MATLAB codes and specification of cods for Example 1*.
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They are capable of implementing massively parallel computations for mapping, function approximation, classification, and pattern recognition processing that require less formal statistical training. Moreover, ANNs have the ability to identify very high complex nonlinear relationships between outcome (dependent) and predictor (independent) variables using multiple training algorithms [1, 2]. Generally speaking, in terms of network architectures, ANNs tend be classified into two different classes, feedforward and recurrent ANNs, each may have several subclasses.
Feedforward is a widely used ANN paradigm for, classification, function approximation, mapping and pattern recognition. Each layer is fully connected to neurons in another layer with no connection between neurons in the same layer. As the name suggests, information is fed in a forward direction from the input to the output layer through one or more hidden layers. MLP feed forward with one hidden layer can virtually predict any linear or non-linear model to any degree of accuracy, assuming that you have a appropriate number of neurons in hidden layer and an appropriate amount of data. Adding more neurons in the hidden layers to an ANN architecture gives the model the flexibility of fitting extremely complex nonlinear functions. This also holds true in classification modeling in approximating any nonlinear decision boundary with great accuracy [2].
Recurrent neural networks (RNNs) emerged as an operative and scalable ANN model for several learning problems associated with sequential data. Information in these networks incorporate loops into the hidden layer. These loops allow information to flow multi-directionally so that the hidden state signifies past information held at a given time step. Thus, these network types have an infinite dynamic response to sequential data. Many applications, such as Apple’s Siri and Google’s voice search, use RNN.
The most popular way to train a neural network is by backpropagation (BP). This method can be used with either feedforward or recurrent networks. BP involves working backward through each timestep to calculate prediction errors and estimate a gradient, which in turn is used to update the weights in the network. For example, to enable the long sequences found in RNNs, multiple timesteps are conducted that unrolls the network, adds new layers, and recalculates the prediction error, resulting in a very deep network.
However, standard and deep RNN neural networks may suffer from vanishing or exploding gradients problems. As more layers in RNN containing activation functions are added, the gradient of the loss function approaches zero. As more layers of activating functions are added, the gradient loss function may approach zero (vanish), leaving the functions unchanged. This stops further training and ends the procedure prematurely. As such, parameters capture only short-term dependencies, while information from earlier time steps may be forgotten. Thus, the model converges on a poor solution. Because error gradients can be unstable, the reverse issue, exploding gradients may occur. This causes errors to grow drastically within each time step (MATLAB, 2020b). Therefore, backpropagation may be limited by the number of timesteps.
Long Short-Term Memory (LSTM) networks address the issue of vanishing/exploding gradients and was first introduced by [3]. In addition to the hidden state in RNN, an LSTM block includes memory cells (that store previous information) and introduces a series of gates, called input, output, and forget gates. These gates allow for additional adjustments (to account for nonlinearity) and prevents errors from vanishing or exploding. The result is a more accurate predicted outcome, the solution does not stop prematurely, nor is previous information lost [4].
Practical applications of LSTM have been published in medical journals. For example, studies [5, 6, 7, 8, 9, 10, 11, 12, 13, 14] used different variants of RNN for classification and prediction purposes from medical records. Important, LSTM does not have any assumption about elapsed time measures can be utilized to subtype patients or diseases. In one such study, LSTM was used to make risk predictions of disease progression for patients with Parkinson’s by leveraging longitudinal medical records with irregular time intervals [15].
The purpose of this chapter is to introduce Long Short-Term Memory (LSTM) networks. This chapter begins with introduction of multilayer feedforward architectures. The core characteristic of an RNN and vanishing of the gradient will be explained briefly. Next, the LSTM neural network, optimization of network parameters, and the methodology for avoiding the vanishing gradient problem will be covered, respectively. The chapter ends with a MATLAB example for LSTM.
Artificial Neural Networks (ANNs) are powerful computing techniques that mimic functions of the human brain to solve complex problems arising from big and messy data. As a machine learning method, ANNs can act as universal approximators of complex functions capable of capturing nonlinear relationships between inputs and outcomes. They adaptively learn functional structures by simultaneously utilizing a series of nonlinear and linear activation functions. ANNs offer several advantages. They require less formal statistical training, have the ability to detect all possible interactions between input variables, and include training algorithms adapted from backpropagation algorithms to improve the predictive ability of the model [2].
Feed forward multilayer perceptron (Figure 1) is the most used in ANN architectures. Uses include function approximation, classification, and pattern recognition. Similar to information processing within the human brain, connections are formed from successive layers. They are fully connected because neurons in each layer are connected to the neurons from the previous and the subsequent layer through adaptable synaptic network parameters. The first layer of multi-layer ANN is called the input layer (or left-most layer) that accepts the training data from sources external to the network. The last layer (or rightmost layer) is called the output layer that contains output units of the network. Depending on prediction or classification, the number of neurons in the output layer may consist of one or more neurons. The layer(s) between input and output layers are called hidden layer(s). Depending on the architecture multiple hidden layers can be placed between input and output layers.
(adapted from Okut, 2016). Artificial neural network design with 4 inputs (
Training occurs at the neuronal level in the hidden and output layers by updating synaptic strengths, eliminating some, and building new synapses. The central idea is to distribute the error function across the hidden layers, corresponding to their effect on the output. Figure 1 demonstrates the architecture of a simple feedforward MLP where
Because the two stages, outlined in Figure 1 above, are used in all neural networks, we can extend MLP feedforward neural networks for use in sequential data or time series data. To do so, we must address time sequences. Recurrent neural networks (RNNs) address this issue by conducting multiple time steps that unrolls the network, adds new layers, and recalculates the prediction error, resulting in a very deep network. First, the connections between nodes in the hidden layer(s) form a directed graph along a temporal sequence allowing information to persist. Through such a mechanism, the concept of time creates the RNNs memory. Here, the architecture receives information from multiple previous layers of the network.
