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A Comprehensive Study of Deep Learning and Machine Learning methods for Land Application and Cover Segmentation

A Comprehensive Study of Deep Learning and Machine Learning ...

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International conference on

作者： Kimmi Gupta Abhinav Raghav Heena Khera R. Srinivasan School of Computer Science Engineering & Technology Bennett University Greater Noida India IILM University Greater Noida India IILM University Greater Noida Galgotias University Greater Noida

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Research in the Delhi NCR area from 2000 to 2024 is compared in this study. Firstly, a flash flood occurred due to the heavy rain in July 2013. Grasslands are among the vegetative types in L ULC, which cannot be univocally classified as either natural in-land or artificial urban. But some areas of grassland, when it came time to classify L ULC, three different machine learning techniques were considered: random forest, support vector machine, and feedforward neural networks. Optimization The model best for random forest performed better than those two others. The study used statistical analysis tools, the “sustainable development prism” and “patch-corridor matrix” models, a landscape index, and time series data to explore how BGS changed in new urban areas. The research made use of Landsat datasets and optimized machine learning algorithms to classify L ULC, extract built-up areas, analyse landscape fragmentation and investigate built-up expansion in Delhi NCR by means of frequency analysis. In the EFPCNNM ensemble deep learning model, convolutional neural networks, feature pyramids, and ensemble learning are combined to increase the accuracy of remote sensing picture classification. The EFPCNNM process includes everything necessary for image analysis: pre-processing, feature extraction, feature pyramid creation, and ensemble learning. Exciting new possibilities for environmental monitoring, urban planning and property mapping emerge from this study. Sustainable urban development and resource management require effective monitoring and interpretation of the changes in L ULC, as well as employing remote sensing data alongside machine learning and deep learning technology.

关键词： Deep learning Grasslands Machine learning algorithms Feature extraction Classification algorithms Ensemble learning Data mining Convolutional neural networks Remote sensing Random forests

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A New Deep Fuzzy Based MSER Model for Multiple Document images Classification 3rd

A New Deep Fuzzy Based MSER Model for Multiple Document Imag...

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3rd International conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Biswas, Kunal Shivakumara, Palaiahnakote Sivanthi, Sittravell Pal, Umapada Lu, Yue Liu, Cheng-Lin Ayub, Mohamad Nizam Bin Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China National Laboratory of Pattern Recognition Institute of Automation of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy Beijing China

ISBN: (纸本)9783031090363

Understanding document images uploaded on social media is challenging because of multiple types like handwritten, printed and scene text images. This study presents a new model called Deep Fuzzy based MSER for classification of multiple document images (like handwritten, printed and scene text). The proposed model detects candidate components that represent dominant information irrespective of the type of document images by combining fuzzy and MSER in a novel way. For every candidate component, the proposed model extracts distance-based features which result in proximity matrix (feature matrix). Further, the deep learning model is proposed for classification by feeding input images and feature matrix as input. To evaluate the proposed model, we create our own dataset and to show effectiveness, the proposed model is tested on standard datasets. The results show that the proposed approach outperforms the existing methods in terms of average classification rate. © 2022, Springer Nature Switzerland AG.

关键词： image classification

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statistical Modeling of ISAR Imaging based on Bayesian Compressive Sensing of Pareto Distribution Family 5

Statistical Modeling of ISAR Imaging based on Bayesian Compr...

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5th International conference on Communication, image and Signal processing (CCISP)

作者： Xia, C. Y. Zhou, Z. L. Guo, Chun-Bo Hao, Y. S. Civil Aviat Adm China Res Inst 2 Chengdu 610041 Sichuan Peoples R China China Co Air Control Chengdu Peoples R China

ISBN: (纸本)9781728185897

The paper aims to compare the potential of two popular flexible laws in Pareto distribution family, the Exponentiated generalized Pareto distribution (exGPD) and the Gamma-ParetoIV distribution (GPIVD), for the statistical modeling of inverse synthetic aperture radar (ISAR) imaging data. From the perspective of enhancing compressibility and sparsity of the target echo, a novel model named GPIVD-prior-based Bayesian compressed sensing (GPIVCS) is deducted, the analysis proves that the sparsity and compressibility of the GPIVCS model are obtained, further improve the image quality. And from the perspective of reducing imaging complexity, a Bayesian compressed sensing method based on exGPD prior is proposed (EX-GPCS), compared with the conventional methods, the EX-GPCS method is capable of imaging targets in a short period of time. This paper gives a detailed formula derivation of the imaging process of the above two models. Finally, the state-of-the-art performance of these proposed methods is verified by experiments with a wide range of ISAR images.

