1. Introduction

With the growth of communication especially using satellites and cameras, the remote sensing images was appeared with the importance of processing and dealing with this type of images (remote sensing images). One of these substantial image processing is classification which done using machine learning technology. Machine learning is one of the artificial intelligence branches that based on training computers using real data which result that computers will have good estimations as an expert human for the same type of data [1]. Deep learning is a branch of machine learning that counts on the Artificial Neural Networks (ANNs) which can be utilized in the remote sensing image classification [2]. In the recent years, which the latest satellites versions and its updated cameras with high spectral and spatial resolution are released, the very high resolution (VHR) remote sensing images are appeared. As logical results, the VHR remote sensing images have redundancy pixels that can cause an over-fitting problem through training process with using the ordinary machine learning or ordinary deep learning in classification. So it must optimize the ANNs and extract convenient features from remote sensing images as a preprocessing before training [3]. The convolution neural networks (CNNs) are derived from the ANNs who haven’t fully connected layers as the ANNs layers; it have an excited rapid advance in computer vision [4]. It is based on some blocks can enforced on images as filters and then extracting a convolution object features from the input images which solving many of computer vision problems, one of these problems is classification [5]. The need of dealing with the huge data, that be contained in the VHR remote sensing images, produced the need of specific CNNs deep networks architectures that can generate an elevated accuracy in the classification problems. So, the classic networks are appeared. Many of classic networks are mentioned by researchers in their research papers. In this paper we use three widely used classic networks as features extraction in our proposed approaches. These classic networks are; the DenseNet 196, the VGG 16, and the ResNet 50 models. There are many researchers used these networks in their researches that related to the remote sensing images classification. In [6], authors proposed a new model to improve the classification accuracy using the DenseNet model. In [7], authors built dual channel CNNs for the remote sensing images using the DenseNet as features extraction. In [8], authors built small number of convolutional kernels using dense connections to achieve large number of reusable feature maps. It was lead them to propose a convolutional network based on the DenseNet for the remote sensing images classification. In [9], authors used the VGG and the ResNet models to propose the RS-VGG classifier for classifying the remote sensing images. In [10], authors were built a combination between CNNs algorithms outputs, the VGG model is one of these algorithms, so a representation of the VHR remote sensing images understanding were established by their proposed method. In [11], authors used the remote sensing images classification to distinguish the airplanes by using the pre-trained VGG model. In [12], authors built a classifier model that classifying the high spatial resolution remote sensing images by using a fully convolution network that based on the VGG model. In [13], authors proposed the use of the ResNet model to extract the VHR remote sensing images features, then concatenated with low level features to generate a more accurate model using the SVM. In [14], authors used the ResNet to extract the 3-D features by transfer learning. They proposed a classification method for the hyper-spectral remote sensing images. In [15], authors proposed a method for classifying forest tree species using high resolution RGB color images that captured by a simple grade camera mounted on an unmanned aerial vehicle (UAV) platform. They used the ResNet in their proposed. In [16], authors proposed aircraft detection methods that based on the deep ResNet and super vector coding. In [17], authors combined edge and texture maps to propose a remote sensing image usability assessment method based on the ResNet. In [18], authors presented a comparative study that discussed the difference between the using of the SVM and the deep learning in classifying the remote sensing images. In [19], authors proposed a remote sensing image classification method that based on the SVM and sequential classifier. In [20], authors proposed a remote sensing images classifier using the genatic algorithm (GA) and the SVM.

The problem that attended by this paper is the difficulty of reaching a high accuracy assessment in the VHR remote sensing images classification models. Many researchers proposed solutions for this problem in their researches but they depend on the deep learning only or the machine learning only. Few of them are proposed hybrid classification techniques that consist of combination of the deep learning and the machine learning such as [13] and [20]. The deep learning and the machine learning combination can lead to considerable results.

The aim of this paper is to propose a combination of a classic network and the SVM to build a novel remote sensing images classifier. In this paper, the desired classic networks that combined with the SVM classifier are the DenseNet 169, The VGG16, and the ResNet 50 models. We used the ImageNet pre-trained weights with these mentioned models, transfer learning, and then extract features. These extracted features are considered as input features for the SVM classifier which trained to achieve the desired performance classification models. A comparative study is done for the use of the mentioned three convolution models as features extraction that combined with the SVM classifier, as proposed in this paper, to determine the performance of using each model. This comparison is based on calculating the overall accuracy (OA) to determine the performance of each model as a features extraction. There are two used datasets in this study; the UC Merced land use dataset and the SIRI-WHU dataset.