Figure 2 outlines the hidden layer for RNN and demonstrates the nonlinear function of the previous layers and the current input (
A typical RNN that has a hyperbolic tangent activation function
An unrolled RNN with a hidden state carries pertinent information from one input item in the series to others. The blue and red arrows in the figure are indicating the forward and the backward pass of the network, respectively. With backward pass, we sum up the contributions of each time step to the gradient. In other words, because W is used in every step up to the output, we need to backpropagate gradients from
Similar to feedforward MLP networks, RNNs have two stages, a forward and a backward stage. Each works together during the training of the network. However, structures and calculation patterns differ. Let us first consider the forward pass.
The forward pass will be summarized into 5 steps:
Summation step. In this step two different source of information are combined before nonlinear activation function will be take place. The sources are the values of weighted input
The weight of the previous hidden state and current input are placed in a trainable weight matrix. Element-wise multiplication of the previous hidden state vector with the hidden state weights (
Hyperbolic tangent activation function is applied to the summed of the two parameterized vectors
The network input to the output unit at time
The output of the network at time t is calculated (the activation function applied to the output layer depends on the type of target (dependent) variable and the values coming from the hidden units. Again, a second activation function (mostly sigmoid) is applied to the value generated by the hidden node. The predicted value of a RNN block with sigmoid:
During the training of the forward pass of the RNN, the network outputs predicted value (
Then the error (loss function, cost function) at each time step is calculated to start the “
Here
After the forward pass of RNN, the calculated error (loss function, cost function) at each time step is injected backwards into the network to update the network weights at each iteration. The idea of RNN unfolding in Figure 3 takes place the bigger part in the way RNNs are implemented for the backward pass. Like standard backpropagation in feed forward MLP, the backward pass consists of a repeated application of the chain rule. For this reason, the type of backpropagation algorithm used for an RNN to update the network parameters is called backpropagation through time (BPTT). In BPTT, the RNN network is unfolded in time to construct a feed forward MLP neural network. Then, the generalized delta rule is applied to update the weights
Here,
The total error is calculated by the summation of the error from all time steps as:
The value of gradients
To calculate the error gradient given in Eq. (6):
To calculate the overall error gradient, the chain rule of differentiation given in (7) is used.
Then the network weights can be updated as follow:
Note that, as given in (2), the current state (
The Jacobians of any time
while the Jacobian matrix for hidden state is given by:
Putting the Eqs. (7) and (8) together, we have the following relationship:
In other words, because the network parameters are used in every step up to the output, we need to backpropagate gradients from last time step (
As mentioned before, the output from RNNs is dependent on its previous state or previous N time steps circumstances. Conventional RNN face difficulty in learning and maintaining long-range dependencies. Imagine the unfolding RNN given in Figure 3. Each time step requires a new copy of the network. With large RNNs, thousands, even millions of weights are needed to be updated. In other word,
The long short-term memory networks (LSTMs) are a special type of RNN that can overcome the vanishing gradient problem and can learn long-term dependencies. LSTM introduces a memory unit and a gate mechanism to enable capture of the long dependencies in a sequence. The term “long short-term memory” originates from the following intuition. Simple RNN networks have long-term memory in the form of weights. The weights change gradually during the training of the network, encoding general knowledge about the training data. They also have short-term memory in the form of ephemeral activations, which flows from each node to successive nodes [17, 18].
The neural network architecture for an LSTM block given in Figure 4 demonstrates that the LSTM network extends RNN’s memory and can selectively remember or forget information by structures called cell states and three gates. Thus, in addition to a hidden state in RNN, an LSTM block typically has four more layers. These layers are called the cell state (
Illustration of long short-term memory block structure. The operator “
A block diagram of LSTM at any timestamp is depicted in Figure 4. This block is a recurrently connected subnet that contains the same cell state and three gates structure. The
A sigmoid activation function
As shown in the upper part of Figures 4 and 5, the cell state is the key to LSTMs and represents the memory of LSTM networks. The process for the cell state is very much like to a conveyor belt or production chain. The information about the parameters runs straight forward the entire chain, with only some linear interactions, such as multiplication and addition. The state of information depends on these interactions. If there are no interactions, the information will run along without changes. The LSTM block removes or adds information to the cell state through the gates, which allow optional information to cross [19].
The cell state, the horizontal line running through the top of the diagram of an LSTM.
The
The forget gate controls what information to throw away from the memory.
The function takes the old output (
The
The input-update gate decides what new information should be stored in the cell state, which has two parts: A sigmoid layer and a hyperbolic tangent (tanh) layer. The sigmoid layer is called the “input gate layer” because it decides which values should be updated. The tanh layer is a vector of new candidate values
Memory update is done using old memory via the forget gate and new memory via the input gate.
where
Next, a vector of new candidate values,
In the next step, the values of the input state and cell candidate are combined to create and update the cell state as given in (14). The linear combination of the input gate and forget gate are used for updating the previous cell state (
The
The output state decides what information will be output using a sigmoid
First, the previous hidden state (
Like the RNN networks, an LSTM network generates an output
Illustration of the (A) an LSTM unit from 3-time steps with input data (demographic and clinical data). LSTM network takes inputs from to the current time step to update the hidden state and (
Similarly, the value of gradients
and for the overall error gradient using the chain rule of differentiation is:
As Eq. (19) illustrates, the gradient involves the chain rule of
Recall the Eq. (14) for cell state is
Note the term
Several modifications to original LSTM architecture have been recommended over the years. Surprisingly, the original continues to outperform, and has similar predictive ability compared with variants of LSTM over 20 years.
