检索结果-内蒙古大学图书馆

Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

computer Modeling in Engineering & sciences 2024年第6期139卷 3101-3123页

作者： Mohammad Mehedi Hassan Salman A.AlQahtani Mabrook S.AlRakhami Ahmed Zohier Elhendi Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Science Technology and Innovation Department King Saud UniversityRiyadh11543Saudi Arabia

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly *** its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is *** bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning *** integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data *** approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model *** pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s *** unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic *** method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare *** innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

关键词： Explainable AI COVID-19 CT images deep learning

来源：评论

学校读者我要写书评

暂无评论

Effects of feature selection and normalization on network intrusion detection

引用

Data science and Management 2025年第1期8卷 23-39页

作者： Umar, Mubarak Albarka Chen, Zhanfang Shuaib, Khaled Liu, Yan College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China Department of Computer Science Shantou University Guangdong Shantou515063 China

The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence (AI) techniques (such as machine learning (ML) and deep learning (DL)) to build more efficient and reliable intrusion detection systems (IDSs). However, the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs. Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues. While most of these researchers reported the success of these preprocessing techniques on a shallow level, very few studies have been performed on their effects on a wider scale. Furthermore, the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used, which most of the existing studies give little emphasis on. Thus, this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets: NSL-KDD, UNSW-NB15, and CSE–CIC–IDS2018, and various AI algorithms. A wrapper-based approach, which tends to give superior performance, and min-max normalization methods were used for feature selection and normalization, respectively. Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization. The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. The RF models also achieved an excellent performance compared to recent works. The results show that normalization and feature selection positively affect I

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A multi-modal deep learning system for Arabic emotion recognition

引用

International Journal of Speech technology 2023年第1期26卷 123-139页

作者： Abu Shaqra, Ftoon Duwairi, Rehab Al-Ayyoub, Mahmoud Department of Computer Information Systems Jordan University of Science and Technology Irbid Jordan Department of Computer Science Jordan University of Science and Technology Irbid Jordan

Emotion analysis is divided into emotion detection, where the system detects if there is an emotional state, and emotion recognition where the system identifies the label of the emotion. In this paper, we provide a multimodal system for emotion detection and recognition using Arabic dataset. We evaluated the performance of both audio and visual data as a unimodal system, then, we exposed the impact of integrating the information sources into one model. We examined the effect of gender identification on the performance. Our results show that identifying speaker’s gender beforehand increases the performance of emotion recognition especially for the models that rely on audio data. Comparing the audio-based system with the visual-based system demonstrates that each model performs better for a specific emotional label. 70% of the angry labels were predicted correctly using the audio model while this percentage was less using the visual model (63%). The accuracy obtained for the surprise class was (40.6%) using the audio model while it was (56.2%) using the visual model. The combination of both modalities improves accuracy. Our final result for the multimodal system was (75%) for the emotion detection task and (60.11%) for emotion recognition task and these results are among the top results achieved in this field and the first which focus on Arabic content. Specifically, the novelty of this work is expressed by exploiting deep learning and multimodal models in emotion analysis and applying it on a natural audio and video dataset for Arabic speaking persons. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

引用

computers, Materials & Continua 2023年第6期75卷 6305-6322页

作者： Muhammad Imran Khan Khalil Syed Adeel Ali Shah Izaz Ahmad Khan Mohammad Hijji Muhammad Shiraz Qaisar Shaheen Department of Computer Science&Information Technology University of Engineering&TechnologyPeshawar25000Pakistan Department of Computer Science Bacha Khan UniversityCharsadda24541Pakistan Computer Science Department University of TabukTabuk47512Saudi Arabia Department of Computer Science Federal Urdu University of ArtsScience and Technology Islamabad44000Pakistan Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan

Data centers are being distributed worldwide by cloud service providers(CSPs)to save energy costs through efficient workload alloca-tion *** CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload *** research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers(DCs).However,oper-ating cost savings during workload processing,which also considers string-matching techniques in geo-distributed DCs,remains *** this research,we propose a novel string matching-based geographical load balanc-ing(SMGLB)technique to mitigate the operating cost of the geo-distributed *** primary goal of this study is to use a string-matching algorithm(i.e.,Boyer Moore)to compare the contents of incoming workloads to those of documents that have already been processed in a data center.A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save *** the contrary,if no match can be discovered,the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices,the number of active servers,on-site green energy,and traces of incoming *** results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.

