Morphological assessment of blastocyst quality is one of the most significant challenges in the IVF process because the current assessment is based on evaluation by an embryologist; thus, it is still manual and subjective and lacks precision. Artificial intelligence (AI) plays a role in overcoming the limitations of the manual assessment system, and its use is expected to increase implantation rates in IVF. This study aims to optimize the convolutional neural network (CNN) model using the grid search method and to evaluate the effectiveness of different machine learning models in classifying the blastocyst quality in a small dataset. The reliability of the proposed model will be compared with that of other machine learning methods, such as logistic regression (LR), support vector machine (SVM), k-nearest neighbors (KNN), the boosting algorithm, and with the addition of the Canny operator as a segmentation process and principal component analysis (PCA) as a feature extraction approach. We evaluated the results using various performance measures, such as the precision, recall, F1-measure, accuracy, and area under the curve of the receiver operating characteristic curve (AUC-ROC). The final results showed that our proposed CNN model achieves a validation accuracy of 84.00%, a test accuracy of 83.33%, and an AUC of 0.844. McNemar’s statistical test results support that our CNN model outperforms the other classifiers.

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.

Grouping of data is entailed to facilitate the process of data analysis in the great numbers, especially the data whose has fluctuated condition and multi spectral properties. This study aims to create a concept of grouping data with several new approaches in the analysis of its data grouping. The signal fluctuation used is HF (high fluctuation). There will be a way of approach to represent the pattern into one value that can represent data from the pattern. Furthermore, a representative point for each set of data is set and a unique code is made to compare between one data set in 1 material with data sets contained on different materials. The materials objected to the study are H2O and H2O mixed with NaOH. We analyze the data set by performing the mean values, standard deviation values and variance to mean ratio (VMR) values. Then, their goals are to gain, which dataset groups have the fairly stable value and also can represent the data set for subsequent processing. The results show that the groupings of data sets per 200 data sets are sufficient to represent a good percentage value. In other world, it is noticeable to say that we can obtain the characteristics of a material based on its fluctuation pattern.

This paper presents a high efficiency 2.44-GHz rectifier that can harvest low input RF power effectively. A new rectifier with a simple structure is proposed for the rectenna application. The proposed single-band rectifier has been implemented and the measurements show good agreement with simulation. With an input matching network, the measurement results for an input power level of 20 dBm show peak RF-to-DC efficiencies of 76% and output dc voltage of 3.27 V at frequency 2.44 GHz.

A microwave imaging system for scanning the presence of cancer in the body is designed. The system uses a pair of dipole antennas as a transmitter and a receiver at frequency of 3 GHz based on Algebraic Reconstruction Tecnique (ART) algorithm. Two types of numerical phantom are used for validating the image reconstruction system, a cylindrical phantom and a Shepp-Logan phantom. The reconstructed image and its histogram show that the system is successful in representing the tissue characteristics of the phantoms for identifying the presence of tumorous tissue.

This study discusses the influence of WBA (wide band amplifier) supply against the performance of consistency level of multi-spectral fluctuation pattern, which is the output of the MCS (Multi Frequency Capacitive Sensor). In order to obtain some significant impacts on the sensor output, there are several methods that are employed in this study. Firstly, the sensor equipment MSC is designed, followed by setting up the equipment used in this study. Furthermore, there will be some setting circuits of WBA supply by using two input sources, such as source of AC (alternating current) and DC (direct current). The material used is material mixture between H₂O and HCl. In addition, the results will be analyzed statistically that the data is observed based on the value of HHF (high-high fluctuation) and fluctuations value in VMR (variance to mean ratio). Whereas, the result reveals that the WBA supply with battery supply performs the highest of consistency level fluctuations pattern compared to others WBA supply, which is the average value obtained by statistical observations is 98.67% and the value of VMR is 0.0073. It is noticeable to say that by achieving the biggest consistency value of multi spectral fluctuation pattern, then a comparison between two of different materials can perform well.