2022 International Conference on Connected Systems & Intelligence (CSI)最新文献_第2页

Analysis of Link Prediction Methods in Weighted and Unweighted Citation Network 加权与非加权引文网络链接预测方法分析

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924105

P. Radhika Dileep, L. R. Deepthi

{"title":"Analysis of Link Prediction Methods in Weighted and Unweighted Citation Network","authors":"P. Radhika Dileep, L. R. Deepthi","doi":"10.1109/CSI54720.2022.9924105","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924105","url":null,"abstract":"Networks can be used to represent a variety of real world complex interacting systems in which vertices represents interacting entities and a network link represents a connection between two nodes or entities. Citation graphs are widely utilized in a variety of graph mining situations like citation recommendation and locating research hotspots. Link prediction is considered as a significant task in data and graph mining and deals with prediction of the future or missing network links based on the given network knowledge. In this research, the problem of prediction of links in weighted citation network is addressed and also we compare how much weighing the network can improve the link prediction accuracy. Normally link prediction problems consider only the existence of links. This might lead to a less accurate prediction as it will not give the strength of the relationship between the two entities. In this study, we analyzed the Search Path Count method, which is used to assign weights to the citation links. So rather than just considering the presence of the links, two weighted path methods using Search Path Count weights are proposed in this research for link prediction. Experiments on real citation dataset show that using the Search Path Count weights to evaluate the relevance of the edges in citation networks improves the accuracy of link prediction systems.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122637854","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

Red Palm Weevil Detection System for Early Warning and Mitigation of Crop Loss 红棕象鼻虫预警与减灾系统

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924005

Parvathy S.R., Deepak Jayan P., Nimmy Pathrose, Rajesh K.R., Lekshmy Janardhanan R, James Varghese, V. S., Nimmy Mathew, Sujith B. Kallara

引用次数: 0

GAN-generated Fake Face Image Detection using Opponent Color Local Binary Pattern and Deep Learning Technique 基于对手颜色局部二值模式和深度学习技术的gan生成假人脸图像检测

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924077

K. Remya Revi, Meera Mary Isaac, R. Antony, M. Wilscy

{"title":"GAN-generated Fake Face Image Detection using Opponent Color Local Binary Pattern and Deep Learning Technique","authors":"K. Remya Revi, Meera Mary Isaac, R. Antony, M. Wilscy","doi":"10.1109/CSI54720.2022.9924077","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924077","url":null,"abstract":"Advancements in AI techniques like Generative Adversarial Network (GAN) facilitate the creation of realistic-looking fake face images and these images are used to create fake profiles on various social media platforms. In this work, we develop deep learning-based binary classification models to distinguish GAN-generated fake face images from camera-captured real face images. The classification models are developed by fine-tuning three lightweight state-of-the-art pre-trained Convolutional Neural Networks (CNNs) - GoogLeNet, ResNet-18, and MobileNet-v2 -using the transfer learning approach. In this method, instead of RGB images, joint color texture feature maps of the images obtained using Opponent Color-Local Binary Pattern (OC-LBP) are used as input to the CNN. For the experimental analysis, we use datasets that contain fake face images generated by Progressive Growing GAN (PGGAN) and Style-based GAN (StyleGAN2), and camera-captured real face images from CelebFaces Attributes- High Quality (CelebA-HQ) and Flickr Faces High Quality (FFHQ) datasets. The proposed method shows remarkable performance in terms of test accuracy, generalization capability, and robustness against JPEG compression. Also, the method exhibits excellent performance when compared with state-of-the-art methods.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126822273","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

Continuous and Realtime Road Condition Assessment Using Deep Learning 基于深度学习的连续实时路况评估

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924135

Ahmed Abul Hasanaath, AbuMuhammad Moinuddeen, Nazeeruddin Mohammad, M. Khan, Ahmed A. Hussain

{"title":"Continuous and Realtime Road Condition Assessment Using Deep Learning","authors":"Ahmed Abul Hasanaath, AbuMuhammad Moinuddeen, Nazeeruddin Mohammad, M. Khan, Ahmed A. Hussain","doi":"10.1109/CSI54720.2022.9924135","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924135","url":null,"abstract":"Continuous and real-time monitoring of road quality conditions is essential for the maintenance of roads and to ensure the safety of drivers and their vehicles. However, the continuous monitoring of thousands of kilometers of roads and highways is a very tedious, time-consuming, error-prone, and expensive operation. A deep learning based approach that can automatically classify the road condition can help tremendously in cutting down the time, effort, accuracy, and cost for monitoring and maintenance of vast road infrastructure. This paper proposes a mechanism to continuously monitor deteriorating road conditions at the city or municipality level in real time and classify them into four different categories (good, medium, bad and unpaved) using custom-built and transfer learning from pre-trained deep learning models (VGG16 and MobileNetV2). The dataset is collected from different roads in the Kingdom of Saudi Arabia. The dataset is composed of close-up road images taken in real time (while driving the car) at regular intervals using an Android App. In the data capture model, the Android App helps to easily tag (label) the captured images for model training purposes. In the classifier mode, the Android app uses the developed deep learning model to classify the captured image and then transmits the medium, bad or unpaved road condition to the central server along with longitude and latitude information to update the centralized map of the city (or municipality). The proposed approach provides an accuracy of 98.6 % to classify the road condition based on images captured during real time driving of the vehicle.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128970948","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

