1. Introduction

This paper is an extension of work initially presented in 2018 Fifth HCT Information Technology Trends (ITT), IEEE [1]. On average, students have different talents, distinct attitudes, and different levels of enthusiasm in their educational programs.  Although various students may share a major, it is, however, important to analyze students in many aspects, including the knowledge and skills they have learned. The students have a wide range of learning styles that reflect how they can analyze, plan, and respond to the learning environment [2]. Some students may be comfortable thinking of hypotheses and abstract ideas, while others feel better being a little more concrete with data. Some of the student body prefers active learning, interaction by taking notes and seeing the material presented [3]. It shows how different the students are to each other even within the same major.  There are three learners of the course. One learner who has begun to learn by auditory means, another who has begun with tactile activities and finally another learner who is visually dominated [4]. The challenge of the day is to equip our students with the information and skills they will need to accomplish their goals into their future professional careers, regardless of their personal preference. There are many majors in computing that require different knowledge and essential skills than those required for their future careers. To memorize everything here is not exactly effective; i.e., memorization is inefficient.  An inability to memorize is a type of learning disorder. Students with such a condition do not expend much effort in studying or preparing for exams. There are two different behaviours that those students might do. Some students might adopt a more in-depth approach when understanding the meaning, whereas the others might make a more strategic approach to understand such type of knowledge, get the best knowledge of their peers and gain related skills [5,6]. As it should be, learners often have their own personal values that affect their own personal ability to absorb new knowledge and learn new functional skills [7].

Knowing students’ personality patterns is extremely valuable in finding out their skills; the Myers-Briggs Type Indicator (MBTI) is very much in use. as a means to do just that. Neural dynamics testing techniques, as used in MBTI, helps with understanding the form of human personality. The four main personality traits are eye contact, emotional perception of facial expressions, sensitivity to language, and sensitivity to others’ physical appearance [8].

The first pattern is the orientation of energy: Extroversion (E) is preferable to those who prefer to be energized for situations, people and things (that is, for the external world) compared with their complement, preferring to direct their energy towards information, beliefs, ideas and explications (that is, the internal world) (I).

The second pattern is the information and things an individual wants to deal with; Those who prefer to deal with facts and describe clearly situations are sensitive (S). They prefer to deal with ideas, expectations and unknown factors that are not obvious, rather than their additions dependent on intuition. (N).

The third pattern is the sort of decision-making, People would rather make decisions based on objective logic and analysis- Thinking (T) than those who prefer to make decisions based on the values they believe important — feelings Feelings (F).

Fourth, how people make their planning, Those who prefer well-designed and planned lifestyles judge (J) against those who like flexibility, flow and react to things, i.e. perception (P) [9].

• Extraversion(E) vs Introversion (I)
• Sensing (S) vs Intuition(N)
• Thinking(T) vs Feeling(F)
• Judging vs Perceiving(P)

A person may be more inclined to be extroverted than an introvert. Likewise, a person could be more sensing than intuitive, more thoughtful than feeling and judging than perceived. The table below decipher examples of detected personalities.

Table 1: Sample types of personality

Each personality fits more than the others to a particular career; for example, the ISTJ personality type could fit more into the Systems administrator.

I: Propensity to think about things in the mind

S: Prefer ideas with practical applications

T: Take decisions on an impersonal basis, using a logical reasoning

J: Prefer detailed step-by-step instructions

Combining these four couples generates 16 alternative personality types that enable scientists to understand individuals and advance in a specific area through measurement (s). Students with a preference for introversion, probably doing a lot of thorough work or considering a problem, can find it difficult to live in a noisy or interactive environment [10]. Consequently, they can tend to pick less active and interrupted concentrations. The measurement of computer skills in vital terms and concepts, which are understandable and learning for students during their studies. We have to measure skills such as troubleshooting, network setup, audit protection framework, etc. In addition, it is possible to develop applications, systems, networks, etc.

Maintaining a system to ensure students’ personality types is extremely important for understanding their emotions, thinking, and behaviour. For example, understand if a student prefers to work alone or prefers to work with others. Prefers an extremely organized and fixed career, or someone who would like a flexible, open schedule, which allows him or her to be spontaneous. This information helps to determine which career(s) are appropriate for the preferences of students. There are no special advantages or disadvantages over these patterns as they complement each other. If a student has been detected as an Extroversion with a specific percentage, it is an Introversion of the supplement percentage; therefore, each pair is valid for each person but with different strength. The networking employee is expected to be self-driven to deal with network issues and not wait for outside directions. Extraverts tend to be more active when learning, while introverts tend to be more reflective when learning.

