2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)最新文献_第6页

Mapping of Business Intelligence Research Themes: Four Decade Review 商业智能研究主题映射:四十年回顾

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530790

A. Purnomo, M. Firdaus, Dian Utami Sutiksno, R. S. Putra, Uswatun Hasanah

{"title":"Mapping of Business Intelligence Research Themes: Four Decade Review","authors":"A. Purnomo, M. Firdaus, Dian Utami Sutiksno, R. S. Putra, Uswatun Hasanah","doi":"10.1109/COMNETSAT53002.2021.9530790","DOIUrl":"https://doi.org/10.1109/COMNETSAT53002.2021.9530790","url":null,"abstract":"Research on business intelligence continues to develop but is limited to one country and/or one field. From the perspective of bibliometric reviews, this study purposes to visually research mapping and research trends in the field of business intelligence on an international scale. This study used bibliometric techniques with secondary data from Scopus. Analyze and visualize data using the VOSViewer program and the analyse search results function on Scopus. This study analyzed 244 scientific documents published from 1975 to 2020. According to the research, Monash University, and Yeoh, W. had the most active affiliated institutions and individual scientists in business intelligence research. Business, management, and accounting; and Lecture Notes in Business Information Processing were the most studied and disseminated outlets of business intelligence research. There were five category maps of collaborative researchers from around the world. Based on the identification of a collection of knowledge accumulated from over forty-five years of publication, this research proposes a grouping of business intelligence research themes: Business analytics, Element of marketing, Competitive intelligence, Intelligence system, Industry, and Business process abbreviated as BECIIB themes.","PeriodicalId":148136,"journal":{"name":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131352847","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

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment 动态环境下无人机四旋翼避障的人工势场算法

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530803

A. Ma’arif, Wahyu Rahmaniar, M. A. M. Vera, Aninditya Anggari Nuryono, Rania Majdoubi, Abdullah Çakan

引用次数: 12

Interpolation Effect on FBP-Based Image Reconstruction of Measured L-Band Microwave Tomography 基于fbp的l波段微波层析成像图像重构插值效果研究

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530773

Mohammad Ridwan Effendi, Fauzia Anis Sekar Ningrum, Nurul Amalia Amri, Bakhtiar Abubakar, Slamet Risnanto, A. Munir

{"title":"Interpolation Effect on FBP-Based Image Reconstruction of Measured L-Band Microwave Tomography","authors":"Mohammad Ridwan Effendi, Fauzia Anis Sekar Ningrum, Nurul Amalia Amri, Bakhtiar Abubakar, Slamet Risnanto, A. Munir","doi":"10.1109/COMNETSAT53002.2021.9530773","DOIUrl":"https://doi.org/10.1109/COMNETSAT53002.2021.9530773","url":null,"abstract":"This paper presents an interpolation effect on image reconstruction using Filtered Back Projection (FBP) algorithm for L-band microwave tomography. The FBP-based image reconstruction is conducted by changing the image of the object using radon transformation. The FBP method is chosen for the reconstruction due to no time consuming on its computation process. After conducting the reconstruction process, the interpolation is applied on the reconstructed image so that the image produced can be more stable. Here, the interpolation method is using linear, nearest, and cubic method at the L-band frequency of 1.5 GHz. The results show that the interpolation using linear method has Mean Square Error (MSE) value of 0.1335. Meanwhile, the nearest and cubic methods has the MSE value of 0.1392 and 0.1323, respectively. In case of using the Structure Similarity Indexing Method (SSIM), it shows that the linear method produces greater similarity than other methods, with the value of 0.3399. Whilst nearest-neighbor and cubic methods have the SSIM value of 0.3506 and 0.3404, respectively.","PeriodicalId":148136,"journal":{"name":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121343708","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

Steganalysis of Adaptive Image Steganography using Convolution Neural Network and Blocks Selection 基于卷积神经网络和块选择的自适应图像隐写分析

