Introduction: The field of rural landscape design deals with the design and recovery of rural areas and landscape in a way that it can support natural biodiversity in addition to human needs in sustainable ways as well as maintaining its cultural character. It employs the use of plants, landforms and some water without interference with the environment in order to come up with useful and beautiful spaces. Recognizing the importance of preserving the environment while at the same time investing in developmental projects, it is centralized and focuses on the entire eco-system with the aim of enhancing the lives of the people. Aim: This study aims to develop theoretical aspect of an innovative computer simulation model for designing rural landscapes by applying the technology of remote sensing image. Research methodology: We suggest a new Starling Murmuration search-driven Adaptive YOLOv7 algorithm to identify and categorize several rural buildings and setting types. For the image data, we collected abundant data from several environments using UAV devices to train our proposed model. It is not surprising that our proposed model combined the use of three dimensional (3D) geographic information system (GIS) virtual imaging design model in the simulation of the rural landscape designs. Our recommended model is then extended using SM optimization to improve object detection with YOLOv7. By repeated adjustments of the network parameters in a somewhat similar fashion like flocking, we managed to enhance both accuracy and efficiency. This framework exploits crowdsourcing for delimiting rural buildings and landscapes with high-fidelity. Findings and Conclusion: We implemented our recommended model in Python software. During the phase of evaluation, we evaluate the efficacy of our recommended SM-AYOLOv7 model across a variety of parameters such as precision (91.72%), recall (92.34%), Intersection over Union (IoU) (90.23%), and f1 score (93.64%). Our experimental results precisely indicate that our approach outperforms traditional approaches. We demonstrate significant increases in accuracy and adaptability, especially when adjusting to dynamic configurations.
导言：乡村景观设计涉及乡村地区和景观的设计和恢复，使其能够以可持续的方式支持自然生物多 样性和人类需求，并保持其文化特色。它利用植物、地貌和一些水，在不干扰环境的情况下，创造出有用而美丽的空间。由于认识到在投资发展项目的同时保护环境的重要性，它采用集中式设计，关注整个生态系 统，目的是改善人们的生活。研究目的：本研究旨在通过应用遥感图像技术，从理论方面开发一种创新的农村景观设计计算机模拟模型。研究方法：我们提出了一种新的斯塔琳-默默搜索驱动的自适应 YOLOv7 算法，用于识别和分类几种乡村建筑和环境类型。在图像数据方面，我们利用无人机设备从多个环境中收集了丰富的数据来训练我们提出的模型。毫不奇怪，我们建议的模型结合使用了三维地理信息系统（GIS）虚拟成像设计模型来模拟农村景观设计。我们推荐的模型利用 SM 优化技术进行扩展，以改进 YOLOv7 的目标检测。 通过以有点类似于植群的方式反复调整网络参数，我们成功地提高了准确性和效率。该框架利用众包技术对农村建筑和景观进行了高保真划界。研究结果和结论：我们用 Python 软件实现了我们推荐的模型。在评估阶段，我们评估了我们推荐的 SM-AYOLOv7 模型在精度（91.72%）、召回率（92.34%）、交集大于联合（IoU）（90.23%）和 f1 分数（93.64%）等多个参数上的功效。实验结果准确地表明，我们的方法优于传统方法。我们证明了准确性和适应性的显著提高，尤其是在适应动态配置时。
{"title":"Computer Simulation of Rural Landscape Design Based on Remote Sensing Image Technology","authors":"Kun Xing, YuQing Xia","doi":"10.52783/jes.4266","DOIUrl":"https://doi.org/10.52783/jes.4266","url":null,"abstract":"Introduction: The field of rural landscape design deals with the design and recovery of rural areas and landscape in a way that it can support natural biodiversity in addition to human needs in sustainable ways as well as maintaining its cultural character. It employs the use of plants, landforms and some water without interference with the environment in order to come up with useful and beautiful spaces. Recognizing the importance of preserving the environment while at the same time investing in developmental projects, it is centralized and focuses on the entire eco-system with the aim of enhancing the lives of the people. \u0000Aim: This study aims to develop theoretical aspect of an innovative computer simulation model for designing rural landscapes by applying the technology of remote sensing image. \u0000Research methodology: We suggest a new Starling Murmuration search-driven Adaptive YOLOv7 algorithm to identify and categorize several rural buildings and setting types. For the image data, we collected abundant data from several environments using UAV devices to train our proposed model. It is not surprising that our proposed model combined the use of three dimensional (3D) geographic information system (GIS) virtual imaging design model in the simulation of the rural landscape designs. Our recommended model is then extended using SM optimization to improve object detection with YOLOv7. By repeated adjustments of the network parameters in a somewhat similar fashion like flocking, we managed to enhance both accuracy and efficiency. This framework exploits crowdsourcing for delimiting rural buildings and landscapes with high-fidelity. \u0000Findings and Conclusion: We implemented our recommended model in Python software. During the phase of evaluation, we evaluate the efficacy of our recommended SM-AYOLOv7 model across a variety of parameters such as precision (91.72%), recall (92.34%), Intersection over Union (IoU) (90.23%), and f1 score (93.64%). Our experimental results precisely indicate that our approach outperforms traditional approaches. We demonstrate significant increases in accuracy and adaptability, especially when adjusting to dynamic configurations. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yilin Yin, Wenyu Liu, Hang Yin, Huimin Mao, Xiaoyu Li