This is a type of LSTM by adding “
This configuration was offered to improve the predictive ability of LSTMs to count and time distances between rare events [21].
Gated recurrent units (GRU) is a simplified version of the standard LSTM designed in a manner to have more persistent memory in order to make it easier for RNNs. A GRU is called gated RNN and introduced by [22]. In a GRU, forget and input gates are merged into a single gate named “update gate”. Moreover, the cell state and hidden state are also merged. Therefore, the GRU has fewer parameters and has been shown to outperform LSTM on some tasks to capture long-term dependencies.
This configuration of LSTM was introduced by Krause et al., [23]. The architecture is for sequence modeling combines LSTM and multiplicative RNN architectures. mLSTM is characterized by its ability to have different recurrent transition functions for each possible input, which makes it more expressive for autoregressive density estimation. Krause et al. concluded that mLSTM outperforms standard LSTM and its deep variants for a range of character-level language modeling tasks.
The core idea behind the LSTM with Attention frees the encoder-decoder architecture from the fixed-length internal illustration. This is one of the most transformative innovations in sequence to uncover the mobility regularities in the hidden node of LSTM. The LSTM with attention was introduced by Wu et al., [24] for Bridging the Gap between Human and Machine Translation in Google’s Neural Machine Translation System. The LSTM with attraction consists of a deep LSTM network with 8 encoder and 8 decoder layers using attention and residual connections. Most likely, this type of LSTM continues to power Google Translate to this day.
Two examples using MATLAB for LSTM will be given for this particular chapter.
Total daily number of positively tested COVID19 and the rate (positively tested/number of test) conducted in the USA. (a) Plot of the training time series of the number of positively tested COVID19 with the forecasted values, (b) compare the forecasted values of the number of positively tested with the test data set. This graph shows the total daily number of virus tests conducted in each state and of those tests, how many were positive each day. (c) Plot of the training time series of the rate of positively tested COVID19 (d) compare the forecasted values of the rate of positively tested with the rates in the test data set. The trend line in blue shows the actual number of positive cases and the trend line in red shows the number predicted for the last 38 days.
Example 1. MATLAB Codes and descriptions of the codes | |
---|---|
a. Data prepretion | Descriptions |
numTimeStepsTrain = floor(0.95*numel(Tpositive)); dataTrain = Tpositive(1:numTimeStepsTrain+1); dataTest = Tpositive(numTimeStepsTrain+1:end); | Partition the training and test data. Train on the first 95% of the sequence and test on the last 5% |
numHiddenUnits = 300; layers = […sequenceInputLayer(numFeatures) lstmLayer(numHiddenUnits) fullyConnectedLayer(numResponses) regressionLayer]; | Define LSTM Network Architecture and create an LSTM regression network. Specify the LSTM layer to have 300 hidden units |
options = ‘MaxEpochs’,1000, … ‘GradientThreshold’,1, … ‘InitialLearnRate’,0.005, … ‘LearnRateSchedule’,\'piecewise’, … ‘LearnRateDropPeriod’,125, … ‘LearnRateDropFactor’,0.2, … ‘Verbose’,0, … ‘Plots’,\'training-progress’); | Training with Adam (adaptive moment estimation) To prevent the gradients from exploding, set the gradient threshold to 1. The software updates the learning rate every certain number of epochs by multiplying with a certain factor. Number of epochs for dropping the learning rate Factor for dropping the learning rate when ‘LearnRateSchedule’,\'piecewise’ |
net = trainNetwork(XTrain,YTrain,layers,options); | Train the LSTM network with the specified training options |
net = predictAndUpdateState(net,XTrain); [net,YPred] = predictAndUpdateState(net,YTrain(end)); numTimeStepsTest = numel(XTest); for i = 2:numTimeStepsTest [net,YPred(:,i)] = predictAndUpdateState(net,YPred(:,i-1),\'ExecutionEnvironment’,\'cpu’); end; | The training and update data Next, make the first prediction using the last time step of the training response YTrain(end). Loop over the remaining predictions and input the previous prediction to predictAndUpdateState. |
net = resetState(net); net = predictAndUpdateState(net,XTrain); | Update Network State with Observed Values |
YPred = []; numTimeStepsTest = numel(XTest); for i = 1:numTimeStepsTest [net,YPred(:,i)] = predictAndUpdateState(net,XTest(:,i),\'ExecutionEnvironment’,\'cpu’); end | Predict on each time step. For each prediction, predict the next time step using the observed value of the previous time step. |
MATLAB codes and specification of cods for Example 1*.
All descriptions are based on MATLAB 2020b and related examples from the MATLAB.
