bluuebunny/crossref_metadata_2025 huggingface dataset:
Educational Big Data Analytics Using Sentiment Analysis for Student Requirement Analysis on Courses
Authors: Wang Meida , Yang Qingfeng
Published: August 2024

The online learning become a choice of most educational institution which creates enormous data on learning platform. This study introduces a novel framework that leverages Big Data analytics, with a focus on sentiment analysis, to decipher student requirements and preferences regarding course offerings and content. The objective is to harness the vast amounts of unstructured feedback generated by students in the form of reviews, forum posts, and surveys to inform and enhance educational strategies. We propose a sentiment analysis model multi attention fusion with CNN-BiLSTM model, that is adept at processing natural language and identifying the polarity of sentiments expressed by students. By analyzing this sentiment data, our system can capture the nuanced preferences and needs of students. The model is trained and validated on a diverse dataset encompassing various educational domains and student demographics, ensuring robustness and generalizability of the results. The outcomes indicate that sentiment analysis is an effective tool for uncovering hidden patterns and trends in student feedback. Our findings reveal correlations between student satisfaction and specific course features, such as module content, teaching methodologies, and resource availability. Additionally, the results evaluate precision, recall, accuracy and F1-score.
Green Plant Landscape Design for Urban Air Quality Purification with Computer Image Processing in Cloud, Grid, and Cluster Computing
Authors: Ni Jingjing
Published: August 2024

This research paper explores the innovative integration of green plant landscape design with advanced computer image processing in cloud, grid, and cluster computing environments to enhance urban air quality purification. The study begins by highlighting the critical need for improving air quality in urban areas, considering the rising levels of pollution and its impact on public health and the environment. The methodology involves the use of sophisticated image processing techniques to analyze various sensors on air quality measures and plant species their effectiveness in air purification, facilitated by the computational power of cloud, grid, and cluster computing. A diverse range of green plants was selected, and their air purification capabilities were assessed through a series of computer-simulated models. These models were developed using complex algorithms to predict the plants’ performance in real-world urban settings. The research uniquely combines landscape architecture with technology, emphasizing the role of green spaces in urban areas for environmental sustainability. The results demonstrate that certain plant species are more effective than others in purifying urban air. The study provides a comprehensive ranking of these plants based on their purification capabilities, growth requirements, and suitability for various urban landscapes. The paper concludes by proposing practical guidelines for urban landscape designers and policymakers, recommending the strategic incorporation of specific green plants in urban areas to maximize air purification. Additionally, it highlights the potential of leveraging advanced computing technologies in environmental research and urban planning. This research contributes to the fields of environmental science, urban planning, and computer science by showcasing how multidisciplinary approaches can address pressing environmental issues. It opens avenues for further research in the optimization of urban green spaces using advanced computing techniques. The results demonstrate that certain plant species are more effective than others in purifying urban air. The study provides a comprehensive ranking of these plants based on their purification capabilities, growth requirements, and suitability for various urban landscapes. The paper concludes by proposing practical guidelines for urban landscape designers and policymakers, recommending the strategic incorporation of specific green plants in urban areas to maximize air purification.
Learners Behaviour Prediction and Analysis Model for Smart Learning Platform using Deep Learning Approach
Authors: Feng Liyuan , Ji Yunfeng
Published: August 2024

In the quickly changing field of instructional technology, intelligent educational systems are now essential for individualized and effective instruction. To forecast and understand learners’ actions in intelligent educational settings, this research suggests an analytical framework that makes use of deep learning techniques. By offering real-time information on user activities, the goal is to improve these platforms’ reactivity and flexibility. Using state-of-the-art deep learning designs, our technique examines large datasets that include interactions between users, interest trends, and efficiency measures. The proposed method classifies the e-learning based behaviour classification and then the e-learning performance prediction using CNN-LSTM. The suggested framework incorporates the temporal relationships and sequential patterns present in learners’ actions on the platform by fusing convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). Furthermore, using multimedia information like simulations that are interactive and video lectures, convolutional neural networks (CNNs) are used to gather spatial data. The present study advances smart learning technology by providing a stable and expandable structure for behavior analysis and prediction in students. Through proactive customization of learning events, instructors, content producers, and platform developers can create a setting that is both enjoyable and effective for students. This is made possible by the knowledge gained from this approach.
Design of 0-day Vulnerability Monitoring and Defense Architecture based on Artificial Intelligence Technology
Authors: Hu Jian , Zhang Zhenhong , Hang Feilu , Xie Linjiang
Published: August 2024

