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Ltd., Atsugi, Japan, Researcher/Senior Researcher, Researches on Semiconductor Quantum Dots for Quantum Information, Semiconductor Optoelectronic Materials and Devices. \nApril, 2012 – March 2014: University of Tokyo, Tokyo, Japan, Senior Researcher, Researches on Quantum Information Processing Devices. \nApril, 2014 – now: Southwest Institute of Technical Physics, Chengdu, China, Professor, Researches on Semiconductor Optoelectronic Materials and Devices. \nJune, 2015 – now: University of Electronic Science and Technology, Chengdu, China, Professor, Researches on Nanoscaled Semiconductors and Quantum Information Processing Devices.\n \nAchievements\nSystematically studied the property of porous silicon materials and verified their mechanism; found green and ultraviolet luminescence, and clarified the multiple luminescence mechanisms of nanocrystalline-silicon embedded in SiO2, which is valuable to silicon-based optoelectronic integration; realized enhanced hole mobility in amorphous silicon, verified the existence of deep trap states in amorphous selenium, providing ways to improve amorphous optoelectronic materials. \nDiscovered lateral coupling between self-assembled quantum dots (QDs) and their tuning effect to 2D electron gas; illustrated and deeply explained the metal-insulator transition in 2D ordered QD arrays, all of which are worth in optoelectronic application of semiconductor QDs. \nDeveloped Sb-free technique to double the InAs/GaAs QD density and suppress the atomic interdiffusion, helped producing 1.3 um QD lasers, which won Japanese national prizes and had been merchandized; developed 1.06 um quantum-well lasers, which have been used to produce pure-green lasers robust against high temperature. \nFound a way to access buried QDs by scanning tunneling microscope; achieved a way to prepare diluted QDs by post-annealing and clarified its mechanisms; invented a technique to control the size and site of QDs by atomic-force microscopy lithography, and an apparatus to detect single electron spin states by optically-detected magnetic resonance; designed a few types of micropillar cavities applicable to realize 1.55 um highly-efficient, even coherent (strongly coupled) InAs/InP QD single photon sources; produced fiber-integrated photon-entangled sources, all of which are very useful to the applications of QDs in quantum information processing. \nDeveloped focal-plane single-photon avalanche detectors, providing central devices for 3D laser detecting and ranging system; explored antimonide middle- and long-wavelength infrared detectors and the surface plasmon enhancement effect in such detectors; advanced the acetone-sensing function of Eu-doped SnO2 nano-belt; found Nickle Phosphide serving as a good catalyst in hydrogen-producing. 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Appl”\nMember of APS (American Physics Society)\nMember of OSA (Optical Society of America)\nPermanent Member of China Physical Science and Technology\nPermanent Member of the Chinese Optical Society\nTechnical committee member of PIERS, organizing a series of “quantum information processing and devices” sessions\nTechnical committee member of ICICM",institutionString:"Southwest University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Southwest University",institutionURL:null,country:{name:"China"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"20",title:"Physics",slug:"physics"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"453623",firstName:"Silvia",lastName:"Sabo",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/453623/images/20396_n.jpg",email:"silvia@intechopen.com",biography:null}},relatedBooks:[{type:"book",id:"8356",title:"Metastable, Spintronics Materials and Mechanics of Deformable Bodies",subtitle:"Recent Progress",isOpenForSubmission:!1,hash:"1550f1986ce9bcc0db87d407a8b47078",slug:"solid-state-physics-metastable-spintronics-materials-and-mechanics-of-deformable-bodies-recent-progress",bookSignature:"Subbarayan Sivasankaran, Pramoda Kumar Nayak and Ezgi Günay",coverURL:"https://cdn.intechopen.com/books/images_new/8356.jpg",editedByType:"Edited by",editors:[{id:"190989",title:"Dr.",name:"Subbarayan",surname:"Sivasankaran",slug:"subbarayan-sivasankaran",fullName:"Subbarayan Sivasankaran"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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There is no lack of predictions stating that CAVs will solve many of the current problems experienced on roads today, such as congestion, traffic accidents and lost time [2].
Traffic state prediction and traffic control are two key modules in transportation systems with CAVs [3]. Traffic states such as flow, speed, congestion, etc., plays vital roles in traffic management, public service and traffic control [4]. By predicting the evolution of traffic state timely and accurately, decision-maker and traffic controller can make effective policy and control input to avoid traffic congestion ahead of time and thus ITS (Intelligent Transportation Systems), advanced traffic management systems and traveler information systems rely on real-time traffic state prediction. Traffic control can be divided into a decision-making module and a vehicle control module. The former is used to optimize the mobility, safety and energy consumption by using the vehicle trajectory prediction results to calculate vehicle platoon sizes, speed, flow, density, traffic merging, diverging flow and traffic signals, while the latter is used for vehicle path control, vehicle fleet control and steering wheel, throttle, brake, and other actuator control by using onboard units based on the control commands [3]. How to timely and accurately predict the future traffic state and deliver an effective traffic control strategy are fundamental issues in ITS.