This example trains an LSTM network to forecast the number of positively tested given the number of cases in previous days. The training data contains a single time series, with time steps corresponding to days and values corresponding to the number of cases. To make predictions on a new sequence, reset the network state using the “
filename = “filename” data = readtable(filename,\'TextType’,\'string’); | To import the text data as strings, specify the text type to be \'string’. |
crossval = cvpartition(‘DataName.ClassVaribleName’,0.30); dataTrain = DataName(training(crossval),:); dataValidation = DataName (test(crossval),:); | Partition data into sets for training and validation. Partition the data into a training partition and a held-out partition for validation and testing. Specify the holdout percentage to be 30%. |
textDataTrain = dataTrain.TextVariableName_; textDataValidation = dataValidation. TextVariableName _; YTrain = dataTrain. TextVariableName; YValidation = dataValidation. TextVariableName; | Extract the text data and labels from the partitioned tables. Here TextVariableName is column name for the group variable (for example age group) and TextVariableName is the column name for the text variable (in this example the name of cancer types) |
figure wordcloud(textDataTrain); | To check that you have imported the data correctly, visualize the training text data using a word cloud. |
documentsTrain = preprocessText(textDataTrain); documentsValidation = preprocessText(textDataValidation); | Preprocess the training data and the validation data using the preprocessText function. |
enc = wordEncoding(documentsTrain); sequenceLength = 10; XTrain = doc2sequence(enc,documentsTrain,\'Length’,sequenceLength); XTrain(1:5) XValidation = doc2sequence(enc,documentsValidation,\'Length’,sequenceLength); | Encoding to convert the documents into sequences of numeric indices. |
inputSize = 1; embeddingDimension = 30; numHiddenUnits = 200; numWords = enc.NumWords; numClasses = numel(categories(YTrain)); layers = [… sequenceInputLayer(inputSize) wordEmbeddingLayer(embeddingDimension,numWords) lstmLayer(numHiddenUnits,\'OutputMode’,\'last’) fullyConnectedLayer(numClasses) softmaxLayer classificationLayer] | Initialize the embedding weights |
options = trainingOptions(‘adam’, … ‘MiniBatchSize’,30, … ‘GradientThreshold’,8, … ‘Shuffle’,\'every-epoch’, … ‘ValidationData’,{XValidation,YValidation}, … ‘Plots’,\'training-progress’, … ‘Verbose’,false); | MiniBatchSize: Classifies data using mini batches of size ‘GradientThreshold: Clip gradient values for the threshold |
net = trainNetwork(XTrain,YTrain,layers,options); | Train the LSTM network using the trainNetwork function. |
reportsNew = [… “The text definition here.”]; | Classify the event type of three new reports. Create a string array containing the new reports. |
documentsNew = preprocessText(reportsNew); XNew = doc2sequence(enc,documentsNew,\'Length’,sequenceLength); labelsNew = classify(net,XNew) |
MATLAB codes and specification of cods for Example 2*.
All descriptions are based on MATLAB 2020b and related examples from the MATLAB.
Visualizing the training data text file for SEER-2017 cancer types by age groups using a word cloud of LSTM. The MATLAB codes to create this figure are given in
LSTM is a very powerful ANN architecture for disease subtypes, time series analyses, for the text generation, handwriting recognition, music generation, language translation, image captioning process. The LSTM approach is effective to make predictions as equal attention is provided for all input sequences by the information flows through the cell state. Because of the mechanism adopted, the small change in the input sequence does not harm the prediction accuracy done by LSTM. Future work on LSTM has several directions. Most LSTM architectures are designed to handle data evenly distributed between elapsed times (days, months, years, etc.) for the consecutive elements of a sequence. More studies are needed to improve the predictive ability of LSTM for nonconstant consecutive observations elapsed times. Moreover, further studies are needed for possible overfitting problems for training with smaller data sets. Rather than using early stopping to avoid the overfitting, Bayesian regularized approach would be more effective to ensure that the neural network halts training at the point where further training would result in overfitting. As the Bayesian regularized approach uses a different loss function with different hyperparameters, this approach demands costly computation resources.
The author would like to sincerely thank to Rosey Zackula for reading and revising the manuscript carefully.
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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\n\n\n\nAll chapters will be published via IntechOpen's 'Online First' service meaning chapters will be published individually, immediately after review and before the entire book is ready for publication, allowing content to be shared, searched and cited straightaway, thereby generating early stage interest and momentum for your research
\n\nOnline First Chapters are considered published on the day they are posted and are citable from that date.
\n\nChapters will remain listed as Online First until the final versions of the books are published online. Following publication of the full monograph, Chapters will be redirected from the Online First version and will be available only through the final link of the official published page.
\n\nYou are invited to download, use, reproduce, make derivative works of, display, distribute and cite the Online First works. You can find "How to Cite and Reference" by following the link at the end of each online book chapter. Please be aware that it is possible that further editing and changes might be made before the final release of the book.
\n\nIf there are supplemental materials to the chapter, these will be published at the time the final book is published online.
\n\nReaders and Authors can notify us if they find any errors in the works published under Online First. All major errors will be accompanied by a separate correction notice, erratum or corrigendum (Retraction and Correction Policy.)
\n\nIntechOpen books are available online by accessing all published content on a chapter level.
