1. Introduction

This paper is an extension of work initially presented at the 2022 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE2022)[1]. Planning and estimation are imperative for any Information Technology (IT) project. Estimation aids in tracking progress and delivery velocity. However, due to the close relationship between cost and time factors, any project delay might result in cost overruns.

The investigators [2][3] revealed that the top-ranked IT project risk is “Underestimated Costs and Time”. According to the authors [4], 60% of IT projects have cost and time problems. Budget and timeline underestimation seems to occur at various stages of the project lifecycle. The most undesirable scenario happens when the budget and duration are underestimated at the beginning of the project lifecycle.

Artificial intelligence (AI) can improve decision-making in complex environments with clear objectives. A study concluded that, in terms of accuracy, artificial intelligence tools outperform traditional tools [5]. The value of AI can only be activated as humans and machines function complementarily integrated.

Hybridizing Machine Learning (ML) models are getting their popularity recently. According to researchers [6], hybridization effectively advances prediction models. This article focuses on the performance of various hybrid ML models in prediction accuracy enhancement to improve cost and duration estimation to address the critical IT failure problem.

2. Methodology

2.1.  The Machine Learning Model Evaluation

This study was designed to demonstrate to the research community that the evaluations are comprehensive and can explain their significance. Five hybrid ML models were developed using Python and evaluated using three different datasets, including two public datasets. These models were trained and tested on three different datasets to reduce bias caused by data quality. The best-performing ML model was selected based on the performance measured by six different metrics. It was then put forward for live project verification to determine its performance in predicting project cost and duration.

These five hybrid ML models were: Hybrid Multiple Linear Regression Deep Neural Network (MLR-DNN), Particle Swarm Optimised DNN (PSO-DNN), Hybrid Gradient Boosting Regression DNN (GBR-DNN), Hybrid Random Forest Regression DNN (RFR-DNN), and Hybrid eXtreme Gradient Boosting DNN (XGB-DNN).

Controlled experiments play a vital role in applied machine learning, and the behaviour of algorithms on specific problems must be learned empirically. A machine learning experiment procedure involves a series of steps, 1. Data collection. 2. Data pre-processing: cleaning and manipulating acquired data to prepare it for modelling. 3. Model training: the model is trained on a training dataset, usually a subset of the data collected. 4. Model tuning: change in hyperparameters to optimize the model’s performance. ML performance is measured by the defined performance metrics indicated in section 2.2.  5. Model evaluation: determine the model’s performance on a testing dataset or another subset of the data collected. 6. Model deployment: the best model is then used to make predictions on live project data.

2.2.  Performance Metrics

Evaluating the performance of ML models is essential to ensure their effectiveness. The choice of the performance metric is an important factor in this evaluation process. It depends on the specific ML problem being solved and the project’s goals. The performance parameter used in this study is accuracy, which evaluates the number of correct predictions made as a percentage of all predictions made. The associated “accuracy” performance metrics used were , , , ,  and ( ).

The Root Mean Square Error ( ) acts as a heuristic model for testing and training measures differences between predicted values and actual values from 0 to ∞. The smaller the , the better the model [7].   is predicted output or forecasted values and  is the actual or observational values.

The Root Mean Squared Log Error ( ) is a logarithmically calculated  commonly used metric or loss function in the regression-based machine learning model. The lesser error, the better the model is.

The Mean Absolute Error ( )  measures the magnitude of errors regardless of their direction in a series of estimates.  is superior to  in terms of explanation-ability.  has a distinct advantage over  using absolute values, which is undesirable in many mathematical calculations. The smaller value, the better the model is.

The Mean Magnitude of Relative Error ( ) and Median Magnitude of Relative Error ( ) are two important performance metrics derived from the overall mean and median errors. The primary function of  is to serve as an indicator for differentiating between prediction models. The model with the lowest  typically being chosen typically implies low uncertainty or inaccuracy. The better the model, the smaller the values are.

Percentage of Estimate, , is an alternative to the  that is a commonly used prediction quality metric. It simply measures the proportion of forecasts within % the actual value. The bigger the , the less information and confidence in a prediction’s accuracy [8].

