2021 IEEE World AI IoT Congress (AIIoT)最新文献_第8页

Education System for Bangladesh Using Augmented Reality, Virtual Reality and Artificial Intelligence 使用增强现实、虚拟现实和人工智能的孟加拉国教育系统

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454247

Haymontee Khan, Faria Soroni, Syed Jafar Sadek Mahmood, Noel Mannan, Mohammad Monirujjaman Khan

引用次数: 3

Remote Crop Sensing with IoT and AI on the Edge 边缘物联网和人工智能的远程作物遥感

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454237

Panagiotis Savvidis, G. Papakostas

引用次数: 0

Multi-Modal Multi-Stream UNET Model for Liver Segmentation 肝脏分割的多模态多流UNET模型

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454216

Hagar Louye Elghazy, M. Fakhr

{"title":"Multi-Modal Multi-Stream UNET Model for Liver Segmentation","authors":"Hagar Louye Elghazy, M. Fakhr","doi":"10.1109/AIIoT52608.2021.9454216","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454216","url":null,"abstract":"Computer segmentation of abdominal organs using CT and MRI images can benefit diagnosis, treatment, and workload management. In recent years, UNETs have been widely used in medical image segmentation for their precise accuracy. Most of the UNETs current solutions rely on the use of single data modality. Recently, it has been shown that learning from more than one modality at a time can significantly enhance the segmentation accuracy, however most of available multi-modal datasets are not large enough for training complex architectures. In this paper, we worked on a small dataset and proposed a multi-modal dual-stream UNET architecture that learns from unpaired MRI and CT image modalities to improve the segmentation accuracy on each individual one. We tested the practicality of the proposed architecture on Task 1 of the CHAOS segmentation challenge. Results showed that multi-modal/multi-stream learning improved accuracy over single modality learning and that using UNET in the dual stream was superior than using a standard FCN. A “Dice” score of 96.78 was achieved on CT images. To the best of our knowledge, this is one of the highest reported scores yet.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130666763","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

Study of Behaviors of Motion Models in High-Order Systems 高阶系统运动模型的行为研究

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454228

MinhTri Tran, A. Kuwana, Haruo Kobayashi

{"title":"Study of Behaviors of Motion Models in High-Order Systems","authors":"MinhTri Tran, A. Kuwana, Haruo Kobayashi","doi":"10.1109/AIIoT52608.2021.9454228","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454228","url":null,"abstract":"This paper presents several proposed motion models of high-order physical systems in three main concepts called macro-scale, regular-scale, and nano-scale. In fact, it is very difficult to find an exact numerical solution for the high-order differential equations because all numerical methods only yield the approximate solutions. In addition, loop gain is not widely used in many negative feedback systems because it is an approximation value. To overcome the limitations of the high-order differential equations and the loop gain, the waveforms of the physical periodic motions are expressed by helix functions at time variation, and the characteristics of complex functions are used to examine the behaviors of the transmission spaces and the transmission networks in the different motion models including the Earth's motion, the simple pendulum systems, and the electronic systems. Furthermore, the force of attraction and the friction or the resistance in the different scales obey the conservation law and the superposition principle; therefore, three superposition formulas are introduced to derive the transfer functions in high-order mechatronic systems. The operating regions, the effects of the overshoot phenomena, the breaking chemical bonds, and the difference between negative and positive feedbacks in these systems are also introduced. As a result, the use of complex functions, helix waves, and superposition principle leads to a complete control theory with which many behaviors of the physical systems can be explained and predicted easily.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116426903","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

Hardware Deployment of HBONext using NXP Bluebox 2.0 HBONext基于NXP Bluebox 2.0的硬件部署

