检索结果-内蒙古大学图书馆

engineering & Computing Technologies (ICECT), International Conference on

作者： Asma Bibi Umara Noor Zahid Rashid Department of Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Department of Computer Science Kohsar University Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350349719

ISBN: (纸本)9798350349726

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of “Mobile Twin” is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of “Revolutionary Ultra-low latency (RULL)” in the metaverse.

关键词： computers Metaverse computer architecture Network architecture Data processing Mobile handsets Digital twins

来源：评论

学校读者我要写书评

暂无评论

LEMS: Optimized Large Model Framework for Edge-AI in Consumer Internet of Things Devices

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Rehman, Abdul Mahmood, Khalid Hassan, Mahmood Ul Javed, Muhammad Wasim Aurangzeb, Khursheed Anwar, Muhammad Shahid University of Haripur Department of Information Technology Hattar Road Khyber Pakhtunkhwa Haripur22620 Pakistan Applied College Muhayil Engineering and Technical Specializations Unit King Khalid University Aseer Saudi Arabia IIC University of Technology Department of Computer Science Phnom Penh Cambodia King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia Department of Ai and Software Gachon University Seongnam-si13120 Korea Republic of

Edge computing plays a critical role in enabling real-time deployment of artificial intelligence (AI) on resource-constrained consumer IoT devices. These devices face significant challenges in balancing energy efficiency, latency, accuracy, and security. To address these issues, an Optimized Lightweight Large Model Framework (LEMS) is proposed that enhances advanced model compression techniques, including pruning and quantization, to reduce computational and memory demands. Additionally, LEMS employs a hybrid edge-cloud processing architecture that optimizes resource utilization by offloading complex tasks to the cloud while maintaining low-latency performance at the edge. To ensure data security, the framework integrates lightweight cryptographic protocols, ensuring privacy without overwhelming the constrained devices. The LEMS evaluated on various IoT platforms, including Raspberry Pi 4 and ESP32 microcontrollers, using real-world dataset: MIMIC-III. Results show that LEMS reduces model size by up to 40% and cuts energy consumption by 15%, while preserving 91% inference accuracy. Moreover, the hybrid processing reduced latency by 60%, and the security mechanisms incurred less than 5% computational overhead. © 2025 IEEE.

关键词： Computation offloading

来源：评论

学校读者我要写书评

暂无评论

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Gooseneck Barnacle Optimization and Artificial Neural Networks

Air Pollutant (CO) Forecasting: A Hybrid Approach Using Goos...

引用

AI in Cybersecurity (ICAIC), International Conference on

作者： Marzia Ahmed Ahmad Johari Mohamad Md Maruf Hassan Touhid Bhuiyan Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) Malaysia Department of Computer Science and Engineering Southeast University Dhaka Bangladesh School of IT Washington University of Science and Technology (WUST) Virginia USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Air quality forecasting is critical for environmental monitoring and public health, and in this study, we propose a hybrid approach utilizing Gooseneck Barnacle Optimization (GBO) and Artificial Neural Networks (ANN) to enhance predictive accuracy. The air quality dataset, comprising pollutant concentrations (CO, NOx, benzene) and meteorological factors (temperature, humidity), is sourced from the UCI Machine Learning Repository. GBO is employed to fine-tune the ANN’s hyperparameters (especially the weight and bias), as well as the number of layers, neurons, and activation functions, to improve regression-based forecasting. The GBOANN model achieves a Mean Squared Error (MSE) of 0.562 and a Root Mean Squared Percentage Error (RMSPE) of 5.63%, outperforming other metaheuristic methods like BMO, SSA, DA, MVO, and EMA. This study demonstrates the GBO-ANN model’s potential for accurate real-time air quality forecasting, offering a robust, sustainable solution.

关键词： Accuracy Atmospheric modeling Artificial neural networks Predictive models Air quality Data models Real-time systems Environmental monitoring Forecasting Public healthcare

来源：评论

学校读者我要写书评

暂无评论

A Sample Detection Model Applying IoT technology

A Sample Detection Model Applying IoT Technology

引用

Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Li Wang Shu Huang Computer and Information Engineering College Wuhan Railway Vocational College of Technology Wuhan Hubei China School of Computing Wuhan Vocational College of Software and Engineering Wuhan Hubei China

This article first analyzes the development trend of IoT technology to determine its process framework in the industry. Then, through sample analysis, the application value and direction of intelligent instruments in sample analysis are identified. Finally, by integrating intelligent instruments in sample analysis with IoT technology, the application of intelligent instruments in sample analysis under IoT technology is studied. This article is based on the application of Internet of Things technology to intelligent instruments, obtaining a new type of intelligent instrument and exploring its application in blood cells. It mainly conducts comparative experiments on the counting and classification of blood cells. The experimental results of the comparison between the model algorithm and instrument method in this article indicate that the algorithm of this model has a certain degree of accuracy and effectiveness, and the intelligent analysis in this article further improves the research on samples.

