1          Introduction and Purpose

In Interactive Learning Environment (ILE) the evaluation of learners is based on interactions analysis when accessing e-learning systems.

Several studies [1, 2, 3] look into calculation and deduction of indicators from analysis of interaction traces, in order to extract meaningful information about the learner activity and behavior, this allows tutors to observe, assess and regulate the activities of learners .

To some extent, the choice of data to analyse mainly depends on the objectives of observation, supposedly with individual or collective character, using quantitative or qualitative value, knowledge’s study. The use of extracted knowledge from traces has enabled the development of several sectors such machine learning, artificial intelligence, e-learning and e-commerce. A concept that turns more and more to realization in real time [4, 5]. In fact, exploring traces as a background information identifies learners’ navigation paths and reconstruct their activities. [6] demonstrate the relevance of navigation in systems oriented toward the elderly, indicating that linear navigation is more suitable for these users, through comparing two designs of the same email Web application with linear and hypertextual navigation styles. However, the use of traces is a common approach by focusing on the processing on digital traces; just a few works involves another kind of traces in this aspect of the research. [7] propose a system that evaluates the activity of learners using log files data, based on four indicators; classification indicator of learners based on their activity (weekly), indicator of activity of each learner throughout the course (weekly), learner classification indicator according to their performance (for each event) and performance indicator of each learner.

The active involvement of learners in their learning process can be reinforced by a meta-cognitive regulation strategy by alternating learning with repeated tests of their knowledge [8]. [9] propose that the computer support provided to the tutor enable him/her to follow up individually a collective activity in which the learner is engaged. In complicated learning conditions, learners have an increased commitment to learn with increased cognitive effort that would paradoxically result in more sustained attention [10, 11]. The voluntary efforts

of the learner result in the acquisition of knowledge, however the quality of learning returns with added value, which positively influences cognitive performance.

Effort, or as [12] call, motivational excitement, occurs only if a number of conditions are met. First, there should be expectation that some behavior will lead to some desirable outcome values (task incentive). Secondly, the behavior must be difficult but considered to be at its capacity and justified by the potential gain. A concept that is very close to mental effort is the mental load [13].

The effort is expected to be associated with cognitive processes reflected by the Electroencephalography measurements. Where cerebral activity in default mode [14, 15] and cortical inhibition [16, 17] suggests that this measure reflects different levels of effort, with a high alpha for low effort levels. The human cognitive system is a complex processing device capable of acquiring, conserving, processing and transmitting information. Cognition improves gradually from birth through the interactions of the child with his/her environment. Cognitive development appears punctuated by acquisitions that are necessary for the establishment of subsequent cognitive processes. Actually, cognitive science has identified four key factors for learning: attention, active learner engagement, feedback and consolidation [18]. These four factors are based on the functioning characteristics of the cognitive system and are deeply rooted at an early age.

On the basis of the [19] model, the cognitive architecture underlying the learning phenomena consists of working memory and long-term memory. In neuroscience, memory is defined as the ability to acquire, retain and restore information. Stimulated by more than a century of neuropsychological study, researchers have come to distinguish these forms of memory. Maintaining a certain amount of memory in short-term memory would eventually allow it to be transferred into long-term memory for more sustainable storage. This process would be facilitated by mental work of repetited information , hence the expression ”working memory” [20]. The working memory can be defined as the temporary maintenance and the manipulation of information during the realization of cognitive spots. However, longterm memory can be seen as an adaptive function of the human being to his cultural, social and emotional environment [21].

Knowledge is an element of our memory that allows us to recognize the world around us, to interpret, to understand our environment, but also to act on it. Knowledge is mainly evaluated in the form of a simple reminder or through comprehension activities. In the didactic of learning environments, knowledge given to be study always comes from an institutional demand and what individuals have to build are knowledge that will enable them to analyze a situation or to allow an action.

When designing a pedagogical scenario, knowledge derived from the psychology of learning, [22] presents human knowledge by categorizing it into six different formats, these formats corresponding to particular learning processes. Contrary to the proposal of [23], which updated the taxonomy of Bloom, these authors do not establish any hierarchy between the different knowledge formats and even consider that some knowledge does not exist according to certain formats.

