2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)最新文献_第5页

Steel Defect Classification Using Machine Learning 基于机器学习的钢缺陷分类

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/IMCOM53663.2022.9721728

Syeda Rabia Arshad, Ishwa Obaid, Rameesha Gull, M. Shahzad

{"title":"Steel Defect Classification Using Machine Learning","authors":"Syeda Rabia Arshad, Ishwa Obaid, Rameesha Gull, M. Shahzad","doi":"10.1109/IMCOM53663.2022.9721728","DOIUrl":"https://doi.org/10.1109/IMCOM53663.2022.9721728","url":null,"abstract":"Ensuring the quality of industrial production for the steel industry is very crucial. For complete defect detection, it is important to know the exact location and class of defect, due to which it becomes difficult to apply this method and attain accuracy in both location and classification. Methods used for the detection of steel defects include the YOLO detection network, acoustic emission method, end-to-end steel surface defect classification, and detection of defects by magneto-optical imaging and neural networks. But as data is too large, deployment and training of these systems become expensive and time-consuming, therefore the algorithms used for the detection of defects should have good generalization. With the growth in computer vision and deep learning automation it is possible to classify images with maximum accuracy. We used machine learning algorithms KNN and transfer learning using VGG-16 for this task to help in quality improvement, quick detection, and classification. KNN used for the classification of defects provided fairly improved results with a significant gain in the accuracy. Detection of defects done by transfer learning via VGG-16 provided promising results. The model trained using VGG-16 achieved high accuracy of 97.54%. These techniques provide an optimal solution for both the classification and detection of defects.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131771202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A General Model for Long-short Term Anomaly Generation in Sensory Data 传感器数据长短期异常生成的通用模型

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721783

Thien-Binh Dang, D. Le, Moonseong Kim, Hyunseung Choo

引用次数: 1

Person Identification Based on Accelerations on Drawing Figures with a Smartphone 基于智能手机绘图加速度的人物识别

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721744

Yoshihaya Takahashi, Atsuya Sonoyama, Takeshi Kamiyamaton, M. Oguchi, Saneyasu Yamaguchi

{"title":"Person Identification Based on Accelerations on Drawing Figures with a Smartphone","authors":"Yoshihaya Takahashi, Atsuya Sonoyama, Takeshi Kamiyamaton, M. Oguchi, Saneyasu Yamaguchi","doi":"10.1109/imcom53663.2022.9721744","DOIUrl":"https://doi.org/10.1109/imcom53663.2022.9721744","url":null,"abstract":"Several methods to estimate the user who is holding a smartphone by analyzing the acceleration obtained from the smartphone's accelerometer using deep learning have been proposed. However, these methods have some issues such as insufficient accuracy or the need for the user to hold a smartphone for a long time. In this paper, we discuss the estimation of the user based on acceleration measured in a shorter aperiod of time. We propose a method to identify a user by make a user draw a figure in the air. The proposed method is based on the assumption that a user is estimated from users given in advance. Acceleration data of all users is acquired in advance, and learning is performed by deep learning using these acceleration data to create a model for estimation. The acceleration data measured for identification are analyzed using this model, and the user who is holding the smartphone is idenfitied. We evaluated the proposed method using two networks, LSTM and DeepConvLSTM, and showed that the proposed method can identify the user with high accuracy. In particular, the accuracy of the method using DeepConvLSTM is high.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131256933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Efficient Multi-Criteria Path Selection Approach in Road Networks through Influencer Nodes and K-hop Search 基于影响者节点和K-hop搜索的道路网络多准则路径选择方法

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721805

Zeeshan Ali, Waqas Nawaz, Kifayat-Ullah Khan

{"title":"An Efficient Multi-Criteria Path Selection Approach in Road Networks through Influencer Nodes and K-hop Search","authors":"Zeeshan Ali, Waqas Nawaz, Kifayat-Ullah Khan","doi":"10.1109/imcom53663.2022.9721805","DOIUrl":"https://doi.org/10.1109/imcom53663.2022.9721805","url":null,"abstract":"We present an efficient approach for a multi-criteria path selection problem in road networks. Since, computing shortest paths among all pairs of nodes in a large network is computationally expensive even with a single criterion. Therefore, performing multi-criteria path search becomes costly and inefficient due to excessive traversals over the network with comparisons between all possible paths. Existing algorithm solved the aforementioned problem by introducing a reference point but they performed a large number of offline pre-computations. In this paper, we introduce an efficient multi-criteria-based approach, in which we uniquely model a road network with places of interest into an attributed graph and restrict our algorithm to explore a minimum number of hops while satisfying maximum criteria. Our objective is to get an optimal path, having minimum path length in terms of number of hops while meeting maximum criteria. Experiments are conducted to verify the effectiveness of the proposed approach. The experimental results depict that the proposed algorithm achieves high accuracy in terms of fulfilling the criteria while avoiding pre-processing steps with minor change in execution time.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114814527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few Shot Model based on Weight Imprinting with Multiple Projection Head 基于多投影头权重印迹的少镜头模型