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530784

Saeed M. Hashim, D. Alzubaydi

{"title":"Steganalysis of Adaptive Image Steganography using Convolution Neural Network and Blocks Selection","authors":"Saeed M. Hashim, D. Alzubaydi","doi":"10.1109/COMNETSAT53002.2021.9530784","DOIUrl":"https://doi.org/10.1109/COMNETSAT53002.2021.9530784","url":null,"abstract":"The method of detecting secret information in images in order to discriminate between a suspect and carrier images are known as image steganalysis. Since content-adapted steganographic methods adaptively integrate knowledge into regions with distortion-based rich textures, state-of-the-art steganalysis approaches with the development of steganography technology cannot achieve the required detection performance. This makes it difficult to extract effective steganalysis features. Due to the impressive performance of Convolution Neural Network (CNN) in the field of image processing, a growing number of research papers are focusing on developing steganalysis methods based on the CNN. Moreover, most research works rely on the entire image to extract the steganalysis features. In this paper, we introduce a new image steganalysis method by designing an entropy-based regional selecting method, and a new CNN framework to discriminate between a stego and cover image. We design a new approach for image block selection that finds the blocks with the highest entropy, allowing the CNN to concentrate on complex textures in image regions while reducing computational complexity. To allow feature diversity, we built a new CNN framework composed of two subnets of different kernel sizes, which we then repeatedly combined and separated. Those two aspects are consequently improved the performance with a reasonable number of epochs for training. When the results of the experiments are compared, our proposed method can lead to improving the detection accuracy of content-adaptive steganographic methods.","PeriodicalId":148136,"journal":{"name":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115693616","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

Image Classification for Egg Incubator using Transfer Learning of VGG16 and VGG19 基于VGG16和VGG19迁移学习的孵卵器图像分类

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530826

Apri Junaidi, Jerry Lasama, Faisal Dharma Adhinata, A. Iskandar

{"title":"Image Classification for Egg Incubator using Transfer Learning of VGG16 and VGG19","authors":"Apri Junaidi, Jerry Lasama, Faisal Dharma Adhinata, A. Iskandar","doi":"10.1109/COMNETSAT53002.2021.9530826","DOIUrl":"https://doi.org/10.1109/COMNETSAT53002.2021.9530826","url":null,"abstract":"Research in the field of image classification is proliferating and providing benefits to the community. This research focuses on image classification on incubators, incubators in addition to monitoring temperature and humidity. Monitoring of conditions in incubators is also required. This study proposes a classification of eggs, hatching eggs, and chicks. Many images of each object are needed, a total of 3,924 images from all three classes. The data set collected from google image and the collection of images owned by the author obtained from shooting with a smartphone camera on a private farm. Data preprocessing is carried out, such as changing the shape of the image to a square that VGG required 224x224 pixels, augmentation to reproduce data, and sharing training data are the input size 80 and 20 percent validation. After preprocessing the data, the model formation and training process was carried out with the results for each model: Custom CNN model yielded an accuracy of 0.8687, VGG16 produced an accuracy of 0.90, and VGG19 produced an accuracy of 0.92. This study shows that transfer learning has the highest accuracy in image classification.","PeriodicalId":148136,"journal":{"name":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332403","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: 5

A Comparison of Several Approaches for Image Recognition used in Food Recommendation System 食品推荐系统中几种图像识别方法的比较