Based on the idea that cognition groups together the various mental processes of perception, memorization and reasoning, [24] made an important contribution when it differentiates four types of cognitive reasoning: deductive reasoning, inductive reasoning, abductive reasoning and reasoning by analogy. However, reasoning takes the form of a cognitive process to produce a conclusion based on premises. The studies conducted in the cognitive psychology help to structure behavioral models. However, the analysis of interactions provides a large amount of data approach that identify the motivational aspect as a point that cannot be detached from cognitive and activity aspect of learner. The number of definitions of the motivation term is almost a large topic. This happens because motivation represents a hypothetical construct and not a material entity, which means that we use observable manifestations to infer a state of motivation.

This paper is an extension of work originally reported in Proceedings of the 11th International Conference on Intelligent Systems: Theories and Applications (SITA). Thereby, we expose firstly the methodology of structuring a course content on which we base our modeling indicators approaches [33]. Secondly we modeled cognition [34], activity and motivation [35] indicators also approached in [36], by addressing their structure that use navigation traces on a learning session, likewise as a concept of procedures integrated in the adaptive learning content system we named S-CAMO. Developing those approaches complete studies that use simpler types of indicators [1, 2, 3], which is not sufficient to reflect the learner profile in a general way. The last section expose the statistical validation of the proposed indicators modeling.Motivation is not a skill, but a state of mind influenced by internal and external factors to the individual. [25] defines it as ”the orientation of the conduct, the efforts deployed and finally the persistence of this effort over time”. In a psychological perspective, [26] emphasizes the characteristics of the environment in which behavior is manifested: motivation is defined as the set of ”physiological and psychological processes responsible for initiation, maintenance and cessation behavior and the appetitive or aversive value conferred on the elements of the environment on which this behavior is exercised ”. The work environment is likely to influence motivation, and also the work environment of the worker and, more specifically, the social beliefs of the employee [27]. The work of [28] on the theory of objectives setting establishes that an individual is motivated when he is working towards a clear, challenging and meaningful objective for which he will have a feedback. The proponents of the theory of self-determination, on the other hand, consider that the individual is motivated by the needs of social belonging, competence and autonomy, which would be the most decisive need [29]. The theory [30] relates motivation to three variables: valence (value attributed to the result), instrumentality (relation between performance and retribution) and expectation (results) which combined, can induce motivation. On the other hand, analysing resistance to learn is directly observable at a large scale as some students fail assessments on a course. [31] suggests, that this resistance manifests through a reduction in motivation, suggested by lateness or absence from sessions, or a reduction in participation in group exercises and discussions. The individual learning resistance is linked to his/her defense mechanisms as a set of actions that purpose to the reduction or elimination of any change in relation to the integrity and stability of his/her biopsychic [32].

2          Educational collections for course structure

Conceiving a flexible learning content toward learner profile with his different needs requires and includes structured components.

We propose an educational collections Ct that offer the possibility of being redesigned under different personalization in proportion to the same degree of subordination of the collection to form a semantic model of dynamic content.

Following the conceptualization of a structured educational content (”As shown in Algorithm 1”), educational collections can be one of four types; A course Cr_i ”(2)”, a chapter Ch_j ”(3)”, a unit U_k ”(4)” or an educational component Cpt_h ”(5)” as a pedagogical concept Cp_h or a pedagogical activity Ap_h.

2.1         Pedagogical objective

The pedagogical objective O^Ct ”(1)” of a collection is conceptualized from the objective to learn dimension Ld and the junction of the cognitive Cd with the knowledge Kd dimensions (”As shown in Figure 1”).

                                    O^Ct = {Ld, Cd, Kd}                               (1)

Algorithm 1: Structuring pedagogical content

{OCpth}h∈[1···n+m]

Figure 1: Pedagogical objective

Tutors create educational collections with taking into account the progress of the objectives to acquire throughout the learning process (”As shown in Algorithm 1”). By means of joining to each collection a learning objective, however, for each one is assigned one of the 24 levels (”As shown in Table 1”) arranged by the junction of the knowledge type and the cognitive process identified in the revised Bloom Taxonomy [23]. Each level is represented by the coefficient P d(Ct) (”As shown in Table 1”) which we propose is equal to the product of the value of the

knowledge type {1..4} and the value of the cognitive process {1..6} compared to all levels of the taxonomy. Table 1: Coefficient of learning objectives

3          Modeling Cognitive, activity and motivation indicators

In the framework of developing Cognitive, Activity and Motivation aspects of learners, deduced from navigation traces and consulting concepts presented in course content. As a fundament of an adaptive learning content, we detail these indicators that are updated each time the learner progresses and pass his unit test.