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Sensing",subtitle:null,isOpenForSubmission:!1,hash:"6f91748e9b1463ce5e7352ea982c3128",slug:"environmental-applications-of-remote-sensing",bookSignature:"Maged Marghany",coverURL:"https://cdn.intechopen.com/books/images_new/5104.jpg",editedByType:"Edited by",editors:[{id:"96666",title:"Prof.",name:"Prof. Dr. Maged",middleName:null,surname:"Marghany",slug:"prof.-dr.-maged-marghany",fullName:"Prof. Dr. Maged Marghany"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"409",title:"Updates in Volcanology",subtitle:"A Comprehensive Approach to Volcanological Problems",isOpenForSubmission:!1,hash:"39ff133e87b1d1f1a07d872ff755762b",slug:"updates-in-volcanology-a-comprehensive-approach-to-volcanological-problems",bookSignature:"Francesco Stoppa",coverURL:"https://cdn.intechopen.com/books/images_new/409.jpg",editedByType:"Edited by",editors:[{id:"57017",title:"Prof.",name:"Francesco",middleName:null,surname:"Stoppa",slug:"francesco-stoppa",fullName:"Francesco Stoppa"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:8,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"25980",doi:"10.5772/25264",title:"Hydrovolcanic vs Magmatic Processes in Forming Maars and Associated Pyroclasts: The Calatrava -Spain- Case History",slug:"hydrovolcanic-vs-magmatic-processes-in-forming-maars-and-associated-pyroclasts-the-calatrava-spain-c",totalDownloads:2879,totalCrossrefCites:8,totalDimensionsCites:25,abstract:null,book:{id:"409",slug:"updates-in-volcanology-a-comprehensive-approach-to-volcanological-problems",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - A Comprehensive Approach to Volcanological Problems"},signatures:"F. Stoppa, G. Rosatelli, M. Schiazza and A. Tranquilli",authors:[{id:"57017",title:"Prof.",name:"Francesco",middleName:null,surname:"Stoppa",slug:"francesco-stoppa",fullName:"Francesco Stoppa"},{id:"62737",title:"Dr.",name:"Gianluigi",middleName:null,surname:"Rosatelli",slug:"gianluigi-rosatelli",fullName:"Gianluigi Rosatelli"},{id:"62738",title:"Mr.",name:"Mariangela",middleName:null,surname:"Schiazza",slug:"mariangela-schiazza",fullName:"Mariangela Schiazza"},{id:"62739",title:"Mr.",name:"Andrea",middleName:null,surname:"Tranquilli",slug:"andrea-tranquilli",fullName:"Andrea Tranquilli"}]},{id:"51948",doi:"10.5772/64129",title:"Fumarolic Minerals: An Overview of Active European Volcanoes",slug:"fumarolic-minerals-an-overview-of-active-european-volcanoes",totalDownloads:2279,totalCrossrefCites:8,totalDimensionsCites:24,abstract:"The fumarolic mineralogy of the Icelandic active volcanoes, the Tyrrhenian volcanic belt (Italy) and the Aegean active arc (Greece) is investigated, and literature data surveyed in order to define the characteristics of the European fumarolic systems. They show broad diversity of mineral associations, with Vesuvius and Vulcano being also among the world localities richest in mineral species. Volcanic systems, which show recession over a longer period, show fumarolic development from the high-temperature alkaline halide/sulphate, calcic sulphate or sulphidic parageneses, synchronous with or immediately following the eruptions, through medium-temperature ammonium minerals, metal chlorides, or fluoride associations to the late low-temperature paragenesis dominated by sulphur, gypsum, alunogen, and other hydrous sulphates. The situation can be different in the systems that are not recessing but show fluctuations in activity, illustrated by the example of Vulcano where the high-temperature association appears intermittently. A full survey of the mineral groups and species is given in respect to their importance and appearance in fumarolic associations.",book:{id:"5311",slug:"updates-in-volcanology-from-volcano-modelling-to-volcano-geology",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - From Volcano Modelling to Volcano Geology"},signatures:"Tonči Balić-Žunić, Anna Garavelli, Sveinn Peter Jakobsson, Kristjan\nJonasson, Athanasios Katerinopoulos, Konstantinos Kyriakopoulos\nand Pasquale Acquafredda",authors:[{id:"183593",title:"Dr.",name:"Tonci",middleName:null,surname:"Balic-Zunic",slug:"tonci-balic-zunic",fullName:"Tonci Balic-Zunic"},{id:"183700",title:"Prof.",name:"Anna",middleName:null,surname:"Garavelli",slug:"anna-garavelli",fullName:"Anna Garavelli"},{id:"183701",title:"Dr.",name:"Sveinn Peter",middleName:null,surname:"Jakobsson",slug:"sveinn-peter-jakobsson",fullName:"Sveinn Peter Jakobsson"},{id:"183702",title:"Prof.",name:"Athanasios",middleName:null,surname:"Katerinopoulos",slug:"athanasios-katerinopoulos",fullName:"Athanasios Katerinopoulos"},{id:"188833",title:"Dr.",name:"Kristjan",middleName:null,surname:"Jonasson",slug:"kristjan-jonasson",fullName:"Kristjan Jonasson"},{id:"188834",title:"Dr.",name:"Konstantinos",middleName:null,surname:"Kyriakopoulos",slug:"konstantinos-kyriakopoulos",fullName:"Konstantinos Kyriakopoulos"},{id:"188835",title:"Dr.",name:"Pasquale",middleName:null,surname:"Acquafredda",slug:"pasquale-acquafredda",fullName:"Pasquale Acquafredda"}]},{id:"51105",doi:"10.5772/63486",title:"How Polygenetic are Monogenetic Volcanoes: Case Studies of Some Complex Maar‐Diatreme Volcanoes",slug:"how-polygenetic-are-monogenetic-volcanoes-case-studies-of-some-complex-maar-diatreme-volcanoes",totalDownloads:1952,totalCrossrefCites:5,totalDimensionsCites:16,abstract:"The increasing number of field investigations and various controlled benchtop and large‐scale experiments have permitted the evaluation of a large number of processes involved in the formation of maar‐diatreme volcanoes, the second most common type of small‐volume subaerial volcanoes on Earth. A maar‐diatreme volcano is recognized by a volcanic crater that is cut into country rocks and surrounded by a low‐height ejecta rim composed of pyroclastic deposits of few meters to up to 200 