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inference</title> <meta name="description" content="Search results for: network inference"> <meta name="keywords" content="network inference"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="network inference"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4984</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: network inference</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4984</span> Identification of Bayesian Network with Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Raouf%20Benmakrelouf">Mohamed Raouf Benmakrelouf</a>, <a href="https://publications.waset.org/abstracts/search?q=Wafa%20Karouche"> Wafa Karouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Rynkiewicz"> Joseph Rynkiewicz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20network" title="Bayesian network">Bayesian network</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20learning" title=" structure learning"> structure learning</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20search" title=" optimal search"> optimal search</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=causal%20inference" title=" causal inference"> causal inference</a> </p> <a href="https://publications.waset.org/abstracts/151560/identification-of-bayesian-network-with-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151560.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">176</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4983</span> Effective Supply Chain Coordination with Hybrid Demand Forecasting Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurmail%20Singh">Gurmail Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective supply chain is the main priority of every organization which is the outcome of strategic corporate investments with deliberate management action. Value-driven supply chain is defined through development, procurement and by configuring the appropriate resources, metrics and processes. However, responsiveness of the supply chain can be improved by proper coordination. So the Bullwhip effect (BWE) and Net stock amplification (NSAmp) values were anticipated and used for the control of inventory in organizations by both discrete wavelet transform-Artificial neural network (DWT-ANN) and Adaptive Network-based fuzzy inference system (ANFIS). This work presents a comparative methodology of forecasting for the customers demand which is non linear in nature for a multilevel supply chain structure using hybrid techniques such as Artificial intelligence techniques including Artificial neural networks (ANN) and Adaptive Network-based fuzzy inference system (ANFIS) and Discrete wavelet theory (DWT). The productiveness of these forecasting models are shown by computing the data from real world problems for Bullwhip effect and Net stock amplification. The results showed that these parameters were comparatively less in case of discrete wavelet transform-Artificial neural network (DWT-ANN) model and using Adaptive network-based fuzzy inference system (ANFIS). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bullwhip%20effect" title="bullwhip effect">bullwhip effect</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20techniques" title=" hybrid techniques"> hybrid techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=net%20stock%20amplification" title=" net stock amplification"> net stock amplification</a>, <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20flexibility" title=" supply chain flexibility"> supply chain flexibility</a> </p> <a href="https://publications.waset.org/abstracts/94150/effective-supply-chain-coordination-with-hybrid-demand-forecasting-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94150.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4982</span> The Effects of the Inference Process in Reading Texts in Arabic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=May%20George">May George</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inference plays an important role in the learning process and it can lead to a rapid acquisition of a second language. When learning a non-native language, i.e., a critical language like Arabic, the students depend on the teacher’s support most of the time to learn new concepts. The students focus on memorizing the new vocabulary and stress on learning all the grammatical rules. Hence, the students became mechanical and cannot produce the language easily. As a result, they are unable to predict the meaning of words in the context by relying heavily on the teacher, in that they cannot link their prior knowledge or even identify the meaning of the words without the support of the teacher. This study explores how the teacher guides students learning during the inference process and what are the processes of learning that can direct student’s inference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inference" title="inference">inference</a>, <a href="https://publications.waset.org/abstracts/search?q=reading" title=" reading"> reading</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20acquisition" title=" language acquisition "> language acquisition </a> </p> <a href="https://publications.waset.org/abstracts/35360/the-effects-of-the-inference-process-in-reading-texts-in-arabic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35360.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">531</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4981</span> Deep learning with Noisy Labels : Learning True Labels as Discrete Latent Variable</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Azeddine%20El-Hassouny">Azeddine El-Hassouny</a>, <a href="https://publications.waset.org/abstracts/search?q=Chandrashekhar%20Meshram"> Chandrashekhar Meshram</a>, <a href="https://publications.waset.org/abstracts/search?q=Geraldin%20Nanfack"> Geraldin Nanfack</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, learning from data with noisy labels (Label Noise) has been a major concern in supervised learning. This problem has become even more worrying in Deep Learning, where the generalization capabilities have been questioned lately. Indeed, deep learning requires a large amount of data that is generally collected by search engines, which frequently return data with unreliable labels. In this paper, we investigate the Label Noise in Deep Learning using variational inference. Our contributions are : (1) exploiting Label Noise concept where the true labels are learnt using reparameterization variational inference, while observed labels are learnt discriminatively. (2) the noise transition matrix is learnt during the training without any particular process, neither heuristic nor preliminary phases. The theoretical results shows how true label distribution can be learned by variational inference in any discriminate neural network, and the effectiveness of our approach is proved in several target datasets, such as MNIST and CIFAR32. