bluuebunny/crossref_metadata_2025 huggingface dataset:
English Teaching Quality Evaluation Model for Higher Education Based on Educational Data Mining Technology
Authors: Xiaohua Su
Published: May 2024

Constructing teaching quality evaluation systems for higher vocational education is beneficial for schools, serving as the cornerstone for developing abilities and enabling precise education management. In this publication, a Dual Relational Graph Attention Network (ETQE-DRGAN)-Based Educational Data Mining Technology English Teaching Quality Evaluation Model for Higher Education is proposed. In three ways, this study assesses and defends the system. An overview of the system's primary components, such as the user and data administration modules, data mining, online assessment, and inquiry modules, is given in the first section. Frequent item sets are divided into two stages and the development of invalid candidate sets is limited to avoid the formation of negative association rules. The Tid-list Vertical Partitioning Query-Apriori Algorithm (TPQ-Apriori algorithm) is improved by these two approaches. The system analysis is covered in section three. In order to assess the success of instruction, this work incorporates a Dual Relational Graph Attention Networks method that takes into account several factors such as age, gender, education, teaching attitude, teaching topic, teaching technique, and teaching effectiveness. The proposed ETQE-DRGAN is implemented in MATLAB, using the School Database for calculation. Numerous performance metrics like response time, precision, accuracy, sensitivity, and F1-score are utilized to calculate the effectiveness of ETQE-DRGAN technique. The obtained outcomes indicate that the ETQE-DRGAN approach achieves the highest precision of 98.5%, Accuracy of 98% and fastest response time of 6.95swhen compared to existing methods like ETQE-AA, ETQE-DTA and ETQE-ML.
Enhancing English Oral Teaching through Pyramidal Convolution Shuffle Attention Neural Network and Sea-Horse Optimizer using Virtual Reality Technology
Authors: Cuimin Sun
Published: May 2024

The ultimate objective of teaching English to students is to help them become self-sufficient language learners and users, proficient in efficient language learning techniques, and capable of transmitting information in English. As a result, good English language instruction requires language communication training for both students and teachers, as well as between students. Compared to classroom instruction, English learning could easily facilitate English learning and provides a comfortable environment by reducing the drawback of the conventional classroom, which could lead to lower ratings for mental strain, absence of communication, and fear of making mistakes. To avoid these challenges, the pyramidal convolution shuffle attention Neural Network with sea-horse optimizer is proposed for classifying pronunciation, speaking proficiency, fluency, and intonation, of the English oral teaching. Initially, the data’s are gathered via the dataset of oral English teaching in virtual reality dataset. Afterward, the data’s are fed to pre-processing. In pre-processing segment; it removes the noise and enhances the input images utilizing federated neural collaborative filtering. The pre-processing output is fed to Feature extraction segment. Here, four statistical features such as kurtosis, mean, skewness, and standard deviation are extracted based on Adaptive and concise empirical wavelet transforms. After that, the extracted features are given to the pyramidal convolution shuffle attention neural network optimized with sea-horse optimizer algorithm for effectively classify the pronunciation, speaking proficiency, fluency, and intonation. The proposed EOT-VRT-PCSANN-SHO approach is implemented in MATLAB.  The performance of the proposed EOT-VRT-PCSANN-SHO approach attains 99%, 98%, 97.5%, and 97%, as high accuracy, 98%, 98.5%, 95%, and 99% in F1 score, and 98.7%, 98%, 99%, and 97.5%, in precision, are high, when compared with existing methods.
Graph Neural Networks optimized with Gazelle Optimization Algorithm for Urban Plantscape Design Based on Large-scale Street View
Authors: Yue Liu
Published: May 2024

The center of human activity and a key point of contact for social interaction between city people and the built environment is the street. With more street-level photos becoming available, urban landscape studies have more chances to examine and evaluate streetscapes closely and from various angles. This manuscript present the graph neural networks (GNN) optimized with gazelle optimization algorithm (GOA) for urban plants cape design based on large-scale street view (UPLS-GNN-GOA). Initially data is taken from ADE20K dataset. Afterward the data is fed to General Robust Subband Adaptive Filtering (GRSAF) based pre-processing process. The pre-processing output is given to Discrete Fractional Fourier Transforms (DFrFT) to extract the street greenery and openness of street canyons. After that, the extracted features are fed to Graph Neural Networks (GNN) to classify the public health, urban microclimate, human perception. The weight parameters of the GNN are optimized using Gazelle Optimization Algorithm (GOA).The UPLS-GNN-GOA method is implemented in Python, and its efficiency is determined using a series of performance evaluation metrics analysis, including accuracy, precision, recall, f1-score, and sensitivity. The proposed UPLS-GNN-GOA method shows the highest accuracy of 98%, precision of 98%, and F1-score of 97% while comparing other existing methods such as urban plantscape design based on convolutional neural network (UPLS-CNN), urban plantscape design based on deep convolutional neural network (UPLS-DCNN) and urban plantscape design based on deep learning and convolutional neural network (UPLS-DL-CNN) respectively.
Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation
Authors: Yang Liu
Published: May 2024

