检索结果-内蒙古大学图书馆

Early Detection of Alzheimer’s Disease Based on Laplacian Re-Decomposition and XGBoosting

computer Systems science & Engineering 2023年第9期46卷 2773-2795页

作者： Hala Ahmed Hassan Soliman Shaker El-Sappagh Tamer Abuhmed Mohammed Elmogy Information Technology Department Faculty of Computers and InformationMansoura UniversityMansoura35511Egypt Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Faculty of Computers and Artificial Intelligence Benha UniversityBanha13518Egypt College of Computing and Informatics Sungkyunkwan UniversitySuwonKorea

The precise diagnosis of Alzheimer’s disease is critical for patient treatment,especially at the early stage,because awareness of the severity and progression risks lets patients take preventative actions before irreversible brain damage *** is possible to gain a holistic view of Alzheimer’s disease staging by combining multiple data modalities,known as image *** this paper,the study proposes the early detection of Alzheimer’s disease using different modalities of Alzheimer’s disease brain ***,the preprocessing was performed on the ***,the data augmentation techniques are used to handle ***,the skull is removed to lead to good *** the second phase,two fusion stages are used:pixel level(early fusion)and feature level(late fusion).We fused magnetic resonance imaging and positron emission tomography images using early fusion(Laplacian Re-Decomposition)and late fusion(Canonical Correlation Analysis).The proposed system used magnetic resonance imaging and positron emission tomography to take advantage of *** resonance imaging system’s primary benefits are providing images with excellent spatial resolution and structural information for specific *** emission tomography images can provide functional information and the metabolisms of particular *** characteristic helps clinicians detect diseases and tumor progression at an early ***,the feature extraction of fused images is extracted using a convolutional neural *** the case of late fusion,the features are extracted first and then ***,the proposed system performs XGB to classify Alzheimer’s *** system’s performance was evaluated using accuracy,specificity,and *** medical data were retrieved in the 2D format of 256×256 *** classifiers were optimized to achieve the final results:for the decision tree,the maximum depth of a tree was *** best number of trees for the random forest was 60;for

关键词： Alzheimer’s disease(AD) machine learning(ML) image fusion Laplacian Re-decomposition(LRD) XGBoosting

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Machine Learning and Metaheuristic Based Model for E-Business Risk Management

引用

SN computer science 2024年第7期5卷 1-13页

作者： Elhoseny, Mohamed Darwiesh, Abdelaziz El-Baz, A.H. Tarabia, A.M.K. College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Faculty of Computers and Information Mansoura University Mansoura Egypt Department of Mathematics Faculty of Science Damietta University New Damietta 34517 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University New Damietta 34517 Egypt

This study introduces a novel approach to enhance e-business firms by identifying potential risks through the analysis of customers’ Twitter posts. Unlike traditional lexicon-based methods, which can be complex, we propose using machine learning models and metaheuristic optimization techniques for more effective risk detection. Additionally, we employ natural language processing techniques to efficiently handle and analyze text data. This approach aims to assist firms in developing competitive e-business strategies by identifying risks based on customer indications on the platform. To achieve this, we construct three datasets: one for risk detection, one for risk assessment, and one for identifying types of risks, which are used in the training phase. We collected tweets from Amazon customers and found that over a quarter of the sample expressed risks in their tweets, with less than a quarter indicating high risks. The most common types of risks identified were operational and financial risks. Furthermore, various performance measures were calculated to evaluate the effectiveness of the proposed approach. The mean accuracy for the processes of risk detection, risk identification, and risk assessment was found to be 81.66%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-business firms Machine learning Metaheuristic techniques Natural language processing Risks detection Social media

来源：评论

学校读者我要写书评

暂无评论

Ensemble Classifiers for Acute Leukemia Classification Using Microarray Gene Expression Data under uncertainty

引用

Neutrosophic Sets and Systems 2022年 49卷 164-183页

作者： Gamal, Mona Zaied, Abdel Nasser H. Rushdy, Ehab Faculty of Computers and Informatics Zagazig University Egypt Vice Dean for students' affairs Faculty of Computer Science MIU Egypt

One of the most prevalent cancers in children and adults, acute leukemia has the potential to lead to death if left untreated. Within a few weeks after diagnosis, childhood ALL has spread throughout the body, posing a serious health risk to the patient. Evaluation of acute leukemia contains uncertainty and incomplete information. Due to the subjective nature of the expectations, this rating procedure incorporates ambiguity and inaccuracy. To illustrate the ambiguity of our subjective judgments, we can use the triplet T, F, and I, truth, falsity, and indeterminacy (I). Therefore, a Single-Valued Neutrosophic Sets (SVNSs) approach based on AHP, TOPSIS, and VIKOR is designed and implemented in this article. Neutrosophic AHP is used to determine the weighting of criteria in this methodology. A neutrosophic TOPSIS and VIKOR model are used to rank alternatives. There is further validation and verification of the proposed methodology in the application. To demonstrate the adaptability of the offered decisions under various circumstances, sensitivity assessments and comparative analyses were carried out. © 2022. All Rights Reserved.

