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Proceedings - 2022 7th international conference on Communication, Image and Signal processing, CCISP 2022

Proceedings - 2022 7th International Conference on Communica...

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7th international conference on Communication, Image and Signal processing, CCISP 2022

ISBN: (纸本)9781665459594

the proceedings contain 97 papers. the topics discussed include: optical fiber dispersion measurement system based on frequency phase shift and its FPGA implementation;research on PCEP extension for VLAN-based traffic forwarding in cloud network integration;electromagnetic pulse protection technology of high frequency communication system;visualization analysis of domestic and foreign cyber security research based on CiteSpace;individual recognition for satellite communication based on feature fusion method;hybrid echo data generation of natural scenes including moving targets for low frequency ultra-wideband one-stationary bistatic SAR;root cause analysis of network fault based on random forest;shared tower communication platform system based on adaptive mounting;a reliable intra-relay cooperative relay network coupling with spatial modulation for the dynamic v2v communication;analysis of the effect of carbon emissions on meteorological factors in Yunnan province;design of a portable ETC radio environment monitoring equipment;a network business quality intelligent assessment and fault location method based on IFIT;and construction technology of software defined modular swarm network information architecture.

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In-Vehicle Occupant Detection System Using mm-Wave Radar 7

In-Vehicle Occupant Detection System Using mm-Wave Radar

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7th international conference on Communication, Image and Signal processing, CCISP 2022

作者： Li, Wei Gao, Yang Hu, Zekun Liu, Nanqi Wang, Kexin Niu, Siyi School of Information Science and Technology North China University of Technology Beijing China Brunel College North China University of Technology Beijing China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

this paper proposes a 77GHz millimeter wave radar-based rear occupant detection system in a vehicle. the major focus is detecting forgotten children in a vehicle's rear seats. the radar TX chirp signal configuration is selected to detect slight movements of people (like breathing). the radar RX IF signal is processed through 2D-FFT to build a two-dimension heat map. then, the Constant False Alarm Detection (CFAR) algorithm is used for object detecting and a 2D cloud points image is obtained which can visualize the presence of people in the vehicle. Moreover, in the simulated vehicle environment experiment, our system can correctly identify if the vehicle is occupied or not in real time. © 2022 IEEE.

关键词： Vehicles

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Modeling Customer Satisfaction based on Kano Model from Online Reviews: Focused on Deep Learning Natural Language processing 7

Modeling Customer Satisfaction based on Kano Model from Onli...

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7th IEEE/ACIS international conference on Big Data, Cloud Computing, and Data Science, BCD 2022

作者： Oh, Myeongseok Kim, Taeyeon Im, Euntack Soongsil Univ. Dept of IT Policy and Management Seoul Korea Republic of Soongsil Univ. Department of Business Administration Seoul Korea Republic of

ISBN: (数字)9781665465823

ISBN: (纸本)9781665465823

Online reviews are data written voluntarily by customers and have become relatively easy to collect in recent years. Because it can be easily collected, online reviews can be used as alternative data to understand customer satisfaction and many studies have attempted customer satisfaction modeling by converting online reviews, which are unstructured data, into structured data. Hence, this study presented an improved framework for conducting customer satisfaction modeling from online reviews and mixed unsupervised learning and supervised learning methods. Also, this study proposed a methodology using deep learning-based natural language processing techniques to supplement the methodological limitations of existing studies and to secure structured data for customer satisfaction modeling in the absence of labeled data. the study is expected to demonstrate the necessity of a detailed product improvement strategy through modeling according to the improvement of processing techniques for review text in customer satisfaction modeling research and to provide efficient product development/improvement guidelines to promote customer satisfaction. © 2022 IEEE.

关键词： Customer satisfaction

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DL Based System for On-Board Image Classification in Real Time, Applied to Disaster Mitigation 7

DL Based System for On-Board Image Classification in Real Ti...

