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Construction of quantitative prediction model of metal mineral resources based on big data mining 3

Construction of quantitative prediction model of metal miner...

引用

3rd international conference on Computer Technology, Information Engineering, and Electron Materials, CTIEEM 2023

作者： Fu, Taiyu Chongqing Test Center of Geology and Minerals Chongqing400042 China

ISBN: (纸本)9781510672925

With the vigorous development of big data technology, the application of data mining program to complete quantitative prediction and evaluation of mineral resources has become a new trend in the development of mineral exploration industry and an important way to promote the digital transformation of traditional prospecting technology. In this regard, this paper will combine geological theory analysis and data mining algorithm to build a quantitative prediction model of metal mineral resources, realize the analysis, processing and visual interpretation of deep geoscience data, and provide necessary data science support for actual mineral exploration. The prediction model takes the machine learning algorithm as the core, and relies on the class libraries such as Numpy, Pandas and Sklearn in Python environment to complete the development and training. Combined with the spatial attribute database in ArcGIS, the prediction elements and importance evaluation indicators are obtained, and the final quantitative prediction results are obtained, and the result evaluation is completed. Practice has proved that the quantitative prediction model of mineral resources based on machine learning algorithm has high prediction accuracy, and the visual results can clearly show the high probability mineral resources areas, which has high practical promotion value. © 2024 SPIE.

关键词： Minerals

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Research on the relationship between electrical signal and growth state of plants based on temperature factor 3

Research on the relationship between electrical signal and g...

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Shang, Chunhu Tian, Liguo Li, Meng Wang, Yuesong Cui, Xudong Han, Han Tianjin University of Technology and Education College of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9781665490788

It is a weak signal that changes the resting potential of plant cells or tissues under the stimulation of external environmental factors and can be transmitted between cells and tissues. Plant electrical signals are closely related to plant movement, growth, metabolism, etc., and the characteristic changes of plant electrical signals can then respond to the growth condition of the plant itself. In this paper, the electrical signals of Aloe vera are related to plant movement, growth, and metabolism. In this paper, according to the time-frequency domain distribution of this Aloe vera electrical signal, we choose to improve the wavelet threshold noise reduction to process the original electrical signal and then use the empirical mode decomposition into multiple eigenmode functions from 0-5Hz, multiple eigenmode functions form a two-dimensional time-frequency matrix signal and input the two-dimensional time-frequency matrix signal into the convolutional neural network for training to get the general law of plant electrical signal and plant growth state under different temperature levels. To further improve the model accuracy, the convolutional neural network model is optimized in this paper by adding a two-layer bidirectional LSTM behind the CNN. © 2023 IEEE.

关键词： Metabolism

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Optimizing Large-Scale data processing with Artificial Intelligence 3

Optimizing Large-Scale Data Processing with Artificial Intel...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Kumar, Ritesh Beemkumar, N. Shukla, Anupam Nirmala, D. Meshram, Vidula V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

massive-scale statistics processing presents precise demanding situations in extracting insights, as the sheer amount of records can be overwhelming for classic analytics systems. Artificial Intelligence (AI) can offer a powerful answer for coping with large-scale records units, because it leverages system mastering algorithms to construct sophisticated fashions and automate complex evaluation. AI algorithms are also adaptive and incredibly green, which reduces the time it takes to mine valuable insights from big information units. This paper will study the diverse approaches that AI can enhance large-scale information processing and explore the numerous advantages of deploying AI in statistics processing workflows. The dialogue will include the advantages of leveraging AI for both supervised and unsupervised getting to know tasks in addition to the technical concerns around deciding on the proper AI technology for information processing jobs. Subsequently, the paper will present actual- world applications of AI in huge-scale records processing and evaluate the ability for groups to gain an aggressive aspect by deploying AI of their information pipelines. © 2023 IEEE.

关键词： Aggressive Applications Information Numerous Present

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Automation of data Transmission for Wallet with Novel Merchant Payment System Using Cluster Classification over Sequence Analysis Method 3

Automation of Data Transmission for Wallet with Novel Mercha...

