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A novel pixel-based deep neural network in posterior probability space for the detection of agriculture changes using remote sensing data

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Remote Sensing applications: Society and Environment 2025年 38卷

作者： Singh, Gurwinder Vyas, Narayan Dahiya, Neelam Singh, Sartajvir Bhati, Neha Sood, Vishakha Gupta, Dileep Kumar Department of Computer Application Chandigarh School of Business Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department Advanced Centre of Research & Innovation Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Application Vivekananda Global University Rajasthan Jaipur 303012 India Department of Computer Science Punjabi University Punjab Patiala 140307 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab 140401 India University Institute of Engineering Chandigarh University Gharuan Punjab 140413 India Department of Research and Development Flexxited Karnataka Bengaluru 560061 India Department of Civil Engineering Indian Institute of Technology Ropar 140001 India Department of Electronics School of Engineering Galgotias University Greater Noida Gautam Buddh Nagar Uttar Pradesh 203201 India

Agricultural land classification is a crucial and demanding task, essential for managing resources and tracking changes in farming activities. Remote sensing (RS) is an excellent technology for monitoring agricultural land and detecting seasonal fluctuations globally. Deep learning models offer promising prospects for crop monitoring. While traditional machine learning methods struggle to capture temporal variations in agricultural land efficiently. This study addresses the challenge of accurately classifying land cover and detecting year-to-year changes using a deep learning (DL)-based approach. The novelty of this research is an integration using a pixel-based deep neural network (PDNN) classifier, which will advance the classification abilities for identifying land cover classes. By comparing images taken over time, the PDNN can help identify different land cover efficiently. The thematic images using the PDNN were derived, and change detection was carried out by adopting a posterior probability space (PPS)-based change detection. The application of the proposed model is demonstrated using the Landsat-9 dataset over Moga District, Punjab, India. Compared to the random forest (RF) and support vector machine (SVM), the PDNN model achieved great performance. While PDNN had an accuracy ranging from 90.6 % to 93.6 %, RF and SVM had lower accuracies, with RF ranging between 86.8 % and 92.2 % and SVM between 88 % and 92.4 %. The PDNN model also excelled in detecting changes in land cover, showing an accuracy between 87.4 % and 90 %, while RF achieved 82.9 %–86.2 % and SVM ranged from 79 % to 83.9 %. The proposed model was adept at capturing changes in agricultural land cover, such as year-to-year variations. The PDNN model demonstrated superior proficiency in capturing seasonal and year-to-year variations in agricultural land cover, effectively identifying subtle transitions in crop cycles. This highlights its potential for long-term agricultural monitoring and precisio

关键词： Agriculture Change detection Landsat-9 Pixel-based Pixel-based deep neural network (PDNN)

Machine learning based calculation of refractive index of polyethylene glycol polymer

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Results in Chemistry 2025年 16卷

作者： Walid Abdelfattah Munthar Kadhim Abosaoda Hardik Doshi H.S. Shreenidhi Manoranjan Parhi Devendra Singh Prabhjot Singh Bilakshan Purohit Kamal Kant Joshi Ahmad Abumalek Department of Mathematics College of Science Northern Border University Arar Saudi Arabia College of pharmacy the Islamic University Najaf Iraq College of pharmacy The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Marwadi Universitly Research Center Department of Computer Engineering Faculty of Engineering & Technology Marwadi University Rajkot Gujarat India Assistant Professor Department of Computer Science and Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha 751030 India Department of Computer science & Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 Uttarakhand India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Department of Allied Science Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun Uttarakhand India Faculty of Engineering Balkh University Balkh Afghanistan

This study develops advanced machine learning algorithms to accurately predict the refractive index of polyethylene glycol (PEG) polymers using temperature and molecular weight as key input variables. A databank comprising 99 experimental measurements were utilized to train and assess multiple algorithms, including Extra Trees, Random Forests, Decision Trees, Ensemble Learning, AdaBoost, K-Nearest Neighbors, Multilayer Perceptron Artificial Neural Networks (MLP-ANN), Linear Regression, and Elastic Net. The dataset demonstrated a strong correlation between molecular weight and refractive index ( r = 0.64), and an inverse correlation with temperature ( r = −0.72). Model performance was assessed using R 2 , MSE, and AARE%, revealing that MLP-ANN, Linear Regression, and Elastic Net models yielded the highest accuracy on the testing dataset. Furthermore, SHAP analysis identified molecular weight as the dominant factor influencing refractive index predictions. The proposed approach significantly reduces the need for experimental measurements and offers a reliable, time-efficient solution for estimating the optical behavior of PEG polymers under varying conditions.

