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共 585 条记录，以下是481-490 订阅

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A Subblock Partition of Multi-Layer pattern Based Image Classification Approach 1

A Subblock Partition of Multi-Layer Pattern Based Image Clas...

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1st international Symposium on Water System Operations, ISWSO 2018

作者： Lv, Xuan Ma, Zezhong Liu, Qing Chongqing Land Resources Housing Surveying and Planning Institute China School of Remote Sensing and Information Engineering Wuhan University Huangshandadao Road No.64 Yubei Chongqing China

Since traditional partition approach may construct very different image representation because of the changed locations of objects in the same image, a subblock partition of multi-layer pattern method for image representation is proposed. The saliency windows straddled by superpixels are utilized to partition the image into multi-layer pattern subblocks. Then all the subblocks are combined to a three order tensor. Comparing to the results of image classification item of Pascal Voc 2007 Challenge, it indicates that the proposed representation method is robust to the varied object locations and achieves better performance than other approaches. CCS Concepts Computing methodologies? Computer vision • Computing methodologies? machine learning. © The Authors, published by EDP Sciences, 2018.

关键词： Image classification

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Seeds Classification and Quality Testing Using Deep learning and YOLO v5 1

Seeds Classification and Quality Testing Using Deep Learning...

引用

1st international Conference on data Science, machine learning and Artificial Intelligence, DSMLAI 2021

作者： Kundu, Nidhi Rani, Geeta Dhaka, Vijaypal Singh Department of Computer and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9781450387637

Segregation of seeds of different crops grown in the mixed cropping is a major cause of concern for the farmers as well as the food industry. Also, the classification and packaging of seeds based on their quality is a challenging task for farmers and agro-industries. Moreover, Post-thrashing separation of seeds by the traditional techniques such as sieving, hand-picking, etc. is a time-consuming and tedious task. Thus, there is a need to automate seed segregation. The potential of deep learning and machine learning techniques in object detection, classification, and pattern recognition motivated the researchers to employ these techniques for the automatic segregation of seeds at the harvesting site. The techniques proposed so far focus on the classification of seeds of different crops. Limited research work is observed that focuses on the classification of seeds of crops grown as a part of mixed cropping as well as seeds of different quality standards. Also, there is a huge scope to improve the classification performance of the proposed models. The purpose of this research to develop the deep learning-based system 'Mixed Cropping Seed Classifier and Quality Tester (MCSCQT)' for accurate classification and quality testing of seeds based on their shape, color, and texture. The system is trained on the dataset comprising labelled images of healthy and diseased seeds of pearl millet and maize. It reports the highest precision and recall of 99%. The efficacy of the system in discriminating the seeds of pearl millet and maize may prove a game-changer for the food industry. Also, its capability in recognition of diseased and healthy seeds of maize enhances its utility in the food industry. © 2021 ACM.

关键词： Object detection

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Generation of memes to engage audience in social media 1

Generation of memes to engage audience in social media

引用

1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Kurochkin, Andrew Bokhan, Kostiantyn Ukrainian Catholic University Applied Sciences Faculty Lviv Ukraine Whirl Software Kyiv Ukraine

In digital marketing, memes have become an attractive tool for engaging online audience. Memes have an impact on buyers and sellers online behavior and information spreading processes. Thus, the technology of generating memes is a significant tool for social media engagement. The primary purpose of the project is to develop a new approach and compare it to the existing baselines in the field of social media content generation, more precisely - meme generation. A meme is an image superimposed with text, which has humoristic or sarcastic sense;a meme is just another type of visual online content. This project is aimed at applying state of the art Deep learning techniques as Transformer architecture to the meme generation problem. To achieve project objectives, we are going to collect dataset;create a model for generation of memes and titles based on the input text;create a model for defining optimal time to make a post;measure and analyze system performance in terms of social network audience engagement. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Deep learning

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Determining sentiment and important properties of Ukrainian language user reviews 1

Determining sentiment and important properties of Ukrainian ...

