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Research and application of Legal Consultation database based on multimedia Network 4

Research and Application of Legal Consultation database base...

引用

4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Yu, Jing Changchun University of Architecture and Civil Engineering Changchun130022 China

In order to relieve the pressure of people's appeal for legal inquiry, the research and application of legal consultation database based on multimedia network is proposed. the legal consultation system designed in this essay, combined with the current situation of the legal industry, takes the Chinese question answering system as the prototype, combined with the open source data retrieval project ***, expands the data storage types. In this essay, the Chinese word segmentation system developed by Chinese Academy of Sciences is integrated into *** platform to make up for its word segmentation deficiency. And the mutual information technology is used to make the legal related words with the same meaning be searched first. When extracting answers in Chinese question answering system, there are often "missing" and "wrong" answers. In this essay a method of sentence similarity calculation of questions and answers based on Latent Semantic analysis (LSA) is proposed, which uses spatial vector model as representation method and latent semantic analysis theory. A low-dimensional semantic space is constructed by using the dimension reduction method of singular value decomposition, and the sentence similarity between question and answer is computed on the semantic space. Experimental results show that most retrievals can be completed within 20 seconds, and the average unit retrieval time is kept at the millisecond level. Conclusion: this system has more accurate query accuracy and less running time. © 2024 the Authors.

关键词： Question answering

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Dermcare-Skin Disease Classification from Image 4

Dermcare-Skin Disease Classification from Image

引用

4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Arora, Vishnupriya Johnson, Aleen Aggarwal, Lakshita Goswami, Puneet Alam, Sana SRM University Dept. of Computer Science Technology Delhi India Dept. of Biochemistry New Delhi India

ISBN: (纸本)9798350306118

Being one of the most exposed organs, the human skin acts as the body's main line of protection against microbiological dangers. Nevertheless, systematic approaches to product utilization and skincare regimens are still largely unexplored. Due to the variety and dynamic nature of skin illnesses, as well as their rising frequency among skin types from different cultural backgrounds, effective methods of diagnosis and treatment are required. the current system for diagnosing skin diseases is complicated and mainly depends on expert medical opinion and information from skin biopsies. Objective: the goal of the study is to increase the accuracy of diagnosis while overcoming the difficulties in differentiating between skin disorders that appear physically identical. By lowering diagnosis time and encouraging more practical and accurate treatment options, this method aims to improve patient care. Methodology: the classification model for skin diseases will be created using a convolutional neural network. the preprocessed picture dataset will be used to train the newly created CNN model. the model's parameters will need to be optimized during the training process. A separate validation dataset will be used to test the trained model's effectiveness in correctly categorizing various skin conditions. Results: the proposed deep learning-based skin disease classification system demonstrated promising results in accurately diagnosing various skin conditions. the model achieved an overall accuracy of 91 % on the validation dataset, indicating its effectiveness in distinguishing between different diseases. © 2023 IEEE.

关键词： Deep learning

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A Review on DDoS attack Detection in SDN using ML 4

A Review on DDoS attack Detection in SDN using ML

引用

4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Raj, Ritu Singh Kang, Sandeep Chandigarh University University Institute of Engineering Computer Science & Engineering Mohali India

ISBN: (纸本)9781665474368

the world has completely transformed in terms of technology as a result of virtualization. One application of this virtualization is SDN. In recent years, it has become a popular and widely utilised network architecture. Using SDN, or a software-defined network, the control network is divided from the data plane. Although Software Defined Networks provide a simple and obvious method for network control, they have also created a new security danger. DoS attacks, man-in-themiddle attacks, and other threats are examples of these dangers. the most frequent and common danger on this list is DDoS. By impacting the server, DDoS made it simpler to start the attack. this results in the network as a whole failing. therefore, it is essential to identify this threat in order for a server to operate safely. this research analyses the various machine learning approaches that are employed in the SDN context to identify DDoS attacks. © 2022 IEEE.