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=label%20noise" title="label noise">label noise</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20latent%20variable" title=" discrete latent variable"> discrete latent variable</a>, <a href="https://publications.waset.org/abstracts/search?q=variational%20inference" title=" variational inference"> variational inference</a>, <a href="https://publications.waset.org/abstracts/search?q=MNIST" title=" MNIST"> MNIST</a>, <a href="https://publications.waset.org/abstracts/search?q=CIFAR32" title=" CIFAR32"> CIFAR32</a> </p> <a href="https://publications.waset.org/abstracts/142809/deep-learning-with-noisy-labels-learning-true-labels-as-discrete-latent-variable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142809.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">128</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4980</span> Anticipation of Bending Reinforcement Based on Iranian Concrete Code Using Meta-Heuristic Tools</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Sadegh%20Naseralavi">Seyed Sadegh Naseralavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Bemani"> Najmeh Bemani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, different concrete codes including America, New Zealand, Mexico, Italy, India, Canada, Hong Kong, Euro Code and Britain are compared with the Iranian concrete design code. First, by using Adaptive Neuro Fuzzy Inference System (ANFIS), the codes having the most correlation with the Iranian ninth issue of the national regulation are determined. Consequently, two anticipated methods are used for comparing the codes: Artificial Neural Network (ANN) and Multi-variable regression. The results show that ANN performs better. Predicting is done by using only tensile steel ratio and with ignoring the compression steel ratio. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro%20fuzzy%20inference%20system" title="adaptive neuro fuzzy inference system">adaptive neuro fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=anticipate%20method" title=" anticipate method"> anticipate method</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete%20design%20code" title=" concrete design code"> concrete design code</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-variable%20regression" title=" multi-variable regression"> multi-variable regression</a> </p> <a href="https://publications.waset.org/abstracts/75588/anticipation-of-bending-reinforcement-based-on-iranian-concrete-code-using-meta-heuristic-tools" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75588.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">284</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4979</span> Research on Knowledge Graph Inference Technology Based on Proximal Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yihao%20Kuang">Yihao Kuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bowen%20Ding"> Bowen Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graphs and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improved strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain a better and more efficient inference effect by introducing PPO into knowledge inference technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=PPO" title=" PPO"> PPO</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20inference" title=" knowledge inference"> knowledge inference</a> </p> <a href="https://publications.waset.org/abstracts/168844/research-on-knowledge-graph-inference-technology-based-on-proximal-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168844.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">243</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4978</span> Research on Knowledge Graph Inference Technology Based on Proximal Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yihao%20Kuang">Yihao Kuang</a>, <a href="https://publications.waset.org/abstracts/search?q=Bowen%20Ding"> Bowen Ding</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing scale and complexity of knowledge graph, modern knowledge graph contains more and more types of entity, relationship, and attribute information. Therefore, in recent years, it has been a trend for knowledge graph inference to use reinforcement learning to deal with large-scale, incomplete, and noisy knowledge graph and improve the inference effect and interpretability. The Proximal Policy Optimization (PPO) algorithm utilizes a near-end strategy optimization approach. This allows for more extensive updates of policy parameters while constraining the update extent to maintain training stability. This characteristic enables PPOs to converge to improve strategies more rapidly, often demonstrating enhanced performance early in the training process. Furthermore, PPO has the advantage of offline learning, effectively utilizing historical experience data for training and enhancing sample utilization. This means that even with limited resources, PPOs can efficiently train for reinforcement learning tasks. Based on these characteristics, this paper aims to obtain better and more efficient inference effect by introducing PPO into knowledge inference technology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reinforcement%20learning" title="reinforcement learning">reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=PPO" title=" PPO"> PPO</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20inference" title=" knowledge inference"> knowledge inference</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a> </p> <a href="https://publications.waset.org/abstracts/173972/research-on-knowledge-graph-inference-technology-based-on-proximal-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173972.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">67</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4977</span> Fuzzy Inference System for Risk Assessment Evaluation of Wheat Flour Product Manufacturing Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yas%20Barzegaar">Yas Barzegaar</a>, <a href="https://publications.waset.org/abstracts/search?q=Atrin%20Barzegar"> Atrin Barzegar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=failure%20modes" title="failure modes">failure modes</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20assessment" title=" risk assessment"> risk assessment</a> </p> <a href="https://publications.waset.org/abstracts/169565/fuzzy-inference-system-for-risk-assessment-evaluation-of-wheat-flour-product-manufacturing-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169565.