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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是671-680 订阅

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Classification of Fruit Plants Leaf and Comparative Analysis of machine learning and Deep learning Algorithms 3

Classification of Fruit Plants Leaf and Comparative Analysis...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Hrithik, Aaryan Kumar Kumar, Vipin Mahatma Gandhi Central University Department of Computer Science and Information Technology Bihar Motihari India

ISBN: (纸本)9781665462006

Leaf is the essential part of plant which have significant feature for the identification of Plant. But without an expert, it is an exceedingly difficult task to find the type of plant. Fruits Plant have significant role in the fulfillment of proper nutrition. Therefore, automatic fruit plant detection is highly required, and it can be accomplished using image processing algorithms. In this paper, the authors themselves have collected 30 categories of fruit plant leaf(i. e., 11321 RGB images) leaves using a smartphone device and comparative studies have been performed for the classification of the leaf of the fruit plant using multiple machine learning (ML) algorithms i.e., Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector machine (SVM), Decision tree (DT), Multi-layer Perceptron (MLP), and Naive Bayes (NB). According to the performance analysis, MLP performed better than other classifiers including LR, KNN, SVM, DT, and NB, with an accuracy rate of 93.18%. In this paper, used DL models are ResNet, AlexNet, VggNet, SqueezeNet, GoogLeNet, ShuffleNet, Resnext50, and DenseNet. Based on the analysis of DL models, we can conclude that ShuffleNet has performed better than the others i.e., 99.96% accuracy. © 2022 IEEE.

关键词： Fruits

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Identifying key interactions between process variables of different material categories using mutual information-based network inference method 3

Identifying key interactions between process variables of di...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Tripathi, Shailesh Jodlbauer, Herbert Mittermayr, Christian Emmert-Streib, Frank Univ Appl Sci Upper Austria Prod & Operat Management Wels Austria Greiner Packaging Int GmbH Greinerstr 70 A-4550 Kremsmuenster Austria Tampere Univ Predict Soc & Data Analyt Lab Tampere Finland Inst Biosci & Med Technol Tampere Finland

This paper analyzes production data from injection molding processes to identify key interactions between the process variables from different material categories using the network inference method called "bagging conservative causal core network" (BC3net). This approach is an ensemble method with mutual information that is measured between process variables to select pairs that show significant shared information. We construct networks for different time intervals and aggregate them by calculating the proportion of significant pairs of process variables (weighted edges) for each production process over time. The weighted edges of the aggregated network for each product are used in a machine learning model to optimize the network interval size (interval split) and feature selection, where edge weights are the input features and material categories are the output classification labels. The time intervals are optimized based on the classification accuracy of the machine learning model. Our analysis shows that the aggregated edge features of inferred networks can classify different material categories and identify critical features that represent interdependence in the associated process variables. We further used the "one vs. other" labels for the machine learning models to identify material-specific interactions for each material category. Additionally, we constructed an aggregated network over all samples in which the process variable interactions were steady over time. The resulting network showed modular characteristics where process variables of similar categories were grouped in the same community. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： injection molding process variable interactions process variable network network inference machine learning models

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Spam Recognition Model Based on TextCNN 3

Spam Recognition Model Based on TextCNN

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3rd international conference on Pattern Recognition and machine learning, PRML 2022

作者： Yixuan, Gao Yuanyuan, Zhang Ye, Cheng Fuzhou University Maynooth International Engineering College Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (数字)9781665499507

ISBN: (纸本)9781665499507

With the advancement of modern Internet technology, email is widely used in people's daily lives and has become one of the common communication tools. However, at the same time, email-based spam has also been widely spread, flooding people's lives and not only wasting public resources but also infringing on people's legitimate rights and interests. Based on this, we propose a Spam recognition model based on TextCNN by combining Convolutional Neural Network and Natural Language Processing, where natural language processing techniques are used to pre-process the text in the spam corpus to obtain text features, which are then imported into the constructed Convolutional Neural Network analysis model. A CNN spam recognition model is then designed and analyzed hierarchically using the TensorFlow framework. After extracting the features from the email dataset, the model was fed into the convolutional neural network again for several iterations of training. The results indicated that the model achieved an accuracy of 97.7% in spam recognition, which is an exceptionally significant improvement in accuracy and stability compared to the SVM model used previously, demonstrating good filtering performance. © 2022 IEEE.