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454210

S. Joshi, M. El-Sharkawy

{"title":"Hardware Deployment of HBONext using NXP Bluebox 2.0","authors":"S. Joshi, M. El-Sharkawy","doi":"10.1109/AIIoT52608.2021.9454210","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454210","url":null,"abstract":"Deep learning models require a lot of computation and memory, so they can only be run on high-performance computing platforms such as CPUs or GPUs. However, due to resource, energy, and real-time constraints, they often fail to meet portable requirements. As a result, there is an increasing interest in real-time object recognition solutions based on CNNs, which are typically implemented on embedded systems with limited resources and energy consumption. Recently, hardware accelerators have been developed to provide the computing power needed by AI and machine learning tools. These edge accelerators deliver high-performance hardware while maintaining the needed accuracy for the task at hand. This paper takes a step forward by suggesting a design approach for porting CNNs to low-resource embedded systems, bridging the gap between deep learning models and embedded edge systems. To complete our task, we employ closer computing approaches to minimize the computational load and memory consumption of the computer while maintaining impressive deployment performance. HBONext is one of those models that was designed to be easily deployable on embedded and mobile devices. We demonstrate how to use NXP BlueBox 2.0 to introduce a real-time HBONext image classifier in this work. Incorporating this concept into this hardware has been a huge success due to its limited architectural scale of 3 MB. This model was trained and validated using the CIFAR10 data set, which performed exceptionally well due to its smaller size and higher accuracy.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129706789","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

Educational Web Application for Young People to Raise Awareness on Menstruation 教育网页应用程式，提高青少年对月经的认识

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454177

Sudman Bin Manjur, Nahian Noshin Nur, Md. Mushfiqur Rahman, Rohimul Basunia, Mohammad Monirujjaman Khan

{"title":"Educational Web Application for Young People to Raise Awareness on Menstruation","authors":"Sudman Bin Manjur, Nahian Noshin Nur, Md. Mushfiqur Rahman, Rohimul Basunia, Mohammad Monirujjaman Khan","doi":"10.1109/AIIoT52608.2021.9454177","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454177","url":null,"abstract":"Proper menstrual hygiene management is vital to the poise and strength of women and young ladies. In any case, it is a disregarded issue both in the overall individuals and health sectors, prompting an emergency of information, offices and hygienic practice. To eliminate the feminine cleanliness of the board issues and social issues, we present to you a web application to raise awareness among individuals about menstruation. This is essentially intended for the youthful ages to show them evidently. We have chiefly utilized PHP 7, HTML, C# and CSS for all coding and information putting away system. Word Press, Adobe Flash and Blender are utilized to do the animations, videos, design and other kinds of things. There will be interactive animation questions according to the child's understanding. There will also be short fun quiz games, different methods of explanation for both boys and girls, options to ask questions from experts and many more things. This is also an attempt to normalize menstruation among people and to minimize taboos and misconceptions on this topic.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131167574","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

Blind Attack Flaws in Adaptive Honeypot Strategies 自适应蜜罐策略中的盲攻击缺陷

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454206

Muath A. Obaidat, Joseph Brown, Awny Alnusair

{"title":"Blind Attack Flaws in Adaptive Honeypot Strategies","authors":"Muath A. Obaidat, Joseph Brown, Awny Alnusair","doi":"10.1109/AIIoT52608.2021.9454206","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454206","url":null,"abstract":"Adaptive honeypots are being widely proposed as a more powerful alternative to the traditional honeypot model. Just as with typical honeypots, however, one of the most important concerns of an adaptive honeypot is environment deception in order to make sure an adversary cannot fingerprint the honeypot. The threat of fingerprinting hints at a greater underlying concern, however; this being that honeypots are only effective because an adversary does not know that the environment on which they are operating is a honeypot. What has not been widely discussed in the context of adaptive honeypots is that they actually have an inherently increased level of susceptibility to this threat. Honeypots not only bear increased risks when an adversary knows they are a honeypot rather than a native system, but they are only effective as adaptable entities if one does not know that the honeypot environment they are operating on is adaptive as wekk. Thus, if adaptive honeypots become commonplace - or, instead, if attackers even have an inkling that an adaptive honeypot may exist on any given network, a new attack which could develop is a “blind confusion attack”; a form of connection which simply makes an assumption all environments are adaptive honeypots, and instead of attempting to perform a malicious strike on a given entity, opts to perform non-malicious behavior in specified and/or random patterns to confuse an adaptive network's learning.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127750161","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

Improving the Relevance of a Web Navigation Recommender System Using Categorization of Users' Experience 利用用户体验分类改进Web导航推荐系统的相关性

