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2024 Winter Simulation Conference, WSC 2024

作者： Wilsdorf, Pia Zschaler, Steffen Haack, Fiete Uhrmacher, Adelinde M. Institute for Visual and Analytic Computing University of Rostock Rostock Germany Department of Informatics King’s College London London United Kingdom

ISBN: (纸本)9798331534202

Simulation studies require thorough validation to ensure model accuracy, reliability, and credibility. While validation typically focuses on the simulation model itself, additional artifacts also influence study outcomes. Conceptual models, comprising research questions, requirements, inputs and outputs, model content, assumptions, and simplifications, provide context information for interpreting results and assessing model suitability. Validating other simulation artifacts for their fitness-for-purpose is complex, necessitating structured arguments to increase confidence. This paper explores when and how validation arguments should be constructed throughout the modeling and simulation lifecycle. Drawing on concepts from safety assurance cases, it defines key claims for the various artifacts, discusses validation arguments from different perspectives – including process, product, people, and project – and illustrates them through a computational biology case study. We conclude with a discussion of the suitability of such structured arguments for the comprehensive validation of simulation studies. © 2024 IEEE.

关键词： Digital elevation model

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Exploring Capital and Privilege in computing and Engineering Education Through Gamification 54

Exploring Capital and Privilege in Computing and Engineering...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Tadimalla, Sri Yash Kunkeler, Thom College of Computing and Informatics University of North Carolina CharlotteNC United States Uppsala University Department of Information Technology Uppsala Sweden

ISBN: (纸本)9798350351507

This full paper presents an innovative board game, 'Game of Life: Educational Pathways' developed as an engaging pedagogical tool to explore the theories of social reproduction, opportunity structures, and educational attainment through the concept of capital. Drawing inspiration from the traditional 'Game of Life,' this adaptation provides a participatory learning experience that delves into the complexities of educational pathways, privilege, socioeconomic factors like capital, and their cumulative impact on life outcomes. The game challenges players to navigate through a series of life stages and decision points related to education, where their choices and the inherent randomness of dice rolls simulate the unpredictable nature of life's opportunities. By simulating the journey through different educational and life outcomes based on various forms of capital - Talent, Wealth, Goodwill, Legacy - the game offers a hands-on approach to understanding the complexities of opportunity and achievement in computing and engineering education. This interactive tool not only aids in grasping complex educational theories but also highlights the importance of personal effort, policy, and practice in overcoming barriers for educational participation. Ideal for educational settings, 'Game of Life: Educational Pathways' prepares future computing and engineering professionals to navigate and contribute to a more equitable field. © 2024 IEEE.

关键词： Gamification

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Madapatha, Savin Fernando, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Longitudinal Evaluation of Casual Puzzle Tablet Games by Older Adults

Longitudinal Evaluation of Casual Puzzle Tablet Games by Old...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Chaudhry, Beenish Moalla Islam, Muhammad Usama Chawla, Nitesh Vinay School of Computing and Informatics University of Louisiana Lafayette United States Department of Computer Science and Engineering University of Notre Dame United States

ISBN: (纸本)9798400705830

Despite growing interest in mobile games for older adults, there is limited exploration of older adults’ gaming behaviors, perceptions, and experiences as they engage with casual puzzle games over a period of time. To address this, we conducted a 9-month study with 20 older adults, examining training needs, in-situ experiences, and preferences. Participants were trained on tablet PCs and ten selected games. During the study, participants documented their experiences and attended technology workshops. Gaming behaviors were logged and analyzed using descriptive and inferential statistics, revealing patterns and statistically signifcant diferences in play frequency and duration over the course of the study. Thematic analysis identifed facilitators and barriers to engagement such as customization, co-play experiences, and health issues. Based on these fndings, we recommend incorporating educational elements, enhancing user control, leveraging identity and nostalgia, supporting social interactions, designing for tangible interaction, and emphasizing the importance of learning aids. Future research should test the efectiveness of these recommendations in increasing older adults’ engagement with casual games. © 2024 Copyright held by the owner/author(s).

关键词： Personal computers

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Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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Computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

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A Comparative Study on Embedding Models for Keyword Extraction Using KeyBERT Method 13

A Comparative Study on Embedding Models for Keyword Extracti...