关键词： Machine learning Artificial intelligence Polyethylene glycol Refractive index Outlier detection SHAP analysis

Hypercomplex Low Rank Reconstruction for Nmr Spectroscopy with Cloud Computing ⋆

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SSRN

SSRN 2022年

作者： Guo, Yi Zhan, Jiaying Tu, Zhangren Zhou, Yirong Wu, Jianfan Hong, Qing Orekhov, Vladislav Qu, Xiaobo Guo, Di School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China Swedish NMR Center Department of Chemistry and Molecular Biology University of Gothenburg Gothenburg41390 Sweden

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. Multi-dimensional NMR offers a major improvement in resolution with multi-dimensional spectrum, but significantly increases data acquisition time and produces hypercomplex data that is difficult to be handled. To reduce this time, non-uniformly sampling can be applied to obtain undersampled data and using a reconstruction approach, such as the state-of-the-art low-rank method, to remove the spectral artifacts introduced by undersampling. However, only complex format of signal, including the real and imaginary parts, is considered in previous low-rank approach, which is less efficient when dealing with hypercomplex data that has multiple components. To deal with this problem, a hypercomplex low-rank model is proposed by introducing an adjoint matrix operation and then solved with a fast matrix factorization algorithm. This method efficiently uses redundant information among all the components of hypercomplex signal. Using simulated data and real protein data, we demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex NMR spectroscopy. Furthermore, an open-access and easy-to-use cloud-computing platform is developed for this method. © 2022, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

Improvement on the mechanical and thermal properties of polypropylene by modulating its crystallization behaviors

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Polymer Bulletin 2025年

作者： Xue, Yu Dun, Dongxing Gu, Yongjiang Sun, Wenbo Ma, Shuai Zhou, Hongfu Li, Jian Wang, Xiangdong Key Laboratory of Processing and Application of Polymeric Foams of China National Light Industry Council School of Light Industry Science and Engineering Beijing Technology and Business University Beijing100048 China Wanhua Chemical Group Co. LTD Yantai264000 China School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing100048 China

In order to enhance the mechanical and thermal properties of polypropylene (PP) and expanded its application potential in the automobile industry and the home appliance manufacturing industry, this study examined the impact of α-nucleating agent addition on the crystalline properties of PP specimens. Isothermal and non-isothermal crystallization kinetics were utilized for analysis, alongside an analysis of crystallization behavior, mechanical properties, and heat distortion temperatures of the PP specimens. The findings revealed that the incorporation of α-nucleating agent significantly increased the crystallization temperature of PP from 123.32 to 130.45 °C, enhancing the crystallization rate. The flexural modulus of the modified PP specimens significantly increased from 839.26 to 1499.4 MPa, marking a 78.7% improvement, while the heat deflection temperature rosed from 83 to 124 °C, a 49.4% increase. This research provides an effective and practical approach for producing PP with elevated modulus and heat deflection temperature. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Heating temperature

QCluster: Clustering Packets for Flow Scheduling

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arXiv 2020年

作者： Yang, Tong Wang, Hao Li, Jizhou Jiang, Jie Zhao, Yikai Zhang, Yinda Yang, Kaicheng Zhang, Nicholas School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Peking University China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Huawei Theory Lab China

Flow scheduling is crucial in data centers, as it directly influences user experience of applications. According to different assumptions and design goals, there are four typical flow scheduling problems/solutions: SRPT, LAS, Fair Queueing, and Deadline-Aware scheduling. When implementing these solutions in commodity switches with limited number of queues, they need to set static parameters by measuring traffic in advance, while optimal parameters vary across time and space. This paper proposes a generic framework, namely QCluster, to adapt all scheduling problems for limited number of queues. The key idea of QCluster is to cluster packets with similar weights/properties into the same queue. QCluster is implemented in Tofino switches, and can cluster packets at a speed of 3.2 Tbps. To the best of our knowledge, QCluster is the fastest clustering algorithm. Experimental results in testbed with programmable switches and ns-2 show that QCluster reduces the average flow completion time (FCT) for short flows up to 56.6%, and reduces the overall average FCT up to 21.7% over state-of-the-art. All the source code in ns-2 is available in Github [45]. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

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arXiv 2022年

作者： Liu, Jia Xuan, Wenjie Gan, Yuhang Zhan, Yibing Liu, Juhua Du, Bo Research Center for Graphic Communication Printing and Packaging Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Institute of Artificial Intelligence Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China JD Explore Academy Beijing China

Change detection is a crucial but extremely challenging task in remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods try to elaborately design complicated neural networks with powerful feature representations. However, they ignore the universal domain shift induced by time-varying land cover changes, including luminance fluctuations and seasonal changes between pre-event and post-event images, thereby producing suboptimal results. In this paper, we propose an end-to-end supervised domain adaptation framework for cross-domain change detection named SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions. Specifically, our SDACD presents collaborative adaptations from both image and feature perspectives with supervised learning. Image adaptation exploits generative adversarial learning with cycle-consistency constraints to perform cross-domain style transformation, which effectively narrows the domain gap in a two-side generation fashion. As for feature adaptation, we extract domain-invariant features to align different feature distributions in the feature space, which could further reduce the domain gap of cross-domain images. To further improve the performance, we combine three types of bi-temporal images for the final change prediction, including the initial input bi-temporal images and two generated bi-temporal images from the pre-event and post-event domains. Extensive experiments and analyses conducted on two benchmarks demonstrate the effectiveness and generalizability of our proposed framework. Notably, our framework pushes several representative baseline models up to new State-Of-The-Art records, achieving 97.34% and 92.36% on the CDD and WHU building datasets, respectively. The source code and models are publicly available at https://***/Perfect-You/SDACD. Copyright © 2022, The Aut

