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Towards Metahospital: augmented and virtual reality in medicine 14

Towards Metahospital: augmented and virtual reality in medic...

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14th international conference on Emerging Ubiquitous systems and Pervasive networks / 13th international conference on Current and Future Trends of Information and communication Technologies in Healthcare, EUSPN/ICth 2023

作者： Daineko, Yevgeniya Ipalakova, Madina Tsoy, Dana Alipova, Bakhyt Kozhakhmetov, Azamat Mustafina, Akkyz International Information Technology University Manas St. 34/1 Almaty Kazakhstan Regional Medical Center Abai Av. 65 Kyzylorda Kazakhstan

Augmented and virtual reality technologies are actively making their way into the field of healthcare, revolutionizing traditional approaches to diagnosis, prevention, and treatment. this article discusses how these concepts are combined and their profound impact on the emergence of the Metahospital. the authors provide an overview of this concept, highlighting its dual role in aiding healthcare professionals and empowering patients. Central to this paradigm shift is technology's potential to enhance specialized training and elevate the precision and efficiency of diagnostics, all while prioritizing patient safety. this model's core features are expounded upon, with a keen emphasis on ethical considerations and safeguarding personal data, a critical aspect explored within the paper. Furthermore, the article elaborates on the creation of a surgical simulator tailored for procedures like angioplasty and coronary artery stenting, showcasing the tangible advancements within the realm of medical technology. © 2024 Elsevier B.V.. All rights reserved.

关键词： Augmented reality

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Features of modeling the online real estate market in Almaty 14

Features of modeling the online real estate market in Almaty

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作者： Sapakova, Saya Sapakov, Askar International Information Technology University Manas str. 34/1 Almaty050000 Kazakhstan Kazakh National Agrarian Research University Valikhanov str. 137 Almaty050000 Kazakhstan

the article is devoted to the description of the real estate market, the situation in the market and services in the real estate market of Almaty and Kazakhstan as a whole. the factors influencing the market, such as external and internal, are considered, and further prospects for the development of the real estate market in the context of digitalization are presented the housing market in Kazakhstan is intricate, comprising numerous unique features among hundreds of thousands of apartments. It's important to note that even minor shifts in this market can trigger speculative behavior and intentional inflation of property prices. thus, grasping the actual worth of an apartment and recognizing its prime location becomes crucial. the primary focus of the research was to construct a predictive model for real estate valuation using machine learning techniques. Various machine learning algorithms were employed, encompassing linear regression, lasso, ridge, decision tree regression, random forest regression, and SVM (utilizing a Gaussian kernel). the study illustrates the outcomes and precision of these algorithms, offering insight into their effectiveness. Based on the findings of this study, the Random Forest regression algorithm demonstrated the highest performance, achieving a 90% score. © 2024 Elsevier B.V.. All rights reserved.

关键词： Commerce

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Information system for remediation and cleanup of contaminated soil with machine learning 14

Information system for remediation and cleanup of contaminat...

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作者： Naizabayeva, L. Nurzhanov, Ch A. Satymbekov, M.N. Elle, V. Zh International Information Technology University Almaty Kazakhstan Institute of Information and Computational technologies Almaty Kazakhstan Al-Farabi Kazakh National University Almaty Kazakhstan

this study introduces an innovative information system leveraging machine learning and deep learning techniques, aiming to forecast and facilitate decision-making in the remediation of soils laden with toxic elements. Our investigation utilizes an ensemble of decision trees to project plant productivity from climatic data, with a particular emphasis on gradient boosting for estimating soil contamination levels. An integrated approach to reclamation is espoused through the XGBoost-based model. the culmination of our research posits that the proposed system markedly augments the efficacy of soil detoxification endeavors, an imperative for environmental conservation and public health. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision trees

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Channel Learning-Based Relay Selection in Underwater Acoustic Sensor Network 14

Channel Learning-Based Relay Selection in Underwater Acousti...

