1. Introduction

Music genre classification is used to categorize musical data into suitable categories based on shared characteristics. In the modern era, numerous musical genres are appreciated, such as blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock. New technologies and globalization have been beneficial for the evolution of music, leading to the emergence of world music. These developments have increased research interest in music information retrieval (MIR), a field of automatic music management including music recommendation [1], music mood classification [2], and musical instrument classification [3]. As musical genres are most commonly used to manage music databases, a previous study investigated the extraction of audio features and techniques for classifying musical genres [4, 5, 6, 7]. However, genre classification has remained a challenge[8, 9], as it requires systems that are capable of performing automatic grouping [10] and querying retrieving data from large a music data set [8]. The identification of commonalities between genres requires feature extraction [11]. In the case of audio files, combinations of relevant features are used for modeling the musical genre [4]. These features may include timbral texture, rhythmic content, and pitch content [12]. Musical genres have been modeled using different features including short-term Mel-frequency cepstral coefficients (MFCCs) [13, 14], and Daubechies wavelet coefficient histograms (DWCHs), depending on the required application. Features are selected to improve performance and accuracy [15]. Achieving optimal feature selection and extracting appropriate features and dimensions of the features reduction are very challenging tasks [16, 17]. There are many different approaches that have been proposed to classify music genre, such as data mining [18, 19], deep learning strategy [20, 21, 22, 23, 24], and machine learning stretegies such as hidden Markov model, AdaBoost, and support vector machine [25, 26, 27, 28, 29, 30, 31, 32, 33, 34].

In addition to the above, musical genre classification problems can be solved with constant evolutionary algorithms [35]. Among them, a genetic algorithm (GA) based optimization has become a widely used approach for optimizing the selection of relevant features [36, 37, 38, 39, 40].

Among the several methods developed for musical genre classification, the performance of classification algorithms has become the benchmark [41]. In this paper, we propose a model for musical genre classification using low-level time and frequency-domain features for short and mid-term feature extraction. In this model, feature extraction is performed using a GA, and musical genre classification is conducted using a range of machine learning algorithms, including the K-Nearest Neighbor (KNN) [42], Nave Bayes Classifier (NBC) [43], and the Support Vector Machine (SVM) [25] were used for music genre classification. In the performance evaluation, it was found that the GA-based feature selection strategy can improve the F-measure rate from 5% to 20% for the KNN, NBC and SVM-based algorithms. In addition, the proposed support vector machine genetic algorithm (SVMGA) algorithm can exactly better than other comparison algorithms

The remainder of this paper is organized as follows. Section 2 provides background information and discusses related studies. The proposed algorithm is presented in Section 3, and its performance is evaluated in Section 4. Finally, the conclusions and future research are presented in Section 5.

2. Literature review

The primary challenge in genre identification is selecting features for extraction from audio data [8]. Audio data comprise a series of samples that together represent an audio signal. Classification cannot simply be applied to the audio samples. In previous studies, audio analysis has been prominently performed by extracting numerical values for representative features. In music signals, these features correspond to primary dimensions such as pitch, rhythm, harmony, melody, timbre, and spatial location. Low-level features, such as temporal, energy, and spectral features, are some of the most prominently used features in sound signal analysis. In audio signal analysis, low-level features are generally extracted from the time and frequency domains [44]. Moreover, 11 sets of low-level time- and frequency-domain features are prominently used.

2.1. Time-domain audio features

In general, time-domain features are directly extracted from samples of audio signals. The features include short-term energy, short-term zero-crossing rate, and entropy. These features provide a simple approach to analyzing audio signals, and combining these features with sophisticated frequencydomain features is generally essential.

• Energy: The sum of squares of signal values, normalized by the respective frame length.
• Zero-Crossing Rate: The rate at which the sign of the signal changes within a particular frame.
• Entropy of Energy: The entropy of the normalized energies of subframes, which can be interpreted as a measure of abrupt changes in the signal.

2.2. Frequency-domain audio features

The discrete Fourier transform (DFT) is extensively used in audio signal analysis, because it provides a convenient representation of the frequency content distribution of a signal as a sound spectrum. Numerous audio features are based on the DFT of a signal. These are termed frequency-domain (or spectral) audio features.

