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The role of GPT in promoting inclusive higher education for people with various learning disabilities: a review

PeerJ computer Science 2025年 11卷 e2400-e2400页

作者： Gadekallu, Thippa Reddy Yenduri, Gokul Kaluri, Rajesh Rajput, Dharmendra Singh Lakshmanna, Kuruva Fang, Kai Chen, Junxin Wang, Wei Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon School of Computer Science Engineering and Information Systems Vellore Institute of Technology University Tamil Nadu India Zhongda Group Jiaxing China College of Information Science and Engineering Jiaxing University Jiaxing China Division of Research and Development Lovely Professional University Phagwara India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India School of Mathematics and Computer Science Zhejiang Agricultural and Forestry University Hangzhou China School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Medical Technology Beijing Institute of Technology Beijing China

The generative pre-trained transformer (GPT) is a notable breakthrough in the field of artificial intelligence, as it empowers machines to effectively comprehend and engage in interactions with humans. The GPT exhibits the capacity to enhance inclusivity and accessibility for students with learning disabilities in the context of higher education, hence potentially facilitating substantial advancements in the field. GPT can provide personalized and diverse solutions that successfully cater to the distinct requirements of students with learning disabilities. This motivated us to conduct an extensive review to assess the effectiveness of GPT in enhancing accessibility and inclusivity in higher education for students with learning disabilities. This review offers a comprehensive analysis of the GPT and its significance for enhancing inclusivity in the field of higher education. In this research, we also examined the possible challenges and constraints associated with the integration of GPT into inclusive higher education, along with potential solutions. Overall, this review is intended for educators, students with and without learning disabilities, policymakers, higher education institutes, researchers, and educational technology developers. This review aims to provide a comprehensive understanding of GPT in promoting inclusive higher education for people with various learning disabilities, its impacts on inclusive higher education, emerging challenges, and potential solutions. Copyright 2025 Gadekallu et al.

关键词： Contrastive Learning

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Hyperparameter optimization in cardio vascular disease prediction

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AIP Conference Proceedings 2025年第1期3159卷

作者： Saraswathi Kuppusamy Renukadevi Thangavel Deepan Kumar Janani Yamunadevi Department of Computer Technology-UG Kongu Engineering College Perundurai Erode Tamilnadu India BSc Software Systems Department of Computer Technology-UG Erode Kongu Engineering College Tamilnadu India

A condition affecting the heart with blood vessels is called Cardio Vascular Disease (CVD). It is the main reason for death. Early detection can help prevent or lessen it, which lowers mortality. Various study articles describe the application of algorithmic machine learning to identify cardiac diseases. When the algorithm is applied to the dataset’s records, a faster and more precise prediction of cardiovascular illnesswill enable the patient to receive therapy. Cardiologists can make judgments quickly with the aid of these projections. The suggested study employs self-defined Decision Tree, random forest, Logistic Regressions, Support Vector Machine (SVM), grid search to identify the presenceof cardiovascular illness. We examine and assess its performance to forecast it.

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Harnessing optimization with deep learning approach on intelligent transportation system for anomaly detection in pedestrian walkways

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Scientific Reports 2025年第1期15卷 1-28页

作者： Alotaibi, Sultan Refa Alrayes, Fatma S. Maashi, Mashael Maray, Mohammed Alliheedi, Mohammed A. Badawood, Donia Alotaibi, Moneerah Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University PO Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University PO Box 103786 Riyadh 11543 Saudi Arabia Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Computing Faculty of Computing and Information Al-Baha University Al Baha Saudi Arabia Department of Data Science School of Computers Umm Al-Qura University Makkah Saudi Arabia

Anomaly Detection (AD) in pedestrian walkways is significant in urban safety and security methods. It is generally employed for perceiving unusual or abnormal situations, behaviours, or actions in regions devoted to pedestrian traffic, like pedestrian bridges, sidewalks, or crosswalks. The main aim is to enhance security, efficiency, and safety in the urban environment by classifying changes and observing the activities of pedestrians from recognized models. This type of AD usually has sensors, surveillance cameras, and advanced software methods. By applying innovative computer vision (CV) and machine learning (ML) methods, this approach always examines the pedestrian area to detect potential anomalies and attacks. Deep learning (DL) aided AD in pedestrian walkways, which displays a new and very effective method to progress safety and security in urban environments. Therefore, this study designs a novel Harnessing Optimization with a DL Approach on an Intelligent Transportation System with Anomaly Detection in Pedestrian Walkways (HODLAITS-ADPW) method. The presented HODLAITS-ADPW method utilizes the CV and DL process to detect pedestrians accurately. To attain this, the HODLAITS-ADPW method performs image pre-processing using Median Filtering to enrich the quality of input frames. Next, the HODLAITS-ADPW method utilizes an enhanced YOLOv7 object detector for the object’s detection in the frame. Besides, the Chameleon Swarm Algorithm is employed for the optimum hyperparameter tuning method. The HODLAITS-ADPW method uses an Attention Pyramid Convolutional Neural Network model for anomaly recognition. The experimental values of the HODLAITS-ADPW technique were certified on the pedestrian dataset, and the outcomes can be examined in numerous aspects. The empirical findings reported better outcomes for the HODLAITS-ADPW technique when compared to other models. © The Author(s) 2025.

