1. Introduction

Network security based on information technology for protecting personal information and system resources from various types of threats may be defined through policies and methods. Various methods for network administrator have been developed to protect networks and cyber assets including detection mechanism against active attacks [1]. However, few studies have been conducted to analyze the characteristics of phishing attacks, which steal entire input information from users. Phishing attack in its broadest sense can be defined as a scalable act of deception whereby impersonation is used by an attacker to obtain the information from an individual [2]. Considering that the most common form of online phishing attack is malicious hyperlinks embedded in messages, the recent technological trend in which personal connections are reinforced due to the explosive growth of social media services is particularly vulnerable [3].

Existing security systems primarily conduct rule-based detection mechanism using phishing databases to identify malicious URLs [4]. However, phishing URLs based on web applications have zero-day exploit characteristics that frequently involve novel attack instances, as URLs can be generated very conveniently in such applications. For this reason, phishing URLs hardly detected by predefined databases or simple detection rules [2, 5, 6].

Meanwhile, previous study based on ensemble of the convolutional neural network (CNN) and recurrent neural network (RNN) for the modeling the character and word-level features found that classification of malicious URLs was improved [7,8].

In Figure 1, we visualize the phishing URLs into feature space generated by the t-SNE dimension reduction method. Blue and red dots represent normal URLs and phishing URL instances, respectively. The Euclidean distance was determined based on the similarity of character combinations constituting the URL, and a cluster of short and regular URLs was mainly formed at the bottom. On the other hand, in the center, instances where it is difficult to distinguish between normal and phishing URLs due to subdomains are intricately confused.

Figure 1: The feature space of phishing URLs and the necessity of ensemble learning

Phishing URL features can be distinguished into a syntactic feature consisting of a sequence of randomly arranged characters and a semantic feature consisting of a sequence of domain and subdomain words. In the existing deep learning-based ensemble approach, a simple rule-based ensemble that averages the output of syntactic-semantic convolution and recurrent networks at log-scale was applied, but it showed the limitation to effectively model the complex nonlinear correlation of features and resulted the degradation of accuracy and recall.

Taken together, we propose an ensemble learning network based on CNN that can systematically utilize the syntactics and semantics of URL features using CNN and RNN. The proposed ensemble network is a deep learning algorithm that can extract effective features for phishing URL classification using filter operations that can be trained using data. The joint learning of ensemble rule based on deep representations of URLs provides plausible solution for phishing detection. We collected a total of 45,000 benign URLs and 15,000 phishing URLs and the proposed method was validated through 10-fold cross-validation, and chi-squared test. The analytic results indicated the best performance among the machine learning-based phishing detector. To the best of our knowledge, this is the first attempt that convolutional neural network is incorporated to learn the ensemble rule for phishing detection. The main findings of this research can be summarized as follows:

• The convolutional neural network works well for learning the ensemble rule of fusing heterogeneous features of URL representations, resulting the best accuracy and recall for phishing detection.
• We categorized the features of URLs into character and word levels, and demonstrated the convolutional and recurrent neural networks to effectively model each feature.

The remainder of this paper is organized as follows. In Section 2, we review the previous URL modeling methods based on machine learning and clarify the contributions of this paper by discussing the differences between them. In Section 3, we illustrate how the heterogeneous URL features are extracted by the deep learning and fused with convolutional neural network. The performance of the model is evaluated in Section 4 through various experiments, including the 10-fold cross-validation and ROC curve analysis. Finally, section 5 concludes the paper with some discussion of future directions.

2. Related Works

 Previous studies on phishing URL classification can be classified into the following categories as summarized in Table 1: those on phishing URL detection based on the blacklist, which were mainly performed before 2010; those on modeling of words extracted from the text based on traditional machine learning; and those on text feature extraction through the latest deep learning algorithms.