2.3.  Degree of Augmentation

The degree of augmentation (DOA), , is a prediction enhancement measurement in error reduction to measure a hybrid model. A dual-layer hybrid cascaded ML model comprises two ML models represented as layers one and two (Figure 1). In stage one, the layer one ML model makes a prediction value  as inputs to stage two ( ) to be processed by the layer two ML model with prediction output . The difference (or error) in the predicted result versus the actual result at stage one is denoted as .

Figure 1: The Degree of Augmentation Scale

The assumption of difference in stage two is more diminutive than in stage one. The effect of convergence resulted in  reduction; therefore, augmentation occurred.

By using equation (12), the  for stage one ( ) and stage two ( ) enables to calculate of the degree of augmentation, , for each of the hybrid models. The degree of augmentation,  is bi-directional. A negative value indicates  increases or diverging, whereas a positive value specifies  decrease or converges. The positive magnitude of  shows the strength of augmentation. The higher the  means the better the hybrid model. The more significant negative value of  means the hybrid model is ineffective.  is considered effective,  is ineffective. For   is marginally effective, which means its augmentation is not significant enough to remain effective.

In an optimistic augmentation scenario, the Interquartile Range (IQR) becomes narrower, whereas the range becomes wider in an adverse augmentation scenario. This convergent phenomenon indicates the  decreases in positive augmentation. Contrary, in a divergent case,  increases in negative boost.

2.4.  Data Collection

Figure 2 illustrates the data collection procedure. Each dataset was randomly split into two groups in a 70:30 ratio, 70% for training and 30% for testing. The relevant dataset was acquired online or gathered from previous project material. The collected data was then converted (if necessary) and pre-processed using scaling (for example, the scikit-learn scaling package) to prepare for ML assessment.

Figure 2: The data collection procedure

2.5.  ML Evaluation

These ML models were evaluated in three steps depending on their algorithm settings. First, the respective models were trained using historical data in the learning or training step. Later in the testing step, these ML models were tested based on a peer comparison of their performance indicators. Each ML model was optimized through hyperparameter tuning until the best results were obtained (Figure 3).

2.6.  Dataset Descriptions

A study concurs that the model may poorly correlate with a dataset that makes learning “incomplete” [9]. This evaluation used three dataset sources to minimize potential bias due to the dataset’s influences. Two are publicly available, and the third dataset is a collection of actual historical project data named EVP. Both Desharnais and ENB datasets were selected in this study because of their multi-target attributes.

Figure 3: The ML evaluation workflow

It is challenging to ensure the quality of an ML dataset, mainly because the relationship between the qualities of the data and their effect on the ML system’s compliance with its requirements is infamously complex and hard to establish [10]. In this study, dataset quality was defined as its appropriateness in terms of accuracy and value.

1)       Desharnais Dataset

Jean-Marc Desharnais gathered the Desharnais dataset from ten organizations in Canada between 1983 and 1988. There are 81 projects (records) and 12 attributes [11], a relatively small public dataset of which four nominal fields are considered redundant in ML model evaluation. Table 1 provides statistical information about this dataset. Four entries have missing data. Most studies that use this dataset use 77 of the 81 records [12]. This study backfilled the missing fields with a “-1” value. Small dataset size issues could be compensated by adopting data-efficient learning or data augmentation strategies [13]. Desharnais datasets were used in many research. Therefore, it can benchmark the investigation against other published results.

2)       ENB Dataset

The Energy Building Dataset [14] contains 768 instances of eight measured building parameters as feature variables. The dataset includes the two corresponding target heating load and cooling load attributes. A nominal field is considered redundant in this dataset.

Table 2 provides statistical information about this public dataset. The data comes from real-world applications and reflects real-world events with a multi-target. ENB is another popular dataset being used by many studies. The data size is deemed appropriate with more than 300 samples [15]. The ENB dataset is interesting, with only two targets closely associated, while the features have no interdependency, making prediction more complicated.