• Spectral Centroid: The center of gravity of a spectrum.
• Spectral Spread: The second central moment of a spectrum.
• Spectral Entropy: The entropy of the normalized spectral energies for a set of sub-frames.
• Spectral Flux: The squared difference between normalized magnitudes of the spectra of two successive frames.
• Spectral Rolloff: The frequency below which 90% of the magnitude distribution of a spectrum is concentrated.
• MFCCs: MFCCs are used to capture short-term, spectral-based features. The logarithm of the amplitude spectrum based on a short-time Fourier transform is derived for each frame. Frequency bins are then grouped and smoothed using Mel-frequency scaling to ensure that they agree with predetermined concepts. MFCCs are generated by decorrelating Mel-spectral vectors by using a discrete cosine transform.
• Chroma Vector: A chroma vector is a 12-element representation of spectral energy, wherein bins represent 12 equal-tempered pitch classes of Western music (semitone spacing).
• Harmonic: Harmonic features represent beats, and the harmonic ratio and fundamental frequency are two harmonic features.

Although studies have been conducted on music genre classification, the most accurate results have been achieved using the GTZAN dataset. A comprehensive set of features was proposed for the direct modeling of music signals [4]. When these features were employed for genre classification by using KNNs and Gaussian Mixture models (GMMs), an accuracy rate of 61% was achieved. A novel feature extraction method was proposed for musical genre classification, wherein DWCHs are combined with machine learning classification algorithms, including SVMs and linear discriminant analysis [14]. An accuracy of 78.5% was obtained with this method. Feature integration methods and late information fusion were examined in a previous study [12], by using majority voting for classification of all short-time features. A novel feature integration technique using an autoregressive (AR) model was proposed. This approach was reported to exhibit superior performance to that obtained by the use of mean variance features. Another study [45] addressed musical genre classification from a multilinear perspective. Multiscale spectro-temporal modulation features were extracted on the basis of an auditory cortical processing model, which provided an accuracy of 78.2%. Nonnegative matrix factorization was used to obtain a novel description of the timbre of musical sounds[13]. A spectrogram was factorized, which provided a characteristic spectral analysis. Gaussian Mixture Models were then applied, to achieve a reported accuracy of 74%. An ensemble approach [37], involving the use of multiple feature vectors and time and space decomposition strategies was also reported in a previous study. In the study, time decomposition was performed using feature vectors, which were extracted from music segments obtained from the beginning, middle, and end of the original signal. The study also employed four machine learning algorithms, namely NBC, decision trees, SVMs, and multilayer perceptron (MLP) neural nets, which provided a maximum accuracy of 65.06%. In an alternative approach [46], a method based on the classification accuracy of an SVM classifier was proposed for selecting training instances. These instances comprised feature vectors representing short-term, low-level characteristics in an audio signal. This method was reported to provide an accuracy of 59.6%. An invariance of MFCCs to musical key and tempo was explored in a previous study[47]. The study indicated that MFCCs encode both timbral and key information. An accuracy of 69.3% was achieved by applying GMMs. The acoustic signal feature can be derived using a mathematical model that represents the acoustic signal [39]. In this approach, features were selected using a GA. Classification was then performed by generating an adjusted KNN classifier. The reported accuracy was 67.6%. In a previous study, a subspace cluster analysis process was used to automate the construction of a classification tree[48]. Experimental results validated the tree-building algorithm, and the study provided a new research direction for automatic genre classification. Several machine learning algorithms were employed, such as KNNs, J48, logistic regression (LOG), LibSVM with a radial basis function kernel, MLP neural networks, and sequential minimal optimization (SMO) SVM with a polynomial kernel. This method was reported to achieve an accuracy of 72.9%.

Another study[49] proposed automatic musical genre classification by using spectral, time-domain, tonal, rhythmic, sound effect, and high-level descriptors. An analysis was conducted using KNNs, an SVM, and a GMM, with a resulting accuracy of 79.7%.

3. Methods

3.1. Feature extraction

Feature extraction is a crucial step in audio analysis, because it is also required in other pattern recognition and machine learning tasks. Extraction is performed in the following two steps: short- and mid-term processes. In short-term feature extraction, the input signal is divided into brief windows (or frames), and the number of features is computed for each frame, generating a sequence of short-term feature vectors from the entire signal. In mid-term extraction, statistical analysis is generally applied to these short-term feature sequences to obtain the signal. Several statistics, including mean, median, standard deviation, standard deviation by mean, max, and min, are derived for each short-term feature sequence. The data are then normalized to avoid the introduction of anomalies and minimize redundancy

3.2. Machine learning methods (naive Bayes classifier (NBC), K-nearest neighbor (KNN), support vector machine (SVM)) combined with genetic algorithm (GA)

Fig. 1 shows the system design. GA optimization is combined with the KNN, NBC, and SVM learning methods.

