Reviewerly/exHarmony huggingface dataset:
Enhanced Energy Optimization of Structured Random Matrix Model in Industrial Automation

The Structured Random Matrix (SRM) model is an increasingly popular approach to industrial automation. This model is based on the idea that the control systems of industrial machines are "structured" in a certain way, with certain properties that can be predicted and used to model the behavior of the system. SRM incorporates the principles of random matrix theory in order to provide an efficient, reliable, and realistic model for industrial automation. The SRM model works by taking a large set of variables, such as the type of machine, the speed of the machine, the number of required parts, and the number of different components to be used in the system. These variables are then used to generate a random matrix, which can then be analyzed to identify patterns and correlations between the different variables. This allows engineers to develop more accurate models of the system, as well as to identify potential problems and solutions. In addition to providing a more accurate model, the SRM model also allows engineers to simplify the design process by reducing the number of variables used in the system. This simplification means that fewer parts are needed in the system, which reduces the cost and complexity of the system. Furthermore, by reducing the complexity of the system, engineers can reduce the amount of time spent on maintenance and repairs, which saves money and increases efficiency.
Classification Learning Model of Environmental Protection Data Based on Intelligent Optimization Algorithm

Environmental issues are becoming increasingly prominent, and people are paying more and more attention to environmental protection. Intelligent optimization algorithms have good global search capabilities, high accuracy, and real-time performance. Therefore, with the help of intelligent optimization algorithms, this paper established a classification learning model for environmental protection data, and analyzed the data from aspects such as water, solid waste, ecological governance, and ESG. The accuracy of the data obtained by the intelligent algorithm in the analysis results is less than 82%. However, it can still improve the processing capacity of relevant environmental data. This article mainly uses experimental comparison and algorithm analysis methods to analyze the results of environmental protection data classification learning. At the same time, the accuracy and stability of the entire algorithm were tested, and the accuracy of the average K, LWF, and IAE during the incremental learning process of MNIST data was compared. Experimental data show that the data accuracy rate obtained by the improved intelligent optimization algorithm exceeds 85%. Therefore, in data mining, existing environmental protection data can be used for statistical analysis and prediction of these classification objects.
A Smart Innovation Development of Agriculture Based Irrigation Systems for Rural Heritages

Agriculture-based irrigation systems are essential to the rural heritage due to their ability to deliver water to crops and prevent soil erosion. Irrigation systems provide a reliable source of water for crops, improving crop yields and quality. In areas with limited access to water, irrigation systems are often the only reliable way to provide water for crops. Irrigation systems have been used for centuries in rural areas and have been a valuable asset to society. In many cases, irrigation systems are the only way to keep crops alive and thriving. Without irrigation, crops may fail to thrive due to lack of water, leading to decreased yields and even crop failure. While irrigation systems are necessary for rural heritages, there are many potential risks associated with using them. Irrigation systems can be difficult to maintain, and the potential for water loss or over-irrigation is always present. Additionally, irrigation systems can be expensive to install and maintain, leading to an increased cost burden on farmers. In order to mitigate the risks associated with agriculture-based irrigation systems, farmers should implement water conservation practices, such as drip irrigation and water recycling. Additionally, farmers should research the different irrigation systems available to them, to ensure that they are using the most efficient irrigation system for their particular climate and soil conditions. Finally, farmers should monitor their irrigation systems carefully and take steps to address any issues that may arise.
Research on a Machine Vision-based Image Tracking System for Sports

Most of the information perceived by humans is obtained visually, and all the dynamic information in visual information is attractive to people. Along with the rapid development of information technology, network technology and multimedia technology, sports target tracking technology has received more and more attention. Today, intelligent moving target tracking technology has become one of the research hotspots. The aim of this paper is to study a sports image tracking system based on machine vision. OpenCV, an open-source computer visualisation library, is chosen as the implementation tool for the system design, and a system process view and software system hierarchy are planned based on the research content. The KNN-based motion recognition model proposed in this paper was tested and the test results show that the average recall of the KNN action recognition model for this subject is 90.1% under three different lights and the average recall when the cross-merge ratio is greater than 0.5 is 89.1% with some performance. the system functions properly and has a certain performance.
Breast Cancer Detection Using Deep Learning Technique