In urban centers across China, the actual annual land supply frequently fails to meet government projections, significantly impacting local economic and social development. This study bridges the gap in prospective analyses of governmental decision-making concerning urban housing land supply. Employing fuzzy set qualitative comparative analysis, this research examines the housing land supply in 50 Chinese cities, including 16 first-tier and 34 non-first-tier cities. The goal is to explore the decision-making combinations that influence the supply of housing land, thereby aiding in the formulation of governmental policies. Our findings indicate that in first-tier cities, forward-looking decisions rely on low fiscal pressure, with purchase restrictions and land supply restructuring acting in tandem. In contrast, in non-first-tier cities, high population density or significant fiscal pressure necessitate enhancements in land supply structures without implementing purchase restrictions to sustain forward-looking governance. Additionally, while forward-looking decisions depend on numerous conditions, it is generally simpler to circumvent non-forward-looking decisions. This investigation integrates forward-looking theory into real estate research, offering valuable insights for the formulation of governmental land supply strategies.
{"title":"Forecasting Urban Housing Land Needs: A Comparative Analysis of Chinese Cities","authors":"Yilin Yin, Wenyu Liu, Hang Yin, Huimin Mao, Xiaoyu Li","doi":"10.52783/jes.4252","DOIUrl":"https://doi.org/10.52783/jes.4252","url":null,"abstract":"In urban centers across China, the actual annual land supply frequently fails to meet government projections, significantly impacting local economic and social development. This study bridges the gap in prospective analyses of governmental decision-making concerning urban housing land supply. Employing fuzzy set qualitative comparative analysis, this research examines the housing land supply in 50 Chinese cities, including 16 first-tier and 34 non-first-tier cities. The goal is to explore the decision-making combinations that influence the supply of housing land, thereby aiding in the formulation of governmental policies. Our findings indicate that in first-tier cities, forward-looking decisions rely on low fiscal pressure, with purchase restrictions and land supply restructuring acting in tandem. In contrast, in non-first-tier cities, high population density or significant fiscal pressure necessitate enhancements in land supply structures without implementing purchase restrictions to sustain forward-looking governance. Additionally, while forward-looking decisions depend on numerous conditions, it is generally simpler to circumvent non-forward-looking decisions. This investigation integrates forward-looking theory into real estate research, offering valuable insights for the formulation of governmental land supply strategies.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141388808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microgrids are becoming popular because they can meet the needs of people who want energy from natural sources and are using more and more energy. Itis important to emphasis on numerous safety and control features of a microgrid. During the change from following the grid to forming the grid, the instability of frequency and voltage due to control problems becomes the main concern. Then, the paper uses a method to control the frequency and voltage of power generators so they can share power efficiently. Furthermore, we are suggesting a way to handle situations when there is not enough control and to keep the system strong. We are proposing a method to prioritize and shed different parts of the system in three stages to help with this. The effectiveness of the method depends on how quickly the system reacts and is calculated based on the changing speed of the frequency. The process combines the battery capacity system and D-STATCOM in the microgrid to provide a reliable power supply to customers for a long time without sudden power cuts. We test the proposed procedures on a smaller version of an IEEE 13-bus microgrid using MATLAB. We recreate the time-domain to see if the procedures work well.