In response to the difficulty in detecting attacks caused by the unknown nature of 0-day vulnerabilities, the author proposes a knowledge graph based 0-day attack path prediction method. By extracting concepts and entities related to attacks from existing research on the ontology of network security and network security databases, a network defense knowledge graph is constructed to extract discrete security data such as threats, vulnerabilities, and assets into interrelated security knowledge. Using a knowledge graph reasoning method based on path sorting algorithm to explore possible 0-day attacks in the target system. Experimental results have shown that the proposed method can rely on the knowledge system provided by the knowledge graph to provide comprehensive knowledge support for attack prediction, reduce the dependence of prediction analysis on expert models, and effectively overcome the adverse effects of unknown 0-day vulnerabilities on prediction analysis. It improves the accuracy of 0-day attack prediction and utilizes the path sorting algorithm to infer based on the explicit feature of graph structure, being able to effectively backtrack the reasons behind the formation of reasoning results, this to some extent improves the interpretability of attack prediction analysis results.
Digital Media Internet Modeling System under Computer Artificial Intelligence Technology
Authors: Li Miaojun , Li Qi , Peng Changrong , Zhang Xiaodong
Published: August 2024

With the continuous expansion of network scale, the hierarchical and modular characteristics of network structure are becoming increasingly prominent. The traditional single-layer network research paradigm has certain limitations in characterizing the complex relationships between various network systems. Analyzing and constructing models for multi-layer networks has gradually become an important direction in complex network research. The author proposes a multi-layer network model for the Internet and the significance of constructing multi-layer network models in the field of the Internet, based on the characteristics of the multi-layer network structure presented in the application process of the Internet. By analyzing the characteristics of internet data, a multi-layer network model covering three types of networks, namely the internet infrastructure layer, business application layer, and user account layer, was designed. The experimental results show that the average shortest path lengths of the three networks, namely routing relationship network, web hyperchain network, and email address network, are 3.8, 2.9, and 1.7, respectively. Due to the existence of inter layer edges in multi-layer networks, nodes can reach each other across layers in addition to intra layer edges. Experimental results show that the average shortest path length of the three networks has been shortened to some extent, with values of 3.1, 2.7, and 1.6, respectively. On the basis of the single-layer network generation model, a layer correlation method for multi-layer networks was designed, and the model construction of multi-layer networks was achieved.
The Application of Deep Learning Intelligent Robots in the Design and Implementation of Information Retrieval Systems
Authors: Miao Yuqi
Published: August 2024

Traditional information retrieval algorithms ignore user needs and are unable to obtain user gaze coordinates and gaze times, resulting in low retrieval accuracy. The author proposes a new interactive information retrieval algorithm for this purpose. Divide eye tracking technology evaluation indicators, visually process eye movement information, obtain user gaze coordinates and gaze time, and calculate the influence coefficients of each gaze area and each point in the area. Weighted visual words are accumulated to get a visual word list with the weight of the associated area, and visual word list and Rocchio algorithm are combined to build a hidden Relevance feedback retrieval model in semantic space to judge information retrieval preferences. Intelligent robot is introduced, and Jensen Shannon divergence is used to calculate the Kullback–Leibler divergence distance between the probability distributions of document sets, calculate the similarity matching, and complete the interactive information retrieval. The simulation results prove that, due to the introduction of intelligent robot strategy in this method, the amount of irrelevant retrieval information is reduced by matching user needs and retrieval results. Therefore, the amount of messages generated by information retrieval is significantly lower than that of the two literature methods, without adding additional network load. The network system is in a stable operation state, and users can quickly grasp their own required information, and the retrieval speed is also improved to a certain extent. It is proved that the proposed algorithm has high retrieval accuracy, can effectively reduce network load and achieve high-quality human-computer interactive information retrieval.
Application of Measurement Robots based on Deep Learning in Building Tilt Stability Monitoring
Authors: Zhang Wei , Li Junhua
Published: August 2024

In order to better understand the application of measuring robots in monitoring the stability of building inclination, this paper proposes a deep learning based algorithm for analyzing the trend of building inclination using measuring robots. The author first proposes a method of using measuring robots to monitor the tilting stability of buildings. Based on the principle of laser ranging, construct a robot structure and laser ranging model to achieve information exchange between different units. Secondly, based on simulation analysis, the position relationship between the laser and the building was marked with triangular coordinates, and the inclination of the building was marked with robot benchmarks. The simulation process was designed. By using the forward crossing method to evaluate the accuracy of tilt monitoring and comprehensively monitoring the three-dimensional angle of free station setting, the problem of low monitoring accuracy in traditional methods has been effectively solved. Finally, the experimental results show that using this method, the accuracy of building tilt monitoring can reach 99%, which is 7% higher than traditional measurement methods. Due to the low efficiency, high cost, and low accuracy of traditional manual monitoring work, it can no longer meet the requirements of modern engineering measurement. Therefore, high-precision measurement robots are used for tilt stability monitoring. Compared with traditional monitoring, high-precision measurement robots can achieve high-precision, high-efficiency, and low-cost monitoring with faster speed, higher efficiency, and strong automation capabilities.
The Integration and Innovation of Sports Social Platforms and Information Technology
Authors: Chen Yongjun
Published: August 2024