                                       CUk , AUk , MUk                                  (6)

3.1         Navigation traces

The navigation traces are collected in the form of a web browsing data, the flow of these interactions data reflects the interaction of a learner (”As shown in Figure 3”).

A trace T r^k(t_i) in a learning session with an index k regroups all interactions data captured at every time of consultation t_i, it is characterized by; t: Duration of learning a unit.

N: Total number of traces visited when learning a unit. f_{T r}k_(ti): Frequency of consultation of an interaction trace.

d_{T r}k_(ti): Duration of an interaction trace.

3.2         Cognitive indicator

The cognitive activity indicator ”(7)” for a learner at a given unit is a component defined by a 3−tuple of explanatory variables related to that unit.

^CUk = (Dknowledge_Uk ,Dmemorizing_Uk ,Dreasoning_Uk )                     (7)

D_knowledgeUk : The knowledge degree is calculated after the learner accessed and proceeded to a unit as well as having passed this unit test T ^Uk (”As shown in Figure 2). Indeed, a unit test is a transit from one unit to another in order to ensure the assimilation of the pedagogical objectives and knowledge presented to the learner related to each pedagogical component of this unit (”As shown in Algorithm 2”). However, the calculation of knowledge acquisitions follows a linear process in the learning activity.

Algorithm 2:                  Processing the level of knowledge

^{Dknowledge_Uk ^}

1: for each U_k ∈ [U₁ ···U_c] do

2:               for each [Cpt₁ ^···Cpt_n+m] do

3: for v⁰ questions Q_i^Cpth associated with the pedagogical component Cpt_h. Each acquisition Aq_Cpth of this pedagogical component Cpt_h is linked to a vector [1..v⁰] ⊂ [1..v] of questions

Cpt^h

        Q_i           . do

4: if Pv10 R(QiCpth) ≥ v20 , with R(QiCpt) as answers given by a learner then

5:                                 Aq_Cpth = 1,          Cpt_h is acquired.

6:                          else if

7:  then Aq_Cpth = 0,              Cpt_h is non acquired.

8:                          end if

9:                   end for

10:          end for

                                   D                          = Pn1+mAqCpth

11:          Calculate        knowledge_{Uk                  n+m}           ,

Dknowledge_Uk ^∈ [0,1].

12:        if Pn1+mAqCpth ≥ (n+2m). then

13: T ^Uk is assessed validated with state_DknowledgeUk = acquired and learner is redirected to unit U_k+1. 14: else if

15: then T ^Uk is assessed not validated with state_DknowledgeUk = non ⁻ acquired and the learner is redirected to the same unit U_k.

16:           end if

17: end for

18: Save the learner degree and state of knowledge

{DknowledgeUk ,stateDknowledgeUk } .

D_memorizingUk : The memorization degree is the quantification of memorized knowledge in a unit U_k. This degree represents the sum of the acquisitions multiplied by the coefficient of objective of a pedagogical component to which they correspond, divided by the number of acquisitions to be acquired (n+m) for each unit (”As shown in Algorithm

3”).

Algorithm 3: Processing the memorization degree

^{Dmemorizing_Uk ^}

1: for each U_k ∈ [U₁ ···U_c] do

2:               for each [Cpt₁ ^···Cpt_n+m] do

3:  for All acquisitions ^Aq_Cpth of a pedagogical component Cpt_h do

4:                                                  Calculate DmemorizingUk =

Pnh=1+mAqCpth×P d(Cpth)

Pnh=1+mP d(Cpth)

5:                   end for

6:            end for

7: end for

8: Save the degree of memorization {D_memorizingUk }

D_reasoningUk : The reasoning degree refers to the ability of an effective knowledge understanding, proposed on a unit U_k, it represents the sum of all

Cpt the given answers R(Q_i ) multiplied by their logical reasoning coefficient divided by the total number v of questions present in the unit test T ^U (”As shown in Algorithm 4”).

