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Research on Information processing System Under Computer Artificial Intelligence Big data Technology

Research on Information Processing System Under Computer Art...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Lei Peng Qiang Fu Zhi Li Musong Lin Yihua Qian Guangdong Key Laboratory of Electric Power Equipment Reliability Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou China

In this paper, a virtual view image post-processing method using background information is proposed. Firstly, a depth-based method is used to deal with overlapping between two images. Secondly, the artifacts of the left and right virtual view are located by the motion direction of the observation point, and the artifacts are eliminated by the background pixels in the supplementary view Angle. Then the background image is filled with holes and combined with the foreground image to form a virtual perspective image. The experimental results show that the peak signal noise of this method is 0.6357 dB better than that of the traditional method. Experiments show that the method proposed in this paper can improve the rendering effect significantly.

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Construction of Enterprise Financial data Visualization Analysis System Based on data Mining Technology 3

Construction of Enterprise Financial Data Visualization Anal...

引用

3rd international conference on Artificial Intelligence, Big data and Algorithms, CAIBDA 2023

作者： Wu, Xueqing Wu, Guoqiang School of Economy and Management Huainan Normal University Anhui Province Huainan City232038 China

ISBN: (纸本)9781643684444

With the rapid development of digital economy, enterprises need to make financial management activities and development decisions based on a large number of financial data and information. This also exposes the shortcomings of the traditional financial analysis and processing mode in adaptability, functionality and efficiency. In this regard, based on the actual needs of enterprises, this paper will put forward a set of construction scheme of enterprise financial data visualization analysis system, give full play to the practical application advantages of data mining technology in the era of big data, change the original processing methods, and provide an efficient, reliable and convenient new way for enterprise financial data analysis. The whole system adopts B/S architecture design, the client design is based on VUE framework, and the server construction is based on Django framework, and the financial data is analyzed, mined and visually displayed by using Numpy, Pandas, Matplotlib and other class libraries in Python environment. Practice has proved that the system can support users to call K-means, Decision Tree, Logistic and other algorithm models online for multi-dimensional analysis of financial data, which realizes an important supplement to traditional financial analysis, and achieves the purpose of improving financial management efficiency and assisting enterprise development decision-making. © 2023 The Authors.

关键词： Python

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Research on the Strategic Expansion Information System of Electric Power Enterprises Based on Computer Big data

Research on the Strategic Expansion Information System of El...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Wenfeng Wang Xuyang Li Xin Niu Yue Yin Zheng Sun Economic and Technological Research Institute of State Grid Henan Electric Power Company Zhengzhou China Tiandidian Research (Beijing) Technology Co. Ltd Beijing China

This article proposes a new type of power enterprise testing model based on computer big data technology, which can effectively enhance the informationization of power enterprise strategic development. The model adopts the core strategy of “resource specification business data application” level interaction, and implements it with the help of computer big data technology, evaluating power enterprise engineering projects, reviewing engineering budgets, and electronic pre settlement, The entire process of data collection is collected by the computer big data system into the model, and the power enterprise is simulated and calculated through the computer big data system. When the calculation occurs, the required information is adjusted based on communication and operation costs, and the dynamic changes in the entire process of strategic expansion are calculated. The Stackelberg expansion model is used to demonstrate that strategic expansion of power enterprises can enhance their digital construction.

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Evaluation Model and Algorithm of Physical Education Teaching 3rd

Evaluation Model and Algorithm of Physical Education Teachin...

引用

3rd EAI international conference on Application of Big data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Li, Lei Suiying, Liu Nanchang University College of Science and Technology Jiangxi Nanchang330001 China Foreign Languages Schoolichuan University Jinjiang Collegeeishan Sichuan620800 China

ISBN: (纸本)9783031631320

This article proposes an evaluation model and algorithm based on data mining and machine learning for physical education teaching evaluation, based on the needs of educational evaluation. In this study, we quantified the key factors and elements in the physical education teaching process by developing relevant indicators and standards, and transformed them into a form that can be used for data analysis and processing for evaluation and analysis. The system consists of four steps. The first step is to plan and evaluate. In this step, we should plan our activities based on the goals of each lesson or unit. We can also use some simple tools, such as scales, checklists, etc., to facilitate the preparation of the evaluation. The evaluation teaching model of physical education teaching refers to the process in which teachers evaluate and provide feedback on students’ performance in physical education teaching. This process helps teachers improve their skills and physical education knowledge. The feedback provided by the teacher should be positive so that students can learn from it. This also helps to improve their performance in sports. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： data mining

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GV Black Inspired Hierarchical Multiclass Classification using Panoramic Radiographic Synthetic data

GV Black Inspired Hierarchical Multiclass Classification usi...