In order to better achieve the integration and innovation of sports social platforms and information technology, the author proposes an SFD (Sport Friend Discover) sports friend recommendation model based on physical testing big data. The core idea of this model is to use physical measurement data to match the similarity between athletes and recommend suitable exercise partners. Specifically, we collected a large amount of physical measurement data, including height, weight, body fat percentage, muscle mass, etc. Then, through data mining algorithms, these data are transformed into feature vectors of the movers. Next, we use a similarity algorithm to calculate the similarity between different athletes and find the most matching motion partner with the user. The results show that the SFD method outperforms the other two traditional recommendation methods on the dataset, with P @ 10, P @ 20, P @ 30, and P @ 40 of SFD reaching 0.099, 0.095, 0.085, and 0.591, respectively. SFD not only utilizes more neighboring information than FOAF based on local graph structure, but also compared to TRW based on global graph structure method, at the same time, the importance of the node itself is also considered, resulting in higher accuracy. It has been proven that the SFD sports friend recommendation model based on physical testing big data has achieved good results in recommending sports partners. Users can quickly find sports partners with similar body types and health conditions, improving the fun and effectiveness of exercise.
Multi Objective Data Transformation in Hybrid Clouds Networks for Offloading Data
Authors: V Sridhar Reddy , N Jayanthi , Juvvanapudi Sharon Rose Victor , Bachu Srinivas , Madipalli Sumalatha
Published: August 2024

Recently hybrid cloud solutions integrating public and private cloud is proposed to address the privacy and security concerns faced by Enterprises in their data offloading decisions. In these solutions, the transformed data is kept in public cloud while transformation keys are kept in private cloud. The existing works for data transformation used in hybrid clouds does not address multiple objectives of privacy, security, fine grained access control, utility preservation for mining and data retrieval efficiency. This work proposes a multi objective data transformation technique for hybrid cloud to address all these objectives. The proposed solution is built on attribute based hierarchical data access control with hierarchy selection based on joint consideration of security, utility preservation and retrieval efficiency. The proposed solution is able to provide 5% higher security strength, 1.34% higher clustering accuracy over perturbed data and 29.95 % higher data retrieval efficiency over perturbed data compared to existing works.
A Secure Data Storage Approach for Online Examination Platform using Cloud DBAAS Service
Authors: Banothu Srinu , G Janardhan , G Sirisha , Shepuri Srinivasulu , Karnam Madhavi , Allam Balaram
Published: August 2024

For the time being, many government or private organizations for recruitment of staff or educational institutions moving towards online based tests. The online examination system is a software application used for conducting examination using computer systems. It helps to the recruitment agency or any govt. or private organizations for conducting any job recruitment examinations transparently. Due to this system results are processed without delay and efficiently evaluated to assess the candidate’s abilities. But the biggest challenge for online examination system is data integrity, security and privacy. The current system is resolving the privacy issue by providing authentication credentials such as user name, password to the candidates. So that only authorized users with proper credentials can login to the system and attempt the exam. But the data confidentiality and integrity are biggest challenges for the system. As the data stored in system database is in plain text format, hence it may be modified or misused by the internal staff of the organization. This paper presents the frame work for secure storage and management of candidate’s data using encryption scheme, distributed databases in cloud database system. The proposed framework enhances the data confidentiality, integrity and avoids any cheating by internal staff or third party institutions. This paper conducts experimental work on proposed framework and analyses the results of the system.
Application of Multi-objective Optimization Algorithm based on Artificial Fish School Algorithm in Financial Investment Portfolio Problems
Authors: Zhang Hongxing
Published: August 2024

In order to comprehensively measure these two indicators and make reasonable portfolio investment decisions, the author proposes using swarm intelligence optimization algorithm - artificial fish swarm algorithm to solve multi-objective investment portfolio problems, and has achieved good results. In order to verify the effectiveness and superiority of the artificial fish school algorithm, the author used MATLAB programming to conduct simulation experiments using AFSA algorithm and genetic algorithm (GA), and compared the results obtained. The results show that compared to the GA algorithm, the artificial fish school algorithm can obtain better investment portfolio decision-making solutions for investing in five types of assets, making investment returns as large as possible while minimizing risks, indicating the efficiency and superiority of the algorithm in solving multi-objective investment portfolio problems.
Machine Learning-based Risk Prediction and Safety Management for Outdoor Sports Activities
Authors: Lu Yan
Published: August 2024

