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A Battery Management System (BMS) can prolong the life of the battery but it depends on the accuracy of the adopted scheme. Different techniques have been developed to enhance the BMS by monitoring the State of Health (SOH) of the battery. In this paper, the detection of battery voltage is analyzed by using the cycle counting method, which is a conventional technique and compared with Artificial Neural Network (ANN), a heuristic method. The advantage of the proposed ANN method is that SOH can be monitored without disconnecting the battery from the load. Also, the sampling data to the ANN are derived from various techniques including Open Circuit Voltage (OCV) method, Ambient temperature measurement, and valley point detection. A feed-forward backpropagation algorithm is used to achieve the purpose of real-time monitoring of the LAB. The results show that the precise estimation of SOH can be obtained by Feed-Forward Neural Network (FFNN) when trained with more sampling data.

New advancements in deep learning issues, motivated by real-world use cases, frequently contribute to this growth. Still, it’s not easy to recognize the speaker’s emotions from what they want to say. The proposed technique combines a deep learning-based brain-inspired prediction-making artificial neural network (ANN) through social ski-driver (SSD) optimization techniques. When assessing speaker emotion recognition (SER), the recognition results are compared with the existing convolutional neural network (CNN) and long short-term memory (LSTM)-based emotion recognition methods. The proposed method for classification based on ANN decreases the computational costs. The SER algorithm allows for a more in-depth classification of different emotions because of its relationship to ANN and LSTM. The SER model is based on ANN and the recognition impact of the feature reduction. The SER in this proposed research work is based on the ANN emotion classification system. Speaker recognition accuracy values of 96.46%, recall values of 95.39%, precision values of 95.21%, and F-Score values of 96.10% are obtained in this proposed result, which is higher than the existing result. The average accuracy results by using the proposed ANN classification technique are 4.38% and 2.89%, better than the existing CNN and LSTM techniques, respectively. The average precision results by using the proposed ANN classification technique are 4.67% and 2.49%, better than the existing CNN and LSTM techniques, respectively. The average recall results by using the proposed ANN classification technique are 2.90% and 1.42%, better than the existing CNN and LSTM techniques, respectively. The average precision results using the proposed ANN classification technique are 3.80% and 3.10%, better than the existing CNN and LSTM techniques, respectively.

Volatility of gold price is of great significance for avoiding the risk of gold investment. It is necessary to understand the effect of external events and intrinsic regularities to make accurate price predictions. This paper first compared EMD with CEEMD algorithm, and the results find that CEEMD algorithm performance is better than that of EMD in analysis gold price volatility. Then this paper uses the complementary ensemble empirical mode decomposition (CEEMD) to decompose the historical price of international gold into price components at different frequencies, and extracts a short-term fluctuation, a shock from significant events and a long-term price. In addition, this paper combines the Iterative cumulative sum of squares (ICSS) with Chow test to test the three event prices for structural breaks, and analyzes the effect of external events on volatility of gold price by comparing the external events with the test results for structural breaks. Finally, this paper constructs support vector machine (SVM) models and artificial neural network (ANN) on three series for prediction, and finds that the SVM performed better in gold price prediction in one-step-ahead and five-step-ahead, and when we combine the SVMs and ANNs with price components to make predictions, the error of the combined prediction is smaller than SVMs and ANNs with separate terms of series extracted.

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

Water quality is one of the major concerns of countries around the world. Monitoring of water quality is becoming more and more interesting because of its effects on human life. The control of risks in the factories that produce and distribute water ensures the quality of this vital resource. Many techniques were developed in order to improve this process attending to rigorous follow-ups of the water quality. In this paper, we present a comparative study of the performance of three techniques resulting from the field of the artificial intelligence namely: Artificial Neural Networks (ANN), RBF Neural Networks (RBF-NN), and Support Vector Machines (SVM). Developed from the statistical learning theory, these methods display optimal training performances and generalization in many fields of application, among others the field of pattern recognition. In order to evaluate their performances regarding the recognition rate, training time, and robustness, a simulation using generated and real data is carried out. To validate their functionalities, an application performed on real data is presented. Applied as a classification tool, the technique selected should ensure, within a multisensor monitoring system, a direct and quasi permanent control of water quality.

In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.

A bottleneck of laboratory analysis in process industries including steelmaking plants is the low sampling rate. Inference models using only variables measured online have then been used to made such information available in advance. This study develops predictive models for key mechanical properties of seamless steel tubes, by strength, ultimate tensile strength and hardness. A plant in Brazil was used as the case study. The sample sizes of some steel tube families given namely, yield a particular property are discrepant and sometimes very small. To overcome this sample imbalance and lack of representativeness, committees of predictive neural network models based on bagging predictors, a type of ensemble method, were adopted. As a result, all steel families for all properties have been satisfactorily described showing the correlations between targets and model estimates close to 99%. These results were compared to multiple linear regression, support vector machine and a simpler neural network. Such information available in advance favors corrective actions before complete tube production mitigating rework costs in general.