关键词： String matching optimization geo-distributed data centers geographical load balancing green energy

来源：评论

学校读者我要写书评

暂无评论

Read for me: developing a mobile based application for both visually impaired and illiterate users to tackle reading challenge

引用

i-com 2023年第3期22卷 225-240页

作者： Alfayez, Zainab Hameed Alfayez, Batool Hameed Abdul-Samad, Nahla Hamad Department of Computer Information Systems College of Computer Science and Information Technology University of Basrah Basrah Iraq Department of Library and Information College of Art University of Basrah Basrah Iraq

In recent years, there have been several attempts to help visually impaired and illiterate people to overcome their reading limitations through developing different applications. However, most of these applications are based on physical button interaction and avoid the use of touchscreen devices. This research mainly aims to find a solution that helps both visually impaired and illiterate people to read texts present in their surroundings through a touchscreen-based application. The study also attempts to discover the possibility of building one application that could be used by both type of users and find out whether they would use it in the same efficiency. Therefore, a requirements elicitation study was conducted to identify the users’ requirements and their preferences and so build an interactive interface for both visually impaired and illiterate users. The study resulted in several design considerations, such as using voice instructions, focusing on verbal feedback, and eliminating buttons. Then, the reader mobile application was designed and built based on these design preferences. Finally, an evaluation study was conducted to measure the usability of the developed application. The results revealed that both sight impaired and illiterate users could benefit from the same mobile application, as they were satisfied with using it and found it efficient and effective. However, the measures from the evaluation sessions also reported that illiterate users had used the develop app more efficiently and effectively. Moreover, they were more satisfied, especially with the application’s ease of use. © 2023 the author(s), published by De Gruyter.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Robust steganographic approach using generative adversarial network and compressive autoencoder

引用

Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

引用

Tsinghua science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

来源：评论

学校读者我要写书评

暂无评论

Small object detection in diverse application landscapes: a survey

引用

Multimedia Tools and Applications 2024年第41期83卷 88645-88680页

作者： Iqra Giri, Kaisar J. Javed, Mohammed Department of Computer Science Islamic University of Science & Technology Pulwama India Computer Vision & Biometrics Lab Dept. of IT Indian Institute of Information Technology Allahabad India

The importance of object detection within computer vision, especially in the context of detecting small objects, has notably increased. This thorough survey extensively examines small object detection across various applications, consolidating and outlining the available methodologies. Traditional papers on small object detection have focused on specific domains. However, this survey paper incorporates insights from a multitude of domains, providing a comprehensive understanding of the versatility and applicability of small object detection techniques. This paper sheds light on the key challenges faced and delves into potential solutions to address the challenges, offering insights into viable solutions to enhance small object detection performance, setting it apart from existing literature. The strategies identified in our survey encompass a spectrum of approaches, categorized as transformer-based, CNN, and traditional methods. Also, this paper collates prevalent datasets relevant to small object detection, simplifying access to these resources. Further, it provides a succinct overview of diverse evaluation metrics used for performance assessment in this field, enhancing understanding of the effectiveness and proficiency of these methods. This survey paper not only consolidates established knowledge but also highlights innovative viewpoints, providing a comprehensive and enlightening compilation that contributes to the advancement of small object detection in the field of computer vision. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Enhanced Object Detection and Classification via Multi-Method Fusion

引用

computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： Muhammad Waqas Ahmed Nouf Abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

来源：评论

学校读者我要写书评

暂无评论

uFOIL: An Unsupervised Fusion of Image Processing and Language Understanding

引用

IEEE Access 2025年 13卷 31683-31700页

作者： Rahman, Md. Abdur Hasan, Md. Tanzimul Farooq Howlader, Umar Kader, Md. Abdul Islam, Md. Motaharul Hung Pham, Phuoc Mehedi Hassan, Mohammad United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh Providence College Department of Mathematics and Computer Science ProvidenceRI02918 United States King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively;and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively. © 2025 The Authors.

关键词： Optical character recognition

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：