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This is essential to our rational as well as intelligent decisions. It helps us to match and understand the feelings of others by conveying our feelings and giving feedback to others. Research has revealed the powerful role that emotion play in shaping human social interaction. Emotional displays convey considerable information about the mental state of an individual. This has opened up a new research field called automatic emotion recognition, having basic goals to understand and retrieve desired emotions. In prior studies, several modalities have been explored to recognize the emotional states such as facial expressions [1], speech [2], physiological signals [3], etc. Several inherent advantages make speech signals a good source for affective computing. For example, compared to many other biological signals (e.g., electrocardiogram), speech signals usually can be acquired more readily and economically. This is why the majority of researchers are interested in speech emotion recognition (SER). SER aims to recognize the underlying emotional state of a speaker from her voice. The area has received increasing research interest all through current years. There are many applications of detecting the emotion of the persons like in the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies, entertainment, banking, call centers, cardboard systems, computer games, etc. For classroom orchestration or E-learning, information about the emotional state of students can provide focus on the enhancement of teaching quality. For example, a teacher can use SER to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. That is why learner’s emotional state should be considered in the classroom.
\nThree key issues need to be addressed for successful SER system, namely, (1) choice of a good emotional speech database, (2) extracting effective features, and (3) designing reliable classifiers using machine learning algorithms. In fact, the emotional feature extraction is a main issue in the SER system. Many researchers [4] have proposed important speech features which contain emotion information, such as energy, pitch, formant frequency, Linear Prediction Cepstrum Coefficients (LPCC), Mel-frequency cepstrum coefficients (MFCC), and modulation spectral features (MSFs) [5]. Thus, most researchers prefer to use combining feature set that is composed of many kinds of features containing more emotional information [6]. However, using a combining feature set may give rise to high dimension and redundancy of speech features; thereby, it makes the learning process complicated for most machine learning algorithms and increases the likelihood of overfitting. Therefore, feature selection is indispensable to reduce the dimensions redundancy of features. A review for feature selection models and techniques is presented in [7]. Both feature extraction and feature selection are capable of improving learning performance, lowering computational complexity, building better generalizable models, and decreasing required storage. The last step of speech emotion recognition is classification. It involves classifying the raw data in the form of utterance or frame of the utterance into a particular class of emotion on the basis of features extracted from the data. In recent years in speech emotion recognition, researchers proposed many classification algorithms, such as Gaussian mixture model (GMM) [8], hidden Markov model (HMM) [9], support vector machine (SVM) [10, 11, 12, 13, 14], neural networks (NN) [15], and recurrent neural networks (RNN) [16, 17, 18]. Some other types of classifiers are also proposed by some researchers such as a modified brain emotional learning model (BEL) [19] in which the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) are merged for speech emotion recognition. Another proposed strategy is a multiple kernel Gaussian process (GP) classification [17], in which two similar notions in the learning algorithm are presented by combining the linear kernel and radial basis function (RBF) kernel. The Voiced Segment Selection (VSS) algorithm also proposed in [20] deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification.
\nIn previous work [21], we present a system for the recognition of «seven acted emotional states (anger, disgust, fear, joy, sadness, and surprise)». To do that, we extracted the MFCC and MS features and used them to train three different machine learning paradigms (MLR, SVM, and RNN). We demonstrated that the combination of both features has a high accuracy above 94% on the Spanish database. All previously published works generally use the Berlin database. To our knowledge, the Spanish emotional database has never been used before. For this reason, we have chosen to compare them. In this chapter, we concentrate to improve accuracy; more experiments have been performed. This chapter mainly makes the following contributions:
The effect of speaker normalization (SN) is also studied, which removes the mean of features and normalizes them to unit variance. Experiments are performed under a speaker-independent condition.
Additionally, a feature selection technique is assessed to obtain good features from the set of features extracted in [21].
The rest of the chapter is organized as follows. In the next section, we start by introducing the nature of speech emotions. Section 3 describes features we extracted from a speech signal. A feature selection method and machine learning algorithms used for SER are presented. Section 4 reports on the databases we used and presents the simulation results obtained using different features and different machine learning (ML) paradigms. Section 5 closes this chapter by analyses and conclusion.
