Multiparametric Optimization on Influence of Ethanol and Biodiesel Blends on Nanocoated Engine by Full Factorial Design

Venkatesh, A.; Latchoumi, T. P.; Babu, S. Chezhian; Balamurugan, K.; Ganesan, S.; Ruban, M.; Mulugeta, Lijalem

doi:10.1155/2022/5350122

Cited by 24 publications

(5 citation statements)

References 29 publications

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“…Subsolid lesions greater than 3 cm require considerable evaluation. As a result, the goal of our research would be to see how well an available commercially DL method performed by measuring the solid fraction of clinically proved pulmonary malignant tumors that manifested as second tranche lesions [18]. Traditional segmentation algorithms divide the subject according to the surface properties of the image such as brightness, roughness, gradients, etc.…”

Section: Literature Surveymentioning

confidence: 99%

Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm

Sivasankaran,

Dhanaraj

2024

RIA

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Deep learning was a valuable and effective modeling object classification technique. Furthermore, since huge datasets for clinical images were not always accessible, there are not many accurate algorithms used in diagnostic imaging diagnostics. The researchers used data from Lung Image Database Consortium (LIDC) dataset to explore the feasibility of utilizing deep learning models to detect lung-cancer. Abnormalities on every Computed Tomography (CT) slice are classified by doctor brands. Humans got 174412 examples with 52 images already and related reality data after dimension reduction and rotation. Deep Belief Networks (DBN) Stacked Denotion Autoencoders (SDAE) and Convolutional Neural Networks (CNN) were three Deep Learning techniques that have been designed and developed. The researchers created a method with 28 feature representations and just an Support Vector Machine (SVM) classifier to monitor the effectiveness of deep learning models with those of traditional Computer-Assisted Diagnosis (CADx) systems. CNN, DBNs and SDAE have levels of accuracy of 0.7976, 0.8119 and 0.7929, correspondingly; their proposed conventional CADx has an efficiency of 0.7940 that is rather lesser than DBNs and CNN. Researchers and discovered that a incorrectly labeled nodules utilizing DBNs were 4 percentage bigger than standard CADx, which could be due to the downstream sample selection losing some lesions showing the significance.

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Section: Literature Surveymentioning

confidence: 99%

Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm

Sivasankaran,

Dhanaraj

2024

RIA

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“…In order to deliver the most complete solution to a solvable problem, process improvement practitioners frequently employ the Design of Experiments (DOE) method. This method differs from the norm in that it makes use of statistical data acquired from a limited number of trials to foresee the outcomes of a process that is both complicated and changeable (Venkatesh et al, 2022;Vicente et al, 1998). The design of experiments is the most prevalent numerical technique for maximizing outcomes, in contrast to any other research approach.…”

Section: Optimization Of Wcb and Mea Blend Ratio On Emissionmentioning

confidence: 99%

Impact of Oxy-Additives on diesel engine performance and emission parameters using Waste cooking oil biodiesel

2023

Global NEST: The International Journal

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<p>From the last two decades, the amount of waste materials recycled into useable energy sources has grown in popularity due to the fast rise in energy demand caused by the world's population growth. In other words, the conversion of garbage into energy will undoubtedly become an essential topic in the near future. Biodiesel has garnered a great deal of interest recently to reduce pollution generated by the burning of petroleum fuels. Biodiesel is made from vegetable oils and animal-based oils in most cases. Waste cooking oil biodiesel (WCB) is the most suitable resource for biofuel development because it is cost-effective and environmentally friendly, minimizing the amount of waste generated. In order to reduce harmful pollutants from engines, the use of oxygenated fuels seems to be a realistic solution. To minimize the impact on air quality, it is important to use oxy-additives that are specifically formulated for biodiesel and comply with environmental regulations. It is also recommended to use biodiesel blends with lower percentages of oxy-additives and to properly maintain the engines and exhaust systems of vehicles that use biodiesel. The purpose of this exploration is to improve the outcomes of an engine running at various concentrations of methoxyethyl acetate (MEA) additives and WCB biodiesel. The findings were compared to pure diesel, and the results were optimized for different load circumstances and combinations of oxygenated additives for the assessment of emission characteristics.on characteristics.</p>

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“…It also indicates that the amount of company-generated tweets, retweets, and likes is what matters most in determining the efficacy of social media marketing practices [15]. Social media users are perceived as contributing to marketing material as a result of the increasing interest in social media and user-generated content on websites like Tube, Facebook, and LinkedIn.Using big data analytics, they examine client perceptions and attitudes towards social media in this article.Related Works 14-year-olds are probably at risk for depression as a strategy to deal with social media or interact with others [16]. This is a widespread fallacy even in developing nations; there are depressed and suicidal persons who for a variety of reasons avoid seeing psychologists [17].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Analyse User Psychology in Social Media

Ansari,

Pawar,

Inukollu

et al. 2023

Recent Trends in Data Science and Its Applications

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In this study, we attempt to identify the emotion levels, such as positive, negative, & neutral feelings, from postings and comments on social networking sites on depression. Social media sites like Facebook and Twitter are becoming effective for helping those in need who require extra care or attention in terms of mental support. They are also utilized for communication and network development among relationships. There are several depressive support groups on Facebook, and they are quite helpful in giving the sufferers mental assistance. In this study, we attempt to formalize the posts and comments on depression into a succinct lexical database and identify the emotion levels from each occurrence. The complete amount of work has been divided into two sections: sentiment analysis and the use of machine learning techniques to examine the capability of extracting sentiment from such a unique category of texts. To determine the sentiment levels, we used the Python textblob module and typical machine learning techniques on the linguistic characteristics. For each of the classifiers, we have calculated the precision, recall, F-measure, accuracy, and ROC values. Random Forest outperformed the other classifiers, successfully classifying 60.54% of the instances. We think that conducting sentiment analysis on a particular class of texts may inspire additional research into how natural language is understood.

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Multiparametric Optimization on Influence of Ethanol and Biodiesel Blends on Nanocoated Engine by Full Factorial Design

Cited by 24 publications

References 29 publications

Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm

Lung Cancer Detection Using Image Processing Technique Through Deep Learning Algorithm

Impact of Oxy-Additives on diesel engine performance and emission parameters using Waste cooking oil biodiesel

Using Machine Learning to Analyse User Psychology in Social Media

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