Traffic state prediction approaches can be broadly divided into two parts: parametric and non-parametric approaches [5]. Parametric approaches utilize parametric models that capture all the information about its predictions within a finite set of parameters. The popular techniques in parametric approaches include ARIMA (Autoregressive Integrated Moving Average) [6, 7, 8, 9], linear regression [10] and Kalman Filter (KF) based method [11], which are linear models and able to have high accuracy with linear characteristics of traffic data. ARIMA model is based on the assumption that the future data will resemble the past and widely used in time series analysis, which can be made to be stationary by differencing. It can be specified three values that represent the order of autoregressive (
Non-parametric models such as DL (Deep Learning) outperform parametric models because of stochastic, indeterministic, non-linear and multidimensional characteristics of traffic data [5]. DL is a subset of machine learning (ML) which is based on the concept of deep neural network (DNN) and it has been widely used for data classification, natural language processing (NLP) and object recognition [5]. The most popular DL models used for traffic state prediction includes Convolution Neural Network (CNN) [12, 13, 14], Deep Belief Network (DBN) [15, 16], Recurrent Neural Network (RNN) [17, 18, 19] and Autoencoder (AE) [20] etc. CNN is useful for traffic prediction because of the two-dimensional characteristics of traffic data and its ability to extract the spatial feature. CNN is only connected to a smaller subset of input and thus decreases the computational complexity of the training process. DBN is a stacking of multiple RBMs (Restricted Boltzmann Machines), which can be used to estimate the probability distribution of the input traffic data. LSTM is the special type of RNN, which can capture the temporal feature of traffic data, and LSTM can overcome the gradient vanishing problem caused by the standard RNN.
Traffic control strategies can be generally divided into classical methods and learning-based methods. Classical methods develop traffic controller based on control theory or optimization-based techniques, which include dynamic traffic assignment based nonlinear controller [21], standard proportional-integral (PI) controller [22, 23], robust PI controller [24], model-based predictive control (MPC) [25, 26], linear quadratic controller [27], mixed-integer non-linear programming (MINLP) [28], multi-objective optimization based decision-making model [29]. Learning-based methods refer to the utilization of artificial intelligence technologies to achieve decision-making and control for CAVs, which can be further divided into three categories: statistic learning-based method, deep learning-based (DL) method and reinforcement learning-based (RL) method. The RL-based method is currently one of the most commonly used learning-based techniques for traffic control and decision-making because RL can solve complex control problems by using the Markov decision process (MDP) to describe the interaction states of agent and environment [4]. The most popular RL-based methods include Q-learning for adaptive traffic signal control [30, 31], multi-agent RL approaches [32, 33, 34, 35], Nash Q-learning strategy [36]. Many other RL-based approaches are also available in the literature. Q-learning based traffic signal control aims to minimize the average accumulated travel time by greedily selecting action at each iteration. Multi-agent RL approaches are more popularly used in network signal optimization and can be generally divided into centralized RL and decentralized RL, while the former considers the whole system as a single agent and the latter distributes the global control to each agent. Nash Q-learning strategy is a decentralized multi-agent RL strategy, which performs iterated updates based on assuming Nash equilibrium behavior over the current Q-values. It can be shown that traffic signal control using the Nash Q-learning strategy can converge to at least one Nash equilibrium for stationary control policies. However, Nash Q-learning is unable to achieve the Pareto Optimality without consideration of cooperation among different agents.
This chapter provides a comprehensive survey about state-of-the-art traffic state prediction and traffic control techniques. It is organized as follows: In Section 2, we firstly introduce the fundamental structure and main characteristics of two important DL models: CNN and LSTM (Long Short-Term Memory), as well as their advantages in traffic state prediction, then we introduce how to realize hybrid traffic state prediction by combining two models to achieve better accuracy. In Section 3, we detail RL fundamentals and introduce how it can be applied in traffic control and decision-making. We focus on multi-agent RL approaches. Pros and cons are discussed. Section 4 gives the summary of this chapter.
In this section, we first briefly overview the machine learning and deep learning concept. Then, we focus on introducing the architectures of two DL models: CNN and LSTM, which show good performance in processing high-dimensional and temporal correlated data. Finally, a hybrid model of CNN and LSTM is described and the research potential is about how to improve the prediction accuracy by incorporating spatio-temporal correlation.