Table 1: Descriptive Statistics for Desharnais Dataset

Table 2: Descriptive Statistics for ENB Dataset

Table 3: Descriptive Statistics for EVP Dataset

3)       EVP Dataset

Earned Value Management (EVM) is widely acknowledged as the most reliable contemporary project management instrument or cost and timeline forecasting technique. EVM calculates the amount of work performed to measure project performance and progress. The Earned Value Plus dataset is based on the conventional EVM attributes and added two new attributes related to the project management and size indexes. It contains 8,470 (more than 8000 records) instances from more than 600 historical project data in EVM format was deemed sufficient to train the ML model effectively (Table 3).

3. Experimental Results

Each optimized model was tested in four cycles. Evaluation results were obtained through each testing cycle and tabulated for each performance indicator. Each performance metric was calculated based on the average performance. The following subsections describe how the ML model performed, illustrated by graphical presentation in two graphs. The first graph shows performance results in ,  and . The second graph shows the performance results in ,  and .

3.1.  Desharnais Dataset

MLR-DNN was the most optimal model for predicting the probability of a given experiment, while PSO-DNN appeared as the worst. MLR-NNN had the highest  value and the best  and  values among all models tested in this study (Figure 4 and Figure 5). MLR-DNN is a hybrid cascaded ML model comprising MLR (Multiple Linear Regressor) and cascading with DNN (Deep Neural Network) embedded with four hidden layers and 64 neurons in each hidden layer.

Figure 4: The , , and  results in the Desharnais dataset

Figure 5: The , , (25) results in the Desharnais dataset

3.2.  ENB Dataset

MLR-DNN outplayed all other performance metrics, with the lowest  value being the least desirable model. The optimum  value was .011, and the highest  value was .492, according to the most favourable  value. The most accurate  value was .004 (Figure 6 and Figure 7).

Figure 6: The , , and  results in the ENB dataset

Figure 7: The , , (25) results in the ENB dataset

3.3.  EVP Dataset

MLR-DNN ranked as the top-performing ML model, with the lowest  value and highest  value. The most favourable  value was .003, the best  value was .003 and the most accurate  value of <.001 (Figure 8 and Figure 9).

Figure 8: The , , and  results in the EVP dataset

3.4.  Degree of Augmentation

The degree of augmentation, , is used as an error reduction indicator in a cascaded hybrid ML model using equation (12). The  for stage one ( ) and stage two ( ) enables us to calculate the degree of augmentation,  , for each of the hybrids cascaded ML models (Figure 1). The hybrid model MLR-DNN demonstrated an average error reduction of .026 compared to the MLR model alone. PSO-DNN was excluded from the DOA comparison because PSD-DNN is not a cascaded standalone ML model but part of DNN with Particle Swarm Optimization (PSO) backpropagation. Overall results revealed that MLR-DNN outperformed all three other hybrids cascading DNN models, suggesting that cascading two different ML models may not produce positive results. Both GBR-DNN and XGB-DNN did not improve prediction accuracy, whereas the RFR-DNN model performed worse than RFR or DNN alone.

Figure 9: The , , (25) results in the EVP dataset

Based on the performance results, the MLR-DNN model performed exceptionally well on all three datasets. The dependency on the quality of the dataset remains significant. This finding indicated that the PSO-DNN model was the most underwhelming performer in ENB and EVP datasets. However, for all three datasets, the least compelling performer was PSO-DNN. The runner-up position for both ENB and EVP datasets was GBR-DNN. However, the runner-up for the Dasharnais dataset was XGB-DNN.

The results also indicated that hybrid cascaded ML models such as GBR-DNN & XGB-DNN do not guarantee a positive gain and may sometimes have detrimental effects, for example, the RFR-DNN model. GBR-DNN performed relatively well in Desharnais and ENB datasets. However, it performed poorly in the EVP dataset. The result indicated that the quality of the dataset remains significant. This finding opens the door for future research.