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1st Masters Symposium on Advances in data mining, machine learning, and Computer Vision, MS-AMLV 2019

作者： Babenko, Dmytro Dyomkin, Vsevolod Ukrainian Catholic University Lviv Ukraine

The main idea of this project to develop a model, which can recognize emotions from user review text. Many websites contains review text with marks (stars or another representation) about some products or services. Sometimes user can write only text without a mark, in this case, such a model can help us to understand the estimation of this user. It can be a review of some special product, about booking a hotel or renting a car or something like that. Furthermore, another purpose of this project to detect which characteristics were influenced for such review. Here, the model should detect sentiments from text and classify for positive and negative. Moreover, it will understand which reasons were caused by these emotions and why the user wrote such texts. As this project mostly works with text processing, it is classical natural language processing task using deep learning. Probably, as a result, it would be some neural network, which was selected as the best from several experiments. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Deep learning

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learning with configurable operators and RL-based heuristics

Learning with configurable operators and RL-based heuristics

引用

1st international workshop on New Frontiers in mining Complex patterns, NFMCP 2012

作者： Martínez-Plumed, Fernando Ferri, Cèsar Hernández-Orallo, José Ramírez-Quintana, María José DSIC Universitat Politècnica de València Camí de Vera s/n 46022 València Spain

ISBN: (纸本)9783642373817

In this paper, we push forward the idea of machine learning systems for which the operators can be modified and finetuned for each problem. This allows us to propose a learning paradigm where users can write (or adapt) their operators, according to the problem, data representation and the way the information should be navigated. To achieve this goal, data instances, background knowledge, rules, programs and operators are all written in the same functional language, Erlang. Since changing operators affect how the search space needs to be explored, heuristics are learnt as a result of a decision process based on reinforcement learning where each action is defined as a choice of operator and rule. As a result, the architecture can be seen as a 'system for writing machine learning systems' or to explore new operators. © 2013 Springer-Verlag.

关键词： Reinforcement learning

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How Effective is the Salp Swarm Algorithm in data Classification 1st

How Effective is the Salp Swarm Algorithm in Data Classifica...

引用

1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Panda, Nibedan Majhi, Santosh Kumar Department of Computer Science and Engineering Veer Surendra Sai University of Technology BurlaOdisha768018 India

ISBN: (纸本)9789811390418

In this paper, the Salp Swam Algorithm (SSA) is deployed in training the Multilayer Perceptron (MLP) for the task of data classification. The UCI machine learning repository standard datasets are used for evaluation of the proposed SSA based MLP. The performance of the proposed method is verified by considering various standard classification measures over the considered benchmark datasets. The result obtained by the proposed method is compared with other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimization (GWO), etc. From the simulation study, it has been verified that the proposed method shows supremacy in results as compared with other evolutionary algorithm based MLP. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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IEEE international Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023 - Proceedings

IEEE International Conference on Advances in Electronics, Co...

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1st IEEE international Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

ISBN: (纸本)9798350348057

The proceedings contain 127 papers. The topics discussed include: automated Bengali abusive text classification: using deep learning techniques;bio algorithms for resource optimization and analysis of data;prediction of rheumatoid arthritis susceptibility using gene mutation rate;a survey paper on emerging techniques used to translate audio or text to sign language;a novel development of blockchain based messaging application;deep machine learning based usage pattern and application classifier in network traffic for anomaly detection;analyzing paralinguistic information from human speech and its applications in medicine;strategic placement of electric vehicle charging stations using grading algorithm;malware detection in android applications using machine learning;the impact of online reviews on product perception and purchase intention;design and implementation of smart classroom using cisco packet tracer;intrusion detection in networks using gradient boosting;real-time lane detection and departure caution gadget for automobiles based on Raspberry pi;development of a Bengali speech-based emotion analysis system;large vocabulary continuous speech recognition system for Marathi;and low-cost smart glasses for people with visual impairments.

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Towards statistical machine learning for Edge Analytics in Large Scale Networks: Real-Time Gaussian Function Generation with Generic DSP 1

Towards Statistical Machine Learning for Edge Analytics in L...