引用

international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Pravar Kulbhushan Jaiyesh Chahar Indrajit Kar Siemens Advanta Bengaluru Karnataka India

The purpose of this research is to demonstrate that using the generated synthetic dataset will produce better results than the original teeth x-rays and thus be more efficient for carrying out hierarchical multiclass classification, this will also allow dental caries classification to be automated in accordance with the GV Black Standards. Five distinct models that were trained and tested using both the new and original datasets are compared. As the first step in each of these tasks, simple object detection will be used to identify each tooth, and then hierarchical classification will be used to achieve the desired results for classifying the cavities so that appropriate treatment can be determined based on the class of caries.

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

3rd International Conference on Smart Technologies, Systems ...

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3rd international conference on Smart Technologies, Systems and Applications, SmartTech-IC 2022

ISBN: (纸本)9783031322129

The proceedings contain 37 papers. The special focus in this conference is on Smart Technologies, Systems and Applications. The topics include: Colombian Sign Language Classification Based on Hands Pose and Machine Learning Techniques;spanish Stylometric Features to Determine Gender and Profession of Ecuadorian Twitter Users;identification of Tropical Dry Forest Transformation from Soundscapes Using Supervised Learning;atmospheric Dispersion Prediction for Toxic Gas Clouds by Using Machine Learning Approaches;facial Recognition System with Liveness Detection Integrated into Odoo for Attendance Registration in a Software Laboratory;Classification of Epileptic Seizures Based on CNN and Guided Back-Propagation for Interpretation Analysis;an Approach to the Presumptive Detection of Road Incidents in Cuenca, Ecuador Using the data from the Social Media Twitter and Spanish Natural Language processing;financial Credit Risk Measurement Using a Binary Classification Model;FPGA-Based Four-Band Multispectral Camera Prototype for Precision Agriculture Applications;data Mining Application for the Generation of User Profiles in Serious Games Aimed at Attention and Memory Training;small-Scale Precision Agriculture: A Case Study Applied to a Seed Germination Chamber;diagnosis and Degree of Evolution in a Keratoconus-Type Corneal Ectasia from Image processing;finite Element Analysis of Uncovered Coplanar Interdigital Electrodes for Capacitive Sensor Applications;algorithm for Medical Diagnostic Support Using Machine and Deep Learning for Depressive Disorder Based on Electroencephalogram Readings;Effects of a Robotic Lower-Limb Orthosis on Gait Based on the Analysis of sEMG signals and Kinematic data: Preliminary Results;design and Evaluation of a Prototype of Dual Channel Electrical Stimulator: Application in the Assessment of Current Perception Thresholds to Multiple Stimuli;system for Postural Stability Assessment Based on an Inertial Sensors Network;perspectives on Climate Change

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Noisy Text data: foible of popular Transformer based NLP models 3

Noisy Text Data: foible of popular Transformer based NLP mod...

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3rd international conference on AI-ML Systems, AIMLSystems 2023

作者： Bagla, Kartikay Gupta, Shivam Kumar, Ankit Gupta, Anuj Chaos Genius India Ninja Salary India Clearfeed Inc. United States Gradient Advisors India

ISBN: (纸本)9798400716492

In the past few years, researchers working in natural language processing have created a number of new models based on transformer architecture. These models have shown remarkable performance for various NLP tasks on benchmark datasets, often surpassing SOTA results. Buoyed with these results, industry practitioners are actively experimenting with fine-tuning these models to build NLP applications for industry use cases. However, for most datasets used by practitioners (to build these applications), it is hard to guarantee the complete absence of any noise in the data. While most transformer-based NLP models have performed exceedingly well in transferring the learnings from one dataset to another for various tasks, it remains unclear how these models perform when fine-tuned on noisy text. In this paper, we precisely do this. We explore the sensitivity of popular transformer-based NLP models to noise in the text data. We show (via experimental results) that the popular transformer-based NLP models such as BERT, RoBERTa, ALBERT, XLNet and T5 perform badly on fundamental NLP tasks (namely, text classification, textual similarity, NER, question answering, text summarization) when fine-tuned on a noisy version of benchmark datasets. We further show that as the quantum of noise in the data increases, the performance of these models degrades rapidly. Our findings suggest that one must cautious of presence of any noise in their datasets before fine-tuning popular transformer-based NLP models on their datasets. © 2023 ACM.

