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

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Applying the Design Sprint to Interactive machine learning Experience Design: A Case Study from Aveni 24th

Applying the Design Sprint to Interactive Machine Learning E...

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24th international conference on Human-computer Interaction (HCII)

作者： Poulter, Chloe Wang, Choon Gayo, Iria DelRio Aveni Ltd 25 South Lauder Rd Edinburgh EH9 2NB Midlothian Scotland

ISBN: (纸本)9783031217067;9783031217074

Design Thinking was applied to the Aveni product in the format of a Design Sprint. The sprint challenge was to identify & design the first Interactive machine learning user experience for the product. Design Thinking was applied to the project to (a) lower the risk of user rejection of the Interactive machine learning interaction and (b) decide which data to collect from users first. The outcome of the sprint was an experience-based roadmap towards the selected Interactive machine learning interaction. After the sprint, participants structured Human-in-the-loop designs by user workflow where previously they were structured by model or data type. This case study provides an example of the application of Design Thinking, through a Design Sprint, to design an Interactive machine learning Human computer Interaction, in order to lower risk, which might be employed by researchers or other industry professionals. Our main contribution is to present the Design Sprint as an approach for defining which aspects of a machine learning solution are target for user-Interactive machine learning, and successfully designing interactions to capture the user input.

关键词： Design Sprint Design thinking process Interactive machine learning Product design and development

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Touchless Advertising Mobile application - TaMa 3

Touchless Advertising Mobile Application - TaMa

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3rd international conference on Next Generation Computing applications, NextComp 2022

作者： Tannoo, Dyan Chiniah, Aatish Jaunkeepersad, Meekshi Baichoo, Humaira Bibi Foicdt University of Mauritius Department of Digital Technologies Reduit Mauritius University of Mauritius Centre for Information Technology & Systems Cits Reduit Mauritius

ISBN: (数字)9781665469548

ISBN: (纸本)9781665469548

Hand gesture recognition has been a promising area of research in the past decades, especially with breakthroughs in the field of computer vision, but the COVID-19 pandemic has brought much attention to this field. The emphasis on a higher sanitary standard has pushed for more touchless interactions to help mitigate viral contagion. This technology could prove to be highly beneficial for interactive devices located in public spaces, such as self-serving information kiosks and self-service check-outs. This paper proposes a touchless advertising mobile application as a proof of concept, to display and interact with advertisements, as a proof of concept. The application uses an implementation of the TensorFlow library to detect, extract, and classify hand gestures. This system is tested and verified to show its robustness. The results obtained show favourable performance and accuracy. The application is designed to offer a smoother learning curve, making it easy to use. © 2022 IEEE.

关键词： computer vision

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Identification of Flowers using machine learning 3

Identification of Flowers using Machine Learning

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3rd international conference on Innovations in Science and Technology for Sustainable Development, ICISTSD 2022

作者： Pawar, Shital Raj, Wagisha Bibikar, Suruchi Patil, Bhakti Kumari, Shubhangi Patil, Anjana Bharati Vidyapeeth's College of Engineering for Women Computer Engineering Department Pune India

ISBN: (纸本)9781665499361

There are so many flower species in the world today that registering and identifying them all is difficult and time-consuming. Every day, we notice a large number of flower species in our houses, parks, roadsides, fields, and on our rooftops, but we have no idea what those flower species are or where they came from. We don't even know what it's called. There are various guidebooks for flower knowledge however, finding the name when you have a picture can be challenging. However, it is impossible for the human brain to remember all of the species it encounters. In that case, we will use this concept to conduct research and develop our project. That will inform people about an unknown flower that they have seen but are unaware of. machine learning for flower classification can pave the way to a solution. Flower classification is a system for identifying and researching specific flower types. Our technology will advance in identifying specific flowers on the spot. The goal of this system is to help those who want to learn about flowers: botanists, travellers and environmental enthusiasts. The proposed flower detection system is implemented by developing a convolutional neural network, which is a very efficient model for image classification. Convolutional neural networks are a popular machine learning domain that is commonly employed in image classification. As a result, we chose this topic to research picture recognition using CNN and Tensorflow. Though we created a web application for our research project, we have many plans to conduct an additional study in order to improve our system. © 2022 IEEE.

关键词： Image classification

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Construction of Fusion Visualization Platform for Meteorological Station and Monitoring application Scenarios 3rd

Construction of Fusion Visualization Platform for Meteorolog...

