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Automating the Interactions among IoT Devices using Neural Networks

Automating the Interactions among IoT Devices using Neural N...

引用

IEEE Annual conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Javier Rojo Daniel Flores-Martin Jose Garcia-Alonso Juan M. Murillo Javier Berrocal University of Extremadura Caceres Spain

ISBN: (数字)9781728147161

ISBN: (纸本)9781728147178

The number of Internet of Things (IoT) devices is growing at an unstoppable pace. In most cases, the proper functioning of these devices requires human intervention. Due to this growth, people will have to configure more and more devices leading to the investment of a considerable effort and time. Nowadays, some works try to automate the user actions with IoT devices by using machine learning algorithms based on the relationships among devices or the previous human behaviour. However, these proposals do not make use of contextual information obtained from the devices themselves or they employ complex sets of prediction models. This paper proposes a neural network-based solution to predict the devices behaviour by using previous interactions and contextual information as input variables. Thanks to this, device interactions can be predicted and automated, even without having previous records, by knowing behaviours of devices of the same type in similar environment.

关键词： Performance evaluation Biological neural networks Predictive models data models Proposals Task analysis

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IOT Based Predictive Maintenance Using LSTM RNN Estimator

IOT Based Predictive Maintenance Using LSTM RNN Estimator

引用

international conference on Electrical, Communication, and Computer Engineering (ICECCE)

作者： Jamal S. Rahhal Dia Abualnadi ECE Dept. Al-Ahliyya Amman University Amman Jordan EE Dept. The University of Jordan Amman Jordan

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171173

Predictive maintenance is a smart solution for many industrial and commercial plants; it enables users to fix their devices before they fail. It is based on a mathematical model that predicts the failure time of the connected devices. In order to optimize the mathematical model large amount of data is required that increases the accuracy of prediction. Internet of Things (IoT) can provide these models with the required large data. Machine learning techniques have been used to produce increasingly effective solutions to predict the remaining useful life (RUL) of devices accurately. In this research a mathematical model is devised for general device that is connected to a central processing unit via IoT. The IoT provides a means of communication for the devices. It collects a huge amount of data from various sites. The large amount of collected data makes the prediction much closer to the true values. Two types of Neural Nets is used, Vanilla-RNN and LSTM-RNN, both showed good prediction results for a simple example of light bulbs. LSTM-RNN demonstrates better prediction results, where we recommend for critical devices. Other devices can use Vanilla-NN for its simplicity.

关键词： Predictive maintenance Mathematical model Sensors Internet of Things Meteorology data models

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Generating Open-Source datasets for Power Distribution Network Using OpenStreetMaps

Generating Open-Source Datasets for Power Distribution Netwo...

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international conference on Renewable Energy Research and Applications (ICRERA)

作者： Muhammad Ali Carlos A. Macana Krishneel Prakash Robi Islam Ilhami Colak Hemanshu Pota School of Engineering and Information Technology University of New South Wales Australia Zeppelin Bend ACT Canberra Australia University of Sunshine Coast Queensland Australia Faculty of Electrical Engineering Nisantasi University Istanbul Turkey

ISBN: (数字)9781728173696

ISBN: (纸本)9781728173702

In scientific research, the debate on open data is still evolving. The development of open-source benchmark data set is essential to the entire community as it enables empirical analysis of developed algorithms, speedy development of novel solutions, and strategies for solving real problems. In this paper, we presented a hybrid approach based on OpenStreetMap (OSM) and Python integration to generate an open-source dataset for power distribution networks. Using Overpass Query Language, multiple queries are constructed to collect the different features of distribution networks such as power line segments, transformers, poles, end consumers (households), and substation locations. The queried data is further accessed and downloaded with the Overpass API in Python. The validation of concept is provided with a case study of small region in Australia and results are illustrated with geographical visualizations. The results show that developed datasets are effective as it supports the geographical validation of realistic distribution network models.

关键词： Substations data visualization Distribution networks Spatial databases Australia Open source software Standards

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Using SURF to Improve ResNet-50 Model for Poultry Disease Recognition Algorithm

Using SURF to Improve ResNet-50 Model for Poultry Disease Re...

