Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494456
Qiao Lin, Xin Chen, Chao Chen, J. Garibaldi
Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have concentrated on modifying convolutional kernel size to achieve multi-scale spatial information. In this paper, we introduce a novel fuzzy integral module to the CNNs for fusing the information across feature channels. The fuzzy integral is a mathematical aggregation operator and is widely used in decision level fusion. Herein, we utilize a special case of fuzzy integrals namely ordered weight averaging (OWA) to merge information at feature level. Three publicly available datasets were used to evaluate the proposed fuzzy CNN model for image segmentation. The results show that the proposed fuzzy module helps in reducing the baseline model parameters by 58.54% while producing higher segmentation accuracy (measured by Dice) than the baseline method and a similar method reported in the literature.
{"title":"FuzzyDCNN: Incorporating Fuzzy Integral Layers to Deep Convolutional Neural Networks for Image Segmentation","authors":"Qiao Lin, Xin Chen, Chao Chen, J. Garibaldi","doi":"10.1109/FUZZ45933.2021.9494456","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494456","url":null,"abstract":"Convolutional neural networks (CNNs) have achieved the state-of-the-art performance in many application areas, due to the capability of automatically extracting and aggregating spatial and channel-wise features from images. Most recent studies have concentrated on modifying convolutional kernel size to achieve multi-scale spatial information. In this paper, we introduce a novel fuzzy integral module to the CNNs for fusing the information across feature channels. The fuzzy integral is a mathematical aggregation operator and is widely used in decision level fusion. Herein, we utilize a special case of fuzzy integrals namely ordered weight averaging (OWA) to merge information at feature level. Three publicly available datasets were used to evaluate the proposed fuzzy CNN model for image segmentation. The results show that the proposed fuzzy module helps in reducing the baseline model parameters by 58.54% while producing higher segmentation accuracy (measured by Dice) than the baseline method and a similar method reported in the literature.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115278921","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494432
P. Grzegorzewski, Oliwia Gadomska
A new statistical goodness-of-fit for comparing distributions of two or more populations and based on fuzzy data is proposed. Its idea goes back to the k-nearest neighbor technique applied in pattern recognition, where it simply consists in classifying an object by the majority vote of its neighbors. In our paper we show that by an appropriate test statistic construction which counts the number of nearest neighbors between and within samples it is possible to check whether available fuzzy samples come or not from the same distribution. It is worth underlying that the suggested testing procedure is completely distribution-free which seems to be of extreme importance in statistical reasoning with fuzzy data. Our test proposal is completed with a study of its properties and a case study related to quality assessment.
{"title":"Nearest Neighbor Tests for Fuzzy Data","authors":"P. Grzegorzewski, Oliwia Gadomska","doi":"10.1109/FUZZ45933.2021.9494432","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494432","url":null,"abstract":"A new statistical goodness-of-fit for comparing distributions of two or more populations and based on fuzzy data is proposed. Its idea goes back to the k-nearest neighbor technique applied in pattern recognition, where it simply consists in classifying an object by the majority vote of its neighbors. In our paper we show that by an appropriate test statistic construction which counts the number of nearest neighbors between and within samples it is possible to check whether available fuzzy samples come or not from the same distribution. It is worth underlying that the suggested testing procedure is completely distribution-free which seems to be of extreme importance in statistical reasoning with fuzzy data. Our test proposal is completed with a study of its properties and a case study related to quality assessment.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124916631","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494401
F. Lilik, S. Nagy, Melinda Kovács, S. Szujó, L. Kóczy
In computer aided diagnostics image processing and classification plays an essential role. Image processing experts have been developing solutions for different types of problems, that can be related to image processing, however, due to the sensitivity of the data and the high cost of medical experts, these experimental methods usually have very limited use in real medical practice. The databases that are available are very limited, thus the elsewhere usual and extremely effective convolutional neural network or other automated learning methods are not so easy to adjust for medical image processing. To overcome this difficulty, this paper presents an expert knowledge based method which describes the decision structures by fuzzy signatures. Values of various properties of Computer Tomography images as e.g. density or homogeneity are being considered in these signatures that are different in all case of liver diseases. Because of the low number of samples available, fuzzy sets that describes the leafs of the signatures leads to sparse systems, hence interpolation is needed. However further investigations of other interpolation methods are planned, Stabilized Koczy-Hirota interpolation seems to be appropriate.