\nThis section is concerned with defining the term emotion, presenting its different models. Also for recognizing emotions, there are several techniques and inputs that can be used. A brief description of all of the techniques is presented here.
\nA definition is both important and difficult because the everyday word “emotion” is a notoriously fluid term in meaning. Emotion is one of the most difficult concepts to define in psychology. In fact, there are different definitions of emotions in the scientific literature. In everyday speech, emotion is any relatively brief conscious experience characterized by intense mental activity and a high degree of pleasure or displeasure [22, 23]. Scientific discourse has drifted to other meanings and there is no consensus on a definition. Emotion is often entwined with temperament, mood, personality, motivation, and disposition. In psychology, emotion is frequently defined as a complex state of feeling that results in physical and psychological changes. These changes influence thought and behavior. According to other theories, emotions are not causal forces but simply syndromes of components such as motivation, feeling, behavior, and physiological changes [24]. In 1884, in
The categorization of emotions has long been a hot subject of debate in different fields of psychology, affective science, and emotion research. It is mainly based on two popular approaches: categorical (termed discrete) and dimensional (termed continuous). In the first approach, emotions are described with a discrete number of classes. Many theorists have conducted studies to determine which emotions are basic [27]. A most popular example is Ekman [28] who proposed a list of six basic emotions, which are anger, disgust, fear, happiness, sadness, and surprise. He explains that each emotion acts as a discrete category rather than an individual emotional state. In the second approach, emotions are a combination of several psychological dimensions and identified by axes. Other researchers define emotions according to one or more dimensions. Wilhelm Max Wundt proposed in 1897 that emotions can be described by three dimensions: (1) strain versus relaxation, (2) pleasurable versus unpleasurable, and (3) arousing versus subduing [29]. PAD emotional state model is another three-dimensional approach by Albert Mehrabian and James Russell where PAD stands for pleasure, arousal, and dominance. Another popular dimensional model was proposed by James Russell in 1977. Unlike the earlier three-dimensional models, Russell’s model features only two dimensions which include (1) arousal (or activation) and (2) valence (or evaluation) [29].
\nThe categorical approach is commonly used in SER [30]. It characterizes emotions used in everyday emotion words such as joy and anger. In this work, a set of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral, corresponding to the six emotions of Ekman’s model, were used for the recognition of emotion from speech using the categorical approach.
\nThere is vigorous debate about what exactly individual can express nonverbally. Humans can express their emotions through many different types of nonverbal communication including facial expressions, quality of speech produced, and physiological signals of the human body. In this section, we discuss each of these categories.
\nThe human face is extremely expressive, able to express countless emotions without saying a word [31]. And unlike some forms of nonverbal communication, facial expressions are universal. The facial expressions for happiness, sadness, anger, surprise, fear, and disgust are the same across cultures.
\nIn addition to faces, voices are an important modality for emotional expression. Speech is a relevant communicational channel enriched with emotions: the voice in speech not only conveys a semantic message but also the information about the emotional state of the speaker. Some important voice feature vectors that have been chosen for research such as fundamental frequency, mel-frequency cepstral coefficient (MFCC), prediction cepstral coefficient (LPCC), etc.
\nThe physiological signals related to autonomic nervous system allow to assess objectively emotions. These include electroencephalogram (EEG), heart rate (HR), electrocardiogram (ECG), respiration (RSP), blood pressure (BP), electromyogram (EMG), skin conductance (SC), blood volume pulse (BVP), and skin temperature (ST) [32]. Using physiological signals to recognize emotions is also helpful to those people who suffer from physical or mental illness thus exhibit problems with facial expressions or tone of voice.
\nOur SER system consists of four main steps. First is the voice sample collection. The second features vector that is formed by extracting the features. As the next step, we tried to determine which features are most relevant to differentiate each emotion. These features are introduced to machine learning classifier for recognition. This process is described in Figure 1.