Figure 1: Learner comprehension layers

2. Literature Review

We cannot think of a single method of teaching suitable for all pupils of the same major. Most academic institutions follow the same method, which relies on the delivery of lectures, where students have to absorb the material and then repeat it in the review. This approach does not solve the gaps in student skills, expertise and competencies. Different learning styles define students they prefer to interpret knowledge in different ways [11]. Several models were developed to classify the way people learn; Jung’s psychological theory, also known as the Myers-Briggs Type Indicator, is the most famous model (MBTI) [12]. This test provides ample information about a person’s personality and the consequences of his or her education. The engineering department of the University of Western Ontario has demonstrated that in the first year of engineering courses men introverts, intuition, thinkers and judges are more likely to excel than extraverts, felt, sensed, and interpreted people at the lower end of their range [13]. They used an MBTI test and performance test on 119 students to determine whether that particular group of seniors would perform very well later in a class like that. The type of personality that is more likely to drop out of the curriculum is recommended to be one that is not overly concerned with grades.

Sensors personality ratings were significantly lower than Intuitors in courses with few high abstractions, an especially important practical question posed by the tests. Introverts may be well-suited for team activities, but extraverts will enjoy working together. Persons who score highly in the Intuitive cluster score higher on creativity and problem-solving skills than those with a Sensing personality [14]. For example, in understanding academic success, another model created in recent years is the Felder and Silverman [15]. Its purpose is to answer four questions, which are as follows.

On the student’s preferred topic, what information do they preferentially perceive? For the student who has a sensory personality, they prefer to be focusing on interacting with what they see, hearing the sounds, and using their physical sensations as a way of experiencing. Those who think more specifically are more comfortable with their abstracts (theories, math, memories, thoughts, and are more likely to solve the problems faster and innovative. What kind of information are our senses perceived most effectively? That is visual personality (exhibited in the eyes) or verbal personality (expressed through speech). What process is the student used to incorporate this information into their outside learning?  Actively through participation in physical activity, or through meditation. How does the student characteristically get from “not understanding” to “understanding?” The classroom needs to be set up with logical steps in place, with students thinking linearly and being able to only work with a partial understanding (logic step), or where students think globally and applying their knowledge until they fully understand the picture and have a holistic perspective (global system step) [16]. The Kolb Model assumes that every class possibly includes students of every category. This leads to the belief that these learners are often more likely to become better learners, leading them to perform better [17].

The key issue is how to find an appropriate method of learning for new students when the knowledge is lacking. There are numerous approaches that solutions tend to be used in solving a new state: a cold start problem, [18] Develop the algorithm that finds the closed likeminded learner, and so it generates its recommendations accordingly. [19] Researchers found that students with characteristics that esteem the parental world among students in technology majors were more likely to be of an ESFJ, ISFJ, ESFP, INTJ, and ISTP. On the other hand, students majoring in management information systems tend to be more ESTJ and ISTJ. In an article by  [20]ISTJ, INTJ, and ISFJ are the most common personality types found in the computing industry. A study that was done by  [21] shows that the relationship between the students’ personality and their academic performance depends on the correspondence between the students’ personality and the chosen major. Researchers have found, first of all, that system analysts are more extroverted than other thought-provoked people, that computer designers are more introverted and less extroverted than other thought-provoked people, and that programmers are more introverted and less extroverted than other thought-provoked people.

3. The Proposed Approach

The details of the proposed approach are to be applied in four phases.  In the first phase, we build the courses’ knowledge and skills matrix by decomposing the course’s learning outcomes into its knowledge and skills segments. This will help apply machine learning algorithms to mine learners’ performance on a smaller scale of concept segments rather than on the entire course’s performance or an entire block of concepts. In the second phase, we collected the personalities of students using the MBTI test. In the third level, we assess learners’ success (Knowledge & Skills) considering their personality and concentration. Finally, apply a recommendation framework to enhance the performance of learners using machine learning.

3.1. Construct a matrix of knowledge and skills

Analyze courses in terms of skill and knowledge. The starting point was to decompose the courses into their relevant knowledge and skills, representing all the concepts used in each CLO course and laid out in the PLO curriculum, as seen in the following analysis for CIS1403, fundamental to the programming course.

Table 2: Fundamental of programming knowledge & Skills

The method retains four key components: intelligence, abilities, attitude and soft skills of the learner. The developed machine learning model used by these components to assess learners of the greatest importance, learners’ key causes of At-risk, and we strive to calculate their employability ratio in the light of these core components.