关键词： Change detection

Where to look and how to describe: Fashion image retrieval with an attentional heterogeneous Bilinear network

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arXiv 2020年

作者： Su, Haibo Wang, Peng Liu, Lingqiao Li, Hui Li, Zhen Zhang, Yanning School of Computer Science Northwestern Polytechnical University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China School of Computer Science University of Adelaide Australia MinSheng FinTech Corp. Ltd China

Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extract both appearance (i.e., "how to describe") and localization (i.e., "where to look") information, and their interactions. To this end, we propose a biologically inspired framework for image-based fashion product retrieval, which mimics the hypothesized two-stream visual processing system of human brain. The proposed attentional heterogeneous bilinear network (AHBN) consists of two branches: a deep CNN branch to extract fine-grained appearance attributes and a fully convolutional branch to extract landmark localization information. A joint channel-wise attention mechanism is further applied to the extracted heterogeneous features to focus on important channels, followed by a compact bilinear pooling layer to model the interaction of the two streams. Our proposed framework achieves satisfactory performance on three image-based fashion product retrieval benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Image retrieval

Grand Challenge on Respiratory Sound Classification for SPRSound Dataset

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Grand Challenge on Respiratory Sound Classification for SPRS...

2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022

作者： Zhang, Qing Zhang, Jing Yuan, Jiajun Huang, Huajie Zhang, Yuhang Zhang, Baoqin Lv, Gaomei Lin, Shuzhu Wang, Na Liu, Xin Tang, Mingyu Wang, Yahua Ma, Hui Liu, Lu Yuan, Shuhua Zhou, Hongyuan Zhao, Jian Li, Yongfu Yin, Yong Zhao, Liebin Wang, Guoxing Lian, Yong Shanghai Jiao Tong University Department of Micro-Nano Electronics MoE Key Lab of Artificial Intelligence China Shanghai Jiao Tong University School of Medicine Pediatric Ai Clinical Application and Research Center Shanghai Children's Medical Center China Shanghai Jiao Tong University School of Medicine Shanghai Children's Medical Center Department of Respiratory Medicine China China Shanghai University School of Computer Engineering and Science China Sanya Maternity and Child Care Hospital China Taicang Affiliated Hospital of Soochow University The First People's Hospital of Taicang Department of Paediatrics China Linyi City People Hospital China Fengcheng Hospital Department of Paediatrics China Linyi City Maternal Child Health Hospital China Fifth Affiliated Hospital of Harbin Medical University Department of Pediatrics China Shanghai Tuoxiao Intelligent Technology Co. Ltd China

ISBN: (数字)9781665469173

ISBN: (纸本)9781665469173

It is important to continuously monitor our respiratory system to prevent us from suffering respiratory-releated diseases. This demands for an automatic respiratory sounds software to speed up diagnosis and to reduce the workload of physicians. In the IEEE BioCAS 2022 conference, we have organized the first grand challenge on respiratory sound classification using the paediatric respiratory sound (SPRSound). This event has invited 45 teams with more than 100 open source entries and the top 5 teams are invited to present their works in the IEEE BioCAS 2022. © 2022 IEEE.

关键词： Open source software

Inter-slice context residual learning for 3D medical image segmentation

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arXiv 2020年

作者： Zhang, Jianpeng Xie, Yutong Wang, Yan Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China

Automated and accurate 3D medical image segmentation plays an essential role in assisting medical professionals to evaluate disease progresses and make fast therapeutic schedules. Although deep convolutional neural networks (DCNNs) have widely applied to this task, the accuracy of these models still need to be further improved mainly due to their limited ability to 3D context perception. In this paper, we propose the 3D context residual network (ConResNet) for the accurate segmentation of 3D medical images. This model consists of an encoder, a segmentation decoder, and a context residual decoder. We design the context residual module and use it to bridge both decoders at each scale. Each context residual module contains both context residual mapping and context attention mapping, the formal aims to explicitly learn the inter-slice context information and the latter uses such context as a kind of attention to boost the segmentation accuracy. We evaluated this model on the MICCAI 2018 Brain Tumor Segmentation (BraTS) dataset and NIH Pancreas Segmentation (Pancreas-CT) dataset. Our results not only demonstrate the effectiveness of the proposed 3D context residual learning scheme but also indicate that the proposed ConResNet is more accurate than six top-ranking methods in brain tumor segmentation and seven top-ranking methods in pancreas segmentation. Code is available at: https://***/ConResNet © 2020, CC-BY-NC-SA.

关键词： Mapping