{"title":"Improvement of the Transient Stability of Grid Connected Microgrids Including Inverter Based DGs Using DSTATCOM","authors":"Maithem almousawi","doi":"10.52783/jes.3927","DOIUrl":"https://doi.org/10.52783/jes.3927","url":null,"abstract":"Microgrids are becoming popular because they can meet the needs of people who want energy from natural sources and are using more and more energy. Itis important to emphasis on numerous safety and control features of a microgrid. During the change from following the grid to forming the grid, the instability of frequency and voltage due to control problems becomes the main concern. Then, the paper uses a method to control the frequency and voltage of power generators so they can share power efficiently. Furthermore, we are suggesting a way to handle situations when there is not enough control and to keep the system strong. We are proposing a method to prioritize and shed different parts of the system in three stages to help with this. The effectiveness of the method depends on how quickly the system reacts and is calculated based on the changing speed of the frequency. The process combines the battery capacity system and D-STATCOM in the microgrid to provide a reliable power supply to customers for a long time without sudden power cuts. We test the proposed procedures on a smaller version of an IEEE 13-bus microgrid using MATLAB. We recreate the time-domain to see if the procedures work well.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alaa Sabeeh Salim, Mohamad Mahdi Kassir, Amir Lakizadeh
As smart cities continue to grow and the number of connected devices increases, power consumption becomes a critical concern. By offloading computationally intensive tasks from resource-constrained devices to more powerful edge servers, energy efficiency can be significantly improved. The research proposes a framework for managing power consumption in smart cities by offloading computational tasks to edge servers. This approach, considering factors like device capabilities, network conditions, and energy profiles, can improve energy efficiency. The framework's effectiveness is evaluated through real-world data simulations and performance metrics. Results show that offloading tasks to edge servers significantly reduces power consumption, conserving energy and prolonging battery life. The framework's adaptability ensures optimal resource allocation, maximizing energy efficiency without compromising performance. This research offers practical solutions for sustainable and energy-efficient operations in smarter cities.
{"title":"Managing and Decreasing Power Consumption of Devices in a Smart City Environment","authors":"Alaa Sabeeh Salim, Mohamad Mahdi Kassir, Amir Lakizadeh","doi":"10.52783/jes.3810","DOIUrl":"https://doi.org/10.52783/jes.3810","url":null,"abstract":"As smart cities continue to grow and the number of connected devices increases, power consumption becomes a critical concern. By offloading computationally intensive tasks from resource-constrained devices to more powerful edge servers, energy efficiency can be significantly improved. The research proposes a framework for managing power consumption in smart cities by offloading computational tasks to edge servers. This approach, considering factors like device capabilities, network conditions, and energy profiles, can improve energy efficiency. The framework's effectiveness is evaluated through real-world data simulations and performance metrics. Results show that offloading tasks to edge servers significantly reduces power consumption, conserving energy and prolonging battery life. The framework's adaptability ensures optimal resource allocation, maximizing energy efficiency without compromising performance. This research offers practical solutions for sustainable and energy-efficient operations in smarter cities. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kshirod Sarmah, Swapnanil Gogoi, Hem Chandra Das, Bikram Patir, M. J. Sarma
In sophisticated Human-Computer Interfaces (HCI), the emotional state of the user is becoming a crucial component that is closely linked to emotional speech recognition. Spoken expressions, which can be a part of human-machine interaction, are an important source of emotional information. Speech emotion recognition (SER) in deep learning (DL) continues to be a hot topic, especially in the field of emotional computing. Current deep learning (DL) and neural network methods are applied in this highly active field of research. This is as a result of its expanding potential, advancements in algorithms, and practical uses. Quantitative factors such as pitch, intensity, accent and Mel-Frequency Cepstral Coefficients (MFCC) can be employed to model the paralinguistic data contained in human speech. To achieve SER, three key procedures are usually followed: data processing, feature selection/extraction, and classification based on the underlying emotional qualities. The nature of these processes and the peculiarities of human speech lend support to the employment of DL techniques for SER implementation. A variety of DL methods have been used for SER tasks in recent affective computing research works; however, only a small number of them capture the underlying ideas and methodologies that can be used to facilitate the three main steps of SER implementation. With a focus on the three SER implementation processes, we provide a state-of-the-art assessment of research conducted over the last ten years that tackled SER tasks from DL perspectives in this work. Various issues are covered in detail, including the problem of low classification accuracy of Speaker-Independent experiments and the related remedies. The review offers principles for SER evaluation as well, emphasizing indicators that can be experimented with and common baselines.
