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Tech-Enabled Banking Revolt:the Transformational Era of IT in the Financial Sector 7

Tech-Enabled Banking Revolt:The Transformational Era of IT i...

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7th international conference on Image information processing, ICIIP 2023

作者： Ghantasala, G. S. Pradeep Kunchala, Anjaneyulu Vidyullatha, Pellakuri Banu, Shaik Rehana Bhaumik, Amiya Banu, Shaik Balkhis Gupta, Gaurav Alliance University Department of Computer Science And Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science And Engineering Guntur AP Vaddeswaram India Lincoln University College Dept of Business Management Malaysia Lincoln University College Faculty of Busniess And Accountancy Dept of Business Management Malaysia Fatima College of Heaith Sciences Dept of Physiotherapy Al Ain United Arab Emirates Shoolini University of Biotechnology And Management Sciences Yogananda School of Artificial Intelligence Computers And Data Science HP Solan India

ISBN: (纸本)9798350371406

information technology is been widely spread all over the world. the benefits that the IT sector provides are unmeasurable. though there are numerous benefits there are scams going around with the software developed under IT. the growing need for IT has made IT one of the necessary systems that need to be provided in an organization to enhance productivity and progress. the developed countries are making full use of IT in all sectors. the banking sector plays a vital role in the growth of the nation's economy working along the side of IT will only result in the enhancement of the banking sector in many perspectives. the present paper views on the impact of IT on the banking sector. © 2023 IEEE.

关键词： Banking sector Banking technology information Communication technology information technology

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Comparison Model for Forecasting the Total Population in Bangkok 9

Comparison Model for Forecasting the Total Population in Ban...

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Joint 9th international conference on Digital Arts, Media and technology with 7th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Tipwong, Nattanicha Jitpattanakul, Anuchit Phaphan, Wikanda Nontapa, Chalermrat Mekruksavanich, Sakorn King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Chiang Mai University Faculty of Science Department of Statistics Chiang Mai50200 Thailand Research Group in Statistical Learning and Inference Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

ISBN: (纸本)9798350318241

the primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok's overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, Department of Provincial Administration. this dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. the second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. the evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis. © 2024 IEEE.

关键词： Mean square error

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th international conference on Signal processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. the DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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Research on information Source Detection based on Machine Learning Algorithm 7

Research on Information Source Detection based on Machine Le...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Hu, Gebiao Liu, Han Wang, Jiale Ye, Jiahui Gong, Wenkai Song, Shiyu Construction Branch State Grid Jiangxi Electric Power Company Nanchang China Hebei University College of Physics Science and Technology Baoding China Northwest University School of Information Science and Technology Xi'an China Northwest University School of Mathematics Science Xi'an China

ISBN: (纸本)9781665478571

Estimating the information source is an essential indicator in large-scale complex social networks. Previous researchers have mainly concentrated on network or graph structures with relationship strengths among vertex. However, relationship strengths are randomly distributed in network. We propose a four layers novel conventional neural network that train and investigate the source of each information in network. We also develop a method to evaluate the credibility of founded information source into positive or negative vertexes based on their training results. To evaluate our model, we utilize two large data set gemsec-deezer and emergent from real life. Experimentally training results demonstrate 80%-85% accuracy of determining message source nodes with reasonable computation costs. Finally, we show our positive and negative information sources performance in terms of the accuracy after testing step. © 2022 IEEE.

关键词： Machine learning

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Research on Airport Pavement Condition information System 7

Research on Airport Pavement Condition Information System

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Li, Zonghe Zhao, Kunyang Zheng, Panfei Beijing Super-Creative Technology Co. Ltd. Beijing100621 China China Airport Construction Group Co. Ltd. Beijing100101 China

ISBN: (纸本)9781665478571

the current level of informatization in the collection and use of pavement condition information at civil airports is relatively low. In order to meet the needs of scientific maintenance decision-making of the airport, a set of airport pavement condition information system which accurately and quickly collects pavement disease data and intelligently identifies and manage diseases is researched the system is mainly composed of three parts: the acquisition system, the analysis system and the airport pavement condition database. the acquisition system collects airport pavement data through the handheld acquisition instrument and the fast acquisition vehicle, and uploads the pavement condition data to the analysis system for intelligent identification and evaluation. Finally, it is summarized into the airport pavement condition database to form the pavement disease database, pavement damage curve database, pavement disease development curve database and maintenance technology database, which could be used as the basis for predicting the development trend of pavement damage performance and facilitate the decision-making of airport operation managers. © 2022 IEEE.

