An Ensemble Machine Learning Technique for Functional Requirement Classification

Rahimi, Nouf; Eassa, Fathy; Elrefaei, Lamiaa A.

doi:10.3390/sym12101601

Cited by 33 publications

(16 citation statements)

References 36 publications

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“…The support vector machine (SVM) is a supervised machine learning classifier that combines the influence of conventional theoretical statistical approaches [46]. SVMs are commonly used classifiers in various machine learning-based healthcare areas, such as medical imaging [47] and bioinformatics [48].…”

Section: Classification 351 Support Vector Machinementioning

confidence: 99%

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Mehak¹,

Ashraf²,

Zafar³

et al. 2022

Computers, Materials &Amp; Continua

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Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but nuclear atypia in breast cancer histopathology has not received much consideration. Nuclear atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast, non-invasive breast (usual ductal hyperplasia, atypical ductal hyperplasia) and pre-invasive breast (ductal carcinoma in situ) and invasive breast lesions. We proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification process. The classification results show the value of the multilayer autoencoder model in the evaluation of nuclear polymorphisms. The proposed method has indicated promising results, making them more fit in breast cancer grading.

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Section: Classification 351 Support Vector Machinementioning

confidence: 99%

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Mehak¹,

Ashraf²,

Zafar³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…The developing form of the ML technique that employs ensembles of regressions is gaining attention and this ensemble learning technique employs the same basic process to generate repeated numerous predictions, which are then averaged to form a unique model [37]. RF is one of ensemble learning, which is rapidly used for land-cover categorization from sensed data, as well as other domains connected to the environment and water resources [38].…”

Section: Assimilation Of Iot and ML (References)mentioning

confidence: 99%

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML

et al. 2022

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According to the United Nations, the Sustainable Development Goal ‘6’ seeks to ensure the availability and sustainable management of water for all. Digital technologies, such as big data, Internet of Things (IoT), and machine learning (ML) have a significant role and capability to meet the goal. Water quality analysis in any region is critical to identify and understand the standard of water quality and the quality of water is analyzed based on water quality parameters (WQP). Currently, water pollution and the scarcity of water are two major concerns in the region of Uttarakhand, and the analysis of water before it is supplied for human consumption has gained attention. In this study, a big data analytics framework is proposed to analyze the water quality parameters of 13 districts of Uttarakhand and find the correlation among the parameters with the assimilation of IoT and ML. During the analysis, statistical and fractal methods are implemented to understand the anomalies between the water quality parameters in 13 districts of Uttarakhand. The variation in WQP is analyzed using a random forest (RF) model, and the dataset is segmented location wise and the mean, mode, standard deviation, median, kurtosis, and skewness of time series datasets are examined. The mean of the parameters is adjusted with the coefficient of variation based on the standard values of each parameter. The turbidity in almost all the experimental sites has a normal distribution, with the lowest mean value (0.352 mg/L) and highest (11.9 mg/L) in the Pauri Garhwal and Almora districts, respectively. The pH of the water samples is observed to be in the standard range in all the experimental sites, with average and median values being nearly identical, at 7.189 and 7.20, respectively. However, the pH mode is 0.25. The Cl− concentration varies with mean values from the lowest (0.46 mg/L) to the highest (35.2 mg/L) over the experimental sites, i.e., the Bageshwar and Rudraprayag districts, respectively. Based on the analysis, it was concluded that the water samples were found to be safe to drink and in healthy condition in almost all the districts of the state Uttarakhand, except for the Haridwar district, where some increase in contaminants was observed.

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“…The first node is called the root node, and the nodes that represent other characteristics before reaching the final node of the structure are called the leaf nodes. The leaf node represents the target class or classification label, which is the final decision or prediction made after following the path from the root to the leaf (classification rule) [17,18]. The main advantages of DT are as follows: (1) missing data can be accommodated; (2) the data do not need to conform to a normal distribution; (3) outliers have almost no effect on the final classification; (4) categorical data and numerical data can be used as predictors; (5) Transforming predictors have no effect on the tree structure [19].…”

Section: Dt Algorithm Modelmentioning

confidence: 99%

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

et al. 2021

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Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.

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An Ensemble Machine Learning Technique for Functional Requirement Classification

Cited by 33 publications

References 36 publications

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

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