ML approaches are broadly classified into two categories, i.e., Supervised Learning and Unsupervised Learning [5]. Supervised Learning requires input data to be clearly labeled. It involves a function
DL is a branch of ML which aims to construct a computational model with multiple processing layers to support high-level data abstraction. It can automatically extract the feature from data, without any human interference to explore hidden data relationships among different attributes of the dataset [37]. Concepts of DL are inspired by the thinking process of the human brain. Hence, the majority of DL architectures are using the framework of Artificial Neural Network (ANN), which consists of input, hidden and output layers with nonlinear computational elements (neurons and processing units). The network depth (the number of layers) can be adjusted according to the feature dimensions and complexity of the data. The number of neurons at the input layer is equal to the number of independent variables, while the number of neurons at the output layer is equal to the number of dependent variables, which can be single or multiple. Neurons of two successive layers are connected by weights which are updated while training the model. The neurons at each layer receive the output from the previous layer, which is generated by a weighted summation over inputs and then passed to an activation function (Figure 1).
(left) ANN with one input layer, two hidden layers and one output layer,
Let us take the four-layer ANN in Figure 2 for example. During the training process, the value of
CNN structure.
where
In this section, we examine two popular DL architectures: CNN and LSTM, which are used popularly for multidimensional and time sequential dataset. CNNs have been extensively applied in various fields, including traffic flow prediction [14, 40, 41], computer vision [42], Face Recognition [43], etc., while LSTMs are special kinds of RNNs, which are mainly applied in the area of temporal data processing, such as traffic state prediction [34, 44], speech processing [45] and NLP (Natural Language Processing) contexts [46], etc.
The significant difference between fully connected ANN and CNN is that CNN neurons are only connected to a smaller subset of input which decreases the total parameters in the network [47]. CNNs have the ability to extract important and distinctive features from multidimensional by making use of filtering operations. A commonly used type of CNNs, which is similar to multi-layer perception (MLP), consists of numerous convolution layers preceding pooling layers and fully connected layers. CNN structure is illustrated in Figure 2, where it consists of the input layer, convolution layer, pooling layer and fully connected layer. Convolution layer outputs higher abstraction of the feature. Each convolution layer uses several filters, which are designed to have a distinct set of weights. Filters used by the convolution layer have the smaller dimensions compared to the data size. In the training phase, filter weights are automatically determined according to an assigned task. The filters of each convolution layer are applied through the input layer by computing the sum of the product of input and filter, leading to a feature map of each filter. Each feature map detects a distinct high-level feature which is then processed by a pooling layer and a fully connected layer. ReLU activation function is applied to remove all negative values in the feature map.
The benefits of CNNs over other statistical learning methods and DL methods are listed followings [48]:
CNNs have the weight sharing feature, which reduces the number of trainable network parameters and in turn helps the network speed up the training process and avoid overfitting.
Concurrently learning the feature extraction layers and the classification layer causes the model output to be both highly organized and highly reliant on the extracted features.
Large-scale network implementation is much easier with CNN than with other neural networks.
CNN and other kinds of ANNs such has MLP are not designed for sequences and time series data because they do not have memory element. In such cases, RNN can deliver more accurate results. RNNs are widely used in traffic state prediction because traffic data has spatiotemporal characteristics, which cannot be captured by CNN or other kinds of ANNs. RNN structure is illustrated in Figure 3, where RNNs involve an internal memory element that memorizes the previous output. The current output
RNN structure.
where
LSTM is firstly proposed in [49] to overcome the gradient vanishing problems generated by other RNNs. A typical LSTM network consists of an input layer, a recursive hidden layer and an output layer. In the recursive hidden layer, each neuron is made up of four structures: a forget gate, an input gate, an output gate and a memory block. The state of the memory cell reflects the features of the input, while the three gates can read, update and delete features stored in the cell. The LSTM structure is illustrated in Figure 4.
LSTM structure.
The past information carried by the cell state
where
Generally, LSTM can address the vanishing gradient problem that makes network training difficult for a long-sequence temporal data. The long-term dependencies in the data can be learned to improve the prediction accuracy.
Although, CNN and LSTM have advantages in dealing with traffic data with spatiotemporal dependencies, due to the complex and non-linear models of traffic data, it is hard to predict accurate results by using a single model [5]. Some literature proposed that prediction accuracy can be improved by hybrid modeling such as combining CNN and LSTM [50, 51, 52, 53].
The spatial and temporal features can be fully extracted by hybrid models, where CNN in this model is used to capture spatial features of traffic data whereas LSTM is used to extract temporal features. Suppose that we have traffic state data of
Note that
There are mainly two hybridization manners: the first one is to extract spatio-temporal features by concatenating CNN and LSTM, that is, each column of
model to capture the temporal features; the second one is to parallelize CNN and LSTM modeling process by considering the extracted spatial and temporal features are of the same importance, that is, the same traffic state data is input into two models, the final prediction is obtained by passing the output of two models through a FC (Fully Connected) layer. The structure of the two hybridizations is illustrated as follows (Figure 5).