The interquartile range ( ) is a reliable measure of variability representing the dispersion of the middle 50% of the data [16]. The  is calculated as  = Q3 − Q1 statistically; the smaller  indicates the error range is relatively small. MLR-DNN showed the narrowest  and largest Mann-Whitney U effect size to strengthen its position as the most accurate ML model among the other models in this study. MLR-DNN enhanced the overall prediction accuracy compared to other models with a significant magnitude of error reduction.

From observation of the statistical value in Table 4 for the degree of augmentation   and Mann-Whitney U test effect size , it seems like there is some form of proportion. The investigators [17] explained that effect size is the difference between the variable’s value in the control and test groups. The magnitude of  increases and  increases, . The significant difference between  and  is that the effect size does not cater to attributes of positive or negative augmentation. This finding reflects that the degree of augmentation is a more appropriate performance indicator for measuring cascaded hybrid ML models.

Table 4: Degree of Augmentation Statistical Data

4. Verification Results

Three types of live project data (Waterfall, Hybrid, and Agile) were used to verify MLR-DNN performance. The live performance results explained how effective MLR-DNN could be used practically in project management.

4.1.  Waterfall Project

XYZ is one of the largest telecommunications operators in South East Asia. Due to exponential growth in customer demand, XYZ decided to enhance its operations support capability. MLR-DNN was used during the live project verification stage to forecast the budget and duration. Two EVM data samples were collected at 43% and 53% completion points. Table 5 displays the results.

MLR-DNN outperformed traditional EVM by 8.4% and 54.1% in average cost at Estimate At Completion (EAC) and average schedule prediction at Estimate Duration At Completion (EDAC), respectively. These findings align with a study which indicates CPI (cost) accuracy is relatively better than SPI (time) accuracy in EVM calculation [18].

Table 5: Waterfall Project Verification

The MLR-DNN model improved and significantly enhanced the performance of project effort and duration estimation. Work Breakdown Structure (WBS) and EVM remain moderately accurate despite being less dependent on humans. The result indicated that the dataset’s quality continues to have a significant impact, opening future research opportunities.

4.2.  Hybrid Waterfall-Agile Project

Hybrid Agile-Waterfall projects combine agile approaches with waterfall methodologies to deliver projects. The waterfall method to record specific requirements and the agile methodology to deliver gradually in sprints are examples of hybrid projects. Another hybrid agile-waterfall model is software development teams adopting the agile methodology, while hardware implementation teams stick to the waterfall approach. The amount of agile versus waterfall project technique adoption in scope coverage determines the blending ratio.

STU is a major telecommunications operator in South East Asia with millions of customers. It would like to optimize and enhance its operations support and telemarketing capability. The project cost is moderately high: hardware, commercial out-of-shelf products, software customization, system integration, consulting, and professional services.

Table 6: Hybrid Waterfall-Agile Project Verification

Five samples were collected from the same project at different stages and times (Table 6). One noticeable phenomenon is that prediction accuracy depends on the percentage of completion points. The closer the project’s end, the more accurate the forecast is. At 31% completion, it was a less accurate prediction than the 54% completion point. The characteristic of EVM is inherited and aligned with findings shared by Urgilés et al. [10].

The predicted EDAC was accurate enough, with an average variance of 16% compared to any existing PM techniques and tools with 35-60%. There were insufficient details as to why there was a higher variance of EDAC than compared to EAC. Nevertheless, the project details revealed many change requests initiated that might impact prediction accuracy.

4.3.  Agile Project

The MLR-DNN was fed with live agile project-scaled EVP data to predict project duration and cost in this verification test. Agile projects are typically shorter in duration and use fixed-length iterations. These projects usually have a low to medium budget, fixed period, and flexible scope.

ABC is a popular online banking software offering various electronic payment services to customers and financial institutions. A backlog of enhancements was prioritized in a different sprint by adopting a 100% agile methodology for the whole software development life cycle. Project resources were relatively small, usually less than ten people.

Project size was determined by the amount of project value in USD. Project is considered “small” < 500k; 1 million > “medium” ≥ 500k, and “large” > 1 million. The percentage of completion was defined as the average project delivery progress

Table 7: Agile Project Verification

Three project-type live data samples were collected at different stages, iterations, sprints, and releases comprised of Agile, Hybrid, and Waterfall projects (Table 7). The overall prediction accuracy comparison between traditional EVM vs MLR-DNN in three project types is illustrated in Figure 10.

