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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Zuo, Xiuji Guo, Xin Yin, Zilong Tseng, Shih-Pang School of Information Science and Technology Sanda University Shanghai201209 China Department of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China School of Software and Big Data Changzhou College of Information Technology Changzhou213164 China

ISBN: (纸本)9789819994113

With the development of science and technology, the application of data mining in medical field is becoming more and more popular. Machine learning methods also plays an important role in disease prediction. Stroke is characterized by high incidence rate, high disability rate, high mortality rate and high recurrence rate, and it is also likely to cause other kinds of complications. In this paper, each feature in the stroke dataset was analyzed in order to find out the factors affecting stroke and conducts classification and prediction research on whether there is a disease risk. Specifically, the PCA (Principal Component Analysis) algorithm is used to extract the main feature components of data, the SMOTE (Synthetic Minority Oversampling Technique) algorithm is used to adjust imbalanced feature categories. Traditional machine learning classification algorithms, such as decision tree, SVM(support vector machines), and various ensemble learning algorithms are used for the prediction of stroke risk, so as to study the relationship between stroke disease and each feature, and the classification prediction model, so that we can prevent strokes in time and reduce the risk of stroke. Among all the models, Bagging (Bootstrap aggregating) has the best performance with an ROC value of 0.97. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Principal component analysis

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The Application of big data and AI Analytics in Rating Industrial and Smart Manufacturing Districts: Challenges and Opportunities 3

The Application of Big Data and AI Analytics in Rating Indus...

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3rd International Conference on big data, Information and Computer Network, BDICN 2024

作者： Zhou, Chenchen Wang, Wei Tian, Maohong Liang, Jian Zhang, Dequan Li, Hualin School of Software and Artificial Intelligence Chongqing Institute of Engineering Chongqing China Intelligent Application of Financial Big Data Chongqing Colleges Universities Engineering Research Center Chongqing China Shu Yi Xin Credit Management Co LTD Chongqing China

ISBN: (纸本)9798350363821

As an important carrier and component of industrial clusters, industrial districts are an important way for regional economic growth, and the economic effects of industrial districts have attracted more and more attention. Therefore, how to identify high-quality districts with good development trends and obvious competitive advantages from a large amount of district information has become an increasingly important topic. This article takes industrial and intelligent manufacturing districts as an example and proposes an innovative industrial and intelligent manufacturing zone rating method that combines rule-based modeling, Analytic Hierarchy Process (AHP), and Artificial Intelligence (AI) models. Collect structured and unstructured data, including real-Time operational data, historical records, market trends, and expert opinions, from multiple sources through big data technology. Apply rule-based models and Analytic Hierarchy Process (AHP) to prioritize and weight various factors that affect regional performance, and obtain district ratings;And use the Kmeans method for clustering to obtain district level classification. Finally, the paper combines the outputs of the rule-based models, AHP, and AI models to create an overall rating system. This system continuously monitors district performance, providing accurate and comprehensive ratings to support decision-making in industry and government. © 2024 IEEE.

关键词： Smart manufacturing

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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Explainable Deep Learning for Coffee Leaf Disease Classification in Smart Agriculture: A Visual Approach 2

Explainable Deep Learning for Coffee Leaf Disease Classifica...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Mridha, Krishna Tola, Fistum Getachew Khalil, Ibrahim Jakir, Sheikh Md. Jamiul Wilfried, Pieboji Noubissie Priyok, Masrur Ahsan Shukla, Madhu Marwadi University Computer Engineering Rajkot India Marwadi University Computer Engineering - Artificial Intelligence Rajkot India Tianjin University Software Engineering China Marwadi University Computer Engineering Artificial Intelligence & Big Data Rajkot India

ISBN: (纸本)9798350347456

In recent years, the application of deep learning techniques for plant disease classification has become increasingly important for smart agriculture. Early classification and treatment of plant diseases are crucial for maintaining the quality and quantity of crops, and deep learning algorithms have the potential to provide accurate and efficient solutions to this problem. In this study, we used two datasets collected from Mendeley for coffee leaf disease classification. The first dataset contained two classes of images, while the second dataset contained the remaining three types of images. The two datasets were combined to create a more robust dataset for training the two-transfer learning and CNN model. We update the pre-trained model MobileNet and RestNet50 by adding a flattened and dense layer to classify the five types of coffee diseases. The model was trained using 100 epochs, and the performance was evaluated using evaluation metrics such as accuracy and loss error for training and validation. The best accuracy was obtained from ResNet50, then MobileNet, and then CNN. The accuracy is 100% for Proposed ResNet50, 98% for CNN, and 99% for MobileNet. To understand the affected areas and help farmers comprehend the reason for the prediction, we used visualization techniques such as Grad-CAM and Grad-CAM++. These techniques generated heat maps of the regions of interest, highlighting the areas that contributed most to the classification decision. Our study highlights the potential of using deep learning techniques for accurate and efficient plant disease detection. By automating the disease classification process, farmers can identify and treat diseases early, leading to better crop yields and quality. The results of our study demonstrate the effectiveness of the proposed method and can be used to develop efficient and cost-effective solutions for plant disease detection. © 2023 IEEE.