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14th international conference on Ubiquitous and Future networks, ICUFN 2023

作者： Seol, Seunghwan Kim, Yongcheol Park, Bonggyu Chung, Jaehak Inha University Department of Electronics and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350335385

this paper proposes a channel learning-based relay selection method of cooperative communications to improve packet delivery ratio and reduce latency under the low propagation channel characteristics in underwater acoustic sensor networks. the proposed method estimates the time-varying channel using the sound speed profile based estimator to calculate the link capacity and transmits data packets to the selected best relay and candidate relays based on link capacity. Simulation results show that the proposed method performs better packet delivery ratio, latency, and throughput than the conventional SNR feedback and random selection methods. © 2023 IEEE.

关键词： Cooperative communication

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Topological Attention and Deep Learning Integration for Electricity Consumption Forecasting 14

Topological Attention and Deep Learning Integration for Elec...

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14th international conference on Smart Cities and Green ICT systems, SMARTGREENS 2025

作者： Salem, Ahmed Ben Amayri, Manar Higher School of Communication of Tunis Tunis Tunisia Concordia University Canada

ISBN: (纸本)9789897587511

In this paper, we consider the problem of point-forecasting of univariate time series with a focus on electricity consumption forecasting. Most approaches, ranging from traditional statistical methods to recent learning-based techniques with neural networks, directly operate on raw time series observations. the main focus of this paper is to enhance forecasting accuracy by employing advanced deep learning models and integrating topological attention mechanisms. Specifically, N-Beats and N-BeatsX models are utilized, incorporating various time and additional features to capture complex nonlinear relationships and highlight significant aspects of the data. the incorporation of topological attention mechanisms enables the models to uncover intricate and persistent relationships within the data, such as complex feature interactions and data structure patterns, which are often missed by conventional deep learning methods. this approach highlights the potential of combining deep learning techniques with topological analysis for more accurate and insightful time series forecasting in the energy sector. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Time series

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Business Intelligence Architecture to Improve Decision Making 14

Business Intelligence Architecture to Improve Decision Makin...

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14th IEEE international conference on Computational Intelligence and communication networks, CICN 2022

作者： Moreno, Cristhian Villasante Rodriguez, Ciro Puente, Favio Guevara Petrlik, Ivan Lezama, Pedro Pomachagua, Yuri Universidad Nacional Mayor de San MarcosUNMSM Software Engineering Department Lima Peru Universidad Nacional Federico Villarreal Unfv Department of Electronic Engineering and Informatics Lima Peru Universidad Nacional Federico Villarreal Unfv Systems Engineering Department Lima Peru

ISBN: (纸本)9781665487719

Summary - Currently in different organizations and public entities, have large amounts of data and many times the tools or technologies that achieve the analysis of such data are unknown. in this context, business intelligence emerged, which allows data to be analyzed and provided information to supports decision- making for senior executives of public entities. the objective of this research is to propose a business intelligence architecture that provides a solution to manage large amount of information. the methodology used was based on scrum as part of a good organization, structure and time control by the implementation of the proposed architecture in any system or tool of the public entities. as part of the review and part of the results, the research of various articles was carried out, and 20 were selected for sample and proof that the proposed architecture is the one indicated for decision making. in the results of part of the research, was found evidence that a business intelligence architecture is not only the most used, but it provides various response mechanisms for the large amount of information possessed and, in many cases, not used in the best way. © 2022 IEEE.

关键词： Decision making

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Subspace-Based Adaptive Approach for Blind Channel Estimation and Equalization 15th

Subspace-Based Adaptive Approach for Blind Channel Estimatio...

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15th international conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Acharya, Badal Parida, Priyadarsan Panda, Ravi Narayan Mohapatra, Pradyumna Kumar Department of Electronics and Communication Engineering School of Engineering and Technology GIET University Odisha Gunupur India Department of Electronics and Communication Engineering Gandhi Institute for Technology Odisha Bhubaneswar India Department of Electronics and Communication Engineering Vedang Institute of Technology Odisha Bhubaneswar India

ISBN: (纸本)9783031810855

In this work, we undertake a thorough analysis of the estimation of the blind channel and apply the equalization technique based on subspaces. Addressing the challenges in modern digital communication systems employing QAM for multiple concurrent signal transmissions, we establish a robust signal model. Building upon this model, we introduce an innovative channel estimator that is based on subspace is designed to minimize a cost function. Our investigation delves into the asymptotic performance of this estimator, scrutinizing metrics such as bias, mean square error (MSE), NRMSE, and effecting of evaluation of the channel. Additionally, we evaluate the channel's effectiveness, providing insights into its prospective applications. the study contributes to advancing blind channel estimation and equalization techniques, offering potential applications in diverse communication scenarios. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cost functions