3.2. Evaluation of student personalities

Understanding the personality type will help clarify the preferences, and how or why students may be different. Personality types are useful for understanding how a person can learn, manage, monitor, communicate, collaborate, compromise and cope with stress. Understanding the behaviours of learners helps prepare students to improve their knowledge and skills. This helps to apply a self-awareness tool to recognize strengths, consider and manage growth needs, and establish a career and personal development strategies.

Figure 2: Course knowledge and skills decomposition

We collected data about students’ personality using https://www.16personalities.com/, where students’ future profession can be categorized into MBTI Sixteen Personality Types shown below [22].

Figure 3: Personality types main features

4.3.Build a recommendation system

Build a recommendation framework that considers learners’ personalities from on the one hand and the knowledge and skills they have from the other, as shown in the figure below.

Figure 4: Three-layered course personalization model.

4. Research Procedures

For this research, 180 students were asked twice to use different platforms to take the Myers-Briggs Style Test. Eliminating all irregularities, only correct and clean data for treatment has been processed. Data collection incorporates the Type of Personality and the Personality Code; Extraversion (E), Introversion (I), Sensing (S), and Intuition (N) (P).

Variables

Three factors influence the development of students and thus their academic status in this research. First, we will consider the personality type created through a four-letter combination, each of which represents a set of independent characteristics. : extroverted versus introduced, sensing versus intuitive, thought versus feeling and judging versus prospecting. The second factor is the students’ knowledge based on each class: the third factor is the students’ skills that meet their concentration needs. We can determine if a student is in good academic standing by considering their first-year grades, time in school, etc.

4.1. Create Learners profiles

We have developed student profiles that incorporate their personality style and academic knowledge and skills. The figure below shows the types of students,  which is used to analyze whether the student’s personality affects their achievements in the three majors of computer networking.

Table 3: Learners measured personality

The following two tables show a sample of students’ knowledge and skills performance in the programming course.

Table 4: Learners knowledge performance

Figure 5: Flow chart for detecting the best program

Table 5: Learners skills performance

Figure 6: Recommendation Education system for At-risk learners

In order to find the best predictor for student performance, we have used different machine learning algorithms (Decision Tree, Random Forest, SVM and Association rules); then we evaluated and refined the used algorithm. The main aim is to develop a primitive platform that can be used to develop an education recommendation system to support At-risk learners 24/7 and teachers to coach students with poor knowledge, skills, and to provide them with the right advice.

Applying online coaching to at-risk students needs  to build a recommendation framework that predicts at-risk students considering their knowledge and skills performance in segments of concepts rather than an entire course, considering their personality style. This helps to give coaching in the form of advice crumbs, as shown in the implementation figure 9 below, which may be tutorials, segment coding, exercises, peer discussions, etc.

The association rules are used to created recommendations considering student knowledge and skills performance.

Figure 7: Applied Association rules for creating recommendations

4.2. Explore profiles of students

First, we purified information from all NA instances, measured the mean and variance and the range for each pattern. As shown in the table below, we measured the mean and variance and the three majors’ GPA quartile.

Table 6: Descriptive statistical of the GPA – CIS three-concentrations

In the application development department, the students typically have the highest average GPA, the Security and Forensics students have the second-highest average GPA, and the Networking students have the lowest average GPA. In the three conditions, we have been able to identify different personalities. Security and forensic students share the introverted sensing function type, which means they are more likely to feel, and judge – ISFJ.

It should be clear that, as demonstrated below, we have security students with various types of personalities, however, the GPA scores declined slightly accordingly.

Figure 8: Personality type’s weight-Security and Forensics Major.

Students form Networking Major are more ISTJs, which means more Introduction (I), Sensing (S), Thinking (T), and Judging (J). As shown in the below figures, major students in the App development are more ISTJ.

Figure 9: Personality type-Networking major.

Figure 10: Personality type – App Development major.

5. Experimental Results

In the following tables, we summarized the statistics on student success.

Table 7: Four courses performance statistics for personality types – Security and Forensics major

Table 8: Four courses performance statistics for personality types- Networking major

Table 9: Four courses performance statistics of personality types-App Development major.

The count feature shown in the above table should be clear, referring to each major’s personality styles. In addition, the mean, minimum and standard deviation of GPA values from each course is calculated, and the four courses and personalities are also calculated. Apart from CSF 3103, the course for the security major, the App development students did not receive the results of NaN but took the networking course CIN-2003. We evaluated students’ performance with their personalities in the four classes. In every course, according to the college grading system, we determined the GPA value of students.

Table 10: College GPA grades conversion values

1. The personalities patterns in the four courses for Security and Forensics major.

Figure 11: The impact of personality as a measure for four courses on security and forensics major.