{"title":"Interpolative decisions in the fuzzy signature based image classification for liver CT","authors":"F. Lilik, S. Nagy, Melinda Kovács, S. Szujó, L. Kóczy","doi":"10.1109/FUZZ45933.2021.9494401","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494401","url":null,"abstract":"In computer aided diagnostics image processing and classification plays an essential role. Image processing experts have been developing solutions for different types of problems, that can be related to image processing, however, due to the sensitivity of the data and the high cost of medical experts, these experimental methods usually have very limited use in real medical practice. The databases that are available are very limited, thus the elsewhere usual and extremely effective convolutional neural network or other automated learning methods are not so easy to adjust for medical image processing. To overcome this difficulty, this paper presents an expert knowledge based method which describes the decision structures by fuzzy signatures. Values of various properties of Computer Tomography images as e.g. density or homogeneity are being considered in these signatures that are different in all case of liver diseases. Because of the low number of samples available, fuzzy sets that describes the leafs of the signatures leads to sparse systems, hence interpolation is needed. However further investigations of other interpolation methods are planned, Stabilized Koczy-Hirota interpolation seems to be appropriate.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127552555","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494406
G. Büyüközkan, Deniz Uztürk
Urban agriculture/farming is a promising solution for cities, yet it cannot exist horizontally in urban areas, so the vertical farming (VF) approach is suggested. VF produces food and medicine in vertically stacked layers, vertically inclined surfaces, and/or integrated into other structures. Accordingly, this paper aims to present a novel ELICIT MOORA method for VF technology assessment. The MOORA model, which supplies fast and easy decision-making environments to practitioners, is modified to emphasize its benefits with linguistic variables. Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) model is suggested to extend the MOORA thanks to its several advantages such as interpretability of the results, providing an assessment environment closer to the way of human thinking. Moreover, a case study about an organic farm from Turkey is presented with the comparative results and discussions.
城市农业/农业是一个很有前途的解决方案，但它不能在城市地区横向存在，因此建议采用垂直农业(VF)方法。VF生产食品和药品在垂直堆叠层，垂直倾斜的表面，和/或集成到其他结构。因此，本文旨在提出一种用于VF技术评估的新型引出MOORA方法。对MOORA模型进行了改进，强调了其在语言变量方面的优势，该模型为从业者提供了快速简便的决策环境。扩展比较语言表达与符号翻译(Extended Comparative Linguistic Expressions with Symbolic Translation，简称ELICIT)模型具有结果可解释性、评估环境更接近人类思维方式等优点，可作为MOORA的扩展。此外，本文还以土耳其一家有机农场为例进行了对比研究和讨论。
{"title":"Novel ELICIT Information-based MOORA Approach for Vertical Farming Technology Assessment","authors":"G. Büyüközkan, Deniz Uztürk","doi":"10.1109/FUZZ45933.2021.9494406","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494406","url":null,"abstract":"Urban agriculture/farming is a promising solution for cities, yet it cannot exist horizontally in urban areas, so the vertical farming (VF) approach is suggested. VF produces food and medicine in vertically stacked layers, vertically inclined surfaces, and/or integrated into other structures. Accordingly, this paper aims to present a novel ELICIT MOORA method for VF technology assessment. The MOORA model, which supplies fast and easy decision-making environments to practitioners, is modified to emphasize its benefits with linguistic variables. Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) model is suggested to extend the MOORA thanks to its several advantages such as interpretability of the results, providing an assessment environment closer to the way of human thinking. Moreover, a case study about an organic farm from Turkey is presented with the comparative results and discussions.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127553864","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494545
A. Gersnoviez, I. Baturone
A large number of rules increases the complexity of fuzzy classifiers and reduces the linguistic interpretability of the classification. A tabular rule simplification method that extends the Quine-McCluskey algorithm of Boolean design to fuzzy logic is analyzed in detail in this paper. The method obtains a few compound rules from many initial atomic rules. The influence of membership functions as well as t-norms and s-norms operands, which can be even null if many atomic rules are used, becomes apparent in the classification regions (decision boundaries) induced by the compound rules. Since the compound rules can be ordered according to the covering indexes that measure the number of atomic rules covered, more or less generic classification rules and rules with particular indexes can be further identified, which could ease subsequent classification or decision-making.
{"title":"Rule Simplification Method Based on Covering Indexes for Fuzzy Classifiers","authors":"A. Gersnoviez, I. Baturone","doi":"10.1109/FUZZ45933.2021.9494545","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494545","url":null,"abstract":"A large number of rules increases the complexity of fuzzy classifiers and reduces the linguistic interpretability of the classification. A tabular rule simplification method that extends the Quine-McCluskey algorithm of Boolean design to fuzzy logic is analyzed in detail in this paper. The method obtains a few compound rules from many initial atomic rules. The influence of membership functions as well as t-norms and s-norms operands, which can be even null if many atomic rules are used, becomes apparent in the classification regions (decision boundaries) induced by the compound rules. Since the compound rules can be ordered according to the covering indexes that measure the number of atomic rules covered, more or less generic classification rules and rules with particular indexes can be further identified, which could ease subsequent classification or decision-making.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128051882","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494427
Hugo Leon-Garza, H. Hagras, A. Peña-Ríos, A. Conway, G. Owusu
Semantic segmentation models help with the extraction of information from images. Currently, Convolutional Neural Networks (CNNs) are the state of the art for performing such tasks but the interpretability in their predictions is low. Previous work had proposed the use of Fuzzy Logic Rule-based systems (FRBS) as an explainable AI classifier of pixels for segmentation of images. In this paper, we extend that approach by using the similarity between image patches as context information for our model. The type-1 FRBS that uses the proposed set of context information features reaches an average Intersection over Union (IoU) value 3.51% higher than the type-1 FRBS using colour information. The difference in average IoU is significant due to the importance of colour in the testing images and the already high IoU value from the type-1 FRBS using colour.
