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Abstract: Aiming at the problems of noise and irrelevant feature filtering in data processing, a feature selection method based on the stacking framework is proposed. Use the K-Fold cross-validation method to train and save DNN and SVM-based learners. The prediction results of the base learners are used as the input of the meta-learner, and the logistic regression learning model is trained and saved; comprehensively analyze the correlation coefficients of the fully connected neural network weight matrix and support vector machine, According to the learning results of the meta-learner model, assign different weights to each base learner, calculate the influence factors of each feature, and call the sequence backward search algorithm (SBS) to generate the optimal feature subset. In the experimental stage, a disease diagnosis model was constructed based on the open data set of heart disease research on the Kaggle website, and Stacking-SBS was called to generate the optimal feature subset in the feature space, and the performance comparison experiment of the diagnostic model before and after feature selection was performed, and the method was improved with information. (IG), Chi-square test (Chi) and correlation-based feature selection method (CFS) are compared. The results show that the application of this method can not only reduce model training time, but also significantly improve the model's recall rate and F1 value. In addition, this method is significantly better than the other three feature selection methods in terms of performance improvement. Finally, the open data set of cardiovascular research on the Kaggle website is used to verify the generalization ability of Stacking-SBS. The experimental results show that this method can also significantly improve the performance of the disease diagnosis model.Abstract: Aiming at the problems of noise and irrelevant feature filtering in data processing, a feature selection method based on the stacking framework is proposed. Use the K-Fold cross-validation method to train and save DNN and SVM-based learners. The prediction results of the base learners are used as the input of the meta-learner, and the logistic regr...Learn More
Abstract: With the continuous development of the Internet industry in recent years, traditional enterprises in various industries are gradually informatized, bringing new development opportunities to the software industry, the software market is expanding, facing a dramatic increase in software process management and software project management activities, the software industry for the software process and software project management gradually pay attention to it. How to choose a suitable software process to better meet the project requirements goals, software project managers as the core of the entire software project determine the success of the project, how to strengthen the software process management and how to become a qualified project manager, so as to ensure the overall progress and quality of the software project, are worth to study and research. In this paper, we outline some core concepts of software process and its management, introduce software organization and management methods, and provide an integrated, three-dimensional software reform framework provided by PSP (Personal Software Process), TSP (Team Software Process), and CMM (Capability Maturity Model Integration for Software) and RUP (Rational Unified Process)-based process model. Outlines the concept of project management and refinement to software project management in the software industry, for the role of software project manager, analyze its importance from multiple perspectives such as schedule, personnel, resources and communication, and refine its software project management related activities methods to better meet the needs of modern software management.Abstract: With the continuous development of the Internet industry in recent years, traditional enterprises in various industries are gradually informatized, bringing new development opportunities to the software industry, the software market is expanding, facing a dramatic increase in software process management and software project management activities, t...Learn More
Abstract: An Intrusion Detection System (IDS) is an essential part of the network as it contributes towards securing the network against various vulnerabilities and threats. Over the past decades, there has been a comprehensive study in the field of IDS, and various approaches have been developed to design intrusion detection and classification system. With the proliferation in the usage of Deep Learning (DL) techniques and their ability to learn data extensively, we aim to design Deep Neural Network (DNN) based IDS. In this study, we aim to focus on enhancing the performance of DNN-based IDS in Cyber-Physical Systems (CPS). CPS combine physical processes, networking, and computation. The integration of CPS components could seriously jeopardise the security of CPS settings because of the physical limitations. The vulnerability of CPS to cyberattacks has grown with the development of IoT and other physical systems. As cyber-physical systems refer to the intersection of your organization’s technology and IT infrastructure and its physical assets, ensuring access and data security through advanced methods prevents any cyber-attack from damaging your physical assets and thereby disrupting your business flow. Conventional cyber and network security procedures fail to guarantee data privacy and security in CPS contexts. This research aims to provide a cutting-edge attack detection method based on learning for CPS environments. The paper suggests using MLP-based smart attack control systems to increase the CPSs' security. Performance analysis is presented in terms of different evaluation metrics such as accuracy, precision, recall, f-score, and False Positive Rate (FPR), and the results are compared with existing feature selection techniques. The effectiveness of the suggested model was confirmed by comparing the outcomes with those of other successful deep learning-based algorithms, including the Gaussian Naive Bayes algorithm, SVM, and logistic regression. Comparative results demonstrate that the suggested method outperforms existing learning models with an exceptional accuracy of 99.52%.Abstract: An Intrusion Detection System (IDS) is an essential part of the network as it contributes towards securing the network against various vulnerabilities and threats. Over the past decades, there has been a comprehensive study in the field of IDS, and various approaches have been developed to design intrusion detection and classification system. With ...Learn More