关键词： Natural language processing systems

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Real-Time Robot 3D Pose Computation from NIR Imagery and ToF Depth Maps for Space Applications 1

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3rd international conference on Applied Science and Advanced Technology, ICASAT 2023

作者： Martinez, Geovanni Escuela de Ingeniería Eléctrica Universidad de Costa Rica San José11501-2060 Costa Rica

ISBN: (数字)9783031576201

ISBN: (纸本)9783031576195

This paper presents an algorithm capable of determining the three-dimensional position and orientation (3D pose) of an exploration robot from the processing of two multidimensional signals, a monocular near-infrared (NIR) video signal and a time-of-flight (ToF) depth signal, both provided by a monocular NIR ToF camera rigidly attached to the side of a robot facing the terrain. It is shown that if the depth signal is also considered during processing, it is possible to accurately determine the 3D pose of the robot, even on irregular terrain. The 3D pose is calculated by integrating the frame-to-frame robot 3D motion over time using composition rules, where the frame-to-frame robot 3D motion is estimated by minimizing a linear photometric error by applying an iterative maximum likelihood estimator. Hundreds of experiments have been conducted over rough terrain, obtaining excellent absolute position and orientation errors of less than 1 percent of the distance and angle traveled, respectively. This good performance is mainly due to the algorithm’s more accurate knowledge of the depth provided by the monocular NIR ToF camera. The algorithm runs in real time and can process up to 50 fps at VGA resolution on a conventional laptop computer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cameras

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Optimizing Image and signal processing Through the Application of Various Filtering Techniques: A Comparative Study 1

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3rd international conference on Novel and Intelligent Digital Systems, NiDS 2023

作者： Gonzales, Aliza Lyca Ramos, Anna Liza Lacson, Jephta Michail Go, Kyle Spencer Furigay, Regie Boy Saint Michael’s College of Laguna Old National Highway Platero Philippines

ISBN: (数字)9783031441462

ISBN: (纸本)9783031441455

This study aimed to explore different filtering techniques in order to showcase the performance of the filters and improve image and signal detection accuracy. The filters were evaluated using performance metrics such as Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). These performance metrics were supported by a confidence interval assessment to statistically validate the results. The results demonstrated that the median filter for smoothing, Butterworth filter for sharpening, and Inverse filters for restoration showed promising results. In the case of EEG signals, the Least Mean Square (LMS) filter for adaptive filtering was found to be the best choice, while the Notch filter for Infinite Impulse Response (IIR) and the Coiflets3 for Wavelet Transform outperformed other filters. Therefore, these results serve as a reference guide for determining which filters should be applied for denoising image and signal datasets. Additionally, it is worth noting that when selecting a filter, practitioners should carefully consider the characteristics of the filter in relation to the type of noise, resolution, and lighting conditions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Adaptive filters

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GPU Acceleration of Multi-Vehicle Detection and Classification for Unmanned Aerial Vehicle Surveillance Application using YOLOv3 3

GPU Acceleration of Multi-Vehicle Detection and Classificati...

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3rd IEEE international conference on Recent Advances in Systems Science and Engineering, RASSE 2023

作者： Kumar, Devshree Khand, Atharva Satyanarayana Murthy, P.V. Unmanned Aerial Vehicle Design and Integration Division CSIR-NAL Bengaluru India Manipal Institute of Technology Bengaluru India

ISBN: (纸本)9798350341676

This paper describes faster computation technique for Object Classification in Unmanned Aerial Vehicle (UAV), real-Time flight video data by the use of Compute Unified Device Architecture (CUDA) Graphics processing Unit (GPU) Acceleration, an approach called general-purpose computing on GPUs (GPGPU). The implementation was done to proof the concept that frame per second (FPS) doesn't drop much in case of GPU for multi object detection and classification in video captured by Quadrotor from a height of 50 m above Ground Level as compared to Central processing Unit (CPU) usage. Here, in-house designed NAL Quadrotor was used for collection of vehicle dataset which were labelled and classified using You Only Look Once (YOLOv3) Object detector using Darknet53 as feature extractor. This detector was specially selected for its accuracy in detecting small objects as aerially viewed from UAV gimballed camera. The approach was successfully implemented on vehicle dataset and proper identification in different categories like car, truck etc., was done. A novel object counter algorithm was designed to count the objects and assign class identification and object counter number. Each objects were accurately detected and assigned proper IDs. The processing of intelligent object classification increased to 10 times when GPU acceleration was used on tensors in PyTorch. The results are attached for understanding and prove that this detector works best for recognition of objects in aerial view. © 2023 IEEE.

关键词： Graphics processing unit

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