Tea leaf disease dataset in this manuscript.
\r\n\t(i) Quantum dots of very high-quality optical applications, Quantum dot light-emitting diodes (QD-LED) and ‘QD-White LED’, Quantum dot photodetectors (QDPs), Quantum dot solar cells (Photovoltaics).
\r\n\r\n\t(ii) Quantum Computing (quantum bits or ‘qubits’), (vii) The Future of Quantum Dots (broad range of real-time applications, magnetic quantum dots & graphene quantum dots), Superconducting Loop, Quantum Entanglement, Quantum Fingerprints.
\r\n\r\n\t(iii) Biomedical and Environmental Applications (to study intracellular processes, tumor targeting, in vivo observation of cell trafficking, diagnostics and cellular imaging at high resolutions), Bioconjugation, Cell Imaging, Photoelectrochemical Immunosensor, Membranes and Bacterial Cells, Resonance Energy-Transfer Processes, Evaluation of Drinking Water Quality, Water and Wastewater Treatment, Pollutant Control.
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According to statistical data, in 2016 China’s 17 provinces had a total of 2.87 million hectares of tea plantation and production, and the total output of tea reached 2.4 billion tons [1]. As the main tea-producing areas in China are mainly distributed in subtropical regions, the natural environment differs due to geographical latitude and topographical conditions. The tea tree is a perennial evergreen woody plant, which grows in warm and humid growth environment. However, these regions are conducive to the breeding and reproduction of diseases. In recent years, the tea planting area has increased year by year, and the tea leaf diseases have risen continuously, which has seriously threatened the quality and yield of tea. Because the distribution of tea areas in China is mostly in high mountain areas, the infrastructure construction in these areas is relatively lagging behind, and the occurrence of tea leaf diseases is often not controlled in a timely and effective manner, resulting in huge economic losses. Therefore, being able to detect and identify diseases early in the field is an important task to ensure the sustainable development of the tea industry.
The diagnosis of plant diseases is usually based on the appearance of the disease. When the leaves of a plant are infected by a disease, the appearance of the leaves will change significantly. Each disease usually has a discernible leaf color and texture symptom, and plant diseases can be diagnosed based on these characteristics. However, farmers mainly rely on their own experience to diagnose plant diseases with their own senses. Due to the limitation of knowledge background, there are ambiguities in the diagnosis. Most tea trees in China are planted in mountainous areas, which are large, difficult to investigate in the field, and inefficient. Relying on agricultural experts to diagnose tea leaf diseases is not only time-consuming but also costly. The transportation and infrastructure conditions in these places are limited. Finally, the expert must have experience and knowledge in various disciplines and need to understand all the symptoms of the disease and the causes of the diversity of the disease. At the same time, because China’s agricultural population is relatively large and the number of experts engaged in agricultural services is extremely limited, it is necessary to establish a system that can diagnose tea leaf diseases in a timely and accurate manner.
The current diagnostic methods of plant diseases mainly include microscope identification, molecular biology technology, and spectroscopic technology, but the first method is time-consuming and subjective. Even experienced plant pathologists may have wrong judgments, leading to inaccurate conclusion. The latter two methods are currently considered more accurate, and their main disadvantages are the high labor intensity and the requirement of specific instruments.
With the rapid development of intelligent agriculture and precision agriculture, machine learning methods and computer image processing technologies have been applied to the identification of plant diseases [2, 3], providing a new method for detecting plant diseases, which can help farmers and researchers quickly and accurately identify the types of plant diseases. The general approach based on machine learning and computer image processing technology is first to manually design and extract disease image features, namely, global features, such as color features [4], shape features [5], texture features [6], or two or more than three features [7, 8, 9, 10, 11], and local features, using scale-invariant feature transform (SIFT), speeded-up robust features (SURF), dense scale-invariant feature transform (dense SIFT), and pyramid histograms of visual words (PHOW) [12, 13, 14]. After extracting the features, they are identified and classified using different classifiers, such as artificial neural networks [15, 16] and support vector machines [17, 18]. Because traditional machine learning relies on features extracted manually, the resulting recognition system is not fully automated.
At present, most of the researches on tea using computer vision technology focus on tea quality detection [19], tea species identification [20], and tea leaf disease information query and management based on expert systems [21]. Because the expert system has limited knowledge and needs to be updated and maintained on a regular basis, it is also limited for noncomputer professional technicians. For some literatures, the identification of tea diseases is based on hyperspectral [22] or infrared thermal images [1]. These methods are easy to operate and have high accuracy, but the cost of the instrument is not suitable for widespread promotion.
In recent years, the popularity of the Internet has led to the explosive growth of Internet data, and the technical performance of computers and smartphones has continued to improve. These factors are the main reasons that have led to widespread attention for deep learning. Deep learning refers to the process of learning sample data through a certain training method to obtain a deep network structure containing multiple levels [23]. Deep learning is a branch of machine learning. Its essence is also a neural network, but the number of hidden layers is more than one layer, which is an extension of artificial neural networks. “Neural network” is a component of deep learning.
The concept of deep learning was first mentioned by Professor Geoffrey Hinton of the University of Toronto in a paper on back-propagation algorithms. The concept of “depth” was used to represent large artificial neural networks. With the introduction of deep learning, more and more researchers have begun to develop large-scale neural network systems. These deep neural network systems can take the characteristics from the original data, can work alone without human manipulation, and then can use what humans have learned to learn new things.
The advantage of the deep learning is that it does not require artificial feature extraction but this is obtained automatically by the network. It can solve nonlinear separable problems and has strong generalization ability and robustness. Among them, the most widely used is the convolutional neural network, which is a deep neural network. Images can be directly used as input data, eliminating the complicated process of feature extraction and data reconstruction in traditional machine learning algorithms. At the same time, the multilayer network structure of the convolutional neural network maintains a high degree of invariance to image translation, scaling, or lighting changes [18]. At present, convolutional neural networks have been applied to the identification and diagnosis of plant diseases [24, 25, 26].
In recent years, many researchers in the world have used machine learning algorithms to build many disease recognition systems, but because the characteristics of each plant disease are different, the different machine learning methods will have different recognition effects. Hence, based on previous studies, this paper uses deep convolutional neural networks to identify and classify tea leaf diseases. At the same time, the traditional machine learning algorithm is compared with the proposed convolutional neural network, and a recognition system suitable for the tea leaf disease is found through comparative analysis.
The existing databases on the network such as ImageNet, PlantVillage, and CIFAR-1 datasets do not have sufficient tea leaf disease images and some studies have collected disease photos in indoor or controlled environments. These factors have made the recognition system designed to identify diseases under natural light conditions to have certain limitations, so a new disease data set is constructed in this paper.
Tea leaf disease images were all captured using the Canon PowerShot G12 camera in the natural light environment of the tea garden in Chibi and Yichang within Hubei Province. The images were taken about 20 cm directly above the leaves with autofocus mode at resolution of 4000 × 3000 pixels. A total of 3810 disease images were collected, which contained 7 diseases, and all disease images have been identified by plant pathologists. The identification criteria used for the tea leaf diseases were based on the previously described identification schemes [27, 28]. In order to meet the requirements of the model algorithm and reduce the computational complexity of the network, all disease images are resized to 256 × 256 pixels and 750 × 750 pixels, respectively. Figure 1 shows the types of tea leaf diseases used in this experiment. Data amplification processing is performed on a smaller number of disease images so that the number of the seven diseases image is balanced. Data amplification processing improves the generalization ability of the classifier, which is more conducive to network training. Three different methods were used to alter the image input and improve classification (Figure 2). A total of 7905 tea leaf disease images were obtained after the amplification treatment (Table 1). The 80/20 ratio of training/test data is the most commonly used ratio in neural network applications. In addition, a 10% subset of the test dataset was used to validate the dataset [29].
Typical example images of tea leaf diseases used in this manuscript. (1) Red leaf spot (
Examples of data augmentation used for red leaf spot images. (a) Initial; (b) flip horizontal; (c) flip vertical; (d) rotated 180°; (e–g) randomly cropped; (h) right-rotate 90°; (i) left-rotate 90°.
Class | Training | Validation | Testing |
---|---|---|---|
(1) White spot | 941 | 118 | 117 |
(2) Bird’s-eye spot | 955 | 120 | 119 |
(3) Red leaf spot | 890 | 111 | 111 |
(4) Gray blight | 893 | 112 | 111 |
(5) Anthracnose | 880 | 110 | 110 |
(6) Brown blight | 920 | 115 | 115 |
(7) Algal leaf spot | 846 | 106 | 105 |
Total | 6325 | 792 | 788 |
Tea leaf disease dataset in this manuscript.
Traditional machine learning algorithm is a shallow architecture that contains one or two nonlinear transformation layers. It can automatically learn the underlying laws in the data and use the learned rules to make predictions. In the field of computer vision, many models can be realized by manually designing and extracting the visual characteristics of the image in advance, and the image content is converted into a quantitatively calculated information description form, after being processed by the shallow structure model.
The extraction and selection of image visual features is an important means to transform the image content into a quantitatively calculated information description form, which mainly include global features and local features. Global features refer to the overall attributes of the entire image, mainly including color features, texture features, and shape features. These features are features that can be directly observed by the eyes. Global features are pixel-level shallow features with good stability, real-time performance, and simple and easy-to-implement algorithms. However, their shortcomings are high feature dimensions, large amount of calculations, and changes in image scale, lighting, and perspective. Local features are features extracted from local areas of the image, including corners, lines, edges, and areas with special attributes. Local features are distinguishable and robust to changes in lighting, rotation, perspective, and scale, as well as low dimensions and easy implementation.