1. The personalities patterns in the four courses for Networking major.

Figure 12: Impact of personality for Networking major as a measure on the four courses

1. The personalities patterns in the four courses for App Development major

Figure 13: The personality impact on App Development as measured on the four courses.

In each major and different course, the following table summarizes the results of the best personalities.

Table 11: Personality performances in the four courses.

Based on their personality, we may describe the conditions for a major choice to be made, or we have the ability to predict a student’s GPA (grade point average). Take an example such as this study by the hospital and try to analyze it with the below algorithm.

If “ISFJ” or “ESTJ”

• The best major is Security & Forensics

ELSE IF “ISTJ” or “ENTP”

• The best major is Networking

ELSE IF “ISTJ” or “ISTP”

• The best major is App Development.

 In the case of teaching computers to “understand” the best major, we can also rely on specific patterns, the systematic way, in the algorithm below.

If “S” and “J”

• choose Security & Forensics

ELSE IF (“S” or ‘N’) AND “T”

• The best major is Networking

ELSE IF “S” AND “T”

• The best major is App Development.

It is important to have measured students’ knowledge and skills to recognize getting into trouble students’ academic status and their needs. Students are given an assessment in four courses that cover OOP, Enterprise Networks and Services. A breakdown of students’ knowledge from various academic majors was compiled, and the numbers are listed below.

The factors that we looked at were the students’ different academic performance, as well as the skills they demonstrated in the OOP course.

Table 12: The knowledge of OOP course over the three majors.

Table 13: OOP skills performance in the three major categories.

6. Discussion and Conclusions

This study proposes a model for evaluating university students’ academic status and GPA performance ambitions. Faculty and staff call upon these three factors in decisions: personality preferences, perceived knowledge and skills as well as what they expect from each student. In their attitudes, knowledge and skills, computing students are not identical. We see the disparities between students’ grades because the universities lack an understanding of these disparities between learners’ abilities and a special opportunity for creating a truly unique learning method that matches their individuality. This makes it difficult to apply the same philosophy and expects all students to be highly qualified.

This system allows for specific algorithms to be included, in the students’ guidance, academic major choice or even the selection process. In a class on security, when we use sections for personality types, we can use an algorithm to predict the student’s ranking automatically.

IF “Security & Forensics major.”

     IF “ENFJ” or ‘ISFP’ or ISFJ’  

• Expected high grade

   ELSE IF ‘ENTJ’ or ‘ENPT’

• Expected fail case

  ELSE   

• Expected low grade

 We did not look at all scenarios, only example algorithms.

IF “OBJECT-ORIENTED  COURSE”

     IF “ISTP”  or “ISTJ”  

• Expected high grade

    ELSE

• Expected low grade

In general, consider how OOP awareness is summarized in Table 9. Security students have the ability to implement higher object-oriented concepts than those with a non-security background. Again, we want to emphasize the personality that makes these differences, not the selected major. This clarifies that major security students could perform well than other major students because of the difference in a student’s personality that corresponds to the offered programming course. See the algorithm below that demonstrates it.

For a Major

If Personality ‘ISTJ’ & ‘FUNDAMENTAL OF PROGRAMMING’

• Expect GPA >=2.0

In view of the basics, the students in App developers major with the ‘ISTJ’ personality achieve round 2.91 GPA, while networking students are expected to achieve 2.8 GPA and security and forensics students 2.0 GPA. The differences are seen in the second and third levels as a result of other factors: knowledge and skills.

In the following sections, the benefits of implementing the three-layer model are listed.

1. Maintain high-quality graduates with an appropriate level in their technical knowledge and skills.
2. Minimize cases at risk.
3. Keep an eye on signs of inattentiveness and poor academic performance.
4. A decrease in the number of delayed graduate students.
5. Avoid the disproportionate dropout from prospective students in response to dismissal or academic warnings.
6. Make sure to have a high level of student retention.
7. Provide tools to assist individuals to become more effective in selecting courses and getting the right major.
8. Keep clear knowledge of each student’s academic progress in case of a student falling behind.

7. The Conclusion & Future Work

Educational institutions have a tremendous burden of handling students with low academic performance (At-risk students). Many approaches support this group of pupils, such as psychological therapy, a proper timetable for vulnerable pupils, recall, personal training, mock-tests, direct private education, or success centres. However, these methods are not enough to solve the issue, since other factors could also influence the learner’s success, which could be their family difficulties, cognitive style, prior academic performance, and the foundation of the college level.  Learners personalities are the most important factor that needs a great deal of attention to understand students’ individuality and natural abilities. This greatly helps to adapt the materials supplied to their perceptions and offers solid support during the study programme. This paper proposes a recommendation system based on three key angles: personalities of the learner, skills of the learner and academic knowledge of the learner.  The proposed layered model helps predict at-risk students, provide better advising, enhance learners’ performance and skills, discover their employability competencies, and support learners with a recommendation in terms of tutorials, exercises, etc. during their study period. Based on the association rule, the proposed recommendation framework is structured to predict student success in different college programs and predict their academic status based on their personality analysis and academic performance. It is highly recommended that the admission and consultancy framework be maintained at the university level to fill the fragmentation of students’ knowledge and skills.  This improves student success and clearly identifies the study program’s weaknesses and encourages students to address any observed fragmentations.