Algorithm 4:                      Processing the reasoning degree

^{Dreasoning_Uk ^}

1: for each U_k ∈ [U₁ ···U_c] do

2:               for each [Cpt₁ ^···Cpt_n+m] do

3:                             for each {QiCpth}i∈[1..v] , where R(QiCpth) is

Cpt^h the learner’s answer to the question Q_i  do

Cpt^h

4:                             if ∃ dependence between Q_i                      and

Cpt^h

        Q_i−₁        then

                                           _             Cpt_h                                   Cpt_h

                                            0, R(^Q_i                       ) = 0&R(^Q_i−₁ ) = 0

_

                Cpth | QCpth) = 1                  Cpt

        P (Qi                    i−1                 2, R(Qi            h) = 1&R(QiCpt−1h) = 0



                                          ^            Cpt_h                                   Cpt_h

                                            1, R(Q_i                       ) = 1&R(Q_i−₁ ) = 1

(8)

Cpt

        R                                          Cpt    Cpt           (^Q_iCpth)∗P (^Q_iCpth|^Q_iCpt₋₁h)

_Uk

3.3         Activity indicator

The activity indicator ”(11)” measures the degree to maneuver learning on a given unit. It is a component defined by 3 − tuple of explanatory variables linked to this unit (”As shown in Figure 3”).

                            A^Uk = (Res_Uk , Int_Uk , Ef r_Uk )                     (11)

Res_Uk : Resistance to learn expresses the difficulty of learning all the pedagogical objectives presented in a unit.

P

j∈[1..N]fT rk(ti)(j) ∗

             ResUk = N  dT r2 k(ti)(j)            (12)

Int_Uk : Learning intensity expresses the intensity to learn all the pedagogical objectives presented in a unit.

                                          Int_Uk = N/t                                  (13)

Ef r_Uk :    Learning effort expresses the effort to learn all the pedagogical objectives presented in a unit.

                     Ef rUk = P d(Uk) ∗ ResUk ∗ IntUk 2 ∗ t              (14)

3.4         Motivation indicator

The motivation indicator ”(15)” measures the degree of motivation in learning all the pedagogical objectives presented in a unit (”As shown in Figure

3).

                MUk = X P d(Uk) ∗ fT r(ti)(j) ∗ dT r(ti)(j)                 (15)

                                                         N                  t

j∈[1..N]

4          Method

The perplexity of the proposed indicators modelization, depends on the reliability of the proposed scale. In fact, we implemented all procedures of our approach in an adaptive learning content system we named S-CAMO.

4.1         Learning environment

The system S-CAMO comprises the module ”web navigation tracker” which is an extension that retrieves the data of navigation on the Internet between the time of the connection and the disconnection to the system. This extension will appear as an icon in the browser’s menu bar, where the learner will have to enter his username and password and then log in. If the learner exists in the database, then the profile (Learner name, learner identifier) will persist in the local storage of ”Google Chrome”. However, just after installing this extension, authentication will remain active as long as the learner does not choose to disconnect manually. This extension detects and traces access to any web page in any open tab of the browser ”Google Chrome”, as well as verifies the learner’s adhesion to each learning session opened in the platform, through the validity of its domain name. The extension sends the navigation or interaction data to the web server.

4.1.1         Interception of navigation data

For each successful authentication of a learner, the web service provided by the platform retrieves his identifier (”As shown in Figure 4). However, whenever the learner changes web page (url), the browser adds a new function that will be delegated to the extension level. The process of the new function includes:

1. The retrieval of the information of each navigation data which are url, the url parameters (parameter of the request), the title of the web page;
2. Testing and verifying the authentication at theplatform, in order to send the information associated with this new intercepted and formulated navigation data.

Figure 4: Process of intercepting a navigation trace by ”web navigation tracker” module

4.2         Participants

The sample of learners on which the test was conducted is equal to 50 students, organized into 3 approximately equal groups. The group G₁ and G₂ contained 17 learners, while G₃ contained 16 ones. These learners were heterogeneous in relation to their level of education and had at least an associate degree.

Learners had a laptop, nevertheless, those who could not bring one, had at their disposal laptops or desktop laboratory. For the relevance of the study, we made sure that the module ”web navigation tracker” was installed before the test.

4.3         Materials

We create a course (”As shown in Table 2”) while respecting the learning content structuring described above, also to each educational collection was assigned a pedagogical objective.

All students followed the same course and performed the presented tests unit .

5          Task analysis results

This study recovers values of all indicators’ variables according to the proposed approach, by studying the learners traits.

We have obtained the number of learners who have acquired or not the knowledge concepts presented at level of the chapter Ch₁ structure (”As shown in Table 3).

Table 3: Tests unit of chapter Ch₁ results.