关键词： machine learning

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PPO Based Reinforcement learning Horizontal Pods Auto-Scaling-A Case Study in Simulation Environment 4

PPO Based Reinforcement Learning Horizontal Pods Auto-Scalin...

引用

4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Peng, Fang Ji, Xin Zhang, Le Chen, Ruibo Liao, Xingchuang Zhang, Kui State Grid Corporation of China Big Data Center Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing China

ISBN: (纸本)9798350308082

Microservice has become a state-of-art architecture in the software engineering domain. In the big data scenario, the data can be encapsulated by microservice, providing independent service or orchestrating the business procedure. Much research, such as threshold-based, rule-based, and even deep learning-based resource scaling, has been proposed to tackle this problem. However, the model trained on data like log or monitor index limited the intended dynamic resource adjusting, which caused high latency during adjusting. Moreover, the computation complexity and the data latency can still be a hindrance to the problem-solving. this paper proposed a reinforcement learning-based horizontal pods auto-scaling algorithm to study and respond in low latency. Furthermore, a case study to validate the effectiveness was carried out in a simulation environment. the results show that the proposed algorithm can converge after 0.3 million steps, and three groups of experiments were carried out, which shows that the trained reinforcement learning model can adjust the replica number in real-time, which indicates that the proposed algorithm can be applied in a scenario with low resource consumption. © 2023 IEEE.

关键词： Reinforcement learning

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Evaluation of drilling model of deep extended reach well in Jinzhou based on learning curve 4

Evaluation of drilling model of deep extended reach well in ...

引用

4th international conference on machine learning and computer application, ICMLCA 2023

作者： Geng, Lijun Jiang, Shaolong Zhu, Yulei Tan, Qiang Chen, Zhuo Wang, Xiaopeng Li, Bojia Cnooc Co. Ltd. Tianjin Branch Tianjin300459 China Cnooc Energy Development Co. Ltd. Tianjin300459 China 102200 China

ISBN: (纸本)9798400709449

With the continuous increase in the drilling operations of deep high-displacement wells in the Bohai Oilfield, higher demands are being placed on project management in petroleum engineering. It is imperative for us to have a more comprehensive understanding of project management and conduct more extensive research on deep high-displacement wells. We take the Jinzhou Block in the Bohai Oilfield as an example to study the influencing factors on the drilling cycle of deep high-displacement wells. Furthermore, we have analyzed the application effects of different drilling techniques based on learning curves, and achieved valuable insights. (1) In the drilling process of high-displacement wells in Jinzhou, there is a significant positive correlation between drilling depth and drilling operation cycle. the thickness of bottom gravelly strata shows a positive correlation with the drilling operation cycle. the drilling fluid density exhibits a negative correlation with the drilling operation cycle, while the density window for fluid loss during drilling shows a positive correlation with the drilling operation cycle. (2) Evaluating drilling operation performance based on learning curves helps in the progress management and cost control of high-displacement well projects. (3) In the 16-inch wellbore, the utilization of large borehole drilling technology resulted in an average reduction of 4.57% in the drilling cycle. In the 12-1/4-inch wellbore, the implementation of synthetic-based drilling fluid technology led to an average reduction of 7.55% in the drilling cycle. Furthermore, the application of continuous circulation technology in the 8-1/2-inch wellbore effectively stabilizes the Equivalent Circulating Density (ECD), maintains bottom hole stability, and achieves zero drilling fluid losses. through the study of drilling patterns for deep high-displacement wells in Jinzhou based on learning curves, we have gradually developed a comprehensive technical system and mature ope

关键词： Project management

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NEURALSLICE: Neural 3D Triangle Mesh Reconstruction via Slicing 4D Tetrahedral Meshes 40

NEURALSLICE: Neural 3D Triangle Mesh Reconstruction via Slic...