m thick above the syn‐eruptive surface level. The craters vary from 0.1 km to up to 5 km wide and vary in depth from a few dozen meters to up to 300 m deep. Their irregular morphology reflects the simple or complex volcanic and cratering processes involved in their formation. The simplicity or complexity of the crater or the entire maar itself is usually observed in the stratigraphy of the surrounding ejecta rings. The latter are composed of sequences of successive alternating and contrastingly bedded phreatomagmatic‐derived dilute pyroclastic density currents (PDC) and fallout depositions, with occasional interbedded Strombolian‐derived spatter materials or scoria fall units, exemplifying the changes in the eruptive styles during the formation of the volcano. The entire stratigraphic sequence might be preserved as a single eruptive package (small or very thick) in which there is no stratigraphic gap or significant discordance indicative of a potential break during the eruption. A maar with a single eruptive deposit is quantified as monogenetic maar, meaning that it was formed by a single eruptive vent from which only a small and ephemeral magma erupted over a short period of time. The stratigraphy may also display several packages of deposits separated either by contrasting discordance surfaces or paleosoils, which reflect multiple phases or episodes of eruptions within the same maar. Such maars are characterized as complex polycyclic maars if the length of time between the eruptive events is relatively short (days to years). For greater length of time (thousands to millions of years), the complex maar will be quantified as polygenetic. These common depositional breaks interpreted as signs of temporal interruption of the eruptions for various timescales also indicate deep magma system processes; hence magmas of different types might erupt during the formation of both simple and complex maars. The feeding dikes can interact with groundwater and form closely distributed small craters. The latter can coalesce to form a final crater with various shapes depending on the distance between them. This observation indicates the significant role of the magmatic plumbing system on the formation and growth of complex and polygenetic maar‐diatreme volcanoes.",book:{id:"5311",slug:"updates-in-volcanology-from-volcano-modelling-to-volcano-geology",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - From Volcano Modelling to Volcano Geology"},signatures:"Boris Chako Tchamabé, Gabor Kereszturi, Karoly Németh and\nGerardo Carrasco‐Núñez",authors:[{id:"51162",title:"Dr.",name:"Károly",middleName:null,surname:"Németh",slug:"karoly-nemeth",fullName:"Károly Németh"},{id:"62029",title:"Dr.",name:"Gabor",middleName:null,surname:"Kereszturi",slug:"gabor-kereszturi",fullName:"Gabor Kereszturi"},{id:"182834",title:"Dr.",name:"Boris",middleName:null,surname:"Chako Tchamabé",slug:"boris-chako-tchamabe",fullName:"Boris Chako Tchamabé"},{id:"183809",title:"Dr.",name:"Gerardo",middleName:null,surname:"Carrasco-Núñez",slug:"gerardo-carrasco-nunez",fullName:"Gerardo Carrasco-Núñez"}]},{id:"49656",doi:"10.5772/61974",title:"Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview",slug:"optical-satellite-remote-sensing-of-the-coastal-zone-environment-an-overview",totalDownloads:2496,totalCrossrefCites:7,totalDimensionsCites:15,abstract:"Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment.",book:{id:"5104",slug:"environmental-applications-of-remote-sensing",title:"Environmental Applications of Remote Sensing",fullTitle:"Environmental Applications of Remote Sensing"},signatures:"Ana C. Teodoro",authors:[{id:"18485",title:"Dr.",name:"Ana",middleName:null,surname:"Teodoro",slug:"ana-teodoro",fullName:"Ana Teodoro"}]},{id:"49851",doi:"10.5772/62122",title:"Detection of Tree Crowns in Very High Spatial Resolution Images",slug:"detection-of-tree-crowns-in-very-high-spatial-resolution-images",totalDownloads:3249,totalCrossrefCites:8,totalDimensionsCites:14,abstract:"The requirements for advanced knowledge on forest resources have led researchers to develop efficient methods to provide detailed information about trees. Since 1999, orbital remote sensing has been providing very high resolution (VHR) image data. The new generation of satellite allows individual tree crowns to be visually identifiable. The increase in spatial resolution has also had a profound effect in image processing techniques and has motivated the development of new object-based procedures to extract information. Tree crown detection has become a major area of research in image analysis considering the complex nature of trees in an uncontrolled environment. This chapter is subdivided into two parts. Part I offers an overview of the state of the art in computer detection of individual tree crowns in VHR images. Part II presents a new hybrid approach developed by the authors that integrates geometrical-optical modeling (GOM), marked point processes (MPP), and template matching (TM) to individually detect tree crowns in VHR images. The method is presented for two different applications: isolated tree detection in an urban environment and automatic tree counting in orchards with an average performance rate of 82% for tree detection and above 90% for tree counting in orchards.",book:{id:"5104",slug:"environmental-applications-of-remote-sensing",title:"Environmental Applications of Remote Sensing",fullTitle:"Environmental Applications of Remote Sensing"},signatures:"Marilia Ferreira Gomes and Philippe Maillard",authors:[{id:"177110",title:"Dr.",name:"Philippe",middleName:null,surname:"Maillard",slug:"philippe-maillard",fullName:"Philippe Maillard"},{id:"177172",title:"Ph.D.",name:"Marilia",middleName:"Ferreira",surname:"Gomes",slug:"marilia-gomes",fullName:"Marilia Gomes"}]}],mostDownloadedChaptersLast30Days:[{id:"66703",title:"P-Wave