Tennis is highly explosive, continuous, intense sport that involves numerous continuous short-term explosive actions. This type of training is short-term, high-intensity, and focuses on competitive skills. Athletes must be physically fit and have long-term endurance to excel in technical and tactical skills during competition. In this manuscript, Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation (MMARSCNN-PAM-TT-DM-IR) is proposed. The input data are collected from training levels of technical – sportive actions of 12-14 years old tennis players’ dataset. Initially the input data is preprocessed using Generalized Multi-kernel Maximum Correntropy Kalman Filter for exploring the data. The pre-processed data is given into Gradient Descent Namib Beetle Optimization (GDNBO) for choosing features from the dataset. Then, selected features are given to Dual Tree Complex Discrete Wavelet Transform to extract gray-scale statistical features such as Homogeneity, Entropy, Energy and Smoothness. Finally, the extracted feature attributes are given to MMARSCNN is used to Tennis Teaching Based on Data Mining. In general, MMARSCNN does not express some adaption of optimization strategies for determining optimal parameters to assure accurate Teaching of Tennis. Therefore, Zebra Optimization Algorithm (ZOA) is proposed to enhance weight parameter of MMARSCNN, which precisely Teaching of tennis depend on Data Mining. The proposed model is implemented, its efficacy is assessed utilizing some performance metrics such as accuracy, precision, F1-score, MSE, AUC. The proposed MMARSCNN-PAM-TT-DM-IR method provides 22.55%, 24.72% and 29.63% higher accuracy; 32.66%, 34.97% and 29.57% higher precision; 25.18%, 21.52% and 28.68% higher F1-Score is compared with existing method such as analysis characteristics of Tennis singles matches depend on 5G with data mining technology (AOC-TSM-DM), Tennis online teaching information platform depend on android mobile intelligent terminal (AMIT-TTP-DM), optimization analysis of Tennis players’ physical fitness index depend on data mining with mobile computing  (OA-TP-DM-MC) respectively.
Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network
Authors: Jinqing Luo
Published: May 2024

The creation of strategic pricing models is crucial in the highly competitive centralized energy markets in order to maximize revenues. In order to achieve maximum profit, market participants must act intelligently while bidding on electrical energy purchases or sales. Notwithstanding the many restructuring changes of the Chinese electricity sector, market participants are nonetheless exposed to risks associated with volatility in market prices and uncertainty over demand behavior. The data are collected from New South Wales electricity market. Data-adaptive Gaussian average filtering (DAGAF) is used to pre-process the data during the pre-processing step. Hierarchically Gated Recurrent Neural Networks (HGRNNs) are successfully used to classify both long-term and spot transactions. The neural network's weight parameter is optimized by the Lotus Effect Optimization Algorithm (LEA), which enhances the HGRNN. The RESMB-HGRNN-LEA proposed is implemented on the Python working platform. To calculate the suggested approach, performance measures including accuracy, precision, sensitivity, computation time, and recall were looked at.In comparison to the existing technique, the proposed RESMB-HGRNN-LEA method yields better results in terms of accuracy (16.65% and 18.85%), sensitivity (16.34% and 12.23%), precision (14.89% and 16.89%), and computing time (82.37% and 94.47%).
Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model
Authors: Jian Feng
Published: May 2024

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.
Prediction of Student Employability through Internship based on Big Data Analysis
Authors: Yubin Wang
Published: May 2024