The fitness function uses the largest f-measure value derived using machine learning, and is evaluated using 10-fold crossvalidation. The system then derives the maximum fitness.

Figure 1: Machine learning methods (NBC, KNN, SVM) combined with GA

• The dataset is a feature vector containing normalized data. All necessary parameters are initialized. The initial population is generated and encoded in binary {0,1}. Individual solutions are randomly generated to form the initial population P = {S₁,S₂,S₃,·· ,S_n}; S₁ = S₂ = S₃ = ··· = S_n = {G₁,G₂,G₃,··· ,G_n}. Here S is a solution and G_n represents features, where G_n ∈ {0,1}; feature labeled 0 are not used, whereas those labeled 1 are used.
• The fitness of each solution (S) is evaluated using the highest f-measure value for musical genre classification conducted through the KNN, NBC, and SVM learning methods, following the 10-fold cross-validation method. The highest fitness value indicates the optimal classification result.
• After all fitness values are obtained, tournament selection is used to select parents. This prominently used method probabilistically selects genomes for recombination; the selection is based on fitness. The algorithm randomly selects genomes from the population to compete in tournaments of user-selected size T. The genome with the highest fitness value in each tournament is selected for recombination.
• A uniform crossover is primarily used for problems, wherein elements in the genome are independent of each other. In this case, the locations of genes relative to each other in the genome do not influence fitness. The probability of crossover per gene is set by the user. The uniform crossover process generates a solution in the (t+1)th generation by randomly selecting genes from each of the winners, corresponding to the relevant locations.
• A matrix of 1s and 0s is generated, and mutation is applied individually to each element. The probability of mutation per gene is set by the user. The selected values are interchanged such that 1s become 0s (and vice versa).
• Crossover and mutation then generate a new population (solution), whose fitness value is reevaluated.
• The process is stopped when the required criteria are satisfied; otherwise, the fitness is reevaluated.
• The GA then identifies the optimal solution, which in this case is a set of optimal features.

3.3. Evaluation

For the proposed method, selected audio files were used as validation data in this study. Each file was tested through cross-validation for ensuring its allocation to the accurate class. The result was presented in a confusion matrix summarizing accurately and inaccurately classified cases. The f-measure value was calculated from the matrix to determine the optimal classification. The harmonic mean of precision and recall was used as the f-measure value, with the highest f-measure value indicating the most accurate classification.

4. Experimental results

4.1. Environment setting

The data used in this study comprised instrumental music files from the GTZAN genre collection datasets[4]. The data were obtained from the following source:¹. The dataset comprised 1000 30-s music files in an audio format at a sampling rate of 22050 Hz in 16-bit mono. Samples were classified into the following 10 musical genres: blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock. The raw data were obtained from audio data files. The data were preprocessed by converting the data files into a numerical form before the execution of feature extraction. A total of 11 sets of low-level time- and frequency-domain features were extracted.

All experiments were performed over 15 independent runs and conducted using MATLAB 2016b with the parallel toolbox. The experiments were conducted on a standard laptop with an Intel i5-3320M 2.60-GHz CPU and 4-GB RAM.

4.2. Without feature selection

4.2.1. Experimental results obtained using KNN

As presented in Table 1, the KNN method performed data execution in less than 1 second in all experiments, with the F-measure mean (Fmean) time being 0.87s and a standard deviation (Fsd) time of 0.03s, yielding an Fmean of 66.2%. The difference between the best F-measure (Fbest) and the worst F-measure (Fworst) was not significant, at 1.4%. Moreover, the Fsd value was negligible, at 0.5%. For classification, the f-measure range was 65.5% to 66.9%. In the results shown, the performance of KNN is less than 70%, but very quickly classifies the music data, owing to the fact that KNN does not need the training process..

4.2.2. Experimental results obtained using NBC

Table 2 shows the NBC results. The Fmean time for data execution was 8.93s, with an Fsd time of 0.08s for each experiment and an Fmean of 58%. The difference between Fbest and Fworst was not significant, at 2.3%. Fsd was also very small, at 0.6%. For classification, the f-measure range was approximately 56.8% to 59.1%. In the results show, the performance of NBA is less than 60% and even slower than the KNN.