Abstract: Credit consumption has become the choice of more and more people. While the expansion of credit consumption brings convenience, the consequent problem of discredit brings incalculable loss to trust institutions, such as intentional arrears, malicious overdraft consumption, etc. to the trust in the operation of the huge losses caused. On the one hand, our country is unable to establish perfect personal credit record, customer credit information is difficult to share, access to relevant data is limited, personal credit system is not perfect, therefore, it is necessary to establish a perfect and automatic personal credit evaluation system, identify the personal credit risk scientifically, and realize the maximization of bank credit income. In the bank risk management, the main problem is how to measure and avoid the bank's financial risk, and personal credit assessment is the most difficult and important part. In this paper, we use the XGBOOST algorithm to sort the importance of features, and select the number of features and the specific features according to the relationship between the model accuracy and the number of features, then 80% of the data was taken into the traditional Support vector machine model as a training set and tested with the remaining data with low accuracy, so that, in terms of individual credit risk assessment, this paper introduces the quadric surface into the traditional Support vector machine and builds the kernel-free quadric surface Support vector machine model, which improves the accuracy by 9.8 percentage points and has a guiding significance for the bank reference risk control.Abstract: Credit consumption has become the choice of more and more people. While the expansion of credit consumption brings convenience, the consequent problem of discredit brings incalculable loss to trust institutions, such as intentional arrears, malicious overdraft consumption, etc. to the trust in the operation of the huge losses caused. On the one han...Learn More
Abstract: With the sustained development of power plant of China, the installed base of electric meter grows gradually. It is more and more important to monitor smart meters with their mature use. In order to avoid huge resource wasting and solve the issue because of physical testing of smart meter when smart meter service life time arriving (maximum 8 years), LSTM method related to time series model (TSM) is firstly used to do research on smart meter. Smart meter testing without physical disassembling is realized successfully. Eventually testing abnormity and failure recognition can be obtained through prediction research based on deep learning time series model. Based on electric meter running big data analysis, the electric meter situation including normal and abnormal (error or electricity stealing) and relevant position can be obtained so as to take further action. The research and application of the testing system can avoid physical testing to electric meter. The service time of normal meter can be prolonged by abnormal meters testing. This will lead to saving a lot of resources. The LSTM research method on deep learning is the innovative application in electric power domain.Abstract: With the sustained development of power plant of China, the installed base of electric meter grows gradually. It is more and more important to monitor smart meters with their mature use. In order to avoid huge resource wasting and solve the issue because of physical testing of smart meter when smart meter service life time arriving (maximum 8 years...Learn More
Abstract: In order to solve the problems of UAV flexibility, stability and safety, this paper designs a quadrotor UAV based on MSP432. The UAV includes five parts: main control part, flight height measurement module, gyroscope, fixed point correction module and visual recognition module. In order to protect the main control and other parts, we designed the outer protective carbon plate, so that its security is greatly improved. Based on the in-depth study of the existing remote control flight function, an improved active disturbance rejection controller is added, and the dual control mode of autonomous control and manual control is adopted to prevent unnecessary losses due to operation errors. The combination of GPS and laser rangefinder allows for more precise fixed-point positioning and measurement of the altitude above the ground, allowing the operator to determine the position of the UAV so as to observe its status. The integration function and key technology of each subsystem of UAV are analyzed in detail by simulation software. The experimental results show that each part of the quadrotor UAV and the surrounding protective carbon plate can provide a systematic solution for the intelligent and systematic development of the UAV, further improve the stability, flexibility, safety and the ability of swarm intelligence of the UAV, and make it have a better performance in the flight process.Abstract: In order to solve the problems of UAV flexibility, stability and safety, this paper designs a quadrotor UAV based on MSP432. The UAV includes five parts: main control part, flight height measurement module, gyroscope, fixed point correction module and visual recognition module. In order to protect the main control and other parts, we designed the o...Learn More
Abstract: Natural language processing (NLP) is a field of artificial intelligence (AI) whose primary purpose is to give computers the ability to understand written and spoken language in a human-like manner, mainly by creating computers that can read and respond to information in a human-like manner and then generate their text or speech as a response. However, the process of natural language processing may encounter the problem of inefficiency, so how to make the process of natural language processing more efficient is a direction that is currently being studied. This paper aims to derive a set of programmable rules from helping NLP describe human language by combining computational linguistics and statistics, machine learning and deep learning models. In this way, when text and speech data are combined, computers can understand human language in the form of text or audio data, such as the intent and emotion of the speaker or author. This paper introduces various types of deep learning systems used in NLP analysis and research and describes a pre-training-based approach to natural language processing. The final findings can improve operational efficiency, increase employee productivity, and streamline mission-critical business operations.Abstract: Natural language processing (NLP) is a field of artificial intelligence (AI) whose primary purpose is to give computers the ability to understand written and spoken language in a human-like manner, mainly by creating computers that can read and respond to information in a human-like manner and then generate their text or speech as a response. Howev...Learn More
