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Key Technologies for Dynamic Early Warning of BIM Power Monitoring data in Bridge Operation and Maintenance

Key Technologies for Dynamic Early Warning of BIM Power Moni...

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Artificial Intelligence and Autonomous Robot Systems (AIARS), international conference on

作者： Yu Peng Tian Wang Chongqing College of Electronic Engineering Chongqing China

ISBN: (数字)9798350376173

ISBN: (纸本)9798350376180

Bridge operation and maintenance is an important solution to how to ensure the safe operation of bridges and extend their service life. In this paper, a dynamic early warning technology of BIM (Building Information Modeling) power monitoring data was proposed to supervise the safety of bridges. Through the layout of sensors and monitoring equipment, the power data of the bridge was obtained in real-time. The quality control and data correction of the collected power data were carried out, and data cleaning, data correlation and other technologies were used to process the power data and extract useful characteristic information. An early warning model was also constructed, and machine learning, data mining and other methods were used to analyze and model power data to realize dynamic early warning of bridge power systems. After experiments and analysis, it was concluded that the stability of the method in this paper was maintained at 89%-98%. Through the dynamic early warning of BIM power monitoring data, real-time monitoring and fault diagnosis of the bridge power system can be realized and abnormal conditions and potential problems can be found in a timely manner.

关键词： Bridges Power system dynamics Power system stability data models Stability analysis Real-time systems Maintenance Safety data mining Monitoring

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data mining based Hybrid Latent Representation Induced Ensemble Model Towards Fraud Prediction

Data Mining based Hybrid Latent Representation Induced Ensem...

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international conference on Intelligent Sustainable Systems (ICISS)

作者： J. Anita Smiles T. Kamalakannan Vels Institute of Science Technology & Advanced Studies Chennai India

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

The increasing growth of e-commerce and mobile payments has contributed to a rise in the effects of financial payment services fraud over the last few years. "At 2025, the losses in fraud around the world could amount to about $44 billion, according to McKinsey." Every year, trillions of dollars are lost from fraudulent card transactions. An efficient fraud detection system based on advanced machine learning techniques have gained excellent value in all Financial Institutions to reduce these losses. The proposed algorithm SLREnsemble (Stochastic Latent Representation Ensemble) is a hybrid model that uses deep neural network Autoencoders to obtain the latent representation of genuine and fraudulent transactions and these representations are given as an input to the balanced ensemble model. The technique is tested against other existing methods, i.e., Isolation Forest, Random Forest, and XGBoost algorithms, by various performance metrics such as accuracy, precision, and recall. The results prove that the SLREnsemble model is highly efficient in Fraud detection and performs well with minimal training data and yields good precision, recall and f1-score with high computational speed values.

关键词： data models conferences Correlation Support vector machines Predictive models machine learning algorithms Forestry

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Extracting Clinical entities and their assertions from Chinese Electronic Medical Records Based on machine learning

Extracting Clinical entities and their assertions from Chine...

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2016 3rd international conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Jianhong Wang Yousong Peng Bin Liu Zhiqiang Wu Lizong Deng Taijiao Jiang College of Computer Science and Electronic Engineering Hunan University Center of System Medicine Institute of Basic Medical SciencesChinese Academy of Medical Sciences & Peking Union Medical College

With the rapid growth of electronic medical records（EMRs） in China, large amounts of clinical data have been accumulated. However, limited work for extracting information from EMRs in Chinese has been conducted. In this work, using manually annotated dataset of EMRs in Chinese, we investigated the clinical Named Entities recognition（NER） based on Conditional Random Field（CRF） and further built a Support Vector machine（SVM） classifier to determine their assertion status and evaluate the contributions of different features for assertion classification. For Chinese clinical NER, our CRF-based classifier achieved the best F-measure of 89.07%, while the SVM-based assertion classifier achieved a maximum F-measure of 94.10%. Our work suggests that machine learning methods are helpful in NER and assertion determination for Chinese medical clinical records.

关键词： Chinese Electronic Medical Records Information extraction Named Entity recognition assertion classification machine learning

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Encrypted Web Traffic Classification

Encrypted Web Traffic Classification

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international conference on Contemporary Computing and Informatics (IC3I)

作者： Suman Balhwan Noble Kumari A.K Mohapatra IGDTUW Delhi USIT Delhi

ISBN: (数字)9781538668948

ISBN: (纸本)9781538668955

Traffic analysis is very much essential in many network monitoring/surveillance and middle box appliances installed at the perimeter of the internal and the external network. Internet Service Providers are also required to do traffic analysis for the purpose of providing the Quality of Service (QoS) parameters as mentioned in the Service Level Agreement. A lot of research is being done in the field of traffic analysis based on the machine learning (ML) based approaches. The main factor in ML based approaches is the selection of the features which can uniquely determine and thus classify the traffic flows. In this paper, website fingerprinting (WF) for a particular browser and WF for different browsers has been carried out. The analysis is carried out on encrypted traffic flows, i.e., those using Secure Socket layer/Transport Layer Security (SSL/TLS). The feature extraction phase includes a feature set which are uncorrelated and combines the statistical parameters of a traffic flow as well as information extracted from the meta-data of the encrypted traffic flows. The feature extraction phase is thus the critical step in the later classification strategy for WF. The classification involves machine learning algorithms and for that we have utilized a machine learning tool to automate the process. After training the classifier, the testing and validating shows that the features used for the classification process are indeed very useful in distinguishing the various encrypted web traffic flows captured from visiting various websites and also through the use of different browsers.