One of the harmful types of cancer that can affect females is breast cancer. With the aid of images of the microscopic structure, breast cancer can be identified. This study uses mammography images to categorize various types of breast cancer. Image processing techniques can be used successfully in the classification of mammography images. Deep learning provides wonderful performance for the classification of images in many applications among various image processing algorithms. Convolutional neural network (CNN) designs like VGG19, Inception-Net, ResNet50, and others are used in effective classification.
Virtualized Secure Cloud Computing Using Targetive Dynamic Secured Resource Allocation Algorithm

Currently Cloud Computing (CC) has dramatic changes in secured task scheduling in emerging countries, the volume of the company must cost-effectively process large amounts of data security to process the data for solving task scheduling and network routing problems. Workloads can increase dramatically when virtual machines face inherent challenges. This paper can improve service-based access control for clients to access server data securely. Also, it fails because it consumes time and cannot access adaptive memory. To resolve the problem consideration, we propose a Virtualized secure cloud computing using Targetive dynamic secured resource allocation (TDSRA) algorithm. Distributed computing uses load-balancing technology to increase the security of resource usage in computing environments. Notably, the focus is on improving virtualization application planning compared to other systems and improving cloud user security performance rather than issues such as energy efficiency, throughput, and resource planning.
Signature Based Identification using Locality-Sensitive Hashing for IoT Devices

On current days, the growing applications in Internet of Things (IoT) is increasing in various real-time sectors as smart city applications (air pollution, atmospheric noise canceling and traffic flow management). Now, these technologies are combined to provide the comfort configuration for each application. However, malware / virus are highly possible to extract the device software or hardware information in IoT. All sensed information about the environment or any event is stored in these devices which are hacked by the malicious attackers. To overcome these adversities, we introduced the concept of locality sensitive hash (LSH) for IoT device trustworthiness identification. The main objective of our proposed system is to allow secure transfer of data from the resource constrained devices. In this project, the resource constrained devices are the wireless sensor nodes. The experimental analysis confirmed that our proposed approach is suitable for the application or environment with resource constrained devices. Also, the time, energy, latency and packet overhead is considerably reduced than the existing systems considered for this project.
Data Analysis System for Online Art Education Based on Regression Model Algorithm

The analysis of online art education data is a systematic project, which involves multiple disciplines and needs to consider all aspects comprehensively. This paper improves and perfects the regression algorithm. Firstly, the model based on linear weighting method is studied and verified; Secondly, the regression fitting results are realized and displayed by simulation software, and the feasibility of the model and parameter setting are verified by MATLAB simulation. A general framework design idea of the network art education data analysis system is proposed, that is, the whole scheme is planned from the demand, and then the network and intelligent processing are realized technically. After that, this paper simulates the data analysis system, and the test results show that the online art education data analysis system based on regression model algorithm can operate normally, and the data processing time is fast, with sufficient data volume to meet the needs of users. The online art education data analysis system based on regression model algorithms is an emerging research direction, aiming to improve the quality and effectiveness of online art education through the application of algorithms. The main results of this study include the following aspects. Firstly, this study proposes an online art education data analysis framework based on regression model algorithms, which can analyze students' learning behaviors and results, evaluate students' learning effectiveness, and improve education quality. Secondly, the research has achieved certain results in data acquisition and data preprocessing. Through data collection and processing on the online art education platform, an effective data analysis model has been established. At the same time, the research also proposes a feature selection method based on data mining technology, which effectively extracts students' learning characteristics and improves the accuracy of the regression model. Finally, the research has achieved certain results in experimental verification. Through experimental verification, the feasibility and effectiveness of the online art education data analysis system based on regression model algorithm has been proved. At the same time, the research also conducted a performance test on the system, proving the stability and reliability of the system. In short, the online art education data analysis system based on regression model algorithms is research with important application value and social significance. In the future, we can further improve the system, expand its application range, and apply it to a wider range of education fields. At the same time, we can also strengthen cross research with other fields to further improve the teaching quality and effectiveness of online art education.
Load Balancing of Cloud Resources for Real-Time Tasks Management using Deep Learning