The internship course is among the most important because it gives students a hands-on opportunity to apply their knowledge and get ready to launch a professional career. Internships, yet, don't ensure employability, particularly in cases where a graduate's performance is subpar and internship requirements are not fulfilled. Researchers in the field of higher education face a significant challenge in predicting employability due to the multitude of factors that contribute to this issue. This research presented the methodology for more accurately classifying student data in order to overcome this drawback. Online surveys were used to collect data from Princess Nourahbint Abdulrahman University's College of Computer and Information Sciences information systems (IS) graduates (PNU). The Switching Hierarchical Gaussian Filter (SHGF) is used to preprocess the data at the pre-processing stage. The outcome from the pre-processing is transferred to the feature selection method, which uses Siberian Tiger Optimization (STO) to select the student features. The employed, continuing studies, unemployed, and training are successfully classified using the Multi Fidelity Deep Neural Network. The proposed MFDNN -STO applied to the MATLAB/Simulink platform. To calculate the proposed approach, performance metrics including recall, ROC, computation time, accuracy, precision, sensitivity, and F-score were examined. Higher accuracy of 16.65%, 18.85%, and 17.89%, as well as higher sensitivity of 16.34%, 12.23%, and 18.54%, are achieved by the suggested MFDNN-STO method. The computation time was reduced by 14.89%, 16.89%, and 18.23% as well as 82.37%, 94.47%, and 87.76% in comparison to the existing method.
Enhancing Study Space Utilization at UCL: Leveraging IoT Data and Machine Learning
Authors: Sathishkumar Chintala
Published: May 2024

University College London (UCL) has more than 51,000 students along with a challenge to find available study space. UCL has improved its Internet of Things (IoT) infrastructure to better analyze space occupancy in order to solve this. Current systems offer real-time information, but they are only available in crowded places. Therefore, this study presents a data analysis system that recommends the best study areas by using Machine Learning (ML) and historical data. The main library, student center, and science library at UCL are the top three study places. The goal of this study is to transform raw occupancy data from these venues into useful insights. The development of an independent system written in Python was facilitated by research into dataset optimization, ML regressions, popularity patterns, and feature analysis. This technique uses historical trends to forecast occupancy rates for the future.
The Balance between Artistry and Science in Landscape Design of Memorial Garden Based on the Prediction Model of Multimedia Visual Perception Quality
Authors: Wenjun Si
Published: May 2024

The importance of careful planning and design of the memorial garden's landscape has been steadily rising in recent years as people have grown more aware of the garden's surroundings. The analysis is carried out from two perspectives of the artistic and scientific balance of the memorial garden landscape in this paper, explaining the effect of the balance between artistry and science in landscape design, while illustrating the effect of artistic and scientific balance based on some real memorial garden landscapes. This analysis and explanation play a pivotal role in the landscape design of the memorial garden. The pervasiveness of multimedia programmes and the recognition of their value in enriching life go hand in hand. Researchers need to pay close attention to the spread of multimedia apps that improve such encounters.
A Multi-Source Stream Analysis Model for Adult Education Sustainability Study
Authors: Qi Niu
Published: May 2024

Adult education is instruction intended for students who are elder and have recently gone into employment. People have the opportunity to increase their expertise and develop new abilities by learning as adult students.  Investigation into adult education has been ongoing for a while, and there have been efforts made all around the globe to increase its application. The study's objectives were to investigate the features of the courses provided by education centers (EC), determine if they promote equality, and determine any organizational obstacles to adult education that were connected to these courses. Through the training courses they provide, ECs attempt to satisfy the educational requirements of people and industries in a variety of subjects. As a result, this research suggested a Multi-Source Stream Analysis (MSSA) Model for Adult Education Sustainability Study. The adult education sessions were the source of the information for this investigation. The adult education provided by ECs was evaluated by several criteria, including technical knowledge, communication skill, art and music, customs management, and self confidence. The research demonstrates that adult education enhances educational achievement and other professional abilities and makes adults more capable of realizing their full capability.
Application of BIM+GIS Integration Technology in the Construction of Urban Intelligent Water Informatization
Authors: Qiang Wu
Published: May 2024

Urban intelligent water informatization is the process of managing and operating urban water systems with the use of contemporary information and communication technology in order to increase productivity, security, and sustainability. Establishing the appropriate infrastructure is the initial stage in building an intelligent water system so that management can choose wisely how to manage and operate the water system. Building information modeling (BIM) is a digital depiction of a building's or infrastructure's structural and functional details. In most metropolitan regions, a fast development phase is predicted to cause several water issues, including water resource shortages, floods, poor drainage, and water resource contamination. As a result, a practical approach is required for urban intelligent water information technology with the help of artificial intelligence techniques. Intelligent water systems may be built using Building Information Modeling (BIM) software to increase the system's effectiveness, stability, and durability. By offering a potent tool for storing and analyzing geographical data connected to the water distribution system, a geographic information system (GIS) can play a crucial role in the development of urban intelligent water informatization using ArcGIS mapping Software. The Internet of Things (IoT) system is used to implement intelligent water informatization, hardware implementation using ESP32, and software development using MQTT Broker and Node-Red. As a result, we recommended utilizing BIM+GIS integration technology for building urban intelligent water informatization. The suggested system is evaluated using management efficiency, urban flood control, innovative Construction, intensive management, and computation cost. In this research, the main objective is to identify where there are water leaks broken pipes and early warning system and monitoring system for flash flood disasters in urban areas. The result findings show that the integration of BIM and GIS provides a comprehensive platform for managing complex water informatization infrastructure projects and maximizes efficiency and sustainability.
Environmental Sustainability in the Age of Deep Learning: Balancing Technological Advancement with Ecological Responsibility
Authors: Manesh Patil
Published: May 2024