引用

1st international Colloquium on Smart Grid Metrology (SmaGriMet)

作者： Oyekanlu, Emmanuel Onidare, Samuel Oladele, Paul Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Univ Lancaster Dept Comp & Commun Lancaster England Lulea Univ Technol Dept Comp Sci & Space Tech Lulea Sweden

ISBN: (纸本)9789531842358

The smart grid (SG) is a large-scale network and it is an integral part of the Internet of Things (IoT). For a more effective big data analytic in large-scale IoT networks, reliable solutions are being designed such that many real-time decisions will be taken at the edge of the network close to where data is being generated. Gaussian functions are extensively applied in the field of statistical machine learning, pattern recognition, adaptive algorithms for function approximation, etc. It is envisaged that soon, some of these machine learning solutions and other gaussian function based applications that have low computation and low-memory footprint will be deployed for edge analytics in large-scale IoT networks. Hence, it will be of immense benefit if an adaptive, low-cost, method of designing gaussian functions becomes available. In this paper, gaussian distribution functions are designed using C28x real-time digital signal processor (DSP) that is embedded in the TMS320C2000 modem designed for powerline communication (PLC) at the low voltage distribution end of the smart grid, where numerous devices that generate massive amount of data exist. Open-source embedded C programming language is used to program the C28x for real-time gaussian function generation. The designed gaussian waveforms are stored in lookup tables (LUTs) in the C28x embedded DSP, and could be deployed for a variety of applications at the edge of the SG and IoT network. The novelty of the design is that the gaussian functions are designed with a generic, low-cost, fixed-point DSP, different from state of the art in which gaussian functions are designed using expensive arbitrary waveform generators and other specialized circuits. C28x DSP is selected for this design since it is already existing as an embedded DSP in many smart grid applications and in other numerous industrial systems that are part of the large scale IoT network, hence it is envisaged that integration of any gaussian function based s

关键词： digital signal processor powerline communication smart grid machine learning open-source embedded C big data

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Lung Cancer Detection from Histopathological Images Using Deep learning 1st

Lung Cancer Detection from Histopathological Images Using D...

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1st international Conference on machine Intelligence and Emerging Technologies, MIET 2022

作者： Mohalder, Rahul Deb Hossain, Khandkar Asif Sarkar, Juliet Polok Paul, Laboni Raihan, M. Talukder, Kamrul Hasan Khulna University Khulna Bangladesh North Western University Khulna Bangladesh

ISBN: (纸本)9783031346187

Computed tomography (CT) is critical for identifying tumors and detecting lung cancer. As was the case in the recent past, we wish to incorporate a well-educated, profound learning algorithm to recognize and categorize lung nodules based on clinical CT imagery. This investigation used open-source datasets and data from multiple centers. Deep learning is a widely used and powerful technique for pattern recognition and categorization. However, because large datasets of medical images are not always accessible, there are few deep structured applications used in diagnostic medical imaging. In this research, a deep learning model was created to identify lung tumors from histopathological images. Our proposed Deep learning (DL) model accuracy was 95% and loss was 0.158073%. © 2023, ICst Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Tumors

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Knowledge Graphs for Cybersecurity: A Framework for Honeypot data Analysis 1

Knowledge Graphs for Cybersecurity: A Framework for Honeypot...

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1st IEEE international Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Andrew, Yevonnael Lim, Charles Budiarto, Eka Swiss German University Information Technology Department Banten Tangerang Indonesia

ISBN: (纸本)9798350339437

In the field of cybersecurity, the complexity and diversity of data present significant challenges for effective analysis. This paper explores the use of knowledge graphs as a tool to enhance the analysis of honeypot data. We detail the entire process, from data collection and pre-processing to the creation of the knowledge graph and its subsequent analysis. Our approach enables complex query analysis and provides insights into the sequence and patterns of attacker commands in a specific session, as well as a summary of activities originating from a specific IP address. However, the adaptability of the transformation process may vary depending on the characteristics of the source documents. Our paper underscores the importance of knowledge graphs in enabling more effective threat detection and response mechanisms through a more comprehensive and deeper analysis of honeypot data. Future research could explore real-time graph updating, pattern recognition with machine learning, threat prediction, and attack attribution. © 2023 IEEE.

关键词： Knowledge graph

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