Real-World Application of Machine Learning and Deep Learning

Sree, S. Reema; Vyshnavi, S.B.; Jayapandian, N.

doi:10.1109/icssit46314.2019.8987844

Cited by 29 publications

(3 citation statements)

References 17 publications

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“…DL refers to a category of ML that employs artificial neural networks to address intricate problems by acquiring knowledge from data. DL models have multiple layers of interconnected nodes that process specific aspects of the input data and pass it on to the next layer [116,117]. This enables them to acquire a hierarchical understanding of the data, detecting intricate patterns and characteristics.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Alahi

Sukkuea

Tina

et al. 2023

Sensors

147

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As the global population grows, and urbanization becomes more prevalent, cities often struggle to provide convenient, secure, and sustainable lifestyles due to the lack of necessary smart technologies. Fortunately, the Internet of Things (IoT) has emerged as a solution to this challenge by connecting physical objects using electronics, sensors, software, and communication networks. This has transformed smart city infrastructures, introducing various technologies that enhance sustainability, productivity, and comfort for urban dwellers. By leveraging Artificial Intelligence (AI) to analyze the vast amount of IoT data available, new opportunities are emerging to design and manage futuristic smart cities. In this review article, we provide an overview of smart cities, defining their characteristics and exploring the architecture of IoT. A detailed analysis of various wireless communication technologies employed in smart city applications is presented, with extensive research conducted to determine the most appropriate communication technologies for specific use cases. The article also sheds light on different AI algorithms and their suitability for smart city applications. Furthermore, the integration of IoT and AI in smart city scenarios is discussed, emphasizing the potential contributions of 5G networks coupled with AI in advancing modern urban environments. This article contributes to the existing literature by highlighting the tremendous opportunities presented by integrating IoT and AI, paving the way for the development of smart cities that significantly enhance the quality of life for urban dwellers while promoting sustainability and productivity. By exploring the potential of IoT, AI, and their integration, this review article provides valuable insights into the future of smart cities, demonstrating how these technologies can positively impact urban environments and the well-being of their inhabitants.

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Section: Deep Learning (Dl)mentioning

confidence: 99%

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Alahi

Sukkuea

Tina

et al. 2023

Sensors

147

View full text Add to dashboard Cite

show abstract

“…Deep Learning (DL) is a subset or sub-branch of machine learning [12]. It employs machine learning algorithms and artificial neural networks with multiple layers and has proven to be very successful in dealing with large amounts of data in a variety of research domains e.g.…”

Section: Deep Learning (Dl)mentioning

confidence: 99%

Deep learning-based damage estimation in concrete structures using acoustic emission data

Inderyas¹,

Tayfur²,

Arslan³

et al. 2022

eJNDT

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Conventional structural testing methods assess damaged condition of the structures and they generally base on subjective visual inspection. To take necessary precautions before/during a disaster, real-time monitoring of damage progression is very important especially for critical structures such as bridges, viaducts, tunnels, dams and nuclear power plants. Acoustic emission (AE) has been proved to be a successful method for this purpose and it is used to locate even invisible micro-level cracks in the structures. The most important feature that makes this technique advantageous over other non-destructive testing methods is that it continuously monitors damages in structures subjected to load by sensors converting the waves emitted from the damage into electrical signals. By analyzing the parameters of the recorded signals, critical information such as the type, location, origination-time, size and direction of the damage can be obtained. In this study, it was aimed to estimate damage status of the concrete members exposed to loading by developing a deep learning-based damage detection model. This model was developed by monitoring AE activities of concrete and reinforced concrete specimens with different strength, size, properties and loading conditions. Afterwards, a relationship was established between the properties and load levels of the specimens with features of the AE signals. The developed model was tested on reinforced concrete elements under load. The results show that the model is successful at estimating the damage level.

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“…One of the recent year's trends is deep learning (DL) technology which is used in the digital image processing for solving complex problems (a classification, segmentation and image detection). DL techniques, such as convolutional neural networks (CNNs), have already influenced a wide range of signal processing activities within traditional and new advanced areas, including key aspects of machine learning and artificial intelligence [1]. In particular, CNNs showed superior performance in face detection applications [2,3].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based static hand gesture recognition

Satybaldina

Kalymova

2021

IJEECS

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.

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Real-World Application of Machine Learning and Deep Learning

Cited by 29 publications

References 17 publications

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends

Deep learning-based damage estimation in concrete structures using acoustic emission data

Deep learning based static hand gesture recognition

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