The convergence of technological innovation, particularly deep learning (DL), with the importance of responsibility for the environment in achieving environmental sustainability. Deep learning (DL) offers to improve sustainability in different areas. This paper discusses DL breakthroughs and their applications in accomplishing SDGs, renewable energy, and environmental health. This discovers problems in reconciling technological innovation with caring for the environment by investigating the uses of deep learning in diverse areas and measuring their environmental implications. Furthermore, it explores CNN and LSTM techniques in Deep learning for incorporating environmental factors into the development, application, benefits and challenges of DL technologies to promote sustainability. This study aims to provide insights and recommendations for creating a harmonious link between technical advancement and ecological responsibility in the pursuit of environmental sustainability by conducting a comprehensive review of existing literature. There are three indicators: MAPE, RMSE, and MAE. The MAPE, RMSE, and MAE results are provided based on 7.5, 15, and 30 minutes, indicating low forecast accuracy.
Accessibility and Effectiveness of Online Learning in China's Vocational Education System: A Comparative Study between Urban and Rural Areas
Authors: Hou Yanlin
Published: May 2024

The rapid expansion of online learning in China has revealed significant disparities between rural and urban vocational students, necessitating a thorough investigation. This study explores the accessibility and effectiveness of online education in both urban and rural settings, focusing on key dimensions like internet availability, device readiness, digital literacy, infrastructure support, LMS adoption, feedback methods, student engagement, and satisfaction. Data was collected from 314 participants, and statistical techniques including the Mann–Whitney U test and Pearson correlation were employed for analysis. The study delved into six crucial areas related to online learning accessibility and academic performance, utilizing Principal Component Analysis (PCA) to understand complex interrelationships among variables. The findings indicated that rural students consistently scored lower in all aspects of online learning accessibility (p < 0.01) compared to urban counterparts. Moreover, rural vocational learners exhibited lower engagement and satisfaction levels in online education, resulting in lower academic achievement than their urban peers. The strong correlations among these parameters highlight the need for integrated interventions to bridge the digital divide. In conclusion, this study underscores the urgency of targeted interventions to address the digital divide between urban and rural areas in China.
Realistic Problems and Practical Paths of "Three-Wide Education" in Higher Education Based on Text Analysis and Mining
Authors: Tianyu Gao
Published: May 2024

"Three complete education" is a powerful attempt to meet the requirements of the times. Students must have high quality, and they are compound talents who meet the requirements of the times "Universal education" is to provide students with comprehensive training to improve their cultural level and spiritual and moral level to meet the requirements of future social competition. To study the comprehensive reform and development of the moral education in universities, this paper reviews and standardizes the formation and development of the comprehensive reform in universities, which is related to the development of the comprehensive reform from three aspects: strengthening the system interconnection in the process of all staff training, strengthening the effective interaction between cultures in the whole process, and ensuring the complete organic integration of cultures. It puts forward a solution to the comprehensive reform of "three complete education" for universities. Provide some reasonable suggestions for the intensive development of moral education in universities.
Research on Urban Architecture Design under the Concept of Smart City
Authors: Wei Wang
Published: May 2024

After the digital city and smart city concepts, the latest proposed smart city concept is a general summary of the advanced form of urban information development, which plays an important role in the economic transformation of cities, social governance, improvement of residents' quality of life and energy conservation and environmental protection. The smart city puts forward new requirements for the architectural design of the city, and how to reasonably use the smart city concept in the architectural design is still a major issue for architects and professionals to think about. To this end, this paper proposes a state tracking based full working condition modeling method for nonlinear industrial systems. For the problem that the amount of historical data is too large and it is difficult to screen the modeling data, a sliding window is designed to screen the steady-state data, and a fast recursive algorithm of standard deviation in the window is derived; the influence mechanism of unknown disturbances on the system is analyzed, and the data modeling by dynamic regression to the steady state is selected, and a data-driven modeling algorithm that can eliminate the influence of disturbances is proposed; the model information contained in the process industrial big data is used to apply the higher order function to fit each A linear variable parameter transfer function model based on the characteristic parameters is proposed. The identification of an industrial process is shown to be effective. The main points of urban architectural design should be noted under the concept of smart city from four aspects: safety, diversity, environmental protection and emotionality.
Landscape Design and Green Space Perception Enhancement Based on Panoramic Imaging and Segnet
Authors: Yueqiang Wang
Published: May 2024

