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2nd International Conference on computer and Information sciences, ICCIS 2020

作者： Alharbi, Ebtesam H Alahrbi, Maryam M. Alkhamali, Sahar S. Taibah University College of Computer Science and Engineering Department of Information Systems Saudi Arabia

ISBN: (纸本)9781728154671

Green computing has gained the attention of academia, cloud providers, and governments. The advent of cloud computing has raised sustainability issues (e.g. high power consumption, carbon emissions). Sustainability is the concept of meeting our needs while reducing our impacts on the environment and the life of future generations. It has become a critical concern in the modern environmental life and cloud providers worldwide. Therefore, this paper considers the adaptation of green computing in Saudi Arabia. It proposes a framework that illustrates the factors that affect the adaptation of green computing for current and future cloud computing providers in Saudi Arabia. The framework can be also used as a guideline to ensure that green computing is achieved with best practices. © 2020 IEEE.

关键词： Green computing

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Graph Neural Networks for Job Shop Scheduling Problems: A Survey

arXiv

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arXiv 2024年

作者： Smit, Igor G. Zhou, Jianan Reijnen, Robbert Wu, Yaoxin Chen, Jian Zhang, Cong Bukhsh, Zaharah Zhang, Yingqian Nuijten, Wim Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Eindhoven Artificial Intelligence Systems Institute Eindhoven University of Technology Netherlands College of Computing and Data Science Nanyang Technological University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands College of Economics and Management Nanjing University of Aeronautics and Astronautics China

Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the application of graph neural networks (GNNs) to solve JSSPs, albeit lacking a systematic survey of the relevant literature. This paper aims to thoroughly review prevailing GNN methods for different types of JSSPs and the closely related flow-shop scheduling problems (FSPs), especially those leveraging deep reinforcement learning (DRL). We begin by presenting the graph representations of various JSSPs, followed by an introduction to the most commonly used GNN architectures. We then review current GNN-based methods for each problem type, highlighting key technical elements such as graph representations, GNN architectures, GNN tasks, and training algorithms. Finally, we summarize and analyze the advantages and limitations of GNNs in solving JSSPs and provide potential future research opportunities. We hope this survey can motivate and inspire innovative approaches for more powerful GNN-based approaches in tackling JSSPs and other scheduling problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Plans

CHARLIE: A Chatbot That Recommends Daily Fitness and Diet Pl...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Deepanjali Chowdhury Ahana Roy Sreenivasan Ramasamy Ramamurthy Nirmalya Roy Department of Electrical and Computer Engineering Texas A&M University College Station United States Howard County Public School System Maryland United States Department of Computer Science Bowie State University Bowie United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

Managing a work-life balance has always been challenging, especially after the recent trend of working from home, which has made maintaining one's fitness and diet regime strenuous. Failing to adhere to a fitness and diet plan has been shown to cause long-term effects on a person's health including obesity and shortened lifespans. Currently, people plan their fitness and diet plans around their schedule, but these tasks are challenging to maintain. Users tend to give up these plans due to a lack of time and planning. To help with better planning for a fitness and diet regime, we propose, CHARLIE, a chatbot that works around a user's schedule to intelligently recommend diets and fitness goals based on their calendar. CHARLIE is a recommendation system that suggests the users' a certain meal and fitness activities based on the amount of time they have left in their day and the number of calories they lose.

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Enhancing Predictive Accuracy in Cardiovascular Disease Diagnosis: A Hybrid Approach Using RFAP Feature Selection and Random Forest Modeling

Enhancing Predictive Accuracy in Cardiovascular Disease Diag...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Vikram Pasupuleti Bharadwaj Thuraka Chandra Shikhi Kodete V Priyadarshini Koti Mani Kumar Tirumanadham Vahiduddin Shariff School of Technology Eastern Illinois University Charleston IL USA School of Computer Science and Information Systems Northwest Missouri State University Maryville MO USA Department of CSE SRKR Engineering College Bhimavaram India Department of CSE Sir C R Reddy College of Engineering Eluru India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Cardiovascular disease remains one of the leading causes of death all over the world and is characterized by ailments affecting cardiovascular structures, such as the heart failure, arrhythmia, and coronary artery diseases. It has multiple roots based on genetic influences on the disease, meals, drinks, physical inactivity, or presence of other diseases including hypertension, and diabetes. Heart diseases are still rampant in the world despite the development in the research on diseases and in healthcare. This is mainly because of the increasing average life span of individuals as well as increased cases of urbanization and adoption of poor-quality diets and leading inactive or sedentary lives. Such problems further escalate due to shortcomings in healthcare provision and preventive options by socioeconomic differences, especially affecting the needy populace. Hence, there is a rising focus on using complex technologies such as artifices and machine intelligence, along with big data analytics and predictive models, in handling these issues. These methodologies utilize computational methods and large-scale data to enhance risk assessment, optimize treatment pathways, and alleviate the overall burden of cardiac disease on healthcare systems and individuals. The concentration of this research is on the systematic application of rigorous data preprocessing, class imbalance correction using techniques such as SMOTE, and outlier management using methods such as IQR, in order to improve the effectiveness of predictive modelling. This study intends to improve predictive accuracy and model generalizability, thereby contributing to improvements in healthcare decision-making and patient care outcomes, by utilizing the RFAP hybrid technique for feature selection and Random Forest for modelling.

