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27th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD)

作者： Jin, Wei Ma, Yao Wang, Yiqi Liu, Xiaorui Tang, Jiliang Cen, Yukuo Qiu, Jiezhong Tang, Jie Shi, Chuan Ye, Yanfang Zhang, Jiawei Yu, Philip S. Michigan State Univ Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Comp Sci & Engn Dept E Lansing MI 48824 USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Florida State Univ Tallahassee FL 32306 USA Florida State Univ Comp Sci Tallahassee FL 32306 USA Univ Illinois Chicago IL USA Univ Illinois Comp Sci Chicago IL USA

ISBN: (纸本)9781450383325

Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years. In this tutorial, we systematically review the foundations, techniques, applications and advances in graph representation learning. We first introduce the foundations on graph theory and graph Fourier analysis. We then cover the key achievements of graph representation learning in recent years. Concretely, we discuss the six topics: 1) network embedding theories and systems;2) foundations of graph neural networks (GNNs);3) CogDL toolkit for GNNs;4) scalable GNNs;5) self-supervised learning in GNNs and 6) heterogeneous graphs and heterogeneous GNNs. Finally, we will introduce the applications of graph representation learning with a focus on recommender systems.

关键词： heterogeneous graphs graph neural networks graph representation learning

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An Ensemble of Multiple Boosting Methods Based on Classifier-specific soft Voting for Intelligent Vehicle Crash Injury Severity Prediction 6

An Ensemble of Multiple Boosting Methods Based on Classifier...

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6th IEEE international conference on Big Data computing Service and Applications (IEEE BigDataService)

作者： Liu, Lei Zhang, Xi Liu, Ye Zhu, Wangwang Zhao, Baixuan Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781728170220

To mitigate the crash injury severity of intelligent vehicles when faced with an emergency, it is critical to build an applicable crash injury severity model. An ensemble model (CSSV-AGX) of AdaBoost (Adaptive Boosting), GBDT (Gradient Boosting Decision Tree), and XGBoost (eXtreme Gradient Boosting) based on Classifier-specific soft Voting was proposed in this paper. Several key factors were analyzed to find out relationships between accident factors (for instance, speed) and distribution of different injury severity of occupants via our CSSV-AGX model. When faced with an emergency, our analysis will help intelligent vehicles to make appropriate decisions and therefore mitigate occupants' injury severity. A total of 28071 samples released by National Automotive Sampling System Crashworthiness Data System (NASS-CDS) were used in this study. Experimental results demonstrate that our CSSV-AGX model (50.96%) is superior to traditional machine learning methods (Support Vector machine (49.05%), Multi-layer Perceptron (48.22%), and Random Forest (49.96%)) in multi-classification problem by accuracy. By sensitivity analysis, we concluded that the vehicle speed, weight, and occupants' age, which are key factors to intelligent vehicles, have a great impact on crash injury severity.

关键词： Crash injury severity Classifier-specific soft Voting Traffic safety AdaBoost GBDT XGBoost Ensemble learning

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machine learning Approach for Mitigating Security Threats in IoT Environment

Machine Learning Approach for Mitigating Security Threats in...

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international conference on Inventive Research in computing Applications (ICIRCA)

作者： Harnit Saini Saroj Bala S.Jhansi Ida K. Suresh Kumar Shankerpally Swetha Divyabarathi P Department of CSE Ajay Kumar Garg Engineering College Ghaziabad Department of MCA Ajay Kumar Garg Engineering College Ghaziabad Department of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Department of CSE Kongu Engineering College Erode Department of Information Technology CVR College of Engineering Telangana Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode

In today's world, a huge amount of data and information is transferred through wireless networks. The Internet of Things (IoT) is a complex network that consists of diverse sensors, devices, and things, which often have limited resources and are susceptible to various security threats, including Distributed Denial of Service (DDoS) attacks. These networks are vulnerable to a variety of cyber threats and privacy issues. Intrusions are harmful activities that can damage a network. IoT networks are particularly vulnerable to security threats. The Bi-Layer Intrusion Detection Model (BIDM) identifies intrusions based on the optimal features selected in the Optimal Feature Vector Selection (OFVS) phase. This scheme not only prevents attacks, but also serves as a defense against legitimate threats. The proposed technique was evaluated using the KDD CUP 99 dataset, a well-known Intrusion Detection System (IDS) benchmark. The performance of the proposed scheme was further analyzed using the NSL-KDD and CICIDS-2017 datasets. The performance of OFVS was also investigated using the relatively new IoT Network Intrusion dataset. The implementation was carried out in Jupyter Notebook using the Python programming language. Matplotlib, Scikit-learn, pandas, and NumPy were some of the packages used in the implementation. The proposed framework achieved high accuracy rates, with NB at 97.4%, SVM at 96.1%, and DT at 98.1%. By utilizing this approach, the security of IoT networks can be significantly enhanced, and the risks associated with DDoS attacks can be mitigated.