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13th IEEE International Conference on System Engineering and Technology, ICSET 2023

作者： Issa, Bayan Jasser, Muhammed Basheer Chua, Hui Na Hamzah, Muzaffar University of Aleppo Faculty of Informatics Engineering Syria Sunway University Department of Computing and Information Systems Selangor Sunway City Malaysia Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350340891

KeyBERT is a method for keywords/keyphrases extraction, which has three steps. The first step is selecting candidate keywords from a text using sklearn library, the second step is the embedding operation of the text and its candidate keywords;this operation is done by BERT to get a numerical representation that represents the meanings. The third step is calculating the cosine similarity between individual keywords vectors and document vector. In this paper, we focus on the second step of KeyBERT (embedding step). Although KeyBERT has a lot of supported models for the embedding operation, there are no extensive previous comparative studies to analyze and study the effect of using different supported models in KeyBERT. We introduce a comparative study of two commonly used groups of models;the first group is sentence-transformers pretrained models, supported via the sentence-transformers library, and the second group includes the Longformer model, supported via the Hugginface Transformers library. We conduct the comparative study of models on benchmark datasets, which contain English text documents of multi-domains with different text lengths. Based on the study, we found that the Paraphrase-mpnet-base-v2 model provides the best results among all other models in keyword extraction in terms of effectiveness (f1-score, recall, precision, MAP) on all datasets, with higher efficiency (time) on short text compared with using it on long text;accordingly, we recommend using it in that context. On the other hand, the Longformer model is the most efficient/fastest in keyword extraction among all other models on all datasets and this superiority has been evident, especially in long text;accordingly, we recommend using it in that context. © 2023 IEEE.

关键词： Embeddings

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SQL Queries Using Voice Commands to Be Executed 3rd

SQL Queries Using Voice Commands to Be Executed

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Lakshmi Patibandla, R.S.M. Potturi, Sai Naga Satwika Bhaskaruni, Namratha Department of CSE Koneru Lakshmaiah Education Foundation AP Guntur India Department of IT and CA School of Computing and Informatics VFSTR Deemed to Be University AP Guntur India

ISBN: (纸本)9789819773558

Add tables, delete tables, update tuples, and remove entries all require SQL queries. SQL query execution could appear straightforward, yet even a small mistake could result in serious issues. Keeping track of the queries and ensuring that they are handled flawlessly is a time-consuming, exhausting procedure. Without it, a bad query execution would result in bad data handling and eventual data loss. Speaking a query out loud and clearly and letting the computer handle it are two additional simple alternatives to typing it in. This is accomplished by our software using built-in Python methods and straightforward techniques. Furthermore, the time complexity might be greatly decreased with basic knowledge of NLP methods and how they operate, and comparable outcomes could be seen with low topic knowledge and high productivity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Structured Query Language

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Enhancing Software Cost Estimation Using Feature Selection and Machine Learning Techniques

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Computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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Enhanced Lightweight Facial Emotion Recognition Systems for Visually Impaired People 12

Enhanced Lightweight Facial Emotion Recognition Systems for ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Shehada, Dina Turky, Ayad Rabie, Tamer Hussain, Abir University of Dubai College of Engineering and IT Dubai United Arab Emirates University of Sharjah College of Engineering Department of Electrical Engineering Sharjah United Arab Emirates University of Sharjah College of Computing & Informatics Department of Computer Science Sharjah United Arab Emirates University of Sharjah College of Computing & Informatics Department of Computer Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350391398

Facial expressions are a vital component of human communication, conveying emotional information that enhances the social experience. However, for individuals with visual impairments, perceiving and interpreting facial expressions can be challenging, hindering their ability to engage fully in social interactions. This paper presents an enhanced facial emotion recognition system specifically designed to aid visually impaired individuals. Two enhanced approaches for facial emotion recognition systems are proposed. The first approach consolidates negative emotions (anger, fear, sadness, disgust) into a single class, while the second approach incorporates diverse training datasets from real-world environments. The negative emotion consolidation approach achieved 90.9% and 94.7% accuracy on the FER2013 and CK+ datasets, respectively. While, the diverse dataset integration approach achieved 84.9% and 70.6% accuracy on FER2013 and CK+ datasets, respectively. This is a significant improvement when benchmarked with other approaches available in the literature. The feasibility of the enhanced proposed systems as assistive technology for the visually impaired indicated its ability to be applied in real-world applications. © 2024 IEEE.

关键词： Assistive technology

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Exploring Network Dynamics: Community Detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Andreas Vonitsanos, Gerasimos Karamitsos, Ioannis Al-Hussaeni, Khalil Ionian University Department of Informatics Corfu Greece University of Patras Computer Engineering and Informatics Department Patras Greece Rochester Institution of Technology Dubai United Arab Emirates Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates

ISBN: (纸本)9798350362480

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network's structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health. © 2024 IEEE.

关键词： Tweets

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