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(POSTER) Advancing Non-Terrestrial Networks for Critical Scenarios with the RAIN4C Framework

(POSTER) Advancing Non-Terrestrial Networks for Critical Sce...

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International conference on Distributed Computing in Sensor systems (DCOSS)

作者： Angelo Trotta Alexandre Heideker Ivan Zyrianoff Giovanni Interdonato Sara Pizzi Department of Computer Science and Engineering University of Bologna Bologna Italy Department of Electrical and Information Engineering University of Cassino and Southern Lazio Cassino Italy Department of Information Infrastructure and Sustainable Energy Engineering (DIIES) University Mediterranea of Reggio Calabria Reggio Calabria Italy

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

the transition to sixth-generation (6G) wireless networks underscores the importance of reliable network services in critical scenarios, such as post-disaster and rural areas. Space-Air-Ground Integrated Networks (SAGINs), which include satellite systems, aerial networks, and terrestrial communication, are crucial for global coverage in these contexts. Despite their potential, satellite network latencies and the dynamic nature of Unmanned Aerial systems (UAS) present challenges that diverge from 6G requirements. this paper introduces RAIN4C, a multi-layer framework that integrates hybrid communication and computation solutions across satellite, aerial, and ground segments. RAIN4C focuses on reliable connectivity, energy-efficient computation, and effective caching to address the key issues of deploying SAGIN systems. It proposes methods for optimizing these functionalities and evaluates them through simulations and a test-bed involving satellite links and UAVs. the goal is to enable swift deployment of SAGIN systems for on-demand services in critical environments, aligning with the needs of future wireless networks.

关键词： 6G mobile communication Satellites Wireless networks Computational modeling systems operation system performance Computer architecture

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20th IFIP TC 14 International conference on Entertainment Computing, ICEC 2021

20th IFIP TC 14 International Conference on Entertainment Co...

引用

20th IFIP TC 14 International conference on Entertainment Computing, ICEC 2021

ISBN: (纸本)9783030893934

the proceedings contain 49 papers. the special focus in this conference is on Entertainment Computing. the topics include: Social Gaming Patterns During a Pandemic Crisis: A Cross-cultural Survey;a Real-Time Drum-Wise Volume Visualization system for Learning Volume-Balanced Drum Performance;speech Recognition Game Interface to Increase Intimacy with Characters;game Development as a Serious Game with Live-Programming and Time-Travel Mechanics;AI Game Agents Based on Evolutionary Search and (Deep) Reinforcement Learning: A Practical Analysis with Flappy Bird;instruction Pictograms for Interactive Entertainment;how Awe Affects Players’ Entertainment Experiences Over Six Weeks of Playing;the Gilmorehill Mystery: A Location-Based Game for Campus Exploration;***: A Music Recommendation system Depending on Listener’s Preference by Creating a Branching Playlist;CS:Show – An Interactive Visual Analysis Tool for First-Person Shooter eSports Match Data;towards Suitable Free-to-Play Games for Children;provenance in Gamification Business systems;computational Narrative Blending Based on Planning;linkages Between Gameplay Preferences and Fondness for Game Music;that Sound’s Juicy! Exploring Juicy Audio Effects in Video Games;what Is a Game Mechanic?;a Method for Supporting Verbalization to Facilitate Observation in Illustration Copy-Drawing;optimization of First-Person Shooter Game Control Using Heart Rate Sensor;virtual Guia Fortress: A 3D-printed eXtended Reality Playset;recreating a TransMedia Architectural Location In-Game via Modular Environment Assets;Fun to Enhance Learning, Motivation, Self-efficacy, and Intention to Play in DGBL;interaction Toolkit for Programming Interactions with Marker-Based Tangibles in Virtual Reality;sketch Recognition for Interactive Game Experiences Using Neural Networks;safety Risks in Location-Based Augmented Reality Games.

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Design and Realization of Intelligent Airborne Information Processing Concept Simulation system

Design and Realization of Intelligent Airborne Information P...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Jianchun Xie Linting Bai Qing Luo Aviation Key Laboratory of Science And Technology On Airborne And Missile-borne Computer AVIC Xi'an Aeronautics Computing Technique Research Institute Xi'an China College of Aerospace Engineering Nanjing University of Aeronautics and Astronautics Nanning China Shenyang Aircraft Design&Reserch Institute Shenyang China

Facing the development trend of future distributed cloud operations, intelligent operations, and the technical requirements of multi-machine collaborative operations in the aviation field, this paper further studies the unified data transmission and management of multiple platforms based on the research of the unified cloud intelligent combat system architecture. Distributed data storage and management, multi-platform open information processing resource management and other key technologies, on the basis of the principle prototype designed and implemented by ourselves, a set of conceptual simulation platforms have been constructed for the simulation and technical verification of the entire airborne information processing system.

关键词： Technical requirements Atmospheric modeling systems architecture Collaboration Prototypes Information processing Market research

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A Smart Public Service optimization Model Using Linear Programming to Reduce the Number of Deaths

A Smart Public Service Optimization Model Using Linear Progr...

