检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Jiang, Yuanyuan Ding, Rui Qiao, Tianchi Zhu, Yunan Han, Shi Zhang, Dongmei School of Statistics Renmin University of China Haidian District Beijing100872 China Microsoft Research Asia Haidian District Beijing100080 China School of Computer Science and Engineering Southeast University Jiangsu Province Nanjing211189 China School of Information Science and Technology University of Science and Technology of China Anhui Province He Fei230031 China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence Masdar City Abu Dhabi United Arab Emirates

Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable. In literature, Generalized Additive Model (GAM) is a standard for interpretability. However, due to the one-to-many and many-to-one phenomena which appear commonly in real-world scenarios, existing GAMs have limitations to serve predictive analytics in terms of both accuracy and training efficiency. In this paper, we propose FXAM (Fast and eXplainable Additive Model), a unified and fast interpretable model for predictive analytics. FXAM extends GAM’s modeling capability with a unified additive model for numerical, categorical, and temporal features. FXAM conducts a novel training procedure called Three-Stage Iteration (TSI). TSI corresponds to learning over numerical, categorical, and temporal features respectively. Each stage learns a local optimum by fixing the parameters of other stages. We design joint learning over categorical features and partial learning over temporal features to achieve high accuracy and training efficiency. We prove that TSI is guaranteed to converge to the global optimum. We further propose a set of optimization techniques to speed up FXAM’s training algorithm to meet the needs of interactive analysis. Thorough evaluations conducted on diverse data sets verify that FXAM significantly outperforms existing GAMs in terms of training speed, and modeling categorical and temporal features. In terms of interpretability, we compare FXAM with the typical post-hoc approach XGBoost+SHAP on two real-world scenarios, which shows the superiority of FXAM’s inherent interpretability for predictive analytics. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

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Plant Leaf Recognition Network Based on Feature learning and Metric learning 16th

Plant Leaf Recognition Network Based on Feature Learning and...

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16th International Conference on Intelligent Computing, ICIC 2020

作者： Yang, Hongwei Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhao, Zhongqiu Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuro-Science and Brain-Inspired Intelligence Ministry of Education Shanghai China College of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9783030607982

Plant image recognition is an important thing for protecting plants, protecting the environment, and protecting nature. Recently, most models in the field of plant leaf recognition make classification after extracting global features. In this paper, we propose a plant leaf recognition model based on metric learning. Metric learning calculates the similarity of the extracted feature vectors to obtain the distance between different sample features, so as to determine whether similar pictures belong to the same category, and then achieve the classification effect. In this study, feature triplet are used for metric learning, and the loss function we used is triplet-loss. © 2020, Springer Nature Switzerland AG.

关键词： Image recognition

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Encrypted machine learning of molecular quantum properties

arXiv

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arXiv 2022年

作者： Weinreich, Jan von Rudorff, Guido Falk von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria University of Vienna Vienna Doctoral School in Physics Boltzmanngasse 5 Vienna1090 Austria University Kassel Department of Chemistry Heinrich-Plett-Str.40 Kassel34132 Germany Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Large machine learning models with improved predictions have become widely available in the chemical sciences. Unfortunately, these models do not protect the privacy necessary within commercial settings, prohibiting the use of potentially extremely valuable data by others. Encrypting the prediction process can solve this problem by double-blind model evaluation and prohibits the extraction of training or query data. Unfortunately, contemporary ML models based on fully homomorphic encryption or federated learning are either too expensive for practical use or have to trade higher speed for weaker security. We have implemented secure and computationally feasible encrypted machine learning models using oblivious transfer enabling and secure predictions of molecular quantum properties across chemical compound space. However, we find that encrypted predictions using kernel ridge regression models are a million times more expensive than without encryption. This demonstrates a dire need for a compact machine learning model architecture, including molecular representation and kernel matrix size, that minimizes model evaluation costs. © 2022, CC BY.