关键词： Bayesian compressed sensing GPIVD GPD ISAR Sparsity and Compressibility

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CLUSTER KERNEL FOR LEARNING SIMILARITIES BETWEEN SYMMETRIC POSITIVE DEFINITE MATRIX TIME SERIES

CLUSTER KERNEL FOR LEARNING SIMILARITIES BETWEEN SYMMETRIC P...

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IEEE International conference on image processing (ICIP)

作者： Akodad, Sara Bombrun, Lionel Berthoumieu, Yannick Germain, Christian Univ Bordeaux Grp Signal & Image IMS CNRSUMR 5218 F-33405 Talence France

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163956

The launch of the last generation of Earth observation satellites has yield to a great improvement in the capabilities of acquiring Earth surface images, providing series of multi-temporal images. To process these time series images, many machine learning algorithms have been proposed in the literature such as warping based methods and recurrent neural networks (LSTM, ... ). Recently, based on an ensemble learning approach, the time series cluster kernel (TCK) has been proposed and has shown competitive results compared to the state-of-the-art. Unfortunately, it does not model the spectral/spatial dependencies. To overcome this problem, this paper aims at extending the TCK approach by modeling the time series of second-order statistical features (SO-TCK). Experimental results are conducted on different benchmark datasets, and land cover classification with remote sensing satellite time series over the Reunion Island.

关键词： Multivariate times series classification second-order statistics log-Euclidean remote sensing

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Meta-Learning Stationary stochastic Process Prediction with Convolutional Neural Processes 34

Meta-Learning Stationary Stochastic Process Prediction with ...

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34th conference on Neural Information processing Systems (NeurIPS)

作者： Foong, Andrew Y. K. Bruinsma, Wessel P. Gordon, Jonathan Dubois, Yann Requeima, James Turner, Richard E. Univ Cambridge London England Univ Cambridge Invenia Labs London England Univ Cambridge Microsoft Res London England

ISBN: (纸本)9781713829546

Stationary stochastic processes (SPs) are a key component of many probabilistic models, such as those for off-the-grid spatio-temporal data. They enable the statistical symmetry of underlying physical phenomena to be leveraged, thereby aiding generalization. Prediction in such models can be viewed as a translation equivariant map from observed data sets to predictive SPs, emphasizing the intimate relationship between stationarity and equivariance. Building on this, we propose the Convolutional Neural Process (ConvNP), which endows Neural Processes (NPs) with translation equivariance and extends convolutional conditional NPs to allow for dependencies in the predictive distribution. The latter enables ConvNPs to be deployed in settings which require coherent samples, such as Thompson sampling or conditional image completion. Moreover, we propose a new maximum-likelihood objective to replace the standard ELBO objective in NPs, which conceptually simplifies the framework and empirically improves performance. We demonstrate the strong performance and generalization capabilities of ConvNPs on 1D regression, image completion, and various tasks with real-world spatio-temporal data.

关键词： Random processes

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Method of Structural-statistical Coding of Video Segments in Spectral-Cluster Space

Method of Structural-Statistical Coding of Video Segments in...

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IEEE International conference on Advanced Trends in Information Theory (ATIT)

作者： Andrii Krasnorutsky Vitalii Kolesnyk Anatolii Berchanov Volodymyr Barannik Nataliia Kharchenko Oleksander Malko Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine State Research Institute of Armament and Military Equipment Testing and Certification Chernihiv Ukraine V. N. Karazin Kharkiv National University Kharkiv Ukraine Kharkov National University of Radio Electronics Kharkiv Ukraine National University of Civil Defence of Ukraine Kharkiv Ukraine

