Association for Scientic Computing Electronics and Engineering (ASCEE): Open Journal Systems
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Well-Being support by Sandwich generation in the Films Cinta Pertama, Kedua, dan Ketiga
Get PDFLarge families are very common in Indonesian culture. Individuals living in extended families with multigenerational childcare responsibilities are known as the Sandwich Generation. The challenges involved in multi-generational parenting impact the lifestyle of the Sandwich Generation as a whole, including personal time, career development, and financial stability. The Sandwich Generation is at risk for various mental health problems, including stress, depression, and anxiety, due to increased pressure and demands. The objective of this research is to see the picture of well-being support in the sandwich generation as reflected in the films Cinta Pertama, Kedua, dan Ketiga. The research method used to review the films Cinta Pertama, Kedua, dan Ketiga is Content Analysis by Philip Mayring with an interpretive paradigm. Abstraction, explication, and arrangement were carried out to analyze this film. There are three things that can be concluded from the content analysis of the films Cinta Pertama, Kedua, dan Ketiga. First, the dynamics of the life of the sandwich generation are described as full of problems. Second, the main character Dewa is described as having well-being support from his family. Third, the well-being support obtained cannot be separated from the standard culture that exists in Indonesia, namely Guyub, where people live in harmony and support each other as a family member
Combination of Genetic Algorithm and Neural Network to Select Facial Features in Face Recognition Technique
Get PDFFace recognition methods are computational algorithms that follow aim to identify a person's image according to the bank of images they have of different people. So far, various methods have been proposed for face recognition, which can generally be divided into two categories based on face structure and based on facial features. Based on this, many algorithms have been introduced and used for face recognition. Genetic algorithm has been one of the successful algorithms for face recognition. In this article, we first briefly explained the genetic algorithm and then used the combination of neural network and genetic algorithm to select and classify facial features The presented method has been evaluated using individual features and combined features of the face region. Composite features perform better than face region features in experimental tests. Also, a comprehensive comparison with other facial recognition techniques available in the FERET database is included in this paper. The proposed method has produced a classification accuracy of 94%, which is a significant improvement and the best classification accuracy among the results established in other studies
Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms
Get PDFIn order to minimize surgical site infections, indoor air quality in hospital operating rooms is a major concern. A wide range of literature on the relevant issue has shown that air contamination diminution can be attained by applying a more efficient set of monitoring and controlling systems that improve and optimize the indoor air status level. This paper discusses a fuzzy inference system (FIS) and the integrated model neuro-fuzzy inference system (ANFIS) focusing on the control of contamination via proper airflow distribution in an operating room, which is essential to guarantee the accuracy of the surgical procedure. A deep learning estimation approach is proposed to predict incidence in the presence of airborne contamination. The project's goal is to reduce airborne contamination to improve the surgical environment and reduce the predicted incidence during surgeries. The neuro-fuzzy deep learning model was trained with a neural network structure and tested by considering 3 important parameters that affected the air quality introducing the specialization of the system to control the model’s target. Finally, the proposed approach has been put into practice by making use of data collected by sensors placed within a real operating room in a hospital in Mashhad, Iran. The proposed model attains 97.3% and 95% validation accuracy for estimating the relative humidity and particles, respectively. The efficacy of the proposed neuro-fuzzy indicates that the system significantly lowers risk values and enhances indoor air quality
Forward and Inverse Kinematics Solution of A 3-DOF Articulated Robotic Manipulator Using Artificial Neural Network
Get PDFIn this research paper, the multilayer feedforward neural network (MLFFNN) is architected and described for solving the forward and inverse kinematics of the 3-DOF articulated robot. When designing the MLFFNN network for forward kinematics, the joints' variables are used as inputs to the network, and the positions and orientations of the robot end-effector are used as outputs. In the case of inverse kinematics, the MLFFNN network is designed using only the positions of the robot end-effector as the inputs, whereas the joints’ variables are the outputs. For both cases, the training of the proposed multilayer network is accomplished by Levenberg Marquardt (LM) method. A sinusoidal type of motion using variable frequencies is commanded to the three joints of the articulated manipulator, and then the data is collected for the training, testing, and validation processes. The experimental simulation results demonstrate that the proposed artificial neural network that is inspired by biological processes is trained very effectively, as indicated by the calculated mean squared error (MSE), which is approximately equal to zero. The resulted in smallest MSE in the case of the forward kinematics is 4.592×10^(-8) in the case of the inverse kinematics, is 9.071×10^(-7). This proves that the proposed MLFFNN artificial network is highly reliable and robust in minimizing error. The proposed method is applied to a 3-DOF manipulator and could be used in more complex types of robots like 6-DOF or 7-DOF robots
The teaching of EFL reading at a university level: Teachers' and students' perceptions