在复杂的人机交互界面（HCI）中，用户的情绪状态正成为与情绪语音识别密切相关的重要组成部分。作为人机交互的一部分，口语表达是情感信息的重要来源。深度学习（DL）中的语音情感识别（SER）仍然是一个热门话题，尤其是在情感计算领域。当前的深度学习（DL）和神经网络方法被应用于这一高度活跃的研究领域。这得益于其不断扩大的潜力、算法的进步和实际用途。音高、强度、重音和梅尔频率倒频谱系数（MFCC）等定量因素可用于对人类语音中包含的副语言数据进行建模。要实现 SER，通常需要遵循三个关键程序：数据处理、特征选择/提取和基于基本情感质量的分类。这些过程的性质和人类语音的特殊性支持使用 DL 技术来实现 SER。在最近的情感计算研究工作中，有多种 DL 方法被用于 SER 任务；但是，只有少数方法捕捉到了可用于促进 SER 实施的三个主要步骤的基本思想和方法。在本作品中，我们将重点放在三个 SER 实施过程上，对过去十年中从 DL 角度处理 SER 任务的研究进行了最新评估。其中详细讨论了各种问题，包括与说话人无关的实验分类准确率低的问题及相关补救措施。综述还提供了 SER 评估的原则，强调了可进行实验的指标和通用基线。
{"title":"A State-of-arts Review of Deep Learning Techniques for Speech Emotion Recognition","authors":"Kshirod Sarmah, Swapnanil Gogoi, Hem Chandra Das, Bikram Patir, M. J. Sarma","doi":"10.52783/jes.3745","DOIUrl":"https://doi.org/10.52783/jes.3745","url":null,"abstract":"In sophisticated Human-Computer Interfaces (HCI), the emotional state of the user is becoming a crucial component that is closely linked to emotional speech recognition. Spoken expressions, which can be a part of human-machine interaction, are an important source of emotional information. Speech emotion recognition (SER) in deep learning (DL) continues to be a hot topic, especially in the field of emotional computing. Current deep learning (DL) and neural network methods are applied in this highly active field of research. This is as a result of its expanding potential, advancements in algorithms, and practical uses. Quantitative factors such as pitch, intensity, accent and Mel-Frequency Cepstral Coefficients (MFCC) can be employed to model the paralinguistic data contained in human speech. To achieve SER, three key procedures are usually followed: data processing, feature selection/extraction, and classification based on the underlying emotional qualities. The nature of these processes and the peculiarities of human speech lend support to the employment of DL techniques for SER implementation. A variety of DL methods have been used for SER tasks in recent affective computing research works; however, only a small number of them capture the underlying ideas and methodologies that can be used to facilitate the three main steps of SER implementation. With a focus on the three SER implementation processes, we provide a state-of-the-art assessment of research conducted over the last ten years that tackled SER tasks from DL perspectives in this work. Various issues are covered in detail, including the problem of low classification accuracy of Speaker-Independent experiments and the related remedies. The review offers principles for SER evaluation as well, emphasizing indicators that can be experimented with and common baselines. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Numerous factors influence college students' athletic behaviour and psychological qualities such as sports learning interest, autonomy support in sports play important roles in forming their participation in sports activities. The study used acceptable research methodologies to analyse effect of sports learning interest, autonomy support in sports on college students' sports behaviour, specifically their physical activity levels. In this research work, Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language (QECSSW-IGNN-QCTO) is proposed. The input data are collected from College student data from Sichuan University. Then, the input data are pre-processed using Adaptive-Noise Augmented Kalman Filter (ANAKF) for finding missing data and cleaning the duplicate data. Then the pre-processed data are given to Iso-Geometric Neural Network (IGNN) for evaluating the quality of college