The scale-invariant feature transform (SIFT) is local feature descriptor proposed by David G. Lowe in 1999 [30]. The SIFT descriptor maintains invariance to image rotation, translation, scaling, affine transformation, perspective and brightness changes, and noise and also maintains stability. And it can be combined with other algorithms to form a new optimization algorithm, thereby increasing the operation speed.
The traditional SIFT descriptor mainly extracts stable feature points in the image, which will lead to loss of some information in the image and long calculation time. And the number of feature points extracted from each image is different, which will inevitably lead to different dimensions. Lazebnik et al. improved the number and distribution of SIFT descriptors to obtain dense SIFT [31]. The main difference between the dense SIFT descriptor and the traditional SIFT descriptor is that the sampling method is different. The SIFT descriptor constructs a scale space to detect and filter feature points. The dense SIFT algorithm applies a fixed-size rectangular window for sampling from the left to the right of the image and from the top to the bottom according to the specified step size. The center of the window is used as a key point, and an image block composed of 16 pixels around the center is divided into 4 × 4 pixel-sized units. Within each pixel, the SIFT algorithm is used to calculate the gradient histogram in 8 directions and obtain 4 × 4 × 8 = 128 dimensional feature vectors to form a DSIFT descriptor. The feature points extracted by this method are uniformly distributed, and the specifications are the same; they maintain good stability to illumination, changes in perspective, and affine transformation, scaling, and rotation.
Bag of visual word (BOVW) model was mainly applied to text classification and retrieval technology. The core idea of the bag of visual word model is to treat text as a collection of different words, ignoring the word order, grammar, and syntax of the text, and these words are discrete and independent of each other or do not depend on the presence of other words. The frequency of each word in the text is counted and is represented with histogram so that each text is represented as a vector.
Due to the successful application of the BOVW model in text retrieval, Csurka et al. introduced the BOVW model to the field of computer vision [32]. Think of an image as a document and the features of the image (usually referred to as local features) as the words that make up the image. Unlike the words in the text, there are no ready-made words in the image. We need to extract independent features from the image, which are called visual word. Similar features can be regarded as a visual word. In this way, the image can be described as an unordered set of visual words (local features). Although local features (such as SIFT) also can describe an image, each SIFT is a 128-dimensional vector, and an image contains hundreds or thousands of SIFT descriptor. The calculation amount is very large, so these vectors are clustered, and the cluster center was used to represent a visual word.
The image classification using BOVW model mainly includes the following steps:
Image feature extraction and description: Local feature vectors of the entire training set image are obtained through methods such as point-of-interest detection, dense sampling, or random sampling. Commonly used local features include SIFT descriptor and SURF descriptor.
Construct a visual vocabulary: After obtaining the local feature vectors of all sample images, use the k-means algorithm to cluster the local feature vectors. The k-means algorithm is an unsupervised learning algorithm. It divides the data into different categories through an iterative process and then calculates the Euclidean distance between each data and various types of centers [33]. The smaller the distance, the higher the similarity. k represents the number of clusters, and means represents the mean of the data in the clusters. If there are k cluster centers (i.e., visual words), then the size of the visual vocabulary is also k. This manuscript selects 1000 visual words, and the size of the visual vocabulary is 1000.
Representing images by word frequency: using the vocabulary as a standard, count the number of occurrences of each visual word in the image, and each image becomes a word frequency vector corresponding to the visual word sequence in the vocabulary, that is, each image is represented by a 1000-dimensional numerical vector.
Select classifier to classify the 1000-dimensional numerical vector generated in the previous step as the input of the classification.
Support vector machines (SVMs) were proposed by Corinna Cortes and Vapnik in 1995 [34]. It is a learning method based on VC statistical theory and structural risk minimization criteria. It has advantage in solving small sample, nonlinear, and high-dimensional pattern recognition problems. The basic idea of the SVMs is to map the low-dimensional space vector to the high-dimensional space through the nonlinear transformation defined by the inner product. In this high-dimensional space, the optimal classification hyperplane is determined according to the maximum geometric distance between the support vector and the classification plane. SVMs were initially used to classify two-class problems in the analysis of linear separable cases and require smaller sample sizes and an appropriate train rule, which have led to widespread use in image classification and recognition.
With the deepening of research on support vector machines, many scholars have carried out various toolkits in order to make them suitable for specific fields. In this manuscript a linear classifier LIBLINEAR designed by Professor Lin Zhiren of the National Taiwan University is used, mainly for processing large-scale data and features [35]. LIBLINEAR can be used in the following three cases: when the number of features is much larger than the number of samples; when the number of features and samples is large; and when the number of features is much smaller than the number of samples. Because the complexity of the linear classifier is lower than the nonlinear classifier, the training operation time is greatly reduced, and the training performance of the linear and nonlinear classifiers is also comparable under a large amount of data.
The perceptron was proposed by Rosenblatt in 1958 [36]. It is an artificial neural network structure and the earliest feed-forward neural network. A single-layer perceptron contains only two layers, namely, the input layer and the output layer. Due to its limited mapping capability, it can only achieve linearly separable classification problems. A multi-layer perceptron has one or more hidden layers between the input layer and the output layer, which is mainly used for nonlinear classification and regression. The training algorithm is consistent with the traditional multilayer neural network and also uses a back-propagation algorithm.
Perceptron in this manuscript uses a three-layer structure. Because the extracted features are 1000-dimensional vectors, the input layer contains 1000 nodes, the hidden layer contains 100 nodes, and the output layer contains 7 nodes, which refer to the number of types of tea leaf disease.
The network architecture designed in this manuscript was improved based on the classic model AlexNet model, named as LeafNet. The total number of parameters (weights and deviations) of the classic AlexNet network reaches more than 60 million, the parameters of the convolution layer comprises 3.8% of the total network parameters, and the parameters of the fully connected layer comprises 96.2% of the total. Therefore, by reducing the number of LeafNet’s convolutional layer filters and the number of fully connected layer nodes, the total number of network parameters is reduced, and the computational complexity is reduced. The recognition model has a relatively simple structure and a small amount of calculation, which effectively reduces the problem of overfitting.
LeafNet consists of five convolutional layers, two fully connected layers, and a classification layer. The number of filters for the first, second, and fifth convolutional layers is half of those used in AlexNet’s filters. In addition, the number of neurons in the fully connected layer is set to 500, 100, and 7, respectively. The entire network structure is shown in in Table 2.
Layer | Parameters | Activity function |
---|---|---|
Input | 227 × 227 × 3 | − |
Convolution1(Conv1) | 24 convolution filters (11 × 11) 4 stride | ReLU |
Pooling1(Pool1) | Max pooling (3 × 3) 2 stride | − |
Convolution2(Conv2) | 64 convolution filters (5 × 5) 1 stride | ReLU |
Pooling2(Pool2) | Max pooling (3 × 3) 2 stride | − |
Convolution3(Conv3) | 96 convolution filters (3 × 3) 1 stride | ReLU |
Convolution4(Conv4) | 96 convolution filters (3 × 3) 1 stride | ReLU |
Convolution5(Conv5) | 64 convolution filters (3 × 3) 1stride | ReLU |
Pooling5(Pool5) | Max pooling (3 × 3) 2 stride | − |
Full Connect 6(fc6) | 500 nodes 1 stride | ReLU |
Full Connect 7(fc7) | 100 nodes 1 stride | ReLU |
Full Connect8(fc8) | 7 nodes 1 stride | ReLU |
Output | 1 node | Softmax |
Layer parameters for the LeafNet.
In this experiment, except for the last layer, the rectified linear unit (ReLU) activation function is selected instead of the traditional sigmoid and tanh functions. The main disadvantages of the sigmoid and tanh functions are the large amount of calculations, and when the input is large or small, the output is relatively smooth, the gradient is small, and it is not conducive to the weight update, which ultimately cause the network to fail to complete the training. ReLU is more in line with the principle of neuron signal excitation. It will make some neurons’ output 0, making the network sparse and reducing the interdependence of parameters, effectively alleviating overfitting. At the same time, ReLU has better transmission error characteristics and solves the problem of gradient disappearance, so it makes the training network converge faster.
After the nonlinear neuron output of the first two convolutional layers, a local response normalization operation is introduced. It is a normalization operation and mimics the lateral inhibition phenomenon of neurobiology. Local response normalization creates a competition mechanism for the output of local neurons. Local response normalization creates a competition mechanism for the output of local neurons, making the neurons with large responses larger, thereby enhancing the generalization ability of the model.
The first two fully connected layers have introduced the dropout operation. The dropout technique is an effective solution to overfitting via the training of only some of the randomly selected nodes rather than the entire network [37]. In this article, the dropout ratio is set to 0.5.
Softmax is the activation function of the last fully connected layer, which is mainly used in the output layer of multi-classification problems. It can make the sum of all output values equal to 1. That is, the output value of multiple classifications is converted into a relative probability, in which the category which has a high relative probability is the predicted value.
LeafNet’s training uses stochastic gradient descent (SGD) technique. The weight values of all convolutional layers and fully connected layers are initialized with a Gaussian distribution, and the bias is initialized with a constant of 1. This setting guarantees that the input of the ReLU activation function is a positive number and can also speed up the training speed of the network [25]. Because the number of samples is small, the batch size is set to 16. Batch training can improve the convergence speed of the network and keep the memory usage at a low level. The initial learning rate of all layers of the network is set to 0.1. The learning rate is reduced according to the decline of the error, and each time it is reduced to 0.1 times the original learning rate in subsequent iterations, with the minimum threshold of the learning rate set to 0.0001. The number of epochs was set as 100, while the weight of decay was set to 0.0005 and the momentum was set to 0.9 [38]. LeafNet is implemented using Matlab’s MatConvNet toolbox. The network training is performed on a Windows system, configured with a Core i7-3770K CPU, 8 GB of RAM, and accelerated training via two NVIDIA GeForce GTX 980 GPUs.