\nBlock diagram of the proposed system.
The speech signal contains a large number of parameters that reflect the emotional characteristics. One of the sticking points in emotion recognition is what features should be used. In recent research, many common features are extracted, such as energy, pitch, formant, and some spectrum features such as linear prediction coefficients (LPC), mel-frequency cepstrum coefficients (MFCC), and modulation spectral features. In this work, we have selected modulation spectral features and MFCC, to extract the emotional features.
\nSchema of MFCC extraction [
In our research, we extract the first 12 order of the MFCC coefficients where the speech signals are sampled at 16 KHz. For each order coefficients, we calculate the mean, variance, standard deviation, kurtosis, and skewness, and this is for the other all the frames of an utterance. Each MFCC feature vector is 60-dimensional.
\nProcess for computing the ST representation [
As reported by Aha and Bankert [34], the objective of feature selection in ML is to “reduce the number of features used to characterize a dataset so as to improve a learning algorithm’s performance on a given task.” The objective will be the maximization of the classification accuracy in a specific task for a certain learning algorithm; as a collateral effect, the number of features to induce the final classification model will be reduced. Feature selection (FS) aims to choose a subset of the relevant features from the original ones according to certain relevance evaluation criterion, which usually leads to higher recognition accuracy [35]. It can drastically reduce the running time of the learning algorithms. In this section, we present an effective feature selection method used in our work, named recursive feature elimination with linear regression (LR-RFE).
\nMany machine learning algorithms have been used for discrete emotion classification. The goal of these algorithms is to learn from the training samples and then use this learning to classify new observation. In fact, there is no definitive answer to the choice of the learning algorithm; every technique has its own advantages and limitations. For this reason, here we chose to compare the performance of three different classifiers.
\nInputs: Class models \n
Output: Class of \n
1. \n
2. \n
3. Distance calculation between original and predicted response variables \n
4. Decision is made in favor of the class with the minimum distance \n
Figure 4 shows a basic concept of RNN implementation. Unlike traditional neural network that uses different parameters at each layer, the RNN shares the same parameters (U, V, and W are presented in Figure 4) across all steps. The hidden state formulas and variables are as follows:
\nA basic concept of RNN and unfolding in time of the computation involved in its forward computation [
where \n
The performance and robustness of the recognition systems will be easily affected if it is not well trained with a suitable database. Therefore, it is essential to have sufficient and suitable phrases in the database to train the emotion recognition system and subsequently evaluate its performance. There are three main types of databases: acted emotions, natural spontaneous emotions, and elicited emotions [27, 44]. In this work, we used an acted emotion databases because they contain strong emotional expressions. The literature on speech emotion recognition [45] shows that the majority of studies have been conducted with emotional acted speech. In this section, we detailed the two emotional speech databases used for classifying discrete emotions in our experiments: Berlin Database and Spanish Database.
\nThe Berlin database [46] is widely used in emotional speech recognition. It contains 535 utterances spoken by 10 actors (5 female, 5 male) in 7 simulated emotions (anger, boredom, disgust, fear, joy, sadness, and neutral). This database was chosen for the following reasons: (i) the quality of its recording is very good, and (ii) it is public [47] and popular database of emotion recognition that is recommended in the literature [19].
\nThe INTER1SP Spanish emotional database contains utterances from two professional actors (one female and one male speaker).The Spanish corpus that we have the right to access (free for academic and research use) [48] was recorded twice in the «six basic emotions plus neutral (anger, sadness, joy, fear, disgust, surprise and neutral/normal)». Four additional neutral variations (soft, loud, slow, and fast) were recorded once. This is preferred to other created database because it is available for researchers use and it contains more data (6041 utterances in total). This paper has focused on only seven main emotions from the Spanish database in order to achieve a higher and more accurate rate of recognition and to make the comparison with the Berlin database detailed above.
