1. Introduction

In 2012, deep learning shined on the ImageNet large scale visual recognition competition (ILSVRC), and more and more researchers began to notice deep learning. As an important branch of artificial intelligence, deep learning has been widely used in information processing in the fields of speech, image and text. Convolutional neural networks are the most classical network structures in the field of image processing, and they has excellent spatial feature learning abilities. The classical method to improve the convolution neural networks is to deepen and broaden the convolution neural networks, but it is easy to cause a large increase in the number of neurons. Large increases in the convolutional neural networks of  parameters can lead to over-fitting, so the performance of neural networks will decline. Therefore, the model design of convolutional neural networks is particularly important. The excellent algorithm model can greatly improve the recognition accuracy and effectively reduce the computational complexity of the model, which determines the performance of almost all deep learning algorithms. However, the neural networks has so far been regarded as a black box device. In many cases, neural networks are not interpretative, and their design and improvement only rely on experience.

In this paper, we have improved the Reticulated Convolution Module (RCM) [1] and designed a small high performance convolutional neural network (HPCNet) with less parameters. Networks with fewer parameters are more easily ported to devices with limited transmission bandwidth and storage, such as cloud services, mobile platforms and smart robots. In addition, in the process of improved RCM, the pooling layer of the convolutional neural networks have attracted our attention. Simple changes in the use of different dimensional features lead to significant performance gaps in convolutional neural networks. This difference is instructive for the interpretability of convolutional neural networks and the design rules of convolutional neural networks. This paper mainly includes three parts of work: 1. We improved RCM and named it the second version of Reticulated Convolution Module (RCM-V2). 2. We designed a small convolutional neural network structure called backbone network (BBNet). And based on RCM-V2 and BBNet, a small convolutional neural network with high performance (HPCNet) was designed. 3. Finally, We evaluated RCMNet-V2 and HPCNet performance on two typical image classification datasets.

2. Related Work

Because of the birth of AlexNet [2], the design of convolutional neural networks has become an important research direction for scholars in the field of machine vision. The network structure of AlexNet has eight layers, including five convolution layers and three full connection layers. AlexNet adopts data enhancement methods and adds a dropout layer to prevent over-fitting. And it won the championship with a recognition accuracy of 10.9% ahead of its nearest rival.  Since then, ILSVRC competitions had produced a large number of excellent network structures. VGGNet [3] explores the correlation between deep network and performance by continuously stacking small convolution kernels and pooling layers. GoogLeNet [4] transforms full connection and even general convolution into sparse connection to solve the defect, but it keeps dropout. Two auxiliary softmax are added to the networks for forward conduction gradient to prevent the gradient from disappearing. GoogLeNet innovatively proposes the Inception structure which gathers the features of different scales together, expands the network width and enhances the expressive power of the networks. However, the deeper the network is, the more likely it is to cause degradation. In order to solve this problem, ResNet [5] came out. It introduced identity mapping into network structure and surpassed human in top-5 precision. ResNet is a milestone network, which makes it possible to train deeper networks. DenseNet [6] was born on CVPR2017 which established the dense connection between the front layers and the back layers. The connection of features between channels enables the network to realize high-density feature reuse, which achieves the performance beyond ResNet under the condition of less parameters and calculation cost.

With the increasing scale of the networks, resource asymmetry and other problems are caused in most scenarios. Therefore, compression and acceleration of algorithm models under the guarantee of network performance is a hot research topic in the field of network structure optimization. Lin M et al. [7] first proposed 1×1 convolution kernel in networks in order to greatly lessen network parameters. Iandola, F N et al. [8] proposed a lightweight network called SqeezeNet which got the AlexNet-level accuracy. Howard A G et al. [9] put forward the depthwise separable revolution in MobileNet. The depthwise separable revolution greatly reduces the parameters when the precision is slightly lower than the traditional convolution. However, the depthwise separable convolution reduces the information integration performance of the networks to a certain extent. Later, the idea of ResNext [10] was introduced into ShuffleNet [11] to clean the channels of convolution neural networks. Shufflenet strengthens the information circulation between channels, and improves the information expression ability of networks. The iconic NAS [12] promoted the spring                                                                              tide of automatic machine learning (AutoML), which searched neural network structure according to reinforcement learning such as ENAs [13], DARTS [14] and NAO [15], etc. These models generated by automatic machine learning are not universal. It is also inconvenient to deploy an automated machine learning framework on some simple tasks, so designing an efficient small-convolution neural network is especially important. This paper aims to solve the problems of model design in simple machine vision tasks, and proposes an efficient micro convolution neural network called HPCNet. At the same time, some design inspirations of convolutional neural networks are obtained, which will provide inspiration for the design work of convolutional neural networks in the future.