Table 4: Abbreviation of indicators variables abbreviation         variables

                             Dknowledge_Uk                        v1

                             Dmemorizing_Uk                       v2

                              Dreasoning_Uk                         v3

The three groups present approximately the same characteristics with respect to the educational components of the studied chapter. In fact, all groups reflect a strong correlation among the variables of the cognitive indicator relatively to all the tested units. All groups have no resistance to learn in relation to all units except for group G₁ who presents moderately a resistance to learn unit U₁. All groups have a low intensity to learn relatively all units, however more is the intensity to learn less there is a resistance. The effort to acquire knowledge and to memorize for the group G₁ remains average relatively to unit U₁ while it is low for groups G₂ and G₃, however there is no effort to reason for all groups for all units. All groups show moderate resistance linked to the effort to learn the unit U₂, however, the group G₁ has an effort related to the resistance at unit U₁. Unlike group G₂ and G₃ where resistance is not related to effort. As well as group G₃ has a resitance related to the effort at unit U₃ while group G₂ and G₃ where resistance is not related to effort. Intensity to learn is moderately low compared to all units. Motivation is weakly related to the variables of the cognitive indicator, it is moderately linked to the resistance to learn and weakly related to the intensity of work. All groups have a moderately weak relationship between motivation and effort .

Table 2: Structure of Ch₁.

Table 5: Correlation matrix (Pearson) for group G₁ toward the unit U₁.

Table 6: Correlation matrix (Pearson) for group G₂ toward the unit U₁.

Table 7: Correlation matrix (Pearson) for group G₃ toward the unit U₁.

Table 8: Correlation matrix (Pearson) for group G₁ toward the unit U₂.

Table 9: Correlation matrix (Pearson) for group G₂ toward the unit U₂.

Table 10: Correlation matrix (Pearson) for group G₃ toward the unit U₂.

Table 11: Correlation matrix (Pearson) for group G₁ toward the unit U₃.

Table 12: Correlation matrix (Pearson) for group G₂ toward the unit U₃.

Table 13: Correlation matrix (Pearson) for group G₃ toward the unit U₃.

an educational content structure which was already implemented and which mobilizes the achievement of the specific estimation of the updated indicators after each pedagogical unit. The results show specifically a high relation among the cognition attributes and a moderatly interrelated linkage among all the studied variables. We assume that the scale we have presented in this paper prove the efficiency of the proposed modelization.

  Conclusions and discussion

The many aspects profile of learners serve on adapting their learning environment. However, the lack of holistic measures in learning environment make us propose a compilation of indicators by developing a theoretical conception that include the cognition, activity and motivation in a learning process.The study involve the measurement of the proposed indicators’ structures, it reflects the behavior and trend of learners on processing information in learning interactions. In this context, we develop