students sports work (sports exercise grade). In general, IGNN doesn’t express some adoption of optimization approaches for determining optimal parameters to evaluating the quality of college students’ sports work. Hence QCTO is proposed to optimize IGNN classifier which precisely evaluates the quality of college student’s sports work. The proposed QECSSW-IGNN-QCTO method is implemented in Python, and it assessed with several performance metrics like, Accuracy, Cross validation scores, Recall, F1 score, and ROC. The results show QECSSW-IGNN-QCTO attains 23.4%, 28.3%, and 22.6% higher Accuracy, 25.9%, 17.6%, and 29.4% lower Cross validation scores, 24.6%, 27.5%, and 18.7% higher Recall are analysed with existing methods such as, prediction method of college students’ sports behaviour depend on machine learning method (PMC-SSB-MLM), Designing and implementing an innovative sports training system for college students' mental health education (DII-STSC-SMHE), The effect of sports science students' online learning attitudes on their readiness to learn online in emerging coronavirus pandemic (ESS-SOLA-ECP) methods respectively.
{"title":"Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language","authors":"Yinchun Tang","doi":"10.52783/jes.3736","DOIUrl":"https://doi.org/10.52783/jes.3736","url":null,"abstract":"Numerous factors influence college students' athletic behaviour and psychological qualities such as sports learning interest, autonomy support in sports play important roles in forming their participation in sports activities. The study used acceptable research methodologies to analyse effect of sports learning interest, autonomy support in sports on college students' sports behaviour, specifically their physical activity levels. In this research work, Quality Evaluation of College Students' Sports Work Based on Intellectual or Intuitive Fuzzy Information in Language (QECSSW-IGNN-QCTO) is proposed. The input data are collected from College student data from Sichuan University. Then, the input data are pre-processed using Adaptive-Noise Augmented Kalman Filter (ANAKF) for finding missing data and cleaning the duplicate data. Then the pre-processed data are given to Iso-Geometric Neural Network (IGNN) for evaluating the quality of college students sports work (sports exercise grade). In general, IGNN doesn’t express some adoption of optimization approaches for determining optimal parameters to evaluating the quality of college students’ sports work. Hence QCTO is proposed to optimize IGNN classifier which precisely evaluates the quality of college student’s sports work. The proposed QECSSW-IGNN-QCTO method is implemented in Python, and it assessed with several performance metrics like, Accuracy, Cross validation scores, Recall, F1 score, and ROC. The results show QECSSW-IGNN-QCTO attains 23.4%, 28.3%, and 22.6% higher Accuracy, 25.9%, 17.6%, and 29.4% lower Cross validation scores, 24.6%, 27.5%, and 18.7% higher Recall are analysed with existing methods such as, prediction method of college students’ sports behaviour depend on machine learning method (PMC-SSB-MLM), Designing and implementing an innovative sports training system for college students' mental health education (DII-STSC-SMHE), The effect of sports science students' online learning attitudes on their readiness to learn online in emerging coronavirus pandemic (ESS-SOLA-ECP) methods respectively.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mengzi Zhang, Xiao Chen Yue, Jin Xiaocheng Zhou, Shaowei Zhang
Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.