关键词： Accuracy Systematics Computational modeling Urban areas Medical services Predictive models Feature extraction Socioeconomics Random forests Standards

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Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding 30

Comprehensive Study on German Language Models for Clinical a...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Idrissi-Yaghir, Ahmad Dada, Amin Schäfer, Henning Arzideh, Kamyar Baldini, Giulia Trienes, Jan Hasin, Max Bewersdorff, Jeanette Schmidt, Cynthia S. Bauer, Marie Smith, Kaleb E. Bian, Jiang Wu, Yonghui Schlötterer, Jörg Zesch, Torsten Horn, Peter A. Seifert, Christin Nensa, Felix Kleesiek, Jens Friedrich, Christoph M. Department of Computer Science University of Applied Sciences and Arts Dortmund Dortmund Germany Institute for Medical Informatics Biometry and Epidemiology University Hospital Essen Essen Germany Essen Germany Institute for Transfusion Medicine University Medicine Essen Essen Germany CATALPA FernUniversität in Hagen Germany NVIDIA Santa ClaraCA United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL United States Cancer Informatics and eHealth Core University of Florida Health Cancer Center University of Florida GainesvilleFL United States University of Marburg Marburg Germany University of Mannheim Mannheim Germany University Hospital Essen Institute of Interventional and Diagnostic Radiology and Neuroradiology Essen Germany Essen Germany Heidelberg Germany Department of Physics TU Dortmund Dortmund Germany

ISBN: (纸本)9782493814104

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique domain-specific terminologies, domain-specific abbreviations, and varying document structures are common. This paper explores strategies for adapting these models to domain-specific requirements, primarily through continuous pre-training on domain-specific data. We pre-trained several German medical language models on 2.4B tokens derived from translated public English medical data and 3B tokens of German clinical data. The resulting models were evaluated on various German downstream tasks, including named entity recognition (NER), multi-label classification, and extractive question answering. Our results suggest that models augmented by clinical and translation-based pre-training typically outperform general domain models in medical contexts. We conclude that continuous pre-training has demonstrated the ability to match or even exceed the performance of clinical models trained from scratch. Furthermore, pre-training on clinical data or leveraging translated texts have proven to be reliable methods for domain adaptation in medical NLP tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Classification (of information)

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The Pseudo-Skolem Problem is Decidable 46

The Pseudo-Skolem Problem is Decidable

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46th International Symposium on Mathematical Foundations of computer science, MFCS 2021

作者： D'Costa, Julian Majumdar, Rupak Salamati, Mahmoud Worrell, James Karimov, Toghrul Ouaknine, Joël Soudjani, Sadegh Department of Computer Science University of Oxford United Kingdom Max Planck Institute for Software Systems Kaiserslautern Germany Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9783959772013

We study fundamental decision problems on linear dynamical systems in discrete time. We focus on pseudo-orbits, the collection of trajectories of the dynamical system for which there is an arbitrarily small perturbation at each step. Pseudo-orbits are generalizations of orbits in the topological theory of dynamical systems. We study the pseudo-orbit problem, whether a state belongs to the pseudo-orbit of another state, and the pseudo-Skolem problem, whether a hyperplane is reachable by an pseudo-orbit for every. These problems are analogous to the well-studied orbit problem and Skolem problem on unperturbed dynamical systems. Our main results show that the pseudo-orbit problem is decidable in polynomial time and the Skolem problem on pseudo-orbits is decidable. The former extends the seminal result of Kannan and Lipton from orbits to pseudo-orbits. The latter is in contrast to the Skolem problem for linear dynamical systems, which remains open for proper orbits. © Julian D'Costa, Toghrul Karimov, Rupak Majumdar, Joël Ouaknine, Mahmoud Salamati, Sadegh Soudjani, and James Worrell;licensed under Creative Commons License CC-BY 4.0 46th International Symposium on Mathematical Foundations of computer science (MFCS 2021).