(left) concatenated hybrid model; (right) parallelized hybrid model.
For concatenated hybrid models, the real-time measured data matrix
The high-level spatial feature map output by the one-dimensional CNN can be expressed by
where
where
To extract the temporal features, the high-level spatial feature vector for single or multiple time instants will be selected for the input of each LSTM, which is denoted as
where
where
where
Concatenated hybrid models utilize a one-dimensional CNN to obtain a smaller range of spatial features, in addition, they do not contain a fully connected layer at the output of LSTM models, and thus concatenated hybrid models are with low learning complexity. However, the temporal features delivered by LSTM have a strong correlation with the spatial features output by CNN, which needs some special assumptions about the raw data.
For parallelized hybrid models, the historical data matrix
where
A LSTM is utilized to obtain the high-level temporal feature map. The output of the
By posing a fully connected layer to the output of the
where
In parallelized hybrid models, the spatial and temporal feature maps are considered to be of the same importance, and thus are extracted independently. The fully connected layer merges the output of CNN and LSTM without any special assumptions about the high-level spatial and temporal features.
Traffic state has strong periodic features because people get used to repeating some similar or same behaviors on the same time period of different days or the same day of different weeks, e.g., most people routinely go to work in the morning and go home in the evening during the peak hour [53]; most people routinely go for shopping on weekends rather than weekdays, etc. The periodic features can be used as supplementary information to predict the future traffic state. For the short-term traffic state prediction, the real-time data only contains the data before the prediction time instant, but the historical data on previous days or weeks contain the full data of that period, that means, traffic state information after the inspected time instant on previous days or weeks can be utilized to get the prediction about that on the inspected time instant. Suppose we use parallelized hybrid models, the complete prediction structure should contain CNN and LSTM for the real-time data, CNN and bidirectional LSTM for the historical data, which are connected by using a fully connected layer.
The bidirectional LSTM is composed of two independent forward and backward LSTMs, whose inputs are the time series before and after the inspected time instant. The final prediction of bidirectional LSTM is obtained by concatenating the forward and backward LSTMs. The structure of bidirectional LSTM is depicted in Figure 6.
Bidirectional LSTM structure.
Suppose that additionally, we have historical traffic state data
where
Using Eq. (12), the output of the
Then, the
An accurate and efficient traffic state prediction can provide continuous and precise traffic status and vehicle states based on past information. How to utilize the current and predicted traffic states to make a real-time optimum decision is the main task of the traffic signal control module in ITS. The objectives of traffic signal control include minimizing the average waiting time at multiple intersections, reducing traffic congestion and maximizing network capacity. There exist real-time linear feedback control approaches and MPC (Model-based Predictive Control) that are specifically designed for traffic signal control systems to achieve the targets. The drawback of linear feedback control techniques that have been tried is that the system should always remain in the linear region at all times for the controller. Although, MPC has some advantages such as imposing constraints, the main shortcoming is it needs an accurate dynamic model, which is difficult to be obtained for traffic control systems. Data-driven approaches such as DRL (Deep Reinforcement Learning) based traffic control techniques are widely presented for ITS in recent years because RL can solve complex control problems and deep learning helps to approximate highly nonlinear functions from the complex datasets. In this section, we firstly briefly review the fundamental principles of RL. Then, we focus on multi-agent DRL based traffic signal control techniques such as decentralized multi-agent advantage actor-critic, which can converge to the local optimum and overcome the scalability issue by considering the non-stationarity of MDP transition caused by policy update of the neighborhood; and Nash Q-learning strategy, which can converge to Nash equilibrium by only considering the competition among agents.