{"title":"Talent Cultivation Quality of Software Engineering Majors Based on Deep Learning","authors":"Mengzi Zhang, Xiao Chen Yue, Jin Xiaocheng Zhou, Shaowei Zhang","doi":"10.52783/jes.3738","DOIUrl":"https://doi.org/10.52783/jes.3738","url":null,"abstract":"Online learning, to cultivate talents it is inevitable to encounter some pictures or videos with poor visual quality. Deep-learning algorithms are both data-hungry and expensive to compute. These algorithms work better after being trained on a broad and extensive collection of samples. The current moment deep learning methods must urgently make use of human intellect to address the issue in a way that reduces the most expensive effort computationally. This paper analyzes the current situation of software engineering talent cultivation quality of software engineering to enhance the quality of the education is improved by Hierarchically Gated Recurrent Neural Network (HGRNN). The aim of the work is to foster the development of world-class software engineering talents. Initially, the input data’s are gathered from public dataset train 400 with 400 grey pictures. HGRNN is image de-noising module, as for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such Back Propagation Network (BPN), Artificial Neural Network (ANN) and Decision Tree Algorithm (DTA) our proposed method obtains 98% of accuracy.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study of user perception and interaction with applications is referred to as user experience, or UX. The intricacy and versatility of software products, from requirements engineering to product functionality are well recognized. UX evaluations are often depends on prototypes, but it's important to consider the semantics embedded in software requirements to ensure project success. In this manuscript, Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network (SRE-UEDM-BDA-CBTCN) is proposed. The input data are collected from Requirements dataset. The collected data are given to the Convolution-Bidirectional Temporal Convolutional Network (CBTCN) to Design Modeling of Big Data Analysis user experience based on the dataset. In general, CBTCN does not express any adaption of optimization techniques for determining the ideal parameters to accurate Design user experience. Hence, African Vultures Optimization Algorithm (AVOA) is proposed in this work to improve the weight parameter of CBTCN. The proposed model is implemented and the efficiency is evaluated utilizing some performance metrics like accuracy, precision, specificity, sensitivity and F1-Score. The proposed SRE-UEDM-BDA-CBTCN method provides 28.46%, 21.34 and 33.81% higher accuracy, 22.88%, 26.52% and 34.63% higher Precision and 28.46%, 21.34 and 33.81% higher specificity compared with the existing techniques like Holistic big data integrated artificial intelligent modeling to improve privacy and safety in data management of smart cities (AIM-BDI-SDM), Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach (MHA-UED-MLA) and Towards Measuring User Experience based on Software Requirements (TM-UEB-SR).
{"title":"Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network","authors":"Xu Yang, Chunhua Bian","doi":"10.52783/jes.3737","DOIUrl":"https://doi.org/10.52783/jes.3737","url":null,"abstract":"The study of user perception and interaction with applications is referred to as user experience, or UX. The intricacy and versatility of software products, from requirements engineering to product functionality are well recognized. UX evaluations are often depends on prototypes, but it's important to consider the semantics embedded in software requirements to ensure project success. In this manuscript, Software Requirements Engineering and User Experience Design Modeling of Big Data Analysis using Convolution-Bidirectional Temporal Convolutional Network (SRE-UEDM-BDA-CBTCN) is proposed. The input data are collected from Requirements dataset. The collected data are given to the Convolution-Bidirectional Temporal Convolutional Network (CBTCN) to Design Modeling of Big Data Analysis user experience based on the dataset. In general, CBTCN does not express any adaption of optimization techniques for determining the ideal parameters to accurate Design user experience. Hence, African Vultures Optimization Algorithm (AVOA) is proposed in this work to improve the weight parameter of CBTCN. The proposed model is implemented and the efficiency is evaluated utilizing some performance metrics like accuracy, precision, specificity, sensitivity and F1-Score. The proposed SRE-UEDM-BDA-CBTCN method provides 28.46%, 21.34 and 33.81% higher accuracy, 22.88%, 26.52% and 34.63% higher Precision and 28.46%, 21.34 and 33.81% higher specificity compared with the existing techniques like Holistic big data integrated artificial