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Recently, the automation of the age estimation technique is hoped for in various fields. Therefore, we propose an apparent-age estimation system using empirical mode decomposition (EMD). Conventional study reported that the time-frequency features are important for age estimation. However, these cannot necessarily extract the time-frequency feature in detail, because the classical technique that have a relationship of trade-off between the time resolution and the frequency resolution are used. On the other hand, the EMD is the novel time-frequency analysis technique that do not have the relationship of trade-off between the time resolution and the frequency resolution. The EMD gives a time-frequency analysis decomposing a signal into several intrinsic mode functions (IMFs). The IMF together with their Hilbert transforms are called the Hilbert–Huang spectrum, which leads to instantaneous frequency and amplitude. We use these features effectively for extracting human's age perception. We estimate the age by a neural network that learns pairs of face image and the Hilbert–Huang spectrum. Furthermore, we compress the data for neural network by using the simple principal component analysis (SPCA). In order to show the effectiveness of the proposed method, computer simulations are done by the actual human data.

This paper presents the fuzzy-based Space Vector Pulse Width Modulation (SVPWM) for 5-level Neutral Point Clamped inverter with hybrid energy source fed grid connected system. The proposed scheme is energized using Photovoltaic and Wind energy system combination. The Neural Network-(NN) based Maximum Power Point Tracking algorithm (MPPT) has been implemented to track maximum power under different operating conditions.

First, we proposed fuzzy-based SVPWM, which is developed for 5-level NPC inverter to provide voltage and current control, reduced THD, predetermined switching pattern, capacitor voltage unbalance minimization and maintain good control under nonlinearities and suspicions of PV-Wind energy connected hybrid system.

Second, we proposed the inverter output voltage, which has been synchronized with grid connected system using split inductor (SI). The reference voltages for SVPWM implementation were obtained using feedback current control method.

Third, we proposed different solar radiations, the proposed system has attained better performance by trailing maximum power from hybrid energy system. The simulation and experimental responses have been validated using Matlab/simulink and FPGA processor.

Side-channel attacks (SCAs) are powerful noninvasive attacks that can be used for leaking the secret key of integrated circuits (ICs). Numerous countermeasures were proposed to elevate the security level of ICs against SCAs. Unfortunately, it is quite inconvenient to predict the security levels of these countermeasures since no solid mathematical model exists in the literature. In this paper, neural network (NN)-based entropy is proposed to model the resilience of a system against SCAs. The NN-based entropy model well links the side-channel leakages and probabilities with the neurons and weights of NNs, respectively. In such a circumstance, the NN-based entropy can be used for modeling the robustness of countermeasures since a one-to-one relationship is established between the NN-based entropy and the measurement-to-disclose (MTD) enhancement ratio related with the countermeasures. As demonstrated in the result, the proposed NN-based entropy metric shows 100% consistency with the MTD enhancement ratio if multiple SCA countermeasures are employed into a system.

The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on.

Blood plays an important role in the clinical diagnosis and treatment, the analysis of blood will be of very important practical significance. Study shows that the UV absorption spectrum is of complex shape, there is more absorption peak in 200 to 300nm, it shows that there is a complex absorption phenomenon in blood group macromolecules; there is a degree of displacement of absorption peak to different samples; there is no significant correlation between absorbance at some a wavelength and blood fat concent, but random. Based on the evident correlation between serum UV absorption spectrum and blood fat concentration in the wave band of 265 to 282nm, a neural network model was built to forecast the blood fat concentration, and a relatively good prediction was obtained. It provides a new spectral test method of blood fat concentration.