\nIn this section, experimentation results are presented and discussed. We report the recognition accuracy of using MLR, SVM, and RNN classifiers. Experimental evaluation is performed on the Berlin and Spanish databases. All classification results are obtained under tenfold cross-validation. Cross-validation is a common practice used in performance analysis that randomly partitions the data into N complementary subsets, with \n
\n | \n | \n | \n | Recognition rate (%) | \n\n | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Test | \nFeature | \nMethod | \nSN | \nA | \nE | \nF | \nL | \nN | \nT | \nW | \nAVG. | \n(\n | \n
#1 | \nMS | \nMLR | \nNo | \n45.90 | \n45.72 | \n48.78 | \n77.08 | \n59.43 | \n79.91 | \n75.94 | \n66.23 | \n(5.85) | \n
MFCC | \n56.55 | \n62.28 | \n45.60 | \n54.97 | \n57.35 | \n74.36 | \n91.37 | \n64.70 | \n(3.20) | \n|||
MFCC+SM | \n70.26 | \n73.04 | \n51.95 | \n82.44 | \n69.55 | \n82.49 | \n76.55 | \n73.00 | \n(3.23) | \n|||
#2 | \nMS | \nSVM | \nNo | \n56.61 | \n54.78 | \n51.17 | \n70.98 | \n67.32 | \n67.50 | \n73.13 | \n70.63 | \n(6.45) | \n
MFCC | \n73.99 | \n64.14 | \n64.76 | \n55.30 | \n62.28 | \n84.13 | \n83.13 | \n71.70 | \n(4.24) | \n|||
MFCC+SM | \n82.03 | \n68.70 | \n69.09 | \n79.16 | \n76.99 | \n80.89 | \n80.63 | \n81.10 | \n(2.73) | \n|||
#3 | \nMS | \nMLR | \nYes | \n48.98 | \n35.54 | \n32.66 | \n80.35 | \n55.54 | \n88.79 | \n85.77 | \n64.20 | \n(5.27) | \n
MFCC | \n59.71 | \n59.72 | \n48.65 | \n67.10 | \n67.98 | \n91.73 | \n87.51 | \n71.00 | \n(4.19) | \n|||
MFCC+SM | \n72.32 | \n68.82 | \n51.98 | \n82.60 | \n81.72 | \n91.96 | \n80.71 | \n75.25 | \n(2.49) | \n|||
#4 | \nMS | \nSVM | \nYes | \n62.72 | \n49.44 | \n37.29 | \n76.14 | \n71.30 | \n88.44 | \n80.15 | \n71.90 | \n(2.38) | \n
MFCC | \n70.68 | \n56.55 | \n56.99 | \n59.88 | \n68.14 | \n91.88 | \n85.44 | \n77.60 | \n(4.35) | \n|||
MFCC+SM | \n77.37 | \n69.67 | \n58.16 | \n79.87 | \n88.57 | \n98.75 | \n86.64 | \n81.00 | \n(2.45) | \n
Recognition results with MS, MFCC features, and their combination on Berlin database; AVG. denotes average recognition rate; \n
Berlin (a, fear; e, disgust; f, happiness; l, boredom; n, neutral; t, sadness; w, anger).
