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Climate for Change: New HCI Research for Climate Action

Interactions (N.Y.) 2025年第1期32卷 50-52页

作者： Soden, Robert Sharma, Vishal Mauriello, Matthew Louis Bidwell, Nicola J. Department of Computer Science School of the Environment The University of Toronto Canada Brook Byers Institute for Sustainable Systems Georgia Department of Computer and Information Sciences University of Delaware United States International University of Management Namibia

This forum showcases emerging approaches, new ideas, and promising pathways that draw attention to the diverse, interdisciplinary, and impactful work of global scholars to advance dialogue in the field on how we can b... 详细信息

关键词： Kyoto Protocol

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An enhanced framework for smart automated evaluations of answer scripts using NLP and deep learning methods

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Multimedia Tools and Applications 2025年第11期84卷 8491-8513页

作者： G, Mohanraj R.K, Nadesh M, Marimuthu V, Sathiyapriya School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore632014 India School of Computer Science and Engineering Vellore Institute of Technology Chennai600127 India Knowledge Institute of Technology Salem637504 India

The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need automation of the evaluation process. This work aims to develop an enhanced novel hybrid framework that can evaluate answer scripts and automatically assign marks for different type of questions based on keywords, grammar, symbols, special keywords, and the given factors. First, the proposed system uses Optical Character Recognition (OCR) to convert image answer scripts into an editable text format. Second, the sentence transformers, the Natural Language Processing (NLP) technique flips the answer script and answers key texts into word embedding vectors. To find similarity measures, these vectors are matched using BERT encoding, spearmanś rank-order correlation, and fuzzy search. At last, the proposed model is trained using Deep Columnar Convolutional Neural Network (DCCNN) in the third step with MINST and Kaggle handwritten mathematical symbols and tested with the segmented mathematical equations to find the similarity. The performance of proposed model is measured using precision, recall, accuracy, and F1-score, and its gives highest accuracy of 93% and 95% when compared to the existing methodologies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optical character recognition

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Can I Help Prevent Data Breaches in the Workplace? From Routine Activities to Extra-Role Security Behaviors

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2025年第1期6卷 90-101页

作者： Lee, Jaeung Wang, Jingguo de Guzman, Melchor C. Gupta, Manish Raghav Rao, H. Louisiana Tech Computer Information Systems Department RustonLA71272 United States The University of Texas at Arlington Department of Information Systems and Operations Management ArlingtonTX76019 United States Georgia Gwinnett College Department of Criminal Justice and Criminology LawrencevilleGA30043 United States State University of New York at Buffalo Department of Management Science and Systems Buffalo14260 United States The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

Employees’ extra-role security behaviors can help organizations reduce safety and security threats in their operational environment and protect organizational assets. What antecedents can foster employees’ engagement in such behaviors is an important question. In this study, we consider such extra-role security behaviors as protective behaviors that are driven by employees’ motivation to protect organizational digital assets. This study draws upon Protection Motivation Theory (PMT) and suggests that employees’ threat appraisal and security self-efficacy are key to such extra-role security behaviors. Also, the study opens the black box of threat appraisal using Routine Activity Theory (RAT), proposing that employees’ appraisal of the threat of data breach is from an understanding of routine activities within their organization. The research model is developed based on the above and test the model with employees working in financial organizations. The analyses show that both perceived threat of data breaches and perceived security efficacy increase employees’ engagement in extra-role behaviors. Further, security efficacy reinforces the effect of the threat of data breaches on extra-role behavior. Finally, the suitability of data as well as, interestingly, the presence of guardianship, increases the perceived threat of data breach. 2637-6415 © 2024 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://***/publications/rights/*** for more information.

关键词： Economic and social effects

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IoT-Aware Real-Time Healthcare Diagnostic Framework For Diabetes Using Wearable Sensors through Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18183-18194页

作者： Farman, Haleem Shahzad, Yasir Jan, Bilal Nasralla, Moustafa M. Sallam, Karam M. Munasinghe, Kumudu Jamalipour, Abbas Prince Sultan University Smart Systems Engineering Lab Department of Communications and Networks Engineering College of Engineering Riyadh Saudi Arabia University of Peshawar Department of Computer Science Pakistan FATA University Department of Computer Science Kohat Pakistan University of Sharjah Computer Science Department Sharjah United Arab Emirates University of Canberra School of Information Technology and Systems ACT2617 Australia The University of Sydney School of Electrical and Information Engineering NSW2006 Australia

Machine Learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this paper, a 5G aware real-time diabetes prediction framework is proposed using optimized Bidirectional Long Short-Term Memory (Bi-LSTM) with Deep Reinforcement Learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as Blood Glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of 10 Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters such as blood pressure and body weight. The proposed framework leverages a dataset of 1,830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model's future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient's diabetic condition is categorized using the Surveillance Error Grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly. © 2014 IEEE.

