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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.

Agriculture not only plays a vital role in human survival but also contributes to the nation’s greater economic development. With the use of technologies like IoT, WSNs, remote sensing, camera surveillance, and many more, precision agriculture is the newest buzzword in the field of technology. Its primary goal is to lessen the labour of farmers while increasing the output of farms. Many machine learning techniques are designed to improve the productivity and quality of the crops, but the improper irrigation and disease prediction of the existing techniques leads to loss of productivity and quality. Hence, the IoT based wireless communication system is designed for smart irrigation and rice leaf prediction using ANN and ResNeXt-50 model. In this designed model, smart irrigation is controlled by collecting the temperature and moisture of the soil in the agricultural field by using the WSN. Likewise, a surveillance camera is placed in the agricultural field to capture the rice leaf to find the disease such as rice blast, leaf smut, brown spot and bacterial blight. These collected data are processed and classified for smart irrigation and rice leaf disease prediction. For evaluating the performance of both the ANN and ResNeXt-50 trained model, the performance metrics such as accuracy, sensitivity, specificity, precision, error etc. The performance metrics for the ANN and ResNeXt-50 model are 0.9427, 0.925, 0.903, 0.86, 0.0573 and 0.967, 0.935, 0.943, 0.939 and 0.033. Thus, the evaluation of the designed model results that the proposed approach is performing better compared to the current techniques.

With the increasing demand for security in many fastest growing applications, biometric recognition is the most prominent authentication system. User authentication through speech and face recognition is the important biometric technique to enhance the security. This paper proposes a speech and facial feature-based multi-modal biometric recognition technique to improve the authentication of any system. Mel Frequency Cepstral Coefficients (MFCC) is extracted from audio as speech features. In visual recognition, this paper proposes cascade hybrid facial (visual) feature extraction method based on static, dynamic and key-point salient features of the face and it proves that the proposed feature extraction method is more efficient than the existing method. In this proposed method, Viola–Jones algorithm is used to detect static and dynamic features of eye, nose, lip, Scale Invariant Feature Transform (SIFT) algorithm is used to detect some stable key-point features of face. In this paper, a research on the audio-visual integration method using AND logic is also made. Furthermore, all the experiments are carried out using Artificial Neural Network (ANN) and Support Vector Machine (SVM). An accuracy of 94.90% is achieved using proposed feature extraction method. The main objective of this work is to improve the authenticity of any application using multi-modal biometric features. Adding facial features to the speech recognition improve system security because biometric features are unique and combining evidence from two modalities increases the authenticity as well as integrity of the system.

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.

The geometric topology of a point per event written in the higher dimensional μ-space of data (e.g. 6W's: who, where, when, what, how, and why) can help in the design of information acquisition (IA) systems. Measurement intensity of each W's sensor, or the number of words used to describe a specific W's attribute, represents the length of each vector dimension. Then, N concurrent reports of the same event become a distribution set of N points scattered all over μ-space. To discover the statistically independent components, an unsupervised or unbiased Artificial Neural Networks (ANN) methodology called Independent Component Analysis (ICA) can be used to reveal a new subspace called the feature space. The major and minor axes of the subspace correspond to highly precise and efficient combinations of old attributes (e.g. 2-D feature domains consisting of "where-who-when" and "what-how-why" could be good choices for Internet search indices). Thus, one realizes that the communication of an event is not just the address-where: but who and when are equally important attributes. In principle, the number of new sensors can be reduced (e.g. from 6 W's to 2 features), provided that they are physically realizable. In the combined space of 6N-dimensional Γ-space, one point can represent all N concurrent measurements; the flow of these generates the event behavior in time. The time flow over the reduced 2N feature space generates invariant features called knowledge.

For surveillance against terrorists, legacy electrical power line communication (PLC) will offer a useful relay for the last mile of mobile communications for a Surveillance Sensor Web (SSW) employing ANN: there is no need for "where" addressing for switching because of smart coding and decoding of "who-when." After reviewing Auto-Regression (AR), we generalize AR to a supervised ANN implementation of Principal Component Analysis (PCA) (Appendix A) learning toward unsupervised learning ANN for ICA (Appendix B). This is possible non-statistically because the classical-closed information theory (CIT) of the maximum Shannon entropy S of a closed system must be generalized for open brain information theory (BIT) having non-zero energy exchange E at the minimum Helmholtz free energy H=E-T_oS at isothermal equilibrium (T_o=37°C). For such an open BIT system, we prove the Lyaponov convergence theorem. We compute the ICA features of image textures in order to measure the ICA classifier information content.

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.

The evolution of communication technologies with high-frequency radio-frequency (RF) devices increased the demand for compact and efficient designs. Micro-electromechanical systems (MEMS) technology revolutionized microwave and RF applications because of its ability to be engineered into miniaturized devices that are highly linear and power efficient. It is more challenging to perform numerical analysis and optimization of such complex MEMS devices. Electromagnetic (EM) simulation-based optimization software using different methodologies employing coupled domains and time domain analysis of MEMS devices requires repeated simulation, which makes it computationally expensive. The artificial neural network (ANN) model is an alternative to these conventional simulation-based design methodologies to expedite the design process. ANN models for RF and microwave modeling are known to be effective, precise, and flexible. ANN is capable of producing accurate results with less computational time than sophisticated EM models. An overview of various RF MEMS components and an introduction to ANN are provided in this chapter. In addition, this chapter presents the concept of modeling an RF MEMS shunt switch using ANN as a case study.

Present applications of artificial intelligence technology for wastewater treatment in china are summarized. Expert system was mainly used as the system operation decision-making and fault diagnosis. Artificial neuron network was used as system modeling, water quality forecast and soft measure. The application of fuzzy control technology was the main aspect of intelligence control in wastewater treatment process. Finally, the main application problems of artificial intelligence technology for wastewater treatment in china are analysed.

This paper aims at briefly overview the previous use of process modeling in denim manufacturing sectors, specifically, dyeing and finishing processes. A collection of relevant works is introduced, such as modeling the dyeing process for predicting the depth of shade, the effects of alkali reductive stripping process or cellulase washing process on color properties, and certain physical properties. A specific case in regard to modeling ozone fading denim by artificial neural networks (ANN) was studied as an example at the end, the results simply revealed that modeling process using soft computing techniques is capable to accurately predict the targeted outputs which is obviously promising and potential to make a difference in the future development of denim manufacturing.

Artificial Neural Networks (ANNs) are modelling tools having the ability to adapt to and learn complex topologies of inter-correlated multidimensional data. ANNs are inspired by biological neuron processing, have been widely used in different field of science and technology incorporating time series forecasting, pattern recognition and process control. ANN has been successfully used for forecasting of groundwater table and quality parameters like nitrate, total dissolved solids. In case of groundwater quality prediction, availability of good quality data of better precision is required. ANNs are classified as Feed-forward neural networks (FFNNs), Recurrent neural networks (RNNs), Elman Backpropagation Neural Networks, Input Delay feed-forward Backpropagation Neural Network, Hopfield Network. The artificial neural networks (ANNs) ability to extract significant information provides valuable framework for the representation of relationships present in the structure of the data. The evaluation of the output error after the retraining of an ANN shows us that this procedure can substantially improve the achieved results. Through this review work it is observed that in most hydrological modeling cases FFNN and LM algorithm performed well till today's published research work.