Reinforcement Learning (RL) is a promising data-driven approach for decision-making and control in complex dynamic systems. RL methodology formally comes from a Markov Decision Process (MDP), which is a general mathematical framework sequential decision-making algorithms, and consists of five elements [54]:
A set of states
A set of actions
Transition probability
Reward function
The discount factor
RL aims to maximize a numerically defined reward by interacting with the environment to learn how to behave in an environment without any prior knowledge by learning. In traffic signal control systems, RL is used to find the best control policy
where
RL generally can be classified into model-based RL which knows or learns the transition model from state
where
The stochasticity in Eq. (21) comes from the control policy
where
The learning rate
Value-based RL does not work well for continuous control problems with infinite-dimensional action space or high-dimensional problems because it is difficult to explore all the states in a large and continuous space and store them in a table. In such a case, policy-based RL can provide better solutions than value-based RL. By treating the policy
The optimum policy parameters
Policy-based RL tries to select the optimum actions by using the gradient of the objective function with respect to
where
where
where
Actor-critic RL combines the characteristics of policy-based methods and value-based methods, in which an actor is used to control the agent’s behaviors based on policy, critic evaluates the taken action based on value function. From Eq. (27), the objective function can be rewritten as
The loss function for policy and value updating can be respectively defined as
where
Recall that
where
A real traffic network consists of multiple signalized intersections, each of which can be considered as an agent. The states for the
A set of states space of the
A set of action space of the
Transition probability
Control policy of the
The instantaneous reward function of the
The centralized multi-agent RL considers the multi-agent systems as a single-agent system with joint state space
Suppose we have a multi-intersection traffic network, which can be modeled as
where the global states and policies can be communicated from all other agents in the system as well as the neighborhood
We assume Eq. (33) has continuous state-action space and thus multi-agent A2C can be applied to search the optimum policy parameter. From Eq. (31), the Advantage value for the
where
If each agent follows Eqs. (35) and (36) in a decentralized manner, a local optimum policy
In practice, the information exchange among multiple intersections may not be synchronized and communication delay should be considered, which causes policy changing within the same episode and thus leads to non-stationarity. There is some research that try to stabilize convergence and relieve non-stationarity. Tesauro proposes a “Hyper-Q” learning, in which values of mixed strategies rather than base actions are learned and other agents’ strategies are estimated from observed actions via Bayesian inference [55]. Foerster et al. include low-dimensional fingerprints, such as
To relieve non-stationarity, the key is to keep policies from neighboring agents fixed within one episode. We can apply a DNN network to approximate the local policy
Then, the loss function for policy updating can be rewritten by
Even if the policies from the neighbors are fixed and are considered to be additional input, it is still difficult to approximate
where
Then, the cumulative discounted reward can be obtained by
and the local return and Advantage value
and Eq. (38) can be rewritten as
The loss function for value updating can be expressed as
The decreolized MA2C can overcome the scalability issue and achieve local optimum (Pareto Optimality). How to achieve the global optimum using a decentralized approach when the global reward function is non-convex in the future research direction.
Compared to decentralized MA2C, Nash Q-learning does not consider cooperation among agents and thus it has lower computational complexity but can only achieve the Nash equilibrium. Nash Q-learning aims to find the optimal global control policy
where
Eqs. (45) and (46) show that at each iteration
In traffic signal control application, the state space
By conducting a simulation on SUMO for a two-intersection case, we can observe in Figure 7 that the centralized DQN outperform the centralized Q-learning in terms of reward value (Average Waiting Time/s) and convergence rate (the Number of Iterations). When the number of agents is small (two, in this case), by using the centralized methods, the average waiting time can converge to the local optimum, which is more optimal than the Nash equilibrium delivered by Nash Q learning. However, the convergence rate of Nash Q learning is higher than that of centralized methods.
Comparison of different multi-agent RL methods for traffic signal control.
In this chapter, we introduced deep learning-based traffic state prediction technique, which can provide accurate future information for traffic control and decision making. The traffic state data depicts a strong correlation in the spatial and temporal domain, which can be utilized by applying CNN and LSTM techniques to improve the prediction accuracy. CNN technique is used to capture high-level spatial features while LSTM can provide excellent performance in dealing with time-sequential data by extracting high-level temporal features. We firstly reviewed the fundamentals of deep learning and presented the architecture of CNN and LSTM. Then, we introduced how to combine these two models to form concatenated hybrid models and parallelized hybrid models. Finally, we proposed bidirectional LSTM models to enhance prediction performance by learning additional high-level temporal features from the historical data in previous days.
Furthermore, we introduced the decentralized multi-agent advantage Actor-Critic technique and Nash Q learning for traffic signal control applications. We firstly briefly review the fundamental principles of RL. Then, we focus on multi-agent DRL-based traffic signal control techniques such as decentralized multi-agent advantage actor-critic, which can converge to the local optimum and overcome the scalability issue by considering the non-stationarity of MDP transition caused by policy update of the neighborhood.
The main contribution of this chapter can be summarized as followings:
We reviewed the state-of-the-art technique in traffic state prediction and traffic control strategies, and provide readers with a clear framework for understanding how to apply deep learning models to traffic state prediction and how to deal with multi-agent traffic control by using RL strategies.
We proposed the hybrid prediction models, which can utilize CNN and LSTM to capture the spatio-temporal feature of traffic data.
We proposed a multi-agent deep RL (MARL) strategy, which conducts in a decentralized manner and considers the cooperation among agents and thus can overcome the scalability issue and achieve local optimum.
We compared the centralized RL Q-learning, DQN to the Nash Q-learning strategy in terms of the reward value and convergence rate.