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">102</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4976</span> Fuzzy Inference System for Diagnosis of Malaria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Purnima%20Pandit">Purnima Pandit</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Malaria remains one of the world’s most deadly infectious disease and arguably, the greatest menace to modern society in terms of morbidity and mortality. To choose the right treatment and to ensure a quality of life suitable for a specific patient condition, early and accurate diagnosis of malaria is essential. It reduces transmission of disease and prevents deaths. Our work focuses on designing an efficient, accurate fuzzy inference system for malaria diagnosis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title="fuzzy inference system">fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=malaria%20disease" title=" malaria disease"> malaria disease</a>, <a href="https://publications.waset.org/abstracts/search?q=triangular%20fuzzy%20number" title=" triangular fuzzy number"> triangular fuzzy number</a> </p> <a href="https://publications.waset.org/abstracts/55107/fuzzy-inference-system-for-diagnosis-of-malaria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55107.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">297</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4975</span> Fuzzy Inference System for Risk Assessment Evaluation of Wheat Flour Product Manufacturing Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atrin%20Barzegar">Atrin Barzegar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yas%20Barzegar"> Yas Barzegar</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefano%20Marrone"> Stefano Marrone</a>, <a href="https://publications.waset.org/abstracts/search?q=Francesco%20Bellini"> Francesco Bellini</a>, <a href="https://publications.waset.org/abstracts/search?q=Laura%20Verde"> Laura Verde</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this research is to develop an intelligent system to analyze the risk level of wheat flour product manufacturing system. The model consists of five Fuzzy Inference Systems in two different layers to analyse the risk of a wheat flour product manufacturing system. The first layer of the model consists of four Fuzzy Inference Systems with three criteria. The output of each one of the Physical, Chemical, Biological and Environmental Failures will be the input of the final manufacturing systems. The proposed model based on Mamdani Fuzzy Inference Systems gives a performance ranking of wheat flour products manufacturing systems. The first step is obtaining data to identify the failure modes from expert’s opinions. The second step is the fuzzification process to convert crisp input to a fuzzy set., then the IF-then fuzzy rule applied through inference engine, and in the final step, the defuzzification process is applied to convert the fuzzy output into real numbers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=failure%20modes" title="failure modes">failure modes</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20rules" title=" fuzzy rules"> fuzzy rules</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20assessment" title=" risk assessment"> risk assessment</a> </p> <a href="https://publications.waset.org/abstracts/170997/fuzzy-inference-system-for-risk-assessment-evaluation-of-wheat-flour-product-manufacturing-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170997.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4974</span> Integrating Knowledge Distillation of Multiple Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Jindong">Min Jindong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Mingxia"> Wang Mingxia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title="object detection">object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20distillation" title=" knowledge distillation"> knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20network" title=" convolutional network"> convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20compression" title=" model compression"> model compression</a> </p> <a href="https://publications.waset.org/abstracts/148652/integrating-knowledge-distillation-of-multiple-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148652.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4973</span> Study of ANFIS and ARIMA Model for Weather Forecasting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandreddy%20Anand%20Babu">Bandreddy Anand Babu</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivasa%20Rao%20Mandadi"> Srinivasa Rao Mandadi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Pradeep%20Reddy"> C. Pradeep Reddy</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ramesh%20Babu"> N. Ramesh Babu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title="ARIMA">ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20surmising%20tool%20stash" title=" fuzzy surmising tool stash"> fuzzy surmising tool stash</a>, <a href="https://publications.waset.org/abstracts/search?q=weather%20forecasting" title=" weather forecasting"> weather forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=MATLAB" title=" MATLAB"> MATLAB</a> </p> <a href="https://publications.waset.org/abstracts/13743/study-of-anfis-and-arima-model-for-weather-forecasting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13743.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4972</span> Trusted Neural Network: Reversibility in Neural Networks for Network Integrity Verification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malgorzata%20Schwab">Malgorzata Schwab</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashis%20Kumer%20Biswas"> Ashis Kumer Biswas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this concept paper, we explore the topic of Reversibility in Neural Networks leveraged for Network Integrity Verification and crafted the term ''Trusted Neural Network'' (TNN), paired with the API abstraction around it, to embrace the idea formally. This newly proposed high-level generalizable TNN model builds upon the Invertible Neural Network architecture, trained simultaneously in both forward and reverse directions. This allows for the original system inputs to be compared with the ones reconstructed from the outputs in the reversed flow to assess the integrity of the end-to-end inference flow. The outcome of that assessment is captured as an Integrity Score. Concrete implementation reflecting the needs of specific problem domains can be derived from this general approach and is demonstrated in the experiments. The model aspires to become a useful practice in drafting high-level systems architectures which incorporate AI capabilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trusted" title="trusted">trusted</a>, <a href="https://publications.waset.org/abstracts/search?q=neural" title=" neural"> neural</a>, <a href="https://publications.waset.org/abstracts/search?q=invertible" title=" invertible"> invertible</a>, <a href="https://publications.waset.org/abstracts/search?q=API" title=" API"> API</a> </p> <a href="https://publications.waset.org/abstracts/144758/trusted-neural-network-reversibility-in-neural-networks-for-network-integrity-verification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4971</span> Scour Depth Prediction around Bridge Piers Using Neuro-Fuzzy and Neural Network Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Bonakdari">H. Bonakdari</a>, <a href="https://publications.waset.org/abstracts/search?q=I.