3. Design of Small High Performance Convolutional Neural Network

This chapter first introduces the group convolution and the group convolution used in RCM. Then we introduce the design of RCM-V2 and its key features extraction process. Finally, the overall architecture of BBNet is introduced.

3.1.  Group Convolution

Group convolution plays a crucial role in the design of RCM and RCM-V2. As shown in Figure 1, the group convolution groups the input features and then performs convolution operations within each group. When the size of the input feature maps is H*W*C and the output quantity of model is N, these feature maps are to be divided into G groups. After grouping convolution operation, each set of feature maps is C/G, and the output is N/G. It can be seen that the group convolution can consolidate information in small groups, which functions as feature isolation and cross interaction, and reduces the amount of parameters.

Figure 1.Group Convolution Model and Traditional Convolution Model

If the input of the convolution layer is composed of two different sets of features, and the number of feature maps in these two groups is not equal, great effect will be produced. As shown in Figure 2, A and B are two different sets of features, and the number of feature maps contained in A and B is different. A contains 128 feature maps and B contains 64 feature maps. Group convolution concatenates A and B, and then divides A and B into C and D. Both C and D contain 96 feature maps, and the feature maps in C are all derived from A. In D, 32 feature maps are derived from A, and 64 feature maps are derived from B. The convolution operation æ performs feature extraction on C, which is equivalent to feature selection and fusion on the first 96 convolution maps of A. The convolution operation £ performs feature extraction on D, which is equivalent to the feature fusion of the 32 feature maps of A and all the feature maps of B. As a result, F aggregates the features of A and B, while E merely extracts the features of A. Therefore, the features extracted by E and F will be significantly different.

3.2.  Design of RCM-V2

The traditional convolution neural networks are formed by alternately stacking convolution layers and pooling layers. The desired effect is achieved based on the design of the kernel size of convolution layers and the design of different pooling layers. Single channel and single scale convolution settings tend to make the feature extraction of neural networks inadequate and constrain the performance of neural networks. In this paper, the overall structure of the mesh convolution module is funnel-shaped mesh.

Figure 2.Group convolution of different feature sources.

Figure 3.The structure of the RCM.

The overall structure of RCM is symmetrical, and the number of convolution kernels is unbalanced. RCMNet first integrates different features, and then combines them through cross structure. It not only realizes feature diversification, but also reuses a large number of important features, which makes convolution neural effectively enhance the ability of extracting key features. For miniature machine vision tasks, RCM can extract key features efficiently and ensure the diversity of features, so that convolution kernel can be used efficiently. Figure 3. shows the overall architecture of RCM. RCM is divided into α blocks and β blocks, and  the key features are output of the bottom layer. In general, max-pooling preserves the main features. We focus on the pooling layers to further strengthen the feature selection ability of RCM.

Figure 4.The structure of the RCM-V2. The number of groups for E, F, G and H layers is In addition to the stitching method indicated in the figure, the remaining stitching method is Concat.

RCM-V2 has redesigned the connection between the RCM tail and the pooling layers. The structure of RCM-V2 is shown in Figure 4. The interlacing of different colors indicates the fusion of different features. When the input features enter RCM-V2, they first go through four convolutional layers called A, B, C, and D. They perform four different combinations of input features. B and C are convolution layers with a small number of convolution kernels, which can gather input features well. Next, the feature maps enter four interlaced grouping convolution layers which be named E, F, G and H. These four grouping convolutions interleave and fuse the key features of red and purple, which are like scattering water on the lotus’ leaves and then aggregating into water polo. The reuse of key features effectively reduces the number of redundant features. The pooling layers of RCM-V2 directly extract the maximum value of receptive field from E, F, G and H, which effectively avoid the offset of features. Finally, half of the output features of RCM-V2 are multi-scale fusion results of multi-source features, and half are the results of extracting simple convolution features. Furthermore, RCM-V2 can be equivalent to a set of dual channel convolutions and a set of complex multi-scale convolutions.