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3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： He, Tingting Jiang, Yuantong Hao, Panlin Zheng, Faling He, Yuanrong School of Cultural Industries and Tourism Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Xiamen China Technology Co. Ltd. Xiamen China

ISBN: (纸本)9789819624676

With the continuous development of real-world 3D, the visualization of multi-source real 3D data fusion is significant for digital twins. Based on SketchUp, this study aims to utilize advanced laser point cloud scanning technology to conduct component-level realistic 3D modeling and to construct a high-precision movable meteorological station-based realistic 3D model. Meanwhile, by utilizing the WebGL architecture of a real-time 3D model visualization platform, the meteorological station model is integrated into the large model of the monitoring scene, achieving functions such as browsing, attribute querying, rotating, scaling, dismantling, explosion, and more flexible interaction and spatial analysis of the movable meteorological station 3D model. The real-time 3D model of this study can not only provide intuitive and comprehensive spatial information support for the design, deployment, and operation of meteorological stations, but also provide a visual foundation for environmental monitoring, disaster response, and other fields. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data fusion

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Mini-Resizer: A Minimalist Learnable Resizer for Image Geolocation 3

Mini-Resizer: A Minimalist Learnable Resizer for Image Geolo...

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2022 3rd international conference on computer Information and Big Data applications, CIBDA 2022

作者： Lu, Huayuan Zhu, Ma Qi, Baojun Xu, Jingqian Yang, Chunfang Henan Provincial Key Laboratory of Cyberspace Situational Awareness Zhengzhou450001 China Zhengzhou Science and Technology Institute Zhengzhou450001 China

ISBN: (纸本)9783800758760

For practical application scenarios in the field of image geolocation, it is often necessary to resize images into a uniform size to fit deep learning training tools. However, the traditional resizing process would cause information loss, thus leading to limited geolocation performance. To solve this problem, this paper proposes a new learnable resizer, that maps the features to a high-dimensional space through convolution, then performs feature filtering to obtain a resized image, which is more suitable for deep learning-based geolocation tasks rather than the human eye. Compared with the existing methods, the proposed method has better geolocation performance on the Piitburgh30K dataset. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Deep learning

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Multi-bit Collection and Hierarchical Classification Algorithm of computer application Fundamentals based Teaching Data under the Background of Hybrid Clustering Algorithm 3

Multi-bit Collection and Hierarchical Classification Algorit...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Shao, Dongmei Xi'an Siyuan University Xi'an710038 China

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The principle and defects of general static background extraction algorithms are analyzed, a novel background extraction method based on improved hybrid clustering algorithm is proposed, and the concept of classification analysis is given. This paper analyzes the behavior of learning online courses. Classification, and sorting out and summarizing the current situation of learning behavior data collection and mining analysis methods respectively, to provide a theoretical basis for improving the online course learning behavior data collection model. learning analysis technology is based on massive data, using the results of data mining, targeting the online learning environment and students' online learning, exploring information from the data and analysis models generated by learners, and making predictions and suggestions for learning. © 2022 IEEE.

关键词： Data mining

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3rd international Informatics and Software Engineering conference, IISEC 2022

3rd International Informatics and Software Engineering Confe...

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3rd international Informatics and Software Engineering conference, IISEC 2022

ISBN: (纸本)9781665459952

The proceedings contain 80 papers. The topics discussed include: a secure and intelligent smart home controlling system;mobile platform for livestock monitoring and inspection;feature extraction and dimensionality reduction of cancer data using folded LDA;intrusion detection in computer networks using optimized machine learning algorithms;declarations of software engineering project managers managing remotely: provisions for hybrid working;vision transformers-based hand gesture classification;real-time automated license plate recognition and tracking of runaway vehicles;proposing an augmented reality-based system for fashion and beauty makeover;an augmented reality-based system for fashion and beauty makeover;and time series modeling and forecasting of expected monthly rainfall in some regions of northern Nigeria amid security challenges.

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Artificial Neural Networks and Their application in EEG Signal Classification 1

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3rd international conference on Digital Technologies and applications

作者： Corrales, Eddy Corrales, Byron P. Freire, Luigi O. Benalcazar, Maria J. Salesian Polytech Univ Quito Ecuador Cotopaxi Tech Univ Latacunga Ecuador

ISBN: (数字)9783031298608

ISBN: (纸本)9783031298592;9783031298608

This research shows the performance of a multilayer perceptron (MLP) neural network in the classification of electroencephalographic (EEG) signals, for which the Emotiv Insight headset is used for the signal acquisition stage in order to generate data from EEG recordings, where two types of brain patterns were previously selected for this stage: left winks and right winks;so that through preprocessing stages, feature extraction, the feature matrix of the collected data is generated and thus form the input patterns to be used by the neural network in its training process. The main objective of the research is to make use of the classification model generated as a result of the neural network training in OFF LINE mode, thus an application has been designed in which this model is tested to evaluate if the outputs it emits correspond to the type of recording that is delivered as input. Therefore, the precision performance of the model during the testing phase was 90%, thus, the model can be considered to work in an application that requires to be covered in ON LINE mode, for example, in the so-called BCI systems (Brain computer Interface), that translate the user's brain activity into control commands.

