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The benefits of decision tree to predict survival in patients with glioblastoma multiforme with the use of clinical and imaging features

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Asian Journal of Neurosurgery 2022年第3期13卷 697-702页

作者： Mohtaram Nematollahi Mahdie Jajroudi Farshid Arbabi Amir Azarhomayoun Zohreh Azimifar Anesthesiology and Critical Care Research Center Department of Medical Informatics School of Management and Medical Information Sciences Shiraz University of Medical Sciences Shiraz Department of Oncology School of Medicine Zahedan University of Medical Sciences Zahedan Sina Trauma Research Center Tehran University of Medical Sciences Tehran Department of Neurosurgery Sina Hospital Tehran University of Medical Sciences Tehran Department of Electrical and Computer Engineering Shiraz University Shiraz School of Electrical and Computer Engineering Shiraz University Shiraz

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review

Critical Factors of Immersive Learning in Higher Education: ...

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International Conference on ICT For Smart Society (ICISS)

作者： Sunardi Meyliana Arief Ramadhan Yusep Rosmansyah Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Departement School of information system Bina Nusantara University Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of electrical engineering and informatics Bandung Institute of Technology Bandung Indonesia

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education.

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Enhancing IoT Security and User Experience: Leveraging SGIoT-SURE for Effective Security Implementations

Enhancing IoT Security and User Experience: Leveraging SGIoT...

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Shubhavi Arya Saatvik Arya Minakshi Arya Jaibir Singh Arya Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington USA Information School University of Washington Seattle USA Department of Computer Science Department of Genomics Phenomics and Bioinformatics North Dakota State University Fargo USA Office of Special Secretary Government of Haryana Chandigarh India

The rapid growth of Internet of Things (IoT) devices underscores the need for user-centric science gateways focused on IoT security. This study aims to leverage the insights gathered from the SGIoT-SURE (Science Gateway & Internet of Things -Security User Readiness Evaluation) scale to inform the future design and adaptation of a science gateway dedicated to IoT security. Key factors identified include self-efficacy, user trust and the importance of security measures. Our results highlight user preferences for an intuitive interface, customizable toolkits, and educational resources. Positive correlations with the SGIoT-SURE total score suggest that a more user-friendly and secure gateway is likely to encourage greater user engagement in IoT security. Based on these findings, we recommend an iterative, user-engaged approach for the future design of a science gateway that meets diverse user needs and expectations.

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IoT-UAV Enabled Intelligent Resource Management in Low-Carbon Smart Agriculture Using Federated Reinforcement Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Alasbali, Nada Masood, Fahad Alnazzawi, Noha Ghaban, Wad Alazeb, Abdulwahab Basurra, Shadi Saeed, Faisal King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha 61421 Saudi Arabia Department of Computing Abasyn University Peshawar 25000 Pakistan Royal Commission for Jubail and Yanbu Yanbu Industrial College Computer Science and Engineering Department Yanbu 46411 Saudi Arabia University of Tabuk Applied College Tabuk 47512 Saudi Arabia Najran University College of Computer Science and Information Systems Department of Computer Science Najran 61441 Saudi Arabia Birmingham City University College of Computing Birmingham B4 7XG United Kingdom

The Internet of Things (IoT) and unmanned aerial vehicles (UAVs) continue to advance the low-carbon smart agriculture technologies for next-generation consumer electronics and unlock more informed agricultural practices. Reinforcement learning (RL), federated learning (FL), and federated reinforcement learning (FRL) have demonstrated notable achievements in resolving complex problems, including resource allocation, energy efficiency, anomaly detection, and bandwidth utilization for multimodal tasks. This research explores multimodal data analysis and resource optimization using FRL for agricultural consumer electronics. The proposed framework employs IoT devices to monitor temperature, humidity, soil temperature, and soil moisture in real time, while UAVs provide aerial imagery for soil moisture, crop growth, and pest identification across three fields. This framework supports distributed learning, which trains local RL models on each node and combines them into the global model. The proposed FRL model demonstrated significant enhancements, including a 17% reduction in energy consumption for IoT devices and a 15% reduction for UAVs compared to non-FRL methods. This research emphasizes the effectiveness of FRL in integrating IoT and UAV for efficient resource allocation, energy efficiency, and reduced carbon emissions for low-carbon agricultural consumer electronics. © 1975-2011 IEEE.