Green view rate is an intuitive evaluation criterion used for green space perception. Traditional research on green view rate is mostly calculated based on flat images, which cannot fully reflect the subjective human perception of green volume in three-dimensional space. Based on the panoramic image, we propose the concept of panoramic green perception rate, obtain spherical panoramic photos by panoramic camera, convert isometric cylindrical projection into isoprojective cylindrical projection, and use the convolutional neural network model based on semantic segmentation to automatically identify the area of vegetation to achieve automatic recognition and measurement of panoramic green perception rate. The results were compared with manual discrimination and showed that the average intersection ratio (mIoU) of greenery recognition using Dilated ResNet-105 was 62.53%, and the average difference with manual recognition was 9.17%.
Evolving Power Factor Correction in Solar Water Pumping Systems through Zeta Converter-Based BLDC Motor Drives
Authors: L. Anbarasu
Published: May 2024

The aim of this paper is to present the design and implementation of a new power conditioning system for a stand-alone solar-powered water pumping system. The system integrates a Brushless DC (BLDC) motor drive with a Power Factor Correction (PFC) Zeta converter to improve energy efficiency and reliability. In order to power BLDC motors for water pumping applications—which are essential for irrigation in isolated locations without access to the grid—the suggested solution attempts to overcome the difficulties related to the direct use of variable solar power. The PFC Zeta converter, which is the system's central component, is made to maximize power extraction from photovoltaic (PV) panels under fluctuating solar irradiation, guarantee high power factor, and reduce harmonic distortion in the electrical grid. A BLDC motor that powers a water pump is efficiently operated by this converter, which also optimizes the DC link voltage. A BLDC motor is especially well-suited for solar-powered applications due to its increased efficiency, dependability, and longer lifespan when compared to traditional induction motors. In order to continuously modify the PV panels' operating point to the maximum power point and maximize the amount of energy they capture, a Maximum Power Point Tracking (MPPT) algorithm is incorporated into the PFC Zeta converter's control strategy. The BLDC motor drive control strategy is carefully designed to guarantee seamless operation and flexibility in response to the fluctuating output from the solar panels, upholding the best possible pumping action in any solar situation. The system's capacity to sustain high efficiency and power factor under a variety of operating circumstances is demonstrated by the experimental findings. When the PFC Zeta converter is used, the solar-powered water pumping system performs much better overall. It shows a notable increase in energy conversion efficiency and lowers total harmonic distortion, which prolongs the pumping system's lifespan. By offering a solid method for raising the effectiveness and dependability of solar-powered water pumping devices, this study advances the sector and holds the possibility of a long-term water supply option for agricultural irrigation in isolated locations. In addition to utilizing renewable solar energy's environmental advantages, the suggested solution tackles the real-world difficulties associated with putting dependable and effective water pumping equipment into off-grid applications.
Implementation of Trust Model for Social Internet of Things Using Centrality Feed Forward Networks
Authors: Chethan Raj C
Published: May 2024

