1. Introduction

This paper is an extension of work in [1]. Traditionally, a single machine learning is built to process upon a dataset; this can be served as a recommendation, prediction, classification, etc. Unfortunately, the drawback of the traditional model is realized where machine learning problems and algorithms are far enough that all scenarios can be resolved using a single model [2]. A traditional approach relies on the training process over a whole dataset; data exploration, perhaps, consumes time and resources. Also, the experience in data analytics does not guarantee the effectiveness of the built machine learning systems to specific business scenarios. Moreover, discovering new attributes in real-world scenarios may require a brand-new dataset and retraining process. As business prefers to preserve data as much as possible, this is supported by system design which deals with a new data pattern and the least trade-off and penalty in performance.

This paper innovates a framework under the “Data Blind” concept  – it indicates the knowledge over dataset and scenarios, which are unknown and undiscovered. Hence, the proposed framework performs data analytics for building machine learning models that serve multiple purposes. Overmind, a system conceptual model in distributed artificial intelligence, is named after its theoretical design. Overmind aims for managing dataset, discovering data pattern, and creating collaborative agents, as a complete framework. This paper experiments a demonstration of how it works, and steps, which are taken to gather results and contributions. An interpretation of behaviours and outcomes will be studied and presented in future works.

2. Previous works

The concept of Overmind has been forged base on previous machine learning algorithms and the decentralized systems. We also explore related works on feature transfer and selection. Thus, this section illustrates in two aspects, which are Multi-agent and Decentralized System, and Feature Transfer.

2.1. Multi-Agent and Decentralized System

A term of Distributed Artificial Intelligence (DAI) has been defined based on the concept of distributed intelligent agents. Many projects and methodologies have been reported and summarized into categories proposed in [3] as system conceptual and agent conceptual models. The fine examples of DAI contributions were presented in [4–6] in the area of Intelligent Manufacturing System (IMS) since 1990. Then, a decentralized and distributed machine learning works were published in 1993 [7, 8]. The Cooperative Distributed Problem Solving (CDPS) that involves active agents sharing cooperative behaviours and protocols was invented in 1995 [9]. However, the cooperation is working based on broadcasting negotiation between a system and active agents by receiving the message to perform solving collaboratively.

Undeniably, this paper is highly influenced by the concept proposed in years ago. Although, machine learning has been continuously improved its capability and intelligence to the level that can resolve complex problems in the real-world scenario. Today, a decentralized reinforcement learning [10], which is again inspired by the multiagent system, describes problem statements that require large-scale real-world machine learning whereas the completed work focuses on two dimensions – Networked Evolution Strategies (NES), and Bayesian Optimality in the Wisdom of the Crowd (BOWC). NES describes network structure of communication between parallel-processing agents, that speed up learning process. This network structure of communication inspires Overmind’s concept to integrate network analysis components and allow communication among decentralized machine learnings. Lastly, BOWC draws attention to the modelling on how humans learn from the influence of each other; this also inspires the Overmind concept for incorporating the knowledge management framework to mimic the collaboration in humans.

Decentralized Clustering in Large Multi-Agent Systems [11] draws a concept of using multi-agents in decentralized clustering, which is one of the major machine learning objectives. To overcome data clustering real-world problems such as data is widely distributed, data sets are volatile, and data instances also cannot be compactly processed, decentralization of multi-agent has proven its better performance against the traditional k-mean algorithm. This reviewed work hints decentralized clustering of a single dataset, perhaps, gives significant contributions in pre-data processing.

Recently in 2019, Feature Distributed Machine Learning (FDML) is studied collaboratively called FDML: A Collaborative Machine Learning Framework for Distributed Features [12]. This work inspires privacy preservation in feature engineering when one machine learning model can acquire prediction power by using additional features from other machine learning without sharing actual features. This works fully introduces the collaborative machine learning framework with high capability in transfer learning. The experiments are conducted using a different algorithm, and the results yield significance in real-world application development.