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7th international conference on Parallel, Distributed and Grid Computing, PDGC 2022

作者： Shukla, Amogh Almal, Shubh Gupta, Ankur Jain, Rinisha Mishra, Rishabh Dhabliya, Dharmesh Vellore Institute of Technology School of Cse TL Vellore India Vaish College of Engineering Department of Cse Haryana Rohtak India Vishwakarma Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9781665454018

Natural phenomena such as earthquakes that cause the destruction of bridges and can cause tsunamis, among other phenomena and disasters, are on the rise as a result of climate change, which is affecting our planet. this is because of the rise in average global temperature and the development of extreme rains, which causes rivers to overflow. the use of satellites for terrestrial observation has made it possible to keep tabs on events throughout the world in recent years. However, these satellite systems aren't without their drawbacks;one is that cloud cover may make it difficult to accurately record photographs. It is done based on the clouds present, and another drawback is the visiting time that the satellites have, which means that the area that you want to record the image is not always available, so you have to wait for the day and the time in which the satellite orbit can pass through the area of interest that the image is to be recorded. Drones, which fly at low altitudes and do not have cloud problems, are one such alternative, solving a problem of satellites, on cloud cover. In light of this, in the present work, a methodology based on low-cost systems is proposed for on-board processing in drones of such data in the event of an emergency, such as after the effects of rain or an earthquake. Methodology involves processing the image in an embedded device, which is coupled to the drone and connected to the camera, in such a way that the information that is sent to the ground station, concentrates on the affected area;this allows for faster decision-making and lessens the impact of the natural disaster. therefore, it is shown by testing of the embedded computer based on a Raspberry pi 3 for detecting changes in land cover and transmitting this information to an earth station. © 2022 IEEE.

关键词： Earthquakes

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Distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. this paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 IEEE.

关键词： Smart cities Soft sensors Transportation Data integration Process control Distributed databases Data models

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AFMTD: Anchor-free Frame for Multi-scale Target Detection 7

AFMTD: Anchor-free Frame for Multi-scale Target Detection

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7th international conference on Communication, Image and Signal processing, CCISP 2022

作者： Liu, Xueting Xu, Jingrou Lin, Ruoxi Pan, Jinyang Mao, Junyi Yin, Guangqiang Center for Public Security Technology University of Electronic Science and Technology of China Chengdu China Xinjiang Nowetech Technology Co. Ltd Xinjiang China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

Target detection task plays the most fundamental and important role in computer vision. the appearance of deep learning method has produced a positive effect on target detection, but multi-scale target detection is poor. the reasons could be attributed to two aspects;the first one is that the small target tends to contain less semantic information, which leads algorithm be hard to detect it;the other is that the sample distribution in the practical application scenarios is random, and the different-scaled target features will interfere with each other, which poses negative effect on multi-scale target detection. Based on existing technical issues, we propose an anchor-free frame for the multi-scale target detection (AFMTD) algorithm as solution. First, from the direction of feature fusion, we propose a spatial attention fusion module (SAFM), which designs same scale transformation (SST) based on Bi-FPN, strengthens the valuable information between adjacent feature layers, and suppresses interference features, improving the detection accuracy and resolution ability of targets of different scales. then, from the direction of anchor-free frame detection, the heatmap-based multi-scale detection module (HMDM) is proposed;by introducing a scale distribution mechanism (SDM) and Heatmap-IOU (HIOU) loss function, the module allocates different targets to different corresponding feature maps, which makes the model converge faster and more accurately. through experiments on the MS COCO dataset, our approach achieved 40.5% average precision (AP), and the AP of large, medium, and small-scale targets is 24.5%, 44.1%, and 53.9%, respectively. © 2022 IEEE.