%20Ebtehaj"> I. Ebtehaj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prediction of scour depth around bridge piers is frequently considered in river engineering. One of the key aspects in efficient and optimum bridge structure design is considered to be scour depth estimation around bridge piers. In this study, scour depth around bridge piers is estimated using two methods, namely the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). Therefore, the effective parameters in scour depth prediction are determined using the ANN and ANFIS methods via dimensional analysis, and subsequently, the parameters are predicted. In the current study, the methods’ performances are compared with the nonlinear regression (NLR) method. The results show that both methods presented in this study outperform existing methods. Moreover, using the ratio of pier length to flow depth, ratio of median diameter of particles to flow depth, ratio of pier width to flow depth, the Froude number and standard deviation of bed grain size parameters leads to optimal performance in scour depth estimation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system%20%28ANFIS%29" title="adaptive neuro-fuzzy inference system (ANFIS)">adaptive neuro-fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title=" artificial neural network (ANN)"> artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=bridge%20pier" title=" bridge pier"> bridge pier</a>, <a href="https://publications.waset.org/abstracts/search?q=scour%20depth" title=" scour depth"> scour depth</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20regression%20%28NLR%29" title=" nonlinear regression (NLR)"> nonlinear regression (NLR)</a> </p> <a href="https://publications.waset.org/abstracts/73889/scour-depth-prediction-around-bridge-piers-using-neuro-fuzzy-and-neural-network-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73889.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4970</span> A Model of Empowerment Evaluation of Knowledge Management in Private Banks Using Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nazanin%20Pilevari">Nazanin Pilevari</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamyar%20Mahmoodi"> Kamyar Mahmoodi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this research is to provide a model based on fuzzy inference system for evaluating empowerment of Knowledge management. The first prototype of the research was developed based on the study of literature. In the next step, experts were provided with these models and after implementing consensus-based reform, the views of Fuzzy Delphi experts and techniques, components and Index research model were finalized. Culture, structure, IT and leadership were considered as dimensions of empowerment. Then, In order to collect and extract data for fuzzy inference system based on knowledge and Experience, the experts were interviewed. The values obtained from designed fuzzy inference system, made review and assessment of the organization's empowerment of Knowledge management possible. After the design and validation of systems to measure indexes ,empowerment of Knowledge management and inputs into fuzzy inference) in the AYANDEH Bank, a questionnaire was used. In the case of this bank, the system output indicates that the status of empowerment of Knowledge management, culture, organizational structure and leadership are at the moderate level and information technology empowerment are relatively high. Based on these results, the status of knowledge management empowerment in AYANDE Bank, was moderate. Eventually, some suggestions for improving the current situation of banks were provided. According to studies of research history, the use of powerful tools in Fuzzy Inference System for assessment of Knowledge management and knowledge management empowerment such an assessment in the field of banking, are the innovation of this Research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management" title="knowledge management">knowledge management</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20management%20empowerment" title=" knowledge management empowerment"> knowledge management empowerment</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20Delphi" title=" fuzzy Delphi"> fuzzy Delphi</a> </p> <a href="https://publications.waset.org/abstracts/72815/a-model-of-empowerment-evaluation-of-knowledge-management-in-private-banks-using-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72815.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">359</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4969</span> Syllogistic Reasoning with 108 Inference Rules While Case Quantities Change</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mikhail%20Zarechnev">Mikhail Zarechnev</a>, <a href="https://publications.waset.org/abstracts/search?q=Bora%20I.%20Kumova"> Bora I. Kumova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A syllogism is a deductive inference scheme used to derive a conclusion from a set of premises. In a categorical syllogisms, there are only two premises and every premise and conclusion is given in form of a quantified relationship between two objects. The different order of objects in premises give classification known as figures. We have shown that the ordered combinations of 3 generalized quantifiers with certain figure provide in total of 108 syllogistic moods which can be considered as different inference rules. The classical syllogistic system allows to model human thought and reasoning with syllogistic structures always attracted the attention of cognitive scientists. Since automated reasoning is considered as part of learning subsystem of AI agents, syllogistic system can be applied for this approach. Another application of syllogistic system is related to inference mechanisms on the Semantic Web applications. In this paper we proposed the mathematical model and algorithm for syllogistic reasoning. Also the model of iterative syllogistic reasoning in case of continuous flows of incoming data based on case–based reasoning and possible applications of proposed system were discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=categorical%20syllogism" title="categorical syllogism">categorical syllogism</a>, <a href="https://publications.waset.org/abstracts/search?q=case-based%20reasoning" title=" case-based reasoning"> case-based reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20architecture" title=" cognitive architecture"> cognitive architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=inference%20on%20the%20semantic%20web" title=" inference on the semantic web"> inference on the semantic web</a>, <a href="https://publications.waset.org/abstracts/search?q=syllogistic%20reasoning" title=" syllogistic reasoning"> syllogistic reasoning</a> </p> <a href="https://publications.waset.org/abstracts/24127/syllogistic-reasoning-with-108-inference-rules-while-case-quantities-change" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24127.