Nowadays, a concept of distributed and decentralized artificial intelligence has been widely reported, which shows upcoming era in machine learning integrated fields of research such as artificial intelligence in distributed ledger technology [13], artificial intelligence as IoT [14, 15], distributed security solution [16, 17], etc. Surprisingly, these integrations are all based on a distributed and decentralized concept.

2.2. Transfer Learning

In this sense, transfer learning refers to a process where discovered knowledge is fed to existing machine learning for reinforcement and adaptation over modelling that intents to re-train for better performance. Furthermore, the communication among models in a network is established for transferring particular knowledge from one machine learning to others [18]

Similarly, work in [19] introduces the transferring knowledge over different classes. This work also emphasizes transfer learning in feature selection for machine learning models using a proposed framework. The results of this work influents possibility of transfer learning in real-world dataset across classes and domains.

In sum, this paper draws conceptual designs based on previous works in two dimensions, which are distributed and collaborative systems, and transfer learning. However, the algorithm and process behind our work are different from the origins due to its’ concepts and purposes. Ultimate research questions under our contributions are following: (1) how to establish an end-to-end platform for demonstrating collaborative machine learning models in a dynamic network in order to power up the prediction and (2) how to create a flexible system for transfer learning between models in a network that reflects the reinforcement.

3. Methodology

This work follows a quantitative research method. Firstly, the literature review is performed by limiting the scope of previous works and using keyword-searching. The results of the review are compounded into the conceptual design of the proposed framework, Overmind, which will be described in the next section of this paper. The reviewing of the proposed framework is conducted by researchers and authors. Experiment and testing are conducted on our previous work dataset, Thailand Air Quality Index (AQI) and weather forecast information. The result of applying this proposed framework is studied by comparison against the traditional framework on the same dataset, as presented in sections 5 and 6 of this paper.

4. Overmind: A Framework for Decentralized Machine Learnings

Overmind, a collaborative decentralized machine learning framework, is proposed considering dealing with anonymous dataset before building a machine learning model to serve purposes such as prediction, recommendation and classification. Figure 1 shows the main conceptual design of deploying multiple collaborative machine learning models (“agents”) each of which is responsible for a specific cluster of data. Collaboration means the agents can communicate and exchange knowledge, and share the workload for data processing.

This paper proposes Overmind as a collection of functional processes which are:

1. Determine Data Clustering
2. Assign Agent
3. Establish Network
4. Transfer Learning

In the following section, the aforementioned functional processes are explained in the respective order.

Figure 1: Overmind Architecture

4.1. Determine Data Clustering

Firstly, just the traditional approach, Overmind mimics the data cleansing process by using unsupervised learning algorithms to divide the dataset into clusters. This can be demonstrated as follows:

1. Let be the training dataset where , given that  is the number of instances.
2. Let be the feature set, where , given that  is the number of features in .
3. Overmind deploys unsupervised learning method to divide into clusters, given  is a set of clusters where  and members in  are allocated and distributed over members of , and  is the number of clusters.
4. The framework proposes as centrality node of similarity graph of data under . Hence, each cluster contains centrality node of data which is represented as  , where

Figure 2 illustrates the output of determining data clustering and assigning agent.

4.2. Assign Agent

Agents are the supervised machine learning models trained using data in each cluster. Overmind determines the number of agents and builds a supervised machine learning algorithm to create agents, which can be explained as follows:

1. Let be a set of agents trained by using , given that  is trained by using data under  where
2. Hence, will be agent based on  features, given each feature contains weight/coefficient for particular . Let  be a set of weight/coefficient of features for particular agent

4.3. Establish Network

To build a collaborative framework, there shall be linkages between agents and rules for cooperation. This paper introduces a similarity network of agents based on the graph as below:

1. Let be a graph of pairs  where .
2. Similarity of agents in is measured by  of particular , given that  is a pre-determined threshold of similarity.
3. will be created and linked between agent in  where the similarity measurement of a pair of agents satisfies . This results in a network of agents.
4. Denote the centrality of by . If the centrality of  corresponds to multiple agents, Overmind selects one.