{"title":"A Fuzzy Rule-based System using a Patch-based Approach for Semantic Segmentation in Floor Plans","authors":"Hugo Leon-Garza, H. Hagras, A. Peña-Ríos, A. Conway, G. Owusu","doi":"10.1109/FUZZ45933.2021.9494427","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494427","url":null,"abstract":"Semantic segmentation models help with the extraction of information from images. Currently, Convolutional Neural Networks (CNNs) are the state of the art for performing such tasks but the interpretability in their predictions is low. Previous work had proposed the use of Fuzzy Logic Rule-based systems (FRBS) as an explainable AI classifier of pixels for segmentation of images. In this paper, we extend that approach by using the similarity between image patches as context information for our model. The type-1 FRBS that uses the proposed set of context information features reaches an average Intersection over Union (IoU) value 3.51% higher than the type-1 FRBS using colour information. The difference in average IoU is significant due to the importance of colour in the testing images and the already high IoU value from the type-1 FRBS using colour.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133485567","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494395
Clément Iphar, L. Boudet, Jean-Philippe Poli
Fuzzy logic has been successfully used in various crisis management systems. In such systems, the geographical aspect is usually very important and relies on Geographical Information Systems. Most of the approaches are focused on 2D information. In this paper, we use the fuzzy morpho-mathematics framework to define new relations to reason on the topography with a digital terrain model. In particular, we focus on the characterisation of the line of greatest dip. Without loss of generality, we then illustrate those relations on a case of runoff from a building and a terrain.
{"title":"Topography-based Fuzzy Assessment of Runoff Area with 3D Spatial Relations","authors":"Clément Iphar, L. Boudet, Jean-Philippe Poli","doi":"10.1109/FUZZ45933.2021.9494395","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494395","url":null,"abstract":"Fuzzy logic has been successfully used in various crisis management systems. In such systems, the geographical aspect is usually very important and relies on Geographical Information Systems. Most of the approaches are focused on 2D information. In this paper, we use the fuzzy morpho-mathematics framework to define new relations to reason on the topography with a digital terrain model. In particular, we focus on the characterisation of the line of greatest dip. Without loss of generality, we then illustrate those relations on a case of runoff from a building and a terrain.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123019624","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494505
S. Kakula, Anthony J. Pinar, T. Havens, Derek T. Anderson
The Choquet integral (ChI) is an aggregation operator defined with respect to a fuzzy measure (FM). The FM encodes the worth of all subsets of the sources of information that are being aggregated. The ChI is capable of representing many aggregation functions and has found its application in a wide range of decision fusion problems. In our prior work, we introduced a data support-based approach for learning the FM for decision fusion problems. This approach applies a quadratic programming (QP)-based method to train the FM. However, since the FM of ChI scales as $2^{N}$, where $N$ is the number of input sources, the space complexity for learning the FM grows exponentially with $N$. This has limited the practical application of ChI-based decision fusion methods to small numbers of dimenstions—$N$ ≲ 6 is practical in most cases. In this work, we propose an iterative gradient descent-based approach to train the FM for ChI with an efficient method for handling the FM constraints. This method processes the training data, one observation at a time, and thereby significantly reduces the space complexity of the training process. We tested our online method on synthetic and real-world data sets, and compared the performance and convergence behaviour with our previously proposed QP-based method (i.e., batch method). On 10 out of 12 data sets, the online learning method has either matched or outperformed the batch method. We also show that we are able to use larger numbers of inputs with the online learning approach, extending the practical application of the ChI.