This pilot study offers further insight as to the strengths and weaknesses of the three specific majors who receive the most job postings, “Application Development, Networking and Security and Forensics.” In addition to measuring a student’s actual academic performance, the system also distributes personalized advice based on the student’s digital profile. Collecting data from multiple learners from different programs allows us to understand what the program is trying to accomplish and what students need to know. The data completed throughout the learning process helps guide and get the learners to further levels of understanding. Future research will continue to create more trust in this advice through the study’s continuation to include students from various other programs, like Business Solutions, Applied Media and Engineering. The proposed and implemented solution is very successful and leads to quick results, but it does not offer as much information because it does not account for the students’ learning progress. Advisors constantly have access to learners’ information about their personalities which affect their privacy.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgements

This work was supported by H.C.T. Research Grants (H.R.G.) [Fund No: 113108]. We want to express our gratitude to the Higher Colleges of Technology for the financial grant.

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16. Kitipoth Wasayangkool, Kanabadee Srisomboon, Chatree Mahatthanajatuphat, Wilaiporn Lee, "Accuracy Improvement-Based Wireless Sensor Estimation Technique with Machine Learning Algorithms for Volume Estimation on the Sealed Box", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 108–117, 2023. doi: 10.25046/aj080313
17. Chaiyaporn Khemapatapan, Thammanoon Thepsena, "Forecasting the Weather behind Pa Sak Jolasid Dam using Quantum Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 54–62, 2023. doi: 10.25046/aj080307
18. Der-Jiun Pang, "Hybrid Machine Learning Model Performance in IT Project Cost and Duration Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 108–115, 2023. doi: 10.25046/aj080212
19. Paulo Gustavo Quinan, Issa Traoré, Isaac Woungang, Ujwal Reddy Gondhi, Chenyang Nie, "Hybrid Intrusion Detection Using the AEN Graph Model", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 44–63, 2023. doi: 10.25046/aj080206
20. Roy D Gregori Ayon, Md. Sanaullah Rabbi, Umme Habiba, Maoyejatun Hasana, "Bangla Speech Emotion Detection using Machine Learning Ensemble Methods", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 70–76, 2022. doi: 10.25046/aj070608
21. Deeptaanshu Kumar, Ajmal Thanikkal, Prithvi Krishnamurthy, Xinlei Chen, Pei Zhang, "Analysis of Different Supervised Machine Learning Methods for Accelerometer-Based Alcohol Consumption Detection from Physical Activity", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 147–154, 2022. doi: 10.25046/aj070419
22. Zhumakhan Nazir, Temirlan Zarymkanov, Jurn-Guy Park, "A Machine Learning Model Selection Considering Tradeoffs between Accuracy and Interpretability", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 72–78, 2022. doi: 10.25046/aj070410
23. Ayoub Benchabana, Mohamed-Khireddine Kholladi, Ramla Bensaci, Belal Khaldi, "A Supervised Building Detection Based on Shadow using Segmentation and Texture in High-Resolution Images", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 166–173, 2022. doi: 10.25046/aj070319
24. Osaretin Eboya, Julia Binti Juremi, "iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 185–202, 2021. doi: 10.25046/aj060521
25. Arwa Alghamdi, Graham Healy, Hoda Abdelhafez, "Machine Learning Algorithms for Real Time Blind Audio Source Separation with Natural Language Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 125–140, 2021. doi: 10.25046/aj060515
26. Baida Ouafae, Louzar Oumaima, Ramdi Mariam, Lyhyaoui Abdelouahid, "Survey on Novelty Detection using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 73–82, 2021. doi: 10.25046/aj060510
27. Radwan Qasrawi, Stephanny VicunaPolo, Diala Abu Al-Halawa, Sameh Hallaq, Ziad Abdeen, "Predicting School Children Academic Performance Using Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 08–15, 2021. doi: 10.25046/aj060502
28. Zhiyuan Chen, Howe Seng Goh, Kai Ling Sin, Kelly Lim, Nicole Ka Hei Chung, Xin Yu Liew, "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 376–384, 2021. doi: 10.25046/aj060442
29. Hathairat Ketmaneechairat, Maleerat Maliyaem, Chalermpong Intarat, "Kamphaeng Saen Beef Cattle Identification Approach using Muzzle Print Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 110–122, 2021. doi: 10.25046/aj060413