The rest of this paper is organized as follow. Section 2 gives the methods. The experimental results and setup are shown in section 3. Section 4 presents the conclusions followed by the most relevant references.

2. The Methods

In this section the proposed models will explained with its structures. The classic networks that used in these proposals and the SVM will illustrate in brief as a literature review, ending with how to measure the learning model performance.

2.1. The Proposed Models

The features extraction from the remote sensing images is provided an important basis in the remote sensing images analysis. So, in this paper the classic networks outputs can considered as the features that extracted from the remote sensing images. Where the train of a new model of the CNNs requires large amount of data, so in these three models, we used the ImageNet pre-trained weights, transfer learning, and then extract the desired features, then use these features as input features in training the SVM classifier. In the DenseNet 169 model, we transfer learning to the last hidden layer, before the output layer, that had 1664 neurons. Its outputs considered as the input features for the SVM classifier. In the VGG 16 model, we transfer learning to the last hidden layer, before the output layer, that had 4096 neurons. Its outputs considered as the input features for the SVM classifier. In the ResNet 50 model, we transfer learning to the last hidden layer, before the output layer, that had 2048 neurons. Its outputs considered as the input features for the SVM classifier.

2.2. The Convolution Neural Networks (CNNs)

The CNNs are possessed from the ANNs with exclusion that it is not fully connected layers [21]. The CNNs considered as the magic solution for much computer vision problems. The CNNs depend on some of filters that reduce the image height and width and increase the number of channels, then processing the output with full connected (FC) neural network layers [21, 22]. Figure 1 shows one of the CNNs model structures [22].

Figure 1: One of the CNNs model structures [23]

2.3. The DenseNet Model

In 2017, the DenseNets was proposed in the CVPR 2017 conference (Best Paper Award) [23]. The start point was from endeavored to construct a deeper convolution network that contains shorter connections between its layers close to the input and those close to the output, this deep convolution network can be more accurate and efficient to train. It is different from the ResNet which has skip-connections that bypass the nonlinear transformation, the DenseNet add a direct connection from any layer to any subsequent layer. So the  layer receives the feature-maps of all former layers  to  as (1) [23].

where  refers to the spectrum of the feature-map produced in the layers . Figure 2 shows the 5-layers dense block architecture. Table 1 shows the DenseNet 169 model architecture with the ImageNet pre-trained weights [23].

Figure 2: The 5 layers Dense block architecture [23]

2.4. The VGG Model

In 2015, the VGG network was proposed in the ICLR 2015 conference [24]. The start with inspected the effect of the convolution network depth on its accuracy with using the large-scale images. Through they rectified the deeper networks architecture using (3 3) convolution filters, they showed that an expressive growth on the prior-art configurations can be achieved by pushed the depth to 16-19 weight layers. The VGG model is a deeper convolution network that trained on the ImageNet dataset. Table 2 shows the VGG 16 model architecture with the ImageNet pre-trained weights [24].

The input images of this network is (224 244 3). This network consists of five convolution blocks, each block containing convolution layers and pooling layer, then ending with two FC hidden layers (each layer has 4096 neurons), then ending with the output layer with softmax activation (1000 classes) [24].

2.5. The ResNet Model

In 2016, the ResNet was proposed in the CVPR 2016 conference [25]. They concerted the degradation problem by presenting a deep residual learning framework. Instead of intuiting, each few stacked layers directly fit a desired underlying mapping. The ResNet is based on skip connections between deep layers. These skip connections can skipping one or more non-linear transformation layers. The outputs of these connections are added to the outputs of the network stacked layers as (2) [25].

where H(x) is the final block output, x is the output of the connected layer, and F(x) is the output of the stacked networks layer in the same block. Figure 3 shows the ResNet one building block and tables 3 shows the ResNet 50 model architecture for the ImageNet [25].

