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Water resource management is one of the biggest challenges that are being faced, such as a warming climate, arid land, and toxic chemicals in the water. It is essential to deal with water resource management urgently. In this article, researchers mainly focus on monitoring the water quality in the Lam Pa Thao dam, Chaiyaphum, Thailand. The farmer in that area directly affected by the water quality in the dam because they raise fish in floating fish cages. To prevent losses from fish farming, they should have the ability to monitor and control the factors that affect the water quality. As a result, the farmer can monitor the water quality and the monitor system can report to the farmer in time. In this case, to monitor the water quality, researchers designed the buoys, which is the internet of things device, to collect data from the Lam Pa Thao dam. researchers collected the water quality data from January - March 2021, including 13,608 instances. The five important parameters were obtained, including dissolved oxygen, temperature, pH, total dissolved solids, and electric conductivity. Due to the number of parameters, researchers decided not to apply dimension reduction. In these experiments, researchers proposed using K-means clustering algorithms to group the water data into appropriate clusters. For the K-Means algorithm, we calculated the silhouette coefficient to analyze the effectiveness of cluster separation. The best cluster that was grouped using the K-means algorithm achieved the silhouette score of 0.6839. Furthermore, researchers evaluated the K-means algorithm on Charles river and Fitzroy river datasets. It obtained the silhouette score of 0.5489 and 0.6589, respectively.

Vehicle Type Recognition has a significant problem that happens when people need to search for vehicle data from a video surveillance system at a time when a license plate does not appear in the image. This paper proposes to solve this problem with a deep learning technique called Convolutional Neural Network (CNN), which is one of the latest advanced machine learning techniques. In the experiments, researchers collected two datasets of Vehicle Type Image Data (VTID I & II), which contained 1,310 and 4,356 images, respectively. The first experiment was performed with 5 CNN architectures (MobileNets, VGG16, VGG19, Inception V3, and Inception V4), and the second experiment with another 5 CNNs (MobileNetV2, ResNet50, Inception ResNet V2, Darknet-19, and Darknet-53) including several data augmentation methods. The results showed that MobileNets, when combine with the brightness augmented method, significantly outperformed other CNN architectures, producing the highest accuracy rate at 95.46%. It was also the fastest model when compared to other CNN networks.

Plant disease is the most common problem in agriculture. Usually, the symptoms appear on leaves of the plants which allow farmers to diagnose and prevent the disease from spreading to other areas. An accurate and consistent plant disease recognition system can help prevent the spread of diseases and save maintenance costs. In this research, we present a plant leaf disease recognition system using two deep convolutional neural networks (CNNs); MobileNetV2 and NasNetMobile. These CNN architectures are designed to be suitable for smartphones due to the models being small. We have experimented on training techniques; online, offline, and mixed training techniques on two plant leaf diseases. As for data augmentation techniques, we found that the combination of rotation, shift, and zoom techniques significantly increases the performance of the CNN architectures. The experimental results show that the most accurate algorithm for plant leaf disease recognition is NASNetMobile architecture using transfer learning. Additionally, the most accurate result is obtained when combining the offline training technique with data augmentation techniques.

The real-world food image is a challenging problem for food image classification, because food images can be captured from different perspective and patterns. Also, many objects can appear in the image, not just foods. To recognize food images, in this paper, we propose a modified MobileNet architecture that is applies the global average pooling layers to avoid overfitting the food images, batch normalization, rectified linear unit, dropout layers, and the last layer is softmax. The state-of-the-art and the proposed MobileNet architectures are trained according to the fine-tuned model. The experimental results show that the proposed version of the MobileNet architecture achieves significantly higher accuracies than the original MobileNet architecture. The proposed MobileNet architecture significantly outperforms other architectures when the data augmentation techniques are combined.

Land use is constantly changing, and water plays a critical role in the process. If changes are noticed quickly or are predictable, land use planning and policies can be devised to mitigate almost any problem. Accordingly, researchers present a mask region-based convolutional neural network (Mask R-CNN) for water body segmentation from aerial images. The system's Aerial image water resources dataset (AIWR) was tested. The AIWR areas were agricultural and lowland areas that require rainwater for farming. Many wells were spotted throughout the agricultural areas. The AIWR dataset presents two types of data: natural water bodies and artificial water bodies. The two different areas appear as aerial area images that are different in color, shape, size, and similarity. A pre-trained model of Mask R-CNN was used to reduce network learning time. ResNet-101 was used as backbone architecture. The information gathered in the learning process is limited, and only 720 pictures were produced, Researchers used data augmentation to increase the amount of information for training by using affine image transformation, including scale, translation, rotation, and shear. The experiment found that mask R-CNN architecture can specify the position of the water surface. Measuring method in this case is mAP value. The mAP value is at 0.30 without data augmentation. However, if using the R-CNN mask with data augmentation, the mAP value increased to 0.59.