\n | \n | \n | \n | Recognition rate (%) | \n\n | \n | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Test | \nFeature | \nMethod | \nSN | \nA | \nD | \nF | \nJ | \nN | \nS | \nT | \nAVG. | \n(\n | \n
#1 | \nMS | \nMLR | \nNo | \n67.72 | \n44.04 | \n68.78 | \n46.95 | \n89.58 | \n63.10 | \n78.49 | \n69.22 | \n(1.37) | \n
MFCC | \n67.85 | \n61.41 | \n75.97 | \n60.17 | \n95.79 | \n71.89 | \n84.94 | \n77.21 | \n(0.76) | \n|||
MFCC+SM | \n78.75 | \n78.18 | \n80.68 | \n63.84 | \n96.80 | \n82.44 | \n89.01 | \n83.55 | \n(0.55) | \n|||
#2 | \nMS | \nSVM | \nNo | \n70.33 | \n69.38 | \n78.09 | \n60.97 | \n89.25 | \n69.38 | \n85.95 | \n80.98 | \n(1.09) | \n
MFCC | \n79.93 | \n79.02 | \n81.81 | \n75.71 | \n93.77 | \n80.15 | \n92.01 | \n90.94 | \n(0.93) | \n|||
MFCC+SM | \n84.90 | \n88.26 | \n89.44 | \n80.90 | \n96.58 | \n83.89 | \n95.63 | \n89.69 | \n(0.62) | \n|||
#3 | \nMS | \nMLR | \nYes | \n64.76 | \n49.02 | \n66.87 | \n44.52 | \n87.50 | \n58.26 | \n78.70 | \n67.84 | \n(1.27) | \n
MFCC | \n66.54 | \n57.83 | \n74.56 | \n56.98 | \n94.02 | \n72.32 | \n89.63 | \n76.47 | \n(1.51) | \n|||
MFCC+SM | \n77.01 | \n78.45 | \n80.50 | \n64.18 | \n94.42 | \n80.14 | \n91.29 | \n83.03 | \n(0.97) | \n|||
#4 | \nMS | \nSVM | \nYes | \n69.81 | \n70.35 | \n75.44 | \n52.60 | \n86.77 | \n66.94 | \n82.57 | \n78.40 | \n(1.64) | \n
MFCC | \n77.45 | \n77.41 | \n80.99 | \n69.47 | \n91.89 | \n75.17 | \n93.50 | \n87.47 | \n(0.95) | \n|||
MFCC+SM | \n85.28 | \n84.54 | \n84.49 | \n73.47 | \n93.43 | \n81.79 | \n94.04 | \n86.57 | \n(0.72) | \n
Recognition results with MS, MFCC features, and their combination on Spanish database.
Spanish (a, anger; d, disgust; f, fear; j, joy; n, neutral; s, surprise; t, sadness).
Dataset | \nFeature | \nSN | \nAverage (avg) | \nStandard deviation (\n | \n
---|---|---|---|---|
Berlin | \nMS | \nNo | \n66.32 | \n5.93 | \n
MFCC | \n69.55 | \n3.91 | \n||
MFCC+MS | \nYes | \n63.67 | \n7.74 | \n|
MS | \n68.94 | \n5.65 | \n||
MFCC | \n73.08 | \n5.17 | \n||
MFCC+MS | \n\n | 76.98 | \n4.79 | \n|
Spanish | \nMS | \nNo | \n82.30 | \n2.88 | \n
MFCC | \n86.56 | \n2.80 | \n||
MFCC+MS | \n90.05 | \n1.64 | \n||
MS | \nYes | \n82.14 | \n1.67 | \n|
MFCC | \n86.21 | \n1.22 | \n||
MFCC+MS | \n87.02 | \n0.36 | \n
Recognition results using RNN classifier based on Berlin and Spanish databases.
From Table 1, it can be concluded that applying SN improves recognition results for Berlin database. But this is not the case for the Spanish database, as demonstrated in Tables 2 and 3. Results are the same with the three different classifiers. This can be explained by the number of speakers in each database. The Berlin database contains 10 different speakers, compared to the Spanish database that contains only two speakers and probably the language impact. As regarding the RNN method, we found that combining both types of features has the worst recognition rate for the Berlin database, as shown in Table 3. That is because the RNN model has too many parameters (155 coefficients in total) and a poor training data. This is the phenomena of overfitting. This is confirmed by the fact that when we reduced the number of features from 155 to 59 features, the results show an increase of above 13%, as shown in Table 4. To investigate whether a smaller feature space leads to better recognition performance, we repeated all evaluations on the development set by applying a recursive feature elimination (LR-RFE) for each modality combination. The stability of RFE depends heavily on the type of model that is used for feature ranking at each iteration. In our case, we tested the RFE based on an SVM and regression models; we found that using linear regression provides more stable results. We observed from the previous results that the combination of the features gives the best results. So we applied LR-RFE feature selection only for this combination to improve accuracy. In this work, a total of 155 features were used; best features were chosen from feature selection. Fifty-nine features were selected by RFE feature selection method based on LR from the Berlin database and 110 features from the Spanish database. The corresponding results of LR-RFE can be seen in Table 4. For most setting using the Spanish database, LR-RFE does not significantly improve the average accuracy. However, for recognition based on Berlin database using the three classifiers, LR-RFE leads to a remarkable performance gain, as shown in Figure 5. This increases the average of MFCC combined with MS features from 63.67 to 78.11% for RNN classifier. These results are illustrated in Table 4. For the Spanish database, the feature combination of MFCC and MS after applying LR-RFE selection using RNN has the best recognition rate which is above 94.01%.
