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Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

Constructing Conditional Random Fields for Automated Land Co...

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2024 international conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Prabha, S. Singh, Vaishali Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels. © 2024 IEEE.

关键词： category Conditional evaluating hyper spectral schooling

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MLP-FOX: A Metaheuristic Approach-Based Training Multilayer Perceptron for Data Classification 1

MLP-FOX: A Metaheuristic Approach-Based Training Multilayer ...

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1st international conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Mohapatra, Swagatika Panda, Madhumita Sarangi, Partha Pratim Mishra, Bhabani Shankar Prasad Parihar, Nikhil Sahoo, Biswajit School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Trident Academy of Technology Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350361780

Training a Multilayer Perceptron (MLP) using metaheuristic optimization algorithms serves as a benchmark problem for demonstrating the effectiveness of emerging metaheuristic optimizers. In this study, we explore the efficacy of a novel metaheuristic optimizer namely, Fox algorithm, to optimize the weights and biases of an MLP. However, backpropagation algorithm is a traditional and well-known gradient-based optimizer is commonly used in training MLP, may become trap in local minima in many cases due to complex multimodal training error curves. This issue can diminish the classification accuracy. To overcome this problem, stochastic optimization algorithms play a vital role in improving error convergence and generalization capability of the MLP. In our work, exhaustive experimentation and analysis has been performed using five standard classification medical datasets to show the effectiveness of the Fox algorithm. The experimental results compare with backpropagation (generalized delta rule) and three recently proposed metaheuristic optimizers to evaluate its capability to converge to optimal solutions and enhance the overall performance of the MLP. Based on error convergence rate, accuracy, and F1-score, the proposed approach reveals promising classification performance compared to competitors. © 2024 IEEE.

关键词： Multilayer neural networks

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Smart Connected Future with Millimeter Waves: Challenges and technology Directions 8

Smart Connected Future with Millimeter Waves: Challenges and...

引用

8th international conference on Computers and Devices for Communication, CODEC 2023

作者： Choudhury, Debabani Intel Corporation Santa ClaraCA United States

ISBN: (纸本)9798350317176

The convergence of communication and sensing with AI, security, high-performance computing, human-machine interfaces, is creating new technological paradigms. The advent of new applications calls for complex wireless channels with intelligence and flexibility enabling instantaneous exchange of information. This presentation discusses next generation multi-modal connected future use cases and addresses the millimeter wave challenges, prospects and technology directions. © 2023 IEEE.

关键词： Beamforming

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Bi-directional GRU-Based Approach for Multi-Class Text Error Identification System 9

Bi-directional GRU-Based Approach for Multi-Class Text Error...

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9th IEEE international conference for convergence in technology, I2CT 2024

作者： Reeha, Shaik Nikith, Boda Venkata Mani Prakash Reddy, G. Pati, Peeta Basa Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

In today's highly competitive academic and research landscape, the impression authors leave on readers is paramount to their reputation and success. Spelling errors can significantly undermine an author's credibility and negatively impact their standing within the scholarly community. To mitigate this risk, it is imperative that authors thoroughly proofread their content before submitting it to prestigious conference journals or sharing it with fellow researchers. This paper introduces a novel solution to streamline the error-checking process and empower authors to focus on other critical aspects of their projects. We propose a sophisticated text classification model, leveraging the vast capabilities of the Recurrent Neural Network (RNN) architecture, which was trained on the extensive C4 dataset sourced from the TensorFlow Library. This model exhibits exceptional proficiency in rectifying spelling mistakes across a wide range of contexts, thereby bolstering the overall quality and clarity of written work. Notably, our text classification model trained with Bi-Directional GRU method showcases a remarkable accuracy of 67% in classifying errors into four distinct categories, providing insightful feedback to authors for further improvement. © 2024 IEEE.

关键词： Recurrent neural networks

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convergence of Blockchain, AI and IoT for Effective Monitoring and Securing Smart City Infrastructure 2

Convergence of Blockchain, AI and IoT for Effective Monitori...

