Multistep power consumption forecasting is smart grid electricity management’s most decisive problem. Moreover, it is vital to develop operational strategies for electricity management systems in smart cities for commercial and residential users. However, an efficient electricity load forecasting model is required for accurate electric power management in an intelligent grid, leading to customer financial benefits. In this article, we develop an innovative framework for short-term electricity load forecasting, which includes two significant phases: data cleaning and a Residual Convolutional Neural Network (R-CNN) with multilayered Long Short-Term Memory (ML-LSTM) architecture. Data preprocessing strategies are applied in the first phase over raw data. A deep R-CNN architecture is developed in the second phase to extract essential features from the refined electricity consumption data. The output of R-CNN layers is fed into the ML-LSTM network to learn the sequence information, and finally, fully connected layers are used for the forecasting. The proposed model is evaluated over residential IHEPC and commercial PJM datasets and extensively decreases the error rates compared to baseline models.

The purpose of this study is to look into the components that affect the behavioral intention of Jordanians to use Virtual Reality Technology in the Learning Environment (VRTLE). For the educational and entertainment industries, virtual reality presents both obstacles and opportunities. The goal of this study is to determine the factors that influence VRTLE acceptance and to propose how such technology may be integrated into the educational setting of students and universities. A total of 60 students from private universities took part in the research. Among the participants, 63.3 were females and 36.7 were males. The modified UTAUT model used in this study only looked at the effects of four independent variables and two external factors, namely, acceptability (ACC) and usability (USA). Acceptability and usability have a substantial impact on student acceptance of VRTLE, according to the findings. Furthermore, as moderators, there is a major effect on both Awareness and Experience. This paper contributes significantly to the UTUAT model. It also tackles educational concerns about the transition to a virtual reality learning environment. As a result, it would be useful to look into other factors as well. As a result, a comprehensive empirical study should be designed to allow for the assessment of the effects of other variables as well.

Intelligent transportation systems (ITS) provides a safe and reliable means of transferring data between vehicles. The document describes the transmission systems, protocols, networks, taxonomy, and applications of Intelligent Systems. Detailed analysis of the existing transmission flow systems is required, including classification, standards, coverage, applications, as well as their advantages and disadvantages. The adaptability of transmission networks, such as ad hoc, hybrid, mobile ad hoc networks (MANET), and Vehicular ad hoc networks (VANETs), has a significant advantage. Described protocols for a variety of communication types, including routing techniques, platforms, structures, and the use of information areas as well. The use of intelligent technology can determine reliable, comfortable, safe, and trustworthy vehicular communication. This paper analyzes the current vehicular communication (VC) research flow and their deployments with indicated areas where further development is necessary. This paper examines how emerging technologies in the upcoming markets will enable the development of high-featured VC technologies. The challenges of improving upon existing VC systems in the development of future systems are discussed in this paper, including medium selection, link and service quality, security, channel characteristics, and mobility. The purpose of this study is to identify the need for the development of improved VC technologies, networks, and protocols for a wide range of applications in the future.

Image processing is the main topic of discussion in the field of computer vision technology. With the increase in the number of images used over time, the types of images with different resolution qualities are becoming more diverse. Low image resolution leads to uncertainty in the task of image processing. Therefore, a method with high performance is needed for image processing. In image processing, there is a Convolutional Neural Networks (CNN) architecture for semantic segmentation of pixels called U-Net. U-Net is formed by an encoder network and decoder network that will later produce segmented images. In this paper, researchers applied the U-Net architecture to the lung CT image dataset, which has different resolutions in each image, to segment the image that produces a segmented lung image. In this study, we conducted experiments for many training and testing data ratios while also comparing the model performances between the single resolution dataset and the multiresolution dataset. The results showed that the segmentation accuracy using a single resolution dataset is as follows: 5 to 5 ratio is 66.00%, 8 to 2 ratio is 88.96%, and 9 to 1 ratio is 94.47%. For the multiresolution dataset, the application is: 5 to 5 ratio is 82.42%, 8 to 2 ratio is 90.12%, and 9 to 1 ratio is 93.66%. And for the result, the training time using single resolution dataset are: 5 to 5 ratio is 59.94 seconds, 8 to 2 ratio is 87.16 seconds, and 9 to 1 ratio is 195.34 seconds, as for multiresolution data application are: 5 to 5 ratio is 49.60 seconds, 8 to 2 ratio is 102.08 seconds, and 9 to 1 ratio is 199.79 seconds. Based on those results, we obtained the best accuracy for single resolution at a 9:1 ratio and the best training time for multiresolution at a 5:5 ratio. Doi: 10.28991/ESJ-2023-07-02-014 Full Text: PDF

Breast cancer is currently the most prevalent type of cancer in women, with a growing number of fatalities worldwide. Different imaging methods like mammography, computed tomography, Magnetic Resonance Imaging, ultrasound, and biopsies assist in detecting breast cancer. Recent developments in deep learning have revolutionized breast cancer pathology by facilitating accurate image categorization. This study introduces a novel approach to enhance detection and classification using the Convolutional Neural Network Deep Learning method and Transfer Learning to create a high-speed, accurate image classification model. The model is trained on pre-processed data subjected to thorough analysis and augmentation to ensure the quality of inputs. The experimental results from the Breast Ultrasound Image dataset indicate that our model, with a 0.1 test size ratio, outperforms its counterparts. It achieved an accuracy of 90.12%, with a loss of 0.2641, validation accuracy of 90.15%, and validation loss of 0.31, evidencing its superior classification capability. This research introduces an innovative approach to the automated diagnosis of breast cancer. By combining CNN, Transfer Learning, and data augmentation, we have developed a desktop application that expedites the classification process and significantly improves accuracy. This advancement represents a key development in machine learning applications for breast cancer prognostics and diagnostics. Doi: 10.28991/ESJ-2024-08-06-014 Full Text: PDF

Energy analysis that leads to energy efficiency becomes one of the most important factors in the building design process, especially considering the current energy crisis and the effects of global warming. Building designers greatly benefited from the review and analysis to optimize energy usage for the building in the design stage. While the current design approach is mostly done manually, this paper presents the automated version using the developed BIM plugin. It eases the designer’s choice of alternative plans that yield an effectively designed building. The development of energy analysis in the application aims to promote energy efficiency by calculating the energy consumption estimation based on energy codes MS2680 and MS1525. This application is improved by a simulation that uses the Building Information Modeling (BIM) platform and extracts the necessary parameters from the BIM model with the aid of the created plugins. This study measured energy consumption and efficiency using the two primary parameters of Overall Thermal Transfer Value (OTTV) and Roof Thermal Transfer Value (RTTV). According to the results, OTTV reaches 42.72% and RTTV reaches 8.02%, both of which respectively meet Malaysian Energy Code limits of less than 50% and 25%. Doi: 10.28991/ESJ-2023-07-06-022 Full Text: PDF