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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

共 1034 条记录，以下是161-170 订阅

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An Efficient application of machine learning for Assessment of Terrain 3D Information Using Drone Data 2nd

An Efficient Application of Machine Learning for Assessment ...

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2nd international conference on Unmanned Aerial Systems in Geomatics (UASG)

作者： Agarwal, Ankush Saini, Aradhya Kumar, Sandeep Singh, Dharmendra Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttarakhand India Indian Inst Technol Roorkee Dept Elect & Commun Engn Roorkee 247667 Uttarakhand India

ISBN: (纸本)9783031193118;9783031193095;9783031193088

Plant height is beneficial in defence-related applications during the movement of troops as terrain information is required in advance. This terrain information is of utmost importance to obtain knowledge about possible paths especially in unexplored areas. This information facilitates safe movement of troops. While exploring an unexplored area, vegetation cover area need to be checked carefully, because of the tall and dense bushes. During vegetation monitoring various parameters like plant growth, soil moisture, water availability, the need for fertilizers, etc. are observed. All these parameters are necessarily checked to monitor the growth of the plant. The plant growth parameter is reflected from the plant height. In the modern era, smart farming and precision agriculture have been applied, in which monitoring of plant growth-related parameters are optimized and the necessity of any parameter is fulfilled as per the demand and position in the field. In the extension of smart agriculture, the need for the usage of drones arises while monitoring the fields. Drones provide good spatial resolution and can be flown according to the need and application. In this work, the objective is to calculate the plant height using a drone in order to ensure safe troops movement and on other side to monitor the healthy growth of the plant. The application can be helpful for defense as well as civilian purpose. For achieving this, a machine learning-based model has been proposed in which multiple ground control points (GCPs) of different heights have been used to train the model, which results in minimizing the output error. The challenge is to get drone data and manually recorded GCPs height correctly so that the training can be accomplished successfully for better results.

关键词： Crop health Crop height Digital surface model Drone GCP based height calibration machine learning Smart agriculture

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Prototype for Recognition and Classification of Textile Weaves Using machine learning 1

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2nd international conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2021

作者： Padilha, Rafael da Silva Vasconcelos, Raimundo Cláudio Brasília DF Brazil

ISBN: (数字)9789811698859

ISBN: (纸本)9789811698842

Textile weave of a fabric gives a garment unique characteristics. It is very difficult to manually classify textile patterns because threads are very small and the classes are very similar. The goal for this project was to create a prototype using machine learning and deep learning techniques to classify textile fabrics from their images. Using the bilinear convolutional neural network, 97.91% accuracy was achieved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Textiles

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Semantic Awareness Model For Binary Operator Code 2

Semantic Awareness Model For Binary Operator Code

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2nd international conference on Image Processing, computer Vision and machine learning, ICICML 2023

作者： Gao, Haichao Fang, Liming Li, Yang Li, Minghui Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Shenzhen China

ISBN: (纸本)9798350331417

With the promotion of artificial intelligence in various industries, there have been some organizations and individuals using various means to attack it. Common types of attacks against models include adversarial sample attacks, data poisoning attacks, and model stealing attacks. The above attack methods require a certain understanding of the structure of the model, so it becomes a challenge to restore the category of binary operators from the *** code similarity detection (BCSD) has important applications in code checking, vulnerability detection, and malicious co de analysis. Due to the lack of syntax structure information in binary operator code, it is difficult to determine the type of operator. Recent research has focused on using deep learning models to understand the semantics and structural information of binary code to achieve better results. Recent research has shown that deep learning models, especially natural language processing models, can comprehend the semantics of binary code. In this paper, we propose a method for identifying binary operators which uses a sequence-aware model and a structure-aware model to model binary operators. It combines CNN semantics and Transformer semantics for classification. The evaluation shows that our method can achieve good performance in binary operator classification. © 2023 IEEE.

关键词： BC SD Binary operator code component Deep learning

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The Algorithms' application In Medical Images Gans' applications

The Algorithms' Application In Medical Images Gans' applicat...