4.2.3. Experimental results obtained using SVM

Table 3 shows the SVM results. For all experiments, the Fmean and Fsd times for data execution were 13.16 and 1.69 s, respectively, with an Fmean of 0.762 (76.2%). The difference between Fbest and Fworst was 0.017 (1.7%), which was nonsignificant. Moreover, the Fsd value was 0.005 (0.5%), which was negligible. The f-measure range for classification was 0.7530.770 (75.3%77.0%).

4.3. With GA feature selection

4.3.1. Parameter Setting

The previous result shows that KNN is the best algorithm for F-measure and computation times. Thus, in this section we will try to import the GA-based feature selection strategy into these three algorithms. The basic parameter settings of all experiments are listed in Table 4. All experimental results are collected from 15 independent runs, each of 100 iterations.

Table 4: GA parameter settings

4.3.2. Experimental results obtained using KNNGA

From Table 5, it can be seen that KNN-GA required less than

5s for data execution in each experiment. The Fmean time of 4.74 s and Fsd time of 0.43 s gave an Fmean of 71.3%. The difference between Fbest and Fworst was not significant, at 1.2%. Fsd was also very small, at 0.3%. For classification, the approximate f-measure range was 70.8% to 72%. . In summary, we found that the GA-based feature selection will improve the 5% F-measure rates with the KNN approach.

4.3.3. Experimental results obtained using NBCGA

From Table 6 it can be seen that, for data execution, the NBC-GA required a fairly long Fmean time of 61.46 s. With the Fsd time of 8.50 s for each experiment, this gave an Fmean of 66.8%. The difference between Fbest and Fworst was not significant, at 0.9%. The Fsd was also insignificant, at 0.3%. The f-measure range for classification was approximately 66.4% to 67.3%. In summary, we found that the GA-based feature selection will improve the 10% F-measure rates with the NBC approach.

4.3.4. Experimental results obtained using SVMGA

As can be seen from Table 7, SVM-GA required a fairly long Fmean time for data execution, of 125.614s. With an

Fsd time of 44.29s from each experiment, the Fmean was

79.5%. The difference between Fbest and Fworst was not significant, at 1.1%. The Fsd was also very small, at 0.3%. The f-measure range for classification was approximately

79.0% to 80.1%. In summary, we found that the GA-based feature selection will improve about the 3-5% F-measure rates with the SVM approaches.

4.3.5. Convergence Rates of KNN-GA, NBC-GA, and SVM-GA

The graphs in Figs 2, 3, and 4 reach steady state because they include a stop criterion at the 100th iteration when the process ended.

Figure 2: Convergence rates of KNN with Genetic Algorithm

Figure 3: Convergence rates of NBC with Genetic Algorithm

Figure 4: Convergence rates of SVM with Genetic Algorithm

4.4. Comparison of F-measure values

The performance is assessed based on Eq. 1.

Here, ∆F_M denotes the improvement of the F-measure values, ∆t_avg denotes the improvement of computation time, F_new denotes the F-measure values of the KNN, NBA, and SVM algorithm performing the genetic algorithm feature selection, and F_orig denotes the KNN, NBA, and SVM algorithms without performing any feature selection strategy, and t_avg is the computation time in seconds.

Table 8 shows that the average increase in the Fmean value was approximately 7.24% for KNN-GA, approximately 13.27% for NBC-GA, and 4.23% for SVM-GA. However, in order to perform the feature selection strategy, the computation time was increased to between 500% and 871%. In summary, the feature selection increases the quality of the solution, but with a massive increase in computation time, especially with SVM-GA, which requires spending more than 10 times the computation time..

4.5. Comparison of results in this study with those in previous studies

Previous results showed the ability of a GA to improve the quality of result classification by reducing features. The existing algorithm performs poor quality classification (approximate Fmean 59%66%). Moreover, by feature selection using the GA, the quality classification can be improved until Fmean is 67%80%. In this section, we compare the proposed algorithm with other algorithms to verify the performance or proposed algorithms. Table 9 compares the accuracy of different methods with our proposed algorithms, KNN-GA, NBA-GA, and SVM-GA, using the highest measure achieved by each. All studies used the same GTZAN Genre Collection Data Sets [4]. The KNN-GA method from the current study produced an Fbest value of 72%, which is better than the comparison algorithms of [4, 39, 47, 48]. The NBC-GA method produced an Fbest value of 67.3%, which exceeded that reported in [4]. The SVM-GA method produced an Fbest value of 80.1%, which exceeded previous studies. In summary, the GA combined with the SVM method can obtain a better solution than other algorithms.