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4. El Mehdi Ben Laoula, Marouane Midaoui, Mohamed Youssfi, Omar Bouattane, "Improving License Plate Identification in Morocco: Intelligent Region Segmentation Approach, Multi-Font and Multi-Condition Training", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 262–271, 2023. doi: 10.25046/aj080329
5. Sutham Satthamsakul, Ari Kuswantori, Witsarut Sriratana, Worapong Tangsrirat, Taweepol Suesut, "Landmarking Technique for Improving YOLOv4 Fish Recognition in Various Background Conditions", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 100–107, 2023. doi: 10.25046/aj080312
6. Víctor Manuel Bátiz Beltrán, Ramón Zatarain Cabada, María Lucía Barrón Estrada, Héctor Manuel Cárdenas López, Hugo Jair Escalante, "A Multiplatform Application for Automatic Recognition of Personality Traits in Learning Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 30–37, 2023. doi: 10.25046/aj080204
7. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, Thiagarajan Murugasamy, "An Ensemble of Voting- based Deep Learning Models with Regularization Functions for Sleep Stage Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 84–94, 2023. doi: 10.25046/aj080110
8. Hanae Naoum, Sidi Mohamed Benslimane, Mounir Boukadoum, "Encompassing Chaos in Brain-inspired Neural Network Models for Substance Identification and Breast Cancer Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 32–43, 2022. doi: 10.25046/aj070304
9. Mohammad Givi Efgivia, Al-Bahra, Abdul Karim Halim, R Andi Ahmad Gunadi, "The Influence of Online Learning on Learning Interest and Motivation and Their Impact on Student Achievement at Educational Technology Study Program – Ibn Khaldun University Bogor", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 6, pp. 82–87, 2021. doi: 10.25046/aj060612
10. Piotr Szymkowski, Khalid Saeed, Nobuyuki Nishiuchi, "Hiragana and Katakana Minutiae based Recognition System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 6, pp. 54–59, 2021. doi: 10.25046/aj060608
11. Wided Hechkel, Brahim Maaref, Néjib Hassen, "Coupled Apodization Functions Applied to Enhance Image Quality in Ultrasound Imaging using Phased Arrays", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 149–157, 2021. doi: 10.25046/aj060517
12. Chia-En Yang, Yang-Ting Shen, Shih-Hao Liao, "SyncBIM: The Decision-Making BIM-Based Cloud Platform with Real-time Facial Recognition and Data Visualization", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 16–22, 2021. doi: 10.25046/aj060503
13. Wilawan Inchamnan, Jiraporn Chomsuan, "The Gamification Design for Affordances Pedagogy", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 138–146, 2021. doi: 10.25046/aj060416
14. Valerii Dmitrienko, Serhii Leonov, Aleksandr Zakovorotniy, "New Neural Networks for the Affinity Functions of Binary Images with Binary and Bipolar Components Determining", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 91–99, 2021. doi: 10.25046/aj060411
15. Juyana Islam, M. A. H. Akhand, Md. Ahsan Habib, Md Abdus Samad Kamal, Nazmul Siddique, "Recognition of Emotion from Emoticon with Text in Microblog Using LSTM", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 347–354, 2021. doi: 10.25046/aj060340
16. Mohammad AL-Tahat, Issam Jalham, Abbas Al-Refaie, Mohammed Fawzi Alhaj Yousef, "Activity-Based Cost Estimation Model for The Extrusion of Variant Aluminum Profiles", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 172–181, 2021. doi: 10.25046/aj060320
17. Svetlana Segarceanu, George Suciu, Inge Gavăt, "Environmental Acoustics Modelling Techniques for Forest Monitoring", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 15–26, 2021. doi: 10.25046/aj060303
18. Nadia Jmour, Slim Masmoudi, Afef Abdelkrim, "A New Video Based Emotions Analysis System (VEMOS): An Efficient Solution Compared to iMotions Affectiva Analysis Software", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 990–1001, 2021. doi: 10.25046/aj0602114
19. Murtadha Arif Bin Sahbudin, Chakib Chaouch, Salvatore Serrano, Marco Scarpa, "Application-Programming Interface (API) for Song Recognition Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 846–859, 2021. doi: 10.25046/aj060298
20. Abraham Adiputra Wijaya, Inten Yasmina, Amalia Zahra, "Indonesian Music Emotion Recognition Based on Audio with Deep Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 716–721, 2021. doi: 10.25046/aj060283
21. Phillip Blunt, Bertram Haskins, "A Model for the Application of Automatic Speech Recognition for Generating Lesson Summaries", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 526–540, 2021. doi: 10.25046/aj060260
22. Shahnaj Parvin, Liton Jude Rozario, Md. Ezharul Islam, "Vehicle Number Plate Detection and Recognition Techniques: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 423–438, 2021. doi: 10.25046/aj060249
23. Rosula Reyes, Justine Cris Borromeo, Derrick Sze, "Performance Evaluation of a Gamified Physical Rehabilitation Balance Platform through System Usability and Intrinsic Motivation Metrics", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1164–1170, 2021. doi: 10.25046/aj0601131