Table 1: Parameters of RCM-V2

1. M is the number of output features of the convolutional layers

When the number of the input and output of the model’s features is the same, the parameters of RCM are only equivalent to 91.67% of the conventional convolutional layers. The parameters of RCM-V2 are shown in Table 1.

This paper focuses on the pooling layers to improve the RCM. When the original RCM is connected to the next pooling layer, the characteristics of different dimensions are first eltwise and then input into the pooling layers. Although the original way can retain the features of each dimension to the greatest extent, it weakens the main features to a certain degree. In this paper, the pooling layers are advanced, and the method of post eltwise is used, and max-pooling is used to strengthen ability of expression the main feature of a single convolution layer. The expression of the original RCM is (1).

Where O_I is the output of layer I, α_out is the output of α block, βout is the output of β block, RCM_out is the output of RCM, g (·) represents concat operation, and p(·) represents max-pooling operation. Next_out is the output of the RCM that connects max-pooling to the next layers. The expression of RCM-V2 is (2).

RCM-V2 cleverly applies the max-pooling layers, which reduces the feature cancellation effect of adjacent dimensions to a certain extent, and strengthens the main features extracted by the core convolutional layers.

Figure 5.Inspiration of BBNet

3.3.  Design of BBNet and HPCNet

When people understand or classify new things, they usually divide them into a large category, and then summarize the small classification to which the new things belong. BBNet refers to human cognitive mechanism of images, integrates high-dimensional features of different levels, and effectively uses high-dimensional features. This feature reuse mechanism of BBNet can use fewer convolution kernels for extracting features effectively and avoid extracting repeated features. To a certain extent, it can reduce the parameters of convolution neural networks. Specifically, the tail of BBNet uses cancat layers to splice high-dimensional features together, and then uses 1×1 convolution to check these features for information reorganization. Furthermore, in order to ensure the universality of BBNet, BBNet adopts a unified number of convolution kernels. Figure 5. shows the inspiration of BBNet and the Figure 6. shows the structure of BBNet.

Figure 6.Structure of BBNet

We replaced the convolution module except Conv1 and Conv7 with RCM-V2 to build HPCNet. Table 2. shows the parameter Setting of HPCNet.

Table 2: Parameter Setting of HPCNet

4. Experiments and Results Analysis

In order to better evaluate the performance of RCM-V2 and BBNet, this paper conducted experiments in two datasets, 101_food and Caltech-256. The experiment was divided into two parts. The first part was to compare RCM-V2 with the original RCM, and the second part was to test BBNet and HPCNet.

4.1.  Experimental Platform and Data Processing

Experimental platform: The experiment uses caffe as a deep learning framework, and uses Ubuntu 16.04 as the operating system. The computer has 32GB of RAM, a four-core and eight-thread i7-6700K CPU and a NVIDIA GTX-1080Ti GPU.

Datasets: 101-food contains 101 kinds of food and each kind of food contains 1000 pictures. It contains a total of 101000 pictures. The ratio of training set and test set is 3:1. Caltech-256 includes 256 categories. The number of pictures in each category is not fixed. There are 29781 pictures in total. In this paper, the training set and test set are divided into 4:1. 101_food is a large and medium-sized food classification data set, which has high requirements for fine-grained image recognition of neural networks. Caltech-256 is a kind of data set with many kinds, but it needs convolution neural networks with strong generalization ability because of the uneven distribution of pictures between classes.

Data pre-processing: The entire pre-processing process can be divided into three steps. Firstly, the images of all data sets are unified to the size of 256 * 256 for scale normalization. Secondly, we need to enhance the data. We choose to randomly cut 227 * 227 sub graphs from 5 directions of all the pictures (left upper corner, top right-hand corner, left lower corner, bottom right-hand corner and the middle), and turn the sub graphs horizontally to achieve the effect of expanding the original data set tenfold. Finally, the mean value is normalized to ensure that the mean value of all features is near zero.

