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Attribute grouping-based naive Bayesian classifier

science China(Information sciences) 2025年第3期68卷 125-149页

作者： Yulin HE Guiliang OU Philippe FOURNIER-VIGER Joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) College of Computer Science & Software Engineering Shenzhen University

The naive Bayesian classifier(NBC) is a supervised machine learning algorithm having a simple model structure and good theoretical interpretability. However, the generalization performance of NBC is limited to a large extent by the assumption of attribute independence. To address this issue, this paper proposes a novel attribute grouping-based NBC(AG-NBC), which is a variant of the classical NBC trained with different attribute groups. AG-NBC first applies a novel effective objective function to automatically identify optimal dependent attribute groups(DAGs). Condition attributes in the same DAG are strongly dependent on the class attribute, whereas attributes in different DAGs are independent of one another. Then,for each DAG, a random vector functional link network with a SoftMax layer is trained to output posterior probabilities in the form of joint probability density estimation. The NBC is trained using the grouping attributes that correspond to the original condition attributes. Extensive experiments were conducted to validate the rationality, feasibility, and effectiveness of AG-NBC. Our findings showed that the attribute groups chosen for NBC can accurately represent attribute dependencies and reduce overlaps between different posterior probability densities. In addition, the comparative results with NBC, flexible NBC(FNBC), tree augmented Bayes network(TAN), gain ratio-based attribute weighted naive Bayes(GRAWNB), averaged one-dependence estimators(AODE), weighted AODE(WAODE), independent component analysis-based NBC(ICA-NBC), hidden naive Bayesian(HNB) classifier, and correlation-based feature weighting filter for naive Bayes(CFW) show that AG-NBC obtains statistically better testing accuracies, higher area under the receiver operating characteristic curves(AUCs), and fewer probability mean square errors(PMSEs) than other Bayesian classifiers. The experimental results demonstrate that AG-NBC is a valid and efficient approach for alleviating the attribute i

关键词： naive Bayesian classifier attribute independence assumption attribute grouping dependent attribute group posterior probability class-conditional probability

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SGformer:Boosting transformers for indoor lighting estimation from a single image

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Computational Visual Media 2024年第4期10卷 671-686页

作者： Junhong Zhao Bing Xue Mengjie Zhang Centre for Data Science and Artificial Intelligence&School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Estimating lighting from standard images can effectively circumvent the need for resourceintensive high-dynamic-range(HDR)lighting ***,this task is often ill-posed and challenging,particularly for indoor scenes,due to the intricacy and ambiguity inherent in various indoor illumination *** propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian(SG)distributions—a compact yet expressive lighting *** from previous approaches,we explore underlying local and global dependencies in lighting features,which are crucial for reliable lighting ***,we investigate the structural relationships spanning various resolutions of SG distributions,ranging from sparse to dense,aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component *** harnessing the synergy of local–global lighting representation learning and incorporating consistency constraints from various SG resolutions,the proposed method yields more accurate lighting estimates,allowing for more realistic lighting effects in object relighting and *** code and model implementing our work can be found at https://***/junhong-jennifer-zhao/SGformer.

关键词： lighting estimation transformer spherical Gaussian(SG) augmented reality

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Dynamic Resource Utilization Prediction Model for Cloud Datacenter

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IAENG International Journal of Applied Mathematics 2025年第2期55卷 307-331页

作者： Bliedy, Doaa Khafagy, Mohamed H. Badry, Rasha M. Department of Information Systems Faculty of Computers and Artificial Intelligence Fayoum University Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Fayoum University Egypt

Much research has been done on predicting resource utilization in the cloud to avoid over- and under-provisioning resources. Most existing systems focus on estimating the utilization of one or two resources at most, including memory, CPU, storage, network, or servers dedicated to cloud applications;they do not consider the correlation between resources. A maximum of one or two machine learning algorithms is employed for prediction purposes. Additionally, traditional prediction methods in the cloud provide a one-dimensional output. Most current solutions predict resources, such as memory and CPU utilization, as a single output. Unfortunately, a one-dimensional output in resource provision and usage cannot capture the relationship between the application requirements of different resources, such as CPU, memory, CPU cores, disk, and network;this results in incomplete and inaccurate information and prediction results. Efficient resource allocation and management require predicting several parameters using multivariate state variables. This study presents a multi-resource utilization prediction model that uses several machine learning approaches, such as support vector regression, random forests, MLP regression, neural networks (NN) using Adam and SGD optimizers, and decision tree regression. The prediction model is based on univariate and multivariate time series. Google cluster trace data is used to evaluate the work. Four experiments are executed on the dataset, seeking to predict the resources for different time series interval periods. The various algorithms’ mean absolute errors (MAE), root mean square error, mean absolute percentage error and R-squared are compared to determine which technique achieves the lowest error rate. The experimental results have shown that the prediction model yields better accuracy than previous research. © (2024), (International Association of Engineers). All rights reserved.

