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.

In Indonesia, there is still a disparity in telecommunications access, with most rural areas experiencing “no signal” or “blank spots.” In contrast, urban areas enjoy modern and societally-beneficial technologies. A comprehensive framework is needed to address the disparity in telecommunications access between “rich” and “poor” groups in urban and rural/remote areas, respectively. This paper proposes a framework, built by the mathematical model, that can be used as a reference for the Indonesian government in constructing the nation’s telecommunications infrastructure. The framework categorizes Indonesian administrative regions into four grids: Grid #1: “fostered” districts; Grid #2: “developing” districts; Grid #3: “developed” districts; and Grid #4: “independent-advanced” districts. To determine where each district falls in these grids, we propose a novel statistical approach using 17 indicators involving a telecommunications network and socioeconomic factors. The proposed framework results in a grid visualization of 7232 districts in Indonesia. Finally, as this paper is replete with academic research approaches and mathematical model perspectives, it is expected that the results may be a valuable input to the development of the country’s telecommunications policy.

Voice over Internet Protocols (VoIP) is an IP-based communication technology or commonly known as Internet Protocols (IP). In which, IP is currently widely used for mobile communication activities. However, the main concern in the application of VoIP technology is the system ability to maintain information confidentiality while guaranteeing protection to its primary users. For this reason, it requires to do the addition of Virtual Private Network (VPN) features. Conceptually, it aims to create connection lines in secret by utilizing the internal network structure (intranet) and to be accessed remotely using tunneling protocols in its security system. The purpose of this research is to compare the Quality of Service (QoS) on several tunneling protocols. Moreover, it is conducted to also analyze several security system mechanisms including Delay, Jitter, Throughput and Packet Loss. This analyzation is used for determining the best quality of some of the piloted tunneling security protocols. Furthermore, this work compares several methods of VoIP voice-call-testing in term of Generic Routing Encapsulation with IPSecurity (GRE+IPSec), Internet Protocol in Internet Protocol based Session Initiation Protocol (IPIP+SIP-based), Secure Socket Layer (SSL), and Layer 2 Tunneling Protocol IPSecurity (L2TP+IPSec). Accordingly, the comparative results show the better performance compared to the existing work, which is proven by the ability of the proposed method to provide the VoIP based on ITU-T.G.1010.

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.

Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.

The aim of this paper is to see the characteristics of 5G mmWave band after implementing eMBB scenario in 5G trial network. The method used were trial scheme indoor and outdoor with various cases to test 5G capability in 1 among 3 scenarios. The focus usage scenarios conducted in this paper is enhanced mobile broadband (eMBB) scenario with varying several parameters setting in indoor and outdoor conditions. The result of the trial is that channel bandwidth, power setting, location and reflector existence impacting the 5G mmWave band performance significantly.