Cloud computing is an emerging technology that improves the efficiency of computers by sharing their resources. New cloud-based apps mean more work for existing servers. Although much research has been done in the field of Cloud computing, there are still challenges that must be overcome in order to improve performance and provide the greatest possible customer happiness. In this work, we cover the Machine Learning methods used for assembling VM squads with certain CPUs in mind. Quality of Service (QoS) is achieved through the application of DL methods, which entails the increased efficiency with which resources are used and deployed, the decrease in waiting time, response time, and costs that results from this, and the resulting improvement in system reliability due to the more even distribution of load across all equipment. For those interested in load balancing algorithms, this article provides a thorough discussion of many machine learning and deep learning-based methods.
Design and Implementation of Digital Book Recommendation Platform Based on Data Mining Visualization Technology

The rapid development of Internet technology in the information age incurs serious problems of overloaded network data resources and complicated operation of website pages due to their dynamics and multi-structural operation patterns. Consequently, information resources are obtained from certain interfaces ineffectively. The purpose of this paper is to use the association rules in the data mining visualization technology to complete the design and implementation of the digital book recommendation system. With the Apriori book recommendation algorithm, an in-depth analysis is conducted on the reader borrowing data in this system, and the association rule visualization model is optimized in all aspects to produce a visualization model of diamond graphs. Moreover, the digital book recommendation system is established on the basis of professional knowledge and skills, whose system interface is easy for readers to understand and use such that shortening the time required for book borrowing. The readers’ basic demand can be met, with the provision of scientific technical services which further increases the utilization rate of library resources.
Detecting Image Forgeries: A Key-Point Based Approach

Digital image authenticity has proven to be a challenging issue. The most straightforward picture modification technique is called reprint-pass falsification. To make the altered image appear realistic and believable to the eye, geometric changes are made to elements of the image that were duplicated and then fixated. Further complicating matters, other processing techniques might also be utilized in the fabricated region to cover up any indications of the fabrication. Processing like scale, rotation, AWGN, and JPEG compression fall within this group. The method put forward in this work utilizes the strengths of both the FREAK descriptor and the CenSurE key point detector. Good view of the authors' knowledge, this combination is not used for the purpose for which it is currently employed, making it innovative in and of itself. CenSurE detectors are quick and deliver accurate and consistent output even with rotated images, which they group using the binary descriptor FREAK. Because it uses neighbourhood search and hierarchical clustering to find and identify numerous copy-move forgeries, the system can work as planned. The suggested solution will be applicable for both real-flow image authentication and reprint-pass falsification.
MTPE Model Translation Course Recommendations Based on Mobile Cloud Computing Technology

As long as translators adapt to new technologies and are willing to learn new skills and adapt to the evolving needs of the market, the translation industry will continue to thrive. The purpose of this paper is to study MTPE model translation course recommendation based on mobile cloud computing technology. The characteristics of mobile cloud computing and distributed cloud computing translation course recommendation services and algorithms are studied. On the basis of machine translation, a classification system of error types (science and technology, humanities, medical articles) is established to guide students to identify machine translation errors, evaluate and make statistics and analysis on students' cognitive ability of translation quality and post-translation editing ability, and propose corresponding teaching strategies. After using the MTPE model based on mobile cloud technology for experimental teaching, the overall recognition rate of students is significantly improved, and the average number of vocabulary recognition errors is 88 and 23 times more than before experimental teaching. The average number of grammatical meaning recognition errors is 50 or 9 times more than that before experimental teaching. The recognition rate of contextual meaning is the highest, with an average of 86 errors. Other errors average 82; There are an average of 69 style correction questions. This shows that this technology can improve the students' error recognition rate and improve the learning effect.
Application of Optimized Fuzzy Decision Tree Algorithm in Sports Video Analysis

In sports video prediction, a very important link is the model. The traditional fuzzy algorithm based on classical fuzzy algorithm can no longer meet the needs of the development of the times. So this paper improves and optimizes this problem and gets a more accurate, practical, fast and intuitive result. The objective function-weight vector method replaces the artificial neural network to train the decision tree algorithm, and applies it to the establishment of sports video evaluation index system based on motion control quantity. Finally, the feasibility and effectiveness of the model in practical application are verified by experiments. The verification results show that, The running time of fuzzy decision tree algorithm is within 11-17 seconds, and the running efficiency is more than 80%. This shows that the algorithm has obvious optimization effect on sports video analysis.
Object Motion Tracking and Detection in Surveillance Videos using Resnet Architecture