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Modeling of Regional Closed-Loop international Freight Transportation Network Based on Graph Neural Network

Modeling of Regional Closed-Loop International Freight Trans...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Xu, Suqing Xi'an Fanyi University International Economics Department Shaanxi Province 710015 China

ISBN: (纸本)9798331509828

With the continuous progress of neural networks and mathematical optimization algorithms, their application in transportation and logistics network modeling has become a research trend. therefore, this study proposes a regional closed-loop international freight transportation network modeling framework based on the graph neural network. the original intention of the design of this framework is to solve the challenges of high complexity and multi-objective optimization in traditional logistics network models. In the basic network part, based on the characteristics of convolutional neural network (CNN), this model constructs a 10-layer graph neural network module to transmit information and extract features from nodes and edges in the logistics network. Subsequently, by combining information entropy with the greedy algorithm, the modeling efficiency of the graph neural network was optimized at the theoretical level. Finally, this study proposes an initial transportation network structure that takes into account sensors and communication technologies. this structure estimates the location of logistics nodes through a wireless sensor network (WSN) model to optimize network layout and improve transportation efficiency. Visualization and analysis results show that the proposed method has achieved satisfactory results. © 2025 IEEE.

关键词： Graph neural networks

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3D Photorealistic Radio Map Generation Using Gaussian Splatting toward On-site Surveys 14

3D Photorealistic Radio Map Generation Using Gaussian Splatt...

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14th international conference on Indoor Positioning and Indoor Navigation - Work-in Progress Papers, IPIN-WiP 2024

作者： Toyoda, Yuma Maeoki, Sho Yokono, Naoya Yamada, Wataru NTT Docomo Inc. Chiyoda-ku Tokyo Japan

thanks to mobile network operators who maintain their networks, we can communicate anywhere with high speed and stability over them. Radio maps, data sets of radio information at each location, are essential to maintain communication quality in cellular networks. Radio maps are typically collected by measuring radio information and plotting locations manually in on-site surveys;however, this investigation work is a hard burden. Although several studies have been proposed to efficiently generate radio maps by automatically acquiring location information, those methods are limited to constructing 2D maps or necessitate expensive equipment. this paper proposes a time-efficient and low-cost method to generate a radio map with a 3D photorealistic map (i.e., a 3DP radio map) using an omnidirectional camera and a smartphone. Intuitive comprehension of radio conditions and the measurement environment with the 3DP radio map can help maintainers to analyze and improve communication states. the experiments demonstrated the efficiency of radio map generation by the proposed method and revealed the beneficial factors of the 3DP radio map for improving communication quality. © 2024 Copyright for this paper by its authors.

关键词： Cellular radio systems

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Home Appliances using loT and Machine Learning: the Smart Home 14

Home Appliances using loT and Machine Learning: The Smart Ho...

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14th IEEE international conference on Computational Intelligence and communication networks, CICN 2022

作者： Rathi, Kritika Sharma, Vanshika Gupta, Swati Bagwari, Ashish Tomar, G.S. Uttarakhand Technical University Women Institute of Technology Department of Electronics and Communication Engineering Dehradun India Rajkiya Engineering College U.P. Sonbhadra India

ISBN: (纸本)9781665487719

the term Home Automation describes the technologies used in the home for the essential needs like interconnection, wireless communication and making the quality of the component to use in a smart way just through the internet of things. the term HOME AUTOMATION indicates the quality of the automatic working of the systems and controlling of the different variety of home appliances. In the information communication technology, cloud computing and the IOT are the major types of services used in the model for the advancement of the new generation in which these two are making a great influence for making and deploying new applications. On the other hand, the interfaces between the hardware component, communication, and the programming are a main application for the technology used in the home which works to combine every device through the internet or Wi-Fi. the every device used in the model is connected to the Wi-Fi due to which we can get the output through our mobile phones, it doesn't depends whether we are at home or at the working place or anywhere else in the world, the app in the mobile phone shows the update information of our lights, motor, induction and also face recognize for the safety purpose in the front door. © 2022 IEEE.

关键词： Domestic appliances

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