关键词： Agriculture Consumer Electronics Energy Efficiency Federated Reinforcement Learning IoT UAV

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SusTrainable: Promoting Sustainability as a Fundamental Driver in Software Development Training and Education Teacher Training November 1–5, Nijmegen, The Netherlands Revised lecture notes

arXiv

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arXiv 2022年

作者： Koopman, Pieter Lubbers, Mart Fernandes, João Paulo Korečko, Štefan Paquete, Luís Porkoláb, Zoltán Szalay, Richárd Mauša, Goran Oprescu, Ana Koedijk, Lukas van Oostveen, Sander Kok, Stephan Li, Jianhao Zsók, Viktória Grbac, Tihana Galinac Grbac, Neven Valkanov, Vladimir Petrov, Mihail Radboud University Nijmegen Netherlands University of Porto Portugal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia CISUC Department of Informatics Engineering University of Coimbra Portugal Department of Programming Languages and Compilers Institute of Computer Science Faculty of Informatics Eötvös Lorád University Budapest Hungary University of Rijeka Faculty of Engineering Vukovarska 58 Rijeka51000 Croatia Complex Cyber Infrastructure University of Amsterdam Netherlands KPMG Software Engineering Master University of Amsterdam Netherlands Informatics Institute University of Amsterdam Netherlands Juraj Dobrila Univeristy of Pula Zagrebačka 30 PulaHR-52100 Croatia University Of Plovdiv "Paisii Hilendarski" Faculty of Mathematics and Informatics 24 Bulgaria Blvd. Plovdiv4027 Bulgaria

These are the proceedings of the first teacher training of the Erasmus+ project No. 2020-1-PT01-KA203-078646 -- Sustrainable. The full title of this project is Promoting Sustainability as a Fundamental Driver in Software Development Training and Education and the interested reader may know more about it at: https://*** The flagship contribution is the organization of two summer schools on sustainable software production. The first summer school is moved due to the Corona pandemic. It is now scheduled for July 2022 in Rijeka, Croatia. This summer school will consist of lectures and practical work for master and PhD students in the area of computing science and closely related fields. There will be contributions from Plovdiv University, University of Ko\v{s}ice, University of Coimbra, University of Minho, E\"otv\"os Lor\'and University, University of Rijeka, Radboud University Nijmegen, University of Pula, University of Amsterdam and Babe\c{s}-Bolyai University. To prepare and streamline this summer school, the consortium organized a teacher training in Nijmegen, the Netherlands. This was an event of five full days on November 1-5, 2022 organized by Ingrid Berenbroek, Mart Lubbers and Pieter Koopman on the campus of the Radboud University Nijmegen, The Netherlands. Sustainability is one of the themes within the strategy of the Radboud University called `A Significant Impact', see https://***/sustainability/organisation/radboud-sustainable/. Sustainability plays an important role in education, research, and management. The contributions were reviewed and give a good overview of what can be expected from the summer school. Based on these papers and the very positive and constructive atmosphere, we expect a high quality and pleasant summer school. We are looking forward to contributing to a sustainable future. © 2022, CC BY-NC-ND.

关键词： Personnel training

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Analysis of the Resilience of an Emergency department: The Case of Accident with Multiple Victims

Analysis of the Resilience of an Emergency Department: The C...