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International conference of Computer Science and Information Technology

作者： A M H Pardede Muammar Khadapi Y Maulita L A N Kadim STMIK Kaputama Binjai North Sumatra Indonesia

In everyday life, it is possible for people to need public services. What is meant by public services in this research are health, security, protection, and assistance services that threaten the safety of people's lives. Public services are needed by the community in every normal or emergency incident that must receive life-saving services as soon as possible, and every service that will be provided is not necessarily the same for all communities. this is because the incident, rescue needs, and location of the victim may vary, such as the occurrence of health problems, traffic accidents, crime incidents, and fires. this research uses the concept of “Smart Health and Safe City” which aims to optimize the smart public service model using linear programming to reduce the number of deaths by utilizing hospital agencies' limited resources to provide treatment as soon as possible, police agencies, fire agencies and Basarnas agencies, where when going to carry out public services, medical (m), police (p), fire brigade (f), and Basarnas (b) depart from the hospital location (ih), police location (ip), fire extinguisher location (if), and Basarnas location (ib) to location of victim (j) to provide public services in order for the modeling's output to address the issue of delivering intelligent services as early as possible which can reduce the number of deaths, minimize costs incurred, minimize service time and maximize resource utilization. possible, as well as obtaining the maximum value of the model's objective function. the simulation results, the maximum value of the objective function was obtained at 164,00000 at the 20th iteration step and produced a model that minimized the arrival time at the location of the service recipient.

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A Computational Approach to Recognizing Poetry Genres in Uzbek Texts

A Computational Approach to Recognizing Poetry Genres in Uzb...

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IEEE Region 8 International conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Davlatyor B. Mengliev Vladimir B. Barakhnin Bobur R. Saidov Mukhammadjon Atakhanov Mukhriddin O. Eshkulov Bahodir B. Ibragimov Novosibirsk State University Novosibirsk Russia Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Urgench Uzbekistan Federal Research Center for Information and Computational Technologies Novosibirsk Russia Novosibirsk State Universitety Novosibirsk Russia Jizzakh polytechnic institute Jizzakh Uzbekistan Urgench State University Urgench Uzbekistan

ISBN: (数字)9798331532024

ISBN: (纸本)9798331532031

In the research paper, the authors propose an algorithm for detecting poetic elements, words, and phrases in Uzbek texts. In addition, during analyzing the algorithm classifies poetic tokens in of the of 2 poetic genres (comedy, drama). In particular, the researchers trained a language model of artificial intelligence with an architectural combination of a convolutional neural network and long short-term memory. A custom language corpus was used as training data. this corpus was formed from more than 3,600 literary sentences, which in turn were selected from U zbek poetic works of the 20th century. Words and phrases in the corpus sentences were tagged according to the BIO-scheme, which the authors also talk about in the corresponding section of the article. Moreover, the authors also included information about the morphology of the U zbek language so that the context of this study was more understandable to all readers. In addition, the authors also conducted a comparative analysis of existing alternative works, where they provide the features of each similar work.

关键词： Training Humanities Analytical models Text recognition Biological system modeling Training data Classification algorithms Convolutional neural networks Long short term memory Testing

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Using Machine Learning to Support Prostate Cancer Detection Without a Dedicated Biomarker

Using Machine Learning to Support Prostate Cancer Detection ...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Hadrien Salem Sarah Ben Othman Marc Broucqsault Slim Hammadi CRIStAL CNRS UMR 9189 Nord France Altao Lille Nord France

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Early disease detection through the analysis of laboratory tests is critical for the successful treatment of certain diseases. However, current medical knowledge relies on the use of certain biomarkers in order to diagnose associated diseases. this means that some diseases cannot be detected unless the corresponding biomarker is actually *** this paper, we will explore the possibility of using machine learning to detect abnormal values in biomarkers, even when they are not directly measured. this stems from the fact that, as an ensemble of features, even a set of common biomarkers contains sufficient information to suspect anomalies in a patient’s *** present a complete pipeline for processing medical laboratory data, taking into account the many challenges offered by this use case, such as data cleanliness, the abundance of missing values, and strong class imbalance. Using this framework, we perform a case study on prostate cancer and its associated biomarker, Prostate-Specific Antigen (PSA). Our implementation, based on an imbalance-aware ensemble prediction model, is tested on a real laboratory dataset of more than 8 million records. We find that basing the ensemble on Random Forest models provides the best results, with a ROC-AUC of 75.86%, indicating that it is indeed possible to detect abnormal PSA values without measuring it directly. We also show that a simple baseline of 24 commonly-measured biomarkers provides near-equivalent results to a full set of features. Our work offers new perspectives for routine disease screening.

关键词： Antigens Machine learning algorithms systematics Sensitivity Biological system modeling Pipelines Biomarkers Prostate cancer Medical diagnostic imaging Diseases

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Optimal Scheduling for the Performance optimization of SpMV Computation using Machine Learning Techniques

Optimal Scheduling for the Performance Optimization of SpMV ...