30. Md Mahmudul Hasan, Nafiul Hasan, Dil Afroz, Ferdaus Anam Jibon, Md. Arman Hossen, Md. Shahrier Parvage, Jakaria Sulaiman Aongkon, "Electroencephalogram Based Medical Biometrics using Machine Learning: Assessment of Different Color Stimuli", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 3, pp. 27–34, 2021. doi: 10.25046/aj060304
31. Dominik Štursa, Daniel Honc, Petr Doležel, "Efficient 2D Detection and Positioning of Complex Objects for Robotic Manipulation Using Fully Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 915–920, 2021. doi: 10.25046/aj0602104
32. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, "Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 754–762, 2021. doi: 10.25046/aj060287
33. Antoni Wibowo, Inten Yasmina, Antoni Wibowo, "Food Price Prediction Using Time Series Linear Ridge Regression with The Best Damping Factor", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 694–698, 2021. doi: 10.25046/aj060280
34. Javier E. Sánchez-Galán, Fatima Rangel Barranco, Jorge Serrano Reyes, Evelyn I. Quirós-McIntire, José Ulises Jiménez, José R. Fábrega, "Using Supervised Classification Methods for the Analysis of Multi-spectral Signatures of Rice Varieties in Panama", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 552–558, 2021. doi: 10.25046/aj060262
35. 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
36. Sebastianus Bara Primananda, Sani Muhamad Isa, "Forecasting Gold Price in Rupiah using Multivariate Analysis with LSTM and GRU Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 245–253, 2021. doi: 10.25046/aj060227
37. Byeongwoo Kim, Jongkyu Lee, "Fault Diagnosis and Noise Robustness Comparison of Rotating Machinery using CWT and CNN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1279–1285, 2021. doi: 10.25046/aj0601146
38. Md Mahmudul Hasan, Nafiul Hasan, Mohammed Saud A Alsubaie, Md Mostafizur Rahman Komol, "Diagnosis of Tobacco Addiction using Medical Signal: An EEG-based Time-Frequency Domain Analysis Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 842–849, 2021. doi: 10.25046/aj060193
39. Reem Bayari, Ameur Bensefia, "Text Mining Techniques for Cyberbullying Detection: State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 783–790, 2021. doi: 10.25046/aj060187
40. Inna Valieva, Iurii Voitenko, Mats Björkman, Johan Åkerberg, Mikael Ekström, "Multiple Machine Learning Algorithms Comparison for Modulation Type Classification Based on Instantaneous Values of the Time Domain Signal and Time Series Statistics Derived from Wavelet Transform", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 658–671, 2021. doi: 10.25046/aj060172
41. Carlos López-Bermeo, Mauricio González-Palacio, Lina Sepúlveda-Cano, Rubén Montoya-Ramírez, César Hidalgo-Montoya, "Comparison of Machine Learning Parametric and Non-Parametric Techniques for Determining Soil Moisture: Case Study at Las Palmas Andean Basin", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 636–650, 2021. doi: 10.25046/aj060170
42. Ndiatenda Ndou, Ritesh Ajoodha, Ashwini Jadhav, "A Case Study to Enhance Student Support Initiatives Through Forecasting Student Success in Higher-Education", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 230–241, 2021. doi: 10.25046/aj060126
43. Lonia Masangu, Ashwini Jadhav, Ritesh Ajoodha, "Predicting Student Academic Performance Using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 153–163, 2021. doi: 10.25046/aj060117
44. Sara Ftaimi, Tomader Mazri, "Handling Priority Data in Smart Transportation System by using Support Vector Machine Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1422–1427, 2020. doi: 10.25046/aj0506172
45. Othmane Rahmaoui, Kamal Souali, Mohammed Ouzzif, "Towards a Documents Processing Tool using Traceability Information Retrieval and Content Recognition Through Machine Learning in a Big Data Context", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1267–1277, 2020. doi: 10.25046/aj0506151
46. Puttakul Sakul-Ung, Amornvit Vatcharaphrueksadee, Pitiporn Ruchanawet, Kanin Kearpimy, Hathairat Ketmaneechairat, Maleerat Maliyaem, "Overmind: A Collaborative Decentralized Machine Learning Framework", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 280–289, 2020. doi: 10.25046/aj050634
47. 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
48. 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
49. Khalid A. AlAfandy, Hicham Omara, Mohamed Lazaar, Mohammed Al Achhab, "Using Classic Networks for Classifying Remote Sensing Images: Comparative Study", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 770–780, 2020. doi: 10.25046/aj050594
50. Khalid A. AlAfandy, Hicham, Mohamed Lazaar, Mohammed Al Achhab, "Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 652–659, 2020. doi: 10.25046/aj050580