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">411</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4968</span> Using Cyclic Structure to Improve Inference on Network Community Structure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behnaz%20Moradijamei">Behnaz Moradijamei</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Higgins"> Michael Higgins</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identifying community structure is a critical task in analyzing social media data sets often modeled by networks. Statistical models such as the stochastic block model have proven to explain the structure of communities in real-world network data. In this work, we develop a goodness-of-fit test to examine community structure's existence by using a distinguishing property in networks: cyclic structures are more prevalent within communities than across them. To better understand how communities are shaped by the cyclic structure of the network rather than just the number of edges, we introduce a novel method for deciding on the existence of communities. We utilize these structures by using renewal non-backtracking random walk (RNBRW) to the existing goodness-of-fit test. RNBRW is an important variant of random walk in which the walk is prohibited from returning back to a node in exactly two steps and terminates and restarts once it completes a cycle. We investigate the use of RNBRW to improve the performance of existing goodness-of-fit tests for community detection algorithms based on the spectral properties of the adjacency matrix. Our proposed test on community structure is based on the probability distribution of eigenvalues of the normalized retracing probability matrix derived by RNBRW. We attempt to make the best use of asymptotic results on such a distribution when there is no community structure, i.e., asymptotic distribution under the null hypothesis. Moreover, we provide a theoretical foundation for our statistic by obtaining the true mean and a tight lower bound for RNBRW edge weights variance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hypothesis%20testing" title="hypothesis testing">hypothesis testing</a>, <a href="https://publications.waset.org/abstracts/search?q=RNBRW" title=" RNBRW"> RNBRW</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20inference" title=" network inference"> network inference</a>, <a href="https://publications.waset.org/abstracts/search?q=community%20structure" title=" community structure "> community structure </a> </p> <a href="https://publications.waset.org/abstracts/137181/using-cyclic-structure-to-improve-inference-on-network-community-structure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/137181.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4967</span> Application of Neuro-Fuzzy Technique for Optimizing the PVC Membrane Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Rezayi">Majid Rezayi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sh.%20Shahaboddin"> Sh. Shahaboddin</a>, <a href="https://publications.waset.org/abstracts/search?q=HNM%20E.%20Mahmud"> HNM E. Mahmud</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Yadollah"> A. Yadollah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Saeid"> A. Saeid</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Yatimah"> A. Yatimah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, the adaptive neuro-fuzzy inference system (ANFIS) was applied to obtain the membrane composition model affecting the potential response of our reported polymeric PVC sensor for determining the titanium (III) ions. The performance statistics of the artificial neural network (ANN) and linear regression models for potential slope prediction of membrane composition of titanium (III) ion selective electrode were compared with ANFIS technique. The results show that the ANFIS model can be used as a practical tool for obtaining the Nerntian slope of the proposed sensor in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro%20fuzzy%20inference" title="adaptive neuro fuzzy inference">adaptive neuro fuzzy inference</a>, <a href="https://publications.waset.org/abstracts/search?q=PVC%20sensor" title=" PVC sensor"> PVC sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=titanium%20%28III%29%20ions" title=" titanium (III) ions"> titanium (III) ions</a>, <a href="https://publications.waset.org/abstracts/search?q=Nerntian%20slope" title=" Nerntian slope"> Nerntian slope</a> </p> <a href="https://publications.waset.org/abstracts/57746/application-of-neuro-fuzzy-technique-for-optimizing-the-pvc-membrane-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57746.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">287</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4966</span> Home Legacy Device Output Estimation Using Temperature and Humidity Information by Adaptive Neural Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sung%20Hyun%20Yoo">Sung Hyun Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=In%20Hwan%20Choi"> In Hwan Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Ho%20Jung"> Jun Ho Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon%20Ki%20Ahn"> Choon Ki Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Myo%20Taeg%20Lim"> Myo Taeg Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Home energy management system (HEMS) has been issued to reduce the power consumption. The HEMS performs electric power control for the indoor electric device. However, HEMS commonly treats the smart devices. In this paper, we suggest the output estimation of home legacy device using the artificial neural fuzzy inference system (ANFIS). This paper discusses the overview and the architecture of the system. In addition, accurate performance of the output estimation using the ANFIS inference system is shown via a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20fuzzy%20inference%20system%20%28ANFIS%29" title="artificial neural fuzzy inference system (ANFIS)">artificial neural fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=home%20energy%20management%20system%20%28HEMS%29" title=" home energy management system (HEMS)"> home energy management system (HEMS)</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20device" title=" smart device"> smart device</a>, <a href="https://publications.waset.org/abstracts/search?q=legacy%20device" title=" legacy device"> legacy device</a> </p> <a href="https://publications.waset.org/abstracts/42360/home-legacy-device-output-estimation-using-temperature-and-humidity-information-by-adaptive-neural-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42360.