关键词： Prediction models

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Revolutionizing cardiovascular health: integrating deep learning techniques for predictive analysis of personal key indicators in heart disease

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Neural Computing and Applications 2025年第1期37卷 1-24页

作者： Talaat, Fatma M. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt

Cardiovascular diseases (CVDs) remain a global burden, highlighting the need for innovative approaches for early detection and intervention. This study investigates the potential of deep learning, specifically convolutional neural networks (CNNs), to improve the prediction of heart disease risk using key personal health markers. Our approach revolutionizes traditional healthcare predictive modeling by integrating CNNs, which excel at uncovering subtle patterns and hidden interactions among various health indicators such as blood pressure, cholesterol levels, and lifestyle factors. To achieve this, we leverage advanced neural network architectures. The model utilizes embedding layers to transform categorical data into numerical representations, convolutional layers to extract spatial features, and dense layers to model complex interactions and predict CVD risk. Regularization techniques like dropout and batch normalization, along with hyperparameter optimization, enhance model generalizability and performance. Rigorous validation against conventional methods demonstrates the model’s superiority, with a significantly higher R2 value of 0.994. This achievement underscores the model’s potential as a valuable tool for clinicians in CVD prevention and management. The study also emphasizes the need for interpretability in deep learning models and addresses ethical considerations to ensure responsible implementation in clinical practice. © The Author(s) 2024.

关键词： Blood pressure

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An optimized ensemble model based on meta-heuristic algorithms for effective detection and classification of breast tumors

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Neural Computing and Applications 2025年第6期37卷 4881-4894页

作者： Saber, Abeer Elbedwehy, Samar Awad, Wael A. Hassan, Esraa Information Technology Department Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Department of Data Science Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Damietta University Damietta34517 Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33511 Egypt

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively. © The Author(s) 2024.

关键词： Adversarial machine learning

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Feature-Grounded Single-Stage Text-to-Image Generation

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Tsinghua science and Technology 2024年第2期29卷 469-480页

作者： Yuan Zhou Peng Wang Lei Xiang Haofeng Zhang School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China

Recently,Generative Adversarial Networks(GANs)have become the mainstream text-to-image(T2I)***,a standard normal distribution noise of inputs cannot provide sufficient information to synthesize an image that approaches the ground-truth image ***,the multistage generation strategy results in complex T2I ***,this study proposes a novel feature-grounded single-stage T2I model,which considers the“real”distribution learned from training images as one input and introduces a worst-case-optimized similarity measure into the loss function to enhance the model's generation *** results on two benchmark datasets demonstrate the competitive performance of the proposed model in terms of the Frechet inception distance and inception score compared to those of some classical and state-of-the-art models,showing the improved similarities among the generated image,text,and ground truth.

关键词： text-to-image(T2I) feature-grounded single-stage generation Generative Adversarial Network(GAN)

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Enhancing Healthcare Service with Firebase Integration and Intelligent Chatbot Deployment 2

Enhancing Healthcare Service with Firebase Integration and I...

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Ghanem, Sahar I. Hamshary, Esraa Matar, Lamis Morsy, Adham Pharos University in Alexandria Computer Science and Artificial Intelligence Faculty Artificial Intelligence Department Egypt Pharos University in Alexandria Computer Science and Artificial Intelligence Faculty Computer Science Department Egypt

ISBN: (纸本)9798350362633

The deployment of an efficient Hospital Management System (HMS) is necessary to ensure efficient hospital administration and superior patient care in the constantly evolving healthcare environment. This study explores the significance of an integrated health management system (HMS) which includes a mobile application for patients to receive medication notifications, a website for nurses and doctors to manage patient data, firewall authentication for confidential access, an interface to track donations, and a chatbot to provide hospital information. This system enhances engagement in the community, guarantees data security, improves medication adherence, streamlines data management processes and renders hospital-related information easily accessible. Through the utilization of web-based and mobile technologies, this universal HMS considerably raises patient safety and satisfaction, optimizes hospital operations, and ultimately strengthens healthcare delivery altogether. © 2024 IEEE.

关键词： Remote patient monitoring

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Improved Runge Kutta Optimization Using Compound Mutation Strategy in Reinforcement Learning Decision Making for Feature Selection

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Journal of Bionic engineering 2024年第5期21卷 2460-2496页

作者： Jinpeng Huang Yi Chen Ali Asghar Heidari Lei Liu Huiling Chen Guoxi Liang Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou325035China School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehran***Iran College of Computer Science Sichuan UniversityChengdu610065China Department of Artificial Intelligence Wenzhou PolytechnicWenzhou325035China

Runge Kutta Optimization(RUN)is a widely utilized metaheuristic ***,it suffers from these issues:the imbalance between exploration and exploitation and the tendency to fall into local optima when it solves real-world opti-mization *** address these challenges,this study aims to endow each individual in the population with a certain level of intelligence,allowing them to make autonomous decisions about their next optimization *** incorporating Reinforcement Learning(RL)and the Composite Mutation Strategy(CMS),each individual in the population goes through additional self-improvement steps after completing the original algorithmic phases,referred to as *** is,each individual in the RUN population is trained intelligently using RL to independently choose three different differentiation strategies in CMS when solving different *** validate the competitiveness of RLRUN,comprehensive empirical tests were conducted using the IEEE CEC 2017 benchmark *** comparative experiments with 13 conventional algorithms and 10 advanced algorithms were *** experimental results demonstrated that RLRUN excels in convergence accuracy and speed,surpassing even some champion ***,this study introduced a binary version of RLRUN,named bRLRUN,which was employed for the feature selection *** 24 high-dimensional datasets encompassing UCI datasets and SBCB machine learning library microarray datasets,bRLRUN occupies the top position in classification accuracy and the number of selected feature subsets compared to some *** conclusion,the proposed algorithm demonstrated that it exhibits a strong competitive advantage in high-dimensional feature selection for complex datasets.

关键词： Runge Kutta optimization Metaheuristic algorithm Feature selection Reinforcement learning

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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