Get PDFThis study investigated the teaching of EFL reading at a university level from teacher’s and students’ perspectives. It delved into how the participating lecturer conducted the teaching of reading. Moreover, it shared the difficulties of teaching reading and the difficulties of students in reading. In this research, the researchers used a case study. The researchers used observation and interviews to collect the data. The participants of this study are one lecturer and three students situated in a private Islamic university in Malang, Indonesia. The recruited lecturer has been teaching reading for more than 10 years. Meanwhile, the recruited students were selected because they were close to the researchers and easy to access. The findings suggest that the teaching of reading was done by making a group discussion and cooperative learning such as Think-Pair-Share and Jigsaw. Besides, the minimal access to high-quality books for students’ reading tasks is one of the difficulties in the teaching of reading. Students also found it difficult to read difficult words. Mostly, they skipped the words and struggled to look for the text's main ideas and topic sentences. Finally, based on the result of the study, it is suggested that English lecturers should use appropriate reading books and techniques, select other supplementary materials, and vary the teaching activities. Future researchers are urged to conduct more intensive research in the teaching of reading in different contexts
Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems
Get PDFChatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field
A Review on Energy Management of Community Microgrid with the use of Adaptable Renewable Energy Sources
Get PDFThe main objective of this paper is to review the energy management of a community microgrid using adaptable renewable energy sources. Community microgrids have grown up as a viable strategy to successfully integrate renewable energy sources (RES) into local energy distribution networks in response to the growing worldwide need for sustainable and dependable energy solutions. This study presents an in-depth examination of the energy management tactics employed in community microgrids using adaptive RES, covering power generation, storage, and consumption. Energy communities are an innovative yet successful prosumer idea for the development of local energy systems. It is based on decentralized energy sources and the flexibility of electrical users in the community. Local energy communities serve as testing grounds for innovative energy practices such as cooperative microgrids, energy independence, and a variety of other exciting experiments as they seek the most efficient ways to interact both internally and with the external energy system. We discuss several energy management tactics utilized in community microgrids with flexible RES, Which include various renewable energy sources (wind, solar power, mechanical vibration energy) and storage devices. Various energy harvesting techniques have also been discussed in this paper. It also includes information on various power producing technology. Given the social, environmental, and economic benefits of a particular site for such a community, this paper proposes an integrated technique for constructing and efficiently managing community microgrids with an internal market. The report also discusses the obstacles that community microgrids confront and proposed methods for overcoming them. This paper analyzes future developments in community microgrids with adaptive RES. The study discusses potential developments in community microgrids with flexible energy trading systems
Finite-Time Synchronization of the Rabinovich and Rabinovich-Fabrikant Chaotic Systems for Different Evolvable Parameters
Get PDFThis paper addresses the challenge of synchronizing the dynamics of two distinct 3D chaotic systems, specifically the Rabinovich and Rabinovich-Fabrikant systems, employing a finite-time synchronization approach. These chaotic systems exhibit diverse characteristics and evolving chaotic attractors, influenced by specific parameters and initial conditions. Our proposed low-cost finite-time synchronization method leverages the signum function's tracking properties to facilitate controlled coupling within a finite time frame. The design of finite-time control laws is rooted in Lyapunov stability criteria and lemmas. Numerical experiments conducted within the MATLAB simulation environment demonstrate the successful asymptotic synchronization of the master and slave systems within finite time. To assess the global robustness of our control scheme, we applied it across various system parameters and initial conditions. Remarkably, our results reveal consistent synchronization times and dynamics across these different scenarios. In summary, this study presents a finite-time synchronization solution for non-identical 3D chaotic systems, showcasing the potential for robust and reliable synchronization under varying conditions
Understanding of Convolutional Neural Network (CNN): A Review
Get PDFThe application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet
A generative deep learning for exploring layout variation on visual poster design
Get PDFLayout variation is an essential concept in design and allows designers to create a sense of depth and complexity in their work. However, manually creating layout variations can be time-consuming and limit a designer's creativity. The use of generative art as a tool for creating visual poster designs that emphasize layout variety is explored in this study. Deep learning through generative art offers a solution by using an algorithm to generate layout variations automatically. This paper uses the VQGAN and CLIP approach to describe a generative art system, which renders images via a text prompt and produces a series of variations based on the zoom parameter 0.95 and shifts the y-axis 5 pixels. Our experiment shows that one frame can be generated roughly in 10.108±0.226 seconds, significantly faster than the conventional method for creating layouts on poster design. The model achieved a good quality image, scoring 4.248 using an inception score evaluation. The layout variations can be used as a basis for poster design visuals, allowing designers to explore different visual representations of layouts. This paper demonstrates the potential of generative art to explore layout variation in visual design, offering designers a new approach to creating dynamic and engaging visual designs