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/IMCOM53663.2022.9721726

Paulino Cristovao, H. Nakada, Y. Tanimura, H. Asoh

{"title":"Few Shot Model based on Weight Imprinting with Multiple Projection Head","authors":"Paulino Cristovao, H. Nakada, Y. Tanimura, H. Asoh","doi":"10.1109/IMCOM53663.2022.9721726","DOIUrl":"https://doi.org/10.1109/IMCOM53663.2022.9721726","url":null,"abstract":"Few-shot learning models based on imprinted weights have achieved excellent results on several benchmarks. In these methods, the network model directly sets the weights of the final layers for novel classes from the latent representations of the training classes. As a result, the learned representations lead to good performance accuracy in training classes. However, the performance accuracy may be poor on unseen classes. This paper provides an alternative training technique for imprinted weight models. We find that adding projection heads can yield substantial improvements over the baseline model. Our experiments show that (1) introducing nonlinear projection heads in-between the feature extractor and the classifier substantially improves generalization, (2) imprinting from the task-specific layer does not provide better generalization for novel classes. Instead, we propose imprinting from the task-agnostic layer, and (3) our design choice benefits from a large latent dimension. We validate our findings by achieving 5.6 and 4.1% improvement on the MNIST dataset trained with the Omniglot dataset","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132785452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Data Cleaning of Sound Data with Label Noise Using Self Organizing Map 基于自组织映射的带标签噪声的声音数据清理

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721724

Pildong Hwang, Yanggon Kim

引用次数: 2

Mining Contextual Item Similarity without Concept Hierarchy 挖掘没有概念层次结构的上下文项目相似性

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/IMCOM53663.2022.9721788

Mohammad Fahim Arefin, Chowdhury Farhan Ahmed, Redwan Ahmed Rizvee, C. Leung, Longbing Cao

引用次数: 2

A mobile game approach for energy conservation awareness 通过手机游戏提高节能意识

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/IMCOM53663.2022.9721794

Maslinda Mohd Nadzir, Baktajivan Pillay

引用次数: 2

Music recommendation service based on user impressions: Study of user interface acquiring appropriate impression words 基于用户印象的音乐推荐服务:用户界面获取合适印象词的研究

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/imcom53663.2022.9721747

YuJia Han, M. Nakano, M. Oguchi

引用次数: 0

Sorting of Scrambled Video Frames Using Temporal Order Verification 使用时序验证的混乱视频帧排序

2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM) Pub Date : 2022-01-03 DOI: 10.1109/IMCOM53663.2022.9721759

Mazen Khodier, Ahmed Abdelaziz, Maria Gadelkarim, Abdelrahman Abdelkhalek, W. Gomaa

{"title":"Sorting of Scrambled Video Frames Using Temporal Order Verification","authors":"Mazen Khodier, Ahmed Abdelaziz, Maria Gadelkarim, Abdelrahman Abdelkhalek, W. Gomaa","doi":"10.1109/IMCOM53663.2022.9721759","DOIUrl":"https://doi.org/10.1109/IMCOM53663.2022.9721759","url":null,"abstract":"The Sorting Problem is exceedingly popular in our contemporary world for its contribution in many of our daily activities. In this paper, we tackle the problem of sorting through providing a method that sorts a variable number of scrambled video frames. The sorting algorithm is accompanied by two Machine Learning models. A supervised learning approach using video frames with semantic labels is presented through these models. The learning method was formulated as a sequential verification task, that is, determine whether a sequence of frames from a video is in the correct temporal order. Using two different classes of artificial neural networks, Three Dimensional Convolutional Neural Network (3D-CNN), and Recurrent Neural Network (RNN), temporally shuffled frames (frames in non- chronological order) are taken as inputs, then, the neural network is trained to first determine whether that sequence is sorted or not Afterwards, these models are used as validators in the sorting algorithm, to sort shuffled frames in the input sequence, to end up with a sorted frame sequence. The experimental results show good accuracy as different tests ended up with accuracy above 82%. This accuracy is related to the performance of an individual neural network, which means that it could be higher if both models are combined.","PeriodicalId":367038,"journal":{"name":"2022 16th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130498105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0