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">545</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4965</span> Recommendation Systems for Cereal Cultivation using Advanced Casual Inference Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md%20Yeasin">Md Yeasin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjit%20Kumar%20Paul"> Ranjit Kumar Paul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, recommendation systems have become indispensable tools for agricultural system. The accurate and timely recommendations can significantly impact crop yield and overall productivity. Causal inference modeling aims to establish cause-and-effect relationships by identifying the impact of variables or factors on outcomes, enabling more accurate and reliable recommendations. New advancements in causal inference models have been found in the literature. With the advent of the modern era, deep learning and machine learning models have emerged as efficient tools for modeling. This study proposed an innovative approach to enhance recommendation systems-based machine learning based casual inference model. By considering the causal effect and opportunity cost of covariates, the proposed system can provide more reliable and actionable recommendations for cereal farmers. To validate the effectiveness of the proposed approach, experiments are conducted using cereal cultivation data of eastern India. Comparative evaluations are performed against existing correlation-based recommendation systems, demonstrating the superiority of the advanced causal inference modeling approach in terms of recommendation accuracy and impact on crop yield. Overall, it empowers farmers with personalized recommendations tailored to their specific circumstances, leading to optimized decision-making and increased crop productivity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture" title="agriculture">agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=casual%20inference" title=" casual inference"> casual inference</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=recommendation%20system" title=" recommendation system"> recommendation system</a> </p> <a href="https://publications.waset.org/abstracts/169691/recommendation-systems-for-cereal-cultivation-using-advanced-casual-inference-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169691.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">80</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4964</span> TDApplied: An R Package for Machine Learning and Inference with Persistence Diagrams</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shael%20Brown">Shael Brown</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Farivar"> Reza Farivar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Persistence diagrams capture valuable topological features of datasets that other methods cannot uncover. Still, their adoption in data pipelines has been limited due to the lack of publicly available tools in R (and python) for analyzing groups of them with machine learning and statistical inference. In an easy-to-use and scalable R package called TDApplied, we implement several applied analysis methods tailored to groups of persistence diagrams. The two main contributions of our package are comprehensiveness (most functions do not have implementations elsewhere) and speed (shown through benchmarking against other R packages). We demonstrate applications of the tools on simulated data to illustrate how easily practical analyses of any dataset can be enhanced with topological information. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=persistence%20diagrams" title=" persistence diagrams"> persistence diagrams</a>, <a href="https://publications.waset.org/abstracts/search?q=R" title=" R"> R</a>, <a href="https://publications.waset.org/abstracts/search?q=statistical%20inference" title=" statistical inference"> statistical inference</a> </p> <a href="https://publications.waset.org/abstracts/162711/tdapplied-an-r-package-for-machine-learning-and-inference-with-persistence-diagrams" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162711.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">85</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4963</span> A Comparative Study on ANN, ANFIS and SVM Methods for Computing Resonant Frequency of A-Shaped Compact Microstrip Antennas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Kayabasi">Ahmet Kayabasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akdagli"> Ali Akdagli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=a-shaped%20compact%20microstrip%20antenna" title="a-shaped compact microstrip antenna">a-shaped compact microstrip antenna</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network%20%28ANN%29" title=" artificial neural network (ANN)"> artificial neural network (ANN)</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20neuro-fuzzy%20inference%20system%20%28ANFIS%29" title=" adaptive neuro-fuzzy inference system (ANFIS)"> adaptive neuro-fuzzy inference system (ANFIS)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/31100/a-comparative-study-on-ann-anfis-and-svm-methods-for-computing-resonant-frequency-of-a-shaped-compact-microstrip-antennas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31100.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">441</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4962</span> Tree-Based Inference for Regionalization: A Comparative Study of Global Topological Perturbation Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Orhun%20Aydin">Orhun Aydin</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20V.%20Janikas"> Mark V. Janikas</a>, <a href="https://publications.waset.org/abstracts/search?q=Rodrigo%20Alves"> Rodrigo Alves</a>, <a href="https://publications.waset.org/abstracts/search?q=Renato%20Assuncao"> Renato Assuncao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a tree-based perturbation methodology for regionalization inference is presented. Regionalization is a constrained optimization problem that aims to create groups with similar attributes while satisfying spatial contiguity constraints. Similar to any constrained optimization problem, the spatial constraint may hinder convergence to some global minima, resulting in spatially contiguous members of a group with dissimilar attributes. This paper presents a general methodology for rigorously perturbing spatial constraints through the use of random spanning trees. The general framework presented can be used to quantify the effect of the spatial constraints in the overall regionalization result. We compare several types of stochastic spanning trees used in inference problems such as fuzzy regionalization and determining the number of regions. Performance of stochastic spanning trees is juxtaposed against the traditional permutation-based hypothesis testing frequently used in spatial statistics. Inference results for fuzzy regionalization and determining the number of regions is presented on the Local Area Personal Incomes for Texas Counties provided by the Bureau of Economic Analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=regionalization" title="regionalization">regionalization</a>, <a href="https://publications.waset.org/abstracts/search?q=constrained%20clustering" title=" constrained clustering"> constrained clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20inference" title=" probabilistic inference"> probabilistic inference</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20clustering" title=" fuzzy clustering"> fuzzy clustering</a> </p> <a href="https://publications.waset.org/abstracts/84786/tree-based-inference-for-regionalization-a-comparative-study-of-global-topological-perturbation-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84786.