关键词： Decision making

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DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING thE USAGE OF IMAGES FROM VIRTUAL SCENARIOS 16

DOMAIN ADAPTATION IN IMAGE DEHAZING: EXPLORING THE USAGE OF ...

引用

16th international conference on computer Graphics, Visualization, computer Vision and Image processing, CGVCVIP 2022, 8th international conference on Connected Smart Cities, CSC 2022, 7th international conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2022, and 11th international conference on theory and Practice in Modern Computing, TPMC 2022 - Held at the 16th Multi conference on computer Science and information Systems, MCCSIS 2022

作者： Sappa, Angel D. Suárez, Patricia L. Velesaca, Henry O. Carpio, Darío Escuela Superior Politécnica del Litoral ESPOL Campus Gustavo Galindo Km. 30.5 P.O. Box 09 Vía Perimetral 01-5863 Ecuador Computer Vision Center Campus UAB Bellaterra Barcelona08193 Spain

ISBN: (纸本)9789898704429

this work presents a novel domain adaptation strategy for deep learning-based approaches to solve the image haze removal problem. Firstly, a large set of synthetic images is generated by using a realistic 3D graphic simulator;these synthetic images contain different densities of haze, which are used for training the model that is later adapted to any real scenario. the adaptation process requires just a few images to fine-tune the model parameters. the proposed strategy allows overcoming the limitation of training a given model with few images. In other words, the proposed strategy implements the adaptation of a haze removal model trained with synthetic images to real scenarios. It should be noticed that it is quite difficult, if not impossible, to have large sets of pairs of real-world images (with and without haze) to train in a supervised way haze removal algorithms. Experimental results are provided showing the validity of the proposed domain adaptation strategy. © MCCSIS *** rights reserved.

关键词： Deep learning

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An In-Depth Analysis of Electric Vehicle Charging Station Based on LSTM and SVM Hybrid Model 7

An In-Depth Analysis of Electric Vehicle Charging Station Ba...

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7th international conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Ramkumar, V. Nadaf, Akabarsaheb B Ghamande, Manasi Vyankatesh Dabral, Amar Prakash Bharambe, Priti Mangesh Kumar, Davinder Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Pune Abhijit Kadam Institute of Management and Social Sciences Solapur India E and AS Vishwakarma Institute of Information Technology Maharashtra Pune India Graphic Era Deemed to Be University Department of Hospitality Management Uttarakhand Dehradun India MIT Arts Commerce and Science College Alandi Computer Application Department Maharashtra Pune India Architect Micron Technology Telangana Raidurgam India

ISBN: (纸本)9798350340600

As part of their future smart city ambitions, many countries hope to increase environmental sustainability by electrifying their transportation networks. this means that the percentage of electric vehicles in any particular city's total vehicle population will rise rapidly. Electric vehicle (EV) batteries can be recharged in a number of ways, but charging stations will be given top priority. Charging stations should be dispersed across a city in such a way that all EVs may reach one within a reasonable period of time and then use that charge to travel the entire city. this data preprocessing phase makes use of normalized processing, time interval processing, and outlier processing. the features are derived using principal component analysis. the LSTM method entails a set of four gates (an input, a forget, a learn, and a remember), All of which contribute to the model's training. the models are tested using an LSTM-SVM method. When compared to LSTM and SVM, the proposed method produces higher-quality results. © 2023 IEEE.