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Operation-Aware soft Channel Pruning using Differentiable Masks 37

Operation-Aware Soft Channel Pruning using Differentiable Ma...

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international conference on machine learning (ICML)

作者： Kang, Minsoo Han, Bohyung Seoul Natl Univ Dept Elect & Comp Engn Comp Vis Lab Seoul South Korea Seoul Natl Univ ASRI Seoul South Korea

ISBN: (纸本)9781713821120

We propose a simple but effective data-driven channel pruning algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operations. The proposed approach makes a joint consideration of batch normalization (BN) and rectified linear unit (ReLU) for channel pruning;it estimates how likely the two successive operations deactivate each feature map and prunes the channels with high probabilities. To this end, we learn differentiable masks for individual channels and make soft decisions throughout the optimization procedure, which facilitates to explore larger search space and train more stable networks. The proposed framework enables us to identify compressed models via a joint learning of model parameters and channel pruning without an extra procedure of fine-tuning. We perform extensive experiments and achieve outstanding performance in terms of the accuracy of output networks given the same amount of resources when compared with the state-of-the-art methods.

关键词： Deep neural networks

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Application of Computer Vision and Deep learning in Breast Cancer Assisted Diagnosis 2019

Application of Computer Vision and Deep Learning in Breast C...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Gu Yunchao Yang Jiayao Beihang Univ Sch Comp Sci & Engineer 37 Xueyuan Rd Beijing Peoples R China Xianyang Rainbow Middle Sch 1 Rainbow Rd Xianyang Shaanxi Peoples R China

ISBN: (纸本)9781450366120

In the general process of breast cancer diagnosis, doctors mainly analyze and judge B-mode ultrasound images through vision, which depends heavily on doctors' operational experience and technical level. Artificial intelligence methods represented by machine learning algorithms have made rapid progress in recent years, especially natural image classification, target detection, semantics segmentation based on computer vision technology have been relatively mature, and have been widely used successfully in various fields. So as to improve the automation ability and reduce human errors, etc. By using artificial intelligence technology such as computer vision and in-depth learning, an automated method is established to diagnose breast cancer B-mode ultrasound images. This method can quickly strengthen the correct diagnostic rate of front-line medical staff and reduce the difference of operation level between urban and rural doctors. It has obvious medical needs and wide social significance.

关键词： Ultrasound imaging Deep learning Convolution neural network Target detection

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Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第5期18卷 1914-1923页

作者： Chen, Jiaojiao Jiao, Jianbo He, Shengfeng Han, Guoqiang Qin, Jing South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China Univ Oxford Dept Engn Sci Oxford OX1 2JD England Hong Kong Polytech Univ Dept Nursing Kowloon Hong Kong Peoples R China

Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases in hematoxylin and eosin (H&E) stained whole-slide images (WSI) is still challenging mainly due to two aspects. (1) The resolution of the image is too large. (2) lacking labeled training data. Whole-slide images generally stored in a multi-resolution structure with multiple downsampled tiles. It is difficult to feed the whole image into memory without compression. Moreover, labeling images for the pathologists are time-consuming and expensive. In this paper, we study the problem of detecting breast cancer metastases in the pathological image on patch level. To address the abovementioned challenges, we propose a few-shot learning method to classify whether an image patch contains tumor cells. Specifically, we propose a patch-level unsupervised cell ranking approach, which only relies on images with limited labels. The main idea of the proposed method is that when cropping a patch A from the WSI and further cropping a sub-patch B from A, the cell number of A is always larger than that of B. Based on this observation, we make use of the unlabeled images to learn the ranking information of cell counting to extract the abstract features. Experimental results show that our method is effective to improve the patch-level classification accuracy, compared to the traditional supervised method. The source code is publicly available at https://***/fewshot-camelyon.

关键词： Breast cancer Pathology Tumors machine learning Training data Task analysis Few-shot learning metastases classification unsupervised learning

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machine learning for Web Intrusion Detection: A Comparative Analysis of Feature Selection Methods mRMR and PFI 19th

Machine Learning for Web Intrusion Detection: A Comparative ...