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">229</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4961</span> Mathematical Modeling for Diabetes Prediction: A Neuro-Fuzzy Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vijay%20Kr.%20Yadav">Vijay Kr. Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=Nilam%20Rathi"> Nilam Rathi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=back-propagation" title="back-propagation">back-propagation</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20mellitus" title=" diabetes mellitus"> diabetes mellitus</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=neuro-fuzzy" title=" neuro-fuzzy"> neuro-fuzzy</a> </p> <a href="https://publications.waset.org/abstracts/79054/mathematical-modeling-for-diabetes-prediction-a-neuro-fuzzy-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79054.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4960</span> Diagnosis of the Lubrification System of a Gas Turbine Using the Adaptive Neuro-Fuzzy Inference System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Mahdjoub">H. Mahdjoub</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Hamaidi"> B. Hamaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Zerouali"> B. Zerouali</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Rouabhia"> S. Rouabhia </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The issue of fault detection and diagnosis (FDD) has gained widespread industrial interest in process condition monitoring applications. Accordingly, the use of neuro-fuzzy technic seems very promising. This paper treats a diagnosis modeling a strategic equipment of an industrial installation. We propose a diagnostic tool based on adaptive neuro-fuzzy inference system (ANFIS). The neuro-fuzzy network provides an abductive diagnosis. Moreover, it takes into account the uncertainties on the maintenance knowledge by giving a fuzzy characterization of each cause. This work was carried out with real data of a lubrication circuit from the gas turbine. The machine of interest is a gas turbine placed in a gas compressor station at South Industrial Centre (SIC Hassi Messaoud Ouargla, Algeria). We have defined the zones of good and bad functioning, and the results are presented to demonstrate the advantages of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection%20and%20diagnosis" title="fault detection and diagnosis">fault detection and diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=lubrication%20system" title=" lubrication system"> lubrication system</a>, <a href="https://publications.waset.org/abstracts/search?q=turbine" title=" turbine"> turbine</a>, <a href="https://publications.waset.org/abstracts/search?q=ANFIS" title=" ANFIS"> ANFIS</a>, <a href="https://publications.waset.org/abstracts/search?q=training" title=" training"> training</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/31945/diagnosis-of-the-lubrification-system-of-a-gas-turbine-using-the-adaptive-neuro-fuzzy-inference-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31945.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">489</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4959</span> A Bayesian Model with Improved Prior in Extreme Value Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eva%20L.%20Sanju%C3%A1n">Eva L. Sanjuán</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacinto%20Mart%C3%ADn"> Jacinto Martín</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Isabel%20Parra"> M. Isabel Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20M.%20Pizarro"> Mario M. Pizarro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Extreme Value Theory, inference estimation for the parameters of the distribution is made employing a small part of the observation values. When block maxima values are taken, many data are discarded. We developed a new Bayesian inference model to seize all the information provided by the data, introducing informative priors and using the relations between baseline and limit parameters. Firstly, we studied the accuracy of the new model for three baseline distributions that lead to a Gumbel extreme distribution: Exponential, Normal and Gumbel. Secondly, we considered mixtures of Normal variables, to simulate practical situations when data do not adjust to pure distributions, because of perturbations (noise). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20inference" title="bayesian inference">bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=highly%20informative%20prior" title=" highly informative prior"> highly informative prior</a> </p> <a href="https://publications.waset.org/abstracts/141776/a-bayesian-model-with-improved-prior-in-extreme-value-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141776.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4958</span> Substitutional Inference in Poetry: Word Choice Substitutions Craft Multiple Meanings by Inference</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20Marie%20Hicks">J. Marie Hicks</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The art of the poetic conjoins meaning and symbolism with imagery and rhythm. Perhaps the reader might read this opening sentence as 'The art of the poetic combines meaning and symbolism with imagery and rhythm,' which holds a similar message, but is not quite the same. The reader understands that these factors are combined in this literary form, but to gain a sense of the conjoining of these factors, the reader is forced to consider that these aspects of poetry are not simply combined, but actually adjoin, abut, skirt, or touch in the poetic form. This alternative word choice is an example of substitutional inference. Poetry is, ostensibly, a literary form where language is used precisely or creatively to evoke specific images or emotions for the reader. Often, the reader can predict a coming rhyme or descriptive word choice in a poem, based on previous rhyming pattern or earlier imagery in the poem. However, there are instances when the poet uses an unexpected word choice to create multiple meanings and connections. In these cases, the reader is presented with an unusual phrase or image, requiring that they think about what that image is meant to suggest, and their mind also suggests the word they expected, creating a second, overlying image or meaning. This is what is meant by the term 'substitutional inference.' This is different than simply using a double entendre, a word or phrase that has two meanings, often one complementary and the other disparaging, or one that is innocuous and the other suggestive. In substitutional inference, the poet utilizes an unanticipated word that is either visually or phonetically similar to the expected word, provoking the reader to work to