Table 3: the ResNet 50 model architectures for ImageNet [25]

Figure 3: The ResNet building block[25]

2.6.  The Support Vector Machine (SVM)

The SVM is a machine learning algorithm can act as a classifier or regression. It is based on small sample statistic theory that establishes optimal hyper-planes from the training data. These hyper-plans separate the different classes by building margins between classes. Maximizing these margins between classes, specially the nearest classes, on both sides of hyper-planes is the target for achieving the optimal SVM classifier [26, 27]. Figure 4 shows the possible and the optimal hyper-planes in the SVM model [26, 27].

Figure 4: The possible and the optimal hyper-planes in the SVM model [26, 27]

2.7. The Performance Assessment

There are many matrices for gauge the performance of the learning models. One of them is the overall accuracy (OA). The OA is the main classification accuracy assessment [28]. It is measure the percentage ratio between the corrected estimation test data objects and all the test data objects in the used dataset. The OA is calculated using (3) [28, 29].

3. Experimental Results and Setup

This section will illustrate the experiments setup of the proposed algorithms, and then presents a comparative study for using each convolution model in our proposed. This comparison is based on calculating the OA for each model. The UC Merced land use dataset and the SIRI-WHU dataset are the used datasets. The details of these datasets will introduce in this section and then the experiments setup and results.

3.1. The UC Merced Land Use Dataset

The UC Merced Land use dataset is a collection of remote sensing images which has been prepared in 2010 by the University of California, Merced [30]. It is consists of 2100 remote sensing images divided into 21 classes with 100 images per each class. The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the USA. All images in this dataset are Geo-tiff RGB images with 256 256 pixels resolution and 1 square foot (0.0929 square meters) spatial resolution [30]. Figure 5 shows image examples from the 21 classes in the UC Merced land use dataset [30].

Figure 5: Image examples from the 21 classes in the UC Merced land use dataset [30]

3.2. The SIRI-WHU Dataset

The SIRI-WHU dataset is a collection of remote sensing images which the authors of [31] used this dataset in their classification problem research in 2016. This dataset consists of two versions that must complete each other. The total images in this dataset are 2400 remote sensing images divided into 12 classes with 200 images per each class. The images were extracted from Google Earth (Google inc.) and mainly cover urban areas in China. All images in this dataset are Geo-tiff RGB images with 200 200 pixels resolution and 2 square meters (21.53 square foot) spatial resolution [31]. Figure 6 shows image examples from the 12 classes in the SIRI-WHU dataset [31].

Figure 6: Image examples from the 12 classes in the SIRI-WHU dataset [31].

3.3. The Experimental Setup

All tests were performed using Google-Colab. The Google-Colab is a free cloud service hosted by Google inc. to encourage machine learning and artificial intelligence researches [32]. It is acts as a virtual machine (VM) that using 2-cores Xeon CPU with 2.3 GHz, GPU Tesla K80 with 12 GB GPU memory, 13 GB RAM, and 33GB HDD with Python 3.3.9. The maximum lifetime of this VM is 12 hours and it will be idled after 90 minutes time out [33]. Performing tests has been done by connecting to this VM online through ADSL internet line with 4Mbps communication speed. This connection was done using an Intel® coreTMi5 CPU M450 @2.4GHz with 6 GB RAM and running Windows 7 64-bit operating system. This work is limited by used the ImageNet pre-trained weights because the train of new convolution models needs a huge amount of data and more sophisticated hardware, this is unlike the lot of needed time consumed for this training process. The other limitation is that the input images shape is mustn’t less than 200 200 3 and not greater than 300 300 3 because of the limitations of the pre-trained classic networks. The preprocessing step according to each network requirements is necessary to get efficient results; it must be as done on ImageNet dataset through these models were trained and produced the ImageNet pre-trained weights. The ImageNet pre-trained weights classic networks that used in this paper have input shape (224, 224, 3) and output layer with 1000 neurons according to the ImageNet classes (1000 classes) [34, 35]. So, it must perform transfer learning as stated in section 2.1. The used datasets were divided into 80% training set and 20% testing set before training the SVM model. It is must be notice that the SVM penalty value were determined by iterations and self-intuition. To extract the convolution features, the normalization preprocessing must done before extract features using the DenseNet 169 model, then using these features as input features to train the SVM model with penalty = 1.7. Where the BGR mode convertion is the desired preprocessing before extract features using the VGG 16 and the ResNet 50 models, then using its outputs as input features to train the SVM model with penalty = 2 for the VGG 16 extracted features and penalty =20 for the ResNet 50 extracted features. Figure 7 shows the flow charts of the proposed algorithms in this paper; figure 7.a. for using the DenseNet 169 model as features extraction, figure 7.b. for using the VGG 16 model as features extraction, and figure 7.c. for using the ResNet 50 model as features extraction, and then training the SVM classifier.

