1. Introduction

Gesture Technology is an emerging scope in the field of Computer Science, precisely machine learning. The objective of gesture technology is to interpret or recover the human gesture using intelligent machines. Complex mathematical algorithm is used to interpret the gestures. Fundamentally spoken, the mathematical algorithms contain a large set of test and training data, which are eventually used to compare with the current status of the human, thus come out with an appropriate result that can describe the gesture. Gesture is vastly used in human-machine interaction where the machine detects gesture input from the human and responds accordingly. Advanced technology has created a possible way for communication between 2 or more machines, with no human presence, using gesture input and output. Furthermore, gesture technology can be very useful in human to human interaction, precisely where the persons involved in the communication do not have the same mode of communication. While other body parts are also used in gesture technology, the most used parts are face and hands. In general term, gesture can be divided into 2 types- a) body gesture: where body is used to create expression; and b) hand gesture: where only hand is used to create expression. A sign or symbol is the most commonly used hand gesture [1]. Gesture can be further distinguished on basis of static gesture [2] and dynamic gesture [3]. Face images in gesture technology are mostly used for emotion detection [4] and as a security tool used in gadgets and other devices [5]. Some main application areas for gesture technology include gaming sector, home automation, automotive sector, sign language interpretation. In hand gesture technology, human can interact with machine or with another human through a machine that recognizes the hand gestures created by the human being [6]. Generally still images of hand gestures are sent as inputs to the machines, but there are also machines that can collect gesture data from live video footage. Thanks to advanced technology, touching of the machine is not necessary anymore; strong sensors in the machine can detect hand gestures from distance. Hand gesture technology is verily used in the later two of the previous example. In this domain, we will talk about hand gesture technology and sign language interpretation.

Enjoying the proper lifestyle is the birth right of every human being in this world. Everybody should be able to communicate and share expression with the outer world, but unfortunately not every time people are able to do so. It is always hard for people to communicate with others when mental or physical challenges become a barrier in the way. While it is safe to say that these people are verily able in other ways that help them to communicate, we always try to find a system better than the existing one. The most fundamental and most popular technique used in this field is creating sign language using hand gestures [7]. But the challenges occur when at least one of the people involved in such communications doesn’t know the sign language or how to create them. A few numbers of people who don’t need sign language for communication actually know this language, resulting in a great difficulty when there is a communication between 2 people where one of them only uses sign language for sharing expression. Based on practical experience, it is almost certain that these types of conversations do not proceed easily unless there is a third person who is used as an interpreter. There exist devices, as for example, data gloves, which are used to solve such problems during conversation. Data glove is an interactive device, basically a glove to wear with sensors on it. These sensors create precise capturing of hand gesture information. Though data glove is generally used in robotics, other application areas also include human interaction. This report refers only to camera based static hand gesture recognition [3]. Static hand gesture recognition technology has a useful application on communication between 2 persons where one is able to use a particular sign language (say ASL or American Sign Language) and other isn’t. A hand gesture recognition device is used as the protocol in this communication. As mentioned earlier, the device contains sensors to capture images and to detect gesture data and analyze them.  The basic objective of the device is to eliminate the necessity of a third party interpreter, by allowing itself to do the same job. It captures gesture images as input and creates output suitable for non-ASL speaker.  Generally the output is in textual format. The device is also used in the reverse situation, that is., taking textual or vocal words as inputs and creating sign language as outputs. Since the algorithms used in a device is based on a particular sign language, it is necessary that mode of communication from the sign language user’s side only includes one specific language, such as either Indian Sign Language (ISL)[8] or the American Sign Language (ASL)[9] but not an amalgam of both. The objective of this paper is to illustrate the extraction of unique features from each symbol or gesture in reference to an existing data set (ISL or ASL) and then train the machine with the obtained feature vectors using standard classification models.