Figure 10: Performance Comparison between MLR-DNN and Traditional EVM in both Schedule and Cost Prediction

MLR-DNN model performed well in agile projects. It accurately predicted cost and schedule dimensions for many waterfall projects. Cost forecast accuracy is relatively better than duration forecast accuracy.

5. Machine Learning Biases

Machine learning (ML) algorithms are becoming more used in various industries. These algorithms, however, are not immune to bias, which can have detrimental repercussions. Therefore, it is critical to understand and address potential ML biases in order to ensure that these algorithms are fair and equal.

Type I – Algorithmic bias refers to systematic errors or unfairness resulting from employing algorithms inherited from the ML model, including how the model was constructed or trained, leading to biased outcomes [19]. Type II – Dataset bias is another type of bias that relates to the tendency of ML models to deliver inaccurate or unreliable predictions due to flaws or inconsistencies in the data used to train them [20]. It can result from various factors, including data collection methods and pre-processing techniques. To reduce ML biases, practitioners should evaluate models and datasets for performance and choose the least biased models.

6. Conclusion and Further Research

Traditional project planning in effort and duration estimation techniques remain low to medium accurate. This study seeks to develop a highly reliable and efficient Hybrid ML model that can improve cost and duration prediction accuracy. The results of the experiments indicated that MLR-DNN was the superior, effective, and reliable machine learning model.

The verification results in Agile, Hybrid and Waterfall projects indicated that the MLR-DNN model improved and significantly enhanced project effort performance and duration estimation. Despite WBS and EVM (conventional project management tools) being less dependent on humans, they are moderately accurate.

The results indicated that hybrid cascaded ML models such as GBR-DNN & XBG-DNN do not guarantee a positive gain and may sometimes have detrimental effects, for example, the RFR-DNN model. MLR-DNN inherits other neural network flaws being computationally costly and operating in black boxes with little explanation.

The accuracy of neural networks (including MLR-DNN) depends on the volume and the quality of training data [21]. Therefore, the dataset’s quality significantly impacts the ML model’s performance. This finding opens the door for future research.