As mentioned in [39], the classification accuracy and mean class accuracy (MCA) are used to evaluate the performance of the algorithm. CCRk is first defined as the correct classification rate for class k, as shown in Eq. (1):
Where Ck is the number of correctly identified for class k and Nk is the total number of elements in class k. Classification accuracy is then defined by Eq. (2):
Lastly, MCA is determined using Eq. (3):
In this study, the accuracy of the SVM, MLP, and CNN classifiers in determining disease states for tea leaves from images was evaluated. The results of these analyses are shown in Figure 3. Error matrices were used to evaluate the accuracy of tea leaf disease recognition classifiers (Tables 3–5). From these data, although LeafNet algorithms are significantly better than SVM and MLP algorithms, three recognition algorithms can usually correctly identify most tea leaf diseases. Traditional machine learning algorithms extract the surface features of images, and the number is limited. The ability to represent image features is not strong, resulting in a low accuracy rate for identifying diseases. However, the CNN can automatically extract the deep features of the image, which can more accurately express the features of the disease image, so its recognition accuracy is higher.
Accuracy (%) of disease classification for each of the three classification models in recognizing the seven candidate tea diseases.
White spot | Bird’s-eye spot | Red leaf spot | Gray blight | Anthracnose | Brown blight | Algal leaf spot | Sensitivity | Accuracy | MCA | |
---|---|---|---|---|---|---|---|---|---|---|
White spot | 111 | 3 | 0 | 0 | 3 | 0 | 0 | 94.87% | 90.23% | 90.16% |
Bird’s-eye spot | 1 | 117 | 0 | 0 | 0 | 0 | 1 | 98.32% | ||
Red leaf spot | 0 | 0 | 95 | 7 | 0 | 8 | 1 | 85.59% | ||
Gray blight | 0 | 0 | 4 | 96 | 3 | 7 | 1 | 86.49% | ||
Anthracnose | 5 | 0 | 1 | 6 | 97 | 1 | 0 | 88.18% | ||
Brown blight | 0 | 1 | 15 | 2 | 0 | 97 | 0 | 84.35% | ||
Algal leaf spot | 1 | 1 | 2 | 2 | 1 | 0 | 98 | 93.33% |
Error matrix showing the classification accuracy of the LeafNet algorithm.
White spot | Bird’s-eye spot | Red leaf spot | Gray blight | Anthracnose | Brown blight | Algal leaf spot | Sensitivity | Accuracy | MCA | |
---|---|---|---|---|---|---|---|---|---|---|
White spot | 79 | 11 | 0 | 2 | 19 | 1 | 5 | 67.52% | 60.91% | 60.62% |
Bird’s-eye spot | 12 | 89 | 0 | 4 | 1 | 10 | 3 | 74.79% | ||
Red leaf spot | 2 | 4 | 59 | 23 | 2 | 19 | 2 | 53.15% | ||
Gray blight | 0 | 0 | 13 | 70 | 8 | 17 | 3 | 63.06% | ||
Anthracnose | 19 | 0 | 5 | 13 | 56 | 11 | 6 | 50.91% | ||
Brown blight | 0 | 2 | 19 | 17 | 3 | 73 | 1 | 63.48% | ||
Algal leaf spot | 9 | 10 | 12 | 13 | 3 | 4 | 54 | 51.43% |
Error matrix showing the classification accuracy of the SVM algorithm.
White spot | Bird’s-eye spot | Red leaf spot | Gray blight | Anthracnose | Brown blight | Algal leaf Spot | Sensitivity | Accuracy | MCA | |
---|---|---|---|---|---|---|---|---|---|---|
White spot | 83 | 13 | 0 | 3 | 15 | 1 | 2 | 70.94% | 70.94% | 70.77% |
Bird’s-eye spot | 6 | 100 | 0 | 6 | 1 | 5 | 1 | 84.03% | ||
Red leaf spot | 1 | 1 | 80 | 17 | 0 | 11 | 1 | 72.07% | ||
Gray blight | 0 | 0 | 9 | 81 | 6 | 14 | 1 | 72.97% | ||
Anthracnose | 13 | 0 | 4 | 10 | 73 | 8 | 2 | 66.36% | ||
Brown blight | 0 | 5 | 16 | 15 | 3 | 75 | 1 | 65.22% | ||
Algal leaf spot | 6 | 5 | 9 | 10 | 4 | 4 | 67 | 63.81% |
Error matrix showing the classification accuracy of the MLP algorithm.
It can be seen from the error matrix that the recognition accuracy of MLP and SVM for the seven tea leaf diseases is 70.94% and 60.91%, respectively, and the MCA is 70.77% and 60.62%, respectively. In the two algorithms, the correct rate of the bird’s-eye spot is the highest, but there is no obvious regularity for the rest of diseases. The bird’s-eye spot is clearly distinguishable, characterized by small and dense red-brown dots, which are significantly different from other disease characteristics, so its accuracy of identification is high.
The recognition accuracy of tea leaf disease by SVM and MLP algorithm is not high, which is caused by artificial selection of features. The recognition effect of SVM and MLP algorithm largely depends on whether the artificially selected features are reasonable, and researchers usually rely on personal experience when selecting features. Although better results can be obtained using artificial feature classification, these features are specific to certain datasets. If you use the same features to analyze different data sets, the results may be very different, which is a problem inherent in these technologies.
LeafNet has the best recognition effect on the bird’s-eye spot, which may be due to the obvious plant pathological symptoms and the strong recognition ability of the LeafNet algorithm. The white spot disease was the second, while the other diseases range from 84 to 93%. Because of the similar pathological characteristics of the gray blight, red leaf spot, and brown blight, the classification accuracy of the three diseases is lowest. The symptoms of gray blight and brown blight diseases are too similar, which both exhibit annulations in their late stage and cannot be distinguished. In addition, the symptoms of white spot and bird’s-eye spot diseases both include reddish brown spots at early stages. In addition, both anthracnose and brown blight diseases are typified by waterlogged leaves during early disease stages, while symptoms are different in the later stages. Some diseases can occur in tea plants throughout the year, although some diseases occur at distinct times. Consequently, diseases diagnosed at different times may affect the accuracy of disease identification. Another factor that affects the accuracy may be that the tea leaf can be infected with two or more diseases at the same time. This is because when the tea leaf is infected by one pathogen, the leaves are suffering from physiological weakness, and the second pathogen can easily infect. Therefore, the above factors explain the main reasons for the low accuracy of the test model in some diseases.
In addition, the performance of LeafNet is compared with the method of Reference [40], which contains 10 diseases of 3 crops with a maximum accuracy of 97.3%. Therefore, the performance of LeafNet is slightly lower than Reference [40], which used currently popular transfer learning algorithm. The main advantages of this algorithm are as follows: the network can converge quickly when the data set is small; easy to implement; and shorter training time. Therefore, in the future we will continue to research on and apply transfer learning algorithms to identify more plant diseases.
CNNs have developed into mature techniques that have been increasingly applied in image recognition. The computational complexity needed for neural network analyses is considerably reduced compared to other algorithms, and it also significantly improves computing precision. Concomitantly, the high fault tolerance of CNNs allows the use of incomplete or fuzzy background images, thereby effectively enhancing the precision of image recognition.
Feature extraction is an important step in image classification and directly affects classification accuracies. Thus, two feature extraction methods and three classifiers were compared in their abilities to identify seven tea leaf diseases in the present manuscript. These analyses revealed that LeafNet yielded the highest accuracies among SVM and MLP classification algorithms. CNNs thus have obvious advantages for identifying tea leaf diseases. Importantly, the results from the present study highlight the feasibility of applying CNNs in the identification of tea leaf diseases, which would significantly improve disease recognition for tea plant agriculture. Although the disease classification accuracy of the LeafNet was not 100%, improvements upon the present method can be implemented in future studies to improve the method and provide more efficient and accurate guidance for the control of tea leaf diseases.
In this manuscript, the expansion process of sample data is a time-consuming process, but with the continuous growth of network information resources, the number of tea tree disease images will continue to increase, so we must collect images of different morphological features in the early, middle, and late stages of each disease and continuously expand the tea tree disease data set to make the data set more detailed and comprehensive.
At present, disease recognition is based on computer system operations. However, as the performance of smartphones continues to improve, the recognition model of deep convolutional neural networks is migrated to android-based mobile applications. It can timely and accurately obtain relevant information about diseases and can provide help for the control of tea tree diseases.
We acknowledge the funding support by key R&D projects of Ningxia Hui Autonomous Region (2017BY080) and the National Natural Science Foundation of China (M1942001) and Natural Science Foundation of Inner Mongolia Autonomous Region (2019MS08168).
The authors declare no conflict of interest.