Figure 7: The flow charts of the proposed algorithms in this paper

3.4. The Experimental Results

This section presents the results of the proposed algorithms with the used datasets in this paper. Through the training process we used the training data (80% from the used dataset) and then calculate the OA using the predictions of the test data (20% from the used dataset) to assess the performance of each model. Table 4 and figure 8 show the OA for the using of each model to extract features that act as the input features to train the SVM classifier using the both datasets.

As shown from these results, the ResNet–SVM model had the higher OA in this study where the VGG–SVM model had the lowest OA. In the other hand the DenseNet–SVM model had higher OA than the VGG–SVM model when using the UC Merced land use dataset and had very little lower OA than the VGG–SVM when using the SIRI-WHU dataset. The use of the SIRI-WHU dataset had higher OA than the use of the UC Merced land use dataset. These results illustrated that the OA had an opposite relation with the dataset image resolution and the dataset number of classes, so the use of the SIRI-WHU dataset which has 12 classes, image resolution 200 200 pixels, and spatial resolution 2 square meters gave higher OA than the use of the UC Merced land use dataset which has 21 classes, image resolution 256 256 pixels, and spatial resolution 0.0929 square meters (1 square feet). Extracting features using the deeper convolution networks gave considerable accuracy but the connections between layers may have another influence. The VGG–SVM model, that has deeper network without any layers connections, gave a good OA so it had an efficient result. In the other hand the ResNet–SVM model, that has skip layers connections, and the DenseNet–SVM, that has full layers connections, gave higher OA but still the ResNet–SVM gave the highest OA in this comparison. If keeping in mind the penalty values that used in training the SVM classifier using the three models as features extraction, we saw that the VGG–SVM and the DenseNet–SVM had normal penalty to give its OA in this comparison, where the ResNet–SVM had a high penalty value and given the highest OA in this comparison. The ResNets are based on the skip layers connections so the layer connections can raise the classification accuracy. The DenseNets may have more connections but still the use of the ResNets has the higher OA. As a total the deeper convolution networks may give better accuracy but the deeper networks that have layers connections may give the more better accuracy. The convolution models that have skip connections can give the better OA than the convolution models that have full layers connections. A high penalty value is needed for training the SVM models when using input features that extracted from the skip connection convolution models. For the SVM classifiers, extracting features from the high resolution remote sensing images are preferred with the use of deep convolution models that have layers connections where extracting features from the low resolution remote sensing images are preferred with the use of deep convolution models that haven’t layers connections.

Figure 8: The OA for the use of the convolution models with the SVM classifier using the both datasets

4. Conclusions

This paper proposed remote sensing images classification approaches using the SVM classifier. The proposed approaches were based on using the deep convolution models classic networks as features extraction. The used classic networks in these approaches were the Densenet 169, the VGG 16, and the ResNet 50 models. The remote sensing images convolution features were extracted from these convolution models using the ImageNet pre-trained weights, which used as input features to train the SVM classifier. This paper also presented a comparison between the uses of the mentioned convolution models with the SVM classifier as proposed in this paper. This comparison was based on calculating the OA for using each model with the SVM classifier. There were two used datasets in this study; the UC Merced land use dataset and the SIRI-WHU dataset. This comparison illustrated that the ResNet–SVM model was more accurate than the other models that mentioned in this paper, which the OA of the DenseNet–SVM model was higher than the VGG–SVM model for using the UC Merced land use dataset and very little lower than the VGG–SVM using the SIRI-WHU dataset. The overall accuracy had an opposite relation with the remote sensing images resolution (pixel or spatial) and the number of dataset classes. For the SVM classifiers, it is preferred to use the deep networks without layers connections as features extraction with the low resolution remote sensing images where the deep networks that have layers connection are the preferred with the high resolution remote sensing images.

Conflict of Interest

The authors declare no conflict of interest.