引用

40th international conference on machine learning

作者： Jiang, Chenbo Yang, Jie Hee, Shwai Lai, Yu-Kun Gao, Lin Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Nanjing Univ Sci & Technol Zijin Coll Nanjing Jiangsu Peoples R China Univ Maryland College Pk MD 20742 USA Cardiff Univ Cardiff Wales Univ Chinese Acad Sci Beijing Peoples R China

learning-based high-fidelity reconstruction of 3D shapes with varying topology is a fundamental problem in computer vision and computer graphics. Recent advances in learning 3D shapes using explicit and implicit representations have achieved impressive results in 3D modeling. However, the template-based explicit representation is limited by fixed topology, and the implicit representation, although flexible with arbitrary topology, requires a large number of sampled points to regress the surface, which is computationally expensive. In this work, we propose a novel 3D shape representation named NEURALSLICE, which represents a 3D shape as the intersection of a 4D tetrahedral mesh and a 4D hyperplane. A novel network is designed to incorporate the proposed representation flexibly, which learns a deformable 4D template and a parameter for slicing 4D hyperplane to reconstruct the 3D object. To learn the local deformation of the 4D template, we further propose a spatial-aware network to locate the 4D points within the 3D feature volume of input shape via positional encoding, which leverages the local geometrical feature to guide the 4D deformation. By addressing the 3D problem in a higher 4D space, our method supports flexible topology changes while being highly efficient. Our method is guaranteed to produce manifold meshes. NEURALSLICE outperforms the state-of-the-art explicit-based approaches in terms of reconstruction quality. Compared with implicit approaches, by avoiding point sampling, our method is 10 times faster than the implicit approaches, and better preserves thin structures. NEURALSLICE has the capability of representing various shapes and topologies using a single 4D tetrahedral mesh. the corresponding code can be found on Github at https://***/IGLICT/NEURALSLICE.

关键词： 3D modeling

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AI-Enable Generating Human Faces using Deep learning 12

AI-Enable Generating Human Faces using Deep Learning

引用

12th IEEE international conference on Communication Systems and Network Technologies, CSNT 2023

作者： Varshney, Neeraj Kumar, Gaurav Kumar, Ankit Pandey, Saroj Kumar Singh, Teekam Singh, Kamred Udham Gla University Department of Computer Engineering & Appications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Hill University School of Computing Dehradun India

ISBN: (纸本)9781665462617

Lately, sensible Image processing using deep neural networks has become a fervently discussed issue in machine learning and computer vision. Image can be made at the pixel level by learning from a gigantic variety of pictures. learning to make splendid movement pictures from highdifference draws is not only a captivating investigation issue yet also a reasonable application in innovative delight. In this research, we research the sketch-to-picture mix issue by using prohibitive generative poorly arranged networks. the model can normally deliver reasonable shadings for a sketch. the new model is not only prepared for painting hand-drawn sketches with real tones, yet also allows customers to exhibit supported tones. Test results on two sketch datasets show that the autopainter performs better contrasted with existing picture-topicture methodologies. With creating interest in the development of film, the interest in building a computerized structure to change over the authentic video into action is higher than at some other time. the edge-by-diagram modification of the action age measure is costly and dreary. To help with moving quickly and with no issue in a robotized collaboration we proposed a generative model that changes over genuine pictures into contrasting energy pictures without losing critical nuances of the source picture. We used an assortment of the generative hostile association as a fundamental plan with the custom incident ability to ensure the substance of the source picture, which changed over to an exuberance image. © 2023 IEEE.

关键词： Deep neural networks

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Professional Insights into Benefits and Limitations of Implementing MLOps Principles 26

Professional Insights into Benefits and Limitations of Imple...