关键词： Deep reinforcement learning

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Optimizing Document Classification Using Modified Relative Discrimination Criterion and RSS-ELM Techniques

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 824-833页

作者： Anwaar, Muhammad Gilanie, Ghulam Namoun, Abdallah Sharif, Wareesa Department of Artificial Intelligence-Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan AI Center-Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351 Saudi Arabia

Internet content is increasing daily, and more data are being digitized due to technological advancements. Everincreasing textual data in words, phrases, terms, sentences, and paragraphs pose significant challenges in classifying them effectively and require sophisticated techniques to arrange them automatically. The vast amount of textual data presents an opportunity to organise and extract valuable insights by identifying crucial pieces of information using feature selection techniques. Our article proposes "a Modified Relative Discrimination Criterion (MRDC) Technique and Ringed Seal Search-Extreme Learning Machine (RSS-ELM) to improve document classification", which prioritizes key data and fits corresponding documents into appropriate classes. The proposed MRDC and RSS-ELM techniques are compared with several existing techniques, such as the Relative Discrimination Criterion (RDC), the Improved Relative Discrimination Criterion (IRDC), GA-EM, and CS-ELM. The MRDC technique produced superior classification results with 91.60% accuracy compared to existing RDC and IRDC for feature selection. Moreover, the RSS-ELM optimization technique improved predictions significantly, with 98.9% accuracy compared to CS-ELM and GA-ELM on the Reuter21578 dataset. © (2025), (Science and information Organization). All rights reserved.

关键词： Dynamic programming

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A Hybrid Approach to Raising Digital Literacy among Adolescents in KSA Using a Mobile Application

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 98-114页

作者： Benaida, Mohamed Bin Youssef Shaheen, Abdul Rahman Alam, Tanweer Bin Saad Khalifa, Abdul Hakam Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia Department of Education College of Arabic Language and Human Studies Islamic University of Madinah Madinah Saudi Arabia

As digital technologies and the Internet evolve, digital issues are becoming more prevalent and are a serious concern due to their increased level of integration in our daily lives. Many users lack the necessary digital literacy to protect themselves and their communities from such issues. Background research reveals that adolescents in Saudi Arabia are particularly vulnerable to these issues, also supported by the studies pre-test for students (15.8%). This highlights a gap regarding preventative measures that researchers aim to fill. This study investigates the effectiveness of integrating a dedicated mobile application focused on raising awareness regarding digital issues such as cyberbullying within a hybrid teaching framework amongst primary and middle school students in Saudi Arabia. The application’s features, including but not limited to pre-tests, post-tests, lecture material, and progress monitoring, along with its ease of use, are designed to efficiently achieve digital issue learning outcomes. To assess this hybrid approach toward raising awareness of digital issues, this study gathers 30 participants and splits them into three groups: group one (hybrid), group two (traditional), and group three (online only). The results, which were analysed via one-way analysis of variance (ANOVA) and Tukey’s honest significant difference (HSD) post-hoc test, demonstrate a significant advantage in the increased level of awareness of students from the first group. This indicates that our mobile application in hybrid settings is significantly more effective in enhancing knowledge retention amongst students and raising their level of awareness of digital issues. © 2025 by the authors of this article.