关键词： Hierarchical systems

来源：评论

学校读者我要写书评

暂无评论

Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

引用

Neural Computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

来源：评论

学校读者我要写书评

暂无评论

A Neutrosophic Approach to Robust Web Security: Mitigating XSS Attacks

引用

Neutrosophic Sets and Systems 2025年 79卷 1-22页

作者： Salama, A.A. Aboelfotoh, El-Said F. El-Bakry, Hazem M. Khalid, Huda E. Essa, Ahmed K. Sabbagh, Ramiz El-Morshedy, Doaa S. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Information Systems Faculty of Computers and Information Mansoura University Mansoura Egypt Telafer University Telafer Iraq Department of the Scientific Affairs Telafer University Mosul Iraq

Cross-Site Scripting (XSS) is one of the most grievous vulnerabilities-a pitfall through which web applications are affected. These types of attacks are complex, and the available threat landscape is always changing, making it hard for conventional security tools to effectively detect or prevent these types of attacks. We present here an approach that detects and prevents XSS attacks on Web pages. Since Neutrosophism works with different kinds of data, it aims at the validity degree of the attack. This way the system understands the different types of attacks and allows the system to act more effectively. Our system combines (1) static analysis-to look into the code behind the website-with (2) dynamic analysis-to watch the website in action. Moreover, a proactive defense watches your user behavior and scrubs every input/output. © 2025, University of New Mexico. All rights reserved.

关键词： Websites

来源：评论

学校读者我要写书评

暂无评论

A novel medical steganography technique based on Adversarial Neural Cryptography and digital signature using least significant bit replacement

引用

International Journal of Cognitive Computing in Engineering 2024年第1期5卷 379-397页

作者： Hameed, Mohamed Abdel Hassaballah, M. Abdelazim, Riem Sahu, Aditya Kumar Department of Computer Science Faculty of Computers and Information Luxor University Luxor85951 Egypt Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj16278 Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena Egypt Department of Information Systems College of Information Technology Misr University for Science and Technology Giza Egypt Amrita School of Computing Amrita Vishwa Vidyapeetham Andhra Pradesh Amaravati522503 India

With recent advances in technology protecting sensitive healthcare data is challenging. Particularly, one of the most serious issues with medical information security is protecting of medical content, such as the privacy of patients. As medical information becomes more widely available, security measures must be established to protect confidentiality, integrity, and availability. Image steganography was recently proposed as an extra data protection mechanism for medical records. This paper describes a data-hiding approach for DICOM medical pictures. To ensure secrecy, we use Adversarial Neural Cryptography with SHA-256 (ANC-SHA-256) to encrypt and conceal the RGB patient picture within the medical image's Region of Non-Interest (RONI). To ensure anonymity, we use ANC-SHA-256 to encrypt the RGB patient image before embedding. We employ a secure hash method with 256bit (SHA-256) to produce a digital signature from the information linked to the DICOM file to validate the authenticity and integrity of medical pictures. Many tests were conducted to assess visual quality using diverse medical datasets, including MRI, CT, X-ray, and ultrasound cover pictures. The LFW dataset was chosen as a patient hidden picture. The proposed method performs well in visual quality measures including the PSNR average of 67.55, the NCC average of 0.9959, the SSIM average of 0.9887, the UQI average of 0.9859, and the APE average of 3.83. It outperforms the most current techniques in these visual quality measures (PSNR, MSE, and SSIM) across six medical assessment categories. Furthermore, the proposed method offers great visual quality while being resilient to physical adjustments, histogram analysis, and other geometrical threats such as cropping, rotation, and scaling. Finally, it is particularly efficient in telemedicine applications with high achieving security with a ratio of 99% during remote transmission of Electronic Patient Records (EPR) over the Internet, which safeguards the patien

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

Interactive Chatbot for Improving the Text Classification Data Quality 1

引用

15th International Conference on Interactive Mobile Communication, Technologies, and Learning, IMCL 2023

作者： Elzanfaly, Doaa S. Mohamed, Nada Amr Othman, Nermin Abdelhakim Department of Information Systems Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Informatics and Computer Science British University in Egypt Cairo Egypt