ISBN: (纸本)9798350332636

The article provides a general description of the problems of ensuring the necessary quality indicators of remote video service provision. The reason for this is the imbalance between the reliability of the received video information and time delays in the process of delivering video information over wireless communication channels. The destruction of existing wireless infocommunication technologies, which is the cause of disruption of the decision-making process in critical infrastructure systems, is briefly disclosed. It is proposed to solve the imbalance of video information delivery by using a set of technological solutions. The main component here is the use of technologies to reduce the bit rate of the video stream. The disadvantages of existing methods of reducing the bit rate of the video stream are substantiated. Among such disadvantages of the methods is that they have low efficiency: in terms of bit rate reduction and resistance to channel interference. It is proposed to solve the imbalance of reducing the bit intensity of the video stream by coding video data as part of integrated image processing technologies. Such technologies provide: additional consideration of new types of redundancy that are not related to the exclusion of psycho-visual redundancy; the construction of code structures with greater resistance to the negative impact of channel errors on the process of decoding video data. The technology of structural-statistical coding in the spectral-cluster space of the image transform is proposed. This technology is based on the principle of the duality of transformant components. The clustering of image transforms is proposed to be carried out in the binary space of their components. This creates conditions for simplifying the process of evaluating information characteristics and the formation of code values in binary space. As such information characteristics, it is proposed to use the number of series of units in the binary representation of the

关键词： Wireless communication Resistance image coding Codes Redundancy Bit rate Transforms

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Learning fractional white noises in neural stochastic differential equations 22

Learning fractional white noises in neural stochastic differ...

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Proceedings of the 36th International conference on Neural Information processing Systems

作者： Anh Tong Thanh Nguyen-Tang Toan Tran Jaesik Choi KAIST Johns Hopkins University VinAI Research Vietnam KAIST INEEJI

ISBN: (纸本)9781713871088

Differential equations play important roles in modeling complex physical systems. Recent advances present interesting research directions by combining differential equations with neural networks. By including noise, stochastic differential equations (SDEs) allows us to model data with uncertainty and measure imprecision. There are many variants of noises known to exist in many real-world data. For example, previously white noises are idealized and induced by Brownian motions. Nevertheless, there is a lack of machine learning models that can handle such noises. In this paper, we introduce a generalized fractional white noise to existing models and propose an efficient approximation of noise sample paths based on classical integration methods and sparse Gaussian processes. Our experimental results demonstrate that the proposed model can capture noise characteristics such as continuity from various time series data, therefore improving model fittings over existing models. We examine how we can apply our approach to score-based generative models, showing that there exists a case of our generalized noise resulting in a better image generation measure.

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International conference on Emerging Wireless Communication Technologies and Information Security, EWCIS 2020

International Conference on Emerging Wireless Communication ...

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International conference on Emerging Wireless Communication Technologies and Information Security, EWCIS 2020

ISBN: (纸本)9789813363922

The proceedings contain 41 papers. The special focus in this conference is on Emerging Wireless Communication Technologies and Information Security. The topics include: Modern Radar Topology for Bio-medical Applications;Ensuring Reliability in Vehicular Collision Avoidance Using Joint RFID and Radar-Based Vehicle Detection;dimensional Analysis and Gradation of Rice Grain Using image processing;feature Dimension Reduction for Efficient Classification of Dermoscopic images with Feature Fusion;feature Edge-Detail Preservation of Random-Valued Impulse Noise in images;sparse Auto-encoder Improvised Texture-Based statistical Feature Estimation for the Detection of Defects in Woven Fabric;Evaluation of ML-Based Sentiment Analysis Techniques with stochastic Gradient Descent and Logistic Regression;A Comparative Analysis of Sentiment Analysis Using RNN-LSTM and Logistic Regression;android-Based Assistance System for Visually Impaired Person Using Deep Learning and Augmented Reality;a Study of Black Hole Attacks in Delay Tolerant Network;towards Data Storage, Availability and Scalability with the Aid of Blockchain;countermeasures of Different Jamming Attacks in Wireless Sensor Networks;Detecting Acute Lymphoblastic Leukemia Through Microscopic Blood images Using CNN;blockchain Marketplace—A Novel Overview for Real-Time Implementation;applying Machine Learning Algorithms in Network-Based Intrusion Detection Systems;principal Component Analysis in Body Sensor Networks for Secure Data Transmission;quantum Inspired Multiobjective Optimization in Clustered Homogeneous Wireless Sensor Networks for Improving Network Lifetime and Coverage;A Review of Wireless Charging in WSN;vibration Measurement Using Accelerometer Sensor and Fast Fourier Transform;intelligent Fire Outbreak Detection in Wireless Sensor Networks;A Survey on Open-Source SCADA for Industrial Automation Using Raspberry Pi;lightweight Authenticated Encryption for Cloud-assisted IoT Applications.

