A Learning Transition from Machine Learning to Deep Learning: A Survey

Dol, Mercy; Geetha, Angelina

doi:10.1109/icetci51973.2021.9574066

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…These models played an important role in the pre-development of deep learning because they did not require excessive training data. However, feature engineering is a difficult task for such methods [40]. Before training the classifier, researchers must collect knowledge or experience to extract features from the original text.…”

Section: Machine-learning-based Methodsmentioning

confidence: 99%

Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

Duan

Shen

Luo

2022

IJERPH

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As the relationship between the built environment and the sense of human experience becomes increasingly important, emotional geography has begun to focus on sentiments in space and time and improving the quality of urban construction from the perspective of public emotion and mental health. While youth is a powerful force in urban construction, there are no studies on the relationship between urban youth sentiments and the built environment. With the development of the Internet, social media has provided a large source of data for the metrics of youth sentiment. Based on data from more than 10,000 geolocated Sina Weibo comments posted over one week (from 19 to 25 July 2021) in Shanghai and using a machine learning algorithm for attention mechanism, this study calculates the sentiment label and sentiment intensity of each comment. Ten elements in five aspects were selected to assess the built environment at different scales and also to explore the correlations between built environment elements and sentiment intensity at different scales. The study finds that the overall sentiment of Shanghai youth tends to be negative. Sentiment intensity is significantly associated with most built environment elements at smaller scales. Urban youth have a higher proportion of both happy and sad sentiments, within which sad sentiments are more closely related to the built environment and are significantly related to all built environment elements. This study uses a deep learning algorithm to improve the accuracy of sentiment classification and confirms that the built environment has a great impact on sentiment. This research can help cities develop built environment optimization measures and policies to create positive emotional environments and enhance the well-being of urban youth.

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Section: Machine-learning-based Methodsmentioning

confidence: 99%

Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

Duan

Shen

Luo

2022

IJERPH

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“…Thus, the inputs and neuron weights are constantly modified until we receive the desired outputs. The deep learning method is based on the execution of complex algorithms that run on multilevel neural networks in order for the machine to be able to imitate the human brain in learning new knowledge [4]. In recent years, many researchers have argued that machine learning remains a sub-level of artificial intelligence.…”

Section: The Deep Learning Methods In Machine Learningmentioning

confidence: 99%

Smart Home: Deep Learning as a Method for Machine Learning in Recognition of Face, Silhouette and Human Activity in the Service of a Safe Home

et al. 2022

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Despite the general improvement of living conditions and the ways of building buildings, the sense of security in or around them is often not satisfactory for their users, resulting in the search and implementation of increasingly effective protection measures. The insecurity that modern people face every day, especially in urban centers regarding their home security, led computer science to the development of intelligent systems, aiming to mitigate the risks and ultimately lead to the consolidation of the feeling of security. In order to establish security, smart applications were created that turned a house into a Smart and Safe Home. We first present and analyze the deep learning method and emphasize its important contribution to the development of the process for machine learning, both in terms of the development of methods for safety at home, but also in terms of its contribution to other sciences and especially medicine where the results are spectacular. We then analyze in detail the back propagation algorithm in neural networks in both linear and non-linear networks as well as the X-OR problem simulation. Machine learning has a direct and effective application with impressive results in the recognition of human activity and especially in face recognition, which is the most basic condition for choosing the most appropriate method in order to design a smart home. Due to the large amount of data and the large computing capabilities that a system must have in order to meet the needs of a safe, smart home, technologies such as fog and cloud computing are used for both face recognition and recognition of human silhouettes and figures. These smart applications compose the systems that are created mainly through “Deep Learning” methods based on machine learning techniques. Based on the study we have done and present in this work, we believe that with the use of DL technology, the creation of a completely safe house has been achieved to a large extent today, covering an urgent need these days due to the increase in crime.

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“…ML and DL allow us to learn to identify and recognize objects in images, but their execution makes them different. In [22,23], we can see how both approaches or techniques detect objects.…”

Section: Image Processing Algorithmmentioning

confidence: 99%

“…According to [22,23] in order to perform object recognition using ML, images or videos must be collected and the relevant features of each of them must be selected so that, for example, a feature extraction algorithm can obtain information from these images and videos from edges or corners to differentiate different kinds of data. Based on these characteristics, an ML model would be applied to classify the data into different categories in order to use the information obtained in the analysis and classification of new objects.…”

Section: Image Processing Algorithmmentioning

confidence: 99%

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Enhancing Urban Mobility through Traffic Management with UAVs and VLC Technologies

Garau Guzman,

Baeza

2023

Drones

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This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities poses significant challenges to the daily mobility of citizens, rendering traditional ground-based traffic management methods increasingly inadequate. In this context, UAVs provide a distinctive perspective for real-time traffic monitoring and congestion detection using the YOLO algorithm. Through image capture and processing, UAVs can rapidly identify congested areas and transmit this information to ground-based traffic lights, facilitating dynamic traffic control adjustments. Moreover, VLC establishes a communication link between UAVs and traffic lights that complements existing RF-based solutions, underscoring visible light’s potential as a reliable and energy-efficient communication medium. In addition to integrating UAVs and VLC, we propose a new communication protocol and messaging system for this framework, enhancing its adaptability to varying traffic flows. This research represents a significant stride toward developing more efficient, sustainable, and resilient urban transportation systems.

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A Learning Transition from Machine Learning to Deep Learning: A Survey

Cited by 12 publications

References 12 publications

Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

Smart Home: Deep Learning as a Method for Machine Learning in Recognition of Face, Silhouette and Human Activity in the Service of a Safe Home

Enhancing Urban Mobility through Traffic Management with UAVs and VLC Technologies

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