1. D.-J. Pang, K. Shavarebi, S. Ng, “Development of Machine Learning Models for Prediction of IT project Cost and Duration,” in 2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE: 228–232, 2022, doi:10.1109/ISCAIE54458.2022.9794529.
2. D.-J. Pang, K. Shavarebi, S. Ng, “Project practitioner experience in risk ranking analysis-an empirical study in Malaysia and Singapore,” Operations Research and Decisions, 32(2), 2022, doi:10.37190/ord220208.
3. D.-J. Pang, K. Shavarebi, S. Ng, “Project Risk Ranking Based on Principal Component Analysis – An Empirical Study in Malaysia-Singapore Context,” International Journal of Innovative Computing, Information and Control, 18(06), 1857–1870, 2022, doi:10.24507/IJICIC.18.06.1857.
4. TD. Nguyen, T.M. Nguyen, T.H. Cao, “A conceptual framework for is project success,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 142–154, 2017, doi:10.1007/978-3-319-56357-2_15.
5. D. Magaña Martínez, J.C. Fernandez-Rodriguez, “Artificial Intelligence Applied to Project Success: A Literature Review,” International Journal of Interactive Multimedia and Artificial Intelligence, 3(5), 77, 2015, doi:10.9781/ijimai.2015.3510.
6. A. Mosavi, M. Salimi, S.F. Ardabili, T. Rabczuk, S. Shamshirband, A.R. Varkonyi-Koczy, “State of the art of machine learning models in energy systems, a systematic review,” Mdpi.Com, 12(7), 2019, doi:10.3390/en12071301.
7. S. Bayram, S. Al-Jibouri, “Efficacy of Estimation Methods in Forecasting Building Projects’ Costs,” Journal of Construction Engineering and Management, 142(11), 05016012, 2016, doi:10.1061/(ASCE)CO.1943-7862.0001183.
8. D. Port, M. Korte, “Comparative studies of the model evaluation criterions MMRE and PRED in software cost estimation research,” in ESEM’08: Proceedings of the 2008 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ACM Press, New York, New York, USA: 51–60, 2008, doi:10.1145/1414004.1414015.
9. E. Korneva, H. Blockeel, “Towards Better Evaluation of Multi-target Regression Models,” in Communications in Computer and Information Science, Springer Science and Business Media Deutschland GmbH: 353–362, 2020, doi:10.1007/978-3-030-65965-3_23.
10. S. Picard, C. Chapdelaine, C. Cappi, L. Gardes, E. Jenn, B. Lefevre, T. Soumarmon, “Ensuring Dataset Quality for Machine Learning Certification,” in Proceedings – 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020, 275–282, 2020, doi:10.1109/ISSREW51248.2020.00085.
11. A.K. Bardsiri, “An intelligent model to predict the development time and budget of software projects,” International Journal of Nonlinear Analysis and Applications, 11(2), 85–102, 2020, doi:10.22075/ijnaa.2020.4384.
12. MF Bosu, SG Macdonell, “Experience: Quality benchmarking of datasets used in software effort estimation,” Journal of Data and Information Quality, 11(4), 1–26, 2019, doi:10.1145/3328746.
13. R.M. Thomas, W. Bruin, P. Zhutovsky, G. Van Wingen, “Dealing with missing data, small sample sizes, and heterogeneity in machine learning studies of brain disorders,” Machine Learning, 249–266, 2019, doi:10.1016/B978-0-12-815739-8.00014-6.
14. OpenML enb, May 2021.
15. M.A. Bujang, N. Sa’at, T.M. Ikhwan, T.A.B. Sidik, “Determination of Minimum Sample Size Requirement for Multiple Linear Regression and Analysis of Covariance Based on Experimental and Non-experimental Studies,” Epidemiology Biostatistics and Public Health, 14(3), e12117-1 to e12117-9, 2017, doi:10.2427/12117.
16. D.T. Larose, Discovering Knowledge in Data: An Introduction to Data Mining, 2005, doi:10.1002/0471687545.
17. P. Kadam, S. Bhalerao, “Sample size calculation,” International Journal of Ayurveda Research, 1(1), 55, 2010, doi:10.4103/0974-7788.59946.
18. M. Fasanghari, S.H. Iranmanesh, M.S. Amalnick, “Predicting the success of projects using evolutionary hybrid fuzzy neural network method in early stages,” Journal of Multiple-Valued Logic and Soft Computing, 25(2–3), 291–321, 2015.
19. S.S. Gervasi, I.Y. Chen, A. Smith-Mclallen, D. Sontag, Z. Obermeyer, M. Vennera, R. Chawla, “The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It,” Https://Doi.Org/10.1377/Hlthaff.2021.01287, 41(2), 212–218, 2022, doi:10.1377/HLTHAFF.2021.01287.
20. A. Paullada, I.D. Raji, E.M. Bender, E. Denton, A. Hanna, “Data and its (dis)contents: A survey of dataset development and use in machine learning research,” Patterns, 2(11), 100336, 2021, doi:10.1016/J.PATTER.2021.100336.
21. J. Zhou, X. Li, H.S. Mitri, “Classification of rockburst in underground projects: Comparison of ten supervised learning methods,” Journal of Computing in Civil Engineering, 30(5), 04016003, 2016, doi:10.1061/(ASCE)CP.1943-5487.0000553.