Using a huge number of data in test set and training set creates less ambiguity for the machine while taking decision, and as a result it can give a more accurate output. As for example, a specific hand sign can be described by several textual words, and a set of hand signs can be described by 2 or more different sentences. The more there is data in the device’s memory based on previous trainings (that is, in training data set), the more accurately the device will be able to decode texts into proper sign languages. There are more necessities to be taken care of in order to get more accurate results. Use of white gloves is employed to eliminate unwanted wrinkles or groves on the palm and fingers. From the threshold image of the figure we obtain its minimum fitting polygon through what we call polygon approximation technique. The next step is Convex Decomposition technique. It segregates each of the convex parts of the hand, which is an essential step to identity the crucial aspects of the gesture. The next and final step is to extract the required unique features. The wrist point in the hand is considered to be the mid-point of the lowermost polygon.

The novelty of the proposed method is listed as follows.

• Extraction of individual features for each and every gesture creates the possibility of notable polarity and a chance of further efficient classification of the obtained
• The proposed method is equipped for large scale deployment through recognizing the demand and necessity of real time
• The produced results from trained dataset are favorable with adequate acceptance

2. Related Work

Both the Indian Sign Language (ISL) and American Sign Language (ASL) are comprised of a notable list of hand gestures, each with meaningful expression that can be used for any non-verbal communication. As mentioned earlier, the sign languages come real handy when we have the technology to decode a specific sign language to sounds/texts and vice-versa, thus useful in communication where one person is accustomed with sign language communication where as other person use vocal/ textual communication and does not know the sign language. In probability, Markov Model is a concept for modelling randomly changing system. According to this model, the next state of a random system mostly depends on the current state of the system; and not on the previous states. In 1960 William Stokoe developed a concept based on real time Markov-model based systems [10]. In this system, continuous American Sign Language (ASL) generates long combination of words as outputs. A single camera is used in this concept. The concept is still very much effective and is majorly used technique to recognize gestures. However, this implementation requires polygon approximation followed by convex decomposition.

The convex decomposition method is a special case of alternating sum of volumes (ASV) method. ASV can be defined as a series of convex elements stick together in a way such that for elements A, B, C, D, E, … the series will be: A u B / C u D / E and so on, where u is Union operation and / is Difference Operation [11]. In 1998, Starner proposed the using of convex hulls and set- difference operations [12]. A convergent convex decomposition is proposed that uses a combination of ASV decomposition and remedial partitioning for the non-convergence. The concept uses ASVP decomposition for image feature extraction, in other words, to identify the unique properties of an image that differentiates the image from others.

Polygon Approximation is very necessary in this context. The simplest idea of the process can be described by 4 steps – Identifying original shape (contour), chopping the shape around the corners, generate approximate convex lines for the chopped segments, merging the segments [13].  The decomposition of polygon with 0 or more holes into convex chops can be done approximately using Approximate Convex Decomposition (ACD) [14]. The algorithm used in this context is simple: iterative resolving of the most significant non-convex feature. Approximate Convex Decomposition method is applicable for both 2D and 3D models.

On the basis of simple cut and split paradigm of Chazelle, a method to compute a convex decomposition of a non-convex polyhedron of arbitrary genus (handles) and shells (internal voids) is generalized [15]. The algorithm is extended to work for a certain class of no manifold polyhedral.

A set of topological decimation operations to its dual graph is applied to calculate a hierarchical segmentation of mesh triangles, which is used for 3D mesh approximate convex decomposition [16]. The method of segmentation incudes a) portraying a triangle mesh and its corresponding dual graph, b) contracting of an arc of the dual graph, followed by the marking of the end-points of the two triangles corresponding to the dual arc’s as belonging to the same single cluster [17]. Segmentation can be of two types – direct segmentation and feature based segmentation. This particular model is important due to its adaptation to collision detection.

Inter-class variability enhancement and local-global ambiguity identification for each hand gesture is used in the automatic gesture recognition on Indian Sign Language (ISL) [18]. In ISL both hands can be used to represent an expression. Support Vector Machines (SVM) is used to classify each hand posture. In SVM data items are plotted as points in n-dimensional space, where n is the number of features. To classify dynamic gestures, Dynamic Time Warping (DTW) is used with the trajectory feature vector, and it achieves 86.3% recognition rate.

A pre-trained Google Net architecture representing a real-time sign language interpreter is described while developing the architecture with ILSVRC2012 dataset as training sets [19]. The idea is based on Convolution Neural Network (CNN). The main difference between Convolution Neural Network (CNN) and ordinary Neural Network is that CNN assumes that all the inputs are images, allowing researchers to encode certain properties into the architecture. The architecture is also trained with data sets from Surrey University and Massey University ASL data. This training set allows the architecture to apply transfer learning to this task. The system features a pipeline that captures video of signing a word as input through a designated application. Individual frames of the video are extracted and then they generate letter probabilities using a CNN.