intelligent modeling to improve privacy and safety in data management of smart cities (AIM-BDI-SDM), Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach (MHA-UED-MLA) and Towards Measuring User Experience based on Software Requirements (TM-UEB-SR).","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The expansion of technology and computer science, as well as advancements in language instruction and learning methodologies, has enabled computer-assisted language learning technologies to tackle this challenge. In the field of Chinese learning, a few language learning computerized systems in the country and abroad concentrate mainly on language, grammar acquisition only have one or two assessment indicators as basis of evaluation, that definite functional flaws provide a general assessment to learners' pronunciation. In this manuscript, Language Dissemination Paths and Modes Aided by Computer Technology (LDPM-QICCNN-KOA) are proposed. The input data are collected from Chinese Corpus dataset. Then the data is given into unscented trainable kalman filter for preprocessing the input data. Then the preprocessed data are provided to QICCNN for Language Dissemination. In general, the based Quantum-inspired Complex Convolutional Neural Network doesn’t express adapting optimization approaches to determine optimal parameters to ensure exact identification. Hence, KOA utilized to enhance Quantum-inspired Complex Convolutional Neural Network, which accurately done the Language Dissemination Paths and Modes. The proposed LDPM-QICCNN-KOA method is executed on python. Then performance of proposed technique is analyzed with other existing methods. The proposed technique attains 26.36%, 20.69% and 35.29% higher accuracy; 19.23%, 23.56%, and 33.96% higher F1-Score; 26.28%, 31.26%, and 19.66% higher precision when comparing with the existing methods such as research on network oral English teaching system depend on machine learning (LDPM-DBN), nonlinear network speech recognition structure in deep learning algorithm (LDPM-DNN), research on open oral English scoring system depend on neural network (LDPM-BPNN).
{"title":"Language Dissemination Paths and Modes Aided by Computer Technology","authors":"Yanghong Wu, Tao Huang","doi":"10.52783/jes.3732","DOIUrl":"https://doi.org/10.52783/jes.3732","url":null,"abstract":"The expansion of technology and computer science, as well as advancements in language instruction and learning methodologies, has enabled computer-assisted language learning technologies to tackle this challenge. In the field of Chinese learning, a few language learning computerized systems in the country and abroad concentrate mainly on language, grammar acquisition only have one or two assessment indicators as basis of evaluation, that definite functional flaws provide a general assessment to learners' pronunciation. In this manuscript, Language Dissemination Paths and Modes Aided by Computer Technology (LDPM-QICCNN-KOA) are proposed. The input data are collected from Chinese Corpus dataset. Then the data is given into unscented trainable kalman filter for preprocessing the input data. Then the preprocessed data are provided to QICCNN for Language Dissemination. In general, the based Quantum-inspired Complex Convolutional Neural Network doesn’t express adapting optimization approaches to determine optimal parameters to ensure exact identification. Hence, KOA utilized to enhance Quantum-inspired Complex Convolutional Neural Network, which accurately done the Language Dissemination Paths and Modes. The proposed LDPM-QICCNN-KOA method is executed on python. Then performance of proposed technique is analyzed with other existing methods. The proposed technique attains 26.36%, 20.69% and 35.29% higher accuracy; 19.23%, 23.56%, and 33.96% higher F1-Score; 26.28%, 31.26%, and 19.66% higher precision when comparing with the existing methods such as research on network oral English teaching system depend on machine learning (LDPM-DBN), nonlinear network speech recognition structure in deep learning algorithm (LDPM-DNN), research on open oral English scoring system depend on neural network (LDPM-BPNN).","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The construction industry has experienced important changes in recent years due to advancements in digital, artificial intelligence, and construction technologies, as well as the sector's on-going development and the advancement of science and technology. The creative growth of building industry, creative creation of architectural forms are partially supported technically by sophisticated parametric design apparatuses, the potent computing benefits of computer technology. In this manuscript, Exploration of Natural Element Form Optimization Algorithm using Spatial-Temporal Multi-Scale Alignment Graph Neural Network