Abstract: Data is the "blood" of GIS. GIS focuses on data input, analysis and output, so data collection is particularly important. Traditional surveying and mapping has some shortcomings, such as backward equipment, low accuracy of data acquisition, low acquisition efficiency, and high cost of manpower and material resources. In the information collection of modern urban planning, traditional surveying and mapping has been difficult to meet the needs of data collection. At the same time, the emergence of various mobile intelligent devices and the increasingly mature development of related software and hardware make mobile terminal information acquisition technology gradually replace traditional surveying and mapping in field operations. This paper combines traditional surveying and mapping and modern information technology to develop the mobile terminal information acquisition system based on iServer. First, learn about the development of information collection technology in mobile GIS at home and abroad by consulting a large number of relevant literature, and define the value and significance of developing mobile terminal information collection system. Then, analyze the shortcomings of traditional surveying and mapping in field survey and the problems existing in mobile GIS collection information at present, combine available resource tools to form a project demand analysis, and conceive a project outline design according to the demand analysis, Finally, the information collection system based on iServer and Android is designed in detail, including software functional framework, database design and server side functional design. Theoretical analysis and practical results show that the system can effectively improve the accuracy and speed of data acquisition while reducing the cost of data acquisition.Abstract: Data is the "blood" of GIS. GIS focuses on data input, analysis and output, so data collection is particularly important. Traditional surveying and mapping has some shortcomings, such as backward equipment, low accuracy of data acquisition, low acquisition efficiency, and high cost of manpower and material resources. In the information collection o...Learn More
Abstract: The unified laboratory fire monitoring system aims to centrally monitor laboratory environmental data across the province or even the country, perceive real-time changes in the laboratory's internal environment, accurately assess fire risks, and take timely disaster relief measures. In addition, once a fire occurs in the laboratory, an alarm message can be issued in a timely manner, which can better extinguish the fire in a timely manner to reduce losses or automatically take fire extinguishing measures to avoid the occurrence of a fire. For this purpose, this article designs a cloud platform university laboratory security monitoring system based on ZigBee. This system can monitor the temperature, humidity, and smoke concentration in laboratory rooms of different colleges and universities, and conduct safety monitoring of the rooms. When the monitoring values exceed the safety range, real-time alarms will be given. This system consists of four parts: terminal data acquisition module, ZigBee coordinator, ESP8266 wireless gateway communication module, OneNET cloud platform, and third-party web application. The system collects monitoring data in the laboratory through various sensors and wirelessly transmits the data to the cloud through ZigBee networking. The cloud then transmits the data to the web, thereby achieving real-time monitoring and alerting of the security situation in each laboratory. This article provides a human-machine interaction page on the local web end and a cloud platform monitoring interface. The effectiveness of the system is verified by the measured data validation.Abstract: The unified laboratory fire monitoring system aims to centrally monitor laboratory environmental data across the province or even the country, perceive real-time changes in the laboratory's internal environment, accurately assess fire risks, and take timely disaster relief measures. In addition, once a fire occurs in the laboratory, an alarm messag...Learn More
Abstract: Mangrove is a wetland ecosystem with high productivity in tropical and subtropical regions, which is of great significance for maintaining ecological diversity and protecting wetlands. With the rapid development of urbanization and industrialization, human activities such as the use of agricultural fertilizers and pollutant discharge have brought a series of problems. A large number of heavy metal pollutants enter rivers, lakes, seas and soils in different ways, bringing heavy pressure to the restoration of the ecological environment. Leizhou Peninsula Mangrove Nature Reserve, as one of the largest nature reserves in Chinese Mainland, is highly valued. Aiming at the study of heavy metal pollution in Leizhou Peninsula mangrove wetland, this paper proposes a monitoring system for heavy metal pollution in seawater of mangrove wetland. Wireless packet switching technology based on Global System for Mobile Communications (GSM) system, which provides end-to-end and wide area wireless IP connection. The main function modules of the system include: data input, real-time display of monitoring data, data query, data statistics and analysis, decision-making and early warning, early warning information release, etc. Through this technology, real-time monitoring of heavy metal pollution in sea water of mangroves in Leizhou Peninsula can be realized, which can effectively solve the problem of mangrove protection and maintain the ecological environment.Abstract: Mangrove is a wetland ecosystem with high productivity in tropical and subtropical regions, which is of great significance for maintaining ecological diversity and protecting wetlands. With the rapid development of urbanization and industrialization, human activities such as the use of agricultural fertilizers and pollutant discharge have brought a...Learn More
