You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective 
Chapter Title:  2) SOME RELEVANT RESEARCH IN MACHINE LEARNING 
1. Abstract of Cited Paper (BIB001):  Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services. 
2. Abstract of Cited Paper (BIB002):  A network traffic classifier (NTC) is an important part of current network monitoring systems, being its task to infer the network service that is currently used by a communication flow (e.g., HTTP and SIP). The detection is based on a number of features associated with the communication flow, for example, source and destination ports and bytes transmitted per packet. NTC is important, because much information about a current network flow can be learned and anticipated just by knowing its network service (required latency, traffic volume, and possible duration). This is of particular interest for the management and monitoring of Internet of Things (IoT) networks, where NTC will help to segregate traffic and behavior of heterogeneous devices and services. In this paper, we present a new technique for NTC based on a combination of deep learning models that can be used for IoT traffic. We show that a recurrent neural network (RNN) combined with a convolutional neural network (CNN) provides best detection results. The natural domain for a CNN, which is image processing, has been extended to NTC in an easy and natural way. We show that the proposed method provides better detection results than alternative algorithms without requiring any feature engineering, which is usual when applying other models. A complete study is presented on several architectures that integrate a CNN and an RNN, including the impact of the features chosen and the length of the network flows used for training. 
3. Abstract of Cited Paper (BIB003):  Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the networking and distributed computing system is the key infrastructure to provide efficient computational resources for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of MLN, which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations of their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities in networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help motivate researchers to develop innovative algorithms, standards and frameworks. 
4. Abstract of Cited Paper (BIB004):  The state-of-the-art cloud computing platforms are facing challenges, such as the high volume of crowdsourced data traffic and highly computational demands, involved in typical deep learning applications. More recently, Edge Computing has been recently proposed as an effective way to reduce the resource consumption. In this paper, we propose an edge learning framework by introducing the concept of edge computing and demonstrate the superiority of our framework on reducing the network traffic and running time. 
5. Abstract of Cited Paper (BIB005):  Collaborative applications of physical systems and algorithms bring the rapid development of cyber physical systems (CPS). Establishing CPS with image classification systems, however, is difficult, because both categories of algorithms, deep learning methods and traditional feature extraction methods, are independent and individual currently. Therefore, in this paper, we propose a fast feature fusion algorithm to satisfy the requirement of CPS in the area of image classification from a comprehensive perspective. First, we fuse the shallow-layer network feature, large pre-trained convolutional neural network feature and traditional image features together by genetic algorithm, in order to guarantee high accuracy with little training time and hardware cost. Second, we increase the distance between different centers by dynamic weight assignment to improve distinguishability of different classes. Third, we propose a partial selection method to reduce the length of the fused feature vectors and to improve the classification accuracy further by centralizing the features within the same class. Finally, experimental results show that our method can achieve high classification accuracy with lower training time and hardware consumption, which can greatly improve the efficiency and flexibility of image classification in cyber physical systems. 
6. Abstract of Cited Paper (BIB006):  Cognitive networks (CNs) are one of the key enablers for the Internet of Things (IoT), where CNs will play an important role in the future Internet in several application scenarios, such as healthcare, agriculture, environment monitoring, and smart metering. However, the current low packet transmission efficiency of IoT faces a problem of the crowded spectrum for the rapidly increasing popularities of various wireless applications. Hence, the IoT that uses the advantages of cognitive technology, namely the cognitive radio-based IoT (CIoT), is a promising solution for IoT applications. A major challenge in CIoT is the packet transmission efficiency using CNs. Therefore, a new Q-learning-based transmission scheduling mechanism using deep learning for the CIoT is proposed to solve the problem of how to achieve the appropriate strategy to transmit packets of different buffers through multiple channels to maximize the system throughput. A Markov decision process-based model is formulated to describe the state transformation of the system. A relay is used to transmit packets to the sink for the other nodes. To maximize the system utility in different system states, the reinforcement learning method, i.e., the Q learning algorithm, is introduced to help the relay to find the optimal strategy. In addition, the stacked auto-encoders deep learning model is used to establish the mapping between the state and the action to accelerate the solution of the problem. Finally, the experimental results demonstrate that the new action selection method can converge after a certain number of iterations. Compared with other algorithms, the proposed method can better transmit packets with less power consumption and packet loss. 
7. Abstract of Cited Paper (BIB007):  Currently, a large number of industrial data, usually referred to big data, are collected from Internet of Things (IoT). Big data are typically heterogeneous, i.e., each object in big datasets is multimodal, posing a challenging issue on the convolutional neural network (CNN) that is one of the most representative deep learning models. In this paper, a deep convolutional computation model (DCCM) is proposed to learn hierarchical features of big data by using the tensor representation model to extend the CNN from the vector space to the tensor space. To make full use of the local features and topologies contained in the big data, a tensor convolution operation is defined to prevent overfitting and improve the training efficiency. Furthermore, a high-order backpropagation algorithm is proposed to train the parameters of the deep convolutional computational model in the high-order space. Finally, experiments on three datasets, i.e., CUAVE, SNAE2, and STL-10 are carried out to verify the performance of the DCCM. Experimental results show that the deep convolutional computation model can give higher classification accuracy than the deep computation model or the multimodal model for big data in IoT. 