Eventually, Overmind creates decentralized machine learning as a collection of nodes in a network by using similarity measurement. Each node represents a supervised machine learning model.

This contribution is that machine learning models can be assessed in term of similarity, which can be advanced for future works when one would like to measure affinity among trained models.

Figure 2: Illustration of Determining Data Clustering and Assigning Agent

4.4. Transfer Learning

This section describes two major transfer learnings of Overmind –data and feature transfers.

4.4.1 Data Transfer

Data transfer is involved when new data arrive for processing. Assumption on testing scenario when  is discovered and fed to framework, Overmind determines a suitable  for  by using . This process implies similarity measurement on the centrality of clusters and new data instances. The steps are taken as below:

1. Let be a test dataset of this framework. Hence, { .
2. Overmind determines each in  and measures the similarity of  to .
3. Overmind assigns to  which has the highest similarity measured between  and . This step can be considered as a batch process where  is divided into subsets for feeding to particular .
4. The accuracy of framework is measured as average accuracy of or average accuracy of subsets in .

4.4.2 Feature Transfer

Features and Feature Transfer are challenging components in designing the machine learning model. A traditional approach may require retraining of model when dealing with newly discovered features. A retaining process takes resources and time consuming for a big dataset. Yet, in the real-world scenario, some business may obtain a set of new information, both data and features, whether it contributes significances or not, but they have no willing to dispose of an existing dataset or pre-trained model.

In this sense, feature transfer refers to the ability that the existing trained model determines and deploy the discovered new features without retraining model; this means the transfer process will occur in every agent in the network. To elaborate flexibility in a dynamic machine learning model which accepts changes in both dataset and features, Overmind is conceptually designed to overcome the aforementioned challenges by described steps below:

1. When is discovered within a new dataset , Overmind predetermines a need of  by mimicking the supervised machine learning model of  and process upon  along with . However, to avoid data volume bias, Overmind will proportionally random data in each  to form a dataset as equal as
2. Let accuracy of forming dataset is denoted by and accuracy of model upon  is denoted by .
3. If , Overmind drops from  and takes as data transfer process. This indicates that  cannot improve performance of existing system.
4. If , Overmind considers as significant feature and process further steps for feature transfer process which is described below:
   • Where the existing dataset lacks of true values, Overmind creates imputation model by changing target label to  using forming dataset. This imputation model is denoted by .
   • Overmind inserts for each  in , denoted by . This may result in extreme missing values in  . Hence,  is used for imputation of missingness.
   • Starting from , agent retrains its model along with , the retrained agents are denoted by
   • Retrained agents perform cross-validation and compare a result against . There are two possible outcomes
     • gives a better performance, then  is accepted.  adjusts , denoted by , or else
     • gives a better performance, then  is rejected.  flags weight/coefficient of   as zero in

5. Overmind reshapes a network of agents, this creates . Perhaps, centrality of network may be different from , Overmind updates the new centrality, denoted by
6. is distributed over  following data transfer process.

Figure 3 shows the establishing of network of agents and transferring learning that impacts changes over a network.

Figure 3: (a) Illustrating an established network with transfer learnings for both data and feature (b) Demonstrating a reshaped network resulted from transfer learning process.

Until now, a proposed framework creates a dynamic network which illustrates the transfer process among decentralized agents. Hence, this is called the collaborative decentralized machine learning framework.

5. Performance evaluation: a case study Thailand PM2.5 and weather forecast information database

To evaluate framework performance, this paper simulates the framework over the testing dataset, which is Thailand PM2.5 and weather forecast dataset presented in [1]. This dataset contains 2,383 instances with 21 features (including target). Overmind’s simulation takes place in two functions, Framework Performance and Demonstration on Transfer Learning for Feature Transfer, to predict PM2.5 values from a given set of features.

5.1. Framework Performance

This paper deploys 80% of dataset for training and 20% for testing, for both traditional machining learning model –a single model –and Overmind.

