IoT enabled HELMET to safeguard the health of mine workers

Singh, Ninni; Gunjan, Vinit Kumar; Chaudhary, Gopal; Kaluri, Rajesh; Victor, Nancy; Lakshmanna, Kuruva

doi:10.1016/j.comcom.2022.06.032

Cited by 57 publications

(31 citation statements)

References 33 publications

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“…Studies have been conducted for icon generation using GAN in the past. As an example, the study in [11,31] constructed an icon dataset which included 8 different icon categories, wherein training techniques such as attention mechanism and spectral normalization based on StyleGAN was implemented. The results of the model generated icons which were extremely useful for the designers and reflected superior design quality.…”

Section: Mfigm For Icon Art Designmentioning

confidence: 99%

Icon Art Design with Generative Adversarial Network under Deep Learning

Meng

Yang

Wang

2022

Wireless Communications and Mobile Computing

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With the rapid development of the Internet, application interface design has undergone rapid changes. Numerous new design styles and resources have appeared; thus, a large number of interface icon design needs have been generated. Icons are quite different from ordinary photographed images, because they are all drawn by designers and have certain schematic and artistic features. Moreover, artistic icons can convey their drawn characteristics and meanings faster and better than captured images. The ideation process in icon design is time-consuming, and its design style and method of drawing are influenced by the device and the environment in which it is used. To simplify the process of icon design and enrich the creativity of icon conception, this study proposes to use the generative adversarial network technology in deep learning to train computers to generate artistic icons. This paper completes the construction of the icon generation model with generative adversarial network (GAN) model combined with the actual icon design process. For the problem of automatic icon generation, this paper does the following research work: (1) based on the conditional classification generative adversarial network, a multifeature icon generation model (MFIGM) is proposed. In the discriminator, a multifeature identification module is added to optimize the structure of the conditional feature to ensure that the icon generated by the model meets the given conditional feature. (2) Experiments on the icon dataset show that the MFIGM-based icon generation model proposed in this paper has better performance in designing various feature expressions of icons.

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Section: Mfigm For Icon Art Designmentioning

confidence: 99%

Icon Art Design with Generative Adversarial Network under Deep Learning

Meng

Yang

Wang

2022

Wireless Communications and Mobile Computing

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“…[40] proposed a filter model for system enhancement. Moreover, the clustering technique can also be followed for the collection and aggregation of data, and data can be filtered through machine learning algorithms [41][42][43][44][45][46]. As part of the industry's technological progress, Teferaa et al [47] emphasize emerging different automation approaches such as IoT, wireless communications, machine learning, artificial intelligence, and deep learning.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

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“…The study in [33] implemented DNMSVM using the general linear kernel NEUROSVM. The study in [34,35] used DNMSVM technique for the neuroimaging based diagnosis of Parkinson's disease. Deep neural mapping large margin distribution machine (LDM) was used to perform kernel mapping instead of implementing implicit kernel function in LDM.…”

Section: Unbalanced Development Of Dragon Lion Projectmentioning

confidence: 99%

Teaching Practice of Dragon and Lion Dance in Colleges and Universities with the Support of Big Data Technology

Chen¹,

Liu

Zuo³

et al. 2022

Wireless Communications and Mobile Computing

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Colleges and universities are an important part of China’s higher education. They undertake the important task of cultivating talents and are an important part of the construction of a harmonious society. Building a harmonious campus is not only the cornerstone of building a harmonious society but also the internal need to promote the comprehensive, coordinated, and sustainable development of various undertakings in colleges and universities, but also an important goal of the construction, reform, and development of colleges and universities. Therefore, building a harmonious campus has become a new task for colleges and universities, and the construction of harmonious campus culture is the core content of building a harmonious campus. Dragon and lion dance, which originated in China, is a nationally unique art form and deeply contains broad Chinese traditional culture. Studying the role of dragon and lion dance in promoting the construction of harmonious campus culture in colleges and universities will help to strengthen the school’s sports work and enhance national unity, so as to promote the construction of a harmonious campus. Firstly, combined with the characteristics of dragon and lion dance practice, this paper expounds on the development value and strategy of dragon and lion dance in colleges and universities. Then, with the support of big data, the teaching of dragon and lion dance in colleges and universities was studied. An improved depth neural network is used to analyze the data using deep neural mapping support vector machine (DNMSVM) and cross-connected convolutional network. Simulation experiments verify the effectiveness of the model and promote the promotion and popularization of the dragon and lion dance in colleges and universities.

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IoT enabled HELMET to safeguard the health of mine workers

Cited by 57 publications

References 33 publications

Icon Art Design with Generative Adversarial Network under Deep Learning

Icon Art Design with Generative Adversarial Network under Deep Learning

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Teaching Practice of Dragon and Lion Dance in Colleges and Universities with the Support of Big Data Technology

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