Participant safety is becoming increasingly important as outdoor sports activities gain popularity. A machine learning-based strategy for risk assessment and safety control in outdoor sports activities is presented in this paper. Our framework uses predictive modelling, sophisticated algorithms, and historical data analysis to identify potential dangers and improve safety procedures. It also considers participant profiles and environmental conditions. Comprehensive testing and validation are used to examine the model’s efficacy, showing that it can offer risk evaluations in real-time and support preventive safety measures. Our approach entails placing sensor-based Internet of Things (IoT) devices at building sites to gather extremely detailed temporal and geographic weather, building, and labour data. This data is then cooperatively used on the edge nodes to train Deep Neural Network (DNN) models in a cross-silos way. The present study makes a valuable contribution to sports safety by offering a clever approach that integrates technology and outdoor leisure to ensure participants have a safe and pleasurable experience. The experiment’s outcomes show how well the suggested strategy works to increase the adoption of construction safety management systems and lower the likelihood of future mishaps and fatalities. As a result, the system has improved speed and responsiveness, an important feature for time-sensitive applications like safety prediction.
Optimization of Computer Network Security System Based on Improved Neural Network Algorithm and Data Searching
Authors: Tian Chongfeng , Chen Zhihao , Zhu Yi , Lu Hongfei , Guoxiao Li  , Li Rongquan , Pan Wei
Published: August 2024

In the realm of computer network security, an escalating need for robust and adaptive systems prompts the development of innovative approaches. This paper introduces a novel framework, termed ”ALPSO AutoLSTM-PSO Security Optimization Framework,” designed for the optimization of computer network security systems. The framework synergistically integrates advanced techniques, including Autoencoder (Auto), Long Short-Term Memory (LSTM), and Particle Swarm Optimization (PSO). The Autoencoder, trained on normal network traffic data, serves as a feature learning mechanism, capturing essential representations. The LSTM, adept at modeling temporal dependencies, complements this by recognizing sequential patterns in network behavior. Furthermore, the PSO algorithm is employed to finely tune the parameters of both the Autoencoder and LSTM networks, enhancing their collective performance. The integrated model, forged through this holistic approach, forms the cornerstone of an improved neural network algorithm. To demonstrate the efficacy of the proposed ALPSO, comprehensive experiments are conducted using the NSL-KDD dataset. This dataset provides a realistic and diverse set of network traffic scenarios, enabling a thorough evaluation of the framework’s capabilities. The algorithm, enriched by the dynamic fusion of Autoencoder and LSTM outputs, is adept at anomaly detection and security threat identification. This framework, coupled with efficient data searching techniques, enables real-time analysis of network traffic, thereby fortifying the security infrastructure. The ALPSO Framework represents a comprehensive solution that amalgamates state-of-the-art technologies to address the evolving challenges in computer network security.
Research on Deep Learning-based Algorithm for Digital Image Combination and Target Detection
Authors: Huang Shanlu , Lai Jialin
Published: August 2024

This study uses deep learning techniques to improve target recognition and digital picture processing, combining efficiency and accuracy in the fields of computer vision and image processing. Different situations, heterogeneous circumstances in the environment, and a wide range of image properties present obstacles for the traditional approaches of combining images and target recognition. To address these issues, our research suggests a novel method that makes use of deep learning methods to identify relevant characteristics and trends from a variety of sources that provide diverse pictures. As part of the research process, a complex deep learning system that can recognize ordered representations of input photos is developed and trained. We will investigate whether faster RCNN are suitable for capturing temporal and spatial relationships in the image data. To maximize the model’s performance, deep learning techniques will be used to make use of pre-trained networks on sizable datasets. Benchmark datasets will be used in the method’s assessment, and it will be pitted with conventional image processing techniques. The accuracy and dependability of the algorithm’s performance will be evaluated using quantitative metrics including precision, recall, and F1-score. Furthermore, qualitative evaluations will be conducted to determine the visual appeal and interpretive capacity of the created composite images.
Design of Financial Data Analysis and Decision Support System based on Big Data
Authors: Zheng Sufang
Published: August 2024

A cutting-edge Decision Support System (DSS) utilizing Deep Reinforcement Learning (DRL) for improved financial data analysis is the primary focus of the proposed research. In light of the prospering difficulties presented by large information in the monetary space, our creative methodology outfits the force of DRL to foster a powerful and versatile framework. By flawlessly incorporating DRL into the DSS structure, we mean to improve the framework’s capacity to break down huge and complex monetary datasets. This DSS not only provides financial professionals with intelligent decision-making support but also real-time insights into market trends and patterns. The collaboration between enormous information investigation and DRL works with a dynamic and responsive framework equipped for adjusting to the quickly developing financial scene. Our exploration adds to the headway of choice by tending to the particular requests of monetary information, consequently enabling clients with ideal and informed dynamic abilities. The proposed DRL-based DSS addresses a change in perspective in monetary information examination, offering a versatile and effective answer for exploring the intricacies of enormous information in the financial area. This examination holds huge potential for changing dynamic cycles, advancing monetary security, and at last adding to the progression of the more extensive monetary industry.
Enhanced Throttled Load Balancing for Virtual Machine Allocation in Multiple Data Centers
Authors: Panuganti Hanumanth Rao , Subramanian Rajakumar
Published: August 2024