关键词： Dynamical systems

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A survey of disaster management datasets

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Journal of Information and Telecommunication 2025年

作者： Abbas, Sohail Talib, Manar Abu Nasir, Qasim Belal, Omar Al-Haidary, Mohammed Emad Ahmed, Ibtihal Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

Natural disasters are characterized as a combination of natural hazards and vulnerabilities that endanger communities and result in significant financial and human losses. The uncertain occurrence of these events and the limited resources in affected regions constitute their core attributes. Leveraging information technology to assess, forecast, and depict these occurrences can improve handling such disasters. Artificial Intelligence (AI) technology has recently been employed practically in various disciplines for various applications, and disaster management applications are among the most critical areas. The dataset is essential to any AI system and must be corrected to deliver reliable results. Disaster datasets are presented and described in literature. This study examines the datasets available for disaster evaluation and detection frameworks. A general framework consisting of five main dataset properties is designed for the presentation of the datasets. Some features include general information, data volume, recording environment, data nature, and evaluation. There are two or three sub-properties under each of the five main properties. Each dataset is examined in comparison to the listed attributes. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Artificial Intelligence datasets properties image dataset Natural disasters social media dataset

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Using a Wearable Device and Patient Reported Outcome to Evaluate the Influence of Sleep on Quality of Life Among Breast and Prostate Cancer Patients 18

Using a Wearable Device and Patient Reported Outcome to Eval...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Dhar, Eshita Barsasella, Diana Srikanth, Sruthi Panja, Ajith Kumar Malwade, Shwetambara Syed-Abdul, Shabbir Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Taipei Medical University Taipei Taiwan Health Polytechnic of Health Ministry Tasikmalaya Tasikmalaya West Java Indonesia Department of Computer Science and Engineering College of Engineering Anna University Guindy Chennai India School of Gerontology Health Management College of Nursing Taipei Medical University Taiwan

ISBN: (纸本)9781643682648

Few studies have used the combination of subjective and objective measures to investigate sleep problems as a primary outcome of concern in cancer patients. This study highlights the influence of sleep quality and duration on quality of life among breast and prostate cancer patients. Thirty-one participants were included in this study. Sleep quality and duration was assessed using a wearable actigraphy device and Pittsburgh Sleep Quality Index questionnaire. Quality of life was measured by European-Organization-For-Research-And-Treatment-Of-Cancer Quality of Life Questionnaire. t-test statistics further investigates the influence of sleep quality and duration with quality of life in participants. Results implied that those breast cancer patients who were poor sleepers and short sleepers showed greater deficits in areas of quality of life. In contrast, those prostate cancer patients who were long sleepers and good sleepers displayed poorer quality of life using actigraphy. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Surveys

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A Study of XXE Attacks Prevention Using XML Parser Configuration

A Study of XXE Attacks Prevention Using XML Parser Configura...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Ramsha Shahid Safdar Nawaz Khan Marwat Ala Al-Fuqaha Ghassen Ben Brahim Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia

ISBN: (纸本)9781665487726

Online attacks are outcomes of cyber vulnerabilities. XML (eXtensible Markup Language) is a self-descriptive markup language, and XML eXternal Entity injection (XXE) is a well-recognised web security vulnerability. XXE injection can attack web applications that several popular XML parsers are unable to withstand. Most of the available literature on XXE is based on vulnerability testing, but not much work has been done on ways for prevention of such attacks. The aim of this research is investigation of XXE attacks, and ways of prevention of such attacks. A sample vulnerable web application utilizing the standard XML parser that accompanies the Java Development Kit (JDK) has been developed in this work for executing different attacks and detection of attacks. Additionally, ways for prevention of XXE attacks are suggested as well. A virtual machine image of the developed framework is provided to perform trial and error for experimentation and representation of vulnerabilities. Different techniques and exploitation approaches for evaluating the performance of sample web application in terms of vulner-abilities are utilized to develop a platform for executing such attacks. Since the vulnerabilities of misconfigured XML parsers are depicted in this work in detail, effective methods for securing websites from XXE attacks are provided. Furthermore, this work also includes discourse on tools for developing platform for XXE attacks, and ways to eliminate vulnerabilities.

关键词： Java Codes Standards organizations XML Organizations Virtual machining Security

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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