The author proposed a system that extracts lexical features from the text according to ex-pert-defined rules, constructs a blacklist on known phishing URLs, and detects new phishing URLs through a simple comparison algorithm [9]. However, this method has the limitation of detecting new phishing URLs in terms of generalization performance. To confirm the validity of the machine learning method in the field of phishing URL classification, the authors applied fundamental machine learning methods including naive Bayes classifiers to the word combination found in URLs and classified phishing URLs that were not included in training datasets [10]. The authors enhanced the performance of phishing URL classification systems based on machine learning by applying a support vector machine (SVM), which is widely known to perform more complex nonlinear mapping [11]. Verma significantly increased phishing URL classification accuracy through the implementation of a random forest algorithm that was designed to perform effective modeling of hierarchical elements of lexical features in the URLs [12].

The researchers extracted semantic features from phishing URLs using a word-to-vector model capable of embedding word vectors based on their statistical meaning using deep learning algorithms. Furthermore, they applied long short-term memory (LSTM) and gated re-current unit (GRU) deep learning algorithms specialized for time series modeling, including gate operations, to enhance the phishing URL classification performance of existing modeling methods [8,13]. It was proposed a convolution-recurrent network to effectively model semantics extracted from the word-to-vector model [14].

The majority of the current research in deep learning-based phishing detection focuses mainly on optimizing the operation of the neural network [16]. In particular, the comparative study in [17] proves the superiority of the ensemble approach based on CNN variations. This motivates our decision to consider the ensemble learning approach proposed in this paper. The proposed method deviates from existing work in that it implements and learns the ensemble rule with convolutional operation based on CNN to consider the heterogeneous URL features.

Table 1: Related works on phishing URL detection with respect of URL features and modeling method.

Figure 2: Proposed CNN-based ensemble learning method for phishing URL detection

3. Proposed Method

In this section, we describe the combination of the convolutional neural network and recurrent neural network to extract URL features and its ensemble learning method. Figure 2 visualize the diagrams of deep learning-based phishing classifier that extract the syntactic and semantics from URLs, as well as the proposed CNN-based ensemble learning network for the late-fusion of URL features.

3.1. Deep Learning-based Phishing URL Feature Extraction

Two types of deep learning algorithms and individual preprocessing steps were applied to conduct the modeling of syntactic and semantic features of phishing URLs. First, an integer was assigned to each character, and modeling of a low-level signal obtained through this process was performed by the CNN to model the syntactic features of random characters, including enumerated special characters, which are frequently observed in phishing URLs. Second, each word was embedded based on the word-to-vector model, and the modeling of a sequence of words obtained through this process was performed by the LSTM to model the semantic features of domains and sub-domains composing the internal URLs.

In detail, a preprocessing step for each character was performed to replace the characters with their unique Unicode values based on UTF-8 encoding, and an integer sequence of up to 100 characters was extracted in consideration of the average length of URL characters in the datasets collected. In total, 139 types of characters were used, and a vector in the dimension of  based on  of observations were inputted into the character-level CNN.

The convolution operation  in Equation 1 applies a parameterized filter to the input vector and extracts syntactics from sequence of characters in URL. A filter size  is applied to the th row and th column nodes of the th layer.

The pooling operation  in the th pooling layer performs the extraction of representative value and defined as Equation 2, with the pooling distance  in the region  among the input vectors, and outputs the maximum activation value from the region.

The learning of the convolution operation is the process of optimizing the weight of the filter  that extracts the syntactics while preserving the spatial correlation between characters, and the pooling operation is based on extraction of emphasized features.

Meanwhile, representative features of URLs include semantics that can be derived from a sequence of words such as domains and sub-domains. Phishing URL classification accuracy can be enhanced through the parallel utilization of deep learning algorithms for additional modeling of a sequence of subdomains [18].

The modeling of semantics of phishing URLs was carried out through word embedding based on the word-to-vector model and LSTM deep learning algorithm application for time series modeling. Moreover, 20 words that appeared in sub-domains were additionally extracted since phishing URLs generally included various sub-domains. Each word was replaced as vectors in 32 dimensions using the word-to-vector model, and URLs formed as  sized vector according to  observations were input in the phishing word-level LSTM.