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454181

Ilan Yehuda Granot, C. Wu, Z. Or-Bach

引用次数: 0

Improved Noise Filtering Technique For Wake Detection In SAR Image Under Rough Sea Condition 海浪条件下SAR图像尾迹检测的改进噪声滤波技术

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-05-10 DOI: 10.1109/AIIoT52608.2021.9454171

P. Subashini, P. V. H. Kumar, S. Lekshmi, M. Krishnaveni, T. Dhivyaprabha

{"title":"Improved Noise Filtering Technique For Wake Detection In SAR Image Under Rough Sea Condition","authors":"P. Subashini, P. V. H. Kumar, S. Lekshmi, M. Krishnaveni, T. Dhivyaprabha","doi":"10.1109/AIIoT52608.2021.9454171","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454171","url":null,"abstract":"Sea surface is rough when the weather condition at sea is rough due to strong wind, waves, swell and storms. Under the rough sea condition, the propagation of radar energy and the subsequent radar coverage is strongly influenced by various atmospheric effects, such as, strong wind, wave height, weather condition, oceanic currents and rainstorms. The identification of ship wakes in Synthetic Aperture Radar (SAR) image under the rough sea condition is viewed as a highly complex task for the real time monitoring and surveillance applications. It becomes a quite big challenge due to coherent radiation of backscattering signals and the multiplicative speckle noise found in SAR images. The objective of this work is to develop an optimized Discrete Wavelet Transform (DWT) based on Synergistic Fibroblast Optimization (SFO) algorithm for filtering speckle noise in SAR image which are captured under rough sea condition. An improved filtering technique is tested with the real time SAR images acquired from European Space Agency (ESA) sentinel scientific data hub and its efficacy is further validated by employing Discrete Radon Transform (DRT) method to detect ship wakes (linear signature) in SAR image under rough sea surface. The performance of SFO based wavelet transform is evaluated and compared with conventional DWT families, namely, daubechies, coiflet, symlet, discrete meyer, biorthogonal and reverse biorthogonal to conduct the better investigation of this study. Investigation of results illustrates the effectiveness of optimized method, in terms of, noise suppression and its implication on radon transform method to localize the identification of ship wakes in SAR imagery.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115006537","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

Predictive Maintenance - Bridging Artificial Intelligence and IoT 预测性维护-连接人工智能和物联网

2021 IEEE World AI IoT Congress (AIIoT) Pub Date : 2021-03-20 DOI: 10.1109/AIIoT52608.2021.9454173

Gerasimos G. Samatas, Seraphim S. Moumgiakmas, G. Papakostas

{"title":"Predictive Maintenance - Bridging Artificial Intelligence and IoT","authors":"Gerasimos G. Samatas, Seraphim S. Moumgiakmas, G. Papakostas","doi":"10.1109/AIIoT52608.2021.9454173","DOIUrl":"https://doi.org/10.1109/AIIoT52608.2021.9454173","url":null,"abstract":"This paper highlights the trends in the field of predictive maintenance with the use of machine learning. With the continuous development of the Fourth Industrial Revolution, through IoT, the technologies that use artificial intelligence are evolving. As a result, industries have been using these technologies to optimize their production. Through scientific research conducted for this paper, conclusions were drawn about the trends in Predictive Maintenance applications with the use of machine learning bridging Artificial Intelligence and IoT. These trends are related to the types of industries in which Predictive Maintenance was applied, the models of artificial intelligence were implemented, mainly of machine learning and the types of sensors that are applied through the IoT to the applications. Six sectors were presented and the production sector was dominant as it accounted for 54.55% of total publications. In terms of artificial intelligence models, the most prevalent among ten were the Artificial Neural Networks, Support Vector Machine and Random Forest with 28.95%, 18.42% and 14.47% respectively. Finally, 12 categories of sensors emerged, of which the most widely used were the sensors of temperature and vibration with percentages of 60.71% and 46.42% correspondingly.","PeriodicalId":443405,"journal":{"name":"2021 IEEE World AI IoT Congress (AIIoT)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120959077","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: 8