关键词： Principal component analysis

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Cervical Cancer Cell Image Detection Method Based on Improved YOLOv4 7

Cervical Cancer Cell Image Detection Method Based on Improve...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： Shi, Can Pan, Qiao Rehman, Mustafain Donghua University College of Computer Science and Technology Shanghai China

ISBN: (纸本)9781665478571

Aiming at the problem of frequent false and missed detection due to the dense and scattered distribution of cancer cells in the pathological images of cervical cancer cells, the complex image background, and the rich proportion of small objects, an improved image detection method of cervical cancer cells is proposed by combining the advantage of convolutional neural network and the transformer on the basis of the YOLOv4 network. Firstly, this method introduces the CoT module into the backbone feature extraction network of YOLOv4 to improve the global information acquisition ability of the model and the visual expression ability of the model. Secondly, according to the characteristics of the cervical cancer cell object detection dataset, data augmentation strategies such as Mosaic are used to enrich the dataset and improve the generalization ability of the model. Lastly, the improved method proposed is used to perform experiments on cervical cancer cell image dataset, and the corresponding results are obtained. the results show that the improved network model is significantly improved, which significantly improves the detection ability of cervical cancer cells and fully verifies the effectiveness of the algorithm proposed in this paper. © 2022 IEEE.

关键词： Object recognition

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7th international Russian Automation conference, RusAutoCon 2024

7th International Russian Automation Conference, RusAutoCon ...

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7th international Russian Automation conference, RusAutoCon 2024

ISBN: (纸本)9783031824937

the proceedings contain 37 papers. the special focus in this conference is on Russian Automation. the topics include: Mapping and Path Planning Methods for Highly Automated Vehicles in Agriculture;morphological Analysis and Synthesis Features of Technological Processes;a Modern Method of Wireless Control of Unguarded Railway Crossings;duties and Obligations of the Railway Staff Concerned When the Microprocessorized Contactless Controlling Gauge Device Signal Is Triggered;integration of Automated Management Systems for Enterprises’ Transport and Technical Services;development of an Automation Module for Planning Trajectories for Painting Aircraft Fuselage Elements;identification of Metal Sheets in the Flow, Based on the Marking Imprint, Using Neural Networks;study of the Performance of Adaptive Sensor Networks for Collecting and processing thermoelectric Data;development and Research of a Cartographic Model for Municipal Planning as the Basis of an Intelligent Geoinformation System;BIM Visual Programming of Bionic Architecture Construction Using Dynamo and Revit;development of a Model and Algorithms for Trigger Control of Technological Processes of Resource Provision;system for Statistical Assessment of Means of Controlling Engineers’ Qualification for information Support to Manage the Recruiting Process in Industry;smart Enumeration technology;comparative Analysis of C-Band Conical Horn Antenna Sparse Structures Characteristics at Different Frequencies;software Implementation of Heuristic Methods of Optimization and Integration into a Cloud Service;computer System for Modeling Fluid Flow Around Bodies and Its Potential in Industry;the Effect of Regular and Irregular Segmentations on Characteristics and Charge Distribution Densities of Microstrip Lines;reinforcement Learning with External Teacher for Building Energy Management.

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Which Sentence Representation is More Informative: An Analysis on Text Classification 7

Which Sentence Representation is More Informative: An Analys...

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7th international conference on Dependency Linguistics (Depling, GURT/SyntaxFest 2023), Depling 2023

作者： Bölücü, Necva Can, Burcu Computer Engineering Adana Alparslan Turkes Science and Technology University Adana Turkey Department of Computing Science and Mathematics University of Stirling Stirling United Kingdom

ISBN: (纸本)9781959429326

Text classification is a popular and well-studied problem in Natural Language processing. Most previous work on text classification has focused on deep neural networks such as LSTMs and CNNs. However, text classification studies using syntactic and semantic information are very limited in the literature. In this study, we propose a model using Graph Attention Network (GAT) that incorporates semantic and syntactic information as input for the text classification task. the semantic representations of UCCA and AMR are used as semantic information and the dependency tree is used as syntactic information. Extensive experimental results and in-depth analysis show that UCCA-GAT model, which is a semantic-aware model outperforms the AMR-GAT and DEP-GAT, which are semantic and syntax-aware models respectively. We also provide a comprehensive analysis of the proposed model to understand the limitations of the representations for the problem. © 2023 Association for Computational Linguistics.

关键词： Semantics

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