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10. Win Pa Pa San, Myo Khaing, "Advanced Fall Analysis for Elderly Monitoring Using Feature Fusion and CNN-LSTM: A Multi-Camera Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 6, pp. 12–20, 2024. doi: 10.25046/aj090602
11. 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
12. Joshua Carberry, Haiping Xu, "GPT-Enhanced Hierarchical Deep Learning Model for Automated ICD Coding", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 21–34, 2024. doi: 10.25046/aj090404
13. 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
14. 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
15. 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
16. Nguyen Viet Hung, Tran Thanh Lam, Tran Thanh Binh, Alan Marshal, Truong Thu Huong, "Efficient Deep Learning-Based Viewport Estimation for 360-Degree Video Streaming", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 49–61, 2024. doi: 10.25046/aj090305
17. Sami Florent Palm, Sianou Ezéckie Houénafa, Zourkalaïni Boubakar, Sebastian Waita, Thomas Nyachoti Nyangonda, Ahmed Chebak, "Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 3, pp. 41–48, 2024. doi: 10.25046/aj090304
18. 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
19. 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
20. 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
21. Pui Ching Wong, Shahrum Shah Abdullah, Mohd Ibrahim Shapiai, "Double-Enhanced Convolutional Neural Network for Multi-Stage Classification of Alzheimer’s Disease", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 2, pp. 09–16, 2024. doi: 10.25046/aj090202
22. Toya Acharya, Annamalai Annamalai, Mohamed F Chouikha, "Enhancing the Network Anomaly Detection using CNN-Bidirectional LSTM Hybrid Model and Sampling Strategies for Imbalanced Network Traffic Data", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 1, pp. 67–78, 2024. doi: 10.25046/aj090107
23. 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
24. 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
25. 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
26. Nizar Sakli, Chokri Baccouch, Hedia Bellali, Ahmed Zouinkhi, Mustapha Najjari, "IoT System and Deep Learning Model to Predict Cardiovascular Disease Based on ECG Signal", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 08–18, 2023. doi: 10.25046/aj080602
27. Zobeda Hatif Naji Al-azzwi, Alexey N. Nazarov, "MRI Semantic Segmentation based on Optimize V-net with 2D Attention", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 73–80, 2023. doi: 10.25046/aj080409
28. Kohei Okawa, Felix Jimenez, Shuichi Akizuki, Tomohiro Yoshikawa, "Investigating the Impression Effects of a Teacher-Type Robot Equipped a Perplexion Estimation Method on College Students", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 28–35, 2023. doi: 10.25046/aj080404
29. Yu-Jin An, Ha-Young Oh, Hyun-Jong Kim, "Forecasting Bitcoin Prices: An LSTM Deep-Learning Approach Using On-Chain Data", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 186–192, 2023. doi: 10.25046/aj080321
30. 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
31. 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
32. 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
33. Ivana Marin, Sven Gotovac, Vladan Papić, "Development and Analysis of Models for Detection of Olive Trees", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 87–96, 2023. doi: 10.25046/aj080210
34. 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
35. Víctor Manuel Bátiz Beltrán, Ramón Zatarain Cabada, María Lucía Barrón Estrada, Héctor Manuel Cárdenas López, Hugo Jair Escalante, "A Multiplatform Application for Automatic Recognition of Personality Traits in Learning Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 2, pp. 30–37, 2023. doi: 10.25046/aj080204
36. 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
37. Fatima-Zahra Elbouni, Aziza EL Ouaazizi, "Birds Images Prediction with Watson Visual Recognition Services from IBM-Cloud and Conventional Neural Network", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 181–188, 2022. doi: 10.25046/aj070619
38. 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
39. Bahram Ismailov Israfil, "Deep Learning in Monitoring the Behavior of Complex Technical Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 10–16, 2022. doi: 10.25046/aj070502
40. 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
41. Nosiri Onyebuchi Chikezie, Umanah Cyril Femi, Okechukwu Olivia Ozioma, Ajayi Emmanuel Oluwatomisin, Akwiwu-Uzoma Chukwuebuka, Njoku Elvis Onyekachi, Gbenga Christopher Kalejaiye, "BER Performance Evaluation Using Deep Learning Algorithm for Joint Source Channel Coding in Wireless Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 4, pp. 127–139, 2022. doi: 10.25046/aj070417
42. 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
43. 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
44. Tiny du Toit, Hennie Kruger, Lynette Drevin, Nicolaas Maree, "Deep Learning Affective Computing to Elicit Sentiment Towards Information Security Policies", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 152–160, 2022. doi: 10.25046/aj070317
45. Kaito Echizenya, Kazuhiro Kondo, "Indoor Position and Movement Direction Estimation System Using DNN on BLE Beacon RSSI Fingerprints", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 129–138, 2022. doi: 10.25046/aj070315
46. Jayan Kant Duggal, Mohamed El-Sharkawy, "High Performance SqueezeNext: Real time deployment on Bluebox 2.0 by NXP", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 70–81, 2022. doi: 10.25046/aj070308