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Emdad Haque and M. Salim Uddin",authors:[{id:"163390",title:"Dr.",name:"C. Emdad",middleName:null,surname:"Haque",slug:"c.-emdad-haque",fullName:"C. Emdad Haque"},{id:"168399",title:"Mr.",name:"Mohammed S",middleName:null,surname:"Uddin",slug:"mohammed-s-uddin",fullName:"Mohammed S Uddin"}]},{id:"56731",doi:"10.5772/intechopen.70351",title:"Affective Technology Acceptance Model: Extending Technology Acceptance Model with Positive and Negative Affect",slug:"affective-technology-acceptance-model-extending-technology-acceptance-model-with-positive-and-negati",totalDownloads:2357,totalCrossrefCites:8,totalDimensionsCites:15,abstract:"Research works on TAM, TAM2, TAM3 and UTAUT has always focused on cognitive aspect of technology acceptance in the past two decades. Acceptance of technologies such as eCommerce, Mobile and ERP that considered emotion and affect are still less. This creates a gap in the technology acceptance research, which consider the role of affect into technology acceptance model. This study considers the role of affect of a knowledge worker that work in Multimedia Super Corridor (MSC)-status organizations in Malaysia on their behavioural intention to use knowledge sharing tools (KS tools) in their day-to-day tasks. Hence, Affective Technology Acceptance (A.T.A) model has been proposed. The behavioural intention on the acceptance of KS tools will be hypothesize in the Affective Technology Acceptance (A.T.A) model. Positive (PA) and Negative (NA) affect as the role of affect construct were introduce in this model to investigate its influence on KS tools usefulness and ease of use among employees in Multimedia Super Corridor organizations. The findings of this study highlighted that NA has no impact on perceive usefulness. The findings also showed that PA has very significant positive influence on PU, PEOU and BI with impact on PEOU being the greatest.",book:{id:"5491",slug:"knowledge-management-strategies-and-applications",title:"Knowledge Management Strategies and Applications",fullTitle:"Knowledge Management Strategies and Applications"},signatures:"Angela Lee Siew Hoong, Lip Sam Thi and Mei-Hua Lin",authors:[{id:"190265",title:"Associate Prof.",name:"Angela",middleName:"Siew Hoong",surname:"Lee",slug:"angela-lee",fullName:"Angela Lee"},{id:"195089",title:"Prof.",name:"Lip Sam",middleName:null,surname:"Thi",slug:"lip-sam-thi",fullName:"Lip Sam Thi"},{id:"195090",title:"Prof.",name:"Mei Hua",middleName:null,surname:"Lin",slug:"mei-hua-lin",fullName:"Mei Hua Lin"}]},{id:"55633",doi:"10.5772/intechopen.68933",title:"Parental Self-efficacy in Promoting Children Care and Parenting Quality",slug:"parental-self-efficacy-in-promoting-children-care-and-parenting-quality",totalDownloads:2099,totalCrossrefCites:9,totalDimensionsCites:13,abstract:"Parental self-efficacy (PSE) emerges as a crucial variable into exploring variability in parenting quality. After introducing the link between PSE and parental competence, the role of PSE on parenting quality, its multiple influences, and transactional effects connected to contextual or cultural variables are discussed. The chapter addresses some key issues: (a) the levels of PSE measurement (i.e., domain- or task-specific approach), their interrelationship and magnitude as mutual predictors (study 1); (b) infant-caring, parent’s adjustment, and PSE development in the transition to parenthood (study 2); (c) parenting difficult children and the role of PSE as a “buffer” variable moderating the effects of negative child’s characteristics on parenting skills; and (d) PSE beliefs in family context, the relationships with other family measures (marital self-efficacy and stress), and their associations with children’s adjustments (study 3). Finally, in the study 4, PSE is presented as an outcome variable in a parent training. In all summarized studies, a special attention was devoted to father’s PSE as a specific factor affecting childrearing and parent’s well-being. As Bandura says, PSE is not a personality trait, but a learnable set of beliefs producing positive effects on parenting quality. Suggestions for family-based interventions enhancing PSE are discussed.",book:{id:"5605",slug:"parenting-empirical-advances-and-intervention-resources",title:"Parenting",fullTitle:"Parenting - Empirical Advances and Intervention Resources"},signatures:"Loredana Benedetto and Massimo Ingrassia",authors:[{id:"193200",title:"Prof.",name:"Loredana",middleName:null,surname:"Benedetto",slug:"loredana-benedetto",fullName:"Loredana Benedetto"},{id:"193901",title:"Prof.",name:"Massimo",middleName:null,surname:"Ingrassia",slug:"massimo-ingrassia",fullName:"Massimo Ingrassia"}]},{id:"60027",doi:"10.5772/intechopen.74964",title:"Corporate Governance in Downturn Times: Detection and Alert – The New Italian Insolvency and Crisis Code",slug:"corporate-governance-in-downturn-times-detection-and-alert-the-new-italian-insolvency-and-crisis-cod",totalDownloads:1203,totalCrossrefCites:6,totalDimensionsCites:9,abstract:"In its life cycle, an enterprise may experience periods of crisis. If the crisis