In this paper, we proposed a face verification method. We experiment with a histogram of oriented gradients description combined with the linear support vector machine (HOG+SVM) as for the face detection. Subsequently, we applied a deep learning method called ResNet-50 architecture in face verification. We evaluate the performance of the face verification system on three well-known face datasets (BioID, FERET, and ColorFERET). The experimental results are divided into two parts; face detection and face verification. First, the result shows that the HOG+SVM performs very well on the face detection part and without errors being detected. Second, The ResNet-50 and FaceNet architectures perform best and obtain 100% accuracy on the BioID and FERET dataset. They also, achieved very high accuracy on ColorFERET dataset.

Local gradient feature descriptors have been proposed to calculate the invariant feature vector. These local gradient methods are very fast to compute the feature vector and achieved very high recognition accuracy when combined with the support vector machine (SVM) classifier. Hence, they have been proposed to solve many problems in image recognition, such as the human face, object, plant, and animal recognition. In this paper, we propose the use of the Haar-cascade classifier for the face detection and the local gradient feature descriptors combined with the SVM classifier to solve the gender recognition problem. We detected 4,624 face images from the ColorFERET dataset. The face images data used in gender recognition included 2,854 male and 1,770 female images, respectively. We divided the dataset into train and test set using 2-fold and 10-fold cross-validation. First, we experimented on 2-fold cross-validation, the results showed that the histogram of oriented gradient (HOG) descriptor outperforms the scale-invariant feature transform (SIFT) descriptor when combined with the support vector machine (SVM) algorithm. The accuracy of the HOG+SVM and the SIFT+SVM were 96.50% and 95.98%. Second, we experimented on 10-fold cross-validation and the SIFT+SVM showed high performance with an accuracy of 99.20%. We discovered that the SIFT+SVM method needed more training data when creating the model. On the other hand, the HOG+SVM method provided better accuracy when the training data was insufficient.

Building security is crucial, but guards and CCTV may be inadequate for monitoring all areas. A quadcopter (drone) with manual and autonomous control was used in a trial mission in this project. Generally, all drones can stream live video and take photos. They can also be adapted to assist better decision-making in emergencies that occur inside a building. In this paper, we show how to improve a quadcopter’s ability to fly indoors, detect obstacles and react appropriately. This paper represents a new conceptual framework of hybrid indoor navigation ontology that analyzes a regular indoor route, including detection and avoidance of obstacles for the auto-pilot. An experiment with the system demonstrates improvements that occur in building surveillance and maintaining real-time situational awareness. The immediate objective is to show that the drone can serve as a reliable tool in security operations in a building environment.

We propose a people and object tracking algorithm for an autonomous unmanned aerial vehicle (UAV). It uses as a surveillance camera and can move anywhere. The camera from UAV is not fixed l ocation a s c losed-circuit t elevision. T he face detection and objection detection are applied to support our proposed. In this research, the UAV model for this paper was AR-Drone 2.0. It has a constraint on the front camera because it has fixed the position of view and cannot change the view during flight. W e d esigned t wo e xperiments. F irst, t he f ace detection using images and applied to the popularity of the face detection, is a Haar-cascade classifier and max-margin object detection with convolutional neural network based features because they have high precision in analysis. Second, color detection system, which only focuses on the color of objects which can developed as an obstacle detection system. The results of the experiment can be accepted to adapt to tracking people and objects in the smart-city.

This research aims to present the method for identifying distributed denial of service (DDoS) attacks. Two benchmark dataset, including KDD CUP 1999 and NSL-KDD, were used. The dataset were checked and deleted duplicate data. After the process, the amount of records of KDD Cup 1999 dataset were decreased from 4,898,431 records to 529,655 records, and the amount of records of NSL-KDD dataset were decreased from 125,373 to only 12,354 records. The reduction of the records always happened because of the characteristics of DDoS attacks which send repeated data to the victims’ server. The researchers converted alphabet data to numeric data, then training by K-nearest neighbor (KNN), multi-layer perceptron and support vector machine. The result showed that KNN was the best method to identify the DDoS attacks.