关键词： Feature extraction Cryptography machine learning Browsers Fingerprint recognition Tools Training

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Secure and Efficient IoT Networks: An AI and ML-based Intrusion Detection System

Secure and Efficient IoT Networks: An AI and ML-based Intrus...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Mohammed A. Mahdi Department of Information and Computer Science College of Computer Science and Engineering University of Hail Hail City Saudi Arabia

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The growing deployment of IoT devices has led to unprecedented interconnection and information sharing. However, it has also presented novel difficulties with security. Using intrusion detection systems (IDS) that are based on artificial intelligence (AI) and machine learning (ML), this research study proposes a unique strategy for addressing security issues in Internet of Things (IoT) networks. This technique seeks to address the challenges that are associated with these IoT networks. The use of intrusion detection systems (IDS) makes this technique feasible. The purpose of this research is to simultaneously improve the present level of security in ecosystems that are connected to the Internet of Things (IoT) while simultaneously ensuring the effectiveness of identifying and mitigating possible threats. The frequency of cyber assaults is directly proportional to the increasing number of people who rely on and utilize the internet. data sent via a network is vulnerable to interception by both internal and external parties. Either a human or an automated system may launch this attack. The intensity and effectiveness of these assaults are continuously rising. The difficulty of avoiding or foiling these types of hackers and attackers has increased. There will occasionally be individuals or businesses offering IDS solutions who have extensive domain expertise. These solutions will be adaptive, unique, and trustworthy. IDS and cryptography are the subjects of this research. There are a number of scholarly articles on IDS. An investigation of some machine learning and deep learning techniques was carried out in this research. To further strengthen security standards, some cryptographic techniques are used. Problems with accuracy and performance were not considered in prior research. Furthermore, further protection is necessary. This means that deep learning can be even more effective and accurate in the future.

关键词： Deep learning Accuracy Simulation Intrusion detection Real-time systems pattern recognition Internet of Things

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Research on Negative learning Emotion recognition in English Classroom using Cross Space based Convolutional Neural Network

Research on Negative Learning Emotion Recognition in English...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Xuanxuan Tan Xiaoli Hou Xiaojia Dai Lilanjun Tan Department of International Business and Economics Hunan Sany Polytechnic College Changsha China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In English classroom the teaching behavior, changes according to the student’s facial emotions which are observed by facial expressions and behavioral phases in the class. Nonetheless, in the English classroom, behavior of the students during learning is changeable and complex to understand by teacher’s and it also difficult for understanding the negative emotions such as bored, stressed and not interested in the English classroom. To overcome these limitations, a Cross-Space based Convolutional Neural Network (CS-CNN) model is proposed to recognize the which is observed from students. The proposed method learns multiscale information such as subtle differences between various emotions and complex negative emotion pattern efficiently by cross-space feature fusion. This helps to enhance the recognition performance to assist the teachers to change their teaching strategies to make the students more focused and improve learning environment of students. For extracting significant features from the facial images are preprocessed by histogram equalization and extracted using DeepNet architecture. The experimental results of the proposed CS-CNN method achieved accuracy of 94.33% which is higher when compared to existing recognition approaches such as Support Vector machine (SVM) and Yolo-V3.

关键词： Support vector machines Image quality Emotion recognition Histograms Accuracy Education Computer architecture Feature extraction Convolutional neural networks data mining

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IoT-based Car Driver Assistance System using Computer Vision and machine learning

IoT-based Car Driver Assistance System using Computer Vision...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： Kumar Shubham Kumar Satyam Pranav Kapisway Raghav Memani Aniket Anand C Sathiya Kumar VIT Vellore India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

This paper presents an innovative approach for improving driver convenience and safety by combining Artificial Intelligence (AI) and Internet of Things (IoT) with computer vision techniques like OpenCV. By using intelligent automation, the suggested system, termed the IoT-based Car Driver Assistance System using Computer Vision and machine learning, seeks to both enhance and reduce potential driving-related risks. A camera and sensor suite are installed on the dashboard of the vehicle as part of the system design to enable real-time data collection and processing. By utilizing face recognition algorithms, the system begins by confirming the driver’s identification prior to vehicle activation, which improves security. The system always keeps a watch on the driver’s condition, looking for indications of drowsiness while operating and sending out alerts in a timely manner to help avoid collisions. The technology improves situational awareness by detecting and alerting the driver to approaching obstacles on the road, helping the driver avoid potentially dangerous situations. The technology also facilitates hands-free driving by utilizing hand gesture detection to provide seamless control over in-car audio operations such as track selection and volume adjustment. Call management is integrated with gesture recognition, enabling drivers to use hand gestures to accept or reject incoming calls. The system aims to improve the driving experience by utilizing facial recognition and mood detection to identify the driver’s emotional state and provide tailored Spotify music choices. By recognising traffic signals and lane changes, the technology helps drivers maintain lane discipline, which improves road safety and encourages adherence to traffic laws. In order to lessen the difficulties of parking in crowded areas, the system also offers intelligent parking assistance. With a full feature set that gives drivers more control, awareness, and efficiency while driving, the proposed I