ISBN: (数字)9783031560750

ISBN: (纸本)9783031560743

The pandemic is affecting the global community in many ways. In most developing countries, there is a limitation in the detection facilities, which affect many suspected cases. This paper proposes a chatbot framework to assist and provide guide for the suspected/infected patients with COVID-19. Conversational software agents activated by natural language processing is known as chatbot, are an excellent example of such machine. Our COVID Bot is based on an integrated model between the rule-based model and the class classification model, having the rule-based model integrated with the MongoDB NoSQL database. Chatbot, using Natural Language Processing (NLP) and data mining techniques to assist patients by providing immediate answers for their questions. It also acts as a novel communication mean for impaired people for sharing knowledge and information, through conversing with them. Based on the literature review, this paper compared our methods with three classical classification algorithms: random forest, gradient boosting, and multi-layer perceptron (MLP). Experimental results show that our proposed chatbot greatly improves the classification performance, with IE-Net as 94.80%, 92.79% as recall, 92.97% as precision and 94.93% as AUC for distinguishing COVID-19 cases from non-COVID-19 cases (with only common clinical diagnose data). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Efficient Machine Learning Algorithms for DDoS Attack Detection 6

Efficient Machine Learning Algorithms for DDoS Attack Detect...

引用

6th International Conference on Computing and informatics, ICCI 2024

作者： Al-Eryani, Amal M. Hossny, Eman Omara, Fatma A. Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt

ISBN: (纸本)9798350373875

Distributed Denial of Service (DDoS) attack is a widely spread attack that posing a major threat to organizations dependent on online services. DDoS attacks aim to disrupt services by overwhelming servers with fake traffic from multiple sources. Early and effective detection of DDoS attacks is crucial for mitigating their impact. Recently, the most widely used algorithms for detecting DDoS are based-on Machine Learning (ML) and Deep learning (DL). The work in this paper focuses on providing a comparative study between recently ML algorithms that were tested using the CICDoS2019 dataset. The objective of this comparison is determining the most effective ML algorithm for DDoS detection. Based on the comparative study results, it is found that the Gradient Boosting (GB) and the XGBoost algorithms are extraordinarily accurate and correctly predicted the type of network traffic with 99.99% and 99.98% accuracy respectively, in addition to, a low false alarm rate of approximately 0.004 for GB. © 2024 IEEE.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

Improve the efficiency of network traffic in IoT using dimensionality reduction 6

Improve the efficiency of network traffic in IoT using dimen...

引用

6th International Conference on Computing and informatics, ICCI 2024

作者： Mohamed, Ibrahim Ali Wassif, Khaled Bayomi, Hanaa Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt

ISBN: (纸本)9798350373875

IoT has changed our lives through the increased convenience of automating mundane tasks, enhancing home security systems, wearable devices to improve health and wellness, and improved connectivity. Vast volumes of data, such as images, are generated and then transmitted to the cloud server for processing or storage. This leads to an increase in latency and network traffic. To address these challenges, the concept of edge computing has been introduced as a computing paradigm that moves processing closer to the point of data generation. This paper includes the merging of cloud and edge computing in the realm of IoT. It explores techniques that utilize machine learning, employing the autoencoder-based deep learning method and principal component analysis, to perform image dimensionality reduction at the edge. Subsequently, the encoded data is transmitted to the cloud server, where it can be directly utilized for various machine learning tasks without significantly impacting the accuracy of the processed data in the cloud. The suggested approach has been assessed on an image classification task using a set of 20,580 images from the ILSVRC dataset. The results indicate that a 61% reduction in data did not significantly affect the accuracy of the image classification task. © 2024 IEEE.

关键词： Edge computing

来源：评论

学校读者我要写书评

暂无评论

Efficient Key Management in Lightweight Cryptography: A New Hashing-Based Approach 8

Efficient Key Management in Lightweight Cryptography: A New ...

引用

8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Al-Mahmood, Haider H. Alsaad, Saad N. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Mustansiriyah Department of Computer Science College of Science Baghdad Iraq

ISBN: (纸本)9798350354423

Lightweight encryption methods are introduced to overcome the challenges of high-power consumption and high memory usage in the traditional cryptography system. The primary goal of the lightweight encryption technique is to make it suitable for use in devices provided with lower power and small memories. The importance of lightweight cryptosystem is its ability to provide suitable security for data encryption without consuming high level of power and without need for big memories. As the devices with lower battery life and supplied low memory become widespread, encryption key management problems start to emerge. This may lead to a high possibility of an attack on the encryption key and break the encryption system. In this context, this paper proposes a novel hashing- based approach for key generation in lightweight cryptography. The approach involves generating significant hash digits from a seed input to the hashing system, and then using hash code slicing to select different characters and numbers that can be used as a seed for the key generating system. The practical results, which demonstrate that a small seed length can effectively generate a large key length, are a testament to the effectiveness of the proposed approach. These results can be used in the encryption process to reduce the initial total bits, thereby enhancing the efficiency of the system. © 2024 IEEE.

关键词： Cryptographic Algorithms Encryption Key Management Hashing Techniques IoT Security Lightweight Cryptography

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：