2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)最新文献_第10页

CrossFeat: Multi-scale Cross Feature Aggregation Network for Hand Gesture Recognition CrossFeat:手势识别的多尺度交叉特征聚合网络

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342652

Gopa Bhaumik, Monu Verma, M. C. Govil, S. Vipparthi

{"title":"CrossFeat: Multi-scale Cross Feature Aggregation Network for Hand Gesture Recognition","authors":"Gopa Bhaumik, Monu Verma, M. C. Govil, S. Vipparthi","doi":"10.1109/ICIIS51140.2020.9342652","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342652","url":null,"abstract":"Hand gestures are considered as an effective means of communication in the field of Human-computer interaction. However, the design of an efficient hand gesture recognition (HGR) system is still a challenging task owing to a plethora of complexities such as cluttered background, illumination changes, and occlusion in a real-world environment. The paper proposes a lightweight CNN based network named CrossFeat: Multi-scale Cross Feature Aggregation network for explicit hand gesture recognition (HGR). CrossFeat employs multi-scale convolutional layers and preserves the spatial features from the hand gesture region. The use of multi-scale filters: 1 × 1, 3 × 3, 5 × 5 and 7 × 7 allow the network to learn granular and coarse edges from the different regions of the hand gestures. These complementary features enhance the learning ability of the network. Moreover, the cross-layer connectivity enables the gradient information to reach the top layers and prevent it from diminishing in the upstream layers. The proposed network is investigated on three benchmark datasets: ASL Finger Spelling, NUS-I and NUS-II. The experimental results and analysis show that the aggregation of multi-scale and cross features enhances the performance of the HGR system compared to the existing networks.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124466744","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

Party Identification of Legal Documents using Co-reference Resolution and Named Entity Recognition 使用共同参考决议和命名实体识别的法律文件的当事人识别

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342720

Chamodi Samarawickrama, Melonie de Almeida, Nisansa de Silva, Gathika Ratnayaka, A. Perera

引用次数: 7

Li-Fi: Illuminating the Future of Internet Li-Fi:照亮互联网的未来

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342641

Jasmine Mannil Abraham, Hardeep Kumar, G. Bala

引用次数: 1

Investigation on Transformer Oil Parameters Using Support Vector Machine 基于支持向量机的变压器油参数研究

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342631

Birender Singh, A. H. Kumar, C. Reddy

{"title":"Investigation on Transformer Oil Parameters Using Support Vector Machine","authors":"Birender Singh, A. H. Kumar, C. Reddy","doi":"10.1109/ICIIS51140.2020.9342631","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342631","url":null,"abstract":"Machine Learning has been used to predict the transformer oil parameters by using data obtained from Megger tests and transformer oil test. The relationship among the measured insulation resistance (among distribution transformer’s low tension winding, high tension winding and ground) with breakdown strength, acidity, water content, and interfacial tension of transformer oil is modeled for the prediction. Support Vector Machine is the algorithm used for the prediction of the parameters. A cascaded network approach has been used where stage-wise division has been done to obtain different parameters depending on their correlation with each other. The cascaded network takes insulation resistances as input to predict breakdown and interfacial tension which are further used along with colour as input to predict water content which is further used to predict the acidity. Even though there was a lack of sufficient dataset for training the network the results seemed to be promising. Testing data was used to verify the network and the results were good as evident from the confusion matrices obtained. Therefore it is concluded that SVM is a good technique to predict transformer oil parameters with accuracy.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130503825","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: 1

Vehicle Detection and Localization for Autonomous Traffic Monitoring Systems in Unstructured Crowded Scenes 非结构化拥挤场景下自主交通监控系统的车辆检测与定位

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342663

S. Egodawela, H. Herath, S. M. A. B. Willamuna, M. Ekanayake, G. Godaliyadda, J. Wijayakulasooriya, V. Herath, I. M. S. Sathyaprasad

{"title":"Vehicle Detection and Localization for Autonomous Traffic Monitoring Systems in Unstructured Crowded Scenes","authors":"S. Egodawela, H. Herath, S. M. A. B. Willamuna, M. Ekanayake, G. Godaliyadda, J. Wijayakulasooriya, V. Herath, I. M. S. Sathyaprasad","doi":"10.1109/ICIIS51140.2020.9342663","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342663","url":null,"abstract":"Image/video processing has been one of the major developments in the recent history with its applications in areas of Road safety, military, medical and agriculture fields. Due to its complexity a generic solution for multiple object detection in extremely crowded scenes remains to be found. Traditional methods of optical flow, connected component analysis and image segmentation have been extensively studied in image processing and video processing material. With recent developments of machine learning and numerical optimization techniques the use of deep neural networks are getting frequent in image processing applications. Among such deep learningbased methods commonly used in this context are RCNN variants, Mask RCNN and YOLOv3. An exhaustive comparison of the traditional methods and deep learning-based methods and also deep learning methods are discussed in this paper. This study will be of use in selection of a method for any extremely crowded scene object detection problem.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116385720","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: 1