引用

3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Awasthi, Aishwary Mehta, Richa Rathod, Mohit Salman, Hayder Mahmood Lal, Bechoo Sulaimanalfurhood, Badria Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India Chandigarh Engineering College Department of ECE Jhanjeri India PIT Parul University Department of CSE Gujarat Vadodara India Al-TurathUniversiy College Computer Science Department Baghdad Iraq KL Deemed University Vijayawada Campus Department of CSE AP India Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia

ISBN: (纸本)9798350399264

To improvise a data transmission for wallet with merchant payment system using cluster classification over sequence analysis method is proposed in this paper. The implementation have been carried out for processing the data through MySQL and the algorithms are tested with Net Beans (IDE) applications. The cluster classification paved the way to achieve the enhancement of data transmission of data. With the sample size of 46 and tested around 11 number of times compared with the sequence analysis method. The speed with the accuracy of 92% for data transmission for wallets with merchant payment systems using cluster classification, provides significantly better results compared to sequence analysis methods. There was a statistical significance between cluster classification and sequence analysis *** improvise a data transmission for a wallet with a merchant payment system using cluster classification over sequence analysis method provides more significant accuracy of 94.4 percent than sequence analysis method by analyzing the parameters of time with data and received time. © 2023 IEEE.

关键词： Cryptography

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Network information data processing method based on artificial intelligence 3

Network information data processing method based on artifici...

引用

3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Yang, Hong Meng, Xiaokai Zhang, Guangwei Wang, Dawei Liu, Xiaofei Institute of Electric Power Science Shanxi Electric Power Company National Power Network Taiyuan030001 China Anhui Jiyuan Software Co. Ltd. Hefei230000 China

Because of the complexity and development of network information data processing, many conventional technical means can not process information data, and the artificial itself is powerless, so it needs to use artificial intelligence technology for processing. In view of this, this paper will introduce the basic concept of artificial intelligence technology and its advantages in information data processing, and then combine the technology to put forward the network information data processing method. Through the research, the method in this paper can give full play to the role of artificial intelligence technology, fast and in-depth processing of network information data, and can give artificial decision support. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision support systems

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The Integration of Stream data Models in Modern Transportation Networks 3

The Integration of Stream Data Models in Modern Transportati...

引用

3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naval, Preeti Jain, Sandeep Kumar Krishna, K. Gopala Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Electrical Engineering Jaipur India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka562112 India

ISBN: (纸本)9798350343274

The study proposal proposes an innovative strategy for managing modern transport networks, with an emphasis on the coordinated use of analytics, real-time data processing, and dynamic decision-making utilizing stream data models. To create a solid infrastructure for real-time data analysis, it is required to use a diverse set of data sources, such as sensors, GPS devices, traffic cameras, and mobile application software. A strategy for reaching this goal is described below. The real-time system that integrates transportation models, artificial intelligence, and data mining is at the core of this strategy. In addition to monitoring and assessment, one of the key responsibilities of this system is to develop the transportation network. Without this framework as a foundation, the method would be worthless. This collaborative effort provides proactive traffic control, efficient route planning, and event prediction in real time. The application of machine learning in predictive analytics and traffic analysis, which together give solid traffic forecasts, prompts the creation of tactics that cut wait times and enhance productivity. Event detection algorithms are critical for ensuring commuter safety and reducing wait times in the event of an emergency. Using cloud-hosted data processing technologies, the idea manages massive amounts of real-time data. This is critical for data collection, processing, and storage methods. Finally, we believe that the development of an intelligent transportation networkone that can continually learn, adapt, and drive itselfwill result in less traffic, more safety, shorter travel times, and environmentally friendly legislation and practices. To address the ever-increasing demands placed on dynamic urban settings, it is believed that the technique developed would transform current transport networks into more responsive, user-friendly, and efficient systems. © 2023 IEEE.

关键词： Real time systems

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Measuring the Vehicle-in-Motion, Density and Allocation of Traffic signal Using Transfer Learning 3rd

Measuring the Vehicle-in-Motion, Density and Allocation of T...