关键词： Forecasting

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learning with Group Noise

arXiv

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arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： machine learning

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Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable artificial intelligence

arXiv

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arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Ghassemi, Navid Heras, Jonathan Ling, Sai Ho Beheshti, Amin Zhang, Yu-Dong Wang, Shui-Hua Alizadehsani, Roohallah Gorriz, Juan M. Acharya, U. Rajendra Rokny, Hamid Alinejad Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Mathematics and Computer Science University of La Rioja La Rioja Spain Australia Data Analytics Lab Department of Computing Macquarie University SydneyNSW2109 Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Deakin University VIC3217 Australia Department of Psychiatry University of Cambridge United Kingdom School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed. © 2022, CC BY-NC-ND.

关键词： Diseases

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Reversible adversarial information hiding based on the interpretation of neural network

引用

Digital Signal Processing: A Review Journal 2025年 165卷

作者： Li, Ruiping Wang, Xi Cui, Qingchen Li, Ming Information Construction and Management Office Henan Normal University Xinxiang453007 China School of Artificial Intelligence and Software Engineering Nanyang Normal University Nanyang473061 China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image Henan Nanyang473000 China College of Information Engineering Zhoukou Vocational College of Arts and Science Zhoukou466000 China College of Computer and Information Engineering Henan Normal University Xinxiang453007 China Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province Xinxiang453007 China

The emerging technology of adversarial information hiding can generate adversarial example by embedding useful information instead of meaningless noise into the host image. The obtained image can deceive classification models and therefore equip the function of privacy protection. However, existing research mainly focuses on the effectiveness of attack rather than preserving the integrity of the protected image. It cannot meet the requirements in the fields such as medical imaging, financial transactions, and copyright protection where users' raw data should not be damaged. Therefore, we propose a novel reversible adversarial information hiding based on interpretability of neural networks. The Grad-CAM-generated heatmap is employed to identify a minimal set of high-impact pixels for embedding, ensuring that minor modifications can induce significant misclassification. Then, the user-defined secret bits are embedded into the identified pixels by using difference expansion. After extraction, the original image can be perfectly restored. The technologies of adversarial example and reversible information hiding are combined to accommodate wider applications. The proposed method is tested on CIFAR-10 with three different neural architectures (NiN, AlexNet, ResNet). Experimental results show that the proposed method can completely restore the cover image while ensuring the extraction of the embedded data and misleading the neural network. © 2025 Elsevier Inc.

关键词： Adversarial attacks Adversarial information hiding Neural network interpretability Reversibility

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Robust motion averaging under maximum correntropy criterion

arXiv

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arXiv 2020年

作者： Zhu, Jihua Hu, Jie Lu, Huimin Chen, Badong Li, Zhongyu Lab of Vision Computing and Machine Learning School of Software Engineering Xi'an Jiaotong University Xi'an710049 China Kyushu Institute of Technology Kitakyushu Japan School of Artificial Intelligence Xian Jiaotong University Xi'an710049 China

Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem. This method aims to recover global motions from a set of relative motions, where the original method is sensitive to outliers due to using the Frobenius norm error in the optimization. Accordingly, this paper proposes a novel robust motion averaging method based on the maximum correntropy criterion (MCC). Specifically, the correntropy measure is used instead of utilizing Frobenius norm error to improve the robustness of motion averaging against outliers. According to the half-quadratic technique, the correntropy measure based optimization problem can be solved by the alternating minimization procedure, which includes operations of weight assignment and weighted motion averaging. Further, we design a selection strategy of adaptive kernel width to take advantage of correntropy. Experimental results on benchmark data sets illustrate that the new method has superior performance on accuracy and robustness for multi-view registration. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian distribution

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Correction to: GAMe-BiLSTM: a novel modified metaheuristic deep learning technique for non-ST-segment elevation myocardial infarction classification

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International Journal of Information Technology 2024年 1-1页