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icmlca 2021;2nd international conference on machine learning and computer application

作者： Zhihui Chen Zeyi Hao

Adversarial networks are commonly used in Image reconstruction, segmentation, detection, classification and cross-modal synthesis. In our research programmer, we studied some basic adversarial networks like Generative Adversarial Network (Gan), Convolutional Neural Networks (CNN) and Deep Neural Network (DNN). As a result, we saw a lot of innovations and applications in different fields, especially in the medical field. Based on our observations, we believe there would be ceaseless new improvements in medicine. To assist the professional researchers, find and quote a variety of papers, we will conduct a review of advances in medicine within adversarial networks in the past few years.

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machine learning-based Design of Software to Calculate the Fragmentation Power of the Combat Section of an Explosive Killing Shell

Machine Learning-based Design of Software to Calculate the F...

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icmlca 2021;2nd international conference on machine learning and computer application

作者： HongZhi Zhao ZhiYong Bi Xu Ceng ZhaoMing Tan ShuShan Wang

The fragmentation power is an important parameter to characterize the performance of a combatant. At present, the fragmentation power is mainly calculated by empirical formulae or simulation analysis, which has problems such as large calculation volume, slow calculation speed and low efficiency. In this paper, a machine learning-based method for calculating the lethality of explosives is proposed. On the basis of analyzing the structure and material of explosive kill combat section, combining theoretical calculation data, simulation data and experimental data, the training model of machine learning is constructed, and by introducing BP neural network algorithm, the rapid calculation of debris dynamic field characterization parameters is finally realized.

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Hierarchical Policies of Subgoals for Safe Deep Reinforcement learning 2nd

Hierarchical Policies of Subgoals for Safe Deep Reinforcem...

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2nd international conference on Ubiquitous Security, UbiSec 2022

作者： Yu, Fumin Gao, Feng Yuan, Yao Xing, Xiaofei Dai, Yinglong College of Information Science and Engineering Hunan Normal University Changsha410081 China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China College of Liberal Arts and Sciences National University of Defense Technology Changsha410073 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9789819902712

Reinforcement learning is a machine learning method that relies on the agent to learn by trial and error to solve decision optimization problems. It is well known that an agent based on deep reinforcement learning in complex environments is difficult to train. Moreover, the agent will generate unsafe and strange actions due to the lack of sufficient reward feedback from the environment. To make the agent converge to a better policy and make its behavior safer and more controllable under sparse rewards, we propose a subgoal embedding method based on prior knowledge and hierarchical strategy that can make the training process converge faster. The subgoal embedding method can be combined with existing reinforcement learning methods. In this paper, we combine the subgoal embedding method with REINFORCE algorithm and PPO(Proximal Policy Optimization) algorithm to test the method in the MiniGrid-DoorKey game environment of the gym platform. The experiments demonstrate the effectiveness of the subgoal embedding method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Malware Mobile application Detection Using Blockchain and machine learning 2

Malware Mobile Application Detection Using Blockchain and Ma...

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2nd Global conference for Advancement in Technology, GCAT 2021

作者： Aneja, Naman Suri, Sandeep Papneja, Sachin Khurana, Nikhil Samsung India Electronics Pvt. Ltd Platform RD Delhi India

ISBN: (纸本)9780738132150

The world is seeing a rapid growth in mobile malware applications. Traditional computer malware programmers are shifting to android malware applications. Consequently, mobile security specialists are also working very hard to obtain a robust explication to this current problem. Many anti malware applications have also been launched to tackle this problem. In this paper we have tried to propose a system for detection of malware application based on Blockchain with help of machine learning. We use one internal permissioned blockchain with feature extractor model and one external permissioned blockchain feedback to another machine learning model to accomplish this task. We use dedicated internal blockchain for each application to make our system error free and more accurate. © 2021 IEEE.

关键词： Blockchain

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An Overview of the Theory and application of Reinforcement learning

An Overview of the Theory and Application of Reinforcement L...

