检索结果-内蒙古大学图书馆

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

computer Modeling in Engineering & Sciences 2024年第8期140卷 2035-2051页

作者： Sandeep Dasari Rajesh Kaluri School of Computer Science Engineering and Information Systems Vellore Institute of TechnologyVellore632014India

The increasing data pool in finance sectors forces machine learning(ML)to step into new *** data has significant financial implications and is *** users data from several organizations for various banking services may result in various intrusions and privacy *** a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global ***,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of *** address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global *** improves the privacy of the local *** analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)***,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and *** experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

关键词： Federated learning FedAvg FedProx flower framework privacy preservation financial sectors

来源：评论

学校读者我要写书评

暂无评论

Ontology-Based Business Processes Gap Analysis

引用

International Journal of Advanced computer Science and Applications 2025年第4期16卷 889-904页

作者： Ali, Abdelgaffar Hamed Ahmed Department of Computer Information Systems King Faisal University Al-hasa Saudi Arabia

Business processes are subject to change for quality reasons (i.e., efficiency). However, the gap analysis process is a preliminary and essential step in discovering the gap between the to-be and as-is business processes. It usually resorts to a nonstandard and manual analysis process, making it unpredictable and complex. This paper proposes a standard method based on ontology principles and the business process design methodology (DEMO). The ontology unifies the shared vocabulary among worlds of source and target business process to enable this sort of interoperability. Building an essential model is a core concept behind DEMO that provides an ontological view independent of realization and implementation issues and enables understanding of the enterprises’ behavior. Moreover, this paper provides heuristics for detecting gaps, based on the premise that producing similar institutional facts reflects similar behavior between the to-be and as-is business processes. Since the domains of the source and target are the same, it is also possible to compare the inputs of corresponding actions. The paper proposes a UML activity model for modeling business processes, enriched with DEMO concepts, to provide a foundational and informative ontology for reasoning about gaps. The expected outcome is a contribution to the broader community of business process management, ERP, and strategic planning, enabling more informed decision-making. © (2025), (Science and information Organization). All rights reserved.

关键词： Risk perception

来源：评论

学校读者我要写书评

暂无评论

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

引用

computers, Materials & Continua 2023年第5期75卷 4633-4648页

作者： Sameh Abd El-Ghany A.A.Abd El-Aziz Department of Information Systems College of Computer and Information SciencesJouf UniversityKSA Department of Information Systems Faculty of Computers and InformationMansoura UniversityEgypt Department of Information Systems and Technology Faculty of Graduate Studies for Statistical ResearchCairo UniversityEgypt

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart *** breakdown of coronary cardiovascular disease is one of the principal sources of death all over *** deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained *** heart diseases happen without apparent signs,high-level detection is *** paper proposed a robust and tuned random forest model using the randomized grid search technique to *** proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between ***,the healthcare industry has a lot of data but needs to gain more *** proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham *** datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection ***,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two ***,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)*** randomized grid search selected the best parameters and got the ideal CAD *** proposed framework was evaluated and compared with traditional *** proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

关键词： Coronary artery disease tuned random forest randomized grid search classifier

来源：评论

学校读者我要写书评

暂无评论

Social Media-Based Surveillance systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

引用

computer Modeling in Engineering & Sciences 2024年第5期139卷 1167-1202页

作者： Samina Amin Muhammad Ali Zeb Hani Alshahrani Mohammed Hamdi Mohammad Alsulami Asadullah Shaikh Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information System College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic *** review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their ***,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by *** paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic *** has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation *** recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM *** paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM ***,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

关键词： Social media epidemic machine learning deep learning health informatics pandemic

来源：评论

学校读者我要写书评

暂无评论

Electrocardiogram image classification for six classes of heart diseases

引用

Iran Journal of computer Science 2025年 1-21页

作者： Oke, Oluwafemi Ayotunde Cavus, Nadire Department of Computer Information Systems Near East University Nicosia 99138 Cyprus Computer Information Systems Research and Technology Centre Mersin Türkiye

Heart diseases are among the leading causes of death worldwide, as reported by the World Health Organization. Electrocardiograms (ECGs) are essential for diagnosing various heart conditions. While much of the existing research primarily focuses on accuracy as the key performance metric for ECG classification, this approach overlooks the critical importance of sensitivity, specificity, and F1-score, particularly when dealing with imbalanced datasets. In this study, we propose a hybrid model that integrates machine learning techniques (Support Vector Machines and Random Forest) with deep learning (VGG16) to classify six heart conditions: abnormal heart rhythm, atrial fibrillation, ischaemic heart disease, myocardial infarction, normal heart rhythm, and sinus bradycardia. We utilised a hybrid dataset consisting of 2848 ECG images sourced from both the Kaggle online database and a cardiac centre. The proposed model achieved state-of-the-art performance, attaining 95% accuracy, 99% specificity, 96% precision, 92% sensitivity, and 95% F1-score, thereby surpassing existing benchmarks such as MINA, ExpertRF, and CRNN from the PhysioNet Challenge dataset. These results underscore the model’s potential for facilitating early diagnosis, enabling cardiologists to deliver timely and personalised treatment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cardiac centre data set Deep learning Electrocardiogram (ECG) Hybrid dataset Hybrid model

来源：评论

学校读者我要写书评

暂无评论

Firefly-CDDL:A Firefly-Based Algorithm for Cyberbullying Detection Based on Deep Learning

引用

computers, Materials & Continua 2023年第4期75卷 19-34页

作者： Monirah Al-Ajlan Mourad Ykhlef Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhKSA

There are several ethical issues that have arisen in recent years due to the ubiquity of the Internet and the popularity of social media and community *** them is cyberbullying,which is defined as any violent intentional action that is repeatedly conducted by individuals or groups using online channels against victims who are not able to react *** alarmingly high percentage of people,especially teenagers,have reported being cyberbullied in recent years.A variety of approaches have been developed to detect cyberbullying,but they require time-consuming feature extraction and selection ***,no approach to date has examined the meanings of words and the semantics involved in *** past work,we proposed an algorithm called Cyberbullying Detection Based on Deep Learning(CDDL)to bridge this *** eliminates the need for feature engineering and generates better predictions than traditional approaches for detecting *** was accomplished by incorporating deep learning—specifically,a convolutional neural network(CNN)—into the detection *** this algorithm shows remarkable improvement in performance over traditional detection mechanisms,one problem with it persists:CDDL requires that many parameters(filters,kernels,pool size,and number of neurons)be set prior to *** parameters play a major role in the quality of predictions,but a method for finding a suitable combination of their values remains *** address this issue,we propose an algorithm called firefly-CDDL that incorporates a firefly optimisation algorithm into CDDL to automate the hitherto-manual trial-and-error hyperparameter *** proposed method does not require features for its predictions and its detection of cyberbullying is fully *** firefly-CDDL outperformed prevalent methods for detecting cyberbullying in experiments and recorded an accuracy of 98%within acceptable polynomial time.

关键词： Firefly optimization convolutional neural network(CNN) cyberbullying cyberbullying detection text classification

来源：评论

学校读者我要写书评

暂无评论

Accurate Phase Detection for ZigBee Using Artificial Neural Network

引用

Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

来源：评论

学校读者我要写书评

暂无评论

iSSL-AL: a deep active learning framework based on self-supervised learning for image classification

引用

Neural Computing and Applications 2024年第28期36卷 17699-17713页

作者： Agha, Rand Mustafa, Ahmad M. Abuein, Qusai Department of Computer Information Systems Jordan University of Science and Technology Irbid22110 Jordan

Deep neural networks have demonstrated exceptional performance across numerous applications. However, DNNs require large amounts of labeled data to avoid overfitting. Unfortunately, the labeled data may not be available;annotating large amounts of data is time-consuming, laborious, and requires human expertise, making it unfeasible to rely on manpower for annotation. One of the solutions to address this limitation is active learning (AL), a technique that utilizes unlabeled data while maintaining high performance. AL reduces the annotation cost of large datasets and enhances the training of models with fewer annotations. Uncertainty sampling has been proven to be one of the most effective strategies in AL;however, it lacks diversity. This research proposes iSSL-AL, a novel active learning framework that utilizes self-supervised learning (SSL) to ensure informative yet diverse samples. Three main aspects categorize the novelty of our work. The first is extending the margin uncertainty sampling by incorporating SSL to select informative and diverse points. The second is employing incremental learning for efficient training of the AL base classifier, where the model is trained incrementally in each AL cycle. The third is addressing the cold start problem, as our framework achieved high results in the early stages of training. Experiments show that iSSL-AL outperforms other state-of-the-art algorithms on the MNIST, FashionMNIST, and CIFAR-10 datasets, with accuracy scores of 99%, 98.9%, and 93.5%, respectively, effectively selecting diverse and informative samples. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Supervised learning

来源：评论

学校读者我要写书评

暂无评论

A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

引用

International Journal of Environmental Studies 2024年第2期81卷 661-674页

作者： Kalaivanan, K. Vellingiri, J. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches have been inadequate when water contamination increases. The paper reports an anomaly detection technique based on an autoencoder and an extreme learning machine (AEELM). This approach uses an autoencoder for feature selection and an ELM algorithm for classification purposes. It is estimated using the Cauvery River dataset. The autoencoder-based ELM techniques outperformed other models with 83% accuracy, 77% precision, 83% recall, and 83% F1-level characteristic performance indicators. These results demonstrate its great effectiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water quality

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing online shopping with FITMI: a realistic virtual try-on solution

引用

Neural Computing and Applications 2025年第8期37卷 6125-6144页

作者： Samy, Tassneam M. Asham, Beshoy I. Slim, Salwa O. Abohany, Amr A. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Damanhour University Damanhour Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

In today’s digital age, consumers increasingly rely on online shopping for convenience and accessibility. However, a significant drawback of online shopping is the inability to physically try on clothing before purchasing. This limitation often leads to uncertainty regarding fit and style, resulting in customer post-purchase dissatisfaction and higher return rates. Research indicates that online items are three times more likely to be returned than in-store ones, especially during the pandemic. To address this challenge, we propose a virtual try-on method called FITMI, an enhanced Latent Diffusion Textual Inversion model for virtual try-on purposes. The proposed architecture aims to bridge the gap between traditional in-store try-ons and online shopping by offering users a realistic and interactive virtual try-on experience. Although virtual try-on solutions already exist, recent advancements in artificial intelligence have significantly enhanced their capabilities, enabling more sophisticated and realistic virtual try-on experiences than ever before. Building on these advancements, FITMI surpasses ordinary virtual try-ons relying on generative adversarial networks, often producing unrealistic outputs. Instead, FITMI utilizes latent diffusion models to generate high-quality images with detailed textures. As a web application, FITMI facilitates virtual try-ons by seamlessly integrating images of users with garments from catalogs, providing a true-to-life representation of how the items would look. This approach differentiates us from competitors. FITMI is validated using two widely recognized benchmarks: the Dress-Code and Viton-HD datasets. Additionally, FITMI acts as a trusted style advisor, enhancing the shopping experience by recommending complementary items to elevate the chosen garment and suggesting similar options based on user preferences. © The Author(s) 2025.

关键词： Generative adversarial networks

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：