understand the poetic phrase as written, while unconsciously incorporating the meaning of the line as anticipated. In other words, by virtue of a word substitution, an inference of the logical word choice is imparted to the reader, while they are seeking to rationalize the word that was actually used. There is a substitutional inference of meaning created by the alternate word choice. For example, Louise Bogan, 4th Poet Laureate of the United States, used substitutional inference in the form of homonyms, malapropisms, and other unusual word choices in a number of her poems, lending depth and greater complexity, while actively engaging her readers intellectually with her poetry. Substitutional inference not only adds complexity to the potential interpretations of Bogan’s poetry, as well as the poetry of others, but provided a method for writers to infuse additional meanings into their work, thus expressing more information in a compact format. Additionally, this nuancing enriches the poetic experience for the reader, who can enjoy the poem superficially as written, or on a deeper level exploring gradations of meaning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=poetic%20inference" title="poetic inference">poetic inference</a>, <a href="https://publications.waset.org/abstracts/search?q=poetic%20word%20play" title=" poetic word play"> poetic word play</a>, <a href="https://publications.waset.org/abstracts/search?q=substitutional%20inference" title=" substitutional inference"> substitutional inference</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20substitution" title=" word substitution"> word substitution</a> </p> <a href="https://publications.waset.org/abstracts/52403/substitutional-inference-in-poetry-word-choice-substitutions-craft-multiple-meanings-by-inference" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52403.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">238</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4957</span> Probabilistic Approach of Dealing with Uncertainties in Distributed Constraint Optimization Problems and Situation Awareness for Multi-agent Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sagir%20M.%20Yusuf">Sagir M. Yusuf</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Baber"> Chris Baber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DCOP" title="DCOP">DCOP</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent%20reasoning" title=" multi-agent reasoning"> multi-agent reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20reasoning" title=" Bayesian reasoning"> Bayesian reasoning</a>, <a href="https://publications.waset.org/abstracts/search?q=swarm%20intelligence" title=" swarm intelligence"> swarm intelligence</a> </p> <a href="https://publications.waset.org/abstracts/116869/probabilistic-approach-of-dealing-with-uncertainties-in-distributed-constraint-optimization-problems-and-situation-awareness-for-multi-agent-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116869.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">119</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4956</span> Assessment the Quality of Telecommunication Services by Fuzzy Inferences System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Oktay%20Nusratov">Oktay Nusratov</a>, <a href="https://publications.waset.org/abstracts/search?q=Ramin%20Rzaev"> Ramin Rzaev</a>, <a href="https://publications.waset.org/abstracts/search?q=Aydin%20Goyushov"> Aydin Goyushov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fuzzy inference method based approach to the forming of modular intellectual system of assessment the quality of communication services is proposed. Developed under this approach the basic fuzzy estimation model takes into account the recommendations of the International Telecommunication Union in respect of the operation of packet switching networks based on IP-protocol. To implement the main features and functions of the fuzzy control system of quality telecommunication services it is used multilayer feedforward neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20communication" title="quality of communication">quality of communication</a>, <a href="https://publications.waset.org/abstracts/search?q=IP-telephony" title=" IP-telephony"> IP-telephony</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20set" title=" fuzzy set"> fuzzy set</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20implication" title=" fuzzy implication"> fuzzy implication</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/15543/assessment-the-quality-of-telecommunication-services-by-fuzzy-inferences-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15543.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4955</span> LaPEA: Language for Preprocessing of Edge Applications in Smart Factory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masaki%20Sakai">Masaki Sakai</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsuyoshi%20Nakajima"> Tsuyoshi Nakajima</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazuya%20Takahashi"> Kazuya Takahashi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to improve the productivity of a factory, it is often the case to create an inference model by collecting and analyzing operational data off-line and then to develop an edge application (EAP) that evaluates the quality of the products or diagnoses machine faults in real-time. To accelerate this development cycle, an edge application framework for the smart factory is proposed, which enables to create and modify EAPs based on prepared inference models. In the framework, the preprocessing component is the key part to make it work. This paper proposes a language for preprocessing of edge applications, called LaPEA, which can flexibly process several sensor data from machines into explanatory variables for an inference model, and proves that it meets the requirements for the preprocessing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=edge%20application%20framework" title="edge application framework">edge application framework</a>, <a href="https://publications.waset.org/abstracts/search?q=edgecross" title=" edgecross"> edgecross</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing%20language" title=" preprocessing language"> preprocessing language</a>, <a href="https://publications.waset.org/abstracts/search?q=smart%20factory" title=" smart factory"> smart factory</a> </p> <a href="https://publications.waset.org/abstracts/142882/lapea-language-for-preprocessing-of-edge-applications-in-smart-factory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142882.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">146</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=network%20inference&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=network%20inference&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=network%20inference&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=network%20inference&page=5">5</a></li> <li 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