Multispectral Imaging for Automated Fish Quality Grading 用于鱼类质量自动分级的多光谱成像

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342726

Dhananjaya Jayasundara, Lakshitha Ramanayake, Neranjan Senarath, S. Herath, R. Godaliyadda, Parakrama B. Ekanayake, V. Herath, Sujeewa Ariyawansha

引用次数: 2

Facial Expression Classification Using Convolutional Neural Networking and Its Applications 基于卷积神经网络的面部表情分类及其应用

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342664

R. Jaiswal

{"title":"Facial Expression Classification Using Convolutional Neural Networking and Its Applications","authors":"R. Jaiswal","doi":"10.1109/ICIIS51140.2020.9342664","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342664","url":null,"abstract":"The ability to analyze facial expressions plays a major role in non-verbal communication. If a someone only analyzes what a person’s mouth says and ignores what the person’s face says, then we can only have a part of the story. Humans were the only ones who could distinguish between expressions but not anymore, with advancing technology our computers can learn how to detect emotions as well. This report is a guide to facial expression recognition software using OpenCV, Keras, Tensorflow and CNN, by implementing a program in Python it has become possible to build an algorithm that performs detection, extraction, and evaluation of these facial expressions for automatic recognition of human emotion in real-time. The main features of the face are considered for the detection of facial expressions. To determine the different emotions, the variations in each of the main features are used. To detect and classify different classes of emotions, machine learning algorithms are used by training different sets of images. This paper discusses a real-time emotion classification of a facial expression into one among the seven universal human expressions: Anger, Disgust, Fear, Happy, Neutral, Sad, Surprise by the implementation of a real-time vision system that can classify emotions.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115582983","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

Exploring interdisciplinary nature of postgraduate research in the field of Computing using Text mining: a case study 利用文本挖掘探索计算机领域研究生研究的跨学科性质:一个案例研究

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342734

M. Lall

引用次数: 0

Fire Emergency Detection from Twitter Using Supervised Principal 使用有监督的Principal从Twitter进行火灾紧急探测

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342671

Mohammed Ahsan Raza Noori, Ritika Mehra

{"title":"Fire Emergency Detection from Twitter Using Supervised Principal","authors":"Mohammed Ahsan Raza Noori, Ritika Mehra","doi":"10.1109/ICIIS51140.2020.9342671","DOIUrl":"https://doi.org/10.1109/ICIIS51140.2020.9342671","url":null,"abstract":"Principal Component Analysis (PCA) is primarily a dimensionality reduction technique used in the area of unsupervised machine learning, while the use of PCA in the area of supervised machine learning is still in progress. In the field of supervised event detection from social media, PCA is not well explored by the researchers to avoid the curse of high dimensionality produced by the Vector Space Model (VSM). In this work, we proposed a supervised event detection system, which detect the occurrence of fire emergency from Twitter streaming data in near real-time using supervised PCA as a dimensional reduction technique. Our aim is to find the minimum number of Principal Components (PC’s) that can contribute towards achieving the highest classification performance. We used three machine learning algorithms for classification, Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT). The performance of these algorithms in conjunction with their corresponding PC’s has been compared. Our experimental study has shown that LR outperforms the other two algorithms and achieves the highest accuracy of 91% using 710 PC’s out of 1,000 dimensions. From the results, LR as a classifier is used to build the actual system. To process high dimensional data in batch as well as in near real-time we used Apache Spark framework.","PeriodicalId":352858,"journal":{"name":"2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121307138","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: 1

Empirical Study on Multi Convolutional Layer-based Convolutional Neural Network Classifier for Plant Leaf Disease Detection 基于多卷积层的卷积神经网络分类器在植物叶片病害检测中的实证研究

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS) Pub Date : 2020-11-26 DOI: 10.1109/ICIIS51140.2020.9342729

C. Sunil, C. Jaidhar, Nagamma Patil

引用次数: 8