The LSTM network is a type of RNN in which three types of nonlinear gates are implemented. The LSTM  performs the time-series modeling of sequence of domain and subdomains.

The input gate(i), forget gate (f), output gate (o), and LSTM cell state (c) were defined based on the input domain sequence of  with word sequence length , as shown in Equation 4. refer to the bias added to each neural network, the sigmoid activation function of neural networks, and Hadamard multiplication, respectively.

3.2. Ensemble Learning based on the Convolutional Network

The proposed ensemble network utilizes the deep URL representations with a size of  and  with  observations from the intermediate layer of CNN and LSTM in Section 3.1. Contrary to the existing CNN-LSTM ensemble-based phishing URL detector, the model is optimized to weight the outputs from multi-level URL representations.

The character-level and word-level representations of phishing URL derived from the character-level CNN and word-level RNN were concatenated to form a vector of size . The proposed fusion neural network was trained to minimize errors that might occur in the process of mapping the input vector to the benign or phishing URLs.

Figure 3: Results of 10-fold cross validation comparing the proposed ensemble learning with other existing methods (k-NN: k-nearest neighbor; DT: decision tree; RF: random forest; SVM: support vector machine; MLP: multilayered perceptron; CNN: convolutional neural network; LSTM: long short-term memory).

As the proposed method contains a convolution layer in order to systematically fuse level-based features, effective features are selected from input vectors from 288 dimensions and output the predictive label , as shown in Equation 5.

At this stage, the Softmax function, which is an activation function of the neural network, was applied to facilitate the encoding of the output vector at the probability of [0,1] range and to promote the differentiation process that operates during the optimization of the loss function. The entire mapping results obtained from inputs to outputs in the character-level and semantic-level neural networks, including the proposed ensemble network, is differentiable and can be learnt by the massive URL observations.

The entire weights of neural networks are tuned by applying a backpropagation algorithm based on gradient descent to the cross-entropy loss function  shown in Equation 6.

The proposed ensemble network, which fuses features according to deep URL representations, performs the optimization of ensemble rules in consideration of joint learning of CNN and LSTM.

Figure 4: ROC curve and area under the curve (AUC) of classification result based on ensemble learning approach.

4. Experimental Results

4.1. Phishing URL Dataset

A total of 45,000 of URLs were collected by crawling method and 15,000 of phishing URLs were collected from Phishtank [19,20] where provides a blacklist of phishing URLs. Benign URLs were mainly collected from open directory project(ODP), which is the URL database to categorize URLs. The number of observations was intentionally adjusted because of the data imbalance issue, to reflect the conditions in which the number of phishing URLs was much lower than that of benign URLs. We noted the length of the URL is one of the critical phishing features, given that the average lengths of phishing and benign URLs are 75.74 and 35.83, respectively.

4.2. Phishing URL Classification Performance

Figure 3 shows the result of 10-fold cross validation on the proposed ensemble network and other  machine learning-based models to verify the phishing URL classification performance of the proposed method. The average accuracy of the random forest algorithm, CNN, and RNN were achieved 0.8940, 0.9534, and 0.9461, respectively, with the CNN and RNN exhibiting significantly higher accuracy performance than the random forest algorithm.

The comparative result based on the ensemble of CNN [7] and RNN [8] achieved 0.9641. Based on the performance improvement, we confirmed the significance of proposed ensemble learning approach in consideration of character and word-level URL modeling. Regarding the proposed ensemble network designed to fuse Multi-level URL features, the best classification accuracy was achieved as 0.9803.

Table 2: Qualitative evaluation of complementarity based on the case analysis of CNN and LSTM (0: benign, 1: phishing).

Table 3:  A confusion matrix for phishing URL classification based on the ensemble network

Since it is essential to minimize false negatives and improve recall in the field of phishing URL detection, the ROC curve and AUC are described in Figure 4. Table 3 summarizes the classification results based on the model that exhibited the best accuracy. Considering the false negatives and the recall of phishing instance of 0.9602, it is inferred that additional modeling should be carried out mainly focusing on the generalization strength of the model.