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Crossref Citations

1. Vikas Thammanna Gowda, Landis Humphrey, Aiden Kadoch, YinBo Chen, Olivia Roberts, "Multi Attribute Stratified Sampling: An Automated Framework for Privacy-Preserving Healthcare Data Publishing with Multiple Sensitive Attributes", Advances in Science, Technology and Engineering Systems Journal, vol. 11, no. 1, pp. 51–68, 2026. doi: 10.25046/aj110106
2. David Degbor, Haiping Xu, Pratiksha Singh, Shannon Gibbs, Donghui Yan, "StradNet: Automated Structural Adaptation for Efficient Deep Neural Network Design", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 6, pp. 29–41, 2025. doi: 10.25046/aj100603
3. Glender Brás, Samara Leal, Breno Sousa, Gabriel Paes, Cleberson Junior, João Souza, Rafael Assis, Tamires Marques, Thiago Teles Calazans Silva, "Machine Learning Methods for University Student Performance Prediction in Basic Skills based on Psychometric Profile", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 4, pp. 1–13, 2025. doi: 10.25046/aj100401
4. khawla Alhasan, "Predictive Analytics in Marketing: Evaluating its Effectiveness in Driving Customer Engagement", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 3, pp. 45–51, 2025. doi: 10.25046/aj100306
5. Khalifa Sylla, Birahim Babou, Mama Amar, Samuel Ouya, "Impact of Integrating Chatbots into Digital Universities Platforms on the Interactions between the Learner and the Educational Content", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 1, pp. 13–19, 2025. doi: 10.25046/aj100103
6. Ahmet Emin Ünal, Halit Boyar, Burcu Kuleli Pak, Vehbi Çağrı Güngör, "Utilizing 3D models for the Prediction of Work Man-Hour in Complex Industrial Products using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 6, pp. 01–11, 2024. doi: 10.25046/aj090601
7. Haruki Murakami, Takuma Miwa, Kosuke Shima, Takanobu Otsuka, "Proposal and Implementation of Seawater Temperature Prediction Model using Transfer Learning Considering Water Depth Differences", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 01–06, 2024. doi: 10.25046/aj090401
8. Brandon Wetzel, Haiping Xu, "Deploying Trusted and Immutable Predictive Models on a Public Blockchain Network", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 72–83, 2024. doi: 10.25046/aj090307
9. Anirudh Mazumder, Kapil Panda, "Leveraging Machine Learning for a Comprehensive Assessment of PFAS Nephrotoxicity", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 62–71, 2024. doi: 10.25046/aj090306
10. Taichi Ito, Ken’ichi Minamino, Shintaro Umeki, "Visualization of the Effect of Additional Fertilization on Paddy Rice by Time-Series Analysis of Vegetation Indices using UAV and Minimizing the Number of Monitoring Days for its Workload Reduction", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 29–40, 2024. doi: 10.25046/aj090303
11. Henry Toal, Michelle Wilber, Getu Hailu, Arghya Kusum Das, "Evaluation of Various Deep Learning Models for Short-Term Solar Forecasting in the Arctic using a Distributed Sensor Network", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 12–28, 2024. doi: 10.25046/aj090302
12. Tinofirei Museba, Koenraad Vanhoof, "An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 01–11, 2024. doi: 10.25046/aj090301
13. Toya Acharya, Annamalai Annamalai, Mohamed F Chouikha, "Optimizing the Performance of Network Anomaly Detection Using Bidirectional Long Short-Term Memory (Bi-LSTM) and Over-sampling for Imbalance Network Traffic Data", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 144–154, 2023. doi: 10.25046/aj080614
14. Renhe Chi, "Comparative Study of J48 Decision Tree and CART Algorithm for Liver Cancer Symptom Analysis Using Data from Carnegie Mellon University", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 57–64, 2023. doi: 10.25046/aj080607
15. Ng Kah Kit, Hafeez Ullah Amin, Kher Hui Ng, Jessica Price, Ahmad Rauf Subhani, "EEG Feature Extraction based on Fast Fourier Transform and Wavelet Analysis for Classification of Mental Stress Levels using Machine Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 46–56, 2023. doi: 10.25046/aj080606
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. 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
19. Ossama Embarak, "Multi-Layered Machine Learning Model For Mining Learners Academic Performance", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 850–861, 2021. doi: 10.25046/aj060194
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. Moulay Youssef Smaili, Hanaa Hachimi, "Hybridization of Improved Binary Bat Algorithm for Optimizing Targeted Offers Problem in Direct Marketing Campaigns", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 239–246, 2020. doi: 10.25046/aj050628
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
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