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methods for Describing the Dynamics of Agricultural Machinery Processes 6th

Methods for Describing the Dynamics of Agricultural Machiner...

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6th International conference on Industrial Engineering, ICIE 2020

作者： Kazimirov, A.N. The South-Ural State University Lenin 76 Chelyabinsk454080 Russia

ISBN: (纸本)9783030548131

Agricultural machines are complex dynamic systems. The machine’s operation in a non-uniform environment can be considered as a reaction to input influences: field irregularities, ground resistance, and operating mode parameters. Improving technical means and technologies for soil cultivation, harvesting of agricultural crops improving the design parameters of machines requires correct problem setting and correctly selected methods of solving them. Usually two approaches are used in the study of dynamic processes-deterministic and statistical. It is shown that the complex nature of the dynamics of agricultural machines can be described by a nonlinear dynamic system with a limited number of independent variables. Experimental data on measuring the traction resistance of trailed agricultural machines: pick-up and comb-forming machines are presented. The measurements were carried out on the basis of the Ural testing center for agricultural machinery, and the measuring and computing system was used to register signals in digital form. As a result of mathematical data processing, the spectrum, probability distribution density, and dynamic (fractal) dimension of signals were obtained. Differences in the dynamic characteristics of agricultural machines are established. The value of the dynamic (fractal) dimension of the traction resistance of the comb-forming machine is equal to four. Four differential equations are required to describe the operation of this machine. Workflow optimization is based on a mathematical model that describes an agricultural machine as a dynamic system. The obtained results provide the basis for further work on describing the dynamics of agricultural machines. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Agricultural machines Chaotic dynamics Dynamic systems Fractal dimension stochastic dynamics Traction resistance

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stochastic model of siRNA endosomal escape mediated by fusogenic peptides

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Mathematical biosciences 2025年 387卷 109476页

作者： Nisha Yadav Jessica Boulos Angela Alexander-Bryant Keisha Cook School of Mathematical and Statistical Sciences Clemson University 220 Parkway Drive Clemson 29634 SC USA. Department of Bioengineering Clemson University 118 Engineering Service Drive Clemson 29634 SC USA. School of Mathematical and Statistical Sciences Clemson University 220 Parkway Drive Clemson 29634 SC USA. Electronic address: kcook@***.

Gene silencing via small interfering RNA (siRNA) represents a transformative tool in cancer therapy, offering specificity and reduced off-target effects compared to conventional treatments. A crucial step in siRNA-based therapies is endosomal escape, the release of siRNA from endosomes into the cytoplasm. Quantifying endosomal escape is challenging due to the dynamic nature of the process and limitations in imaging and analytical techniques. Traditional methods often rely on fluorescence intensity measurements or manual image processing, which are time-intensive and fail to capture continuous dynamics. This paper presents a novel computational framework that integrates automated image processing to analyze time-lapse fluorescent microscopy data of endosomal escape, hierarchical Bayesian inference, and stochastic simulations. Our method employs image segmentation techniques such as binary masks, Gaussian filters, and multichannel color quantification to extract precise spatial and temporal data from microscopy images. Using a hierarchical Bayesian approach, we estimate the parameters of a compartmental model that describes endosomal escape dynamics, accounting for variability over time. These parameters inform a Gillespie stochastic simulation algorithm, ensuring realistic simulations of siRNA release events over time. By combining these techniques, our framework provides a scalable and reproducible method for quantifying endosomal escape. The model captures uncertainty and variability in parameter estimation, and endosomal escape dynamics. Additionally, synthetic data generation allows researchers to validate experimental findings and explore alternative conditions without extensive laboratory work. This integrated approach not only improves the accuracy of endosomal escape quantification but also provides predictive insights for optimizing siRNA delivery systems and advancing gene therapy research.

关键词： Cancer Compartmental model Endosomal escape image processing Parameter estimation Peptides stochastic simulation algorithm

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