# Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
## 2 Conceptual Framework
### Data Mining Technologies for Smart Cities
#### 2.4.2 Machine Learning (ML) Algorithms
However, Bayesian Regularized ANNs (BRANNs) tries to overcome the overfitting problem by incorporating Bayes' modeling into the regularization scheme [91]. Convolutional Neural Network (CNN) is now gaining considerable attention leveraging its powerfulness in automatically learning the underlying patterns from the data. A basic structure of CNN generally consists of two stages, namely the feature learning stage and the classifier training stage [103; 104]. The structure of a CNN is more complicated than that of a traditional NN [100]. Recurrent Neural Networks (RNNs) are feedforward NN with a recurrent loop. They are considered a powerful model for sequential data by using past history [105], and they are applied to a wide variety of problems involving time sequences of events and ordered data [92]. Long Short-Term Memory (LSTM) is a three-layer particular type of RNN proposed to identify a correlation between an input sequence and an output sequence. LSTM was used by [PERSON] et al. [105] for energy consumption prediction and anomaly detection in a smart grid application. To detect moving objects with maximum accuracy, [106] used a Counter-Propagation Artificial Neural Network (CP-ANN). The CP-ANN has a three-layer architecture where the first layer is an input layer, the second layer is a Kohonen layer, and the third layer is a Grossberg layer. While ANNs are capable of extracting and modeling the general behavior of the system, Fuzzy Logic (FL) modeling can be used for approximate reasoning, modeling of qualitative data for uncertainties [103] that inherently appear in data to ensure adaptable control even in the presence of noisy, imprecise data [107]. FL also assists in easily incorporating expert domain knowledge into the control system by means of human interpretable linguistic rules. [108] used a firmly structured network, an Adaptive Neuro-Fuzzy Inference System (ANFIS) formed by the combination of ANN and fuzzy systems. In order to achieve near-optimal control, they used the Particle Swarm Optimization (POS) method. Alternatively, Evolutionary Algorithms (EA) may be used for dynamically optimizing both ANN and FL techniques as well as classical control methods. As EAs are inspired by the process of natural selection, they provide the capability of optimizing complex real-world problems [109] and converging on near-optimal results when the search space is too large to be searched exhaustively [78]. Deep Learning (DL) derives from ANNs with many hidden perceptron layers that can help to identify hidden patterns. The basic idea of DL is to replicate what the human brain does in most cases [110]. The Deep Belief Network (DBN) was proposed in 2006 by [PERSON] and has been widely used since then. DL not only changes traditional ML methods but also affects our understanding of human perception [100]. DL has been applied to several classification and regression problems. Part of its success is due to automatic feature extraction at different levels of abstraction. This promotes the easy reutilization of models on different domains without the intervention of a field specialist. Moreover, DL is capable of the representation of nonlinear, complex city data. DL models have been used to achieve state-of-the-art results in the field of computer vision and have also been applied to the problem of time series forecasting [111]. Reinforcement learning (RL) asks users who are already involved in sensing and actuation to reinforce and guide the system toward better accuracy and intuitive actuation [68]. The emerging Deep Reinforcement Learning (DRL) [112] can be considered as a promising technology, which takes a long-term goal into account and can generate optimal control actions to time-variant dynamic systems. [PERSON] et al. [108] argue that ensemble and hybrid models are the future trends in ML. Ensemble meta classifiers adopt classification techniques where multiple classifiers of a different or similar type are being trained over the same or subsets of a training set [94].