Recent years have seen a flurry of activity in the field of security research devoted mostly to the topic of video surveillance. The goal of installing an Intelligent surveillance system is to identify and follow the item in order to foil any malicious intent. Effective methods for using a Video Surveillance System are discussed. Three distinct tasks—Classification, Object Detection, and Tracking—make up the Video Surveillance System’s overall workflow. Background Subtraction, Optical Flow, and Spatially Filtering are just few of the approaches that may be used to identify objects in a scene. It is necessary to separate the identified items into distinct categories, which may be done using feature-based methods. Points-, kernel-, and silhouette-based methods may be used to locate the items of interest. Computation time and reliability rates are considered in a comparison of the methods.
An Intelligent Innovation of Electrochemical Energy Based Smart Industrial Automation

Electrochemical energy based smart industrial automation uses electrochemical energy generated from chemical reactions to power and control industrial processes. This energy is generated through the use of electrolyte and electrodes, which convert chemical energy into electrical energy. This energy can then be used to power and control industrial machines, such as industrial robots and automated assembly lines. Additionally, this energy can be used to monitor, regulate, and control processes, such as temperature and pressure, within an industrial environment. This technology offers numerous advantages over traditional methods of powering and controlling industrial operations. Firstly, it is more efficient and cost effective than traditional methods. Secondly, it has the potential to reduce energy consumption, improve efficiency, and increase productivity. Finally, it is environmentally friendly, as it does not produce harmful emissions or waste materials. The use of electrochemical energy based smart industrial automation is the way of the future for industrial operations. The potential of this technology is immense, and the advantages it can bring to industrial operations are numerous. With the right implementation and application, this technology has the potential to revolutionize how industrial processes are operated and managed.
Influence of Autodesk Series Software on Landscape Planning and Design

Landscape design involves a lot of graphic drawing and topographic survey, which is a large and complex project. The application of digital technology can simplify the workload of landscape design survey. It is very important and urgent to accelerate the theoretical and practical process of modern landscape architecture. For this reason, this paper puts forward the landscape planning and design based on Autodesk software. The research results show that after using Autodesk, the average completion time of four landscape designers is 3.525 hours, which is 29% higher than that without using Autodesk. After using Autodesk, the average document verification error of four landscape designers was 1.5, and the improvement effect reached 92%. This proves that the research based on Autodesk series software has important practical significance for landscape planning and design. It cannot only improve the speed of landscape designers editing documents, but also intelligently identify errors and improve drawing efficiency.
Energy Efficient and Fault Tolerant Area Monitoring for Sensor Networks

The Fault Tolerant Area Monitoring for Sensor Network is a technology that helps to monitor areas for faults that may occur due to environmental changes. It is a system that uses sensors to monitor an area for any faults that may occur. This system has been developed to help detect faults in an area and can be used in areas such as factories, hospitals, and other places where environmental changes may occur. The system is able to detect the changes in the environment and then alert the staff in the area of any potential faults. Energy efficient sensor networks have emerged as a major area of research in recent years, due to the increasing demand for energy efficient solutions in the face of global climate change. Sensor networks are composed of a variety of sensor nodes, connected together wirelessly, that can collect data from the environment and transmit it to a central processing unit. This data is used to detect and monitor environmental changes, in order to optimize energy consumption and reduce greenhouse gas emissions. In order to achieve this goal, energy efficient sensor networks must be designed in a way that minimizes the power consumption of the nodes, while still providing reliable data.
Using Cloud Computing Technology to Promote the Development of Smart Tourism

Tourism is one of the most important economic sectors in the world. Tourism has become an important part of the global economy and has a positive impact on local, regional and national economies. Tourism also contributes to poverty reduction, environmental protection and cultural diversity protection. In this article, we will discuss how to use cloud computing technology to promote the development of smart tourism. The impact of cloud computing on smart tourism studied in this paper aims to deter-mine the impact of cloud computing on the future smart tourism industry, as well as the overall impact of cloud computing on the industry, and identify some challenges that need to be overcome before the technology can be effectively used for this purpose.