”Cloud computing” hosts software and other services in remote data centers that customers can access worldwide. The user may access all the services and applications online. The IT industry has benefited greatly from the proliferation of cloud computing. On the flip side, organizations moved their operations to the cloud as a result of industrial automation. A surge in demand for cloud computing was directly correlated to the quick migration of businesses. Businesses looking to minimize expenses without sacrificing service quality will find this approach to be ideal. Considering the meteoric rise of cloud computing, service providers are delighted. Contrarily, distributing resources is a challenging task. Cloud computing overcomes some of its most fundamental obstacles, one of which is the load-balancing approach employed by load-balancers to economically optimize costs while minimizing time expenditures. Quick services for cloud customers and minimal cost for cloud providers are the goals of the optimal resource allocation method. This research suggests a novel approach to increase task processing time, which can aid in increasing cloud computing’s load balancing capabilities. The proposed method Enhanced Throttled Load Balancing Algorithm (ETLBA) is an upgrade to the original Throttled Algorithm, which efficiently performs resource allocation and load balancing. The proposed ETLBA is contrasted with the existing algorithms, Round Robin, Active Monitoring Load Balancing Algorithm (AMLBA) and Throttled Load Balancing Algorithm (TLBA) to display the efficacy. Cloud Analyst tool simulates the proposed and existing methods. According on the results of the simulations, the proposed algorithm ETLBA achieves better outcomes than the popular existing algorithms in terms of processing time, request processing time, and datacenter cost. It shows 18% reduction in response time, 7% reduction in data center processing time, 16% reduction in data center request processing time and 4% less data center cost compared to the existing solutions. ETLBA performs better by selecting virtual machines using a prioritized indextable and consumption index. It limits idling resources, improves response as well as reduces processing times, and cloud costs compared to conventional solutions.
Design and Development of an Unmanned/Autonomous Ocean Surface Vehicle using Self-Sustaining Dual Renewable Energy Harvesting System
Authors: Murugan Kamalahasan , S Manivannan , B Swapna
Published: August 2024

This study pioneers a breakthrough in sustainable energy solutions by developing a cutting-edge system for powering autonomous Smart Ocean surface vehicles. The research delves into the exploration of renewable energy harvesting techniques, specifically focusing on solar and hydro flow energy systems, with the aim of creating a self-sustaining power infrastructure. Through rigorous experimentation and modeling, we design and implement a versatile test rig setup to analyze the efficacy of these techniques under varying surface water conditions. Additionally, we investigate and assess distributed solar power systems ranging from 100W to 700W, as well as hydro flow power systems within the same power range, to ascertain their viability for real-world applications. Furthermore, we engineer and optimize the necessary electronic hardware utilizing IoT and industrygrade components, enabling efficient harnessing of dual renewable energy sources to power propulsion systems for our autonomous vehicles. This research introduces novel approaches to energy sustainability for autonomous ocean surface vehicles. The study disregards traditional methods and instead aims to unveil unconventional solutions for powering these vehicles. Through a series of experimental investigations, we seek to redefine the boundaries of renewable energy utilization in marine environments. By leveraging cutting-edge technologies and industry-grade components, our work aims to establish a new paradigm in the propulsion systems of autonomous oceanic vehicles.
Adaptation of Scalable Neural Style Transfer to Improve Alzheimer's Disease Detection Accuracy
Authors: Albalawi Eid
Published: August 2024

Creative augmentation methods in medical imaging, particularly in diagnosing Alzheimer’s disease, is a breakthrough approach in the current medical field. Alzheimer’s disease, a condition that causes the gradual deterioration of cognitive abilities, presents considerable difficulties in accurately diagnosing and interpreting brain imaging, particularly in the early stages. Neural-enhance Style Transfer (NST), once recognized in the creative field for its capacity to combine the styles of many images, is currently being adapted to improve the clarity and comprehensibility of brain scans used to diagnose Alzheimer’s disease. The scalability of this technology is incredibly revolutionary in managing the immense amount of neuroimaging data. In addition, this method also includes the transfer of stylistic characteristics from high-resolution, annotated brain MRIs to broader sets of standard scans, which often need to be clarified and defined. Such enhancement greatly enhances important characteristics, such as brain networks and regions of degeneration, which are essential for the early identification of Alzheimer’s disease. An exemplary use of NST in this field has shown a significant enhancement in the discernibility of brain microstructures, vital for early diagnosis of Alzheimer’s disease. This improvement has resulted in a considerable rise of over 25% in the accuracy of detecting first pathological alterations. This technological progress not only assists in the prompt and precise identification of medical conditions but also tackles the difficulty of effectively handling the increasing amount of neurological imaging data.
The Application of Cluster Analysis Algorithm in Supply Chain Risk Identification
Authors: Zhang Qingping , He Yi
Published: August 2024