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19th international conference on Artificial Intelligence and soft computing, ICAISC 2020

作者： Lucas, Thiago José Tojeiro, Carlos Alexandre Carvalho Pires, Rafael Gonçalves da Costa, Kelton Augusto Pontara Papa, João Paulo Department of Computing São Paulo State University Bauru Brazil College of Technology OurinhosSão Paulo Brazil

ISBN: (纸本)9783030614003

Select from the best features in a complex dataset that is a critical task for machine learning algorithms. This work presents a comparative analysis between two resource selection techniques: Minimum Redundancy Maximum Relevance (mRMR) and Permutation Feature Important (PFI). The application of PFI to the dataset in issue is unusual. The dataset used in the experiments is HTTP CSIC 2010, which shows great results with the mRMR observed in a related work[22]. Our PFI tests resulted in a selection of features best suited for machine learning methods and the best results for an accuracy of 97% with logistic regression and Bayes Point machine, 98% with Support Vector machine, and 99.9% using an artificial neural network. © 2020, Springer Nature Switzerland AG.

关键词： Feature Selection

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A New Mammography Lesion Classification Method Based on Convolutional Neural Network 2019

A New Mammography Lesion Classification Method Based on Conv...

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Wei, XinLei Ma, YiDe Wang, RunZe Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China

ISBN: (纸本)9781450366120

Breast cancer is a malignant tumor disease that is extremely high incidence among women around the world. And the cause of the disease is still unclear, the key to prevention and treatment of breast cancer is detection, diagnosis and treatment early. Mammography is the first choice for early diagnosis, however, limited experts have difficulty dealing with a large number of mammography images. Therefore, this paper is mainly devoted to the study of intelligent classification of mammography images, and applies the latest image processing method-Convolutional Neural Network (CNN) to study the classification of mammography images. In addition, we propose a new classification method of mammography images, which is to classify the lesions of normal (N), benign (B) and malignant (M) under different gland types.

关键词： image recognition deep learning mammography convolutional neural network computer-aided diagnosis

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soft computing for solar radiation potential assessment in Algeria

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international JOURNAL OF AMBIENT ENERGY 2020年第13期41卷 1524-1533页

作者： Guermoui, Mawloud Rabehi, Abdelaziz CDER Ctr Dev Energies Renouvelables URAER Unite Rech Appl Energies Renouvelables Ghardaia Algeria Ctr Univ Tissemsilt Inst Sci & Technol Dept Elect Tissemsilt Algeria Univ Djillali Liabes Sidi Bel Abbes Lab Microelect Appl Sidi Bel Abbes Algeria

Precise estimation of solar radiation is a highly required parameter for the design and assessment of solar energy applications. Over the past years, many machine learning techniques have been proposed in order to improve the forecasting performance using different input attributes. The aim of this study is the forecasting of one day ahead of horizontal global solar radiation using a set of meteorological and geographical inputs. In this respect, the Gaussian process regression methodology (GPR) and least-square support vector machine (LS-SVM) with different kernels are evaluated in order to select the most appropriate forecasting model. In order to assess the proposed models, the southern Algerian city, Ghardaia regions, was selected for this study. A historical data of five years (2013-2017) of meteorological data collected at Renewable Energies (URAER) in Ghardaia city are used. The achieved results demonstrate that all the proposed models give approximately similar results in terms of statistical indicators. In term of processing time, all the models showed acceptable computational efficiency with less computational costs of the GPR model among all machine learning models.

关键词： Solar energy forecasting Gaussian process regression (GPR) least-square support vector machine (LS-SVM)

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Predicting Absenteeism at Work Using Tree-Based Learners 2019

Predicting Absenteeism at Work Using Tree-Based Learners

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3rd international conference on machine learning and soft computing (icmlsc)

作者： Wahid, Zaman Satter, A. K. M. Zaidi Al Imran, Abdullah Bhuiyan, Touhid Daffodil Int Univ Dhaka Bangladesh Amer Int Univ Bangladesh Dhaka Bangladesh

ISBN: (纸本)9781450366120

Absenteeism at workplace acts as a crucial role in demonstrating the productive and profitable capacity of a company. Thus the knowledge of absenteeism of employees' becomes the foundation for an organization in its multiple dimensions. Because the proper determination of employees' profile allows the identification of excesses of occurrences of certain morbidities. The early absenteeism research primarily focused on predicting the characteristics and the categories of diseases of employees that make them perform higher absenteeism at workplace. However, predicting the absenteeism time of employees using different machine learning classifiers is able to give the researches a new dimension in line with the intention of revealing the underlying causes and patterns of absenteeism. In this paper, we have applied 4 prominent machine learning algorithms namely Decision Tree, Gradient Boosted Tree, Random Forest, and Tree Ensemble on the absenteeism dataset of a courier company in Brazil in order to predict the absenteeism time of employees at work as well as the best classifier. Based on the 7 evaluation metrics such as True Positive, True Negative, False Positive, False Negative, Sensitivity, Specificity, and Accuracy we found that Gradient Boosted Tree produced the best result with an accuracy rate of 82% whereas Tree Ensemble performed the lowest with the accuracy rate of 79%.

关键词： machine learning Classification Trees Absenteeism

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