关键词： Chameleon swarm algorithm Intelligent transportation system Median filtering Pedestrian walkway YOLOv7

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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IP SafeGuard - An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Daffodil Smart City Birulia Department of Software Engineering Dhaka 1216 Bangladesh Oryx Universal College with Liverpool John Moores University School of Computing and Data Sciences Cybersecurity Department Doha 34110 Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems Al-Salt 19385 Jordan Bangladesh University of Professionals (BUP) Department of Computer Science and Engineering Dhaka Bangladesh Amman Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Artificial Intelligence Cyber attack Cyber defense Cyber Security IP Address SOC Threat Intelligence

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Methodological contributions to the construction of virtual environments on the Internet

Methodological contributions to the construction of virtual ...

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1st International Conference on Internet Technologies and Applications, ITA 05

作者： Castán, Héctor Sanjuán, Oscar Aguilar, Luís Joyanes Segovia, Javier Department of Languages and Systems and Software Engineering Pontifical University of Salamanca Madrid Campus Spain Department of Languages and Systems and Software Engineering Polytechnic University of Madrid Spain

ISBN: (纸本)0946881324

Virtual reality systems should provide a satisfactory immersive experience for the user. However, the time needed to fully download and display them in the user platform, and long-time delays to start an interaction, may negatively influence the users perception of the overall quality. In order to obtain high quality networked virtual environments in the Internet, as it happens with other software development projects, including a "quality phase" seems not to be enough. This paper proposes a series of development guidelines for virtual environments with the aim of both making the development process easier and improving the final system.

关键词： software design

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Process deployment: A taxonomy of critical success factors

Process deployment: A taxonomy of critical success factors

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2010 6th International Joint Conference on computer, Information, and systems Sciences, and engineering, CISSE 2010

作者： Bayona, Luz S. Calvo-Manzano, Jose A. Cuevas, Gonzalo Feliu, Tomás San Faculty of Computer Science Department of Languages and IT Systems and Software Engineering Technical University of Madrid Madrid Spain

ISBN: (纸本)9781461435570

Various methods, models and standards for software process improvement have been adopted by organizations to improve their software processes. However, despite these efforts they still encounter difficulties in their process deployment throughout the organization. This is because the vast majority of these efforts focus more on the technical aspects, bypassing the human aspects. There is a set of factors that influence the successful deployment of new or modified processes. This paper presents taxonomy of critical success factors in software process deployment to achieve the processes institutionalization. The development of a taxonomy related to these critical success factors is based on a systematic review of existing literature on specialized databases and industrial experiences that have deployed or implemented processes. © 2013 Springer Science+Business Media.

关键词： Taxonomies

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Framework for Effective Utilization of Distributed Scrum in software Projects

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computer systems Science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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Determining practice achievement in project management using a two-phase questionnaire on small and medium enterprises

Determining practice achievement in project management using...

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14th European software Process Improvement Conference, EuroSPI 2007

作者： Garcia, Ivan Calvo-Manzano, Jose A. Gonzalo, Cuevas Tomas, San Feliu Languages and Informatics Systems and Software Engineering Department Faculty of Computer Science Polytechnic University of Madrid Spain

ISBN: (纸本)9783540747659

This paper aims to obtain a baseline snapshot of Project Management processes using a two-phase questionnaire to identify both performed and nonperformed practices. The proposed questionnaire is based on the Level 2 process areas of the Capability Maturity Model Integration for Development v1.2. It is expected that the application of the questionnaire to the processes will help small and medium software enterprises to identify those practices which are performed but not documented, which practices need more attention, and which are not implemented due to bad management or unawareness. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Project management

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

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arXiv 2025年

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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