5. Conclusions and Future work

In this study, automatic musical genre classification was conducted by combining GA optimization with three machine learning methods. This approach entails using low-level time- and frequency-domain features. The features are extracted using short- and mid-term processes. The optimal classification accuracy levels achieved in the study were determined to be comparable to those achieved by other stateof-the-art musical genre classification algorithms. However, the feature selection strategy will consume a massive amount of computation time. As part of our future research, we aim to investigate three aspects: 1) applying other meta-heuristic methods such as particle swarm optimization (PSO) to select low-level features; 2) combining deep-learning techniques such as CNN with a meta-heuristic algorithm to improve the accuracy of classification results; and 3) decreasing the computation time by using feature reduction mechanisms to make the algorithm perform more effectively.

6. Acknowledgement

This work was supported in part by the Ministry of Science and Technology, Taiwan, R.O.C., under grants MOST 107-2218-E-992-303-.

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7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. Amine Bouaouda, Karim Afdel, Rachida Abounacer, "Meta-heuristic and Heuristic Algorithms for Forecasting Workload Placement and Energy Consumption in Cloud Data Centers", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 1, pp. 01–11, 2023. doi: 10.25046/aj080101
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. Olena Nosovets, Vitalii Babenko, Ilya Davydovych, Olena Petrunina, Olga Averianova, Le Dai Zyonh, "Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 406–413, 2021. doi: 10.25046/aj060446
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. Amany Khalil, Osama Tolba, Sherif Ezzeldin, "Design Optimization of Open Office Building Form for Thermal Energy Performance using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 254–261, 2021. doi: 10.25046/aj060228
42. 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
43. 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
44. 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
45. Subash Pokharel, Aleksandar Dimitrovski, "Ferromagnetic Core Reactor Modeling and Design Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 810–818, 2021. doi: 10.25046/aj060190
46. 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
47. 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
48. 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
49. Jesus Aguila-Leon, Cristian Chiñas-Palacios, Carlos Vargas-Salgado, Elias Hurtado-Perez, Edith Xio Mara Garcia, "Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 619–625, 2021. doi: 10.25046/aj060167
50. Abdelouahad Bellat, Khalifa Mansouri, Abdelhadi Raihani, Khalili Tajeddine, "Optimizing the Wind Farm Layout for Minimizing the Wake Losses", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 309–315, 2021. doi: 10.25046/aj060135
51. 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
52. 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
53. Karamath Ateeq, Manas Ranjan Pradhan, Beenu Mago, "Elasticity Based Med-Cloud Recommendation System for Diabetic Prediction in Cloud Computing Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1618–1633, 2020. doi: 10.25046/aj0506193
54. Bilal Babayigit, Eda Nur Hascokadar, "A Software-Defined Network Approach for The Best Hospital Localization Against Coronavirus (COVID-19)", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1537–1544, 2020. doi: 10.25046/aj0506184
55. 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
56. 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
57. Marouane EL Midaoui, Mohammed Qbadou, Khalifa Mansouri, "A Novel Approach of Smart Logistics for the Health-Care Sector Using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1143–1152, 2020. doi: 10.25046/aj0506138
58. 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
59. Moulay Youssef Smaili, Hanaa Hachimi, "Hybridization of Improved Binary Bat Algorithm for Optimizing Targeted Offers Problem in Direct Marketing Campaigns", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 239–246, 2020. doi: 10.25046/aj050628
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. Siddikov Isamiddin Xakimovich, Umurzakova Dilnoza Maxamadjonovna, "Fuzzy-logical Control Models of Nonlinear Dynamic Objects", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 419–423, 2020. doi: 10.25046/aj050449
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
91. 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
92. 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
93. Mohd Razif Idris, Imad Mokhtar Mosrati, "Optimization of the Electrical Discharge Machining of Powdered Metallurgical High-Speed Steel Alloy using Genetic Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 255–262, 2019. doi: 10.25046/aj040632
94. 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
95. 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
96. 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
97. Mohamed Hamada, Abdulsalam Latifat Ometere, Odu Nkiruka Bridget, Mohammed Hassan, Saratu Yusuf Ilu, "A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 449–457, 2019. doi: 10.25046/aj040454
98. 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
99. Wanwanut Boongsood, Chiranuwat Jadram, "Effects of Using Fuzzy Material Handling Inputs in the Genetic Algorithm for Machine Layout", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 133–140, 2019. doi: 10.25046/aj040416
100. 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