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Land Use Classification of Shenyang Based on Spatial data 4

Land Use Classification of Shenyang Based on Spatial Data

引用

4th international conference on machine learning and Computer Application, ICMLCA 2023

作者： Liu, Ruizhao Xie, Zhiwei Lv, Xinyv Peng, Bo Shenyang Jianzhu University Liaoning Shenyang China China Railway Jiuju Group Seventh Engineering Co. Ltd Liaoning Shenyang China Liaoning Vocational College of Ecological Engineering Liaoning Shenyang China

ISBN: (纸本)9798400709449

In order to be able to carry out fine urban management by the results of urban land use classification, this paper designs and implements a fine urban land use classification method based on remote sensing images and spatial data. the nearest neighbor classification method is used to classify the images, and the weighted BOW (Bag of Words) model is used to generate a weighted word frequency vector. the innovative combination of the type-word similarity matrix generated by the Word2vec (Word to Vector) model and the weighted BOW model is used to extract urban land classification results by vector calculation. In this paper, Shenyang, Liaoning Province, China, is taken as the study area, and the remote sensing image of Gaofen-2(GF-2) and POI (Point of Interesting) data are used. the experimental results prove that the residential areas and companies in Shenyang are concentrated on a large area and mainly concentrated in the northeast of the design area, the commercial area is the smallest and shows a distribution pattern of more in the west and less in the east and more in the north. the Land for government agency and public life service tends to be scattered and concentrated in the southeaster part of Shenbei New Area. © 2023 ACM.

关键词： Land use

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Improving data Delivery and Energy Efficiency in MANETs: A Stacking Based SVM Approach for Multipath 15th

Improving Data Delivery and Energy Efficiency in MANETs: A S...

引用

15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Venkatesh, D. Narahari, V. Swamy, K. Ranga Bhaskar, M. Vijaya Saravanan, T. Vasundra, S. Bengaluru India Anantha Lakshmi Institute of Technology and Sciences Anantapur India Sri Venkateswara Institute of Technology Hampapuram515001 India Jawaharlal Nehru Technological University Anantapur India

ISBN: (纸本)9783031810824

Recently, new energy-efficient routing algorithms have been developed for multipath multicast routing to find a stable path for data transmission between nodes that is reliable and fault-tolerant. these algorithms aim to reduce the number of failed data transmissions and the time lost due to route disconnection. To improve data delivery and reduce data loss, several existing techniques have been developed for multipath routing. However, the proposed work focuses on MANETs, which are networks with a variable number of mobile nodes scattered randomly throughout the network. the learning model is first intended to determine which neighbouring nodes are most efficient for data transmission by estimating the link quality and data depletion rate. this leads to increased data packet delivery in a shorter time frame. the weak learners are then subjected to a stacking-based SVM classification technique to produce correct classification results for each node. the degree of resemblance between several data packets is assessed using the Jaccard resemblance function. the studies involve a number of performance measures, including end-to-end delay, the packet delivery ratio, normalized control overhead, energy computing, and residual energy. they are carried out using the ns2.34 simulator. the simulation findings show that the suggested stacking-based SVM model enhances the longevity and reliability of MANETs’ data supply, with end delays ranging from 4.659606 ms to 13.610754 ms for node speeds ranging from 10 m/s to 100 m/s. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy efficiency Jaccard Similarity function multicast routing neighbour learning stacking SVM

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MoFLeuR: Motion-based Federated learning Gesture recognition

MoFLeuR: Motion-based Federated Learning Gesture Recognition

引用

IEEE international conference on Human-machine Systems

作者： S. Jamal Seyedmohammadi S. Mohammad Sheikholeslami Jamshid Abouei Arash Mohammadi Konstantinos N. Plataniotis Inst. of Information Systems Engineering Concordia University Montreal Canada Dept. of Electrical and computer Engineering University of Toronto Toronto Canada Dept. of Electrical Engineering Yazd University Yazd Iran

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Most of the recent works on Human Gesture recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: $a)$ the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and $b)$ clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency.