4.2.  Results and  Analysis

The number of images of each category in 101_food is equal, and a dataset with a uniform number of images is very suitable for network benchmarking. Caltech-256 is a dataset containing many categories, but the images of each category are unevenly distributed. It requires that the networks have higher generalization performance and  can extracting features batter. We evaluate RCMNet and RCMNet-V2 on these two datasets. Figure 7, Figure 8. and Table 3. show the accuracy curve of RCMNet and RCMNet-V2 on different datasets.

Table 3:  Performance of RCMNet and RCMNet-V2 on 101_food, Caltech-256

Figure 7.The accuracy of RCMNet and RCMNet-V2 on 101_food

Figure 8.The accuracy of RCMNet and RCMNet-V2 on Caltech-256

RCMNet-V2 has a certain performance improvement on both datasets, which proves the effectiveness of RCM-V2. Compared to RCM, RCM-V2 only changes the connection of the max-pooling layers. The improved version of the RCM-V2 has the same amount of parameters as the RCM and does not increase the amount of calculation.

Next, we will evaluate the performance of BBNet and HPCNet. Similarly, we perform classification tests on these two datasets. Table 4 shows the evaluation results.

Table 4:  Performance of BBNet and HPCNet on 101_food, Caltech-256

Figure 9.The accuracy of BBNet and HPCNet on 101_food

As can be seen from Figure 9 and Figure 10, BBNet has a higher accuracy rate than HPCNet at the beginning of training, but in the middle and late training period, HPCNet’s accuracy is significantly higher than BBNet. It has proven that HPCNet with integrated RCM-V2 has a higher performance improvement than BBNet. Next, We compare HPCNet with the traditional networks.

Figure 10.The accuracy of BBNet and HPCNet on Caltech-256

Table 5:  Performance of BBNet and HPCNet on 101_food, Caltech-256

Table 5 shows that HPCNet has smaller sizes and it has higher accuracy than popular networks. Even with one of the classic compression networks called SqueezeNet, HPCNet still has higher accuracy and smaller sizes. Moreover, HPCNet’s equal convolution kernels design makes it highly modifiable. For different tasks, the number of convolution kernels can be changed to achieve better results.

5. Conclusion

     This paper focuses on the max-pooling layers to improve the performance of RCM and proposes RCM-V2. Experiments have shown that direct pooling that be used in our paper can get good effect for RCM. A simple change of the application mode of pooling can bring about a great improvement in performance. It brings some inspiration to the design of convolutional neural networks. For example, when should we fuse the features rather than strengthen them directly? All in all, the solution of this problem can help us to design convolutional neural networks quickly and understand convolutional neural networks better.

      A small high-performance convolutional neural network named HPCNet is designed and its performance is evaluated on two datasets of different types. HPCNet has higher performance and smaller sizes than the classic networks on two classic datasets.

Conflict of Interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in the work we submitted.

Acknowledgment

This work was supported by the National Natural Science Foundation of China (grant no. 11465004).

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34. Bakhtyar Ahmed Mohammed, Muzhir Shaban Al-Ani, "Follow-up and Diagnose COVID-19 Using Deep Learning Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 971–976, 2021. doi: 10.25046/aj0602111
35. Showkat Ahmad Dar, S Palanivel, "Performance Evaluation of Convolutional Neural Networks (CNNs) And VGG on Real Time Face Recognition System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 956–964, 2021. doi: 10.25046/aj0602109
36. 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
37. Kenza Aitelkadi, Hicham Outmghoust, Salahddine laarab, Kaltoum Moumayiz, Imane Sebari, "Detection and Counting of Fruit Trees from RGB UAV Images by Convolutional Neural Networks Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 887–893, 2021. doi: 10.25046/aj0602101
38. 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
39. 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
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. 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
42. 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
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. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
91. 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
92. 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
93. 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
94. Sudipta Saha, Aninda Saha, Zubayr Khalid, Pritam Paul, Shuvam Biswas, "A Machine Learning Framework Using Distinctive Feature Extraction for Hand Gesture Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 72–81, 2018. doi: 10.25046/aj030510
95. 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
96. 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
97. 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
98. 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
99. 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
100. Turgay Yalcin, Muammer Ozdemir, "Computational Intelligence Methods for Identifying Voltage Sag in Smart Grid", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 412–419, 2017. doi: 10.25046/aj020353