In the training process, five uncorrelated features are removed from the dataset. Two algorithms, Gradient Boosted Trees (GBT) and Neural Net (NN), are used in this paper because the previous work has described Thailand PM.5 prediction using these algorithms. The set of tuning parameters is left by default using RapidMiner 9.6. For GBT, number of trees = 50 with learning rate = 0.01. For NN, training cycle = 200 with learning rate = 0.01. In addition, 10 folds cross-validation with automatic sampling type is used.

Throughout this paper, gradient color codes are used to indicate how good the results are. Green-colored fields indicate the higher accuracy and correlated values and red-colored fields indicate the opposite.

The clustering and agents training results show in Table 1

Table 1: Distributed Agents in Training Process

Table 1 proves a better performance, measured by Root Mean Square Error (RMSE), on agents,  and  than on a traditional single model, , in both algorithms. It may indicate the abnormal data pattern in  which causes variation of performance for a traditional model and assumption of removing these instances are not acceptable by business scenario. In other words, 13.43% of the instances drop performance of agent for from RMSE 6.56 to 13.50, and it seems to be unacceptable tradeoffs.

Establishing a network of agents, we explore the weights of features based on GBT variable importance percentage as shown in Table 2

Table 2: Variable Importance in agents based GBT

By examining variable importance in Table 2, both of three highest importance features and lowest importance features were examined giving a clue that the criteria as feature weight on the prediction of PM2.5 differ for each agent. The data in each cluster should require a specifically trained agent.

Recall, an establishing network of agents based GBT algorithm, Overmind determines the similarity measurement of agents using variable importance. A Euclidean Distance Measurement is used to find the distance among agents. In Table 3, the similarity is measured by using the equation:

where s is source node, d is destination node.

Table 3: Agent Similarity Measurement

Setting a threshold  means the edges will be created when the similarity between nodes is higher or equal to 0.3. However, this threshold is an adjustable parameter, the lower the threshold the higher chance for connected agents, the similarity of agents affects and shapes the transfer process. Where connection exists, a pair of the agent can communicate and transfer both data and feature, in another hand, isolated agent requires specially and specifically consideration. In this study, 0.3 is represented for the demonstration that creates linkages between agents unless the transfer process cannot be illustrated. Considering a tradition model, an agent in a network will results in Figure 4, however, this network will be examined again in the feature transfer section.

Figure 4: Network of Agents (|F|=15,  =0.3)

In the testing process, 477 instances of the dataset were distributed to specific agents based on the data similarity measurement. Moreover, the prediction assessments on both Gradient Boost Tree (GBT) and Neural Network (NN) were shown in Table 4.

The results show high accuracy when distributed agents perform a prediction over the associated clustered dataset. Undeniably, the traditional model is flexible and stable for input data. Perhaps, it should be considered as a tradeoff. We calculate average performance for distributed agents as:

where  is an accuracy of prediction by  on dataset

Table 4: Test Results

The GBT has achieved average performance RMSE of 11.28 whereas the traditional model has obtained RMSE of 14.13. On the other hand, the NN achieve the RMSE of 6.34, while the traditional model achieves at 6.65. These performances described in Figure 5.

Figure 5: Average Performance, measured in RMSE.

5.2. Demonstration on Transfer Learning for Feature Transfer

In this demonstration, one correlated feature from a training dataset is removed, but a testing dataset remains with full features. Based on variable importance in Table 2, AQI is assumedly removed from the training dataset. The results of newly trained agents are shown in Table 5.

Table 5: Distributed Agents in Training Process, |F|=14

The performance of two minor clustered (n =553, and n = 234) significantly drops. However, it is not yet comparable results where |F|=15 because the clustering process may yield a different output. Base on this experiment, a same threshold value,  will result isolated  which indicates that the data in  and  should be removed from the demonstration and only one agent required in this scenario, it is ; the demonstration will be then bias, hence, we lower a threshold to  and the result of networks of agents shows in Figure 6.

Figure 6: Network of Agents (|F|=14,  =0.2)

Figure 6 shows that a centrality of network is , this means centrality of the network is determined neither by agent performance nor numbers of instances. The centrality of the network is responsible for sharing and transferring the discovered both data and feature.