Teleseismic Tomography: Evidence of Imprints of Deccan Mantle Plume below the Kachchh Rift Zone, Gujarat, India",slug:"p-wave-teleseismic-tomography-evidence-of-imprints-of-deccan-mantle-plume-below-the-kachchh-rift-zon",totalDownloads:2717,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"The Indian plate had experienced the Deccan volcanism at 65 Ma when it moved over the Re-union hotspot, which has altered lithospheric structure below the Kachchh rift zone (KRZ). To quantify the influence of Deccan volcanism on the crust-mantle, the present chapter focuses on the delineation of the upper mantle structure below the KRZ, through the modeling of crust corrected P-residuals and P-wave teleseismic tomography. The crust corrected normalized P-residuals suggest dominant negative residuals associated with the central KRZ, indicating crustal and lithospheric thinning below the KRZ. A low velocity down to a depth of 170 km below the central KRZ is detected through the teleseismic tomography using these P-residuals. However, these residuals also show positive values for the surrounding un-rifted zones. Note that a low shear velocity zone extending from 100–120 km to 170–220 km depth beneath the central KRZ has already been revealed by the modeling of P-RFs. This reduction in seismic velocity in the upper mantle could be explained by the presence of trapped carbonatite/partial melts related to the Deccan volcanism. The influx of volatile CO2 emanating from the carbonatite melts in the asthenosphere might be generating lower crustal earthquakes occurring in the KRZ.",book:{id:"7677",slug:"forecasting-volcanic-eruptions",title:"Forecasting Volcanic Eruptions",fullTitle:"Forecasting Volcanic Eruptions"},signatures:"Prantik Mandal",authors:[{id:"279344",title:"Dr.",name:"Prantik",middleName:null,surname:"Mandal",slug:"prantik-mandal",fullName:"Prantik Mandal"}]},{id:"49608",title:"Remote Sensing of Mountain Glaciers and Related Hazards",slug:"remote-sensing-of-mountain-glaciers-and-related-hazards",totalDownloads:2414,totalCrossrefCites:1,totalDimensionsCites:5,abstract:"Mountain glaciers are highly sensitive to temperature and precipitation fluctuations and active geomorphic agents in shaping the landforms of glaciated regions which are direct imprints of past glaciations, providing reliable evidence of the evolution of the past Cryosphere and contain important information on climatic variables. But most importantly, glaciers have aroused a lot of concern in terms of glacier area changes, thickness change, mass balance and their consequences on water resources as well as related hazards. The contribution of glacier mass loss to global sea-level rise and increasing number of glacier-related hazards are the most important and current socioeconomic concerns. Therefore, understanding the dynamics of the changes and constant monitoring of glaciers are essential for studying climate, water resource management and hydropower and also to predict and evade glacier-related hazards. The recent advances in the techniques of earth observations have proved as a boon for investigating glaciers and glacier-related hazards. Remote sensing technology enables extraction of glacier parameters such as albedo/reflectance/scattering, glacier area, glacier zones and facies, equilibrium line, glacier thickness, volume, mass balance, velocity and glacier topography. The present chapter explores the prospective of remote sensing technology for understanding and surveying glaciers formed at high, inaccessible mountains and glacier-induced hazards.",book:{id:"5104",slug:"environmental-applications-of-remote-sensing",title:"Environmental Applications of Remote Sensing",fullTitle:"Environmental Applications of Remote Sensing"},signatures:"Pratima Pandey, Alagappan Ramanathan and Gopalan\nVenkataraman",authors:[{id:"18342",title:"Prof.",name:"Ramanathan",middleName:null,surname:"Alagappan",slug:"ramanathan-alagappan",fullName:"Ramanathan Alagappan"},{id:"177179",title:"Dr.",name:"Pratima",middleName:null,surname:"Pandey",slug:"pratima-pandey",fullName:"Pratima Pandey"},{id:"178231",title:"Prof.",name:"Gopalan",middleName:null,surname:"Venkataraman",slug:"gopalan-venkataraman",fullName:"Gopalan Venkataraman"}]},{id:"60548",title:"Volcanic Glass and its Uses as Adsorbent",slug:"volcanic-glass-and-its-uses-as-adsorbent",totalDownloads:1624,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Volcanic glasses are an amorphous phyllosilicates formed by the fast cooling of the magma. The physicochemical properties of volcanic glasses are directly related to their chemical composition. Thus, the rhyolitic magma, which presents the highest SiO2 percentage, displays a high viscosity, which leads to explosive eruptions by the ex-solution of H2O, CO2, and SO2, when the pressure diminishes generates a macroporous structure with interesting applications in construction, as abrasive, acoustic, filter as well as in the agriculture field. The macroporosity of volcanic glass allows to host large molecules as biomolecules, tensoactives, or dyes. On the other hand, the existence of hydroxyl groups in this amorphous aluminosilicate also favors the adsorption of cations and anions, so the volcanic glass is an economical adsorbent to retain heavy metals or radioactive cations.",book:{id:"6104",slug:"volcanoes-geological-and-geophysical-setting-theoretical-aspects-and-numerical-modeling-applications-to-industry-and-their-impact-on-the-human-health",title:"Volcanoes",fullTitle:"Volcanoes - Geological and Geophysical Setting, Theoretical Aspects and Numerical Modeling, Applications to Industry and Their Impact on the Human Health"},signatures:"Juan Antonio Cecilia, Miguel Armando Autie-Pérez, Juan Manuel\nLabadie-Suarez, Enrique Rodríguez Castellón and Antonia