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His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr.",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Rheinmetall (Germany)",country:{name:"Germany"}}},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. 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Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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By providing an understanding on how SNS can provide an adequate environment for the design of social marketing strategies, we believe our work has practical implications both for academicians and marketers who want to contribute in the transformation of consumer behavior and the achievement of well-being and social change.",book:{id:"6583",slug:"marketing",title:"Marketing",fullTitle:"Marketing"},signatures:"Alicia De La Pena",authors:[{id:"196878",title:"Dr.",name:"Alicia",middleName:null,surname:"De La Pena",slug:"alicia-de-la-pena",fullName:"Alicia De La Pena"}]},{id:"37593",title:"Standard Operating Procedures (What Are They Good For ?)",slug:"standard-operating-procedures-what-are-they-good-for-",totalDownloads:26573,totalCrossrefCites:7,totalDimensionsCites:9,abstract:null,book:{id:"3276",slug:"latest-research-into-quality-control",title:"Latest Research into Quality Control",fullTitle:"Latest Research into Quality Control"},signatures:"Isin Akyar",authors:[{id:"36323",title:"Dr.",name:"Isin",middleName:null,surname:"Akyar",slug:"isin-akyar",fullName:"Isin Akyar"}]},{id:"38348",title:"Globalization and Culture: The Three H Scenarios",slug:"globalization-and-culture-the-three-h-scenarios",totalDownloads:16815,totalCrossrefCites:4,totalDimensionsCites:11,abstract:null,book:{id:"3009",slug:"globalization-approaches-to-diversity",title:"Globalization",fullTitle:"Globalization - Approaches to Diversity"},signatures:"Abderrahman Hassi and Giovanna Storti",authors:[{id:"148330",title:"Dr.",name:"Abderrahman",middleName:null,surname:"Hassi",slug:"abderrahman-hassi",fullName:"Abderrahman Hassi"},{id:"152537",title:"Prof.",name:"Giovanna",middleName:null,surname:"Storti",slug:"giovanna-storti",fullName:"Giovanna Storti"}]}],onlineFirstChaptersFilter:{topicId:"7",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"83075",title:"Practices and Challenges of Community Services at Debre Markos University, Ethiopia: A Case Study",slug:"practices-and-challenges-of-community-services-at-debre-markos-university-ethiopia-a-case-study",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.105896",abstract:"Universities are the main actors that deliver community service in Ethiopia. Community service is among the three pillars of the university’s business along with teaching and research tasks. Employing a qualitative case study design, this research inspects the practices of community services against the ascribed principles and identifes the pitfalls of community service in Debre Markos University. Both primary and secondary data were collected. Primary data were collected through key informants interviews, semistructured interviews, and non-participant observation. Thirteen participants, five through key informant interview and eight through a semistructured interview were addressed. Participants were purposively selected from both the university and the nearby community. Lecturers, vice-presidents, and directors have participated in the interview. Articles, books, different reports, newspapers, and magazines were reviewed and used as sources of secondary data. Thematic data analysis technique was employed to analyze the primary data, and document analysis was used to analyze the data gained from secondary sources. The results show that, though community service is rendered since 2006 at Debre Markos University, there are still limitations in adhering to the principles of community service. These include shortage of budget, low level of University-Industry Linkage (UIL), less commitment of the staff, and the low level of monitoring and evaluation.",book:{id:"11602",title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg"},signatures:"Adane Mengist"},{id:"82845",title:"Revisiting Crisis Governance: Toward Collaborative Crisis Management",slug:"revisiting-crisis-governance-toward-collaborative-crisis-management",totalDownloads:3,totalDimensionsCites:0,doi:"10.5772/intechopen.106129",abstract:"This chapter attends to three main modes of crisis governance: centralization, decentralization, and collaborative crisis management (CCM). While the first two modes focus almost exclusively on government actors, CCM goes beyond them by involving private sectors and civil society. CCM is a more robust form of crisis governance since it combines knowledge and resources from multiple actors, which is a key to managing the more complex nature of modern crises. This chapter uses the case of Indonesia in dealing with the COVID-19 pandemic to show the dynamics of crisis governance. Indonesia moved from a centralized mode of crisis governance toward a more decentralized one. Simultaneously, there were several collaborative initiatives involving multiple stakeholders to deal with the crisis, such as in the case of SONJO. The case illustrates that while CCM provides a more effective response, it has some limitations as it has a smaller scale, may create internal conflict, lacks sustainability, and has a nonbinding character. The experience of Indonesia lends the lesson that for CCM to be robust crisis governance, and there needs to be a clear arrangement to boost its scale, manage internal conflict, improve sustainability, and induce a more permanent and binding framework.",book:{id:"11439",title:"Crisis Management - Principles, Roles and Application",coverURL:"https://cdn.intechopen.com/books/images_new/11439.jpg"},signatures:"Gabriel Lele"},{id:"82858",title:"Corporate Social Responsibility a Case of the Provision of Recreational Facilities",slug:"corporate-social-responsibility-a-case-of-the-provision-of-recreational-facilities",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.105608",abstract:"Corporate social responsibility (CSR) connotes Government agencies and private enterprises services for effective change and in this regards the recreational provision. The inadequate provision of the recreational services thwarted recreation, resulting to unsuitable funding of recreational facilities and unsuccessful synergy between government and the private enterprises embarking on CSR. This paper examines the roles of government and the private enterprises in the services of CSR with the view to enhance their performances in the provision of recreational facilities. The paper applied the qualitative method using atlas ti.8 for the data analysis. The findings reveal inadequate facilities provision for recreation resulting from lack of funding, lacklustre attitude and poor synergy of the stakeholders. The paper recommends that government should be positive in implementing policies that promote recreational activities and improving the efforts of the private enterprises for CSR. With the effectiveness and efficiency of the provision of recreation facilities, CSR will be acknowledged as a case of Greater Jos. Plateau State, Nigeria.",book:{id:"11602",title:"Corporate Social Responsibility",coverURL:"https://cdn.intechopen.com/books/images_new/11602.jpg"},signatures:"Peter Musa Wash, Shida Irwana Omar, Badaruddin Mohamed and Mohd Ismail Isa"},{id:"82786",title:"Discussion of Purchasing Virtual Digital Nature and Tourism",slug:"discussion-of-purchasing-virtual-digital-nature-and-tourism",totalDownloads:6,totalDimensionsCites:0,doi:"10.5772/intechopen.105869",abstract:"This chapter discusses the potential and prospects of consumers purchasing virtual digital nature and smart tourism. During the lockdown period, people experienced a trend toward increased subjective well-being as a result of their familiarity with the digital nature. In order to academically validate these experiences, this study examines how interaction with nature in the digital environment stimulates new consumer behavior in post-pandemic life. The study will apply structural equation modeling (SEM) to 300 data collected through a questionnaire to develop the discussion, with a particular focus on the mediating effects of digital forest bathing. The results show that digital forest bath ing has a mediating effect in stimulating people’s environmentally oriented behavior, and that the more active they are in digital space and interact with others, the more consumers enjoy interacting with nature in cyberspace and, in turn, the more willing they are to commune with digital nature through smart tourism. This can be expected to provide an effective reference for marketing strategies that contribute to the promotion of smart tourism in the age of symbiosis with COVID.",book:{id:"11581",title:"A New Era of Consumer Behavior - Beyond the Pandemic",coverURL:"https://cdn.intechopen.com/books/images_new/11581.jpg"},signatures:"Hiroko Oe and Yasuyuki Yamaoka"},{id:"82777",title:"Sustainability and Social Investment: Community Microhydropower Systems in the Dominican Republic",slug:"sustainability-and-social-investment-community-microhydropower-systems-in-the-dominican-republic",totalDownloads:4,totalDimensionsCites:0,doi:"10.5772/intechopen.105995",abstract:"Sustainability remains an underestimated concept when assessing the impact of philanthropic and social investments in communities due to the difficult task of conciliating human development, economy, and environmental protection. Currently, financial cost-effectiveness is one of the main criteria for decision-making. However, under a social investing and climate justice framework, monetary valuation of impacts is never enough to assess the complexity of livelihoods. A multi-stakeholder approach, based on common objectives and synergy among entities, is key for sustainability and social investments. Public institutions, private sector, international cooperation, and local civil society organizations work together in the development of initiatives that promote integral development. In the Dominican Republic and Haiti, community microhydropower systems have proved to be an effective model of social investment, climate justice, and sustainability. The response to a social need, such as access to electricity, has turned into a means for promoting a different approach, based on community empowerment. This article contains the experience of the successes and challenges of more than 50 community microhydropower systems, managed by local groups, which are working and demonstrating the meaning of sustainability and the positive nonmonetary impacts of social investing, opening future opportunities to expand the