51. Rajesh Kumar, Geetha S, "Malware Classification Using XGboost-Gradient Boosted Decision Tree", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 536–549, 2020. doi: 10.25046/aj050566
52. Nghia Duong-Trung, Nga Quynh Thi Tang, Xuan Son Ha, "Interpretation of Machine Learning Models for Medical Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 469–477, 2020. doi: 10.25046/aj050558
53. Oumaima Terrada, Soufiane Hamida, Bouchaib Cherradi, Abdelhadi Raihani, Omar Bouattane, "Supervised Machine Learning Based Medical Diagnosis Support System for Prediction of Patients with Heart Disease", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 269–277, 2020. doi: 10.25046/aj050533
54. Haytham Azmi, "FPGA Acceleration of Tree-based Learning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 237–244, 2020. doi: 10.25046/aj050529
55. Hicham Moujahid, Bouchaib Cherradi, Oussama El Gannour, Lhoussain Bahatti, Oumaima Terrada, Soufiane Hamida, "Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 167–175, 2020. doi: 10.25046/aj050522
56. Young-Jin Park, Hui-Sup Cho, "A Method for Detecting Human Presence and Movement Using Impulse Radar", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 770–775, 2020. doi: 10.25046/aj050491
57. Anouar Bachar, Noureddine El Makhfi, Omar EL Bannay, "Machine Learning for Network Intrusion Detection Based on SVM Binary Classification Model", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 638–644, 2020. doi: 10.25046/aj050476
58. 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
59. Amar Choudhary, Deependra Pandey, Saurabh Bhardwaj, "Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 589–593, 2020. doi: 10.25046/aj050469
60. Maroua Abdellaoui, Dounia Daghouj, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime El Bekkali, "Artificial Intelligence Approach for Target Classification: A State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 445–456, 2020. doi: 10.25046/aj050453
61. Shahab Pasha, Jan Lundgren, Christian Ritz, Yuexian Zou, "Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 331–343, 2020. doi: 10.25046/aj050439
62. Ilias Kalathas, Michail Papoutsidakis, Chistos Drosos, "Optimization of the Procedures for Checking the Functionality of the Greek Railways: Data Mining and Machine Learning Approach to Predict Passenger Train Immobilization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 287–295, 2020. doi: 10.25046/aj050435
63. Yosaphat Catur Widiyono, Sani Muhamad Isa, "Utilization of Data Mining to Predict Non-Performing Loan", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 252–256, 2020. doi: 10.25046/aj050431
64. Hai Thanh Nguyen, Nhi Yen Kim Phan, Huong Hoang Luong, Trung Phuoc Le, Nghi Cong Tran, "Efficient Discretization Approaches for Machine Learning Techniques to Improve Disease Classification on Gut Microbiome Composition Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 547–556, 2020. doi: 10.25046/aj050368
65. Ruba Obiedat, "Risk Management: The Case of Intrusion Detection using Data Mining Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 529–535, 2020. doi: 10.25046/aj050365
66. 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
67. Dennis Luqman, Sani Muhamad Isa, "Machine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 214–225, 2020. doi: 10.25046/aj050328
68. Gillala Rekha, Shaveta Malik, Amit Kumar Tyagi, Meghna Manoj Nair, "Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 72–81, 2020. doi: 10.25046/aj050310
69. Ahmed EL Orche, Mohamed Bahaj, "Approach to Combine an Ontology-Based on Payment System with Neural Network for Transaction Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 551–560, 2020. doi: 10.25046/aj050269
70. Bokyoon Na, Geoffrey C Fox, "Object Classifications by Image Super-Resolution Preprocessing for Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 476–483, 2020. doi: 10.25046/aj050261
71. Hasung Kang, Gede Putra Kusuma, "The Effectiveness of Personality-Based Gamification Model for Foreign Vocabulary Online Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 261–271, 2020. doi: 10.25046/aj050234
72. Johannes Linden, Xutao Wang, Stefan Forsstrom, Tingting Zhang, "Productify News Article Classification Model with Sagemaker", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 13–18, 2020. doi: 10.25046/aj050202
73. Michael Wenceslaus Putong, Suharjito, "Classification Model of Contact Center Customers Emails Using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 174–182, 2020. doi: 10.25046/aj050123
74. Rehan Ullah Khan, Ali Mustafa Qamar, Mohammed Hadwan, "Quranic Reciter Recognition: A Machine Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 173–176, 2019. doi: 10.25046/aj040621
