2,572 Hits in 6.9 sec

A Sparse Deep Transfer Learning Model and Its Application for Smart Agriculture

Zhikui Chen, Xu Zhang, Shi Chen, Fangming Zhong, Keping Yu
2021 Wireless Communications and Mobile Computing  
The proposed method is evaluated on representative datasets and applied for smart agriculture to train deep identification models that can effectively detect new pests using few data samples.  ...  The introduction of deep transfer learning (DTL) further reduces the requirement of data and expert knowledge in various uses of applications, helping DNN-based models effectively reuse information.  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China (Nos. 61672123 and 62076047) and the Fundamental Research Funds for the Central Universities (Nos.  ... 
doi:10.1155/2021/9957067 fatcat:wjowimah2zhmbnavz5vj6racny

An Efficient Pest Classification In Smart Agriculture Using Transfer Learning

Tuan Nguyen, Quoc-Tuan Vien, Harin Sellahewa
2021 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems  
It is the source of not only food supply, but also providing raw materials for other industries, e.g. plastic, fuel.  ...  In this paper, we introduce an efficient method basing on deep learning approach to classify pests from images captured from the crops.  ...  However, in deep learning, transfer learning [13] is an approach where a model was first trained on one dataset. It is similar to how human can utilise their knowledge across domain.  ... 
doi:10.4108/eai.26-1-2021.168227 fatcat:ixltqdlmdbcgvpwujosewojb7i

Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems [article]

Mustafa Abdallah, Wo Jae Lee, Nithin Raghunathan, Charilaos Mousoulis, John W. Sutherland, Saurabh Bagchi
2021 arXiv   pre-print
While there is a rich literature on anomaly detection in many IoT-based systems, there is no existing work that documents the use of ML models for anomaly detection in digital agriculture and in smart  ...  Then, considering the sparse data from one kind of sensor, we perform transfer learning from a high data rate sensor. We then perform anomaly detection using the predicted sensor data.  ...  Hypothesis 1: Deep learning-based anomaly detection technique is effective for smart agriculture and smart manufacturing.  ... 
arXiv:2102.05814v1 fatcat:o5uks4y6q5dznix33p4eagdvae

Special Issue on Robustness and Efficiency in the Convergence of Artificial Intelligence and IoT

Meikang Qiu, Bhavani Thuraisingham, Mahmoud Daneshmand, Huansheng Ning, Payam Barnaghi
2021 IEEE Internet of Things Journal  
In the article "DDLPF: A practical decentralized deep learning paradigm for Internet-of-Things applications," Wu et al. propose a decentralized DL paradigm with privacypreservation and fast few-shot learning  ...  The article "A model for joint planning of production and distribution of fresh produce in agricultural Internet of Things" presents a mixed-integer programming model, which covers labor and capital constraints  ... 
doi:10.1109/jiot.2021.3073800 fatcat:yyhchydxabfsxjnvvfi7hsoexq

Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [article]

Jing Zhang, Dacheng Tao
2020 arXiv   pre-print
Artificial intelligence (AI), especially deep learning, is now a proven success in various areas including computer vision, speech recognition, and natural language processing.  ...  Then, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.  ...  To prevent it from being a survey on deep learning, we carefully select the topics and technologies that are closely related to and useful for various AIoT applications.  ... 
arXiv:2011.08612v1 fatcat:dflut2wdrjb4xojll34c7daol4

Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture

Uchechi F. Ukaegbu, Lagouge K. Tartibu, Modestus O. Okwu, Isaac O. Olayode
2021 Sensors  
It operated in such a way that when a weed is detected based on the deep learning algorithms deployed on the Raspberry Pi, general purpose input/output (GPIO) 17 or GPIO 18 (of the Raspberry Pi) were activated  ...  Precision agriculture entails solving the problem of poor agricultural yield due to competition for nutrients by weeds and provides a faster approach to eliminating the problematic weeds using emerging  ...  Acknowledgments: The authors would like to thank the University Research Committee (URC) and the Global Excellence and Stature (GES) scholarship board for their financial contributions to the realization  ... 
doi:10.3390/s21134417 pmid:34203187 fatcat:7ww5wxuv4vectl4un4vqwbexp4

Rare and Endangered Plant Leaf Identification Method Based on Transfer Learning and Knowledge Distillation

2021 International Journal of Agricultural and Environmental Information Systems  
, this paper proposes a rare and endangered plant leaf identification method based on transfer learning and knowledge distillation.  ...  Then, knowledge distillation was utilized to transfer Alexnet, VGG16, GoogLeNet, and ResNet models into a lightweight model.  ...  His research fields involve machine learning, agricultural informatics, medical informatics, and their intersection, fusion, and application.  ... 
doi:10.4018/ijaeis.288037 fatcat:twgfiusopnd33g44yytgg4rcbe