\nSN | \nClassifier | \nLR-RFE | \nBerlin | \nSpanish | \n
---|---|---|---|---|
No | \nMLR | \nNo | \n73.00 (3.23) | \n83.55 (0.55) | \n
Yes | \n79.40 (3.09) | \n84.19 (0.96) | \n||
SVM | \nNo | \n81.10 (2.73) | \n89.69 (0.62) | \n|
Yes | \n80.90 (3.17) | \n90.05 (0.80) | \n||
RNN | \nNo | \n63.67 (7.74) | \n90.05 (1.64) | \n|
Yes | \n78.11 (3.53) | \n|||
Yes | \nMLR | \nNo | \n75.25 (2.49) | \n83.03 (0.97) | \n
Yes | \n82.27 (1.12) | \n|||
SVM | \nNo | \n81.00 (2.45) | \n86.57 (0.72) | \n|
Yes | \n86.47 (1.34) | \n|||
RNN | \nNo | \n76.98 (4.79) | \n87.02 (0.36) | \n|
Yes | \n85.00 (0.93) | \n
Recognition results with combination of MFCC and MS features using ML paradigm before and after applying LR-RFE feature selection method (Berlin and Spanish databases).
Performance comparison of three machine learning paradigms (MLR, SVM, RNN) using speaker normalization (SN) and RFE feature selection (FS), for the Berlin database, is shown.
The confusion matrix for the best recognition of emotions using MFCC and MS features with RNN based on Spanish database is shown in Table 5. The rate column lists per class recognition rates and precision for a class are the number of samples correctly classified divided by the total number of samples classified to the class. It can be seen that
Emotion | \nAnger | \nDisgust | \nFear | \nJoy | \nNeutral | \nSurprise | \nSadness | \nRate (%) | \n
---|---|---|---|---|---|---|---|---|
Anger | \n||||||||
Disgust | \n0 | \n|||||||
Fear | \n0 | \n3 | \n||||||
Joy | \n3 | \n1 | \n1 | \n|||||
Neutral | \n2 | \n0 | \n1 | \n0 | \n||||
surprise | \n2 | \n1 | \n0 | \n3 | \n0 | \n|||
Sadness | \n0 | \n0 | \n1 | \n0 | \n2 | \n0 | \n||
Precision (%) | \n91.86 | \n91.78 | \n92.10 | \n94.66 | \n96.29 | \n95.23 | \n94.28 | \n\n |
Confusion matrix for feature combination after LR-RFE selection based on Spanish database.
In this current study, we presented an automatic speech emotion recognition (SER) system using three machine learning algorithms (MLR, SVM, and RNN) to classify seven emotions. Thus, two types of features (MFCC and MS) were extracted from two different acted databases (Berlin and Spanish databases), and a combination of these features was presented. In fact, we study how classifiers and features impact recognition accuracy of emotions in speech. A subset of highly discriminant features is selected. Feature selection techniques show that more information is not always good in machine learning applications. The machine learning models were trained and evaluated to recognize emotional states from these features. SER reported the best recognition rate of 94% on the Spanish database using RNN classifier without speaker normalization (SN) and with feature selection (FS). For Berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection (FS) are applied to the features. From this result, we can see that RNN often perform better with more data and it suffers from the problem of very long training times. Therefore, we concluded that the SVM and MLR models have a good potential for practical usage for limited data in comparison with RNN .