关键词： Performance evaluation Federated learning Inertial sensors Computational modeling Simulation Human-machine systems Gesture recognition

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Advanced Seizure Prediction System for Wearable Health Management Devices using KNN Algorithm

Advanced Seizure Prediction System for Wearable Health Manag...

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： M. Raja Suguna Nithisha J A. Ramesh Babu M.H Ananda R. Venkatesan N. Malathi RMK College of Engineering and Technology Chennai Tamil Nadu India SRM Institute of Science and Technology Chennai Tamil Nadu India Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India REVA University Bangalore Karnataka India Muthayammal Engineering College Namakkal Tamil Nadu India Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

the K-Nearest Neighbors (KNN) algorithm is used to provide a novel approach to predicting seizures in wearable health monitoring devices. Physiological signals, such as EEG and ECG, are used in the preprocessing and analysis of real-time data performed on a Raspberry Pi platform by the system. After being trained on a large dataset that comprised normal and seizure states, the KNN model demonstrates good classification accuracy. the wearable equipment, intended to be unnoticeable, performs continuous analysis of physiological data and triggers alerts upon recognizing patterns that point to the possibility of an oncoming seizure. this cutting-edge gadget has the ability to identify seizures in their earliest stages, allowing for timely medical attention and intervention to be provided to patients. It is easily incorporated into one’s routine and activities. the research highlights the importance of machine learning in healthcare wearables and the efficacy of the KNN algorithm for predictive analytics in neurological illnesses. It is also shown in the research that machine learning is relevant to healthcare wearables. Because of the study’s emphasis on neurological disorders, this result is significant.

关键词： machine learning algorithms Accuracy Medical services machine learning Prediction algorithms Real-time systems pattern recognition

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Short Speech Key Generation Technology Based on Deep learning 4th

Short Speech Key Generation Technology Based on Deep Learni...

引用

4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Lv, Zhengyin Wu, Zhendong Chen, Juan Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9783031200984

With the increasing popularity of biometric identity authentication in important key authentication applications, biometric key generation technology has attracted more and more attention. How to extract secure, stable and high-strength keys from biometrics has become a key research direction of biometric key generation technology. However, the proposed biometric key generation technology can not meet the security requirements as a key, that is, the key is easy to be cracked, resulting in unpredictable consequences. this paper presents a framework of voiceprint key generation based on deep neural network, which is mainly composed of voiceprint feature extraction model and fuzzy extraction model. the voiceprint feature extraction model is based on the deep neural network, and the sigmoid layer is added at the end of the neural network. this operation makes the features extracted by the neural network have high accuracy and low error recognition rate after binary quantization. the voiceprint database used in this paper is aishell-wakeup-1 wakeup word database. the voiceprint key generation model proposed in this paper generates the key on the voiceprint database with the generation intensity of more than 1000bit, the accuracy rate of more than 93%, and the error recognition rate of less than 0.001%. these data can prove that the voiceprint key generation model proposed in this paper can meet the needs of users for generation strength and security. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Development of an object recognition algorithm based on neural networks With using a hierarchical classifier

Development of an object recognition algorithm based on neur...

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Nguyen, V. T. Pashchenko, F. F. Moscow Inst Phys & Technol Moscow Russia Russian Acad Sci VA Trapeznikov Inst Control Sci Moscow Russia

this paper proposes the architecture of a convolutional neural network that creates a neural network system for recognizing objects in images using our own approach to classification using a hierarchical classifier. the architecture will be assigned to find the optimal solution to the problem for many sets of image data and, unlike existing approaches, will have high performance indicators without losing the number of parameters during recognition, and most importantly, the best value of object recognition accuracy compared to existing models of convolutional neural networks. the main attention is paid to the approach to training such a network and conducting experiments on the generated samples of various datasets using graphic processing units (GPUs). (C) 2021 the Authors. Published by Elsevier B.V.

关键词： neural network machine learning image recognition during shooting optimization algorithm for convolutional neural networks hierarchical classifier

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A Novel Approach using machine learning and NLP for Revolutionizing Pali Manuscript Conservation

A Novel Approach using Machine Learning and NLP for Revoluti...