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Computational Intelligence (ICCI),international conference on

作者： Luyl-Da Quach Nghi Pham Quoc Nhien Huynh Thi Duc Chung Tran Mohd Fadzil Hassan FPT University Can Tho Vietnam FPT University Hanoi Vietnam Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia City

ISBN: (数字)9781728184081

ISBN: (纸本)9781728184104

ResNet-50 is an architecture of residual network and known to have numerous advantages. However, the application of the model to the poultry domain for identifying chickens' diseases has demonstrated insufficient and overfitting results. This is due to the limitation in the training data set which comprises the whole images of chicken body, while the diseases in chickens have been known to be involved specific chicken body parts. As such, in this research work, it has been hypothesised that by pre-processing the data, specific features could be effectively identified during training. Therefore, this research uses the combination of SURF feature analysis with K-means model and then re-selects the main characteristics such as head, wings, legs, and other specific parts of chickens where the known diseases could be identified. The obtained data set was later provided into the ResNet-50 model and resulted in 93.56% accuracy, which is 20% higher than the previous research.

关键词： Training Computational modeling Training data Nonhomogeneous media data models Diseases Residual neural networks

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Infer The Input To The Generator Of Auxiliary Classifier Generative Adversarial Networks

Infer The Input To The Generator Of Auxiliary Classifier Gen...

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IEEE international conference on Image processing

作者： Xiaoming Peng Abdesselam Bouzerdoum Son Lam Phung School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Generative Adversarial Networks (GANs) are deep-learning-based generative models. This paper presents three methods to infer the input to the generator of auxiliary classifier generative adversarial networks (ACGANs), which are a type of conditional GANs. The first two methods, named i-ACGAN- r and i-ACGAN-d, are “inverting” methods, which obtain an inverse mapping from an image to the class label and the latent sample. By contrast, the third method, referred to as i-ACGAN-e, directly infers both the class label and the latent sample by introducing an encoder into an ACGAN. The three methods were evaluated on two natural scene datasets, using two performance measures: the class recovery accuracy and the image reconstruction error. Experimental results show that i-ACGAN-e outperforms the other two methods in terms of the class recovery accuracy. However, the images generated by the other two methods have smaller image reconstruction errors. The source code is publicly available from https://***/XMPeng/Infer-Input-ACGAN.

关键词： Generators Gallium nitride Convolutional codes data models Optimized production technology Linear programming

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A Joint Course Knowledge Entity and Relation Extraction Method for Educational data

A Joint Course Knowledge Entity and Relation Extraction Meth...

引用

Information Science and Education (ICISE-IE), international conference on

作者： Feng Qin Zhiwen Zhang Xiao Zheng School of Computer Science and Technology Anhui University of Technology Ma’anshan China

ISBN: (纸本)9781665446549

Considering the current growth of data in Educational Field, especially in recent times, due to the COVID-19 epidemic, the volume of online course data has increased exponentially. How to extract useful entities and relations from mass educational data has turn into an academic hotspot. In traditional models, entity recognition and relation extraction are considered as two separate subtasks, which may cause error propagation. Therefore, the development of a more effective and accurate entity and relation extraction model in Educational Field is being widely studied. Here, we propose an entity and relation joint extraction method based on deep learning in Educational Field. The proposed model (XMMC) includes XLNet, a pretrained language model for obtaining text word vectors, Mogifier BiGRU neural network for obtaining text context information, Multi-headed Attention for focusing document key information, and CRF for extracting text entities. The obtained entity context information and entity label are further passed to the sigmoid function, which can enhance the accuracy of the subsequent relation extraction and achieve the goal of joint processing. The proposed model performs better than the existing model in terms of accuracy rates (P), recall rates (R), and F1 values (F1 scores).

关键词： Deep learning COVID-19 Information science Epidemics Neural networks Education Focusing

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Analysis of Federated Learning as a Distributed Solution for Learning on Edge Devices

Analysis of Federated Learning as a Distributed Solution for...

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Intelligent data Science Technologies and Applications (IDSTA), international conference on

作者： Saba F. Lameh Wade Noble Yasaman Amannejad Arash Afshar University of Tabriz Tabriz Iran Mount Royal University Calgary Canada Aranite Inc. Calgary Canada

ISBN: (数字)9781728183763

ISBN: (纸本)9781728183770

Sensors and smart devices are continuously collecting massive amounts of data. Today's state-of-the-art machine learning (ML) techniques, such as deep networks, are typically trained using cloud platforms, leveraging the elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all due to privacy and network overhead concerns. Sharing life logging photos and videos from cell phones and wearable devices can cause privacy concerns for users. To respond to the needs of such applications, federated learning (FL) is proposed as a distributed ML solution for learning on edge devices, such as cellphones. In FL, data does not leave users' devices. All users collaboratively train a model without sharing their data. Each user trains a local model with their data and shares the model updates with a FL server to aggregate and build a global model. Such training respects users' privacy while reducing the network overhead for transferring data to a central server. In this paper, we aim to study the FL approach for learning on edge devices. We investigate the performance of the FL model with various parameter settings and compare its accuracy and training time to a traditional learning technique that is trained on a central server. For this purpose, we train a convolutional deep learning network for image recognition in traditional and federated format. The results confirm that it is possible to conduct a complex learning task on distributed devices without sharing their data with a central server, and such federated models can achieve comparable accuracy to central models.