is monitored promptly and appropriate measures are taken, not only may the enterprise continue to operate but it may also be able to seize opportunities for growth. The Italian legislator is introducing a procedure aimed at supporting companies to detect the very first warning signs of a crisis. The supervisory board of auditors, the audit firm, and certain qualified creditors will have the right and duty to start the early warning procedure (“allerta”). The board of statutory auditors (Collegio Sindacale) plays a fundamental role: its ex-ante supervisory and control activities over management allow it to effectively play an important role as main recipient of any crisis warning signs. The new regulatory framework lays down certain indicators and critical thresholds, which may trigger the alert process. Initially, the Delegated Legislation (Bill No.3671-bis) sets forth certain specific financial indicators. The new bill (Crisis and Insolvency Code) on the contrary refers to a more complex and sector-specific system of indicators. The findings of an empirical research conducted by analysing a sample of more than 600 enterprises and testing the discriminating capacity of the indicators initially considered are presented herein.",book:{id:"6620",slug:"crisis-management-theory-and-practice",title:"Crisis Management",fullTitle:"Crisis Management - Theory and Practice"},signatures:"Patrizia Riva, Alessandro Danovi, Maurizio Comoli and Ambra\nGarelli",authors:[{id:"230543",title:"Prof.",name:"Patrizia",middleName:null,surname:"Riva",slug:"patrizia-riva",fullName:"Patrizia Riva"},{id:"245409",title:"Prof.",name:"Alessandro",middleName:null,surname:"Danovi",slug:"alessandro-danovi",fullName:"Alessandro Danovi"},{id:"245410",title:"Prof.",name:"Maurizio",middleName:null,surname:"comoli",slug:"maurizio-comoli",fullName:"Maurizio comoli"},{id:"245411",title:"Dr.",name:"Ambra",middleName:null,surname:"Garelli",slug:"ambra-garelli",fullName:"Ambra Garelli"}]}],mostDownloadedChaptersLast30Days:[{id:"60813",title:"Crisis Management: A Historical and Conceptual Approach for a Better Understanding of Today’s Crises",slug:"crisis-management-a-historical-and-conceptual-approach-for-a-better-understanding-of-today-s-crises",totalDownloads:4663,totalCrossrefCites:7,totalDimensionsCites:8,abstract:"We argue that the basic and contemporary concepts related to crisis management, especially in the communication field, share some similarities with what was practiced in ancient civilizations such as the importance of direct contact between the leadership and the public. Other similarities include the accurate diagnosis of the real causes of the crisis, the forbiddance of the dissemination of false news and the reassurance of the public opinion that there is a solution to the crisis, a sound management decision, and a good plan for its implementation. We link the past time crises to the contemporary era, providing a comparison framework. The history of crisis tends to show us that the study of crisis management cannot be linked to a specific civilization or era, especially when humanity had witnessed multiple and complex environmental, political, economic, and military crisis. Moreover, some of the problems and complex issues in the modern era are rooted in history. Thus, many geopolitical crises nowadays are the result of old causes. The study of crisis management from an academic point of view should be a multifaceted analysis, including a historical, a cultural, and an anthropological one, which determines the course of evolution and consequences of the crisis.",book:{id:"6620",slug:"crisis-management-theory-and-practice",title:"Crisis Management",fullTitle:"Crisis Management - Theory and Practice"},signatures:"Khaled Zamoum and Tevhide Serra Gorpe",authors:[{id:"230918",title:"Prof.",name:"T. Serra",middleName:null,surname:"Gorpe",slug:"t.-serra-gorpe",fullName:"T. Serra Gorpe"},{id:"230920",title:"Dr.",name:"Khaled",middleName:null,surname:"Zamoum",slug:"khaled-zamoum",fullName:"Khaled Zamoum"}]},{id:"44219",title:"Disaster Management Discourse in Bangladesh: A Shift from Post-Event Response to the Preparedness and Mitigation Approach Through Institutional Partnerships",slug:"disaster-management-discourse-in-bangladesh-a-shift-from-post-event-response-to-the-preparedness-and",totalDownloads:4101,totalCrossrefCites:4,totalDimensionsCites:26,abstract:null,book:{id:"3054",slug:"approaches-to-disaster-management-examining-the-implications-of-hazards-emergencies-and-disasters",title:"Approaches to Disaster Management",fullTitle:"Approaches to Disaster Management - Examining the Implications of Hazards, Emergencies and Disasters"},signatures:"C. Emdad Haque and M. Salim Uddin",authors:[{id:"163390",title:"Dr.",name:"C. Emdad",middleName:null,surname:"Haque",slug:"c.