关键词： Computer vision machine learning algorithms Mood Face recognition Vehicle safety machine learning Automobiles

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The Cryptographic Algorithm Identification: Using Deep learning to Empower Smart Grids

The Cryptographic Algorithm Identification: Using Deep Learn...

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Networking Systems of AI (INSAI), international conference on

作者： Mingliang Chen Yayun Zhu Lei Mei Xiaojuan Zhang Sheng Yuan Baiji Hu State Grid Jiangxi Electric Power Co. Ltd. Nanchang China China Electric Power Research Institute Co. Ltd. Beijing China University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

The smart grid has now been fully implemented with a robust defense system that is built on the primary security principles of "security zoning, network specialization, horizontal isolation, and vertical authentication." The secure cryptographic module plays a pivotal role as one of the key components in the power system, facilitating horizontal isolation and vertical authentication. Nevertheless, assessing secure cryptographic modules within power systems presents several challenges. These include low recognition efficiency of cryptographic algorithms, the difficulty in identifying the characteristics of encryption algorithm variants, and challenges specific to special business scenarios. To address these issues, this paper introduces an automatic cryptographic algorithm recognition method. Leveraging a deep learning framework in conjunction with modules such as data encryption, data enhancement, feature extraction, model construction, and five-fold cross-validation, this method achieves automated recognition of cryptographic algorithms, enhancing the testing capabilities of secure cryptographic modules. The experiments reveal that cryptographic algorithm detection using deep learning offers significant advantages, including a high recognition success rate, strong adaptability, and a high degree of automation. This method proves beneficial in significantly enhancing the efficiency of security evaluation and modification, effectively detecting the ciphertext features of commercial codes, and providing vital support for the secure operation of the smart grid.

关键词： Deep learning machine learning algorithms Passwords Manuals Feature extraction Smart grids Encryption Classification algorithms Cryptography Security

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Design and Application of Personalized Recommendation Algorithm for Electricity Marketing

Design and Application of Personalized Recommendation Algori...

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data Analytics, Computing and Artificial Intelligence (ICDACAI), international conference on

作者： Yu Yu Lin Bai Xinpo Zhu Hua Liu Tao Zhou Jianbo Wang Beijing China-Power Information Technology Co. Ltd. Beijing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

With the rapid development of the current economy and technology, the electricity consumption behavior of power users has shown an unprecedented trend of diversification, with diverse and rapidly changing demand patterns. To effectively respond to this market change, power companies urgently need to transform and upgrade, and use innovative marketing strategies to accurately meet user needs, improve service quality, and enhance market competitiveness. In response to the significant drawbacks of traditional power marketing methods, such as rigid strategies, difficulty in accurately matching personalized user needs, uneven distribution of power resources, and serious waste, this article explores and proposes a personalized recommendation algorithm for power marketing based on big data analysis and artificial intelligence (AI) technology. This algorithm utilizes advanced machine learning (ML) models to achieve precise prediction of users' electricity habits, preferences, and future demand through deep mining of massive user electricity data. The experimental results show that the algorithm proposed in this paper performs well in improving user satisfaction and enhancing user stickiness, while significantly reducing the operating costs of power enterprises and reducing resource waste, providing strong support for the sustainable development of the power industry.

关键词： data privacy machine learning algorithms Costs Electricity Biological system modeling Big data Prediction algorithms Power industry data models Sustainable development

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A Novel Rule-based Intrusion Detection System Using data mining

A Novel Rule-based Intrusion Detection System Using Data Min...

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2010 3rd IEEE international conference on Computer Science and Information Technology—ICCSIT 2010

作者： Lei Li, De-Zhang Yang, Fang-Cheng Shen School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, China

Network security is becoming an increasingly important issue, since the rapid development of the Internet. Network Intrusion Detection System (IDS), as the main security defending technique, is widely used against such malicious attacks. data mining and machine learning technology has been extensively applied in network intrusion detection and prevention systems by discovering user behavior patterns from the network traffic data. Association rules and sequence rules are the main technique of data mining for intrusion detection. Considering the classical Apriori algorithm with bottleneck of frequent itemsets mining, we propose a Length-Decreasing Support to detect intrusion based on data mining, which is an improved Apriori algorithm. Experiment results indicate that the proposed method is efficient.

关键词： Intrusion Detection Rule-based Length-Decreasing Support Association Rules data mining

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