Recent advances of Internet of Things (IoT) lead to the most promising paradigm called Social Internet of Things (SIoT). These techniques are considered the strong amalgamation of the social networking features with the IoT objects. These networks are characterized by facilitating the IoT objects to establish the social networking between each other. In SIoT, an interconnection of networks formed by considering the important features such a object-object interactions, social relationship, reliable recommendations and mandates the careful attention towards the strong trustworthy connections. Hence Intelligent Trust Model Identification Model System (ITMIS) is required for the SIoT networks to identify the misbehaving objects by selecting only the reliable, credible and trustworthy objects before relying on the services provided by them. However, existing frameworks truly relay on the conventional approaches that is based on linear relationship between the inputs and outputs. These methods may lead to the high misclassification ratio of selecting the untrusted devices that even may cause untrust process in an application. To overcome this problem, in the proposed research work the novel ITMIS which ensembles the non-linear centralities relationships with the architecture Extreme Feed Forward Neural Networks (EFFNN) with the combination of hybrid algorithm of Long Short Term Memory (LSTM) and Gated Recuurent Unit (GRU) for the better accuracy compare with the existing model. The proposed model captures the number of key trust metrics based on centralities measurements and envisages the EFNN to classify the trusty and non-trust objects. The extensive experimentations are conducted using the real world datasets and various trust metrics were evaluated and compared with the other state-of-the-art trust learning models. The results demonstrate that the proposed model has outperformed with the other existing models by maintaining the accuracy of 95% to 94% with the decreasing untrusted rates and It illustrates conclusively that the LSTM's use of enhancing ensemble characteristics has shown to be more advantageous. While other models, like E-LSTM (90%) to 80%, Stacked LSTM (85% to 80%), SVM (85% to 79%), KNN (75% to 68%), and RF (65% to 58%), exhibit decreasing efficiency, the proposed approach illustrates that it is more productive as well as efficient for building an intelligent trust classification system that is appropriate for establishing trusted SIoT network communication.
Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG
Authors: A. Ravi Shankar
Published: May 2024

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances.  The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.
FPGA Implementation of Direct Torque Control for Surface Mounted Permanent Magnet Synchronous Motor using PID Controller
Authors: N. Krishna Kumari
Published: May 2024

This paper presents a real time FPGA implementation of a Direct Torque Controller for Surface Mounted Permanent magnet Synchronous Motor (SPM) using PID controller. The Direct Torque algorithm with PID controller is designed and implemented using VHDL.The complete digital controller is divided into three modules. From first module position of flux vector is found based on the flux error and torque error and the sector. The torque error is obtained from PID controller. From second module the switching state of the inverter is found based on the position of the flux vector, whereas third module indicates the complete digital controller. The digital controller algorithm presented in this paper has been implemented on a Xilinx Spartan-3 FPGA board. The inverter keeps the same state till the outputs of the hysteresis controllers change states. This inverter is fed to the SPM to maintain a desired constant speed when the load varies. Experimental results on FPGA implementation of a Direct Torque Controller for SPM using PID controller are provided in this paper for two reference speeds and two load torques.
Integrated Passive Anti-Islanding Protection in Micro Grid
Authors: Nirali Upadhyay
Published: May 2024

In last two decades due to major interest amongst power system engineers and policy makers penetration of different distributed energy resources (DREs) into power distribution network are significantly increased. Integration of DREs with conventional power grid is a complex issue and will impose some challenges against power engineers. The major challenge is protection of micro grid, which is based on how your protection strategies distinguish islanding and non islanding conditions correctly and timely. Methods used for have strengths and limitations in the areas like speed, accuracy, power quality and Non detection islanding zone.  The prime motive of this research is to presents an efficient new integrated anti-islanding protection strategy using passive parameters. The proposed algorithm having a novel islanding detection technique, which discriminate the islanding events from the non islanding events of similar signature accurately with minimum non detection zone and minimum impact on power quality. The proposed strategy is the combination of different conventional and latest passive relays based on single and multiple parameters. Integration of different relays made in such a strategic way that it combines the strengths and benefits of each method and minimizes their limitations. The presented algorithm tested under MATLAB/SIMULINK environment in different network conditions. Finally, the efficiency of the algorithm has checked by testing it on standard microgrid structure. The results received from simulation confirmed that algorithm is detecting islanding conditions reliably and efficiently, followed by generation of trip/block signal correctly.
Performance Analysis of Gaussian Apodized Fiber Bragg Grating (GA-FBG) Dispersion Compensator for a Long Haul Optical Fiber Link
Authors: Jyoti Prashant Singh
Published: May 2024

This examination gives a complete analysis of the GA-FBG, a generally involved grating for dispersion compensation across a 150 kilometer optical link, from each point. The essential objective of this study is to distinguish how long the GA-FBG can work as an independent dissemination reimbursement gadget and how well it acts in various compensation settings. In this review, the GA-FBG is utilized as an option in contrast to the exorbitant Dispersion-Reimbursing Fiber inside the association to give a more safe optical link. The investigation delves into three specific compensation methods and studies the GA-FBG grating boundaries in light of the relative abundance of real imperatives associated with a Fiber Bragg Grating (FBG) in the design phase. Using a Piece Blunder Rate analyzer to examine the streamlined links' exhibition highlights allows us to evaluate their productivity. Results show that the GA-FBG exhibits a common dispersion compensation adequacy when the link's channel boundaries are tuned. Using gearbox over distances easily exceeding 500 km, the balanced compensation strategy delivers a remarkable Q-factor respect. Utilizing OptiSystem programming, we validate the proposed system's functional viability. The results of our study indicate that the GA-FBG is capable of efficiently counteracting dispersion, leading to an optical link that is both more economical and effective, with significant potential for extended transmission ranges.
Unleashing Innovation in Electrical and Electronic Engineering: The Role of New Technology in Driving Progress
Authors: B. Ravi Chandra
Published: May 2024