-emdad-haque",fullName:"C. Emdad Haque"},{id:"168399",title:"Mr.",name:"Mohammed S",middleName:null,surname:"Uddin",slug:"mohammed-s-uddin",fullName:"Mohammed S Uddin"}]},{id:"74444",title:"Flood Disaster Hazards; Causes, Impacts and Management: A State-of-the-Art Review",slug:"flood-disaster-hazards-causes-impacts-and-management-a-state-of-the-art-review",totalDownloads:732,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Floods are among disasters that cause widespread destruction to human lives, properties and the environment every year and occur at different places with varied scales across the globe. Flood disasters are caused by natural phenomena, but their occurrences and impacts have been intensified through human actions and inactions. The practice of flood disaster management have evolved over the years from traditional approaches of ad-hoc response measures to integrated approaches involving technologically advanced tools in flood disaster awareness, preparedness and response measures. This chapter proffers understanding into flood disaster awareness, preparedness and management, mitigation and adaptation strategies. Most importantly, the chapter presents a review on the relevance of modern technological tools namely Geographic Information System, Remote Sensing, Internet of Things and Big Data, that are available to flood managers, in the creation of efficient early warnings and Flood decision support systems that elevates the resilience of societies to flood disasters.",book:{id:"7712",slug:"natural-hazards-impacts-adjustments-and-resilience",title:"Natural Hazards",fullTitle:"Natural Hazards - Impacts, Adjustments and Resilience"},signatures:"Frank Jerome Glago",authors:[{id:"325046",title:"M.A.",name:"Frank Jerome",middleName:null,surname:"Glago",slug:"frank-jerome-glago",fullName:"Frank Jerome Glago"}]},{id:"59667",title:"Information Security Awareness in Public Administrations",slug:"information-security-awareness-in-public-administrations",totalDownloads:1614,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Government digital agendas worldwide go hand in hand with the digital transformation in businesses and public administrations as well as the digital changes taking place in society. Information security (IS) and awareness (ISA) must be an integrated part of these agendas. The goal of IS is to protect information of all types and origins. Here, the employees play a necessary and significant role in the success of IS, and the entire staff of an institution need to know about their specific roles and be aware of the information security management system (ISMS). As there are still fundamental strategic deficiencies in the institutions themselves, humans should not be called “the weakest link” in the security chain. Rather, sustainable awareness-raising and training for people should be established in the institutions using interactive, authentic, and game-based learning methods. Psychological studies show the great importance of emotionalization when communicating IS knowledge and the reliable exchange of experience about IS. However, in many institutions, a change in culture is becoming necessary. IS must be integrated into all (business) processes and projects, and viable safeguards must be included. This chapter summarizes the most important scientific findings and transfers them to the practice of public administrations in Germany. Moreover, it shows examples of learning methods and provides practical assistance for IS sensitization and training.",book:{id:"6689",slug:"public-management-and-administration",title:"Public Management and Administration",fullTitle:"Public Management and Administration"},signatures:"Margit Scholl",authors:[{id:"235819",title:"Dr.",name:"Margit",middleName:"C.",surname:"Scholl",slug:"margit-scholl",fullName:"Margit Scholl"}]},{id:"71351",title:"Supply Chain FMEA Risk Analysis for the Heavy Industry Sector",slug:"supply-chain-fmea-risk-analysis-for-the-heavy-industry-sector",totalDownloads:769,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The discussed problem is associated with the analysis of risk factors affecting supply chain management in the heavy industry sector based on the analysis of entities operating in this industry. During the research, several aspects of key importance in supply chain management in the heavy industry sector were identified. The use of the failure mode and effects analysis (FMEA) method in research has enabled the detection of defects in supply chain management and analysis of factors that may negatively affect the flow of goods. During the research, potential design flaws and the effect of these flaws were identified, indicating the class, cause, and occurrence.",book:{id:"9256",slug:"risk-management-and-assessment",title:"Risk Management and Assessment",fullTitle:"Risk Management and Assessment"},signatures:"Małgorzata Dendera-Gruszka and Ewa Kulińska",authors:[{id:"313072",title:"Prof.",name:"Ewa",middleName:null,surname:"Kulińska",slug:"ewa-kulinska",fullName:"Ewa Kulińska"},{id:"313373",title:"Ph.D.",name:"Małgorzata",middleName:null,surname:"Dendera-Gruszka",slug:"malgorzata-dendera-gruszka",fullName:"Małgorzata Dendera-Gruszka"}]}],onlineFirstChaptersFilter:{topicId:"272",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. 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Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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