引用

3rd international conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Prabhakaran, K. Manoj Kumar, K. Nithin Sai Valli, Shaik Nath, K. Kartheek Amrita Vishwa Vidhyapeetam Dept ECE Chennai Campus Chennai Tamil Nadu India

ISBN: (纸本)9783031686160;9783031686177

There are various deep-learning methods available for detecting and classifying vehicles. In addition to detection, a total count of vehicles helps build an intelligent traffic signal by allocating the time according to the vehicle density. But in real-world applications, the multi vehicles cannot be identified in the traffic signal, and the motion of vehicles identification rate is slow in real-time implementation through traffic signal-CCTV cameras. To overcome this, a transfer learning approach has been implemented to speed up detecting, classifying, and counting the vehicles in the traffic signal which helps to allocate time for traffic signal. There are many existing models which use different deep-learning techniques for the vehicle's detection and classification. One such is YOLO V4, a recent super-fast model, one-stage object detector, and is best for real-time applications for object detection. YOLOV4 has been used for vehicle detection because of its very high inference speed when compared with other models. Having high inference speed is good advantage for real time test scenarios. Hence YOLO V4 has been used for implementation. The total estimation time is calculated using Transfer learning, where it's based on the number of vehicles at each traffic signal. In this work, our model attains a mAP of 80% with 14-15 fps of speed with multiple vehicles in traffic signal.

关键词： Multiple Vehicle Detection and Classification YOLO V4 and Vehicle counting

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Research on target attitude estimation method based on image 3

Research on target attitude estimation method based on image

引用

3rd IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Zhao, Tengfei Zhang, Rui Cao, Jianxin ShaanXi Institute of International Trade&Commerc College of Information Engineering ShanXi China Lvliang Vocational Technical Institute College of Big Data ShanXi China

ISBN: (纸本)9781665490788

Aiming at the real time and accuracy of door handle pose estimation, this paper improved ORB matching algorithm and combined with least square plane fitting algorithm. In order to improve the real-time performance of the system to obtain the location information of the door handle, the ORB matching algorithm was used to reduce the time complexity of the algorithm itself and the polar constraint algorithm was added to reduce the search dimension to straight line search to reduce the matching time and reduce the mismatching. Finally, the least square plane fitting algorithm is used to obtain the spatial attitude information of the door handle under the camera plane. The accuracy and real-time performance of the improved ORB matching algorithm and the accuracy of the door handle pose are verified by experiments. The proposed method can meet the requirements in practical application. © 2023 IEEE.

关键词： Door handles

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VREP Platform: A Virtual Reality-Based Human-Computer Interaction Experimental Platform 3

VREP Platform: A Virtual Reality-Based Human-Computer Intera...

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3rd international conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Tan, Bozhi Liu, Shikai He, Yuqi Zhu, Yi Nanjing Audit University School of Computer Science Nanjing China Nanjing Audit University School of Statistics and Data Science Nanjing China

ISBN: (纸本)9798350360813

The deployment of human-computer interaction (HCI) experimental platforms across various domains is of paramount importance. Presently, conventional HCI platforms continue to be rooted in Personal Computer (PC), exhibiting limitations such as low immersion, reduced real-time responsiveness, and diminished accuracy. In light of these constraints, this research advocates for a Virtual Reality (VR)-based HCI experimental platform, with the aim of overcoming these limitations. The platform seeks to enhance user experiences, evaluate emerging interaction technologies, and facilitate scholarly research. Integrating VR technologies, this platform offers a simulated, realistic interactive environment supporting multimodal inputs. The architecture includes three main components: users, processing, and the computer, with submodules facilitating scene construction, tracking, and connectivity. The platform showcases its potential for immersive interaction through an intelligent collaboration experiment between humans and unmanned aerial vehicles. © 2023 IEEE.

关键词： Virtual reality

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Application of 3D digital handover technology in infrastructure projects 3

Application of 3D digital handover technology in infrastruct...

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3rd international conference on Computer Vision and data Mining, ICCVDM 2022

作者： Cong, Baosong Anhui Electric Power Engineering Quality Supervision Center Station Anhui Hefei230041 China

ISBN: (纸本)9781510661363

In order to display the phased information of infrastructure projects, visually process the real-time data of infrastructure projects, and study the application of three-dimensional digital handover technology in infrastructure projects. Divide the processing stages of capital construction projects, divide the grid structure of capital construction buildings, realize the real-time display of numerical value of visual processing by changing the grid operation items, and complete the application of three-dimensional digital handover technology in capital construction projects. The test results of an example show that it is of practical significance to adjust the parameters of capital construction projects online and visually display the parameters of capital construction projects. © COPYRIGHT SPIE.

关键词： data visualization

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