\nEnhancement of the robustness of emotion recognition system is still possible by combining databases and by fusion of classifiers. The effect of training multiple emotion detectors can be investigated by fusing these into a single detection system. We aim also to use other feature selection methods because the quality of the feature selection affects the emotion recognition rate: a good emotion feature selection method can select features reflecting emotion state quickly. The overall aim of our work is to develop a system that will be used in a pedagogical interaction in classrooms, in order to help the teacher to orchestrate his class. For achieving this goal, we aim to test the system proposed in this work.
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Many researches have been conducted to determine the best control method for vortex flow in pump sumps so that the pump lifespan can be maximized. In this study, a vortex control principle designed to minimize the impact of submerged vortex flow in pump sump on major pump components is presented. This principle employs a device called the plate type floor splitter which serves the function of eliminating vortices formed on the sump floor and reduces the intensity of swirling motion in the intake flow. A pump sump model was built to carry out the study by installing a floor splitter plate sample under the pump suction inlet and the corresponding parameters used to quantify the swirl intensity known as the swirl angle was measured. Procedures for the measurement were conducted based on ANSI/HI 9.8-2018 standard. A numerical simulation was performed to study the flow in a full-scale pump sump. The results showed that the installation of floor splitter plate can eliminate vortices efficiently and reduce swirl angle significantly. However, optimization of floor splitter design is needed to achieve a reduction effect that can reduce swirl angles to an acceptable value of lower than 5° according to ANSI/HI 9.8-2018 standard.",book:{id:"10080",slug:"vortex-dynamics-theories-and-applications",title:"Vortex Dynamics Theories and Applications",fullTitle:"Vortex Dynamics Theories and Applications"},signatures:"Zambri Harun, Tajul Ariffin Norizan and Wan Hanna Melini Wan Mohtar",authors:[{id:"243152",title:"Dr.",name:"Zambri",middleName:null,surname:"Harun",slug:"zambri-harun",fullName:"Zambri Harun"},{id:"313310",title:"Mr.",name:"Tajul Ariffin",middleName:null,surname:"Norizan",slug:"tajul-ariffin-norizan",fullName:"Tajul Ariffin Norizan"},{id:"317421",title:"Dr.",name:"Wan Hanna Melini",middleName:null,surname:"Wan Mohtar",slug:"wan-hanna-melini-wan-mohtar",fullName:"Wan Hanna Melini Wan Mohtar"}]},{id:"75307",title:"Prototyping and Production of Polymeric Microfluidic Chip",slug:"prototyping-and-production-of-polymeric-microfluidic-chip",totalDownloads:600,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"Microfluidic chips have found many advanced applications in the areas of life science, analytical chemistry, agro-food analysis, and environmental detection. This chapter focuses on investigating the commonly used manufacturing technologies and process chain for the prototyping and mass production of microfluidic chips. The rapid prototyping technologies comprising of PDMS casting, micro machining, and 3D-printing are firstly detailed with some important research findings. Scaling up the production process chain for microfluidic chips are discussed and summarized with the perspectives of tooling technology, replication, and bonding technologies, where the primary working mechanism, technical advantages and limitations of each process method are presented. Finally, conclusions and future perspectives are given. Overall, this chapter demonstrates how to select the processing materials and methods to meet practical requirements for microfluidic chip batch production. 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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},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. 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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. 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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:"117248",title:"Dr.",name:"Andrew",middleName:null,surname:"Macnab",slug:"andrew-macnab",fullName:"Andrew Macnab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of British Columbia",country:{name:"Canada"}}},{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. Gonzalez-Sanchez",slug:"juan-a.-gonzalez-sanchez",fullName:"Juan A. Gonzalez-Sanchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico System",country:{name:"United States of America"}}},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}}]}},subseries:{item:{id:"2",type:"subseries",title:"Prosthodontics and Implant Dentistry",keywords:"Osseointegration, Hard Tissue, Peri-implant Soft Tissue, Restorative Materials, Prosthesis Design, Prosthesis, Patient Satisfaction, Rehabilitation",scope:"