4.3. Discussion

Table 2 indicates the advantages of each model based on practical classification cases, to aid in the classification of the performance of deep learning models according to multi-level URL representations. The two upper rows show the robustness against random character enumeration of CNN. The character-level CNN classified URL as phishing instance with a probability of 0.9874, considering that a phishing URL feature is hidden in the sequence of random characters. On the other hand, in the second case, the word-based LSTM misclassified benign URL as phishing with a probability of 0.8441 because the benign words are included in the URL.

The word-level LSTM supplements the entire system by reflecting a sub-domain that was not used by the character-level CNN in the form of words. The LSTM was able to classify certain words such as ‘security’ and ‘bitcoin’ based on the massive observations that such words are frequently used in phishing URLs. The CNN, however, misclassified benign URLs as phishing based on the number of sub-domains.

5. Concluding Remarks

5.1. Conclusion

This study introduced a character-level CNN and word-level RNN for phishing URL representation and proposed an ensemble network that can effectively fuse the syntactics and semantics of phishing URLs extracted from each model. The proposed ensemble network, implemented as convolutional neural network, provide the plausible solution of parameterization and optimization of the ensemble rule. Specifically, it exhibited a classification accuracy of 0.9804, which is the highest compared to other machine learning methods including deep learning models.

5.2. Future Work

Further studies should be performed to guarantee the generalization strength of model, in consideration of zero-day attack characteristics of phishing attacks. The latest deep learning algorithms, such as one-shot learning, should be thoroughly examined. Meanwhile, the scope of this study is limited to the modeling of syntactics and semantics of URL and optimizing the ensemble rule. In this regard, a symbolic AI approach to fully exploit and utilize the domain knowledge is promising. In addition, the neural-symbolic integration approach that can calibrate the deep learning model should be additionally considered in the future studies.

Conflict of Interest

The authors declare no conflict of interest.

Acknowledgment

This work has supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2021R1F1A1063085).