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530793

Quang-Linh Tran, Gia-Huy Lam, Quang-Nhat Le, T. Tran, Trong-Hop Do

{"title":"A Comparison of Several Approaches for Image Recognition used in Food Recommendation System","authors":"Quang-Linh Tran, Gia-Huy Lam, Quang-Nhat Le, T. Tran, Trong-Hop Do","doi":"10.1109/COMNETSAT53002.2021.9530793","DOIUrl":"https://doi.org/10.1109/COMNETSAT53002.2021.9530793","url":null,"abstract":"In food recommendation systems, users can use mobile devices to capture images of the dishes they eat. The types of dishes in images will be automatically recognized and input to the recommendation system to suggest other dishes which the users likely to enjoy. Food image recognition is therefore an essential part of the food recommendation system. This used to be a hard problem in computer vision as many foods are very similar in color and texture. Thanks to the development of artificial intelligent and especially deep learning techniques, it is much easier to build a program to recognize the type of foods in the image. This paper examines several approaches, from traditional machine learning to state-of-the-art deep learning techniques for food image recognition to provide a comparison of the performance of these techniques. To this end, a new dataset of Vietnamese cuisine including 12,017 photos of 15 dishes has been built to test algorithms. Traditional machine learning techniques including Histogram of Gradient (HOG) and Scale-Invariant Feature Transform (SIFT) and state-of-the-art deep learning models including VGG16, MobileNet, ANN, Resnet18, Resnet50, Densenet121 have been used for extracting features in the food images. Logistic Regression (SF) and SoftMax (SM) classification have been used for classification using the extracted features. Based on the comparison results provided in this paper, one can choose appropriate techniques for image recognition to build a good food recommendation system.","PeriodicalId":148136,"journal":{"name":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125990497","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

[COMNETSAT 2021 Program Schedule] [COMNETSAT 2021项目时间表]

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/comnetsat53002.2021.9530835

引用次数: 0

Orbit Shifting Analysis Satellite Telkom 4 with Cowell and Encke Method Caused by Perturbation 用Cowell和Encke方法分析telkom4卫星摄动引起的移轨分析

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530783

Ahmad Fahrizal Ramadhan, Solichah Larasati, PB Imam Muhammadi

{"title":"Orbit Shifting Analysis Satellite Telkom 4 with Cowell and Encke Method Caused by Perturbation","authors":"Ahmad Fahrizal Ramadhan, Solichah Larasati, PB Imam Muhammadi","doi":"10.1109/COMNETSAT53002.2021.9530783","DOIUrl":"https://doi.org/10.1109/COMNETSAT53002.2021.9530783","url":null,"abstract":"Satellites are objects that revolve around other objects in certain period of revolution and rotation. There are two types of satellites, natural and artificial satellites. There are several types of artificial satellites based on their use such as weather satellites, military satellites, science education satellites, and communications satellites. Artificial satellites can follow a position according to the movement of the earth, which is called the geostationary orbital position. When a satellite is in a geostationary orbit position, its speed will be relatively low, allowing the position satellite to be moved. The surveillance area has been regulated in ITU-R Rec. S. 484. about the control area tolerance limit of less ±0,5° for operational fixed satellite. In this case, the mobile satellite requires a lot of power and shortens the life of the satellite. Predicting the orbital displacement time interval involves two orbital methods, namely Cowell method and Encke method. Both of these methods can be used to determine the length of time that occurs when the satellite is moved. This study aims to compare which method can determine time intervals more accurately. The advantage of this research is that there are two methods that can be used to determine the time interval of the satellite orbitals. Quantitative comparative research method for field survey through comparison of applied research in main control satellites. The results of the comparison of the two methods will be used for orbital purposes, combined with positional and mathematical factors to determine the duration of orbital shift.","PeriodicalId":148136,"journal":{"name":"2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132032221","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

Analysis of GFDM-OQAM Performance Using Zero Forcing Equalization 基于零强迫均衡的GFDM-OQAM性能分析

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530809

Elsa Nur Oktaviar Herawati, A. F. Isnawati, Khoirun Ni’amah

引用次数: 2

Real-time Signboards using Panels P5 RGB and NodeMCU esp8266 with Library PxMatrix 实时标牌使用面板P5 RGB和NodeMCU esp8266与库PxMatrix

2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT) Pub Date : 2021-07-17 DOI: 10.1109/COMNETSAT53002.2021.9530785

P. Yuliantoro, Shinta Romadhona, A. Hikmaturokhman

引用次数: 2