8. Abstract of Cited Paper (BIB008):  With the rapid development of Internet of things devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in this paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model, which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient. 
9. Abstract of Cited Paper (BIB009):  The deep computation model has been proved to be effective for big data hierarchical feature and representation learning in the tensor space. However, it requires expensively computational resources including high-performance computing units and large memory to train a deep computation model with a large number of parameters, limiting its effectiveness and efficiency for industry informatics big data feature learning. In this paper, a tensor-train deep computation model is presented for industry informatics big data feature learning. Specially, the tensor-train network is used to compress the parameters significantly by converting the dense weight tensors into the tensor-train format. Furthermore, a learning algorithm is implemented based on gradient descent and back-propagation to train the parameters of the presented tensor-train deep computation model. Extensive experiments are carried on STL-10, CUAVE, and SNAE2 to evaluate the presented model in terms of the approximation error, classification accuracy drop, parameters reduction, and speedup. Results demonstrate that the presented model can improve the training efficiency and save the memory space greatly for the deep computation model with small accuracy drops, proving its potential for industry informatics big data feature learning. 
10. Abstract of Cited Paper (BIB010):  Deep computation, as an advanced machine learning model, has achieved the state-of-the-art performance for feature learning on big data in industrial Internet of Things (IoT). However, the current deep computation model usually suffers from overfitting due to the lack of public available labeled training samples, limiting its performance for big data feature learning. Motivated by the idea of active learning, an adaptive dropout deep computation model (ADDCM) with crowdsourcing to cloud is proposed for industrial IoT big data feature learning in this paper. First, a distribution function is designed to set the dropout rate for each hidden layer to prevent overfitting for the deep computation model. Furthermore, the outsourcing selection algorithm based on the maximum entropy is employed to choose appropriate samples from the training set to crowdsource on the cloud platform. Finally, an improved supervised learning from multiple experts scheme is presented to aggregate answers given by human workers and to update the parameters of the ADDCM simultaneously. Extensive experiments are conducted to evaluate the performance of the presented model by comparing with the dropout deep computation model and other state-of-the-art crowdsourcing algorithms. The results demonstrate that the proposed model can prevent overfitting effectively and aggregate the labeled samples to train the parameters of the deep computation model with crowdsouring for industrial IoT big data feature learning.

Literature Review:
A variety of research investigations already exist towards utilizing machine learning for networking applications. For example, Jiang et al. BIB002 reviewed and investigated a variety of machine learning techniques (e.g., supervised learning, unsupervised learning and reinforcement learning) as tools to improve the performance of next generation networks with various compelling applications, such as massive MIMO, ultra-dense small cell network, D2D, and so on. In addition, Zhu et al. BIB007 leveraged the reinforcement learning algorithms (i.e., Q-learning) to optimize the packet transmission scheduling in the cognitive network environment for IoT applications. The transmission scheduling algorithm selects the appropriate actions (transmission power, spectrum access, scheduling, etc.) for the cognitive nodes in the multi-channel environment to maximize the system throughput. Also, Lopez-Martin et al. BIB003 used neural networks to conduct traffic classification for IoT networks. The experimental results of their study demonstrated the fantastic detection accuracy by using the combination of different neural networks (recurrent neural network (RNN) and convolutional neural networks (CNN)), in which features were extracted from packet headers. Furthermore, Wang et al. BIB004 reviewed a promising means of applying machine learning algorithms to address networking challenges and optimize the network design and management. In addition, the existing research efforts have demonstrated the application of machine learning techniques for I-IoT systems BIB001 - BIB008 . For example, to facilitate power generation, monitoring, and control related to energy-based I-IoT systems, Mocanu et al. BIB001 designed different machine learning models to perform tasks, including energy disaggregation, flexibility classification and prediction. Likewise, Huang et al. BIB005 developed a deep learning-based scheme to conduct the forecast of electrical loads in the smart grid. Wang et al. BIB006 addressed the image classification issue and designed a feature fusion algorithm. In addition, Li et al. BIB009 proposed efficient and robust deep learning models to inspect and detect defects in manufactured products. Furthermore, Li et al. BIB008 designed a deep convolutional computation model, which can be used to perform hierarchical feature learning on big data in IoT. In addition, research efforts have been devoted to improving machine learning performance in terms of efficiency and accuracy. For instance, the time and computationconsuming nature of the training phase raises efficiency issues for machine learning models, especially for big industrial data. To improve the efficiency of machine learning, Zhang et al. BIB010 proposed a tensor-train deep compression model to learn hierarchical features for industrial informatics in an efficient manner. The large number of parameters in the model were greatly compressed to decompose the tensors in the deep learning model and improve the speed. Another issue of adopting machine learning for industrial informatics is the lack of training samples. Because of this, deep learning models could degrade classification accuracy due to overfitting. To address this issue, Zhang et al. BIB011 adopted adaptive dropout to prevent overfitting, where the dropout rate of each hidden layer is computed by the adaptive distribution function.