The risk control model of the power supply chain system is established. A fault information identification method based on fuzzy clustering is proposed. This method fully considers the power grid’s characteristics and uses terrible data. A risk assessment model based on fuzzy set theory is established by the COWA operator weight method and grey cluster evaluation method. The security risk identification model of power grid enterprises uses insufficient data. The security risk identification data are normalized and classified. Empirical analysis determines various risk factors that may appear in power projects. The applicability and feasibility of the index system and evaluation model are verified.
Retrieval of Telugu Word from Hand Written Text using Densenet-CNN
Authors: Boddu Rajasekhar , Edara Sreenivasa Reddy
Published: August 2024

The recognition of telugu hand written text is been one of the problems in many applications. To overcome the problem a deep learning technique is proposed in this work i.e. a Dense convolutional neural network (DCNN) model. A telugu dataset which is taken form IIIT-HW-Telugu is utilized to perform the proposed model. In this paper a four stage telugu word retrieval is performed, initially thinning of image is performed using morphological operation, secondly Densenet-CNN is applied for thinning image, thirdly perform OCR based image segmentation, finally two models like HARRIS and BRISK features to extract the features and evaluate information from the given input HWT images. The parameters evaluated are hamming distance, PSNR, MSE, Noise Sensitivity and rate of thinning. The proposed model outperformed well compared to other methods. The PSNR obtained using proposed model is 54.74, hamming distance is 1.2.
Sports Data Privacy Protection and Information Security Management
Authors: Jin Biao
Published: August 2024

In order to achieve the protection of personal privacy, the author proposes research on sports data privacy protection and information security management. The author used the two-dimensional fractional Fourier transform (2D-FRFT) method to encrypt the detected human body parts, which can be decrypted when needed for viewing. Compared to traditional Fourier transform, Fractional Fourier Transform (FRFT) can better express the time-frequency characteristics of signals and is very sensitive to the order of the transform. It is widely used in image encryption systems. 2D-FRFT increases the range of keys, further enhancing the security of the system. The author achieved encryption by extracting the detected human body parts and then performing a certain order of FRFT in the x and y directions respectively; When decrypting, use the same order of encryption to perform inverse fractional Fourier transform. Finally, based on research on pedestrian detection and encryption technology, the author designed a human-machine interaction interface that integrates the functions of detection and encryption interfaces, making the entire operation more intuitive and concise.
Simulation of Segmented Clustering of Cloud Storage Data Based on Neural Network Models and Python
Authors: Xia Guoqing , Chen Huazhen
Published: August 2024

In order to improve the operational efficiency of traditional cloud storage data segmentation clustering methods, the author proposes a machine learning based cloud storage data segmentation clustering method. Reasonably extract multiple small datasets from the cloud storage database, which contain all natural clusters in the cloud storage database. Construct a similarity matrix based on the definition of similarity. Using nonlinear kernel principal component algorithm to measure the similarity of data in the similarity matrix, data with the same features are grouped together through similarity measurement, and a mixed Gaussian distribution probability density model is used to calculate the posterior probability of different categories of data, implement segmented clustering of cloud storage data by comparing probability sizes. The experimental results show that the proposed method can shorten the clustering running time, reduce the clustering variation to 29%, and effectively improve the smoothness of the clustering results.
Plateau Altitude Disaster Prevention and Reduction Platform based on Beidou System
Authors: Zhang Tongwei , Liu Yongqi , Li Lingying
Published: August 2024

This project is based on the Beidou satellite navigation and positioning technology developed by China. The integrated analysis method is used to obtain the monitoring data of high-precision power cables in high-altitude areas with highprecision millimeters to realize the whole process of monitoring various disasters. An early warning system for power line swing and tower bottom geological hazards is established. The track data of cable swing is obtained by using single-frequency dynamic data post-processing (PPK) based on synchronous power grid monitoring. The applicability and trajectory accuracy of the scheme are evaluated by combining theory with practical engineering. This provides essential technical support for the mechanism and prevention of cable sloshing. The experimental simulation shows that the monitoring efficiency of the system is high.
Research on Distributed Scheduling Algorithm for Virtual City Power Plants Based on Blockchain Technology
Authors: Zhu Qing , Zhang Yufeng , Sun Jize
Published: August 2024