This research paper explores the dynamic landscape of electrical and electronic engineering, focusing on the pivotal role of new technologies in driving progress and innovation. The study conducts a comprehensive review of recent technological innovations, with a particular emphasis on the integration of artificial intelligence (AI) and machine learning (ML), the pervasive influence of the Internet of Things (IoT), and the transformative capabilities of 5G technology. Through a meticulous evaluation, the paper underscores the profound impact of these innovations on the field, emphasizing their contributions to enhanced efficiency, reliability, and communication capabilities within electrical and electronic systems. The implications for the future are discussed in the context of fostering continued innovation. Recommendations are presented to address the challenges and opportunities posed by these advancements. Key suggestions include investing in research and development, promoting collaboration through industry-academia partnerships, and encouraging interdisciplinary research to address complex challenges. The paper concludes by envisioning a future where electrical and electronic engineering, propelled by ongoing innovation and collaboration, plays a central role in addressing global challenges and driving societal advancements.
Linking Renewable Energy in Protected Areas for Sustainable Ecotourism Development
Authors: Shanker Bhattarai
Published: May 2024

The tourism sector has been booming in South Asia for several years. Many local people have also started tourism with traditional approaches in various protected areas and homestay places in Nepal. In recent years, the “sustainable tourism” approach has been prioritized too even from the community level. The interrelationship between “renewable energy” and “ecotourism” can be better explored by looking at the link between “renewable energy” and “environment” and “renewable energy” and “tourism” simultaneously. Thus, to know how the use of renewable energy has fostered sustainable Eco-tourism development, a systematic review is conducted to find all peer-reviewed articles in English published between 2009 and 2023 associated with the impact of the use of renewable energy for sustainable ecotourism development. The important databases “Google Scholar, JSTOR, and ScienceDirect,” search yielded 18,113 articles. Among 167 full articles, only 24 articles fulfilled our inclusion criteria. This comprehensive review concludes that integrating “renewable energy sources" into various aspects of tourism in protected areas not only transforms the tourism industry into eco-friendly ecotourism but also increases tourist arrivals ultimately fostering economic growth in the long run. Maintaining and protecting ecotourism and renewable energy is the process of the IoE and IED. These two new protection protocol technologies are decentralized to collect the data and store them in a safe and secure. The protocol helps to maintain sustainability without affecting the environment
Comparative Analysis of SMC and INC MPPT Algorithm for Dual Input Single Output System
Authors: Prabhakar Holambe
Published: May 2024

Many MPPT algorithms present to date have maximum output power from solar PV. This paper compares the SMC and INC algorithm results for the double input single output (DISO) system simulated on MATLAB/Simulink and verified with hardware. As the system is DISO, power is fed to the load, one from solar PV and another from the battery, with the help of a boost converter and bidirectional chopper. A control technique is designed to have constant voltage across the load and extract maximum power from solar PV under variable irradiance. In this project, the PI controller is used on the battery side, which helps to decide whether the battery works in undercharging or discharging mode. Different modes of operation are also shown in the paper, and the MPPT algorithm is used to extract maximum power from solar PV.
Optic Cup and Optic Disc Segmentation Based on improved TransUnet
Authors: Lina Jia
Published: May 2024

Glaucoma ranks as the second leading cause of blindness globally, surpassed only by cataracts. It inflicts irreversible harm to the optic nerve, and once vision is lost, restoration is unattainable for life[1]. Therefore, early detection of glaucoma is imperative.The cup-to-disc ratio serves as a primary diagnostic tool for identifying glaucoma. Generally, an excessively large cup-to-disc ratio in fundus photographs strongly suggests glaucoma. However, human error in diagnosing fundus photographs by clinicians is prevalent, leading to time-consuming, labor-intensive, expensive, and potentially inaccurate diagnoses during extensive screenings.To combat this challenge, an enhanced TransUnet-based method is proposed for optic cup and optic disc segmentation, aiding clinicians in large-scale glaucoma screenings. The model incorporates the Focus structure and CBAM structure. The Focus structure addresses information loss during single downsampling of images, preserving more data without changing image dimensions. Meanwhile, the CBAM structure integrates channel and spatial attention, enhancing the model's ability to extract features.On the ORIGA-650 dataset, this enhanced method achieved a mean Dice coefficient of 0.984 for the disc and 0.947 for the cup, along with a mean IOU value of 0.968 for the disc and 0.902 for the cup. Compared to alternative algorithms, the segmentation results exhibit superior accuracy, showcasing the efficacy of our proposed model.
PEMIKIRAN MAHMUD YUNUS TENTANG PENDIDIKAN ISLAM
Authors: Depita Teti
Published: May 2024

