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检索条件"任意字段=2021 13th International Conference on Machine Learning and Computing, ICMLC 2021"

共 633 条记录，以下是91-100 订阅

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Automatic machine learning based Real Time Multi-Tasking Image Fusion 24

Automatic Machine Learning based Real Time Multi-Tasking Ima...

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16th international conference on machine learning and computing (icmlc)

作者： Karim, Shahid Tong, Geng Li, Jinyang Yu, Xiaochang Hao, Jia Yu, Yiting Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Northwestern Polytech Univ Coll Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Micro Nano Syst Aerosp Minist Educ Xian 710072 Peoples R China Northwestern Polytech Univ Shaanxi Prov Key Lab Micro & Nano Elect Mech Syst Xian 701172 Peoples R China

ISBN: (纸本)9798400709234

Imaging systems work diversely in the image processing domain, and each system contains specific characteristics. We are developing models to fuse images from different sensors and environments to get promising outcomes for different computer vision applications. the multiple unified models have been developed for multiple tasks such as multi-focus (MF), multi-exposure (ME), and multimodal (MM) image fusion. the careful tuning of such models is required to get optimal results, which are still not applicable to diverse applications. We propose an automatic machine learning (AML) based multi-tasking image fusion approach to overcome this problem. Initially, we evaluate source images with AML and feed them to the task-based models. then, the source images are fused with the pre-trained and fine-tuned models. the experimental results authenticate the consequences of our proposed approach compared to generic approaches.

关键词： automatic ML multi-tasking image fusion imaging systems

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machine learning Based Recommendation System for Android Apps 13

Machine Learning Based Recommendation System for Android App...

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13th international conference on Electronics, Computers and Artificial Intelligence, ECAI 2021

作者： Khan, Muhammad Uzair Khalid, Rao Muhammad Umair Burhan, Syeda Airas Nauman, Muhammad National University of Computer and Emerging Sciences Department of Computer Science Peshawar Pakistan Forman Christian College University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665425346

Due to the extensive and ever-growing collection of android apps, it has become quite difficult for users to find apps that they are truly interested in. When looking for android app, the user has a particular goal in mind and wishes to find an app that can truly meets their needs and expectations. App recommendation systems play a vital role in this regard by recommending apps to users based on their preference and requirements. the goal of this project was to develop a machine learning based app recommendation system to recommend Android Apps to the users. We collected the screenshots and metadata of a total of 12000 android apps, divided across ten different categories. CNN model architecture was trained on Vgg16 ImageNet weights. the model's prediction was followed by Feature Extraction which was further used as a basis for Linear Regression to finally recommend an app of interest to the users. © 2021 IEEE.

关键词： Recommender systems

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An incremental ensemble learning system for Vietnamese e-commerce product classification 13

An incremental ensemble learning system for Vietnamese e-com...

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13th international conference Knowledge and Systems Engineering, KSE 2021

作者： Linh, Nguyen Tran Ngoc Quan, Vu Hong Ngan, Le Hoang Phu, Tran Duy Le, Hoang-Quynh Data Analytics Center Viettel Group Viettel Telecom Hanoi Viet Nam University of Engineering and Technology Vietnam National University Hanoi Viet Nam

ISBN: (纸本)9781665499750

With the booming of e-commerce platforms, text classification models play an increasingly important role in businesses. Major challenges that businesses would face include dataset imbalance, continuously added data, language specificity and product specificity. In this paper, we propose a scalable incremental machine learning system for industrial-scale deployment in real-world business. the system also includes steps to optimize e-commerce product specifics. the proposal tactics including keyword dictionary mapping, sampling technique and ensemble learning delivered better performance when compared to models without them. Our experiments also showed that minibatch SVM produced good results and might be considerable in a lighter system. © 2021 IEEE.

关键词： Support vector machines

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Transfer learning-Based Intelligent Diagnosis for Skin Lesion in Cloud-Edge computing Networks 13

Transfer Learning-Based Intelligent Diagnosis for Skin Lesio...