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2023 Winter Simulation Conference, WSC 2023

作者： Rodriguez, Mariela Boixader, Francesc Bruballa, Eva Epelde, Francisco De Giusti, Armando Wong, Alvaro Rexach, Dolores Luque, Emilio National University of Jujuy Faculty of Engineering 10 Italo Palanca Jujuy4600 Argentina Computer Science School Universitat Autonoma de Barcelona Barcelona Sant Cugat del Vallès08174 Spain University Hospital Parc Tauli Universitat Autonoma de Barcelona Internal Medicine Sadabell Barcelona08208 Spain National University of la Plata Faculty of Informatics Calles 50 y 120 Buenos Aires La Plata1900 Argentina Universitat Autonoma de Barcelona Computer Architecture and Operating Systems Department Barcelona Edific Q Campus UAB Bellaterra08193 Spain

ISBN: (纸本)9798350369663

The care of multiple victims such as in natural disasters in an Emergency department is critical. This differs from ordinary care by the number of patients that arrive, their severity and insufficient staff for these events. Designing and simulating this real life scenario will be useful for disaster management decision makers. The objective of this simulation is to model a system with resilience to critical situations. To model the input of this research, we worked with the percentage of patients received by Cauquenes Hospital during the Chilean Earthquake on February 27th, 2010. A comparison of two situations has been carried out: the admission of patients before an earthquake with normal daily attention versus the admission of patients before an earthquake and the activation of the relief chain. The latter situation allows the system to be resilient and adapt quickly to its new reality. © 2023 IEEE.

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Numerical solution of the problem of turbulent currents that occur in the case of edge hardening

Numerical solution of the problem of turbulent currents that...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Marius Codrean Mihaela Cornelia Novac Mihaela Codrean Cornelia Emilia Gordan Ovidiu Constantin Novac Gyongyi Bujdoso Francisc Slovac Department of Electrical Engineering Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania National Geothermal Research Center Technology Transfer Center NGRC-TTC University of Oradea Oradea Romania Department of Electronics and Telecommunications Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Computers and Information Technology Electrical Engineering and Information Technology Faculty University of Oradea Oradea Romania Department of Computer Science and Library and Information Science Faculty of Informatics University of Debrecen Debrecen Hungary

The aim of this work is to develop numerical procedures for analyzing edge hardening using induction heating by turbulent currents. The section through the steel bar, whose lower edge needs to be hardened, is presented. For the inductor adapted to the piece configuration, the 2D model is assumed. The surface treatment of the piece must be differentiated: in the tip area, the heating must be greater than in the rest of the surface. Only the tip area needs to be subjected to hardening, while the rest of the piece must remain elastic to ensure the desired mechanical strength. The main purpose of this work is to propose inductor models for achieving differentiated heating of the blade.

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Development of a chickpea disease detection and classification model using deep learning

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informatics in Medicine Unlocked 2022年 31卷

作者： Belay, Abebech Jenber Salau, Ayodeji Olalekan Ashagrie, Minale Haile, Melaku Bitew Department of Information Technology College of Informatics University of Gondar Ethiopia Department of Electrical/Electronics and Computer Engineering Afe Babalola University Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences India

Ethiopia is the largest producer of chickpeas in Africa. Crop production and yield in Ethiopia is greatly affected by plant diseases which cause loss of agricultural products every year. One of these plant diseases is chickpea disease which is a fungal disease. Ascochyta blight and Fusarium wilt are the most common chickpea diseases in Ethiopia that affect crop production quality and quantity. The identification of these diseases requires experienced experts or systems. Although numerous methods have been previously adopted in literature, deep learning (DL) is suggested as an efficient approach for disease recognition and classification since it can automatically learn features from the input image. In this paper, a chickpea disease detection model was developed using deep learning techniques by combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for feature extraction and Softmax for classification. To develop the proposed model, various image preprocessing stages such as image resizing, normalization, and noise filtering using a combination of Gaussian filter (GF) and Median filter (MF) were performed. To prevent the problem of overfitting, augmentation was applied, while to train and test the effectiveness of the developed model, 8391 images were used. From the acquired images, 80% of the dataset was used for training, 20% of the dataset was used for testing and out of the 80% training data, 20% was used for validation. The proposed CNN-LSTM performed well in identifying chickpea disease, with an accuracy of 92.55%. According to the study's findings, the proposed CNN-LSTM outperforms existing methods. © 2022 The Authors