Initially made use of Histogram of Oriented Gradients method extraction of feature, a feature extraction method uses two neural networks based recognition of 10 gestures [20]. The HOG [21] for an image describes the elementary property (such as color, shape and size) of an image. The objective of HOG method is object detection.

Data availability and analyzing their relative merits to determine features are essential for sign-language recognition (SLR) [22]. The objective is to continuous sign recognition and design a model that is independent of signer. The model should also be adapting to larger and noisy data sets.  A statistical analysis based on four parameters: position, posture, orientation, and motion is used for a large vocabulary sign language interpreter with real-time continuous gesture recognition of sign language [23].

3. Data Set

The dataset for the proposed method has been centered on the popular and ubiquitous ASL (American Sign Language). It was formulated by the University of Surrey’s Center for Vision, Speech and Signal. The images referred have been indicated in the figure (1). We were able to gather approximately more than 500 colored images however the algorithm is constrained to static gesture recognition.

Figure 1: Different Gestures of ASL

4.  Methodology

4.1.  Pre-Processing

In the first step where we remove uneven parts of the image, dilate and erode and then generate a contour based on color edge detection. In the above set of figures, figure (2.a.I) is the initial figure, which is followed by figure (2.a.II) where we get the processed image. Using the processed image, we can find out the contour of the dark area using color edge detection technique [24] shown in the figure (2.a.III).

Once the contour is obtained then the rest of the area apart from the contour can be removed. We assumed that the hand will fit to (having minimal space between the hand and the frame border) the frame with the frame dimensions being constant. Once the background is removed, we need figure out the edges of the fingers and hands marked by black contrasting borders. Figure (2.a.IV) shows the finally obtained contour after preprocessing.

4.2.  Approximation of Minimal Fitting Polygon

In digital image processing the approximation of arbitrary two-dimensional curves by polygonal figures is an imperative technique. In number applications, this technique is widely used for a purpose to represent lines and boundaries by means of polygons that have a minimum number of vertices so that fit condition is satisfied. Using island finding algorithm, we will be able to separate the fingers by inner smaller polygons.

Figure 2 (a): Images Describing the Pre-Processing stage

To find the initial point we need to move by traversing alone the x-axis of the bottom frame and then we can find the first pixel that is colored black. The adjacent pixel can be located by a technique where we check the neighboring 8 pixels for a black pixel. The algorithm navigates through the contour of the figure from pixel to pixel, marking them based on their connectivity and the relative values of their neighbors. Connection between the pixels can be determined with the help of a 3×3 grid as shown in Figure (2.b). This grid can be either 4-connected or 8-connected.

Figure 2 (b): 8 neighboring pixels of a pre-determined pixel

Now we need to find the 2D polygon by the following algorithm:

1. Now take a black pixel at the starting point and continue moving along the pixel.
2. Now joining the starting point pixel and adjacent point pixel (pivotal point) with a line to forms the base. Now we calculate the angle between the base and the next black point and store the calculated angle.
3. Now if calculated angle is not considerably larger or smaller than the previously calculated angle, then we need to eliminate the current pixel point, and take the next pixel point lying in succession as the pivotal point. Else, if the angle is substantially more or less by a threshold value, then consider the current point and initialize the point as starting point. Go to step
4. Repeat the above steps until we reach the initial point.

Figure 3 (a): Determining the Base Angles

Figure 3 (b): Compare the recent angle with previously stored one. If the value is considerably similar (that is, not much greater or much smaller) to it, discard the pixel and move onto next pixel. Here the angle-135^o is not considerably different from 108^o hence discarded.