作者： Patil, Sachin Sambhaji Mohite-Patil, Tanajirao Bapuso Kalipindi, Murali Sarvani, Chalamalasetty P, Ramesh Babu Maheshwari, Shikha Department of Electronics and Communication Engineering School of Engineering and Sciences MIT Art Design and Technology University Pune India Department of Electronics and Telecommunication Engineering D. Y. Patil College of Engineering and Technology Kasaba Bawada Kolhapur India Department of Artificial Intelligence and Machine Learning Vijaya Institute of Technology for Women (Affiliated to JNTUK) Enikepadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science College of Engineering and Technology Wollega University Nekemte Ethiopia Centre for Distance & Online Education Manipal University Jaipur India

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Triples-to-Text Generation with Reinforcement learning Based Graph-augmented Structural Neural Networks

arXiv

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arXiv 2021年

作者： Gao, Hanning Wu, Lingfei Zhang, Hongyun Wei, Zhihua Hu, Po Xu, Fangli Long, Bo Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University 4800 Cao’an Road Shanghai201804 China *** Silicon Valley Research Center 675 E Middlefield Rd Mountain ViewCA94043 United States Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University School of Computer Central China Normal University Hubei Wuhan China Squirrel AI Learning NY United States *** Beijing China

Considering a collection of RDF triples, the RDF-to-text generation task aims to generate a text description. Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence. Nevertheless, these approaches fail to clearly model the local and global structural information between and within RDF triples. Moreover, the previous methods also face the non-negligible problem of low faithfulness of the generated text, which seriously affects the overall performance of these models. To solve these problems, we propose a model combining two new graph-augmented structural neural encoders to jointly learn both local and global structural information in the input RDF triples. To further improve text faithfulness, we innovatively introduce a reinforcement learning (RL) reward based on information extraction (IE). We first extract triples from the generated text using a pretrained IE model and regard the correct number of the extracted triples as the additional RL reward. Experimental results on two benchmark datasets demonstrate that our proposed model outperforms the state-of-the-art baselines, and the additional reinforcement learning reward does help to improve the faithfulness of the generated text. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

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PriBeL: A primary betel leaf dataset from field and controlled environment

引用

Data in Brief 2025年 61卷

作者： Gauri Mane Raghav Bhise Rutuja Kadam Gagandeep Kaur Devika Verma Rupali Chopade Gitanjali Shinde Ghanshyam G. Tejani Seyed Jalaleddin Mousavirad Department of Computer Science Engineering – Artificial Intelligence and Machine Learning Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science Engineering Vishwakarma Institute of Technology Pune India School of Engineering and Technology DES Pune University Pune India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai 600077 India Applied Science Research Center Applied Science Private University Amman 11937 Jordan Department of Computer and Electrical Engineering Mid Sweden University Sundsvall 851 70 Sweden

Essentially, visual identification of plant health is vital for research in agriculture and medicinal plants for important crops, both in terms of economics and pharmacology, such as betel leaves. The strong integration of AI-based methods in precision agriculture and herbal medicine quality control makes these systems effective only when trained on well-structured, diversified *** plant betel leaf (Piper betle) is cultivated throughout the world for its medicinal, cultural, and economic importance, but improper classification and quality assessment of this plant occur because of environmental conditions and variations in handling. To solve this problem, we hereby present the Betel Leaf Dataset, which is systematically curated, consisting of 1,800 high-resolution images (1080 × 1080 pixels) exhibiting the three different conditions of betel leaves: Healthy (Fresh), Diseased, and Dried. The dataset was collected from Veer, Taluka-Purandar, Pune, India, under both natural and controlled conditions so that different appearances could be ensured. Categories include images that have been taken under varied light, backgrounds, and orientations, which comprehensively can cover all real variations in betel leaves. Hence, this systematically collected, standardized, and accessible dataset can enhance agricultural research in leaf classification studies and quality assessment techniques to facilitate better documentation and understanding of betel leaf characteristics. This dataset can be utilized in machine learning applications for plant disease detection, precision agriculture, and automated quality control systems.

关键词： Agriculture Betel leaves Primary dataset On field Controlled environment

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