47. Sreela Sreekumaran Pillai Remadevi Amma, Sumam Mary Idicula, "A Unified Visual Saliency Model for Automatic Image Description Generation for General and Medical Images", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 119–126, 2022. doi: 10.25046/aj070211
48. Idir Boulfrifi, Mohamed Lahraichi, Khalid Housni, "Video Risk Detection and Localization using Bidirectional LSTM Autoencoder and Faster R-CNN", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 6, pp. 145–150, 2021. doi: 10.25046/aj060619
49. Seok-Jun Bu, Hae-Jung Kim, "Ensemble Learning of Deep URL Features based on Convolutional Neural Network for Phishing Attack Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 291–296, 2021. doi: 10.25046/aj060532
50. 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
51. 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
52. 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
53. 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
54. Fatima-Ezzahra Lagrari, Youssfi Elkettani, "Traditional and Deep Learning Approaches for Sentiment Analysis: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 01–07, 2021. doi: 10.25046/aj060501
55. 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
56. Kanjanapan Sukvichai, Chaitat Utintu, "An Alternative Approach for Thai Automatic Speech Recognition Based on the CNN-based Keyword Spotting with Real-World Application", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 278–291, 2021. doi: 10.25046/aj060431
57. 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
58. Anjali Banga, Pradeep Kumar Bhatia, "Optimized Component based Selection using LSTM Model by Integrating Hybrid MVO-PSO Soft Computing Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 62–71, 2021. doi: 10.25046/aj060408
59. 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
60. Nadia Jmour, Slim Masmoudi, Afef Abdelkrim, "A New Video Based Emotions Analysis System (VEMOS): An Efficient Solution Compared to iMotions Affectiva Analysis Software", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 990–1001, 2021. doi: 10.25046/aj0602114
61. 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
62. 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
63. 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
64. Binghan Li, Yindong Hua, Yifeng Liu, Mi Lu, "Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 801–807, 2021. doi: 10.25046/aj060292
65. 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
66. Susanto Kumar Ghosh, Mohammad Rafiqul Islam, "Convolutional Neural Network Based on HOG Feature for Bird Species Detection and Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 733–745, 2021. doi: 10.25046/aj060285
67. 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
68. 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
69. 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
70. 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
71. 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
72. 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
73. 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
74. Bryan Huaytalla, Diego Humari, Guillermo Kemper, "An algorithm for Peruvian counterfeit Banknote Detection based on Digital Image Processing and SVM", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1171–1178, 2021. doi: 10.25046/aj0601132
75. 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
76. 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
77. 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
78. Anass Barodi, Abderrahim Bajit, Taoufiq El Harrouti, Ahmed Tamtaoui, Mohammed Benbrahim, "An Enhanced Artificial Intelligence-Based Approach Applied to Vehicular Traffic Signs Detection and Road Safety Enhancement", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 672–683, 2021. doi: 10.25046/aj060173
79. 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
80. 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
81. 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
82. 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
83. 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
84. Sinh-Huy Nguyen, Van-Hung Le, "Standardized UCI-EGO Dataset for Evaluating 3D Hand Pose Estimation on the Point Cloud", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1–9, 2021. doi: 10.25046/aj060101
85. 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
86. 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
87. Chaymae Ziani, Abdelalim Sadiq, "SH-CNN: Shearlet Convolutional Neural Network for Gender Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1328–1334, 2020. doi: 10.25046/aj0506158
88. 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
89. 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
90. 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
91. 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
92. 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
93. 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
94. Fei Gao, Jiangjiang Liu, "Effective Segmented Face Recognition (SFR) for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 36–44, 2020. doi: 10.25046/aj050605
95. 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
96. 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
97. 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
98. 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
99. 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
100. 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