In this paper, we propose to use deep convolutional neural network (CNN) architectures, namely the deep residual networks (ResNet), the GoogLeNet, the AlexNet, and the AlexNet architectures, for pornographic image dataset. Also, the local descriptors, namely the local binary patterns (LBP), the histogram of oriented gradients, and the scale invariant feature transform (SIFT) combined with a support vector machine (SVM), a multilayer perceptron (MLP), or a K-nearest neighbor (KNN) techniques are proposed. Additionally, a bag of visual words (BOW) and the BOW using extracted HOG features (HOG-BOW) are compared. To classify the pornographic images, we compare the CNN architectures to well-known local descriptor techniques combined with the SVM, the MLP, and the MLP methods. Experimental results indicate that the ResNet architecture yields higher accuracies than all other approaches.

© 2016 IEEE.Most research in image classification has focused on applications such as face, object, scene and character recognition. This paper examines a comparative study between deep convolutional neural networks (CNNs) and bag of visual words (BOW) variants for recognizing animals. We developed two variants of the bag of visual words (BOW and HOG-BOW) and examine the use of gray and color information as well as different spatial pooling approaches. We combined the final feature vectors extracted from these BOW variants with a regularized L2 support vector machine (L2-SVM) to distinguish between classes within our datasets. We modified existing deep CNN architectures: AlexNet and GoogleNet, by reducing the number of neurons in each layer of the fully connected layers and last inception layer for both scratch and pre-trained versions. Finally, we compared the existing CNN methods, our modified CNN architectures and the proposed BOW variants on our novel wild-animal dataset (Wild-Anim). The results show that the CNN methods significantly outperform the BOW techniques.

The support vector machine SVM is a supervised learning algorithm used for recognizing patterns in data. It is a very popular technique in machine learning and has been successfully used in applications such as image classification, protein classification, and handwriting recognition. However, the computational complexity of the kernelized version of the algorithm grows quadratically with the number of training examples. To tackle this high computational complexity, we have developed a directive-based approach that converts a gradient-ascent based training algorithm for the CPU to an efficient graphics processing unit GPU implementation. We compare our GPU-based SVM training algorithm to the standard LibSVM CPU implementation, a highly optimized GPU-LibSVM implementation, as well as to a directive-based OpenACC implementation. The results on different handwritten digit classification datasets demonstrate an important speed-up for the current approach when compared to the CPU and OpenACC versions. Furthermore, our solution is almost as fast and sometimes even faster than the highly optimized CUBLAS-based GPU-LibSVM implementation, without sacrificing the algorithm's accuracy. Copyright © 2014 John Wiley & Sons, Ltd.

The Single Sample per Person Problem is a challenging problem for face recognition algorithms. Patch-based methods have obtained some promising results for this problem. In this paper, we propose a new face recognition algorithm that is based on a combination of different histograms of oriented gradients (HOG) which we call Multi-HOG. Each member of Multi-HOG is a HOG patch which belongs to a grid structure. To recognize faces, we create a vector of distances computed by comparing train and test face images. After this, a distance calculation method is employed to calculate the final distance value between a test and a reference image. We describe here two distance calculation methods: mean of minimum distances (MMD) and a multi-layer perceptron based distance (MLPD) method. To cope with aligning difficulties, we also propose another technique which finds the most similar regions for two images compared. We call it the most similar region selection algorithm (MSRS). The regions found by MSRS are given to the algorithms we proposed. Our results show that, while MMD and MLPD contribute to obtaining much higher accuracies than the use of a single histogram of oriented gradients, combining them with the most similar region selection algorithm results in state-of-the-art performances.

In this paper we propose to use local gradient feature descriptors, namely the scale invariant feature transform keypoint descriptor and the histogram of oriented gradients, for handwritten character recognition. The local gradient feature descriptors are used to extract feature vectors from the handwritten images, which are then presented to a machine learning algorithm to do the actual classification. As classifiers, the k-nearest neighbor and the support vector machine algorithms are used. We have evaluated these feature descriptors and classifiers on three different language scripts, namely Thai, Bangla, and Latin, consisting of both handwritten characters and digits. The results show that the local gradient feature descriptors significantly outperform directly using pixel intensities from the images. When the proposed feature descriptors are combined with the support vector machine, very high accuracies are obtained on the Thai handwritten datasets (character and digit), the Latin handwritten datasets (character and digit), and the Bangla handwritten digit dataset. HighlightsThis paper provides a new standard Thai handwritten character dataset for comparison of feature extraction techniques and methods.This paper propose to use local gradient feature descriptors for handwritten character recognition.This paper makes use of three complex datasets, namely Bangla, Thai, and Latin, for which very high recognition accuracies have not been obtained before.