关键词： Convolutional neural networks

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Predicting Sorption Behavior in Edible Bionanocomposite Films with machine learning Algorithms 3

Predicting Sorption Behavior in Edible Bionanocomposite Film...

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3rd international conference on Computing, Analytics and Networks, ICAN 2022

作者： Sharma, Aashish Josan, G.S. Raju, N.P. Sharma, A.K. Punjabi University Department of Computer Science Patiala India ICAR-NDRI Dairy Technology Divisiom Karnal India ICAR-NDRI DES&M Division Karnal India

ISBN: (纸本)9781665499446

Two machine learning (ML) algorithms, viz., neural network (NN) and support vector regression (SVR) machine have been experimentally investigated to intelligently predict sorption isotherms in a biodegradable nanocomposite food packaging material. These materials were developed using dairy residuals with byproducts, which were, otherwise, wasted by the industry. The predication performances of the ML models were compared with that of conventional mathematical sorption methods, viz., Brunauer-Emmitt-Teller (BET) and Guggenhiem-Anderson-de Boer (GAB) models. The prediction efficiency of the ML models, expressed in terms of coefficient of determination (R2), mean absolute error (MAE), percent root mean squared error (%RMSE) and Accuracy (%), were experimentally found to be in the range of 0.99-1.0, 0.11-0.76, 0.32-9.78, and 82.41-100, against that of the conventional sorption models being in the range of 0.96-0.99, 0.03-1.98, 0.72-10.28, and 43.34-100, respectively. Evidently, the ML models performed fairly better than the conventional sorption methods. © 2022 IEEE.

关键词： Forecasting

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machine learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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Prediction of Geological Conditions Ahead of the Tunnel Face: Comparing the Accuracy of machine learning Models Trained on Real and Synthetic Data 3rd

Prediction of Geological Conditions Ahead of the Tunnel Face...

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3rd international conference of international Society for Intelligent Construction, ISIC 2022

作者： Sapronova, Alla Unterlass, Paul J. Dickmann, Thomas Hecht-Méndez, Jozsef Marcher, Thomas Institute of Rock Mechanics and Tunnelling Graz University of Technology Rechbauerstraße 12 Graz8010 Austria Amberg Technologies AG Trockenloostraße 21 Regensdorf-Watt8105 Switzerland

ISBN: (纸本)9783031202407

Risk assessment during the construction of an underground structure (e.g., tunnel) should rely on accurate information of the rock mass that will be excavated. Advances in engineering equipment allowed vast data collection and thus opened a possibility for data-driven prediction of geological conditions. For an accurate prediction, the integration of various data sources is required, which makes the predictions complex and time-consuming for specialists. The application of machine learning (ML) methods provides a shortcut in analyzing complex data to predict geological conditions. In this work, the authors present a new approach for predicting the geological information ahead of the tunnel face by using an ensemble of ML methods combined with an oversampling technique. Implicit dimensionality reduction is introduced to deal with nonlinear coupling between seismic data: unsupervised machine learning methods are used to cluster the seismic data from two underground construction sites. Obtained information on clusters is then integrated with various seismic and geological variables, and a supervised machine learning model is trained to predict the rock mass class and/or the rock type. The data is over-sampled to avoid biased results when the training datasets are imbalanced and a mix of real and synthesized data is used in training, while an accuracy check is performed only on real data. Our results show that the proposed ML ensemble model has high accuracy in predicting geological conditions. Furthermore, the application of the oversampling technique helps to improve the accuracy of the ML predictors further. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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An I4.0 data intensive platform suitable for the deployment of machine learning models: a predictive maintenance service case study 3

An I4.0 data intensive platform suitable for the deployment ...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Dinten Herrero, Ricardo Zorrilla, Marta Univ Cantabria Grp Ingn Software & Tiempo Real Avda Los Castros 48 Santander 39005 Spain

The Artificial Intelligence is one of the key enablers of the Industry 4.0. The building of learning models as well as their deployment in environments where the rate of data generation is high and their analysis must meet real time requirements lead to the need of selecting a big data platform suitable for this purpose. The heterogeneous and distributed nature of I4.0 environments where data becomes highly relevant requires the use of a data centric, distributed and scalable platform where the different applications are deployed as services. In this paper we present an I4.0 digital platform based on RAI4.0 reference architecture on which a predictive maintenance service has been built and deployed in Amazon Web Service cloud. Different strategies to build the predictor are described as well as the stages carried out for its construction. Finally, the predictor built with k-nearest algorithm is chosen because it is the fastest in producing an answer and its accuracy of 99.87% is quite close to the best model for our case study. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Big data platform Data Stream Mining Predictive Maintenance

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RIS-Assisted Indoor Visible Light Positioning Based on Sparse Bayesian learning 3

RIS-Assisted Indoor Visible Light Positioning Based on Spars...