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35. Binghan Li, Yindong Hua, Mi Lu, "Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 790–800, 2021. doi: 10.25046/aj060291
36. Abraham Adiputra Wijaya, Inten Yasmina, Amalia Zahra, "Indonesian Music Emotion Recognition Based on Audio with Deep Learning Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 716–721, 2021. doi: 10.25046/aj060283
37. Shahnaj Parvin, Liton Jude Rozario, Md. Ezharul Islam, "Vehicle Number Plate Detection and Recognition Techniques: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 423–438, 2021. doi: 10.25046/aj060249
38. Prasham Shah, Mohamed El-Sharkawy, "A-MnasNet and Image Classification on NXP Bluebox 2.0", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1378–1383, 2021. doi: 10.25046/aj0601157
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. Alisson Steffens Henrique, Anita Maria da Rocha Fernandes, Rodrigo Lyra, Valderi Reis Quietinho Leithardt, Sérgio D. Correia, Paul Crocker, Rudimar Luis Scaranto Dazzi, "Classifying Garments from Fashion-MNIST Dataset Through CNNs", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 989–994, 2021. doi: 10.25046/aj0601109
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. Imane Jebli, Fatima-Zahra Belouadha, Mohammed Issam Kabbaj, Amine Tilioua, "Deep Learning based Models for Solar Energy Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 349–355, 2021. doi: 10.25046/aj060140
43. Dionisius Saviordo Thenuardi, Benfano Soewito, "Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 242–249, 2021. doi: 10.25046/aj060127
44. Xianxian Luo, Songya Xu, Hong Yan, "Application of Deep Belief Network in Forest Type Identification using Hyperspectral Data", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1554–1559, 2020. doi: 10.25046/aj0506186
45. Majdouline Meddad, Chouaib Moujahdi, Mounia Mikram, Mohammed Rziza, "Optimization of Multi-user Face Identification Systems in Big Data Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 762–767, 2020. doi: 10.25046/aj050691
46. Kin Yun Lum, Yeh Huann Goh, Yi Bin Lee, "American Sign Language Recognition Based on MobileNetV2", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 481–488, 2020. doi: 10.25046/aj050657
47. Gede Putra Kusuma, Jonathan, Andreas Pangestu Lim, "Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 315–322, 2020. doi: 10.25046/aj050638
48. Lubna Abdelkareim Gabralla, "Dense Deep Neural Network Architecture for Keystroke Dynamics Authentication in Mobile Phone", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 307–314, 2020. doi: 10.25046/aj050637
49. Helen Kottarathil Joy, Manjunath Ramachandra Kounte, "A Comprehensive Review of Traditional Video Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 274–279, 2020. doi: 10.25046/aj050633
50. Kailerk Treetipsounthorn, Thanisorn Sriudomporn, Gridsada Phanomchoeng, Christian Dengler, Setha Panngum, Sunhapos Chantranuwathana, Ali Zemouche, "Vehicle Rollover Detection in Tripped and Untripped Rollovers using Recurrent Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 228–238, 2020. doi: 10.25046/aj050627
51. Andrea Generosi, Silvia Ceccacci, Samuele Faggiano, Luca Giraldi, Maura Mengoni, "A Toolkit for the Automatic Analysis of Human Behavior in HCI Applications in the Wild", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 185–192, 2020. doi: 10.25046/aj050622
52. 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
53. Sherif H. ElGohary, Aya Lithy, Shefaa Khamis, Aya Ali, Aya Alaa el-din, Hager Abd El-Azim, "Interactive Virtual Rehabilitation for Aphasic Arabic-Speaking Patients", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1225–1232, 2020. doi: 10.25046/aj0505148
54. Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah, Galymzhan Abdimanap, "Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 934–943, 2020. doi: 10.25046/aj0505114
55. 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
56. 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
57. Lana Abdulrazaq Abdullah, Muzhir Shaban Al-Ani, "CNN-LSTM Based Model for ECG Arrhythmias and Myocardial Infarction Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 601–606, 2020. doi: 10.25046/aj050573
58. Chigozie Enyinna Nwankpa, "Advances in Optimisation Algorithms and Techniques for Deep Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 563–577, 2020. doi: 10.25046/aj050570
59. Sathyabama Kaliyapillai, Saruladha Krishnamurthy, "Differential Evolution based Hyperparameters Tuned Deep Learning Models for Disease Diagnosis and Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 253–261, 2020. doi: 10.25046/aj050531
60. 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
61. Mohsine Elkhayati, Youssfi Elkettani, "Towards Directing Convolutional Neural Networks Using Computational Geometry Algorithms: Application to Handwritten Arabic Character Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 137–147, 2020. doi: 10.25046/aj050519
62. Nghia Duong-Trung, Luyl-Da Quach, Chi-Ngon Nguyen, "Towards Classification of Shrimp Diseases Using Transferred Convolutional Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 724–732, 2020. doi: 10.25046/aj050486