引用

26th international conference on Enterprise Information Systems, ICEIS 2024

作者： Araujo, Gabriel Kalinowski, Marcos Endler, Markus Calefato, Fabio Brazil Italy

ISBN: (纸本)9789897586927

Context: machine learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. Objective: In this paper, we assess the benefits and limitations of using the MLOps principles in online supervised learning. Method: We conducted two focus group sessions on the benefits and limitations of applying MLOps principles for online machine learning applications with six experienced machine learning developers. Results: the focus group revealed that machine learning developers see many benefits of using MLOps principles but also that these do not apply to all the projects they worked on. According to experts, this investment tends to pay off for larger applications with continuous deployment that require well-prepared automated processes. However, for initial versions of machine learning applications, the effort taken to implement the principles could enlarge the project’s scope and increase the time needed to deploy a first version to production. the discussion brought up that most of the benefits are related to avoiding error-prone manual steps, enabling to restore the application to a previous state, and having a robust continuous automated deployment pipeline. Conclusions: It is important to balance the trade-offs of investing time and effort in implementing the MLOps principles considering the scope and needs of the project, favoring such investments for larger applications with continuous model deployment requirements. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： E-learning

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Weakly Supervised Tower Detection Based on Binary Classification 4

Weakly Supervised Tower Detection Based on Binary Classifica...

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4th international conference on computer Engineering and application, ICCEA 2023

作者： Zhang, Qiang Gao, Jie Niu, Yuzhong Li, Ming Yin, Zhanquan Wei, Shengtao Chen, Hua Li, Zhijun Gao, Xing Chang, Wenchun State Grid Ningxia Electric Power Co. Ltd Ningxia China China Electric Power Research Institute Beijing China State Grid Guyuan Power Supply Company Ningxia China East China Normal University Shanghai China

ISBN: (纸本)9798350347548

the power transmission network is vital to a country's infrastructure, and real-time detection of transmission towers is essential for ensuring transmission safety. Currently, deep learning has dominated in object detection, and most deep learning-based transmission tower detection methods rely on supervised learning. However, supervised detection of transmission towers requires a large amount of tower data to construct the dataset, and requires professionals to perform annotation, which can be a time-consuming and labor-intensive process. In most cases, detecting the exact class of towers is not necessary, but only their location. At this point, weakly supervised tower detection demonstrates its significant advantage. this paper proposes a weakly supervised binary classification method for detecting transmission towers and collects and constructs a binary classification tower dataset. the paper tests the weakly supervised detection performance on the constructed binary classification dataset using two weakly supervised networks. the experimental results show that weakly supervised transmission tower detection based on binary classification can complete the localization task of transmission towers and is feasible in cases where tower class is not required. © 2023 IEEE.

关键词： Deep learning

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Hidden Markov Model Approach to Predict Performance Improvement of 4G Cellular Network 11

Hidden Markov Model Approach to Predict Performance Improvem...

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11th international conference on Electrical Engineering, computer Science and Informatics, EECSI 2024

作者： Kosasih, Eka Oktavia, Tanty BINUS Graduate Program Information System Management Department Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9798350355314

the automation of performance optimization in 4G cellular networks remains a significant research challenge. Although advances in artificial intelligence, machine learning, and deep learning have been made, their application in cellular networks is still in its early stages. the evolution of automated learning algorithms in these networks requires continuous optimization to improve performance. this research aims to develop a data analysis model using a machine learning approach to predict 4G network Key Performance Indicators (KPIs) and enable automated assessments. the study employs Hidden Markov Models (HMM) to predict KPIs such as eRAB Drop Rate and Setup Success Rate. Data were collected from 1,600 newly deployed sites of a mobile operator in Indonesia over six months, from July 2023 to January 2024. the findings indicate that HMM models can significantly contribute to understanding and enhancing cellular network performance. For instance, the model achieved an accuracy of 99.52% and an F1 score of 99.76% in predicting the eRAB Drop Rate. the Inter-Frequency Handover Success Rate showed 96.15% accuracy and a 97.99%F1 score, while the RRC Connection Setup Success Rate achieved 91.35% accuracy and a 95.29% F1 score. these metrics highlight the model's effectiveness in predicting KPIs. By utilizing HMM models for high-accuracy KPI analysis, network operators can gain deeper insights into network performance and implement targeted improvements. this approach presents a promising avenue for automating and enhancing performance in 4G networks. © 2024 IEEE.

关键词： Hidden Markov models

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