In order to solve the problem that allocating the whole virtual power station is not centralized and promoting the free access of power distribution, a research on distributed scheduling of urban power network based on blockchain technology is proposed. and a scheme is proposed. Based on the consistency of decentralized virtual power plant and blockchain in decentralized point-to-point interaction and decentralized cooperation, this paper proposes to use blockchain consensus mechanism to realize distributed scheduling of virtual power plant. According to the principle of continuous micro- increment cost, the optimal economic dispatch of virtual power system is realized by taking the micro- increment characteristic as the variable. and the optimal economic dispatch strategy is realized. As a node of the blockchain, each distributed energy in the virtual power plant has a complete backup of the key data of the whole network. When the load changes, each node uses the PBFT consensus algorithm to independently calculate the new power of each unit. The new power data is stored on the chain, and the global consistency of micro incremental features is maintained to realize the reasonable load distribution among units. The experimental results show that, when the initial t = 1, the λ the values of each unit are different. The system does not meet the principle of constant consumption micro-increase rate, and the system does not operate in the optimal state. After a PBFT consensus, λ variable reaches the same value at t=8, and the system reaches the optimal operating state. Conclusion: The simulation experiment verifies the effectiveness of the algorithm, realizes distributed scheduling by using blockchain, and provides a feasible reference scheme for the operation mode of decentralized virtual power plant.
Dimension Extraction of Remote Sensing Images in Topographic Surveying Based on Nonlinear Feature Algorithm
Authors: Wang Yani , Zhou Yinpeng , Wang Bo
Published: August 2024

In order to solve the problem of inaccurate image feature extraction caused by traditional extraction methods, this paper proposes a remote sensing image size extraction method based on nonlinear multi feature fusion for topographic maps. In this paper, SVM and DS evidence theory are combined to extract image features and classify pre processed remote sensing images. Based on the classification results, basic probability distributions are constructed, and a DS fusion algorithm using matrix analysisis introduced to simplify the complexity of decision level fusion algorithms; We use a multi feature fusion algorithm based on feature proximity, using the proximity vector formed by the attraction between the feature vector and the original graphics pattern as the fusion feature to complete the extraction of remote sensing image features. The simulation results show that after using this method, its soft threshold classifier outputs 0.9865, 0.9965, 0.7852, 0.9921, 0.9847, 0.6879, -0.5898, -0.5678, -0.6897, -0.4785. The algorithm in this paper can distinguish the shape features of terrain images well, and can extract the features of terrain images more accurately, which has strong feasibility.
State Monitoring and Anomaly Detection Algorithms for Electricity Meters Based on IoT Technology
Authors: Wang Chunguang , Huang Tianfu , Wu Zhiwu , Zhang Ying , Huang Hanbin
Published: August 2024

In response to the practical application of the electricity consumption information collection system in the online monitoring business of measuring equipment, the author introduces a method for analyzing the abnormal flying away of electricity meters based on the IoT technology LOF local anomaly detection algorithm. This method can effectively determine whether the abnormal energy representation value belongs to accidental or trend anomalies by calculating the abnormal factor of the energy representation value. After excluding the influence of accidental data, perform a secondary judgment on the abnormal flight of the energy meter. The experimental results show that when calculating the LOF factor of the electricity meter, it can be found that the LOF curve data range is mainly concentrated in the range of 0.8 to 1.3, and there is no significant change in the LOF factor near the mutation point. This proves that this method can effectively improve the accuracy of anomaly detection, avoid misjudgment of faults, and improve the efficiency of on-site fault handling.
A New Multi-Robots Search and Rescue Strategy based on Penguin Optimization Algorithm
Authors: Zedadra Ouarda , Zedadra Amina , Guerrieri Antonio , Seridi Hamid , Ghelis Douaa
Published: August 2024

In response to the challenging conditions that arise after natural disasters, multi-robot systems are utilized as alternatives to humans for searching and rescuing victims. Exploring unknown environments is crucial in mobile robotics, serving as a foundational stage for applications such as search and rescue, cleaning tasks, and foraging. In our study, we introduced a novel search strategy for multi-robot search and rescue operations. This strategy draws inspiration from the hunting behavior of penguins and combines the Penguin Search Optimization Algorithm with the Random Walk Algorithm to regulate the global and local search behaviors of the robots. To assess the strategy's effectiveness, we implemented it in the ARGoS multi-robot simulator and conducted a series of experiments. The results clearly demonstrate the efficiency and effectiveness of our proposed search strategy.
Introduction to the Special Issue on Next Generation Pervasive Reconfigurable Computing for High Performance Real Time Applications
Authors: C Venkatesan , Zhang Yu-Dong , Onn Chow Chee , Shi Yong
Published: August 2024