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icmlca 2021;2nd international conference on machine learning and computer application

作者： Yumeng Zhang

As an important branch of machine learning research, reinforcement learning can obtain strategy improvement through the interaction of trial and environment and is widely applied in various fields. Research on reinforcement learning theory is helpful for subsequent multidisciplinary projects. However, related researches are still relatively scattered. Thus, this paper introduces the theory and application of reinforcement learning by literature analysis and comprehensively introduces the main algorithms theory of reinforcement learning, including temporal difference learning, Q-learning and Sarsa learning, as well as their combination and effect comparison. Besides, this paper also summarizes the current applications of reinforcement learning that have received more attention, namely control systems, autonomous driving, and robots. Moreover, the current research issues and future work directions of reinforcement learning are discussed. Overall, this article concludes that the current basic algorithm construction of reinforcement learning is relatively complete, but the method of combining multiple algorithms remains to be discussed.

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Forecasting the Economic Crisis of Sri Lanka: application of machine learning Algorithms for Time Series Data 2

Forecasting the Economic Crisis of Sri Lanka: Application of...

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2nd international conference on machine learning and Data Engineering, ICMLDE 2023

作者： Raj, A. Stephan Antony Kumarasankaralingam, Lakshmanan Balamurugan, M. Maheswari, B. Gowri, J. Dutta, Anurag Department of Mathematics Rathinam Group of Institutions Kovilpalayam Campus Tamil Nadu Coimbatore India Department of Mathematics Kuwait American School of Education Salmiya Kuwait Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi India Department of Mathematics Dr Mahalingam College of Engineering and Technology Tamil Nadu Pollachi India Department of Mathematics Nallamuthu Gounder Mahalingam College Tamil Nadu Pollachi India Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore Calcutta India

The economic crisis in Sri Lanka began in 2019 and reached a crisis point in 2022 when protesters stormed the presidential palace in Colombo. Acute food, fuel, and other item shortages, soaring inflation, prolonged power outages, and a collapsing economy with few job opportunities all contribute to the populace's unrest. Sri Lanka still faces severe economic difficulties, and no one can say when they will be resolved. This country has seen the most prominent financial collapse since its independence in 1948. Unexpected inflation levels, a close to complete exhaustion of foreign currency reserves, a shortage of medical aid, and a rising price of essential commodities are the results. Numerous compounding reasons, including tax cuts, printing money, a countrywide regulation to move to natural or biological agriculture, the 2019 Sri Lanka Easter bombings, and the repercussions of the COVID-19 epidemic, have been cited as the root cause of the disaster. As early as 2022, these protests in Sri Lanka might be traced back to the country's economic woes. The primary driver of the current financial crisis in Sri Lanka is inflation. This article will look into Sri Lanka's inflation rates beginning in 2021. The inflation Rate of Sri Lanka has been forecasted for the next 6 months (through September 2023) using time series data. We used numerous Time Series Forecasting Algorithms for this multi-step ahead forecast. The results generated by these time series forecasters were in accordion with each other. Thus, we put forward the normalized results from each of these forecasting paradigms to get a better prediction on the inflation Rates, that Sri Lanka is supposed to face shortly (6 months), keeping in consideration of the current scenario being unaltered. The authors hope the Sri Lankan Administration will use the paper's predictions to take charge, which could lead to positive changes. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Research on automatic sentiment analysis of text movie reviews with machine learning methods

Research on automatic sentiment analysis of text movie revie...

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icmlca 2021;2nd international conference on machine learning and computer application

作者： Jia Li

In today’s world, vast amounts of information are available to us. For decades, scientists have sought to develop the most efficient methods of querying these data and extracting the information needed. In this survey, we describe the basics of sentiment analysis and discuss prominent work in natural language processing, focusing on the domain of movie reviews. We discuss methods that operate using lexicon-based approaches, conventional machine learning approaches, deep learning approaches, and hybrid approaches. Compared with previous papers, this article offers more comprehensive coverage, as it includes the latest methodologies in the field, e.g., the use of BERT in sentiment analysis of movie reviews. We also highlight the limitations of research to date and point to potential directions for future work.

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