Infantes\nMolina",authors:[{id:"126325",title:"Dr.",name:"Enrique",middleName:null,surname:"Rodríguez-Castellón",slug:"enrique-rodriguez-castellon",fullName:"Enrique Rodríguez-Castellón"}]},{id:"57502",title:"The Characteristics of Volcanic Eruption in Indonesia",slug:"the-characteristics-of-volcanic-eruption-in-indonesia",totalDownloads:1869,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter discusses the unique characteristics of the volcanic eruptions in Indonesia. We know that Indonesia has 147 volcanoes and 76 of them are active volcanoes and spread along the islands of Java, Lesser Sunda, Sumatra, and Celebes. The characteristics of Indonesian volcanoes are quite unique in terms of the formation process, eruption phenomenon, and the resulting natural disasters. Most volcanoes in Indonesia consist of stratovolcanoes, but this does not mean that the resulting eruptions are always explosive and they have a long period. This can be seen from the activity of Semeru that always erupts effusively every day, Sinabung that has a very short eruption period, Tangkuban Perahu eruption that occurs suddenly with the lack of early signs, and Merapi and Kelud that have eruption period that is getting shorter. Based on the results of our study it can be known that the types of volcanic eruption are influenced by the structure of the constituent rocks of the volcanoes. However, the presence of external control factors in the form of large-scale earthquakes will affect their periodicity. The large earthquakes can affect the stability of the magma chamber that can trigger a premature eruption.",book:{id:"6104",slug:"volcanoes-geological-and-geophysical-setting-theoretical-aspects-and-numerical-modeling-applications-to-industry-and-their-impact-on-the-human-health",title:"Volcanoes",fullTitle:"Volcanoes - Geological and Geophysical Setting, Theoretical Aspects and Numerical Modeling, Applications to Industry and Their Impact on the Human Health"},signatures:"Eko Hariyono and Liliasari S",authors:[{id:"214360",title:"Dr.",name:"Eko",middleName:null,surname:"Hariyono",slug:"eko-hariyono",fullName:"Eko Hariyono"},{id:"219699",title:"Prof.",name:"Liliasari",middleName:null,surname:"S",slug:"liliasari-s",fullName:"Liliasari S"}]},{id:"51105",title:"How Polygenetic are Monogenetic Volcanoes: Case Studies of Some Complex Maar‐Diatreme Volcanoes",slug:"how-polygenetic-are-monogenetic-volcanoes-case-studies-of-some-complex-maar-diatreme-volcanoes",totalDownloads:1953,totalCrossrefCites:5,totalDimensionsCites:16,abstract:"The increasing number of field investigations and various controlled benchtop and large‐scale experiments have permitted the evaluation of a large number of processes involved in the formation of maar‐diatreme volcanoes, the second most common type of small‐volume subaerial volcanoes on Earth. A maar‐diatreme volcano is recognized by a volcanic crater that is cut into country rocks and surrounded by a low‐height ejecta rim composed of pyroclastic deposits of few meters to up to 200 m thick above the syn‐eruptive surface level. The craters vary from 0.1 km to up to 5 km wide and vary in depth from a few dozen meters to up to 300 m deep. Their irregular morphology reflects the simple or complex volcanic and cratering processes involved in their formation. The simplicity or complexity of the crater or the entire maar itself is usually observed in the stratigraphy of the surrounding ejecta rings. The latter are composed of sequences of successive alternating and contrastingly bedded phreatomagmatic‐derived dilute pyroclastic density currents (PDC) and fallout depositions, with occasional interbedded Strombolian‐derived spatter materials or scoria fall units, exemplifying the changes in the eruptive styles during the formation of the volcano. The entire stratigraphic sequence might be preserved as a single eruptive package (small or very thick) in which there is no stratigraphic gap or significant discordance indicative of a potential break during the eruption. A maar with a single eruptive deposit is quantified as monogenetic maar, meaning that it was formed by a single eruptive vent from which only a small and ephemeral magma erupted over a short period of time. The stratigraphy may also display several packages of deposits separated either by contrasting discordance surfaces or paleosoils, which reflect multiple phases or episodes of eruptions within the same maar. Such maars are characterized as complex polycyclic maars if the length of time between the eruptive events is relatively short (days to years). For greater length of time (thousands to millions of years), the complex maar will be quantified as polygenetic. These common depositional breaks interpreted as signs of temporal interruption of the eruptions for various timescales also indicate deep magma system processes; hence magmas of different types might erupt during the formation of both simple and complex maars. The feeding dikes can interact with groundwater and form closely distributed small craters. The latter can coalesce to form a final crater with various shapes depending on the distance between them. 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The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. 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\r\n\tThis topic aims to provide a comprehensive overview of the latest trends in Oral Health based on recent scientific evidence. Subjects will include an overview of oral diseases and infections, systemic diseases affecting the oral cavity, prevention, diagnosis, treatment, epidemiology, as well as current clinical recommendations for the management of oral, dental, and periodontal diseases.
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Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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