关键词：

来源：评论

学校读者我要写书评

暂无评论

SCALAR: A Part-of-speech Tagger for Identifiers

arXiv

引用

arXiv 2025年

作者： Newman, Christian D. Scholten, Brandon Testa, Sophia Behler, Joshua A.C. Banabilah, Syreen Collard, Michael L. Decker, Michael J. Mkaouer, Mohamed Wiem Zampieri, Marcos AlOmar, Eman Abdullah Alsuhaibani, Reem Peruma, Anthony Maletic, Jonathan I. Department of Software Engineering Rochester Institute of Technology RochesterNY United States Department of Computer Science Kent State University KentOH United States Department of Computer Science University of Akron AkronOH United States Department of Software Engineering Bowling Green State University Bowling GreenOH United States Department of Computer Science University of Michigan Flint FlintMI United States George Mason University School of Computing FairfaxVA United States Stevens Institute of Technology School of Engineering and Science HobokenNJ United States Prince Sultan University Department of Software Engineering Riyadh Saudi Arabia University of Hawaii at Manoa Department of Information and Computer Sciences HonoluluHI United States

The paper presents the Source Code Analysis and Lexical Annotation Runtime (SCALAR), a tool specialized for mapping (annotating) source code identifier names to their corresponding part-of-speech tag sequence (grammar pattern). SCALAR’s internal model is trained using scikit-learn’s GradientBoostingClassifier in conjunction with a manually-curated oracle of identifier names and their grammar patterns. This specializes the tagger to recognize the unique structure of the natural language used by developers to create all types of identifiers (e.g., function names, variable names etc.). SCALAR’s output is compared with a previous version of the tagger, as well as a modern off-the-shelf part-of-speech tagger to show how it improves upon other taggers’ output for annotating identifiers. The code is available on Github1 © 2025, CC BY.

关键词： computer software maintenance

来源：评论

学校读者我要写书评

暂无评论

Synthetic Minority Over-sampling Technique for Student Performance Prediction: A Comparative Analysis of Ensemble and Linear Models

Synthetic Minority Over-sampling Technique for Student Perfo...

引用

International Conference on computer and Information technology (ICCIT)

作者： Khaled Mahmud Sujon Rohayanti Hassan Nusrat Jahan Department of Software Engineering Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Faculty of Computing Universiti Teknologi Malaysia (UTM) Johor Malaysia Department of Computer Science and Engineering Chittagong University of Engineering & Technology(CUET) Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study explores the impact of the Synthetic Minority Oversampling Technique (SMOTE) on predictive accuracy in student performance prediction using both linear (Logistic Regression, LR) and ensemble (Random Forest, RF) models. We compare the performance of these models, using a real-time dataset from Universiti Teknologi Malaysia (UTM). While previous studies have examined these models separately, this research uniquely investigates the simultaneous application of SMOTE to both. Our results show that LR’s accuracy improves from 91% to 95% with SMOTE, while RF maintains a consistent 98% accuracy. These findings demonstrate that SMOTE significantly enhances the performance of linear models in handling imbalanced data, providing useful insights for curriculum development in higher education. This research contributes to the literature by guiding higher education institutions in selecting the most effective predictive models.

关键词： Radio frequency Analytical models Logistic regression Accuracy Computational modeling Education Predictive models Data models Real-time systems Information technology

来源：评论

学校读者我要写书评

暂无评论

Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection

Bridging Machine Learning and Formal Concept Analysis for Ef...

引用

Information technology (ACIT)

作者： Anas M. Al-Oraiqat Oleksandr Drieiev Ghassan Samara Sattam Almatarneh Karim A. Al-Oraiqat Hazim Alrawashdeh Hanna Drieieva Ali Elrashidi Yassin M. Y. Hasan Department of Computer Science Faculty of Information Technology Zarqa University Jordan Department of Cybersecurity and Software Central Ukrainian National Technical University Kropyvnytskyi Ukraine Department of Data Science and Artificial Intelligence Faculty of Information Technology Zarqa University Jordan Department of Software Engineering Faculty of Information Technology Zarqa University Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Electrical Engineering Department Assiut University Egypt

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude" low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering" of crowded zones into hierarchical formal concepts and these latter’s "bottom-up parsing" to finally identify the dense zones.

关键词： Formal concept analysis Scalability Prevention and mitigation Decision making Lattices Machine learning Feature extraction Prediction algorithms Real-time systems Data mining

来源：评论

学校读者我要写书评

暂无评论

On the Privacy Preservation and Secure Communication in Fog-Based Mobile Crowdsensing

SSRN

引用

SSRN 2024年

作者： Ogundoyin, Sunday Oyinlola Kamil, Ismaila Adeniyi Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Electrical and Electronic Engineering Programme Bowen University Iwo Nigeria Department of Electrical and Electronic Engineering University of Ibadan Ibadan Nigeria Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and Technology Bando 40601 Kenya