Demonstration on a discovered new dataset and feature (n=477, |F|=14+1), assumedly, AQI is a new feature. Overmind firstly determines the need for AQI data if it can improve the performance of the overall agent or not. Overmind takes sampling proportional data from clusters in a network, then building a model to evaluate performance against a new dataset.

Table 6: Comparing Results for a Transfer Learning

Table 6 shows a better performance with a new feature, hence, a transfer learning should be accepted for the new feature, AQI.

An existing dataset in a network contains no value of AQI, and the business scenario does preserve existing dataset. Hence, an imputation model is built by using a new dataset for training process (n=477, |F|=14+1). Eventually, an existing dataset is imputed for a new feature, AQI; it is deployed to the centrality of the network, all agents accept AQI as a new feature because the comparison identifies AQI can improve performance as shown in Table 7.

Table 7: Distributed Agents in Training Process, |F|=14+1 (imputed AQI)

Overmind update a network of agents based on changing in similarity measurement among agents.

A re-connected nodes between  and  is formed, this results in a fully connected network, and the centrality of  the network is shifted to  because it has the highest performance and controlling a majority of data in the network (N=1119). Overmind then continues data transfer process as described in (A). The data prediction results are shown in Figure 7.

Figure 7: Network of Agents (F=14+1, Y=0.2)

Table 8: Test Result after feature transfer process

The comparison shows average performance in RMSE using GBT with 13.36 in this testing dataset, while a traditional model would give an accuracy, 14.46. For NN, the average performance in RMSE is 8.69, and 8.86 for a traditional model. This comparison is described as shown in Figure 8.

Figure 8: Average Performance, measured in RMSE after feature transfer process

Figure 8 shows penalty in transfer process caused by the imputation model that increases the RMSE after transfer process as shown in Figure 9. However, it should be reminded that demonstrating feature transfer is taken place on the important variable which is the AQI.

To verify the consistency of this approach, the progression of the framework functional processes, Determine Data Clustering, Assign Agent, and Establish Network has been performed. However, in the second round, the dataset is slightly different because (1) 1,906 instances (80%) of training dataset have imputed AQI values, and (2) 477 instances (20%) of the testing dataset have true values of AQI. To avoid confusing, the agents in the second execution is denoted by  where x is a number of clusters produced by this execution.

Figure 9: Penalty after feature transfer process

Table 9: Distributed Agents in Training Process by Second Execution

The result in Table 9 shows consistency over the clustering process which still produce three clusters with similar proportions. Moreover, the overall performance of agents is consistency and even slightly improved. Examining agent similarity using variable importance between the first and second executions are shown in Table 10 and Figure 10. The comparison in Figure 10 shows changes in variable importance, but the agents are keeping their characteristics over the same set of features.

Eventually, a network of agents is re-established as shown in Figure 11; a network of agents also preserves its structure where the centrality of network remains unchanged. However, if the similarity threshold is recalled in the very first demonstration of establishing a network where , the isolated agent ( ) may occur which is assigned to the minority of data (n=316) that contribute poor performance.

Table 10: Variable Importance in agents based GBT by Second Execution

Figure 10: Comparison of Agent Variable Importance based GBT by first and second executions

This scenario implies that the business may consider, (1) a separate studying of agents in order to build a specific algorithm for prediction over the associated dataset, (2) removing associated data instances out of dataset in order to reduce inconsistency and improve prediction performance, (3) reducing a threshold ( ) in order to keep particular agent connected to a network because this agent can  be a specific dataset where a business foresees this data will be increasing over time in soon future.