Table of Contents

2021 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)  
1005 170 A Survey based on PLL and its Synchronization Techniques for Interconnected System Mohan P.  ...  Systems Shailender Kumar, Pankaj Singh, Mohit Ranjan 289 50 Machine Learning Algorithms in Smart Antenna and Arrays for Internet of Things Applications Abhishek Javali, Anindita Sahoo, Jagrati  ... 
doi:10.1109/icais50930.2021.9395763 fatcat:y5filimlujfmbdfxjhgngh44r4

Deep Learning in Industrial Internet of Things: Potentials, Challenges, and Emerging Applications [article]

Ruhul Amin Khalil, Nasir Saeed, Yasaman Moradi Fard, Tareq Y. Al-Naffouri, Mohamed-Slim Alouini
2020 arXiv   pre-print
These enormous number of IoT devices generates a large capacity of data that further require intelligent data analysis and processing methods, such as Deep Learning (DL).  ...  Then, we outline numerous use cases of DL for IIoT systems, including smart manufacturing, smart metering, smart agriculture, etc.  ...  DL in Agriculture Industry DL-based IoT can play a fundamental role in the agriculture industry by making it smart and effective [227] - [232] .  ... 
arXiv:2008.06701v1 fatcat:2kp64xhxhjcojegff3aul4keee

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Sargam Yadav, Abhishek Kaushik, Mahak Sharma, Shubham Sharma
2022 AgriEngineering  
Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population.  ...  The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower.  ...  Fine-tuning was performed on seven different CNN models, and transfer learning was used for this study.  ... 
doi:10.3390/agriengineering4020029 fatcat:mv2tmloy3rcvbek64ezddfu3qy

Deep Learning Approaches for Intrusion Detection in IIoT Networks – Opportunities and Future Directions

Thavavel Vaiyapuri, Zohra Sbai, Haya Alaskar, Nourah Ali
2021 International Journal of Advanced Computer Science and Applications  
In this paper, we present a survey of deep learning-based IDS technique for IIoT.  ...  Recently, the application of machine and deep learning techniques have proved to mitigate multiple security threats and enhance the performance of intrusion detection.  ...  ACKNOWLEDGMENT The authors are very grateful to thank their Deanship of Scientific Research for technical and financial support in publishing this work successfully.  ... 
doi:10.14569/ijacsa.2021.0120411 fatcat:vhguxgz3x5fxvde65ljlvmhanq

Papers by Title

2019 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)  
: A Case Study to Improve Crosswalk Safety in Taipei Smart Home Personalizing Services Based on Adaptive Cloud IOT Devices With Bigdata and Deep Learning Technique Smartphone Mobile-Learning Application  ...  based on Deep Learning and Stereo Camera G 2 3 A B C D E F G H I L M N O P Q R S T U V W Game-based Learning and Augmented Reality Navigation APP for Children's Libraries GaN HEMT Oscillator in Phase  ... 
doi:10.1109/icce-tw46550.2019.8991721 fatcat:62376ymadzge3g5xomicr5tesq

Recent Trends in AI-Based Intelligent Sensing

Abhishek Sharma, Vaidehi Sharma, Mohita Jaiswal, Hwang-Cheng Wang, Dushantha Nalin K. Jayakody, Chathuranga M. Wijerathna Basnayaka, Ammar Muthanna
2022 Electronics  
Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas.  ...  This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing.  ...  Deep Learning Algorithms/Models in Intelligent Sensing Deep Learning is now dominating the industry and research spheres for the growth of a range of smart-world systems for good reasons.  ... 
doi:10.3390/electronics11101661 fatcat:smuark52zjfjverlwlf2tzjpfa

Paper Titles

2019 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)  
Communication and Bluetooth Smart Table Applications Based on Magnetic Resonance Wireless Power Transfer Smoke100k: A Database for Smoke Detection Soft Clustering of Large Scale Power Grid with Pulsed  ...  Using LoRaWAN and e-Paper Smart Glasses Using Deep Learning and Stereo Camera Smart Home Energy Management System to Reduce Monthly Electricity Bill Smart Lighting Control and Services Using Visible Light  ... 
doi:10.1109/gcce46687.2019.9015409 fatcat:6k3r6jixrvglrkrkzek636gb54


2021 2021 International Conference on Communication, Control and Information Sciences (ICCISc)  
Detecting Fissures in Concrete Structures using CNNs and Transfer Learning Ashwin V; Vishal Menon; Deepa Raj K 763 51 169 Plant Disease Detection using CNN and Transfer Learning Vishal Menon  ...  107 Multi-label Bird Species Classification Us- ing Transfer Learning Rajeev Rajan ; Noumida A 752 34 110 Generalized Model Predictive Algorithm For Single-Phase Grid Connected Multi- level Inverters  ... 
doi:10.1109/iccisc52257.2021.9484875 fatcat:n7fllebad5fgrhkogzeywbu7da
« Previous Showing results 1 — 15 out of 2,572 results