present 5% of private investment.",book:{id:"11476",title:"Globalization and Sustainability - Recent Advances, New Perspectives and Emerging Issues",coverURL:"https://cdn.intechopen.com/books/images_new/11476.jpg"},signatures:"Michela Izzo, Alberto Sánchez and Rafael Fonseca"},{id:"81403",title:"Do the Collaboration Dimensions Pay in Manufacturing Reverse Supply Chain? An Empirical Approach",slug:"do-the-collaboration-dimensions-pay-in-manufacturing-reverse-supply-chain-an-empirical-approach",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.103068",abstract:"The purpose of this paper is to examine empirically the enablers and practices of collaboration in relation to reverse supply chain. The research method used in this research was a quantitative method using a survey approach to empirically test if the following collaboration enables and practices are applicable. The statistical approach was AMOS 26. The findings revealed that, the relationship building and management for implementing collaboration was ranked highest, resource investment and development in reverse supply chain was ranked the next. Furthermore, quick response on returned goods and information sharing with suppliers on the returned products were highest ranked. The research was limited because the study was based in the Gauteng region, which means that a generalised statement cannot be made of the finding, as well there is a need for the study to be industry specific such as electronics, online retailers. The practical implications of the findings are that the enablers and practices are needed for reverse supply practices to achieve its aims. There is lack of research in the reverse collaboration space, this has paper has fulfilled the following gap.",book:{id:"11253",title:"Sustainable Rural Development",coverURL:"https://cdn.intechopen.com/books/images_new/11253.jpg"},signatures:"Ifije Ohiomah, Clinton Aigbavboa and Nita Sukdeo"}],onlineFirstChaptersTotal:72},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This book series will focus on various aspects and properties of infectious diseases whose deep understanding is essential for safeguarding the human race from losing resources and economies due to pathogens.",coverUrl:"https://cdn.intechopen.com/series/covers/6.jpg",latestPublicationDate:"August 12th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:13,editor:{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"3",title:"Bacterial Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",slug:"tomas-jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",biography:"Tomasz Jarzembowski was born in 1968 in Gdansk, Poland. He obtained his Ph.D. degree in 2000 from the Medical University of Gdańsk (UG). After specialization in clinical microbiology in 2003, he started studying biofilm formation and antibiotic resistance at the single-cell level. In 2015, he obtained his D.Sc. degree. His later study in cooperation with experts in nephrology and immunology resulted in the designation of the new diagnostic method of UTI, patented in 2017. He is currently working at the Department of Microbiology, Medical University of Gdańsk (GUMed), Poland. Since many years, he is a member of steering committee of Gdańsk branch of Polish Society of Microbiologists, a member of ESCMID. He is also a reviewer and a member of editorial boards of a number of international journals.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",slug:"katarzyna-garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",biography:"Katarzyna Maria Garbacz, MD, is an Associate Professor at the Medical University of Gdańsk, Poland and she is head of the Department of Oral Microbiology of the Medical University of Gdańsk. She has published more than 50 scientific publications in peer-reviewed journals. She has been a project leader funded by the National Science Centre of Poland. Prof. Garbacz is a microbiologist working on applied and fundamental questions in microbial epidemiology and pathogenesis. Her research interest is in antibiotic resistance, host-pathogen interaction, and therapeutics development for staphylococcal pathogens, mainly Staphylococcus aureus, which causes hospital-acquired infections. Currently, her research is mostly focused on the study of oral pathogens, particularly Staphylococcus spp.",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null,editorialBoard:[{id:"190041",title:"Dr.",name:"Jose",middleName:null,surname:"Gutierrez Fernandez",slug:"jose-gutierrez-fernandez",fullName:"Jose Gutierrez Fernandez",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"156556",title:"Prof.",name:"Maria Teresa",middleName:null,surname:"Mascellino",slug:"maria-teresa-mascellino",fullName:"Maria Teresa Mascellino",profilePictureURL:"https://mts.intechopen.com/storage/users/156556/images/system/156556.jpg",institutionString:"Sapienza University",institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"164933",title:"Prof.",name:"Mónica Alexandra",middleName:null,surname:"Sousa Oleastro",slug:"monica-alexandra-sousa-oleastro",fullName:"Mónica Alexandra Sousa Oleastro",profilePictureURL:"https://mts.intechopen.com/storage/users/164933/images/system/164933.jpeg",institutionString:"National Institute of Health Dr Ricardo Jorge",institution:{name:"National Institute of Health Dr. Ricardo Jorge",institutionURL:null,country:{name:"Portugal"}}}]},{id:"4",title:"Fungal Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",slug:"yuping-ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",biography:"Dr. Yuping Ran, Professor, Department of Dermatology, West China Hospital, Sichuan University, Chengdu, China. Completed the Course Medical Mycology, the Centraalbureau voor Schimmelcultures (CBS), Fungal Biodiversity Centre, Netherlands (2006). International Union of Microbiological Societies (IUMS) Fellow, and International Emerging Infectious Diseases (IEID) Fellow, Centers for Diseases Control and Prevention (CDC), Atlanta, USA. Diploma of Dermatological Scientist, Japanese Society for Investigative Dermatology. Ph.D. of Juntendo University, Japan. Bachelor’s and Master’s degree, Medicine, West China University of Medical Sciences. Chair of Sichuan Medical Association Dermatology Committee. General Secretary of The 19th Annual Meeting of Chinese Society of Dermatology and the Asia Pacific Society for Medical Mycology (2013). In charge of the Annual Medical Mycology Course over 20-years authorized by National Continue Medical Education Committee of China. Member of the board of directors of the Asia-Pacific Society for Medical Mycology (APSMM). Associate editor of Mycopathologia. Vice-chief of the editorial board of Chinses Journal of Mycology, China. Board Member and Chair of Mycology Group of Chinese Society of Dermatology.",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"302145",title:"Dr.",name:"Felix",middleName:null,surname:"Bongomin",slug:"felix-bongomin",fullName:"Felix Bongomin",profilePictureURL:"https://mts.intechopen.com/storage/users/302145/images/system/302145.jpg",institutionString:null,institution:{name:"Gulu University",institutionURL:null,country:{name:"Uganda"}}},{id:"45803",title:"Ph.D.",name:"Payam",middleName:null,surname:"Behzadi",slug:"payam-behzadi",fullName:"Payam Behzadi",profilePictureURL:"https://mts.intechopen.com/storage/users/45803/images/system/45803.jpg",institutionString:"Islamic Azad University, Tehran",institution:{name:"Islamic Azad University, Tehran",institutionURL:null,country:{name:"Iran"}}}]},{id:"5",title:"Parasitic Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188881",title:"Dr.",name:"Fernando José",middleName:null,surname:"Andrade-Narváez",slug:"fernando-jose-andrade-narvaez",fullName:"Fernando José Andrade-Narváez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRIV7QAO/Profile_Picture_1628834308121",institutionString:null,institution:{name:"Autonomous University of Yucatán",institutionURL:null,country:{name:"Mexico"}}},{id:"269120",title:"Dr.",name:"Rajeev",middleName:"K.",surname:"Tyagi",slug:"rajeev-tyagi",fullName:"Rajeev Tyagi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRaBqQAK/Profile_Picture_1644331884726",institutionString:"CSIR - Institute of Microbial Technology, India",institution:null},{id:"336849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Izurieta",slug:"ricardo-izurieta",fullName:"Ricardo Izurieta",profilePictureURL:"https://mts.intechopen.com/storage/users/293169/images/system/293169.png",institutionString:null,institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}}]},{id:"6",title:"Viral Infectious Diseases",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. 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After completing his residency in anaesthesiology at AHEPA University Hospital, he worked as a consultant anaesthesiologist in the District General Hospital of Veria, Greece. Later, he completed his fellowship in intensive care at “G. Papageorgiou” General Hospital, Thessaloniki, Greece. Since 2017 he has been working as a consultant at AHEPA University Hospital. He also teaches medical students at the School of Medicine, Aristotle University of Thessaloniki, and students in the Postgraduate Nursing Specialties Program, University General Hospital AHEPA, and the Committee for the European Education in Anesthesiology (CEEA) teaching programs.",institutionString:"AHEPA University Hospital",institution:{name:"AHEPA University Hospital",country:{name:"Greece"}}},{id:"181267",title:"Dr.",name:"Jie",middleName:null,surname:"Tang",slug:"jie-tang",fullName:"Jie Tang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/181267/images/system/181267.png",biography:"Jie Tang, MD, MPH, is an academic nephrologist and associate professor of Medicine at Albert Medical School, Brown University, USA. His research interest is in glomerular disorders and bone mineral metabolism. Dr. Tang has served on journal editorial boards and published many articles in peer-reviewed journals. He is also a well-regarded clinician-educator, mentoring medical students, residents, and nephrology fellows. He gives lectures every year on national and international stages and has authored book chapters on various topics. He is a fellow of the American Society of Nephrology and an active member of the International Society of Nephrology. Dr. Tang is currently serving on the medical advisory boards for the National Kidney Foundation and End-Stage Renal Disease Network.",institutionString:"Brown University",institution:{name:"Brown University",country:{name:"United States of America"}}},{id:"200252",title:"Dr.",name:"Theodoros",middleName:null,surname:"Aslanidis",slug:"theodoros-aslanidis",fullName:"Theodoros Aslanidis",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/200252/images/system/200252.png",biography:"Dr. Theodoros K. Aslanidis received an MD from Plovdiv Medical University, Bulgaria, and a Ph.D. from Aristotle University of Thessaloniki, Greece. After serving as a medical doctor in the Hellenic Army Force and as a rural physician at Outhealth Centre, Iraklia and Serres’ General Hospital, Greece, he completed anesthesiology specialty training at Hippokratio General Hospital of Thessaloniki. He also completed Critical Care subspecialty training at AHEPA University Hospital, and the Prehospital Emergency Medicine postgraduate program, Hellenic National Centre for Emergency Care. He served as an EMS physician and emergency communication center medic before moving to his current post as consultant-researcher at the Intensive Care Unit, St. Paul General Hospital of Thessaloniki, Greece. He also serves as a senior lecturer in the Research Faculty, College of Offshore and Remote Medicine, Pretty Bay, Malta.",institutionString:"Saint Paul General Hospital of Thessaloniki",institution:null},{id:"313921",title:"Dr.",name:"Hassan M.",middleName:null,surname:"Heshmati",slug:"hassan-m.