24. Anass Barodi, Abderrahim Bajit, Taoufiq El Harrouti, Ahmed Tamtaoui, Mohammed Benbrahim, "An Enhanced Artificial Intelligence-Based Approach Applied to Vehicular Traffic Signs Detection and Road Safety Enhancement", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 672–683, 2021. doi: 10.25046/aj060173
25. Deisy Chipana-Collahua, Mariluz Chipana-Collahua, Rosa Villegas-Ortiz, Brian Meneses-Claudio, Hernan Matta-Solis, "Type 2 Diabetes Risk and Physical Activity in outpatients treated in Health Centers in a District of North Lima, 2020", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1651–1656, 2020. doi: 10.25046/aj0506196
26. Khalil Mustafa Ahmad Yousef, Bassam Jamil Mohd, Yusra Abd-Al-Haleem Al-Khalaileh, Ahlam Hani Al-Hmeadat, Bushra Ibrahim El-Ziq, "Automatic License Plate Detection and Recognition for Jordanian Vehicles", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 699–709, 2020. doi: 10.25046/aj050684
27. Katerina Prihodova, Miloslav Hub, "Hand-Based Biometric Recognition Technique – Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 689–698, 2020. doi: 10.25046/aj050683
28. Gede Putra Kusuma, Jonathan, Andreas Pangestu Lim, "Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 315–322, 2020. doi: 10.25046/aj050638
29. Fei Gao, Jiangjiang Liu, "Effective Segmented Face Recognition (SFR) for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 36–44, 2020. doi: 10.25046/aj050605
30. Pamela Zontone, Antonio Affanni, Riccardo Bernardini, Leonida Del Linz, Alessandro Piras, Roberto Rinaldo, "Supervised Learning Techniques for Stress Detection in Car Drivers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 22–29, 2020. doi: 10.25046/aj050603
31. Kodai Kitagawa, Koji Matsumoto, Kensuke Iwanaga, Siti Anom Ahmad, Takayuki Nagasaki, Sota Nakano, Mitsumasa Hida, Shogo Okamatsu, Chikamune Wada, "Posture Recognition Method for Caregivers during Postural Change of a Patient on a Bed using Wearable Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1093–1098, 2020. doi: 10.25046/aj0505133
32. Suni S S, K Gopakumar, "Dense SIFT–Flow based Architecture for Recognizing Hand Gestures", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 944–954, 2020. doi: 10.25046/aj0505115
33. Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah, Galymzhan Abdimanap, "Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 934–943, 2020. doi: 10.25046/aj0505114
34. 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
35. Rohith Raj S, Pratiba D, Ramakanth Kumar P, "Facial Expression Recognition using Facial Landmarks: A Novel Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 24–28, 2020. doi: 10.25046/aj050504
36. Natalya Viktorovna Matveeva, Ludmila Vladimirovna Makar, "Ethics as a Motivation Indicator in Second Language Vocational Digital Teaching", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 776–782, 2020. doi: 10.25046/aj050492
37. Adonis Santos, Patricia Angela Abu, Carlos Oppus, Rosula Reyes, "Real-Time Traffic Sign Detection and Recognition System for Assistive Driving", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 600–611, 2020. doi: 10.25046/aj050471
38. Moulay Smail Bouzakraoui, Abdelalim Sadiq, Abdessamad Youssfi Alaoui, "Customer Satisfaction Recognition Based on Facial Expression and Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 594–599, 2020. doi: 10.25046/aj050470
39. Dmytro Kucherov, Olha Sushchenko, Andrii Kozub, Volodymyr Nakonechnyi, "Assessing the Operator’s Readiness to Perform Tasks of Controlling by the Unmanned Aerial Platforms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 457–462, 2020. doi: 10.25046/aj050454
40. Ali Al-Rashid, "Composition of Methods to Ensure Iris Liveness and Authenticity", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 130–143, 2020. doi: 10.25046/aj050417
41. Chafaa Hamrouni, "Multi-Agent Data Recognition System Based on Received Signal in Antenna on Board Telecom System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 94–98, 2020. doi: 10.25046/aj050412
42. Krina B. Gabani, Mayuri A. Mehta, Stephanie Noronha, "Racial Categorization Methods: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 388–401, 2020. doi: 10.25046/aj050350
43. Suharjito, Atria Dika Puspita, "Face Recognition on Low Resolution Face Image With TBE-CNN Architecture", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 730–738, 2020. doi: 10.25046/aj050291
44. Yeji Shin, Youngone Cho, Hyun Wook Kang, Jin-Gu Kang, Jin-Woo Jung, "Neural Network-based Efficient Measurement Method on Upside Down Orientation of a Digital Document", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 697–702, 2020. doi: 10.25046/aj050286
45. Evan Kristia Wigati, Gede Putra Kusuma, Yesun Utomo, "Combination of Salient Object Detection and Image Matching for Object Instance Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 584–591, 2020. doi: 10.25046/aj050273
