There is some risk of power quality disturbances at many stages of production, transformation, distribution, and energy consumption. The cornerstone for dealing with power quality problems is the characterization of power quality disturbances (PQDs). However, past research has focused on a narrow topic: noise disruption, overfitting, and training time. A new strategy is suggested to address this problem that combines efficient one-dimensional dataset compression with the convolutional neural network (CNN) classification algorithm. First, three types of compression algorithms: wavelet transform, autoencoder, and CNN, are proposed to be evaluated. According to the IEEE-1159 standard, the synthetic dataset was built with fourteen different PQD types. Furthermore, the PQD classification procedure integrated compressed data with the CNN classification algorithm. Finally, the suggested method demonstrates that combining CNN compression and classification methods can efficiently recognize PQDs. Even in noisy environments, PQD signal processing achieved up to 98.25% accuracy and managed the overfitting.

The primary source of the various power-quality-disruption (PQD) concerns in smart grids is the large number of sensors, intelligent electronic devices (IEDs), remote terminal units, smart meters, measurement units, and computers that are linked by a large network. Because real-time data exchange via a network of various sensors demands a small file size without an adverse effect on the information quality, one measure of the power-quality monitoring in a smart grid is restricted by the vast volume of the data collection. In order to provide dependable and bandwidth-friendly data transfer, the data-processing techniques’ effectiveness was evaluated for precise power-quality monitoring in wireless sensor networks (WSNs) using grayscale PQD image data and employing pretrained PQD data with deep-learning techniques, such as ResNet50, MobileNet, and EfficientNetB0. The suggested layers, added between the pretrained base model and the classifier, modify the pretrained approaches. The result shows that advanced MobileNet is a fairly good-fitting model. This model outperforms the other pretraining methods, with 99.32% accuracy, the smallest file size, and the fastest computation time. The preprocessed data’s output is anticipated to allow for reliable and bandwidth-friendly data-packet transmission in WSNs.

Although combining a high-resolution camera with a wireless sensing network is effective for interpreting different signals for image presentation on the identification of face recognition, its accuracy is still severely restricted. Removing the unfavorable impact of ambient light remains one of the most difficult challenges in facial recognition. Therefore, it is important to find an algorithm that can capture the major features of the object when there are ambient light changes. In this study, face recognition is used as an example of image recognition to analyze the differences between Local Binary Patterns Histograms (LBPH) and OpenFace deep learning neural network algorithms and compare the accuracy and error rates of face recognition in different environmental lighting. According to the prediction results of 13 images based on grouping statistics, the accuracy rate of face recognition of LBPH is higher than that of OpenFace in scenes with changes in ambient lighting. When the azimuth angle of the light source is more than +/−25° and the elevation angle is +000°, the accuracy rate of face recognition is low. When the azimuth angle is between +25° and −25° and the elevation angle is +000°, the accuracy rate of face recognition is higher. Through the experimental design, the results show that, concerning the uncertainty of illumination angles of lighting source, the LBPH algorithm has a higher accuracy in face recognition.

Virtual Private Server is a private server whose entire resource is used by one user only and is not influenced by other users. The technology used by VPS is virtualization of physical server hardware which is then divided into several different resources. Called virtual because this division uses software so that in one physical server there are several VPS running. To be more extensive in terms of server control, researchers use Public IP and Dedicated IP as a distributor of VPS systems from local to public. In addition, in this VPS implementation, debian linux and ubuntu linux are used as operating systems to create a VPS server. To get the value of the VPS network performance test from the client using the parameters of the Quality of Service (QoS), namely Delay and Jitter. From the test results, the value of the VPS server resource obtained can be categorized as very good and feasible to be implemented with an average server CPU load of 2.9%, memory 71.23%, disk space 16.1%. And testing the QoS parameters (delay, jitter) also gets good performance and has a fairly low value obtained with an average delay value of 6-37.24%, jitter of 8.95-59.95%.

A student of a three-year vocational program (Diploma III), must work on their final project in the third year. The student's final project must correspond to the output profile of informatics engineering, including Junior Web Programmer, Junior Network Administrator and Junior Programmer. However, the most difficult aspect of the annual graduating design final project is topic selection by students or supervisors. This work intends to use the best recommendation model of neural network (NN) to classify the student into three graduate profiles based on the profile of graduates in the study program with data support in the form of personal abilities, obtained from an IQ test, recap of academic grades and student interest, simultaneously, Chat-GPT algorithm is utilized to generate recommendations for the final project topic. Furthermore, researchers can utilize the data to determine which themes are more famous and to prevent duplicate ones. The primary objectives of the paper are as follows: 1. Supervisors can easily direct students toward topics that are relevant for the graduate profiles. This is mostly utilized as reference by institutions to improve their curriculum. 2. Students study and work on the final project based on the fundamental skills they possess and will master.