Estimation and Interception of a Spiralling Target on Reentry in the Presence of non-Gaussian Measurement Noise 非高斯测量噪声下再入螺旋目标的估计与拦截

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924065

Aastha Dak, Asfia Urooj, R. Radhakrishnan

{"title":"Estimation and Interception of a Spiralling Target on Reentry in the Presence of non-Gaussian Measurement Noise","authors":"Aastha Dak, Asfia Urooj, R. Radhakrishnan","doi":"10.1109/CSI54720.2022.9924065","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924065","url":null,"abstract":"This work addresses the problem of tracking and interception of a ballistic target having spiralling motion on re-entry. Interception is achieved by an interceptor missile which collects the required measurements using an inbuilt seeker, such that accurate estimates for target states are generated. The usual assumption that these measurements are corrupted by Gaussian noise is revisited, as significant outliers are observed in radar measurements. Since the conventional estimators tend to diverge in the presence of measurement outliers, this work propose an accurate and robust estimation algorithm by incorporating the maximum correntropy (MC) criterion. Hence, a Cauchy kernel based MC unscented Kalman filter (CM-UKF) is proposed for accurate state estimation. Also, proportional navigation guidance (PNG) law is implemented such that a possible interception is realized. The estimation accuracy of CM-UKF along with the PNG law is compared with that of the traditional UKF and Gaussian kernel based MC UKF (MC-UKF), by evaluating the average miss-distance and root mean square error (RMSE) in states.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889192","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

Use of Smartphone Goniometer Application for Range of Motion Measurement in Different Joints: Review Article 智能手机测角仪应用于不同关节的运动范围测量:综述文章

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924042

Trupti Pravin Loya, Ragini Dadgal

{"title":"Use of Smartphone Goniometer Application for Range of Motion Measurement in Different Joints: Review Article","authors":"Trupti Pravin Loya, Ragini Dadgal","doi":"10.1109/CSI54720.2022.9924042","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924042","url":null,"abstract":"Background-Range of motion (ROM) is a module of evaluation of the body that is utilized in protocols of identification and rehabilitation. Goniometer is the device which are routinely used to assess musculoskeletal range of motion. Clinicians now have access to smartphone goniometer software. This study proposes a novel gadget that measures different joints ROM applying a smartphone goniometer. Objective- To test the reliability, relevance, use of smartphone goniometer application for ROM measurement at different joints. Sources of information-Four sources of information (Pubmed, Scopus, Web of Science) have been looked from 2012 to 2022. Review method-Studies on the reliability, validity and use of smartphone goniometer application were included. High quality experiment trails were chosen for the study. Result-98 articles were extracted; 4 articles were included in study which emphasized the use of Smartphone goniometer application (SGA) for ROM measurement in different joints. Studies shows inconsistent results on use of smartphone goniometer application on different joints. Conclusion- The measurement produced with the evaluated SGA are accurate as obtained with the UG. Therefore, it's a valid instrument for measuring range of motion for different joints.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131728176","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

Vision based Human Activity Recognition using Hybrid Deep Learning 基于视觉的混合深度学习人类活动识别

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924016

Aishvarya Garg, S. Nigam, R. Singh

{"title":"Vision based Human Activity Recognition using Hybrid Deep Learning","authors":"Aishvarya Garg, S. Nigam, R. Singh","doi":"10.1109/CSI54720.2022.9924016","DOIUrl":"https://doi.org/10.1109/CSI54720.2022.9924016","url":null,"abstract":"Human activity recognition is a wide research area of computer vision that finds applications in smart surveillance system, healthcare, and human robotic interactions. Nowadays, deep learning methods have achieved more interest due to its ability of executing feature extraction and classification steps simultaneously. In this paper, we have focused on the vision based human activity recognition using deep learning algorithms. Long short term memory (LSTM) is a special form of recurrent neural networks (RNN), specifically designed for long term data dependencies. Also it is a known fact that among deep learning algorithms, convolutional neural networks (CNN) have earned high performance in image classification. To overcome the limitation of LSTM in case of classification of static images, a hybrid CNN-LSTM model is proposed in which features are firstly extracted through CNN and then feed to LSTM as a sequence by the means of time distributed layer. This model is utilized for classifying six activities from two datasets which have shown the accuracy of 96.24% and 93.39% on KTH and Weizmann datasets, respectively. We have also implemented the CNN and LSTM models separately on these datasets with same parameters as used in hybrid model to study their impact on accuracy and loss.","PeriodicalId":221137,"journal":{"name":"2022 International Conference on Connected Systems & Intelligence (CSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129018300","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

Improved Bi-Channel CNN For Covid-19 Diagnosis 改进双通道CNN用于Covid-19诊断

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9924106

Nivea Kesav, Jibukumar M.G

引用次数: 0

An Efficient Arithmetic Optimization Algorithm for Solving Subset-sum Problem 解决子集和问题的高效算术优化算法

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9923996

Murali Krishna Madugula, Santosh Kumar Majhi, Nibedan Panda

引用次数: 2

Active Learning Based Audio Tampering Detection 基于主动学习的音频篡改检测

2022 International Conference on Connected Systems & Intelligence (CSI) Pub Date : 2022-08-31 DOI: 10.1109/CSI54720.2022.9923997

Vivek Rahinj, Rashmika K. Patole, S. Metkar

引用次数: 0