The rapid growth of mobile crowdsensing (MCS) presents significant opportunities for large-scale data collection through the collaborative use of smart devices. However, the increasing volume of sensitive data generated by MCS services poses critical challenges in terms of privacy and security, particularly latency-sensitive applications. While some solutions exist, many still lack the scalability, robustness, efficiency, and privacy preservation required by MCS services. This paper proposes a privacy-preserving and secure fog-based MCS (FB-MCS) scheme, comprising a four-tier fog computing architecture that incorporates dynamic lower-tier fog (LTF) and static upper-tier fog (UTF) systems to enable efficient data aggregation, privacy protection, and secure communication. The proposed scheme utilizes secret sharing and homomorphic MAC techniques for efficient and verifiable data aggregation over multi-dimensional data, allowing a data requester to verify the accuracy and integrity of the aggregated results. The scheme employs a lightweight elliptic curve cryptography (ECC) to ensure secure authentication without overburdening resource-constrained devices. An adaptive fog node selection strategy, based on trust and mobility, is proposed for reliable real-time task allocation. Extensive security analysis demonstrates that the scheme not only guarantees privacy preservation, integrity of aggregation results, strong anonymity, un-linkability, traceability, and resistance to well-known attacks but also achieves data confidentiality and unforgeability in the random oracle model under Type I and Type II adversaries, assuming the Computational Diffie-Hellman Problem (CDHP) and Discrete Logarithm Problem (DLP) are intractable. Moreover, performance assessments indicate that the proposed scheme surpasses previous advanced solutions, achieving a 48\% - 280\% improvement in efficiency. © 2024, The Authors. All rights reserved.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Transformer doctor: diagnosing and treating vision transformers 24

Transformer doctor: diagnosing and treating vision transform...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jiacong Hu Hao Chen Kejia Chen Yang Gao Jingwen Ye Xingen Wang Mingli Song Zunlei Feng College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University Bangsheng Technology Co. Ltd. Electrical and Computer Engineering National University of Singapore College of Computer Science and Technology Zhejiang University and Bangsheng Technology Co. Ltd. College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Due to its powerful representational capabilities, Transformers have gradually become the mainstream model in the field of machine vision. However, the vast and complex parameters of Transformers impede researchers from gaining a deep understanding of their internal mechanisms, especially error mechanisms. Existing methods for interpreting Transformers mainly focus on understanding them from the perspectives of the importance of input tokens or internal modules, as well as the formation and meaning of features. In contrast, inspired by research on information integration mechanisms and conjunctive errors in the biological visual system, this paper conducts an in-depth exploration of the internal error mechanisms of Transformers. We first propose an information integration hypothesis for Transformers in the machine vision domain and provide substantial experimental evidence to support this hypothesis. This includes the dynamic integration of information among tokens and the static integration of information within tokens in Transformers, as well as the presence of conjunctive errors therein. Addressing these errors, we further propose heuristic dynamic integration constraint methods and rule-based static integration constraint methods to rectify errors and ultimately improve model performance. The entire methodology framework is termed as Transformer Doctor, designed for diagnosing and treating internal errors within transformers. Through a plethora of quantitative and qualitative experiments, it has been demonstrated that Transformer Doctor can effectively address internal errors in transformers, thereby enhancing model performance. For more information, please visit https://***/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Insulin Management: A Digital Health Solutions for Type 1 Diabetes Support

Evolutionary Insulin Management: A Digital Health Solutions ...

引用

Information technology (ACIT)

作者： Hamzeh Aljawawdeh Randa Salman Rasha Alzaben Ragae Dughmosh Mohammed Khouj Department of Software Engineering Zarqa University Zarqa Jordan Department of Computer Engineering Yarmouk University Irbid Jordan Medicine Department Hamad Medical Corporation Doha Qatar Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Type 1 diabetes is characterised by a deficiency of pancreatic beta cells, resulting in hyperglycemia and insulin deficiency. It usually develops during childhood or adolescence, but symptoms can appear much later. The specific origin of type 1 diabetes is still unknown, but T cell-mediated beta cell death is thought to be responsible for the monogenesis of the disease. New treatments such as insulin pumps, continuous glucose monitoring and hybrid closed-loop systems are being developed. Tight glycemic control has reduced the incidence of microvascular and macrovascular disorders. However, most T1DM patients continue to experience these problems and significant research efforts are needed to improve the quality of life and prognosis of T1DM patients through early diagnosis, prevention of beta cell depletion and better treatment options. Exogenous insulin therapy is needed in this disease to avoid the devastating consequences of hyperglycemia. Psychological issues and comorbidity management are elements of good T1D care, proper nutrition, diabetes treatment, and comprehensive diabetes education. Additionally, several challenges must be overcome at different stages of life. Managing T1D with limited resources Healthcare professionals deal with various difficulties at work. Nonetheless, an alphabetical approach can achieve the best outcome and enable comprehensive T1D care in a scenario where resources are scarce. In the management of children with type 1 diabetes mellitus (T1DM), effective glycemic control is crucial since it is associated with improved short- and long-term health outcomes. However, poor glycemic control lowers quality of life and increases the risk of acute and chronic issues.

关键词： Pediatrics Prevention and mitigation Psychology Medical treatment Insulin pumps Diabetes Insulin Prognostics and health management Information technology Diseases

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：