Figure 3 (c): Since 180^o is considerably large, it is accepted

4.3.  Island Finding Algorithm:

Here, we shall try to find out the minimal cyclic paths that enclose an area. A DFS traversal is done using the following constraints

1. Stack(S) ß Push(R); S is the stack and R is root node.
2. If S==null, end looping, else,
3. If current node X present in Stack S, go to step 6.
4. Else if X not present in Stack S and if it has child nodes then expand and explore child nodes if [Child(X)!=visited or Child(X)!=Parent(X)]. Else return to parent node.
5. Repeat step 2.
6. If substring[X, indexOfLastNode] or reverse (substring[X, indexOfLastNode]) is not already stored, then store (substring[X, indexOfLastNode] + X) as cyclic path C_n. Mark X as visited, POP(X) and PEEP(S). Go to step 5.
7. If substring [X, indexOfLastNode] or reverse (substring [X, indexOfLastNode]) is already stored, then mark X as visited, POP(X) and PEEP(S). Go to step 5.

Once we find all the cyclic paths possible (C₁, C₂, C₃….C_N) then in a sequential order consider each cyclic paths, such that if elements of (C_{n –} lastElement) or elements of reverse (C_{n –} lastElement) is subset of C_m, then discard C_m. Continue until we have minimal unique cyclic paths possible. Then a given cyclic path forms either an island (finger or palm) or it’s the outer contour of the figure.

Figure 4: Illustration of Polygon Approximation of Polygon from the Shape of a Hand

In the above figure 4, the polygon with vertices of color red is sketched out from the curved outline of the image. A simple polygon P is formed when the vertices are joined by the blue lines.

The non-essential curves and bends are removed by the technique of approximation of minimal fitting polygon [25] and give us a figure that is bounded by line segments. The resultant image consists of a simple polygonal region in the plane bounded by a non-self-intersecting, closed, polygonal path. The polygonal path itself is part of the polygon; it is usually called the boundary.

4.4.    Decomposition into Convex Components

Now if we partition the complex models into pieces it will be easy to deal with complex models or many-sided figure. Numerous difficulties can be solved more proficiently when the input is convex. Convex components Decomposition into results in pieces that are easy to process and additional approximation simples and eases computation, ensuring quicker and efficient output with manageable margins of inaccuracy.

Exact and approximate are two type of convex decomposition. Number of clusters in the model should be minimized so we do not use exact decomposition. We can get minimum number of  clusters using Approximate convex decomposition ensuring that each cluster has a concavity lower that a predefined threshold.

Figure 5: Polygon Decomposition into Non-Concave Sub graphs

The figure 5 given above shows the result of convex decomposition on the approximated polygonal segments obtained in the previous section.

Figure 6: Resulting Convex Decomposition

In graph theory, where a complete subset of vertices of a graph G such that every two distinct vertices in the clique are adjacent is known as a clique. Subsequently, a maximum clique is one that has the largest possible number of vertices among other cliques. We then observe the maximum clique possible for any given point and then iteratively check for the points it connects. For given vertices V = {V₁, V₂, V₃…V_N},the we need to form  two groups such that if V_M  V then, put VM into Checking array(C), and the vertices connecting V_M in Pointing array(P). Put P’s last element into C and iteratively check if C elements has common connections with vertices in P. Group those points to find if they share any common point to find the approximate convex partition [26]. The maximum clique in the above mentioned visibility graph G, which is also undirected, is a complete sub graph of G having the maximum number of vertices.

4.5.    Detecting Wrist Point and Reference Line

Deriving the data necessary for feature extraction the wrist point and the reference line are needed. The data are calculated with respect to the reference line. As the image is always considered to have the picture of the gesture in upright position so the wrist point is calculated as the mid-point of the bottom most polygon.

Figure 7: Deduction of Wrist Point and Reference Line

First identify the polygon associated with the wrist to derive the reference line. Then the mid-point of the polygon is calculated. The line that passes though the wrist point and is parallel to the bottom of the frame of reference is known as reference line. The green dotted line in figure 7 indicates the reference line. The bottom-most polygon is the polygon associated with the wrist, and the blue dot is the midpoint associated with the polygon.

4.6.  Angle and Distance calculation with respect to Wrist Point

We now have the line of reference as well as the wrist point which are used to realize the different polygon segments in a given figure. To achieve this by observing the incrementing angles between each line joining the mid points of the polygonal segments and the line of reference. Our main objective here is to find unique feature vector to represent different hand gestures.