关键词： Convolutional neural networks

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SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification 24

SAM-MIL: A Spatial Contextual Aware Multiple Instance Learni...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Fang, Heng Huang, Sheng Tang, Wenhao Huangfu, Luwen Liu, Bo School of Big Data & Software Engineering Chongqing University Chongqing China Fowler College of Business San Diego State University San DiegoCA United States School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current MIL models predominantly rely on instance-level features derived from pretrained models such as ResNet. These models segment each WSI into independent patches and extract features from these local patches, leading to a significant loss of global spatial context and restricting the model's focus to merely local features. To address this issue, we propose a novel MIL framework, named SAM-MIL, that emphasizes spatial contextual awareness and explicitly incorporates spatial context by extracting comprehensive, image-level information. The Segment Anything Model (SAM) represents a pioneering visual segmentation foundational model that can capture segmentation features without the need for additional fine-tuning, rendering it an outstanding tool for extracting spatial context directly from raw WSIs. Our approach includes the design of group feature extraction based on spatial context and a SAM-Guided Group Masking strategy to mitigate class imbalance issues. We implement a dynamic mask ratio for different segmentation categories and supplement these with representative group features of categories. Moreover, SAM-MIL divides instances to generate additional pseudo-bags, thereby augmenting the training set, and introduces consistency of spatial context across pseudo-bags to further enhance the model's performance. Experimental results on the CAMELYON-16 and TCGA Lung Cancer datasets demonstrate that our proposed SAM-MIL model outperforms existing mainstream methods in WSIs classification. Our open-source implementation code is is available at https://***/FangHeng/SAM-MIL. © 2024 ACM.

关键词： Self-supervised learning

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The value of the FTO gene in breast cancer treatment and prevention: a systematic review with Meta-analysis 24

The value of the FTO gene in breast cancer treatment and pre...

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3rd International Conference on Public Health and data Science, ICPHDS 2024

作者： Li, Zheng Gu, Fengming Zhang, Chengan School of Public Health Chengdu University of Traditional Chinese Medicine Sichuan Chengdu China College of Light Industry Science and Engineering Sichuan University Sichuan Chengdu China Chongqing University College of Big Data and Software Chongqing China

ISBN: (纸本)9798400711671

Background: The FTO gene has been shown to be significantly associated with obesity and body fat content, and Recent studies have suggested that it may be strongly associated with a variety of cancers, including breast cancer, by affecting obesity. The aim of this study was to systematically evaluate the role of the FTO gene in the prevention and treatment of breast cancer and to determine the association between the two by meta-analysis. Method: Relevant literature published between 2019 and January 20, 2024 in five online databases including pubmed and others were systematically searched in this study. Estimates of the association between FTO rs9939609 and breast cancer were determined by Meta-analysis and expressed using Odds ratio (OR) and its 95% confidence interval (95% CI). Results: A total of 16 STUDIES, INCLUDING 6, 039 subjects, were included in this study. Seven FTO-related SNPs were involved in the association between FTO and BC. The overall study quality was moderately low. Meta-analysis showed that FTO rs9939609 all comparison groups (including heterozygous, pure, dominant and recessive) were in significantly associated with BC. Conclusion: The FTO gene can be used as a screening indicator for breast cancer risk, and its use as a target for breast cancer treatment is feasible. More research is needed to support its application to the treatment of breast cancer as well as its prevention in practice. © 2024 Copyright held by the owner/author(s).

关键词： breast cancer

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C.M. School of Big Data & Software Engineering Chongqing University Chongqing400000 China School of Automation Chongqing University of Posts and Telecommunications Chongqing400065 China College of Intelligence and Computing Tianjin University 300072 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. Our code is available here. © 2002-2012 IEEE All rights reserved.

关键词： Federated learning

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture 26

A Graph Sequence Generator and Multi-head Self-attention Mec...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wu, Yuejia Zhou, Jian-Tao College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center Intelligent Info. Proc. Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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Discovering All-chain Set with Direction and Graduality Characteristics over Streaming Time Series 19

Discovering All-chain Set with Direction and Graduality Char...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Wang, Shaopeng Feng, Chunkai Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Department of Software Engineering Hohhot China

ISBN: (纸本)9798350346558

Since its introduction over five years ago, time series chain has become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. Recent work has generalized the definition of time series chain, and introduced a novel definition of time series chain with directionality and graduality characteristics (TSC-DG) which can significantly enhance both robustness and usability of the original time series chain. However, previous studies on TSCDG process fixed-length time series. In this work, we focus on the issue of all-chain set with direction and graduality characteristics (all-TSCS-DG) mining over streaming time series for the first time, where all-TSCS-DG is the core of current TSCDG researches. We propose an improved Naive algorithm (IN) to solve this problem. Compared to the Naive, the IN guarantees the same space costs and results firstly, secondly is the IN takes two additional optimal strategies to further improve the time efficiency. The basic ideas of these two strategies are both incremental computing. The first one can make the IN update the IB structure at each time-tick incrementally, where the IB is an important data structure that is used to obtain the all-TSCS-DG. The second one makes the IN obtain mining results at current time-tick based on the ones at the last time-tick incrementally. Extensive experiments on real dataset demonstrate the efficiency and effectiveness of the IN. © 2022 IEEE.

关键词： Time series

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