75. Mehdi Guessous, Lahbib Zenkouar, "An ML-optimized dRRM Solution for IEEE 802.11 Enterprise Wlan Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 19–31, 2019. doi: 10.25046/aj040603
76. Toshiyasu Kato, Yuki Terawaki, Yasushi Kodama, Teruhiko Unoki, Yasushi Kambayashi, "Estimating Academic results from Trainees’ Activities in Programming Exercises Using Four Types of Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 321–326, 2019. doi: 10.25046/aj040541
77. Nindhia Hutagaol, Suharjito, "Predictive Modelling of Student Dropout Using Ensemble Classifier Method in Higher Education", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 206–211, 2019. doi: 10.25046/aj040425
78. Fernando Hernández, Roberto Vega, Freddy Tapia, Derlin Morocho, Walter Fuertes, "Early Detection of Alzheimer’s Using Digital Image Processing Through Iridology, An Alternative Method", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 126–137, 2019. doi: 10.25046/aj040317
79. Abba Suganda Girsang, Andi Setiadi Manalu, Ko-Wei Huang, "Feature Selection for Musical Genre Classification Using a Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 162–169, 2019. doi: 10.25046/aj040221
80. Konstantin Mironov, Ruslan Gayanov, Dmiriy Kurennov, "Observing and Forecasting the Trajectory of the Thrown Body with use of Genetic Programming", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 248–257, 2019. doi: 10.25046/aj040124
81. Bok Gyu Han, Hyeon Seok Yang, Ho Gyeong Lee, Young Shik Moon, "Low Contrast Image Enhancement Using Convolutional Neural Network with Simple Reflection Model", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 159–164, 2019. doi: 10.25046/aj040115
82. Zheng Xie, Chaitanya Gadepalli, Farideh Jalalinajafabadi, Barry M.G. Cheetham, Jarrod J. Homer, "Machine Learning Applied to GRBAS Voice Quality Assessment", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 329–338, 2018. doi: 10.25046/aj030641
83. Richard Osei Agjei, Emmanuel Awuni Kolog, Daniel Dei, Juliet Yayra Tengey, "Emotional Impact of Suicide on Active Witnesses: Predicting with Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 501–509, 2018. doi: 10.25046/aj030557
84. 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
85. Charles Frank, Asmail Habach, Raed Seetan, Abdullah Wahbeh, "Predicting Smoking Status Using Machine Learning Algorithms and Statistical Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 184–189, 2018. doi: 10.25046/aj030221
86. Sehla Loussaief, Afef Abdelkrim, "Machine Learning framework for image classification", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 1–10, 2018. doi: 10.25046/aj030101
87. Ruijian Zhang, Deren Li, "Applying Machine Learning and High Performance Computing to Water Quality Assessment and Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 285–289, 2017. doi: 10.25046/aj020635
88. Batoul Haidar, Maroun Chamoun, Ahmed Serhrouchni, "A Multilingual System for Cyberbullying Detection: Arabic Content Detection using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 275–284, 2017. doi: 10.25046/aj020634
89. 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
90. 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
91. Moses Ekpenyong, Daniel Asuquo, Samuel Robinson, Imeh Umoren, Etebong Isong, "Soft Handoff Evaluation and Efficient Access Network Selection in Next Generation Cellular Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1616–1625, 2017. doi: 10.25046/aj0203201
92. Rogerio Gomes Lopes, Marcelo Ladeira, Rommel Novaes Carvalho, "Use of machine learning techniques in the prediction of credit recovery", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1432–1442, 2017. doi: 10.25046/aj0203179
93. Daniel Fraunholz, Marc Zimmermann, Hans Dieter Schotten, "Towards Deployment Strategies for Deception Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1272–1279, 2017. doi: 10.25046/aj0203161
94. Arsim Susuri, Mentor Hamiti, Agni Dika, "Detection of Vandalism in Wikipedia using Metadata Features – Implementation in Simple English and Albanian sections", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 4, pp. 1–7, 2017. doi: 10.25046/aj020401
95. Adewale Opeoluwa Ogunde, Ajibola Rasaq Olanbo, "A Web-Based Decision Support System for Evaluating Soil Suitability for Cassava Cultivation", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 42–50, 2017. doi: 10.25046/aj020105
96. Arsim Susuri, Mentor Hamiti, Agni Dika, "The Class Imbalance Problem in the Machine Learning Based Detection of Vandalism in Wikipedia across Languages", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 16–22, 2016. doi: 10.25046/aj020103