With reference to figure 8 by the convex decomposition process we begin by finding the mid-points of the segments. These points, marked as P1 through to P6, are then extended to join the wrist point marked as W. What is important to note here is that with each segment mid-point, the angle increases with the minimum being a possible 0° and the maximum being a possible 180°.This gradual expanding gap between the wrist point and each subsequent line signifies the change in finger positions and helps us to differentiate between them. Another aspect of the line joining the wrist point and the mid points of the segments is its length.

Figure 8: Connection of Wrist Point, described by P1, P2, with the Centroid (W) of the Decomposed Polygons

The property which helps us to decide whether a finger is in a folded or unfolded position is the distance between the wrist point and the mid-point of the corresponding polygonal segment of that finger. While the finger is in a folded position, the centroid of the segment gets located in a vertically lower level inside the polygon than it would if the finger was in an unfolded position. As a result, it becomes fairly easily distinguishable to recognize different signs, especially the ones that only differ by the positioning of a single finger.

Outcome of the above stated procedure produces the angle and distance of each mid-point of the different generated convex segments with respect to the reference line and wrist point. It shall be noted that the duality (angle and distance) needs to be converted to a singularity for feature extraction and forming a unique one-dimensional array representation of each distinguishable hand gesture.

Figure 9: Unique Array of Data to Represent a Hand Gesture

Figure 10 (a): Accuracy Calculation for 10 gestures with 20 images for test I.

Figure 10 (b): Equivalent Confusion Matrix for Test-I

Figure 10 (c): Calculating Accuracy for 10 gestures with 20 images for test II

Figure 10 (d):  Equivalent Confusion Matrix for Test-II

Figure 11: Graphical Explanation of Accuracy versus Number of examples.

4.7.  Generation and Training of Feature Vector.

To generate a feature vector, convert the duality (angle and distance) into a singularity is the main challenge. For a given angle and length use a preexisting formula, lsinƟ or the sin component. The following table is a sample of generated feature vector for the data obtained from table 1.

Table 1: Calculating the Elements of Feature Vector

There are cases where we have found that the number of data points are unequal. As a result, the maximum possible number of data points in a gesture from a given set of gestures is considered as the length of the feature vector array. In case of any gesture having less number of data points than the array size, the empty spaces are filled with 0. This is not analogous to padding with 0, because here it means that the figure has more fingers which are at an angle of 0° or 180°. The feature vector for any gesture is highly distinguished from another gesture.

Once ample number of feature vectors have been obtained, then we can proceed to create our feature file for training the machine. The matrix of features can be created using the feature vectors from the training set. We then train the machine with our matrix of features using Artificial Neural Network and compare the accuracy of each on a common test set.

Data obtained from ten images each of ten alphabets are used for training and testing. Learning is done using ANN classifier. For training and testing we have split the data in 8:2 ratio for each alphabet. Our training set consisted of 10 pictures each of 10 alpha-numeric gestures. Thus we have used 80 images in total to train the machine and further 20 to test the accuracy. Our training included the deployment of cross-validation technique to obtain more precise results. Cross-validation randomizes the data used for training and testing respectively.

5. Results

The aforementioned algorithm has been put to test and has yielded an average accuracy of 92.5%. In reference to the results generated by the code, the confusion matrix of a couple of outputs have been mentioned with resultant prediction values.

In the above stated figures, test 1 has revealed an accuracy of 100% stating that all images of the hand gesture has been successfully classified. The confusion matrix indicates true value for the hand gestures it has predicted for a given feature vector and false for the rest. In the second test the algorithm has missed classified two figures and hence it can be noticed in the confusion matrix where the algorithm has wrongly predicted true for hand gestures numbered 2 and 4 respectively. Additional tests have been conducted in order to obtain consolidate the rate of accuracy.

Table 2: A Comparison between the Proposed Frameworks with the Available CNN

The above figures strengthen the advantages of the proposed algorithm over existing CNN or Convolution Neural Network algorithms. Sample training shows that on an average 3 minutes are required for training our algorithm in comparison to CNN which takes roughly 18 minutes for the same number of images. As stated before, storage requirements are highly minimized due to the miniscule feature vector size and the number of images required to obtain adequate accuracy. Thus, for a considerably lesser number of images the algorithm generates acceptable accuracy in contrast to existing algorithms.