in Architectural Design Based on Morphological Theory (ENEF-OA-ADMT) is proposed. The STMSA-GNN and the Chaotic Coyote Algorithm (CCA) are two tools used by the proposed ENEF-OA-ADMT approach to improve architectural design based on morphological theory. The ST-MSA GNN's ability to capture intricate interactions and dependencies between several components in both space and time allows it to perform a comprehensive study of the morphological aspects of architectural designs. This graph neural network's integration of spatial and temporal dimensions enables a deeper understanding of how the architectural structural form design changes over time. The CCA optimized the ST-MSA-GNN to enhance the architectural structural form design. The proposed ENEF-OA-ADMT methodology skill fully combines these methodologies, creating a strong framework that allows architects and designers to work together to explore, refine, and create architectural structural design forms. The framework provided serves as a spur for further research, encouraging a more complete integration of technology and environment in the architectural domain. The effectiveness of proposed method is executed in python, evaluated through performance metrics encompassing accuracy, precision, specificity, Recall, computational time, F1 score, population diversification, randomness. Proposed ENEF-OA-ADMT method 34.56%, 28.63% and 21.89% higher accuracy, 34.97%, 32.13% and 21.89% higher precision and 34.68%, 20.84% and 29.76% higher randomness when compared with the existing methods such as Study of Morphological Design of Architecture from Geometric Logic Perspective (SOT-MDA-GLP), learning deep morphological networks by neural architecture search (LD-MN-NAS) and identifying degrees of deprivation from space utilizing deep learning with morphological spatial analysis of deprived urban areas (IDDS-DLMSA-DUA) respectively.
{"title":"Exploration of Natural Element Form Optimization Algorithm using Spatial-Temporal Multi-Scale Alignment Graph Neural Network in Architectural Design Based on Morphological Theory","authors":"Chen Liu","doi":"10.52783/jes.3730","DOIUrl":"https://doi.org/10.52783/jes.3730","url":null,"abstract":"The construction industry has experienced important changes in recent years due to advancements in digital, artificial intelligence, and construction technologies, as well as the sector's on-going development and the advancement of science and technology. The creative growth of building industry, creative creation of architectural forms are partially supported technically by sophisticated parametric design apparatuses, the potent computing benefits of computer technology. In this manuscript, Exploration of Natural Element Form Optimization Algorithm using Spatial-Temporal Multi-Scale Alignment Graph Neural Network in Architectural Design Based on Morphological Theory (ENEF-OA-ADMT) is proposed. The STMSA-GNN and the Chaotic Coyote Algorithm (CCA) are two tools used by the proposed ENEF-OA-ADMT approach to improve architectural design based on morphological theory. The ST-MSA GNN's ability to capture intricate interactions and dependencies between several components in both space and time allows it to perform a comprehensive study of the morphological aspects of architectural designs. This graph neural network's integration of spatial and temporal dimensions enables a deeper understanding of how the architectural structural form design changes over time. The CCA optimized the ST-MSA-GNN to enhance the architectural structural form design. The proposed ENEF-OA-ADMT methodology skill fully combines these methodologies, creating a strong framework that allows architects and designers to work together to explore, refine, and create architectural structural design forms. The framework provided serves as a spur for further research, encouraging a more complete integration of technology and environment in the architectural domain. The effectiveness of proposed method is executed in python, evaluated through performance metrics encompassing accuracy, precision, specificity, Recall, computational time, F1 score, population diversification, randomness. Proposed ENEF-OA-ADMT method 34.56%, 28.63% and 21.89% higher accuracy, 34.97%, 32.13% and 21.89% higher precision and 34.68%, 20.84% and 29.76% higher randomness when compared with the existing methods such as Study of Morphological Design of Architecture from Geometric Logic Perspective (SOT-MDA-GLP), learning deep morphological networks by neural architecture search (LD-MN-NAS) and identifying degrees of deprivation from space utilizing deep learning with morphological spatial analysis of deprived urban areas (IDDS-DLMSA-DUA) respectively.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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