引用

Sustainable Expert Systems (ICSES), international conference on

作者： Sangita R. Gudadhe Aashish A. Bardekar Amitkumar B. Ranit dept. of Computer Science & Engineering Sipna COET Amravati Amravati India dept.of Civil Engineering Prof. Ram Meghe CEM Badanera Amravati India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

the preservation and analysis of ancient Pali manuscripts are crucial for understanding India's cultural heritage. However, the unique shapes of Pali characters present significant challenges for accurate identification and classification. this research proposes a novel system that utilizes machine learning, image processing, and natural language processing (NLP) to convert Pali characters into understandable formats and decipher the Pali Prakrit language. By combining computer science and linguistics, the proposed model aims to effectively extract data from Pali literature. the proposed model was validated using the Government of India dataset, demonstrating improved character recognition accuracy and reduced memory footprint. this research contributes to the preservation of ancient Pali manuscripts and facilitates the study and understanding of this valuable cultural heritage.

关键词： Training Accuracy Shape Computational modeling machine learning Natural language processing Cultural differences Convolutional neural networks Character recognition Testing

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Pneumonia Image recognition Based On Deep learning

Pneumonia Image Recognition Based On Deep Learning

引用

Information Technology,Big data and Artificial Intelligence (ICIBA), international conference on IEEE international conference on

作者： Zhengyu Jiang Teoh Teik Toe Glasgow College HN University of Electronic Science and Technology of China China NTU Business AI Lab Nanyang Technological University Singapore

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

this article introduces a comprehensive method for diagnosing pneumonia using chest X-ray (CXR) images. By using deep learning models for feature extraction and machine learning methods for classification, the effectiveness of feature extraction has been evaluated using CNN, ResNet.50, and ViT models. the performance of random forest and logistic regression classifiers based on these models were compared, conducting a comprehensive evaluation of accuracy and F1 score to determine the most effective model. this study addresses the challenges of diagnosing artificial pneumonia and emphasizes the urgency of automated solutions. In the end, the combination of ResNet.50 and logistic regression achieved higher accuracy (78.68%), higher precision (99.99%), and more ideal F1 score (0.6239). these results demonstrate that the application of deep learning in clinical diagnosis can improve the clinical efficiency and accuracy of pneumonia detection, which is beneficial for the subsequent treatment of patients.

关键词： Deep learning Training Logistic regression Pneumonia Accuracy machine learning algorithms Feature extraction data models Stability analysis X-ray imaging

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An Approach to pattern recognition for Identification of Devnagari Script Based on Fingertips and Palm 4

An Approach to Pattern Recognition for Identification of Dev...

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4th international conference on Intelligent Circuits and Systems, ICICS 2022

作者： Tantarpale, Ms. Sharvari Deshmukh, C.N. Amravati India PRMIT & R Badnera India

In this paper, we present an finger point based signed language symbol to text identification and classification algorithm that is based upon RGB image datasets. the palm sized images based upon different sizes, backgrounds, orientation are captured to be preprocessed as per the requirements of developing a convolution neural network based algorithm. this algorithm utilizes Alexnet for the preprocessing requisites where in 47 symbols of Devanagari script are augmented based on the reference rulebook created for our requirements as highlighted in the paper. At the primary level this algorithm provides an excellent classification rate which promises upliftment for our research in the upcoming future. We have provided detailed steps and discussion on the classification parameters considered for our algorithm which is implemented on MATLAB platform with the help of machine learning solution libraries. © Published under licence by IOP Publishing Ltd.

关键词： Convolution

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Research on Transferable Patent recognition Based on machine learning 2021

Research on Transferable Patent Recognition Based on Machine...

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4th international conference on data Science and Information Technology, DSIT 2021

作者： Hu, Yifei Yang, Shiwei Shi, Anjie Beijing University of Technology China

ISBN: (纸本)9781450390248

the construction of a transferable patent recognition model is of great significance for increasing patent transformation. To construct the index system affecting patent transformation, this paper adds cross-border dimension index to the existing three-dimensional patent value evaluation index. the results show that the recognition accuracy of Gradient boosting decision tree and Random forest is as high as 85.92%, which indicates that machine learning method can support transferable patent recognition in massive data. © 2021 ACM.

关键词： Decision trees

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