6. Results and Discussion

Till now, demonstrations of both framework performance, and the transfer process are explained. The summary of the performance of Overmind with four comparing scenarios in cross-validation agents’ average performance can be concluded as follows:

1. Scenario I: Overmind assigns distributed agents on a fully completed dataset.
2. Scenario II: Overmind assigns distributed agents on a dataset where importance attributes is removed.
3. Scenario III: Overmind assigns distributed agents to demonstrate a situation where a new feature is discovered and transferred, and
4. Scenario IV: Overmind assigns retrained distributed agents and restates its network

Figure 11: (1) Network of Agents in Second Execution (F=15, Y=0.2) without Traditional Model, (2) Network of Agents in Second Execution (F=15, Y=0.2) with Traditional Model,

Figure 12: Comparison of Average Performance in Cross-Validation Agent Generated Models measured in RMSE

Figure 12 explains, for all scenarios, the cross-validation performance in Overmind generated models is impressive compared to a traditional single model under GBT algorithm. In the other side, the performance is not satisfied when compared to the traditional model under NN algorithm.

To summarize testing performance in testing data, we divide into two scenarios, (1) Scenario I: Overmind assigns distributed agents on a fully completed dataset, (2) Scenario II: Overmind assigns distributed agents to demonstrate a situation where a new feature is discovered and transferred. The results are shown in Figure 13.

Figure 13 indicates a better testing performance in all scenarios, but the performance is slightly better in the NN algorithm. In sum, Overmind can achieve its primary intended design and concept, this achievement can be demonstrated through our previous dataset for PM2.5 prediction in Thailand.

Figure 13: Summary and Comparison on Testing Performance measured in RMSE

7. Conclusion

Overmind is a framework aiming to demonstrate and facilitate a decentralized machine learning-based network analysis. This paper proves the concept and its performance through the mixed dataset between Thailand PM2.5 dataset and weather forecast dataset.

In data prediction, this framework can significantly reduce the data bias scenario, where input data is uncontrollable, variant, and clustered. Moreover, Overmind has proven its performance by comparing an average accuracy measured in RMSE on both traditional models (single machine learning) and distributed agents (multiple machine learning, agents). Overmind performs better performance than the traditional method in selected dataset using the GBT algorithm, reducing the RMSE from 14.13 to 11.28 (20.16% decrease). Furthermore, the RMSE is slightly reduced from 6.65 to 6.34 (4.6% decrease) in the NN algorithm.

A feature transfer process is demonstrated as a conceptual design for data preservation. However, the evaluated performance yields penalty due to imputation method.

In sum, Overmind has successfully formed a collaborative decentralized machine learning framework serving as an alternative in the design of a machine learning system. Possible contributions of Overmind are:

7.1. Higher Performance and Unaffected by Data Bias.

Overmind proves its effectiveness as a prediction system in the selected testing dataset. The design of Overmind is not affected by data bias because distributed agents are designed to function over associated sub-dataset.

7.2. Distributed Resource Utilization

Overmind mimics the concept of a distributed system so that it inherits the advantage where resources are allocated in a decentralized manner. Each agent utilizes computational resources for processing associated input instead of processing the whole dataset. This contribution also involves parallel processing and queue system where particular agents may transfer their queued inputs, as a bottleneck, to similar connected agents in a network for parallel processing where penalty inaccuracy can be traded off. This parallel processing for Overmind is already considered and planned for future work.

7.3. Dynamic Models in Collaborative Manner

Overmind demonstrates dynamic machine learning models for feature discovery and transferring across collaborative network analysis with data preservation. The concept of similarity measurement and network of agents has innovated methods in a dynamic framework. There are still opportunities in exploring the aforementioned methods to study optimization parameters and techniques.

8. Future Work

A study on optimization is recommended on Overmind’s parameters. An interpretation over a network of agents formed by Overmind is an interesting topic to be explored and described how it contributes significances to business viewpoints.

In addition, the enterprise system architecture of Overmind can be improved and enhanced for higher performance and resilience such as queueing system and redundancy in agent level For example, when one agent is in abnormal stage, data can be continuously processed by neighbors or connected agents in the network; this will reduce the impact of disruption and error in artificial intelligence system.

Acknowledgment

We would like to thank the members of Faculty of Information Technology and Digital Innovation, King Mongkut’s University of Technology North Bangkok, for their guidance and suggestions. Importantly, we would also like to thank Bangkok Air Quality for supporting data.

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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. 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. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
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. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
91. 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
92. 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
93. 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
94. 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
95. 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