关键词： Semantics

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Deep Convolution Network Analysis for Crop Growth Prediction 7

Deep Convolution Network Analysis for Crop Growth Prediction

引用

7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Sivakumar, Ramkumar Vasudevan, C.K. Karipp, Sarnaam Mustafa Karthikeyan, Sahaana Sankaranarayanan, Suresh San Jose State University Department of Computer Engineering San Jose United States Deloitte Business Technology Analyst Chennai India Infosys Systems Engineer Bangalore India Tata Consultancy Service Assistant Systems Engineering Chennai India Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9781665421683

Farmers have been facing a lot of difficulties in yielding crops in a healthy state. there has been work where Remote sensing technologies employed for getting real time information towards crop growth. Also work has been done in employing Machine and Deep learning for predicting the stages of apple growth, plant phenological and olive tree phenological stages. But in none of work, there has been an attempt to predict the stage of crop growth with a resource optimised Deep Convolution Neural Network (DCNN) model. So we in this work have performed comparitive analysis of three CNN models like VGG19, Xception and Mobile NetV2 for rice crop growth as case study in terms of accuracy, error and memory usage for resource constrained devices. this could help the farmer in assessing the stages of crop growth to take early action before it drastically affects the entire farm © 2022 IEEE.

关键词： Crops

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Advancement in Agronomy Using Machine Learning Approach 7

Advancement in Agronomy Using Machine Learning Approach

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7th IEEE international conference for Convergence in technology, I2CT 2022

作者： Patel, Darshana H Mirani, Hemangee B. Vasant, Avani R. Chaudhari, Nandini M. Vvp Engineering College Department of Information Technology Rajkot India Computer Science and Engineering Krishna School of Emerging Technology and Applied Research Drs Vadodara India

ISBN: (纸本)9781665421683

Agronomy is the science of dealing with the distribution, management, and productivity of agricultural land. When living in a country where agriculture is the heart of the economy and people's lifestyle, agronomy and the development in agronomy becomes a major responsibility of IT sectors to seek enhancement in existing technologies. All the factors of agronomy are so much dependent on rainfall in India, where unpredictability of monsoon and uneven distribution of rainfall leaves us with the next best option - Irrigation! the key aspect of this paper is to select best suitable solutions from the major irrigation systems namely drip irrigation, surface irrigation, sprinkler irrigation and sub-surface irrigation. Machine learning comes to rescue as the model is trained with thousands of samples of data along with the best irrigation system suitable for respective location. the location of a farm or field is selected using illustrator nominator wherein appropriate crop recommendation report can be generated to improve the productivity using machine learning algorithms. thus, machine learning is on its way to help the farmers to adapt suitable system according to their requirements. © 2022 IEEE.

关键词： Crops

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Supervised Learning of Procedures from Tutorial Videos 7th

Supervised Learning of Procedures from Tutorial Videos

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7th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Arunima, S. Sengupta, Amlan Krishnan, Aparna Venkata Vara Prasad, D. Venkataramana, Lokeswari Y. Department of Computer Science and Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai603110 India

ISBN: (纸本)9783031699818

Online educational platforms and MOOCs (Massive Open Online Courses) have made it so that learning can happen from anywhere across the globe. While this is extremely beneficial, videos are not accessible by everyone, due to time constraints and lower bandwidths. Textual step-by-step instructions will serve as a good alternative, being less time-consuming to follow than videos, and also requiring lesser bandwidth. In this work, the authors aim to create a system that extracts and presents a step-by-step tutorial from a tutorial video, which makes it much easier to follow as per the user’s convenience. this can be mainly accomplished by using Text Recognition for academic videos and Action Recognition for exercise videos. Text Recognition is accomplished using the Pytesseract package from Python which performs OCR. Action Recognition is performed with the help of a pre-trained OpenPose model. Additional information is extracted from both exercise and academic videos with the help of speech recognition. the information extracted from the videos are then segmented into procedural instructions which is easy to comprehend. For exercise videos, a frame-wise accuracy of 79.27% is obtained. Additionally, for academic videos, an accuracy of 78.47% is obtained. © IFIP international Federation for information processing 2024.

关键词： Supervised learning

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE international conference on information technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). the purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. the advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. the method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. the dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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