关键词： data models Servers Training Computational modeling Analytical models Task analysis Privacy

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A Privacy-Preserving and Verifiable Federated Learning Scheme

A Privacy-Preserving and Verifiable Federated Learning Schem...

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IEEE international conference on Communications (ICC)

作者： Xianglong Zhang Anmin Fu Huaqun Wang Chunyi Zhou Zhenzhu Chen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing PR China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Due to the complexity of the data environment, many organizations prefer to train deep learning models together by sharing training sets. However, this process is always accompanied by the restriction of distributed storage and privacy. Federated learning addresses this challenge by only sharing gradients with the server without revealing training sets. Unfortunately, existing research has shown that the server could extract information of the training sets from shared gradients. Besides, the server may falsify the calculated result to affect the accuracy of the trained model. To solve the above problems, we propose a privacy-preserving and verifiable federated learning scheme. Our scheme focuses on processing shared gradients by combining the Chinese Remainder Theorem and the Paillier homomorphic encryption, which can realize privacy-preserving federated learning with low computation and communication costs. In addition, we introduce the bilinear aggregate signature technology into federated learning, which effectively verifies the correctness of aggregated gradient. Moreover, the experiment shows that even with the added verification function, our scheme still has high accuracy and efficiency.

关键词： Servers Machine learning Training Encryption Aggregates Computational modeling Privacy

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A data Mining and Ontology-Based Approach for Predicting the Research Ideas in the Wildlife Sector of Sri Lanka

A Data Mining and Ontology-Based Approach for Predicting the...

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international conference on Industrial and Information Systems, ICIIS

作者： Premisha Premananthan Kumara BTGS Enoka P Kudavidanage Banujan Kuhaneswaran Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

ISBN: (数字)9781728185248

ISBN: (纸本)9781728185255

Sri Lanka, is the global biodiversity hotspot, which contains a wide variety of fauna & flora. Also, wildlife is the key player in the tourism industry of Sri Lanka. But still, Sri Lankan wildlife faced many conflicts to protect their biodiversity because of a lack of technical and research support. There were significant research gaps and data resources but still, researchers retreat to select this domain as their study. This study demonstrates a novel approach to data mining to find hidden keywords and automated labeling for past research work in this area. To model topics and recognize the major keywords for future researchers, we used Latent Dirichlet Allocation (LDA) algorithms to build an ontology model to visualize the relationships between each keyword and similar keywords cluster into creative topics. These techniques are also useful for potential research ideas, for identifying hidden research gaps and to classify domain-related publications. The experiment results demonstrate the validity and efficiency as well as it will make a platform for all researchers to interact easily with the wildlife domain.

关键词： Wildlife Tourism industry Predictive models Ontologies data mining Biodiversity Resource management

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Groundwater Level Prediction Using Modified Linear Regression

Groundwater Level Prediction Using Modified Linear Regressio...

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international conference on Advanced Computing and Communication Systems (ICACCS)

作者： Madhuri Kommineni K Veniha Reddy K Jagathi B Dushyanth Reddy Roshini A V Bhavani Koneru Lakshmaiah Education Foundation AP India

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151984

The non-stop decline of the groundwater stage is one of the crucial factors that affect the development of the countrywide economy and society. Based on the linear regression with PLS regression, the predicting model of the groundwater level is presented. The precision of the version is checked using the tracking facts within the Texas vicinity. Using this approach, a clean equation is generated and on evaluating one of a kind algorithms we selected the first-rate configuration and trained the version of the use of the Texas dataset. The case study shows that the precision of the version is instead excessive and its popularization importance is higher than the alternative models and has some practical fee when being used in the dynamic groundwater level analysis. Groundwater is one of the most significant characteristic asset so as to full fill the water necessities from irrigation, domestic, industrial and research needs. Gauge of the ground water level isn't only the pre-basic for long haul forecast of slant strength in repository bank, yet what's more the key for ensuring the sheltered activity of the supply. In this paper, linear regression with PLS regression is a compelling method for forecast of groundwater level in Texas.

关键词： Predictive models Linear regression Analytical models Sociology data models Artificial neural networks

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