{"title":"Online Sequential Learning of Fuzzy Measures for Choquet Integral Fusion","authors":"S. Kakula, Anthony J. Pinar, T. Havens, Derek T. Anderson","doi":"10.1109/FUZZ45933.2021.9494505","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494505","url":null,"abstract":"The Choquet integral (ChI) is an aggregation operator defined with respect to a fuzzy measure (FM). The FM encodes the worth of all subsets of the sources of information that are being aggregated. The ChI is capable of representing many aggregation functions and has found its application in a wide range of decision fusion problems. In our prior work, we introduced a data support-based approach for learning the FM for decision fusion problems. This approach applies a quadratic programming (QP)-based method to train the FM. However, since the FM of ChI scales as $2^{N}$, where $N$ is the number of input sources, the space complexity for learning the FM grows exponentially with $N$. This has limited the practical application of ChI-based decision fusion methods to small numbers of dimenstions—$N$ ≲ 6 is practical in most cases. In this work, we propose an iterative gradient descent-based approach to train the FM for ChI with an efficient method for handling the FM constraints. This method processes the training data, one observation at a time, and thereby significantly reduces the space complexity of the training process. We tested our online method on synthetic and real-world data sets, and compared the performance and convergence behaviour with our previously proposed QP-based method (i.e., batch method). On 10 out of 12 data sets, the online learning method has either matched or outperformed the batch method. We also show that we are able to use larger numbers of inputs with the online learning approach, extending the practical application of the ChI.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125364796","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494553
Bradley Schneider, Tanvi Banerjee
In this work, we describe a system for classifying activities in first-person video using a fuzzy inference system. Our fuzzy inference system is built on top of traditional object-and motion-based video features and provides a description of activities in terms of multiple fuzzy output variables. We demonstrate the application of the fuzzy system on a well known dataset of unscripted first person videos to classify actions into four categories. Comparing the results to other supervised learning techniques and the state-of-the-art, we find that our fuzzy system outperforms alternatives. Further, the fuzzy outputs have the potential to be much more descriptive than conventional classifiers due to their ability to handle uncertainty and produce explainable results.
{"title":"Bridging the Gap between Atomic and Complex Activities in First Person Video","authors":"Bradley Schneider, Tanvi Banerjee","doi":"10.1109/FUZZ45933.2021.9494553","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494553","url":null,"abstract":"In this work, we describe a system for classifying activities in first-person video using a fuzzy inference system. Our fuzzy inference system is built on top of traditional object-and motion-based video features and provides a description of activities in terms of multiple fuzzy output variables. We demonstrate the application of the fuzzy system on a well known dataset of unscripted first person videos to classify actions into four categories. Comparing the results to other supervised learning techniques and the state-of-the-art, we find that our fuzzy system outperforms alternatives. Further, the fuzzy outputs have the potential to be much more descriptive than conventional classifiers due to their ability to handle uncertainty and produce explainable results.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126331000","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}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494578
Jocines D. F. Silveira, Tiago Rocha Martins, Cristiano Neri da Silva, J. V. D. Reis
This paper proposes a Fuzzy system to assist in the decision making of the deployment plan for the Internet of Things (IoT) communication infrastructure for effective exchange of information between devices (sensors, actuators, controllers, among others) in the Smart Farming scenario. The system offers great potential to assist managers to choose the implementation between the LoRaWAN, LoRaMesh or hybrid technologies, as well reflect on service quality, reduction of implantation costs, sensing and performance of devices in the rural scenario. These technologies were implemented in a real scenario in order to obtain the basis for the rules of the proposed Fuzzy system. The scenario adopted for data validation is a rural area of 162 ha located at the Center of Agricultural Sciences (CCA) of the Federal University of Piauí (UFPI), Teresina, Piauí, Brazil. In which assess the performance of technologies and obtain parameters for the Fuzzy system, data were obtained regarding the Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and the packet loss rate. This resulted in a Fuzzy system capable of recommending among one of the technologies mentioned, helping in the choice of the most appropriate communication infrastructure for a given Smart Farming scenario.
{"title":"New Solution based on Fuzzy System for Planning IoT Communication Infrastructure for Rural Areas","authors":"Jocines D. F. Silveira, Tiago Rocha Martins, Cristiano Neri da Silva, J. V. D. Reis","doi":"10.1109/FUZZ45933.2021.9494578","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494578","url":null,"abstract":"This paper proposes a Fuzzy system to assist in the decision making of the deployment plan for the Internet of Things (IoT) communication infrastructure for effective exchange of information between devices (sensors, actuators, controllers, among others) in the Smart Farming scenario. The system offers great potential to assist managers to choose the implementation between the LoRaWAN, LoRaMesh or hybrid technologies, as well reflect on service quality, reduction of implantation costs, sensing and performance of devices in the rural scenario. These technologies were implemented in a real scenario in order to obtain the basis for the rules of the proposed Fuzzy system. The scenario adopted for data validation is a rural area of 162 ha located at the Center of Agricultural Sciences (CCA) of the Federal University of Piauí (UFPI), Teresina, Piauí, Brazil. In which assess the performance of technologies and obtain parameters for the Fuzzy system, data were obtained regarding the Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and the packet loss rate. This resulted in a Fuzzy system capable of recommending among one of the technologies mentioned, helping in the choice of the most appropriate communication infrastructure for a given Smart Farming scenario.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125907237","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：