Performance analysis and improvement of machine learning algorithms for automatic modulation recognition over Rayleigh fading channels

Hazar, Muhammed Abdurrahman; Odabasioglu, Niyazi; Ensari, Tolga; Kavurucu, Yusuf; Sayan, O. F.

doi:10.1007/s00521-017-3040-6

Cited by 26 publications

(12 citation statements)

References 20 publications

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“…Extra Trees Regressor: Like the random forest tree, the extra trees regressor (ETR) is built using many decision trees [60]. On ETR, all decision trees are trained in their entirety utilizing all training sets.…”

Section: Phasementioning

confidence: 99%

COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

Zain

Alturki

2021

Journal of Control Science and Engineering

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COVID-19 has sparked a worldwide pandemic, with the number of infected cases and deaths rising on a regular basis. Along with recent advances in soft computing technology, researchers are now actively developing and enhancing different mathematical and machine-learning algorithms to forecast the future trend of this pandemic. Thus, if we can accurately forecast the trend of cases globally, the spread of the pandemic can be controlled. In this study, a hybrid CNN-LSTM model was developed on a time-series dataset to forecast the number of confirmed cases of COVID-19. The proposed model was evaluated and compared with 17 baseline models on test and forecast data. The primary finding of this research is that the proposed CNN-LSTM model outperformed them all, with the lowest average MAPE, RMSE, and RRMSE values on both test and forecast data. Conclusively, our experimental results show that, while standalone CNN and LSTM models provide acceptable and efficient forecasting performance for the confirmed COVID-19 cases time series, combining both models in the proposed CNN-LSTM encoder-decoder structure provides a significant boost in forecasting performance. Furthermore, we demonstrated that the suggested model produced satisfactory predicting results even with a small amount of data.

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Section: Phasementioning

confidence: 99%

COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

Zain

Alturki

2021

Journal of Control Science and Engineering

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“…In Fig. 11, the employed network compared with the typical solutions is as follow: LSTM [54] and GRU [55] are the familiar form of RNN; ResNet [56], DensenNet [57], SENet [58] are the sophisticated structure of deep residual neural network with more layer-by-layer connections; PnasNet [59] is the irregular structure generated by reinforcement learning method; Deep neural network (DNN) [29], extensible neural network (ENN) [32], Sparse Autoencoder [35] are in the light of the regular one-dimensional fully connected layer to construct the network; the improved CNN [30], CLDNN (Convolutional Long Short-term Deep Neural Network) [60], fusion neural network (FNN) [34] are made up of CNN and RNN; K Nearest Neighbors [13], Random Forest [15], support vector machines (SVM) [14] are frequently used ML methods in AMR work. All networks are trained and verified at packets = 2187.…”

Section: B Experiments Performancementioning

confidence: 99%

Underwater Communication Signal Recognition Using Sequence Convolutional Network

et al. 2021

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Automatic modulation recognition (AMR) is one of the essential parts in the intelligent communication system. In the underwater acoustic communication, it is a challenging work that promptly and easily recognizes the signal modulation schemes by conventional methods. The deep neural network method is a good solution to the problem, which creates a better recognition effect. The packets of data that are fed to the familiar neural network is constant. However, the packets of signal data on the communication course consistently change, which seriously reflects on the signal recognition veracity. A novel deep learning network with the sequence convolutional network in this paper is proposed, which is composed of one-dimensional sequence convolution of residual network modules and the variable convolution kernel range. By extracting the time-domain signal characteristics, the affection of various signal packets can be mitigated. In experiments, the employed network not only has more concentrated on the modulation recognition veracity, but also owns a lower parameter quantity and a shorter training time, which indicates ideal recognition results in the underwater communication environment. Moreover, it is more valuable to the real underwater communication system.

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“…"hidden_layer_sizes" defines the number of hidden layers and the number of neurons in each hidden layer. In our case, its value is (5,5,5), which shows that three hidden layers with five neurons in each layer are being used in the network. We used some rules of thumb [26] to determine the range of the hidden layer sizes and the neuron sizes.…”

Section: Parking Space Data Setmentioning

confidence: 99%

“…The use of ML/DL algorithms has been compared for different application fields. For example, Hazar et al [5] analyze automatic modulation recognition over Rayleigh fading channels. They trained various ML/DL models for this task, including Random Forest, KNN, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Naïve Bayes, Gradient Boosted Regression Tree (GBRT), Hoeffding Tree, and Logistic regression, and found Naïve Bayes to be an optimal algorithm for this problem.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Awan

Saleem

Minerva

et al. 2020

Sensors

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Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.

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Performance analysis and improvement of machine learning algorithms for automatic modulation recognition over Rayleigh fading channels

Cited by 26 publications

References 20 publications

COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

COVID-19 Pandemic Forecasting Using CNN-LSTM: A Hybrid Approach

Underwater Communication Signal Recognition Using Sequence Convolutional Network

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

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