Thus the aforementioned framework is readily available for application on computation constricted environment in public sectors and also in absence of high data processing infrastructures.

An average accuracy of 92.5 % has been secured by our algorithm while observing each cycle of training and testing set obtained using cross-validation technique. In comparison to existing Convoluted Neural Network models (CNN), it requires substantially less data and produces acceptable accuracy. Further CNN is computationally expensive model of prediction in comparison to ANN model coupled with distinctive feature extraction.

6. Conclusion

Our technology and technique for gesture recognition has provided significant improvements over previously worked on methods in terms of accuracy and reliability. The disabilities experienced by individuals who are incapable of speaking are overcome with the help of this machine which eradicates the need of another individual for translation of the sign language to the commonly used English language. We have trained our system with considerably less and manageable sized dataset consisting of various gestures of every letter in the English alphabet. There are some drawbacks, however, in the fact that the process of recognition of a gesture must be carried out with the assistance of a glove without which the creases hinder with the accurate depiction of the hand model and consequently results in imperfect polygon approximation. Also, the process must be carried out in front of a uni-coloured background to ensure that the outer contours of the hand are well defined. But with better and elevated image preprocessing techniques the need for gloves and background may be eradicated. We hope that our research will help the unfortunate ones become socially acceptable and can express themselves spontaneously and freely.

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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. Hoang Giang Vu, Thi Thuong Huyen Ma, "Observer-Based Method of Feature Extraction for the Fault Detection of Permanent Magnet Synchronous Motors", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 942–948, 2021. doi: 10.25046/aj0602107
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. Mounir Amraoui, Rachid Latif, Abdelhafid El Ouardi, Abdelouahed Tajer, "Feature Extractors Evaluation Based V-SLAM for Autonomous Vehicles", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1137–1146, 2020. doi: 10.25046/aj0505138
51. 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
52. Suni S S, K Gopakumar, "Dense SIFT–Flow based Architecture for Recognizing Hand Gestures", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 944–954, 2020. doi: 10.25046/aj0505115
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. Katleho Moloi, Yskandar Hamam, Jacobus Andries Jordaan, "A Support Vector Machine Based Technique for Fault Detection in A Power Distribution Integrated System with Renewable Energy Distributed Generation", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 577–588, 2020. doi: 10.25046/aj050468
65. 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
66. 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
67. 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
68. 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
69. Amal Hadri, Khalid Chougdali, Raja Touahni, "Nonlinear \(\ell_{2,p}\)-norm based PCA for Anomaly Network Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 234–243, 2020. doi: 10.25046/aj050429
70. 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
71. 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
72. 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
73. Amal Hadri, Khalid Chougdali, Raja Touahni, "Improved Nonlinear Fuzzy Robust PCA for Anomaly-based Intrusion Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 249–258, 2020. doi: 10.25046/aj050332
74. 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
75. 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
76. 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
77. 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
78. 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
79. Daihui Li, Chengxu Ma, Shangyou Zeng, "Design of Efficient Convolutional Neural Module Based on An Improved Module", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 340–345, 2020. doi: 10.25046/aj050143
80. 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
81. Devulapalli Sudheer, Rajakumar Krishnan, "Multiscale Texture Analysis and Color Coherence Vector Based Feature Descriptor for Multispectral Image Retrieval", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 270–279, 2019. doi: 10.25046/aj040634
82. 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
83. 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
84. 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
85. 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
86. Dian Candra Rini Novitasari, Ahmad Lubab, Asri Sawiji, Ahmad Hanif Asyhar, "Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 115–120, 2019. doi: 10.25046/aj040413
87. Md Nasimuzzaman Chowdhury, Ken Ferens, "A Support Vector Machine Cost Function in Simulated Annealing for Network Intrusion Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 260–277, 2019. doi: 10.25046/aj040334
88. 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
89. 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
90. 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
91. 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
92. 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
93. 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
94. Bayan AlSaaidah, Waleed Al-Nuaimy, Mohammed Rasoul Al-Hadidi, Iain Young, "Zebrafish Larvae Classification based on Decision Tree Model: A Comparative Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 347–353, 2018. doi: 10.25046/aj030435
95. 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
96. 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
97. 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
98. 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
99. 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
100. 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