Pendidikan Islam memiliki peran penting dalam pembangunan masyarakat Muslim. Pendidikan Islam bertujuan untuk meningkatkan ketakwaan terhadap Tuhan Yang Maha Esa dan membentuk individu Muslim yang berdaya dan masyarakat yang berkualitas. Pemikiran pendidikan Islam menjadi landasan dalam mengembangkan sistem pendidikan di negara Muslim. Salah satu tokoh yang berkontribusi besar memberikan pemikiran terhadap pendidikan Islam salah satunya adalah Mahmud Yunus. Tujuan dari penulisan artikel ini adalah untuk membahas secara mendalam bagaimana konsep pemikiran pendidikan Islam yang dikemukakan oleh Mahmud Yunus. Metode yang digunakan dalam penelitian ini adalah studi kepustakaan (library research) dimana data diperoleh melalui buku-buku dan jurnal terkait dengan penelitian ini. Mahmud Yunus merupakan tokoh pembaharu dalam dunia pendidikan yang berasal dari Minangkabau. Pemikirannya mengenai pendidikan Islam berkaitan dengan tujuan pendidikan Islam adalah menyiapkan anak-anak didik agar pada waktu dewasa kelak mereka sanggup dan cakap melakukan pekerjaan dunia dan amalan akhirat, sehingga tercipta kebahagiaan bersama dunia akhirat. Selain itu juga membahas tentang kurikulum pendidikan Islam, metode pendidikan Islam dan kelembagaan pendidikan Islam. Meskipun pemikiran Mahmud Yunus terhadap pendidikan Islam digagas sudah cukup lama, namun pemikirannya menarik untuk dikaji kembali karena beberapa pemikirannya masih relevan dengan pendidikan era modern sekarang.
STUDY ON THE IMPACT OF THE MARKET SITUATION ON THE ECONOMIC RESULTS OF A COMPANY SPECIALIZED IN THE SALE OF JEWELRY
Authors:   “Ovidius” University of Constanta, Constanta, Romania, Mariana JUGĂNARU , Felicia RUSU ,   KMG International N.V., Daniela POPESCU ,   University of Craiova, Craiova, Romania
Published: May 2024

The economic performance of any society/company depends on the decisions made in organizing its entire activity (in all aspects, including marketing), as well as its ability to ensure an outward orientation. From a marketing perspective, understanding the multitude of connections, relationships that each company develops, and being aware of the importance of their impact on its activity, have led to an increased interest in studying the external environment. Anticipating the future evolution of external environment components and potential implications for the company's activity are important concerns for marketing activity management. This article presents a study based on the analysis and interpretation of the economic results achieved by a Romanian company during the Covid-19 pandemic. The aspects identified and conclusions drawn from this study highlight the fact that the change in market conditions has been an important element/factor for the company's performance and image.
ANALYSIS OF THE LITERATURE ON DEVELOPMENT ISSUES INNOVATION CLUSTERS
Authors:   Institute for Regional Economics Studies Russian Academy of Sciences, Smirnova E.А.
Published: May 2024

The study is aimed at the innovative clusters’ development problems analysis on the base of literature review. The cluster concept history development, the innovation clusters contemporary understanding approaches and the innovation clusters research’ bibliography indicators. So to summarize, the innovation clusters’ and their role’s in the regional economy research relevance.
THE USE OF COMPARATIVE ANALYSIS IN RESEARCH DEMOGRAPHIC DEVELOPMENT OF THE REGIONS THE EUROPEAN NORTH OF RUSSIA
Authors:   Institute for Regional Economics Studies Russian Academy of Sciences, Solodilov V. V.
Published: May 2024

The article presents the results of use of comparative analysis in researches demographic Eu-ropean North of Russia regions development. The trends and special features of the settlement pattern and urbanization development in these regions are described; the article addresses to some problems of the rural and urban territories development. The author submits the revealed geo-graphical regularities of demographic development of these regions.