Industrial IoT Enabled Fuzzy Logic Based Flame Image Processing for Rotary Kiln Control

Nagadasari, Merrin Prasanna; Bojja, Polaiah

doi:10.1007/s11277-022-09677-z

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…Nagadasari and Bojja [20], Kola et al [21] AmataDesai studied mainly on the classification of lung images cancerous and non-cancerous. Dharani and Bojja [22] proposed a novel method.…”

Section: Review Stagementioning

confidence: 99%

Development of Medical Image Analytics by Deep Learning Model for Prediction and Classification of CT Image Diseases

Pachala¹,

Bojja²

2022

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The CT images of Lung illnesses or diseases that damage the lungs and weaken the respiratory system. Lung cancer is one of the topmost causes of death in humans around the world. Humans have a better chance of surviving if they are detected early. The average survival rate of persons with lung cancer increases from 14 to 49 percent if the disease is detected early. While computed tomography (CT) is significantly more effective than X-ray, a complete diagnosis requires a combination of imaging techniques that complement each other. But, because there are multiple phases of cancer that develop into different types of tumors with varying sizes and risks, finding lung cancer does not predict the risk of cancer. A deep neural network is constructed and tested for detecting lung cancer CT images. This research work analyses different types of tumor sizes such as large cell carcinoma, normal, squamous cell carcinoma, and adenocarcinoma. Also, the lung tumors are detected and predicted with the help of computer vision methods such as Residual neural network (ResNet), Convolutional neural network (CNN). Finally, the results of all the methods are compared and various parameters were calculated. Thus, the proposed method (ResNet) gives an optimal solution on comparison with respect to all the parameters.

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“…Nagadasari and Bojja [20], Kola et al [21] AmataDesai studied mainly on the classification of lung images cancerous and non-cancerous. Dharani and Bojja [22] proposed a novel method.…”

Section: Review Stagementioning

confidence: 99%

Development of Medical Image Analytics by Deep Learning Model for Prediction and Classification of CT Image Diseases

Pachala¹,

Bojja²

2022

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“…Secured deployment [54]; • Event tracking in supervisory control and data acquisition system [55]; • Fuzzy-logic-based flame image processing for rotary kiln temperature control [56]; • IoT-regulated moisture sensor [57]; • Real-time carbon dioxide monitoring based on IoT cloud technologies-MQ135 carbon dioxide sensor, ESP8266 Wi-Fi module, Firebase cloud storage service, and Android application [58].…”

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confidence: 99%

Continuous Assessment of the Environmental Impact and Economic Viability of Decarbonization Improvements in Cement Production

Oguntola,

Simske

2023

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Growing awareness of the importance of mitigating climate change is driving research efforts toward developing economically viable technologies for reducing greenhouse gas emissions. The high energy consumption and carbon-intensive nature of cement manufacturing make it worthwhile to examine the environmental and economic characteristics of process improvements in cement production. This study examines the environmental impact of cement production and its economic considerations and demonstrates an IoT-inspired deployment framework for continuously assessing these. It contributes a practical approach to integrating sustainability into cement manufacturing and analyzes four different scenarios from a combination of two cement types (ordinary Portland cement, Portland-limestone cement) and two energy sources for thermal heating (coal, dried biosolids). It indicates that increased production and adoption of blended cement that has up to 15% limestone as an alternative to ordinary Portland cement can significantly reduce climate change effects from cement production (6.4% lower carbon footprint). In addition, significant emission reduction is possible with the use of waste from sewage sludge as a combustion fuel for heating in the cement production process (7.9% reduction compared with baseline). The information on environmental and financial trade-offs helps informed decisions on cement production improvements and can potentially contribute to greenhouse gas reduction targets.

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Machine Learning Based Automated Disaster Message Classification System Using Linear SVC Algorithm

Prasanna¹,

Mohan

Reddy

et al. 2023

Engineering Cyber-Physical Systems and Critical Infrastructures

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Industrial IoT Enabled Fuzzy Logic Based Flame Image Processing for Rotary Kiln Control

Cited by 10 publications

References 16 publications

Development of Medical Image Analytics by Deep Learning Model for Prediction and Classification of CT Image Diseases

Development of Medical Image Analytics by Deep Learning Model for Prediction and Classification of CT Image Diseases

Continuous Assessment of the Environmental Impact and Economic Viability of Decarbonization Improvements in Cement Production

Machine Learning Based Automated Disaster Message Classification System Using Linear SVC Algorithm

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