-heshmati",fullName:"Hassan M. Heshmati",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313921/images/system/313921.jpg",biography:"Dr. Hassan Massoud Heshmati is an endocrinologist with 46 years of experience in clinical research in academia (university-affiliated hospitals, Paris, France; Mayo Foundation, Rochester, MN, USA) and pharmaceutical companies (Sanofi, Malvern, PA, USA; Essentialis, Carlsbad, CA, USA; Gelesis, Boston, MA, USA). His research activity focuses on pituitary tumors, hyperthyroidism, thyroid cancers, osteoporosis, diabetes, and obesity. He has extensive knowledge in the development of anti-obesity products. Dr. Heshmati is the author of 299 abstracts, chapters, and articles related to endocrinology and metabolism. He is currently a consultant at Endocrinology Metabolism Consulting, LLC, Anthem, AZ, USA.",institutionString:"Endocrinology Metabolism Consulting, LLC",institution:null},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"76477",title:"Prof.",name:"Mirza",middleName:null,surname:"Hasanuzzaman",slug:"mirza-hasanuzzaman",fullName:"Mirza Hasanuzzaman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/76477/images/system/76477.png",biography:"Dr. Mirza Hasanuzzaman is a Professor of Agronomy at Sher-e-Bangla Agricultural University, Bangladesh. He received his Ph.D. in Plant Stress Physiology and Antioxidant Metabolism from Ehime University, Japan, with a scholarship from the Japanese Government (MEXT). Later, he completed his postdoctoral research at the Center of Molecular Biosciences, University of the Ryukyus, Japan, as a recipient of the Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship. He was also the recipient of the Australian Government Endeavour Research Fellowship for postdoctoral research as an adjunct senior researcher at the University of Tasmania, Australia. Dr. Hasanuzzaman’s current work is focused on the physiological and molecular mechanisms of environmental stress tolerance. Dr. Hasanuzzaman has published more than 150 articles in peer-reviewed journals. He has edited ten books and written more than forty book chapters on important aspects of plant physiology, plant stress tolerance, and crop production. According to Scopus, Dr. Hasanuzzaman’s publications have received more than 10,500 citations with an h-index of 53. He has been named a Highly Cited Researcher by Clarivate. He is an editor and reviewer for more than fifty peer-reviewed international journals and was a recipient of the “Publons Peer Review Award” in 2017, 2018, and 2019. He has been honored by different authorities for his outstanding performance in various fields like research and education, and he has received the World Academy of Science Young Scientist Award (2014) and the University Grants Commission (UGC) Award 2018. He is a fellow of the Bangladesh Academy of Sciences (BAS) and the Royal Society of Biology.",institutionString:"Sher-e-Bangla Agricultural University",institution:{name:"Sher-e-Bangla Agricultural University",country:{name:"Bangladesh"}}},{id:"213308",title:"Associate Prof.",name:"Manuel Víctor",middleName:null,surname:"López-González",slug:"manuel-victor-lopez-gonzalez",fullName:"Manuel Víctor López-González",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/213308/images/10301_n.jpg",biography:null,institutionString:null,institution:{name:"University of Malaga",country:{name:"Spain"}}},{id:"169212",title:"Prof.",name:"Pavol",middleName:null,surname:"Svorc",slug:"pavol-svorc",fullName:"Pavol Svorc",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169212/images/system/169212.jpg",biography:"Dr. Pavol Švorc is an Associate Professor, Doctor of the Natural Sciences, Philosophe Doctor. In 1982 he became a Doctor of the Natural Sciences from General Biology, Natural Faculty, Šafarik’s University in Košice. In 1995 he received a PhD. – Physiology and Patophysiology, Natural Faculty Šafarik’s University in Košice. In 2005 he became an Associate Professor from Normal and Patological Physiology, Medical Faculty, Šafarik’s University in Košice. From 1982 to 1983 Dr.Švorc worked as an independent specialist in the local museum in Poprad, Slovakia. In 1983 he started working as a lecturer at the Department of Physiology, Šafarik’s University in Kosice, Slovakia. From\r\n2011 until 2014 he was a Head of the Institute of Physiology and Pathophysiology, Medical Faculty, University of Ostrava, Czech Republic. His research interest includes:\r\nChronobiology of cardiovascular system, respiratory system and autonomic nervous system.",institutionString:"Pavol Josef Safarik University",institution:{name:"University of Pavol Jozef Šafárik",country:{name:"Slovakia"}}},{id:"187859",title:"Prof.",name:"Kusal",middleName:"K.",surname:"Das",slug:"kusal-das",fullName:"Kusal Das",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSBDeQAO/Profile_Picture_1623411145568",biography:"Kusal K. Das is a Distinguished Chair Professor of Physiology, Shri B. M. Patil Medical College and Director, Centre for Advanced Medical Research (CAMR), BLDE (Deemed to be University), Vijayapur, Karnataka, India. Dr. Das did his M.S. and Ph.D. in Human Physiology from the University of Calcutta, Kolkata. His area of research is focused on understanding of molecular mechanisms of heavy metal activated low oxygen sensing pathways in vascular pathophysiology. He has invented a new method of estimation of serum vitamin E. His expertise in critical experimental protocols on vascular functions in experimental animals was well documented by his quality of publications. He was a Visiting Professor of Medicine at University of Leeds, United Kingdom (2014-2016) and Tulane University, New Orleans, USA (2017). For his immense contribution in medical research Ministry of Science and Technology, Government of India conferred him 'G.P. Chatterjee Memorial Research Prize-2019” and he is also the recipient of 'Dr.Raja Ramanna State Scientist Award 2015” by Government of Karnataka. He is a Fellow of the Royal Society of Biology (FRSB), London and Honorary Fellow of Karnataka Science and Technology Academy, Department of Science and Technology, Government of Karnataka.",institutionString:"BLDE (Deemed to be University), India",institution:null},{id:"243660",title:"Dr.",name:"Mallanagouda Shivanagouda",middleName:null,surname:"Biradar",slug:"mallanagouda-shivanagouda-biradar",fullName:"Mallanagouda Shivanagouda Biradar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243660/images/system/243660.jpeg",biography:"M. S. Biradar is Vice Chancellor and Professor of Medicine of\nBLDE (Deemed to be University), Vijayapura, Karnataka, India.\nHe obtained his MD with a gold medal in General Medicine and\nhas devoted himself to medical teaching, research, and administrations. He has also immensely contributed to medical research\non vascular medicine, which is reflected by his numerous publications including books and book chapters. Professor Biradar was\nalso Visiting Professor at Tulane University School of Medicine, New Orleans, USA.",institutionString:"BLDE (Deemed to be University)",institution:{name:"BLDE University",country:{name:"India"}}},{id:"289796",title:"Dr.",name:"Swastika",middleName:null,surname:"Das",slug:"swastika-das",fullName:"Swastika Das",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/289796/images/system/289796.jpeg",biography:"Swastika N. Das is Professor of Chemistry at the V. P. Dr. P. G.\nHalakatti College of Engineering and Technology, BLDE (Deemed\nto be University), Vijayapura, Karnataka, India. She obtained an\nMSc, MPhil, and PhD in Chemistry from Sambalpur University,\nOdisha, India. Her areas of research interest are medicinal chemistry, chemical kinetics, and free radical chemistry. She is a member\nof the investigators who invented a new modified method of estimation of serum vitamin E. She has authored numerous publications including book\nchapters and is a mentor of doctoral curriculum at her university.",institutionString:"BLDEA’s V.P.Dr.P.G.Halakatti College of Engineering & Technology",institution:{name:"BLDE University",country:{name:"India"}}},{id:"248459",title:"Dr.",name:"Akikazu",middleName:null,surname:"Takada",slug:"akikazu-takada",fullName:"Akikazu Takada",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248459/images/system/248459.png",biography:"Akikazu Takada was born in Japan, 1935. After graduation from\nKeio University School of Medicine and finishing his post-graduate studies, he worked at Roswell Park Memorial Institute NY,\nUSA. He then took a professorship at Hamamatsu University\nSchool of Medicine. In thrombosis studies, he found the SK\npotentiator that enhances plasminogen activation by streptokinase. He is very much interested in simultaneous measurements\nof fatty acids, amino acids, and tryptophan degradation products. By using fatty\nacid analyses, he indicated that plasma levels of trans-fatty acids of old men were\nfar higher in the US than Japanese men. . He also showed that eicosapentaenoic acid\n(EPA) and docosahexaenoic acid (DHA) levels are higher, and arachidonic acid\nlevels are lower in Japanese than US people. By using simultaneous LC/MS analyses\nof plasma levels of tryptophan metabolites, he recently found that plasma levels of\nserotonin, kynurenine, or 5-HIAA were higher in patients of mono- and bipolar\ndepression, which are significantly different from observations reported before. In\nview of recent reports that plasma tryptophan metabolites are mainly produced by\nmicrobiota. He is now working on the relationships between microbiota and depression or autism.",institutionString:"Hamamatsu University School of