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2nd international conference on Multidisciplinary Engineering and Applied Science, ICMEAS 2023

作者： Agajo, James Imoru, Odunayo Aigbavboa, Clinton Mishra, Amit Onah, Fidelis Ijala, Abdullahi Danjuma Federal University of Technology Department of Computer Engineering Niger State Minna Nigeria University of Johannesburg Department of Electrical and Computer Engineering South Africa University of Johannesburg Faculty of Engineering and the Built Environment South Africa Department of Computer Science School of Computing and Applied Science Baze University Abuja Nigeria Veritas University Faculty of Engineering Abuja Nigeria Nigeria Communication Commission Abuja Nigeria

ISBN: (纸本)9798350358834

The convergence of block chain, artificial intelligence, and the internet of things (IoT) for effective monitoring and securing smart city infrastructure combines these three hot trends in technology and promises to advance the development of smart cities. Blockchain, AI, and IoT represent one of the most notable technological revolutions since the combination of computing and transaction processing systems, which is now having a positive impact. Due to the actions of cyber assault, the applications of these technologies will soon have such a significant impact on our society that it will be difficult to picture life without them. The Internet of Things Infrastructure is vulnerable to assaults since it is hosted on the cloud. Modern methods are thoroughly examined to talk about the advantages and difficulties in each domain. The combination of these three will change technology and advance a number of fields that will benefit academics and experts in industry, fostering improved service delivery and enhancing corporate structures. The convergence will change the game since it will address security and data privacy concerns. © 2023 IEEE.

关键词： Internet of things

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A Federated Learning Aggregation that Integrates Clustering and Momentum Correction Based on Historical High-Quality Gradients 21

A Federated Learning Aggregation that Integrates Clustering ...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Jian Guo, Bing Chen, Fei Shen, Yan Dai, Shengxin Dai, Cheng Sichuan University School of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China

ISBN: (纸本)9798350329223

Nowadays, federated learning emerges as a new technique to preserve data privacy in distributed machine learning by limiting local data to the client and only aggregating model parameters from multiple parties on the server side. However, it also faces consequent threats from malicious clients, and some methods have been proposed without sufficient defense capability and efficient aggregation. To address these problems, we propose a novel aggregation algorithm, which incorporates PCA operation and clustering to select the excellent client in each round;and brings in a momentum-accelerated historical quality gradient-based approach to increase the weight when the performances of the global model are consistently degraded, which can achieve faster convergence on the basis of ensuring that the update direction of the global model is not manipulated by malicious nodes. Experimental results on the MNIST datasets show that our algorithm is only 0.93% lower than FedAvg under a no-attack scenario, and exhibits faster convergence compared to Krum, Multi-Krum, and Trimmed Mean Aggregation, and the accuracy improves 10.68%,35.83%,19.65% under Black-Box Edge Attack, Sign Flipping Attack, and Same Value Attack. © 2023 IEEE.

关键词： Momentum

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convergence of Geo-IoT with Advanced Technologies

Convergence of Geo-IoT with Advanced Technologies

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2023 international conference on Disruptive Technologies, ICDT 2023

作者： Sinha, Monalisha Shalini Thejaswini, M. Indian Institute of Information Technology Department of Computer Science and Engineering Bhagalpur India

ISBN: (纸本)9798350323887

IoT is an emerging digital technology where every physical object is connected to one another via the internet. IoT devices embedded with GPS sensors are called Geo-IoT systems where spatial data is the most prominent requirement in developing any IoT services and applications. Geo-IoT data can be used in various IoT applications for optimizing routes, tracking assets, real-time traffic notification, auto-driving, precision agriculture, anti-theft prevention, etc. This paper provides a review of the convergence of Geo-IoT with advanced technologies such as artificial intelligence, machine learning, and blockchain technology. This paper mainly discusses the current status and applicability of artificial intelligence and machine learning methods in solving and computing location-based IoT data for developing new advanced Geo-IoT applications, routing protocols, and security issues. © 2023 IEEE.

关键词： Blockchain

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HETEROGENEOUS PERSONALIZED FEDERATED LEARNING BY LOCAL-GLOBAL UPDATES MIXING VIA convergence RATE 12

HETEROGENEOUS PERSONALIZED FEDERATED LEARNING BY LOCAL-GLOBA...