The evolution of scientific computing is reshaping the hardware and software requirements, emphasizing the need for high-performance platforms adaptable to real-time applications. Traditional methods with general-purpose processors often lack the agility needed for swift modifications and fast computations during real-time tasks. Reconfigurable computing offers a compelling solution by integrating hardware speed and software flexibility on a unified platform. This technique promises significant acceleration across diverse applications like image processing, encryption, decryption, runtime operations, and intensive computing tasks such as sequence searching and matching in smart environments.
For high-performance applications, software-programmed microprocessors offer versatility. Artificial Intelligence (AI) has proven more robust than traditional methods in noisy environments, relying on reconfigurable designs for efficient operation. Hybrid machine-learning techniques enhance system reliability without compromising performance. Reconfigurable computing systems have recently accelerated intensive algorithms compared to software-optimized versions, leveraging parallel topologies. Deep Neural Architectures with adaptable computation patterns excel in computer vision tasks. Artificial Neural Networks (ANNs) are crucial for pattern recognition, high-performance machine learning, data manipulation, security threats, data mining, signal processing, and other applications, driving ongoing research into reconfigurable hardware and software implementations for ANNs.
It is a privilege for us to introduce the Special Issue on “Next generation pervasive reconfigurable computing for high-performance real-time applications”. Among the numerous research papers we received (49 in total), we meticulously selected 22 papers for publication. The overarching objective of this special issue is to investigate the recent advancements and disseminate state-of-the-art research related to reconfigurable computing for high-performance real-time applications and the technologies that make this possible. This special issue represents a showcase of new dimensions of research, offering researchers and industry professionals an illuminating perspective on pervasive reconfigurable computing. We sincerely hope that the contributions in this special issue will not only inform but also inspire future research endeavours, leading to a deeper understanding of the multifaceted world of speed computation during real-time applications.
Using TOPSIS to Find the Best Model to Estimate Mean Hourly Irradiation at the "Cirque de Mafate" in Reunion Island
Authors: Fanjirindratovo Tovondahiniriko , Calogine Didier , Chau Oanh , Ramiarinjanahary Olga
Published: August 2024

In this paper, nineteen models were used to estimate the monthly average hourly global solar irradiation from the daily global irradiation value; at the “Cirque de Mafate” which is an isolated high mountain and rugged relief site in Reunion Island. These models are divided into three groups; the first depends on solar parameters like hour angle or solar time, the second implies that the estimation function follows a Gaussian distribution, and the third is a simplified form of the first. The main target is to find, for the site, the best model to estimate the abovementioned monthly average hourly irradiation. The measured data used to validate the models are from an in situ weather station. The following statistical criteria; normalized mean bias error, normalized absolute mean bias error, normalized root mean square, t-statistical test, correlation coefficient, relative standard error and Nash-Sutcliffe Equation were used to evaluate the performance for each model. To rank and compare the nineteen models by the abovementioned seven criteria, the Multi-Criteria Decision Making (MCDM) approach has been used and especially the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The basic principle of TOPSIS is to define the ideal model and the worst model by the set of the statistical criteria’s value for all models. Then the Euclidian distance to the ideal model and/or the worst model is calculated. The best model is the one that is nearest the ideal model and farthest the worst model. To use the TOPSIS, a normalized weight, that indicates the importance or priority, for each statistical criterion has been calculated by objective and subjective way. As result, it was found that the best model came from the first group and it is the Collares-Pereira and Rabl model modified by Gueymard (CPRG) and in second position is the Gueymard model.
Making Christian Rulership on the Peripheries of the Latin World
Authors: Dalewski Zbigniew , Orning Hans Jacob
Published: August 2024

This article explores the introduction and consolidation of Christian rulership ideology in medieval Poland and Norway. Both realms started to be integrated into the Christian European culture around the turn of the first millennium, marked by the introduction of Christianity and the establishment of a kingdom with a Christian rulership ideology imported from Latin Europe. However, the adoption of this ideology and its repercussions were substantially different in the two realms. In both countries, introducing the new ideology increased political tensions, as its notion of undivided power made sharing power a more delicate issue. However, the way that these tensions played out in the two realms differed substantially. In Poland, the new ideology acquired a specific, non-royal dimension, and the result was that Poland was divided into several political entities. In Norway, the new rulership ideology became focused at the rank of kings and promoted sole kingship, which resulted in intense political and ideological struggles. In the long run, however, the ideology of Christian rulership led to consolidated kingdoms in both realms, albeit earlier in Norway (1240) than in Poland (1320).