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International conference on Information and Computer Technologies (ICICT)

作者： Fida Ullah Muhammad Ahmed Muhammad Tayyab Zamir Muhammad Arif E Felipe-Riverón Alexander Gelbukh Instituto Politécnico Nacional (IPN) Centro de Investigación en Computación (CIC) Mexico City Mexico

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

the Sparse Matrix-Vector Multiplication (SpMV) serves as a critical computational kernel within sparse linear system solvers, playing a vital role in diverse scientific, engineering, and application domains such as industrial engineering, robotics, finance, networking, transportation, medicine, and weather forecasting. this study introduces a classification-based predictive model for shared memory, aiming to optimize scheduling policies in terms of execution time. Utilizing a dataset comprised of over 1000 matrices from 25 application domains, stored in block CSR format, the model is trained on 67% of the data using decision tree, random forest, gradient boosting, and artificial neural network algorithms. Results indicate that random forest exhibits superior accuracy with 20%, precision 4%, and recall 23% compared to other algorithms. Moreover, the runtime scheduling policy consistently demonstrates the best execution time across all matrices and features, outperforming static, dynamic, and guided policies. the study provides valuable insights into optimizing SpMV performance under various conditions, enhancing efficiency in solving sparse linear equation systems.

关键词： Machine learning algorithms Processor scheduling Computational modeling Software algorithms Weather forecasting Transportation Optimal scheduling

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Robust Hand Gesture Tracking and Recognition for Healthcare via Recurrent Neural Network

Robust Hand Gesture Tracking and Recognition for Healthcare ...

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International Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Hira Ansar Ahmad Jalal Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

In the emerging fields of computer vision, robotics, and medicine, It's difficult to recognize gestures in dynamic environment. For human-computer hand gesture tracking and recognition, symmetry is required. Recent research has presented many RGB gesture recognition methods as a result of sensor technology advancements. In this research, we propose a sustainable hand gesture tracking and recognition system that can identify and recognize RGB dynamic motions in challenging environments while maintaining accuracy. Firstly, the frames are extracted from dynamic gestures. the frames are preprocessed to reduce noise and adjust light intensity. After that, the hand is detected using the Single-shot Multi-box Detector (SSD). From the extracted hand landmarks are attained via a colour-based fast marching algorithm. the features are extracted, such as polygon mesh, neural gas, and geometric shapes. On the features, the Active Bee Colony Optimizer (ABCO) is applied to reduce dimensionality. then Recurrent Neural Network (RNN) is applied to classify the gestures. the proposed system is tested on Sign word and Egogesture datasets, providing accuracy of 93.33% and 92.67%, respectively.

关键词： Training Recurrent neural networks Accuracy Tracking Shape Dynamics Medical services Feature extraction Robot sensing systems optimization

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Automatic Detection of Various Types of Lung Cancer Based on Histopathological Images Using a Lightweight End-to-End CNN Approach

Automatic Detection of Various Types of Lung Cancer Based on...

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Language Engineering (ESOLEC), International conference on

作者： Ahmed S. Sakr Department of Information System Faculty of Computers and Information Menoufia University Menoufia Egypt

ISBN: (纸本)9781665453233

Lung cancer is one of the main causes of death and illness, and malignant lung tumours are the leading cause of both. According to reports, lung cancer incidence is on the rise. Lung cancer histopathology is an important element of patient care. Using artificial intelligence methods for the identification of lung cancer can become a highly valuable approach. In this article, we offer a modified lightweight end-to-end deep learning strategy based on convolutional neural networks (CNN) to accurately identify lung cancer. In this method, the input histopathology pictures are normalized before being fed into the CNN model, which is then used to detect lung cancer. the effectiveness of our approach is assessed using a publicly accessible database of histopathological pictures and compared to the most advanced cancer detection methods already in use. the examination of the results indicates that the suggested deep model for lung cancer diagnosis yields results of 0.995 percent, which is a better accuracy than other approaches. Due to this excellent outcome, our method is computationally effective.

关键词： Deep learning Histopathology Databases Computational modeling Lung cancer Lung Convolutional neural networks

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the Design Requirements of a Decision Support system for Waste Control 20th

The Design Requirements of a Decision Support System for Was...

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20th International conference on Hybrid Intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired Computing, NaBIC 2020

作者： Ershadi, Mahmoud Jefferies, Marcus Davis, Peter Mojtahedi, Mohammad University of Newcastle Newcastle Australia University of New South Wales Sydney Australia

ISBN: (纸本)9783030730499

Decision support systems (DSS) have a wide variety of applications in management decisions and optimization of engineering processes. Nowadays, the construction industry calls for state-of-the-art mechanisms to take control of the waste generated on construction sites. Executives need advanced systems and technologies to help them get instant information from construction sites and control the activities in a way that minimum waste is generated. this paper explains how real-time multi-agent DSS as robust information technology tools can be applied to help control the environmental risks posed by construction operations. DSS provides a platform in which courses of actions from several teams are coordinated and corresponding massive data from the construction site are analyzed and compiled to support judgments. A qualitative study was conducted to identify how to design such a system using both machine and human agents. the results of this study constitute a basis for the detailed design of a waste control DSS that can be developed by programming experts. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision support systems

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