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3rd international conference on Intelligent Communications and Computing, ICC 2023

作者： Wang, Yuhao Wu, Sifan Yu, Lisu Xu, Chuan Wang, Zhenghai Cai, Xiangxiang School of Information Engineering Nanchang University Jiangxi Nanchang330031 China Digital Technology Application Industry College Shangrao Normal University Jiangxi Shangrao334001 China School of Mathematics and Computer Science Nanchang University Jiangxi Nanchang330031 China

ISBN: (纸本)9798350308327

Visible light communication (VLC) relies heavily on the line of sight (LoS). Once the LoS link is blocked, the communication efficiency will be greatly reduced. The indoor environment is complex, and it is inevitable that the LoS link will be blocked. In order to solve such problem, we have integrated the reconfigurable intelligence surface (RIS) into the indoor visible light positioning (VLP) system to enhance the positioning accuracy when the LoS link is blocked. We have proposed a sparse Bayesian learning (SBL)-Based algorithm to solve the formulated joint-optimization problem of estimated position, noise variance, and sparsity. This algorithm uses Bayesian reconstruction theory and conditional probability expressions to calculate the mean and covariance matrix of the formula, and further obtain the updated noise variance value and sparsity value, thereby finally attain the accurate position. From the simulation results, the positioning accuracy can reach 0.41m when the cascade channel length is 10m. © 2023 IEEE.

关键词： Covariance matrix

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2022 3rd international conference on Human-Centric Smart Environments for Health and Well-Being, IHSH 2022

2022 3rd International Conference on Human-Centric Smart Env...

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3rd international conference on Human-Centric Smart Environments for Health and Well-Being, IHSH 2022

ISBN: (纸本)9781665463218

The proceedings contain 16 papers. The topics discussed include: microgrid energy management strategy based on mas;individual thermal comfort prediction based on upper body thermal imaging and computer vision;enhanced whale optimization algorithm for mesh routers placement problem in wireless mesh networks;an efficient genetic algorithm for solving spectrum assignment problem in elastic optical networks;manta ray foraging optimization algorithm for solving the LEDs placement problem in indoor VLC systems;reference design model for a patient-centric data exchange healthcare environment;toward implementing interoperability in pervasive healthcare systems for chronic diseases by decentralization and modularity;eldercare smart home sensor based system: approach, deployment and insights;and machine learning models to predict cardiovascular events from heart rate variability data.

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A Review on Iris Recognition System using machine and Deep learning 3

A Review on Iris Recognition System using Machine and Deep L...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Panwar, Vaibhav Pooja Sharda University Department of Computer Science and Engineering UP Greater Noida India

ISBN: (纸本)9781665462006

For security purposes, the use of an Iris Recognition System has become standard practice. It is possible to achieve accurate results by employing Iris recognition. Since it never accepts fraudulent values, it is an excellent security tool. In the search for one of the most dependable and accurate biometric technologies available today, Iris recognition is a fantastic location to begin the research. Because of the complex texture of the Iris, which provides strict requirements for identifying individuals, Iris identification systems have caught the interest of many researchers. And its extensive use around the world, Iris recognition has made remarkable strides in recent years. The survey details the Iris recognition technology from the stage of data collection to recognition. Iris recognition involves several procedures, including image capture, pre-processing, feature analysis, recognition, and evaluation. machine learning and deep learning are taken in this article for Iris recognition. A closer look at the essential concepts of various techniques, as well as their inherent connections, will help the researcher to obtain a better understanding of the development of the Iris recognition system. Although many research and review articles have previously been published in this area, we utilized the knowledge from those articles to identify some unexplored areas of iris recognition for researchers. This study will be the beginning for researchers to start their work. © 2022 IEEE.

关键词： Deep learning

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