63. Tran Thanh Dien, Nguyen Thanh-Hai, Nguyen Thai-Nghe, "Deep Learning Approach for Automatic Topic Classification in an Online Submission System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 700–709, 2020. doi: 10.25046/aj050483
64. 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
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. Roberta Avanzato, Francesco Beritelli, "A CNN-based Differential Image Processing Approach for Rainfall Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 438–444, 2020. doi: 10.25046/aj050452
67. Samir Allach, Mohamed Ben Ahmed, Anouar Abdelhakim Boudhir, "Deep Learning Model for A Driver Assistance System to Increase Visibility on A Foggy Road", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 314–322, 2020. doi: 10.25046/aj050437
68. Rizki Jaka Maulana, Gede Putra Kusuma, "Malware Classification Based on System Call Sequences Using Deep Learning", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 207–216, 2020. doi: 10.25046/aj050426
69. Van-Hung Le, Hung-Cuong Nguyen, "A Survey on 3D Hand Skeleton and Pose Estimation by Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 144–159, 2020. doi: 10.25046/aj050418
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. Neptali Montañez, Jomari Joseph Barrera, "Automated Abaca Fiber Grade Classification Using Convolution Neural Network (CNN)", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 207–213, 2020. doi: 10.25046/aj050327
72. Aditi Haresh Vyas, Mayuri A. Mehta, "A Comprehensive Survey on Image Modality Based Computerized Dry Eye Disease Detection Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 748–756, 2020. doi: 10.25046/aj050293
73. Yeji Shin, Youngone Cho, Hyun Wook Kang, Jin-Gu Kang, Jin-Woo Jung, "Neural Network-based Efficient Measurement Method on Upside Down Orientation of a Digital Document", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 697–702, 2020. doi: 10.25046/aj050286
74. Ola Surakhi, Sami Serhan, Imad Salah, "On the Ensemble of Recurrent Neural Network for Air Pollution Forecasting: Issues and Challenges", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 512–526, 2020. doi: 10.25046/aj050265
75. 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
76. Bayan O Al-Amri, Mohammed A. AlZain, Jehad Al-Amri, Mohammed Baz, Mehedi Masud, "A Comprehensive Study of Privacy Preserving Techniques in Cloud Computing Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 419–424, 2020. doi: 10.25046/aj050254
77. Lenin G. Falconi, Maria Perez, Wilbert G. Aguilar, Aura Conci, "Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 154–165, 2020. doi: 10.25046/aj050220
78. 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
79. Noor Syahirah Nordin, Mohd Arfian Ismail, Vitaliy Mezhuyev, Shahreen Kasim, Mohd Saberi Mohamad, Ashraf Osman Ibrahim, "Fuzzy Modelling using Firefly Algorithm for Phishing Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 291–296, 2019. doi: 10.25046/aj040637
80. Andary Dadang Yuliyono, Abba Suganda Girsang, "Artificial Bee Colony-Optimized LSTM for Bitcoin Price Prediction", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 375–383, 2019. doi: 10.25046/aj040549
81. Shun Yamamoto, Momoyo Ito, Shin-ichi Ito, Minoru Fukumi, "Verification of the Usefulness of Personal Authentication with Aerial Input Numerals Using Leap Motion", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 369–374, 2019. doi: 10.25046/aj040548
82. Kevin Yudi, Suharjito, "Sentiment Analysis of Transjakarta Based on Twitter using Convolutional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 281–286, 2021. doi: 10.25046/aj040535
83. Bui Thanh Hung, "Integrating Diacritics Restoration and Question Classification into Vietnamese Question Answering System", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 207–212, 2019. doi: 10.25046/aj040526
84. Priyamvada Chandel, Tripta Thakur, "Smart Meter Data Analysis for Electricity Theft Detection using Neural Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 161–168, 2019. doi: 10.25046/aj040420
85. Ajees Arimbassery Pareed, Sumam Mary Idicula, "A Relation Extraction System for Indian Languages", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 65–69, 2019. doi: 10.25046/aj040208
86. 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
87. An-Ting Cheng, Chun-Yen Chen, Bo-Cheng Lai, Che-Huai Lin, "Software and Hardware Enhancement of Convolutional Neural Networks on GPGPUs", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 28–39, 2018. doi: 10.25046/aj030204
88. Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily, "Auto-Encoder based Deep Learning for Surface Electromyography Signal Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 94–102, 2018. doi: 10.25046/aj030111
89. Myoung-Wan Koo, Guanghao Xu, Hyunjung Lee, Jungyun Seo, "Retrieving Dialogue History in Deep Neural Networks for Spoken Language Understanding", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1741–1747, 2017. doi: 10.25046/aj0203213
90. Marwa Farouk Ibrahim Ibrahim, Adel Ali Al-Jumaily, "Self-Organizing Map based Feature Learning in Bio-Signal Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 505–512, 2017. doi: 10.25046/aj020365
91. R. Manju Parkavi, M. Shanthi, M.C. Bhuvaneshwari, "Recent Trends in ELM and MLELM: A review", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 69–75, 2017. doi: 10.25046/aj020108