Medicine",institution:{name:"Hamamatsu University School of Medicine",country:{name:"Japan"}}},{id:"137240",title:"Prof.",name:"Mohammed",middleName:null,surname:"Khalid",slug:"mohammed-khalid",fullName:"Mohammed Khalid",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/137240/images/system/137240.png",biography:"Mohammed Khalid received his B.S. in Chemistry in July 2000, and his Ph.D. in Physical Chemistry in 2007 from the University of Khartoum, Sudan. In 2009 he joined the Dr. Ron Clarke research group at the School of Chemistry, Faculty of Science, University of Sydney, Australia as a postdoctoral fellow where he worked on the Interaction of ATP with the phosphoenzyme of the Na+, K+-ATPase, and Dual mechanisms of allosteric acceleration of the Na+, K+-ATPase by ATP. He then worked as Assistant Professor at the Department of Chemistry, University of Khartoum, and in 2014 was promoted to Associate Professor ranking. In 2011 he joined the staff of the Chemistry Department at Taif University, Saudi Arabia, where he is currently active as an Assistant Professor. His research interests include:\r\n(1) P-type ATPase Enzyme Kinetics and Mechanisms; (2) Kinetics and Mechanism of Redox Reactions; (3) Autocatalytic reactions; (4) Computational enzyme kinetics; (5) Allosteric acceleration of P-type ATPases by ATP; (6) Exploring of allosteric sites of ATPases and interaction of ATP with ATPases located in the cell membranes.",institutionString:"Taif University",institution:{name:"Taif University",country:{name:"Saudi Arabia"}}},{id:"63810",title:"Prof.",name:"Jorge",middleName:null,surname:"Morales-Montor",slug:"jorge-morales-montor",fullName:"Jorge Morales-Montor",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/63810/images/system/63810.png",biography:"Dr. Jorge Morales-Montor was recognized with the Lola and Igo Flisser PUIS Award for best graduate thesis at the national level in the field of parasitology. He received a fellowship from the Fogarty Foundation to perform postdoctoral research stay at the University of Georgia. He has 153 journal articles to his credit. He has also edited several books and published more than fifty-five book chapters. He is a member of the Mexican Academy of Sciences, Latin American Academy of Sciences, and the National Academy of Medicine. He has received more than thirty-five awards and has supervised numerous bachelor’s, master’s, and Ph.D. students. Dr. Morales-Montor is the past president of the Mexican Society of Parasitology.",institutionString:"National Autonomous University of Mexico",institution:{name:"National Autonomous University of Mexico",country:{name:"Mexico"}}},{id:"217215",title:"Dr.",name:"Palash",middleName:null,surname:"Mandal",slug:"palash-mandal",fullName:"Palash Mandal",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/217215/images/system/217215.jpeg",biography:null,institutionString:"Charusat University",institution:null},{id:"49739",title:"Dr.",name:"Leszek",middleName:null,surname:"Szablewski",slug:"leszek-szablewski",fullName:"Leszek Szablewski",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49739/images/system/49739.jpg",biography:"Leszek Szablewski is a professor of medical sciences. He received his M.S. in the Faculty of Biology from the University of Warsaw and his PhD degree from the Institute of Experimental Biology Polish Academy of Sciences. He habilitated in the Medical University of Warsaw, and he obtained his degree of Professor from the President of Poland. Professor Szablewski is the Head of Chair and Department of General Biology and Parasitology, Medical University of Warsaw. Professor Szablewski has published over 80 peer-reviewed papers in journals such as Journal of Alzheimer’s Disease, Biochim. Biophys. Acta Reviews of Cancer, Biol. Chem., J. Biomed. Sci., and Diabetes/Metabol. Res. Rev, Endocrine. He is the author of two books and four book chapters. He has edited four books, written 15 scripts for students, is the ad hoc reviewer of over 30 peer-reviewed journals, and editorial member of peer-reviewed journals. Prof. Szablewski’s research focuses on cell physiology, genetics, and pathophysiology. He works on the damage caused by lack of glucose homeostasis and changes in the expression and/or function of glucose transporters due to various diseases. He has given lectures, seminars, and exercises for students at the Medical University.",institutionString:"Medical University of Warsaw",institution:{name:"Medical University of Warsaw",country:{name:"Poland"}}},{id:"173123",title:"Dr.",name:"Maitham",middleName:null,surname:"Khajah",slug:"maitham-khajah",fullName:"Maitham Khajah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173123/images/system/173123.jpeg",biography:"Dr. Maitham A. Khajah received his degree in Pharmacy from Faculty of Pharmacy, Kuwait University, in 2003 and obtained his PhD degree in December 2009 from the University of Calgary, Canada (Gastrointestinal Science and Immunology). Since January 2010 he has been assistant professor in Kuwait University, Faculty of Pharmacy, Department of Pharmacology and Therapeutics. His research interest are molecular targets for the treatment of inflammatory bowel disease (IBD) and the mechanisms responsible for immune cell chemotaxis. He cosupervised many students for the MSc Molecular Biology Program, College of Graduate Studies, Kuwait University. Ever since joining Kuwait University in 2010, he got various grants as PI and Co-I. He was awarded the Best Young Researcher Award by Kuwait University, Research Sector, for the Year 2013–2014. He was a member in the organizing committee for three conferences organized by Kuwait University, Faculty of Pharmacy, as cochair and a member in the scientific committee (the 3rd, 4th, and 5th Kuwait International Pharmacy Conference).",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"195136",title:"Dr.",name:"Aya",middleName:null,surname:"Adel",slug:"aya-adel",fullName:"Aya Adel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/195136/images/system/195136.jpg",biography:"Dr. Adel works as an Assistant Lecturer in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. Dr. Adel is especially interested in joint attention and its impairment in autism spectrum disorder",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"94911",title:"Dr.",name:"Boulenouar",middleName:null,surname:"Mesraoua",slug:"boulenouar-mesraoua",fullName:"Boulenouar Mesraoua",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94911/images/system/94911.png",biography:"Dr Boulenouar Mesraoua is the Associate Professor of Clinical Neurology at Weill Cornell Medical College-Qatar and a Consultant Neurologist at Hamad Medical Corporation at the Neuroscience Department; He graduated as a Medical Doctor from the University of Oran, Algeria; he then moved to Belgium, the City of Liege, for a Residency in Internal Medicine and Neurology at Liege University; after getting the Belgian Board of Neurology (with high marks), he went to the National Hospital for Nervous Diseases, Queen Square, London, United Kingdom for a fellowship in Clinical Neurophysiology, under Pr Willison ; Dr Mesraoua had also further training in Epilepsy and Continuous EEG Monitoring for two years (from 2001-2003) in the Neurophysiology department of Zurich University, Switzerland, under late Pr Hans Gregor Wieser ,an internationally known epileptologist expert. \n\nDr B. Mesraoua is the Director of the Neurology Fellowship Program at the Neurology Section and an active member of the newly created Comprehensive Epilepsy Program at Hamad General Hospital, Doha, Qatar; he is also Assistant Director of the Residency Program at the Qatar Medical School. \nDr B. Mesraoua's main interests are Epilepsy, Multiple Sclerosis, and Clinical Neurology; He is the Chairman and the Organizer of the well known Qatar Epilepsy Symposium, he is running yearly for the past 14 years and which is considered a landmark in the Gulf region; He has also started last year , together with other epileptologists from Qatar, the region and elsewhere, a yearly International Epilepsy School Course, which was attended by many neurologists from the Area.\n\nInternationally, Dr Mesraoua is an active and elected member of the Commission on Eastern Mediterranean Region (EMR ) , a regional branch of the International League Against Epilepsy (ILAE), where he represents the Middle East and North Africa(MENA ) and where he holds the position of chief of the Epilepsy Epidemiology Section; Dr Mesraoua is a member of the American Academy of Neurology, the Europeen Academy of Neurology and the American Epilepsy Society.\n\nDr Mesraoua's main objectives are to encourage frequent gathering of the epileptologists/neurologists from the MENA region and the rest of the world, promote Epilepsy Teaching in the MENA Region, and encourage multicenter studies involving neurologists and epileptologists in the MENA region, particularly epilepsy epidemiological studies. \n\nDr. Mesraoua is the recipient of two research Grants, as the Lead Principal Investigator (750.000 USD and 250.000 USD) from the Qatar National Research Fund (QNRF) and the Hamad Hospital Internal Research Grant (IRGC), on the following topics : “Continuous EEG Monitoring in the ICU “ and on “Alpha-lactoalbumin , proof of concept in the treatment of epilepsy” .Dr Mesraoua is a reviewer for the journal \"seizures\" (Europeen Epilepsy Journal ) as well as dove journals ; Dr Mesraoua is the author and co-author of many peer reviewed publications and four book chapters in the field of Epilepsy and Clinical Neurology",institutionString:"Weill Cornell Medical College in Qatar",institution:{name:"Weill Cornell Medical College in Qatar",country:{name:"Qatar"}}},{id:"282429",title:"Prof.",name:"Covanis",middleName:null,surname:"Athanasios",slug:"covanis-athanasios",fullName:"Covanis Athanasios",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/282429/images/system/282429.jpg",biography:null,institutionString:"Neurology-Neurophysiology Department of the Children Hospital Agia Sophia",institution:null},{id:"190980",title:"Prof.",name:"Marwa",middleName:null,surname:"Mahmoud Saleh",slug:"marwa-mahmoud-saleh",fullName:"Marwa Mahmoud Saleh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/190980/images/system/190980.jpg",biography:"Professor Marwa Mahmoud Saleh is a doctor of medicine and currently works in the unit of Phoniatrics, Department of Otolaryngology, Ain Shams University in Cairo, Egypt. She got her doctoral degree in 1991 and her doctoral thesis was accomplished in the University of Iowa, United States. Her publications covered a multitude of topics as videokymography, cochlear implants, stuttering, and dysphagia. She has lectured Egyptian phonology for many years. Her