluation Metrics for Learners in Physical Computing", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 82–87, 2019. doi: 10.25046/aj040510
74. Segundo Moisés Toapanta Toapanta, Allan Fabricio German Diaz, Darío Fernando Huilcapi Subia, Luis Enrique Mafla Gallegos, "Proposal for a Security Model for a Popular Voting System Process in Latin America", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 53–60, 2019. doi: 10.25046/aj040507
75. Héctor L. Núñez-Ramírez, Gloria G. Carvalho-Kassar, Freddy C. Brito-Maestre, Yaremi I. Gamboa-Maldonado, Luis A. Santos-Avendaño, Carlelines Gavidia-Toro, Orlando Villarroel-Ramos, Dino Di Rosa-Ulloa, "Low-Cost and Accurate Computational System for Efficiency Measures over Photovoltaic Arrays", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 39–45, 2019. doi: 10.25046/aj040505
76. Kosuke Gotani, Hiroyuki Takahira, Misumi Hata, Luis Guillen, Satoru Izumi, Toru Abe, Takuo Suganuma, "A Proposal of Control Method Considering the Path Switching Time of SDN and Its Evaluation", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 388–393, 2019. doi: 10.25046/aj040447
77. Constantina Costopoulou, Maria Ntaliani, Filotheos Ntalianis, "An Analysis of Social Media Usage in Winery Businesses", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 380–387, 2019. doi: 10.25046/aj040446
78. Mukundan Kandadai Agaram, "Intelligent Foundations for Knowledge Based Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 73–93, 2019. doi: 10.25046/aj040410
79. Tsutomu Tsuboi, "Quantitative Traffic Congestion Analysis Approach in Ahmedabad", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 183–189, 2019. doi: 10.25046/aj040324
80. Andro Majid, Djoko Budiyanto Setyohadi, Suyoto, "Estimation of Software Development Project Success using Fuzzy Logics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 280–287, 2019. doi: 10.25046/aj040236
81. Janet Bishung, Ooreofe Koyejo, Adaugo Okezie, Boma Edosomwan, Sylvester Ani, Abisola Ibrahim, Austin Olushola, Isaac Odun-Ayo, "A Critical Analysis of Topics in Software Architecture and Design", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 211–220, 2019. doi: 10.25046/aj040228
82. Ahmed Mohammed Alghamdi, Fathy Elbouraey Eassa, "Parallel Hybrid Testing Tool for Applications Developed by Using MPI + OpenACC Dual-Programming Model", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 203–210, 2019. doi: 10.25046/aj040227
83. Halikul Lenando, Mohamad Alrfaay, Haithem Ben Chikha, "Multiple Social Metrics Based Routing Protocol in Opportunistic Mobile Social Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 176–182, 2019. doi: 10.25046/aj040223
84. Jonathan Lockhart, Carla Purdy, Philip Wilsey, "Critical Embedded Systems Development Using Formal Methods and Statistical Reliability Metrics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 231–247, 2019. doi: 10.25046/aj040123
85. Anas Abouzahra, Ayoub Sabraoui, Karim Afdel, "A Practical Approach for Extending DSMLs by Composing their Metamodels", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 358–371, 2018. doi: 10.25046/aj030644
86. Kristóf Csorba, Ádám Budai, "cv4sensorhub – A Multi-Domain Framework for Semi-Automatic Image Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 159–164, 2018. doi: 10.25046/aj030620
87. Maysoon Abdullah Mansor, "Economic and Social Sustainability for Iraqi Middle Provinces", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 447–453, 2018. doi: 10.25046/aj030551
88. Imad Hasan Tahini, Alex Dadykin, "Proposed System of New Generation LMS Using Visual Models to Accelerate Language Acquisition", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 277–287, 2018. doi: 10.25046/aj030533
89. Vittorio Miori, Dario Russo, Luca Ferrucci, "Supporting Active Aging Through A Home Automation Infrastructure for Social Internet of Things", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 173–186, 2018. doi: 10.25046/aj030415
90. Shouq. Al Awadhi, Noor. Al Habib, Dalal Al-Murad, Fajer Al deei, Mariam Al Houti, Taha Beyrouthy, Samer Al-Kork, "Interactive Virtual Reality Educational Application", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 72–82, 2018. doi: 10.25046/aj030409
91. Laud Charles Ochei, Christopher Ifeanyichukwu Ejiofor, "Evaluating the effect of Locking on Multitenancy Isolation for Components of Cloud-hosted Services", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 92–99, 2018. doi: 10.25046/aj030312
92. Khaled Slhoub, Marco Carvalho, "Towards Process Standardization for Requirements Analysis of Agent-Based Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 80–91, 2018. doi: 10.25046/aj030311
93. Kaoutar Jenoui, Abdellah Abouabdellah, "A decision-making-approach for the purchasing organizational structure in Moroccan health care system", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 195–205, 2018. doi: 10.25046/aj030223
94. Htwe Nu Win, Khin Thidar Lynn, "Community Detection in Social Network with Outlier Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 21–27, 2018. doi: 10.25046/aj030203
95. Wei-Hsin Huang, Huei-Ming Chiao, Wei-Hsin Huang, "Innovative Research on the Development of Game-based Tourism Information Services Using Component-based Software Engineering", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 451–459, 2018. doi: 10.25046/aj030155
96. Susan Gottschlich, "A Taxonomy for Enhancing Usability, Flexibility, and Security of User Authentication", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 225–235, 2017. doi: 10.25046/aj020627
97. Michael Pelosi, Michael Brown, Kinza Ahmad, "Improved Hybrid Opponent System for Professional Military Training", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1804–1814, 2017. doi: 10.25046/aj0203220
98. Loretta Henderson Cheeks, Ashraf Gaffar, Mable Johnson Moore, "Modeling Double Subjectivity for Gaining Programmable Insights: Framing the Case of Uber", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1677–1692, 2017. doi: 10.25046/aj0203209
99. Saleh Mohamed Alnaeli, Melissa Sarnowski, Md Sayedul Aman, Ahmed Abdelgawad, Kumar Yelamarthi, "Source Code Vulnerabilities in IoT Software Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1502–1507, 2017. doi: 10.25046/aj0203188
100. Ayano Fujiwara, "The effect of employing knowledge workers from technologically advanced countries: The knowledge spillover caused by the mobility of knowledge workers in electronic industries in Asia", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1342–1349, 2017. doi: 10.25046/aj0203169