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13th international conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Gu, Minjie Li, Xue Chen, Siguang Nanjing University of Posts and Telecommunications Jiangsu Key Lab of Broadband Wireless Communication and Internet of Things Nanjing China Women's Hospital of Nanjing Medical University Nanjing Maternity and Child Health Care Hospital Department of Dermatology Nanjing China

ISBN: (纸本)9781665407854

Currently, artificial intelligence, especially deep learning and convolutional neural network (CNN), have been widely applied in skin lesion diagnosis. However, the limited training samples, imbalanced distribution of disease classes and similarities among different skin lesions make it difficult to accurately classify them. In this paper, we propose a transfer learning-based intelligent diagnosis mechanism for skin lesion in cloud-edge computing networks. First, we construct a cloud-edge computing model, by which users can obtain convenient and accurate diagnosis service. And then an intelligent skin lesion diagnosis mechanism is developed by combining transfer learning, model fine-tuning and data augmentation techniques, it can address the issue of low classification accuracy caused by a shortage of training samples. In addition, a modified cross-entropy loss is designed to further improve the classification accuracy when the class distribution is imbalanced in training dataset. Finally, the experimental results illustrate that our proposed mechanism achieves significant performance advantage compared with other existing methods. © 2021 IEEE.

关键词： Convolutional neural networks

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Fusion Approaches of Heterogeneous Multichannel CNN and LSTM Models for Human Activity Recognition using Wearable Sensors 13

Fusion Approaches of Heterogeneous Multichannel CNN and LSTM...

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13th international conference on Information Technology and Electrical Engineering, ICITEE 2021

作者： Saengthong, Phurich Laitrakun, Seksan Thammasat University Sirindhorn International Institute of Technology Thailand

ISBN: (纸本)9781665443067

Human activity recognition (HAR) is an emerging filed to classify daily activities and is applicable to many applications including healthcare. Several machine learning models and approaches have been investigated to improve the classification performances. In this paper, we investigate several fusion approaches of heterogeneous multichannel models where the channels use different kinds of deep learning (DL) algorithms to better the HAR performances. the DL algorithms exploited in those channels are convolutional neural networks (CNNs), long short-term memory networks (LSTMs), CNN-LSTMs, and convolutional LSTMs. Six different fusion approaches based on feature fusion and decision fusion are applied to combine the outputs of the channels. their classification performances are evaluated and compared. the best fusion approaches achieve 98.96% and 96.35% of accuracy when using the PAMAP2 and DaLiAc datasets, respectively. © 2021 IEEE.

关键词： Long short-term memory

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A machine learning Approach to Classify Network Traffic 13

A Machine Learning Approach to Classify Network Traffic

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13th international conference on Electronics, Computers and Artificial Intelligence, ECAI 2021

作者： Jadav, Nilesh Dutta, Nitul Sarma, Hiren Kumar Deva Pricop, Emil Tanwar, Supeep Marwadi University Dept. of Computer Engineering Rajkot India Sikkim Manipal Inst. of Technology Dept. of Information Technology Majitar India Petroleum-Gas University of Ploiesti Automatic Control Comp and Elect. Dept Ploiesti Romania Nirma University Dept. of Computer Science and Eng. Ahmedabad India

ISBN: (纸本)9781665425346

there is a significant increase of cloud and networking-enabled applications, leading to an exponential growth of network traffic. Monitoring all these applications and their generated traffic is a challenging and complex task, especially in regard to anonymous networks used to access the Dark Web or darknet. Manual investigation of the network traffic to determine patterns of malicious activity is a very difficult task and the results might not be satisfactory. the usage of machine learning is the most viable approach for the classification of the traffic as normal or malign activity. this paper analyzes CIC-Darknet 2020 dataset to classify the benign and darknet traffic. Before applying any classifiers to our dataset, we have balanced it using Synthetic Minority Oversampling Technique (SMOTE). We have applied PCA to reduce dimensionality, furthermore, ensemble techniques, logistic classifiers, tree-classifiers, and Naive Bayes have been compared and evaluated thoroughly with various evaluation metrics Accuracy, Precision, Recall, F1-Score, and Mathew's Correlation Coefficient (MCC). © 2021 IEEE.

关键词： machine learning

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Employee Turnover Prediction: the impact of employee event features on interpretable machine learning methods 13

Employee Turnover Prediction: The impact of employee event f...

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13th international conference on Knowledge and Smart Technology (KST)

作者： Juvitayapun, thee Natl Inst Dev Adm Appl Stat Bangkok Thailand

ISBN: (纸本)9781728176024

the purpose of this study is to study the impact of employee event features on classifier performance to detect employee likelihood to turnover meanwhile maintaining the interpretability of classifiers for benefits of retention interventions development. In order to maintain feature importance four machine learning methods has been selected on this study, consist of (1) Logistic Regressions Method;2) Random Forest Method;(3) Gradient Boosting Tree Method and (4) Extreme gradient boosting method. the result show that employee event features significantly improve classifiers performance. the extreme gradient boosting method has the best performance with 98.03% Accuracy, 90.79% F1 Score, 97.18% Precision and 85.19% recall. Top three feature importance to predict employee turnover are Merit increase compare with market, Ratio of annual leave hours on average - last month and Ratio of annual leave hours on average - last 2 months respectively.

