检索结果-内蒙古大学图书馆

Super-Resolution Based on Curvelet Transform and Sparse Representation

computer Systems science & Engineering 2023年第4期45卷 167-181页

作者： Israa Ismail Mohamed Meselhy Eltoukhy Ghada Eltaweel Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia51422Egypt Department of Information Technology University of JeddahCollege of Computing and Information Technology at KhulaisJeddahSaudi Arabia

Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s).In this paper,we proposed a single image super-resolution *** uses the nonlocal mean filter as a prior step to produce a denoised *** proposed algorithm is based on curvelet *** converts the denoised image into low and high frequencies(sub-bands).Then we applied a multi-dimensional interpolation called Lancozos interpolation over both *** parallel,we applied sparse representation with over complete dictionary for the denoised *** proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher *** experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced *** comparison study shows that the proposed super-resolution algorithm outperforms the *** mean absolute error is 0.021±0.008 and the structural similarity index measure is 0.89±0.08.

关键词： Super-resolution Curvelet transform non-local means filter lancozos interpolation sparse representation

来源：评论

学校读者我要写书评

暂无评论

Improved Dynamic Johnson Sequencing Algorithm(DJS)in Cloud computing Environment for Efficient Resource Scheduling for Distributed Overloading

引用

Journal of Systems science and Systems Engineering 2024年第4期33卷 391-424页

作者： Anurag Sinha Pallab Banerjee Sharmistha Roy Nitasha Rathore Narendra Pratap Singh Mueen Uddin Maha Abdelhaq Raed Alsaqour Department of Computer Science Engineering ICFAI Tech SchoolICFAI UniversityJharkhandRanchi834004India Department of Computer Science Engineering Amity UniversityJharkhandRanchi835303India Department of Computing and Information Technology Usha Martin UniversityRanchi835103India Department of Information Technology Bharti Vidyapeeth’s College of EngineeringPaschim ViharNew DelhiIndia Department of CSE Delhi UniversityNew DelhiIndia College of Computing and Information Technology University of Doha for Science and TechnologyDoha24449Qatar Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdurrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computing and InformaticsSaudi Electronic University93499RiyadhSaudi Arabia

This study introduces an innovative approach to optimize cloud computing job distribution using the Improved Dynamic Johnson Sequencing Algorithm(DJS).Emphasizing on-demand resource sharing,typical to Cloud Service Providers(CSPs),the research focuses on minimizing job completion delays through efficient task *** Johnson’s rule from operations research,the study addresses the challenge of resource availability post-task *** advocates for queuing models with multiple servers and finite capacity to improve job scheduling models,subsequently reducing wait times and queue *** Dynamic Johnson Sequencing Algorithm and the M/M/c/K queuing model are applied to optimize task sequences,showcasing their efficacy through comparative *** research evaluates the impact of makespan calculation on data file transfer times and assesses vital performance indicators,ultimately positioning the proposed technique as superior to existing approaches,offering a robust framework for enhanced task scheduling and resource allocation in cloud computing.

关键词： First come first served(FCFS)scheduling round robin task scheduling queuing model Johnson scheduling algorithm

来源：评论

学校读者我要写书评

暂无评论

SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection Systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

来源：评论

学校读者我要写书评

暂无评论

A Blind Digital Watermarking Approach Using Palmprint-Embedded Local Binary Patterns and Arnold Cat Map Transformations 6

A Blind Digital Watermarking Approach Using Palmprint-Embedd...

引用

6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Ferik, Brahim Laimeche, Lakhdar Meraoumia, Abdallah Laouid, Abdelkader Chait, Khaled Alshaikh, Muath Tebessa Algeria El Oued Algeria Saudi Electronic University College of Computing And Informatics Department of Computer Science Saudi Arabia

ISBN: (纸本)9798350350265

The rapidly evolving healthcare domain and the rise of telemedicine and remote diagnostic techniques underscore the critical demand for secure, authentic, and reliable medical imaging solutions. This study introduces an innovative digital watermarking method designed to safeguard medical images against unauthorized alterations, employing biometric data - specifically, palmprints - as the watermark. Utilizing Local Binary Patterns (LBP) for feature extraction and combining Arnold Cat Map (ACM) with Discrete Cosine Transform (DCT) for image processing, this research implements a novel embedding strategy based on a 2 Least Significant Bits (2LSB) mechanism, facilitating a blind watermarking approach. The efficacy of the proposed method is validated through exceptional Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, achieving values of 51.21675 dB and 0.9999, respectively. These metrics provide substantial evidence of the technique's imperceptibility and robustness, employing state-of-the-art methodologies. The findings of this paper offer a compelling solution for enhancing the security of medical imagery within the telemedicine sector, promoting data integrity and supporting the verification and authentication processes. © 2024 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

E-Commerce Detectives: A Scam Website Detection Application Using Machine Learning 6

E-Commerce Detectives: A Scam Website Detection Application ...

引用

6th IEEE Symposium on computers and informatics, ISCI 2024

作者： Azzuri, Puteri Nursyawati Suffian Sulaiman, Mohd Universiti Tunku Abdul Rahman Department of Computer Science Kampar Malaysia College of Computing Informatics and Mathematics Shah Alam Malaysia

ISBN: (纸本)9798350353853

Many people have chosen to use online-based shopping, which is easy, fast and more accessible to different types of products and services. Some of the popular e-commerce websites nowadays are Shopee, Lazada, eBay, Amazon and Alibaba. These websites serve as a good platform for people to find different kinds of products and services around the world. Most users are younger, ranging in age from 13 to 25 years old. However, some older people love to shop online. Most of them lack exposure to and education on how to tell the difference between a scam and a legitimate website. It is also one of humans' weaknesses to detect and identify a scam website if it looks the same as a legitimate website. Furthermore, there are numerous reports of scam attacks that result in significant financial losses each year. Scammers may target not just an individual but also a company. At times, the situation can worsen, potentially posing a threat to our families and lives. This study has proposed a web-based scam detector as a solution to verify the legitimacy of an e-commerce website. This prototype uses the Random Forest model as a classifier to detect scams. This is due to the good performance and high accuracy of detection compared to the other algorithms such as Decision Tree, Naïve Bayes and Support Vector Machine. The Random Forest model achieved 93% accuracy of detection and it is good enough to deploy on the real prototype. Additionally, this website offers guidance on identifying scam websites and the tactics they typically employ to lure victims. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

CLIP-Flow:Decoding images encoded in CLIP space

引用

Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

来源：评论

学校读者我要写书评

暂无评论

End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Leveraging Large Language Models for Smart Pharmacy Systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

引用

19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

引用

Journal of High Speed Networks 2025年第1期31卷 71-89页

作者： Elhoseny, Mohamed Riad, Khaled College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura35516 Egypt Computer Science Department College of Computer Sciences & Information Technology King Faisal University Al-Ahsa 31982 Saudi Arabia Mathematics Department Faculty of Science Zagazig University Zagazig44519 Egypt

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key g

关键词： Ciphertext

来源：评论

学校读者我要写书评

暂无评论

Multiclass Classification for Cyber Threats Detection on Twitter

引用

computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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：