关键词： Ascochyta blight Chickpea CNN-LSTM Deep learning Fusarium wilt

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Resilience Analysis of an Emergency department in Stressful Situations 11th

Resilience Analysis of an Emergency Department in Stressful ...

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11th Conference on Cloud Computing, Big Data and Emerging Topics, JCC-BD and ET 2023

作者： Rodriguez, Mariela Boixader, Francesc Epelde, Francisco Bruballa, Eva De Giusti, Armando Wong, Alvaro Rexachs, Dolores Luque, Emilio Faculty of Engineering National University of Jujuy San Salvador de Jujuy4600 Argentina Computer Science School Universitat Autonoma de Barcelona Sant Cugat del Vallès Barcelona08174 Spain Consultant Internal Medicine University Hospital Parc Tauli Universitat Autonoma of Barcelona Sadabell Barcelona08208 Spain Faculty of Informatics National University of La Plata Calles 50 y 120 – La Plata Buenos Aires1900 Argentina Computer Architecture and Operating Systems Department Universitat Autónoma of Barcelona Campus UAB Edificio Bellaterra Barcelona08193 Spain

ISBN: (纸本)9783031409417

The performance analysis an Emergency department (ED) is fundamental and necessary to plan the service when it is affected by increasing number of patients due to an external scenario multiple victims from accidents, disasters or catastrophes. In order to perform this analysis, it is important to set up a simulated scenario that allows this study. The proposal is to analyze the ED simulator developed by the HPC4EAS research group at UAB by taking it to a stress situation. The work analyzes key performance indicator (KPI) that measure the performance of an ED (length of stay, waiting queue length, service rate) and make it possible to evaluate the percentage of patients that can be attended without altering the available resources. Study allowed us to determine that the care box (area A - area for the care of seriously ill patients) becomes congested when 40% more patients than usual are received, and the next resource to become congested is the room for the care of milder patients (area B). Analysis that will later allow us to investigate what happens when there is an accident, natural disaster or pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Accidents

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Support Vector Machine based Multi-Class Classification for Oriented Instance Selection

Support Vector Machine based Multi-Class Classification for ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Sandeep Kumar Davuluri Deepak Srivastava Manisha Aeri Madhur Arora Ismail Keshta Richard Rivera Department of Information Technology University of the Cumberlands Williamsburg Kentucky Department of Computer Science and Engineering Himalayan school of Science and Technology Swami Rama Himalayan University Dehradun India CSE Graphic Era Hill University Dehradun India CSE Graphic Era deemed to be University Dehradun India RGPV University Bhopal Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador

Support vector machine has emerged as one of the most crucial classification techniques in recent years in pattern recognition, machine learning, and data mining. The model training will become more challenging when dealing with multi-classification problems, and its training time will increase considerably as the number of samples increases. A quick training data reduction technique MOIS that works with multiple classification problems is provided to address the issues above. This method takes the cluster centre as the reference point. While removing redundant training samples, selects the decisive boundary samples to significantly reduce the training data and reduce the distribution imbalance between categories. Experimental results show that MOIS can maintain or even improve the classification effect of support vector machines. Moreover, it can significantly improve training efficiency. For example, on the Opt digit data set, the proposed method improves the classification accuracy from 98.94% to 99.05% and, at the same time, shortens the training time to 15%; On the data set composed of the first 100 categories, when the accuracy rate is slightly improved (from 99.29% to 99.30%), the training time is significantly shortened to less than 6% of the original. In addition, MOIS itself has High operating efficiency.

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