关键词： component Employee turnover prediction Imbalanced classification Evaluation matrices Employee event features Feature importance

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IoT and machine learning System for Early/Late Blight Disease Severity Level Identification on Tomato Plants 13

IoT and Machine Learning System for Early/Late Blight Diseas...

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13th international conference on Information and Communication Technology and System (ICTS)

作者： Darmawan, Rafif Rahman Rozin, Fauzan Evani, Cynthia Idris, Irman Sumardi, Dadang Inst Teknol Bandung Sch Elect Engn & Informat Bandung Indonesia Inst Teknol Bandung Sch Life Sci & Technol Bandung Indonesia

ISBN: (纸本)9781665440592

Tomato consumption in Indonesia continues to increase every year. Early blight and late blight diseases often attack tomato plants and cause large losses. In this article, an accurate plant disease detection system is designed for the research process of developing high yielding varieties of tomato that are resistant to diseases. the system consists of five subsystems, namely Control, Data Acquisition, Data Storage, machine learning, and Data Visualization. Control and Data Visualization are implemented using an Android application. Data Acquisition is implemented with a robotic framework consisting of a sliding cart, an arm, and a camera. the actuators used are stepper motors and servo motors. the data collection is carried out with an Arducam OV5647 with a capturing speed of 8.23 seconds. Data Storage is implemented on three servers: Firebase, CloudMQTT, and Dataplicity, with MQTT and HTTP as the IoT communication protocol. machine learning is implemented with an SSD MobileNet V2 FPNLite 640x640 which has an mAP value of 77.25% with an average inference time of 3.71 seconds.

关键词： plant diseases detection IoT machine learning

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Supporting Students While Shifting to Online learning During COVID-19: Community College Feedback from Several computing Courses 13th

Supporting Students While Shifting to Online Learning During...

引用

13th international conference on Social computing and Social Media (SCSM) part of the 23rd international conference on Human-Computer Interaction (HCI)

作者： Bartlett, Fred, Jr. Braman, James Community Coll Baltimore Cty Baltimore MD 21237 USA

ISBN: (纸本)9783030776848;9783030776855

the shift in educational strategies and modalities brought on by the COVID-19 pandemic has intensified the need for innovative teaching methods. Both students and educators faced numerous challenges caused or exacerbated by the shift to different learning modalities and compounded by limited services and other restrictions. Adding to this complexity is the diversity in student skillset and technology literacy within the community college environment. At the same time, instructors had to manage their own issues related to the pandemic while juggling critical changes in their courses. In this paper, the authors discuss challenges in teaching several computing courses during the shift to an online format during the COVID-19 pandemic. Several challenges and strategies for supporting students in the computer science/information technology department at a large community college will be discussed, in addition to initial feedback gained from students.

关键词： Online learning COVID-19 Community college Student success

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Controlled Environment for Spinach Cultured Plant with Health Analysis using machine learning 13

Controlled Environment for Spinach Cultured Plant with Healt...

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2021 IEEE 13th international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： Araneta, Mdas Angelo M. Asenjo, Daniel V. Lamprea, Carl Jeremiah L. Reyes, Argilyn Mae L. Medina, Oliver A. Sigue, Anna-Liza F. Cabal, Marielle M. Soriano, Aldrin J. Beano, Mary Grace P. Pamantasan Ng Cabuyao College of Computing and Engineering Laguna Cabuyao Philippines

ISBN: (纸本)9781665401678

the combination of mobile application and machine learning with environmental sensing and image processing device in monitoring the health and growth of the plant in real-time. the goal of the study is to develop a controlled environment for spinach by monitoring its health condition using image processing and supervised machine learning. the device collects real-time data for humidity, temperature, and soil conditions using different sensors. Diagnosis of spinach health status is done by capturing the images of spinach using image processing techniques. Two classifiers were used in detecting spinach health conditions, Green for healthy spinach or no damages on leaves and Green, Yellow, Brown for unhealthy or with holes, damages;this classifier is also called datasets. Spinach health status is monitored using a mobile application called Spinach Monitoring Application (SPIMON) and all collected data are stored in the cloud. the result spinach health status showed that its best to culture spinach in a controlled environment. © 2021 IEEE.

关键词： Image processing

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