关键词： machine learning Neural networks EEG signals Emotiv Insight

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Recognition of Parkinson's disease and Parkinson's dementia based on Gait analysis and machine learning 3

Recognition of Parkinson's disease and Parkinson's dementia ...

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3rd international Symposium on computer Engineering and Intelligent Communications, ISCEIC 2022

作者： Tao, Shuai Wang, Yi Cai, Huaying Lv, Zeping Kong, Liwen Lv, Wen Dalian Key Laboratory of Smart Medical and Health Dalian University Dalian China Department of Neurology Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China National Research Center for Rehabilitation Technical Aids Rehabilitation Hospital Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Key Laboratory of Intelligent Control and Rehabilitation Technology of the Ministry of Civil Affairs Beijing China

ISBN: (纸本)9781510660298

Parkinson's disease (PD) is a common neurodegenerative disease, with a high probability of Parkinson's disease dementia (PDD) in patients with intermediate and advanced PD. Gait disorders and cognitive disorders are common symptoms of PD patients and PDD patients. It is of great clinical significance to identify healthy elderly (HC), PD patients and PDD patients with gait characteristics under cognitive tasks. This study found that stride length, toe-off angle and heel-strike angle are important gait markers for identifying HC and PD as well as HC and PDD. Gait characteristics of multiple 7 task gait consumption can preliminarily identify PD and PDD. The gait features under multiple 7 task were used as input variables of machine learning, and the classification model was modeled by training random forest (RF) and support vector machine (SVM), and the accuracy of machine learning classification was evaluated by using the five-fold cross-validation method. The results found that the classification accuracy of all machine learning can reach more than 80%, and RF has a better classification effect. To further improve the recognition accuracy, this paper introduces recursive feature elimination (RFE) for important feature selection. By screening important features, it is found that the accuracy and AUC value of machine learning are improved to a certain extent. The highest classification accuracy of HC and PD is 91.25%, and the AUC value is 0.9127. The classification accuracy of HC and PDD was up to 97.5%, and the AUC value was 0.95. These findings have important application value for clinical diagnosis of PD and PDD. It also paves the way for a better understanding of the utility of machine learning techniques to support clinical decision-making. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

关键词： Gait analysis

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machine learning based Data Stream Merging in Additive Manufacturing 3

Machine Learning based Data Stream Merging in Additive Manuf...

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

作者： Zenisek, Jan Groening, Holger Wild, Norbert Huskic, Aziz Affenzeller, Michael Univ Appl Sci Upper Austria Ctr Excellence Smart Prod Campus Hagenberg & Wels Wels Austria Johannes Kepler Univ Linz Inst Formal Models & Verificat Linz Austria

Artificial intelligence, especially in the form of machine learning methods, has the potential to stabilize and optimize manufacturing processes in terms of productivity, quality and resource efficiency as numerous publications in the recent past verify. To make this possible, information from various sources (machine controller, external sensory, manufacturing execution system etc.) must be tapped and aligned as a prerequisite for its utilization. Optimally, a suitable primary key (e.g. timestamp or position information) is available and can be used to merge the raw data streams. If such a key does not exist, however, merging data must be carried out manually by domain experts at great expense. To support the envisaged process stabilization and optimization, we present a machine learning based method to generate a valid data stream from various, heterogeneous sources. The herein detailed approach aims to mitigate the problem of data merging using the process knowledge of domain experts and transfer it to machine learning models. The paper demonstrates the applicability of this approach in the context of a Laser Metal Deposition case study. Therein, conducted experiments show unstable deposition heights, which lead to unwanted process changes that affect the geometric and metallurgical component quality. The eventual aim of this use case is to stabilize and optimize the deposition process. The presented approach shows promising results to pave the way for achieving this goal by merging information from various sources in such a way that meaningful conclusions can be drawn. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： machine learning Additive Manufacturing Laser Metal Deposition Data Stream Analysis Domain Knowledge Integration

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