recent research interest is joint attention in autism.",institutionString:"Ain Shams University",institution:{name:"Ain Shams University",country:{name:"Egypt"}}},{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259190/images/system/259190.png",biography:"Dr. Naqvi is a radioanalytical chemist and is working as an associate professor of analytical chemistry in the Department of Chemistry, Government College University, Faisalabad, Pakistan. Advance separation techniques, nuclear analytical techniques and radiopharmaceutical analysis are the main courses that he is teaching to graduate and post-graduate students. In the research area, he is focusing on the development of organic- and biomolecule-based radiopharmaceuticals for diagnosis and therapy of infectious and cancerous diseases. Under the supervision of Dr. Naqvi, three students have completed their Ph.D. degrees and 41 students have completed their MS degrees. He has completed three research projects and is currently working on 2 projects entitled “Radiolabeling of fluoroquinolone derivatives for the diagnosis of deep-seated bacterial infections” and “Radiolabeled minigastrin peptides for diagnosis and therapy of NETs”. He has published about 100 research articles in international reputed journals and 7 book chapters. Pakistan Institute of Nuclear Science & Technology (PINSTECH) Islamabad, Punjab Institute of Nuclear Medicine (PINM), Faisalabad and Institute of Nuclear Medicine and Radiology (INOR) Abbottabad are the main collaborating institutes.",institutionString:"Government College University",institution:{name:"Government College University, Faisalabad",country:{name:"Pakistan"}}},{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.png",biography:"Gyula Mózsik MD, Ph.D., ScD (med), is an emeritus professor of Medicine at the First Department of Medicine, Univesity of Pécs, Hungary. He was head of this department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition, and dietetics. His research fields are biochemical pharmacological examinations in the human gastrointestinal (GI) mucosa, mechanisms of retinoids, drugs, capsaicin-sensitive afferent nerves, and innovative pharmacological, pharmaceutical, and nutritional (dietary) research in humans. He has published about 360 peer-reviewed papers, 197 book chapters, 692 abstracts, 19 monographs, and has edited 37 books. He has given about 1120 regular and review lectures. He has organized thirty-eight national and international congresses and symposia. He is the founder of the International Conference on Ulcer Research (ICUR); International Union of Pharmacology, Gastrointestinal Section (IUPHAR-GI); Brain-Gut Society symposiums, and gastrointestinal cytoprotective symposiums. He received the Andre Robert Award from IUPHAR-GI in 2014. Fifteen of his students have been appointed as full professors in Egypt, Cuba, and Hungary.",institutionString:"University of Pécs",institution:{name:"University of Pecs",country:{name:"Hungary"}}},{id:"277367",title:"M.Sc.",name:"Daniel",middleName:"Martin",surname:"Márquez López",slug:"daniel-marquez-lopez",fullName:"Daniel Márquez López",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/277367/images/7909_n.jpg",biography:"Msc Daniel Martin Márquez López has a bachelor degree in Industrial Chemical Engineering, a Master of science degree in the same área and he is a PhD candidate for the Instituto Politécnico Nacional. His Works are realted to the Green chemistry field, biolubricants, biodiesel, transesterification reactions for biodiesel production and the manipulation of oils for therapeutic purposes.",institutionString:null,institution:{name:"Instituto Politécnico Nacional",country:{name:"Mexico"}}},{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/196544/images/system/196544.jpg",biography:"Angel Catalá studied chemistry at Universidad Nacional de La Plata, Argentina, where he received a Ph.D. in Chemistry (Biological Branch) in 1965. From 1964 to 1974, he worked as an Assistant in Biochemistry at the School of Medicine at the same university. From 1974 to 1976, he was a fellow of the National Institutes of Health (NIH) at the University of Connecticut, Health Center, USA. From 1985 to 2004, he served as a Full Professor of Biochemistry at the Universidad Nacional de La Plata. He is a member of the National Research Council (CONICET), Argentina, and the Argentine Society for Biochemistry and Molecular Biology (SAIB). His laboratory has been interested for many years in the lipid peroxidation of biological membranes from various tissues and different species. Dr. Catalá has directed twelve doctoral theses, published more than 100 papers in peer-reviewed journals, several chapters in books, and edited twelve books. He received awards at the 40th International Conference Biochemistry of Lipids 1999 in Dijon, France. He is the winner of the Bimbo Pan-American Nutrition, Food Science and Technology Award 2006 and 2012, South America, Human Nutrition, Professional Category. In 2006, he won the Bernardo Houssay award in pharmacology, in recognition of his meritorious works of research. Dr. Catalá belongs to the editorial board of several journals including Journal of Lipids; International Review of Biophysical Chemistry; Frontiers in Membrane Physiology and Biophysics; World Journal of Experimental Medicine and Biochemistry Research International; World Journal of Biological Chemistry, Diabetes, and the Pancreas; International Journal of Chronic Diseases & Therapy; and International Journal of Nutrition. He is the co-editor of The Open Biology Journal and associate editor for Oxidative Medicine and Cellular Longevity.",institutionString:"Universidad Nacional de La Plata",institution:{name:"National University of La Plata",country:{name:"Argentina"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",slug:"francisco-javier-martin-romero",fullName:"Francisco Javier Martin-Romero",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",biography:"Francisco Javier Martín-Romero (Javier) is a Professor of Biochemistry and Molecular Biology at the University of Extremadura, Spain. He is also a group leader at the Biomarkers Institute of Molecular Pathology. Javier received his Ph.D. in 1998 in Biochemistry and Biophysics. At the National Cancer Institute (National Institute of Health, Bethesda, MD) he worked as a research associate on the molecular biology of selenium and its role in health and disease. After postdoctoral collaborations with Carlos Gutierrez-Merino (University of Extremadura, Spain) and Dario Alessi (University of Dundee, UK), he established his own laboratory in 2008. The interest of Javier's lab is the study of cell signaling with a special focus on Ca2+ signaling, and how Ca2+ transport modulates the cytoskeleton, migration, differentiation, cell death, etc. He is especially interested in the study of Ca2+ channels, and the role of STIM1 in the initiation of pathological events.",institutionString:null,institution:{name:"University of Extremadura",country:{name:"Spain"}}},{id:"198499",title:"Dr.",name:"Daniel",middleName:null,surname:"Glossman-Mitnik",slug:"daniel-glossman-mitnik",fullName:"Daniel Glossman-Mitnik",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/198499/images/system/198499.jpeg",biography:"Dr. Daniel Glossman-Mitnik is currently a Titular Researcher at the Centro de Investigación en Materiales Avanzados (CIMAV), Chihuahua, Mexico, as well as a National Researcher of Level III at the Consejo Nacional de Ciencia y Tecnología, México. His research interest focuses on computational chemistry and molecular modeling of diverse systems of pharmacological, food, and alternative energy interests by resorting to DFT and Conceptual DFT. He has authored a coauthored more than 270 peer-reviewed papers, 32 book chapters, and 4 edited books. He has delivered speeches at many international and domestic conferences. He serves as a reviewer for more than eighty international journals, books, and research proposals as well as an editor for special issues of renowned scientific journals.",institutionString:null,institution:null},{id:"318757",title:"Associate Prof.",name:"Irina Alexandrovna",middleName:null,surname:"Savvina",slug:"irina-alexandrovna-savvina",fullName:"Irina Alexandrovna Savvina",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/318757/images/18742_n.jpg",biography:null,institutionString:null,institution:null}]}},subseries:{item:{id:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"13633",title:"Prof.",name:"Abdelhamid",middleName:null,surname:"Mellouk",slug:"abdelhamid-mellouk",fullName:"Abdelhamid Mellouk",profilePictureURL:"https://mts.intechopen.com/storage/users/13633/images/1567_n.jpg",institutionString:null,institution:{name:"Paris 12 Val de Marne University",institutionURL:null,country:{name:"France"}}},{id:"109268",title:"Dr.",name:"Ali",middleName:null,surname:"Al-Ataby",slug:"ali-al-ataby",fullName:"Ali Al-Ataby",profilePictureURL:"https://mts.intechopen.com/storage/users/109268/images/7410_n.jpg",institutionString:null,institution:{name:"University of Liverpool",institutionURL:null,country:{name:"United Kingdom"}}},{id:"3807",title:"Dr.",name:"Carmelo",middleName:"Jose Albanez",surname:"Bastos-Filho",slug:"carmelo-bastos-filho",fullName:"Carmelo Bastos-Filho",profilePictureURL:"https://mts.intechopen.com/storage/users/3807/images/624_n.jpg",institutionString:null,institution:{name:"Universidade de Pernambuco",institutionURL:null,country:{name:"Brazil"}}},{id:"38850",title:"Dr.",name:"Efren",middleName:null,surname:"Gorrostieta Hurtado",slug:"efren-gorrostieta-hurtado",fullName:"Efren Gorrostieta Hurtado",profilePictureURL:"https://mts.intechopen.com/storage/users/38850/images/system/38850.jpg",institutionString:null,institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},{id:"239041",title:"Dr.",name:"Yang",middleName:null,surname:"Yi",slug:"yang-yi",fullName:"Yang Yi",profilePictureURL:"https://mts.intechopen.com/storage/users/239041/images/system/239041.jpeg",institutionString:null,institution:{name:"Virginia Tech",institutionURL:null,country:{name:"United States of America"}}}]},onlineFirstChapters:{paginationCount:18,paginationItems:[{id:"82875",title:"Lipidomics as a Tool in the Diagnosis and Clinical Therapy",doi:"10.5772/intechopen.105857",signatures:"María Elizbeth Alvarez Sánchez, Erick Nolasco Ontiveros, Rodrigo Arreola, Adriana Montserrat Espinosa González, Ana María García Bores, Roberto Eduardo López Urrutia, Ignacio Peñalosa Castro, María del Socorro Sánchez Correa and Edgar Antonio Estrella Parra",slug:"lipidomics-as-a-tool-in-the-diagnosis-and-clinical-therapy",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - 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