46. Halima Begum, Muhammed Mazharul Islam, "A Study on the Effects of Combining Different Features for the Recognition of Handwritten Bangla Characters", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 197–203, 2020. doi: 10.25046/aj050225
47. Walaa Gouda, Randa Jabeur Ben Chikha, "NAO Humanoid Robot Obstacle Avoidance Using Monocular Camera", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 274–284, 2020. doi: 10.25046/aj050135
48. Noor A.Hussein, Mohamed Ibrahim Shujaa, "Smart Ambulance: Speed Clearance in the Internet of Things paradigm using Voice Chat", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 280–284, 2019. doi: 10.25046/aj040635
49. Woochun Jun, "Development of Evaluation Metrics for Learners in Unplugged Activity", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 216–219, 2019. doi: 10.25046/aj040628
50. Shanmuganathan Vasanthapriyan, Malith De Silva, "Optical Braille Recognition Software Prototype for the Sinhala Language", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 221–229, 2019. doi: 10.25046/aj040427
51. Hakan Tora, Gursel Karacor, Baran Uslu, "Vowel Classification Based on Waveform Shapes", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 16–24, 2019. doi: 10.25046/aj040303
52. Weiguo Wan, Hyo Jong Lee, "Deep Feature Representation for Face Sketch Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 107–111, 2019. doi: 10.25046/aj040214
53. Ivaylo Penev, Milena Karova, Mariana Todorova, Danislav Zhelyazkov, "Robot Self-Detection System", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 391–402, 2018. doi: 10.25046/aj030647
54. Denise Ashe, Alan Eardley, Bobbie Fletcher, "An Empirical Study of Icon Recognition in a Virtual Gallery Interface", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 289–313, 2018. doi: 10.25046/aj030637
55. Tamarafinide Victory Dittimi, Ching Yee Suen, "Modified HOG Descriptor-Based Banknote Recognition System", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 354–364, 2018. doi: 10.25046/aj030541
56. Mikhail Rodionov, Zhanna Dedovets, "Developing Students’ Motivation for Learning through Practical Problems in School", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 258–266, 2018. doi: 10.25046/aj030531
57. Sudipta Saha, Aninda Saha, Zubayr Khalid, Pritam Paul, Shuvam Biswas, "A Machine Learning Framework Using Distinctive Feature Extraction for Hand Gesture Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 72–81, 2018. doi: 10.25046/aj030510
58. Margaret Lech, Melissa Stolar, Robert Bolia, Michael Skinner, "Amplitude-Frequency Analysis of Emotional Speech Using Transfer Learning and Classification of Spectrogram Images", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 363–371, 2018. doi: 10.25046/aj030437
59. Sherif Said, Samer Al-Kork, Vishnu Nair, Itta Gowthami, Taha Beyrouthy, Xavier Savatier, M Fayek Abdrabbo, "Experimental Investigation of Human Gait Recognition Database using Wearable Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 201–210, 2018. doi: 10.25046/aj030418
60. Ola Surakhi, Mohammad Khanafseh, Yasser Jaffal, "An enhanced Biometric-based Face Recognition System using Genetic and CRO Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 116–124, 2018. doi: 10.25046/aj030316
61. Eleni Bougioukou, Nikolaos Toulgaridis, Maria Varsamou, Theodore Antonakopoulos, "Hardware Acceleration on Cloud Services: The use of Restricted Boltzmann Machines on Handwritten Digits Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 483–495, 2018. doi: 10.25046/aj030159
62. Yuksel Arslan, Abdussamet Tanıs, Huseyin Canbolat, "A Relational Database Model and Tools for Environmental Sound Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 145–150, 2017. doi: 10.25046/aj020618
63. Zdenko Balaž, Davor Predavec, "Cognitive Cybernetics vs. Captology", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 107–118, 2017. doi: 10.25046/aj020614
64. Krystian Kapala, Dawid Krawczyk, Stefan Brachmanski, "Emotional state recognition in speech signal", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1654–1659, 2017. doi: 10.25046/aj0203205
65. Swapnil Vitthal Tathe, Abhilasha Sandipan Narote, Sandipan Pralhad Narote, "Face Recognition and Tracking in Videos", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1238–1244, 2017. doi: 10.25046/aj0203156
66. Mohammed Myasar Ali, Hui Liu, Norbert Stoll, Kerstin Thurow, "Recognition and Position Estimation for Multiple Labware Transportation Using Kinect V2 and Mobile Robots", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1218–1226, 2017. doi: 10.25046/aj0203154
67. Mohammed Faeik Ruzaij Al-Okby, Sebastian Neubert, Norbert Stoll, Kerstin Thurow, "Low-cost Hybrid Wheelchair Controller for Quadriplegias and Paralysis Patients", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 687–694, 2017. doi: 10.25046/aj020388
68. Owais Qayum, Tahreem Sohail, "FenceBook a Geofencing based Advertisements Application Using Android", Advances in Science, Technology and Engineering Systems Journal, vol. 1, no. 5, pp. 27–33, 2016. doi: 10.25046/aj010506