Synthesis and characterization of enhanced silica nanoparticle (SiO2) prepared from rice husk ash immobilized of 3-(chloropropyl) triethoxysilanea

Mezan, Salim Oudah; Absi, Salih Meri Al; Jabbar, Abdullah Hasan; Roslan, M. S.; Agam, Mohd Arif

doi:10.1016/j.matpr.2020.12.564

Cited by 27 publications

(14 citation statements)

References 19 publications

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“…Machine learning is becoming more efficient and accurate because of deep learning algorithms. Using automated machine learning, one may reduce the need for ML expertise while also automating the ML workflow with greater precision [33,34].…”

Section: E Real Purpose Of Smartmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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Farmers’ physical labor and debt are reduced as a result of agricultural automation, which emphasizes efficient and effective use of various machines in farming operations with the purpose of reducing physical labor and debt. It is a revolutionary idea in agriculture to create custom hiring centers, which are intended to make it easier for like-minded farmers to embrace technology/machinery for enhanced resource management practices. The study in question examines the significance of tool renting and sharing in the workplace. Rental and sharing equipment are two approaches that might be used to enable farmers to borrow equipment at a cheaper cost than they would otherwise have to pay for it. The following is a manual pilot study of 562 farmers in India to address the numerous challenges farmers face when looking for tools and equipment, as well as to determine their strong interest in the process of renting and sharing equipment. The study was conducted to address the numerous challenges farmers face when looking for tools and equipment and to determine their strong interest in the process of renting and sharing equipment. Farmers are divided into three groups according to the results of this poll: small, moderate, and large. Training and testing splits were used on the same data set in order to get a better understanding of the target variables. The data set for the survey was standardized in order to remove ambiguity. In this research, three different machine learning models were utilized: nearest neighbors, logistic regression, and decision trees. K-nearest neighbors was the most often used model, followed by logistic regression and decision trees. In order to get the best possible result, a comparison of the aforementioned algorithm models was carried out, which revealed that the decision tree is the better model among the others in this regard. Because the decision tree model is completely reliant on a large number of input factors, such as the kind of crop, the time/month of harvest, and the type of equipment necessary for the crops, it has the potential to have a social and economic impact on farmers and their livelihoods.

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Section: E Real Purpose Of Smartmentioning

confidence: 99%

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Rakhra

Sanober

Naveed

et al. 2022

Journal of Food Quality

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“…As a result, it is critical to diagnose cancer in its early stages in order to limit the chance of death. It can affect any part of the body; however, the heart, back, or legs are more likely to be affected [ 17 , 18 ]. Despite this, still, the complexity of the automated skin lesion segmentation as well as classification methods are need to address due to less upgraded dermoscopic images in various aspects.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

Thapar

Rakhra

Cazzato

et al. 2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals’ visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques’ results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.

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“…Also allows communication and the most efficient utilization of healthcare resources data, and understanding. The code of ethic employs the techniques and technologies of the goal of information sciences to solve issues [ 1 , 2 ]. Renal or kidney disease occurs as a result of nephron dysfunction in the kidney.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

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Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual’s health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient’s stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient’s kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.

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Synthesis and characterization of enhanced silica nanoparticle (SiO2) prepared from rice husk ash immobilized of 3-(chloropropyl) triethoxysilanea

Cited by 27 publications

References 19 publications

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

Implementing Machine Learning for Smart Farming to Forecast Farmers’ Interest in Hiring Equipment

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

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