引用

12th international conference on Learning Representations, ICLR 2024

作者： Jiang, Meirui Le, Anjie Li, Xiaoxiao Dou, Qi Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering The University of British Columbia Canada

Personalized federated learning (PFL) has emerged as a promising technique for addressing the challenge of data heterogeneity. While recent studies have made notable progress in mitigating heterogeneity associated with label distributions, the issue of effectively handling feature heterogeneity remains an open question. In this paper, we propose a personalization approach by Local-Global updates Mixing (LG-Mix) via Neural Tangent Kernel (NTK)-based convergence. The core idea is to leverage the convergence rate induced by NTK to quantify the importance of local and global updates, and subsequently mix these updates based on their importance. Specifically, we find the trace of the NTK matrix can manifest the convergence rate, and propose an efficient and effective approximation to calculate the trace of a feature matrix instead of the NTK matrix. Such approximation significantly reduces the cost of computing NTK, and the feature matrix explicitly considers the heterogeneous features among samples. We have theoretically analyzed the convergence of our method in the over-parameterize regime, and experimentally evaluated our method on five datasets. These datasets present heterogeneous data features in natural and medical images. With comprehensive comparison to existing state-of-the-art approaches, our LG-Mix has consistently outperformed them across all datasets (largest accuracy improvement of 5.01%), demonstrating the outstanding efficacy of our method for model personalization. Code is available at https://***/med-air/HeteroPFL. © 2024 12th international conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Mixing

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Personalized Federated Learning for Fast convergence and Local Adaption: A Double Gradient Descent Method 25

Personalized Federated Learning for Fast Convergence and Loc...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Li, Zhidu He, Songyang Li, Xu Xue, Meiting School of Communication and Information Engineering Chongqing University of Posts and Telecommunications China College of Physical Education Chongqing University of Posts and Telecommunications China School of Cyberspace Hangzhou Dianzi University China

ISBN: (纸本)9798350330014

Federated learning (FL) has emerged as a promising paradigm for privacy-preserving collaborative learning, enabling multiple devices to jointly train a global model without sharing their raw data. However, the statistical heterogeneity of client data in FL often results in slow convergence rates and poor performance of local models. In this paper, we propose a novel personalized federated learning method called Personalized Federated Learning with Double Descent (pFeD3) to mitigate the impact of data heterogeneity. Our approach decouples the up-dates of personalized models from the main FL process, allowing for personalized solutions tailored to each client. Moreover, we introduce a similarity penalty term to accelerate the convergence of the global model, providing generalization information for personalized models. We prove that pFeD3 achieves sublinear convergence rates under general non-convex settings for both personalized models and the global model. Through extensive experiments on real-world datasets, we demonstrate that pFeD3 significantly outperforms various baseline FL methods in terms of convergence speed and accuracy. © 2023 IEEE.

关键词： Learning systems

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AutoCaps-Zero: Searching for Hardware-Efficient Squash Function in Capsule Networks

AutoCaps-Zero: Searching for Hardware-Efficient Squash Funct...

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2024 IEEE international conference on Communications, computing, Cybersecurity and Informatics, CCCI 2024

作者： Kang, Jieui Kwon, Sooyoung Kim, Hyojin Sim, Jaehyeong Ewha Womans University Dept of Artificial Intelligence Convergence Seoul Korea Republic of Ewha Womans University Dept of Computer Science & Engineering Seoul Korea Republic of

ISBN: (纸本)9798350349832

Capsule networks (CapsNets) offer distinct advantages over conventional convolutional neural networks (CNNs) by introducing the concept of a capsule. Specifically, this innovation achieves both rotational invariance and spatial awareness, making CapsNets a powerful tool in the field of machine learning. However, this breakthrough comes with an increased level of computational complexity. In our comprehensive experimental analysis of CapsNets, we meticulously inspected its various components and identified the squash function as the main computational bottleneck. To address this challenge, In this paper, we adapts the principles of neural architecture search (NAS) and introduces AutoCaps-Zero, a framework that automatically searches the hardware-efficient squash function to reduce model inference time. Meanwhile, CapsNet models incorporating the searched squash function have exhibited excellent performance across datasets of various sizes, while retaining robust features that make them resistant to adversarial attacks. Besides, these models maintain high performance even on challenging datasets like multiMNIST. Particularly, our experimental results demonstrate that the squash function searched by AutoCaps-Zero reduces the execution time of the squash function itself by approximately 68 %. Consequently, deploying the searched squash function on our benchmark models can reduce the end-To-end graphic processing unit (GPU) inference time by up to 34%. Overall, with the searched function, the CapsNet code will be released. © 2024 IEEE.

关键词： Graphics processing unit

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