关键词： digital issue education learning mobile application

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Average reward adjusted discounted reinforcement learning

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Neural Computing and Applications 2025年 1-32页

作者： Schneckenreither, Manuel Moser, Georg Department of Information Systems Production and Logistics Management University of Innsbruck Universitaetsstrasse 15 Innsbruck6020 Austria Computer Science University of Innsbruck Technikerstrasse 21a Innsbruck6020 Austria

In this paper, we provide a novel view upon reinforcement learning (RL for short). In particular, we are interested in applications of RL in use cases, where average rewards may be nonzero. While RL methodologies have been extensively researched upon, this particular application area has only received scarce attention in the Literature. In part, our motivation stems from applications in Operation Research (OR for short), where it is typically the case that rewards are profit derived. Similar use cases can be found in more general applications in economics. Based on a principled study of the mathematical background of discounted reinforcement learning we establish a novel adaptation of standard RL, dubbed Average Reward Adjusted Discounted Reinforcement Learning (ARAL for short). Our approach stems from revisiting the Laurent Series expansion of the discounted state value and a subsequent reformulation of the target function guiding the learning process. While the theoretical advance is arguably incremental, we provide ample experimental evidence that the thus obtained novel RL methodology compares favorable to well-established techniques like Q-learning or R-learning. © The Author(s) 2025.

关键词： Adversarial machine learning

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Research in computer information systems at Georgia State University: a balanced approach

Research in computer information systems at Georgia State Un...

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Annual Hawaii International Conference on System Sciences (HICSS)

作者： V.K. Vaishnavi Seok-Joong Yoon G.C. Buchanan Department of Computer Information Systems Georgia State University Atlanta GA USA

An overview is provided of the research conducted in computer information systems at Georgia State in the areas of systems development, software engineering, management of information systems, expert/decision-support systems and AI, data communications, data structures and algorithms, and computer graphics. The balanced approach underlying this research is discussed as it relates to the doctoral program along with how this approach had influenced the program in terms of research, dissertations, and courses taught.< >

关键词： information systems Management information systems Software engineering information management Research and development management Software development management Engineering management Artificial intelligence Data communication Data structures

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Design, development and teaching of innovative Web-based introductory "computer information systems" course

Design, development and teaching of innovative Web-based int...

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Frontiers in Education (FIE) Conference

作者： V. Uskov Department of computer Science and Information Systems Bradley University Peoria IL USA

The state-of-the-art InterLabs Web-lecturing tool has been designed, developed, and tested at the Department of computer Science and information systems and the InterLabs Research Institute, Bradley University, Peoria, Illinois as a part of a National Science Foundation grant. It is based on active utilization of innovative Web-based software engineering, portal, multiagent, streaming multimedia, communication, teaching, learning and information technologies. The introductory "computer information systems" CIS102 course is a required course for all freshman students with major in either engineering, business, science or education areas. As a result, the innovative Web-based CIS102 course was aimed to (1) provide students of various major areas with highest-possible learning convenience, and (2) meet different requirements for CIS102 course of various Bradley departments. The new CIS102 online course was designed and developed by faculty of the computer Science and information systems department in cooperation with faculty from other Bradley colleges. Student formative and summative evaluations and academic performance clearly demonstrate the advantages of the innovative online teaching and learning technologies based on the InterLabs Web-lecturing tool.

关键词： information systems computer science Streaming media System testing Software engineering Portals Multimedia communication information technology computer science education systems engineering education

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Blockchain technology adoption in healthcare: a systematic review and conceptual framework

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International Journal of Industrial and systems Engineering 2025年第1期50卷 1-57页

作者： Abdou, Ashraf Sayed El-Demerdash, Basma E. Mazen, Sherif A. Darwish, Nagy Ramadan Department of Information Systems Faculty of Graduate Studies for Statistical Research Cairo University Egypt Department of Operations Research and Management Faculty of Graduate Studies for Statistical Research Cairo University Egypt Department of Information Systems Faculty of Computer and Artificial Intelligence Cairo University Egypt

Recently, blockchain technology has attracted a lot of interest from different researchers and academics due to its unique properties like immutability, interoperability, and confidentiality. However, to date, their adoption in the healthcare sector is still very limited. Few studies applied a systematic literature review (SLR) for blockchain adoption in healthcare. In this research study, the first contribution is to identify the factors that influence the adoption of blockchain by applying the SLR approach, understand how these factors are interrelated, and discuss the main challenges of blockchain adoption. The findings demonstrated that, the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and its extension were the most popular models used for blockchain adoption. Then, we identified the key research gaps and proposed a conceptual framework to address the identified gaps to be a reference and guide for organisations adopting blockchain in healthcare. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： blockchain adoption blockchain technology healthcare SLR systematic literature review TAM technology acceptance model TOE UTAUT

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