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The world's high-value crop is tea, its aspect plays a powerful role in its marketability. Tea is the utmost extensively absorbed aromatic beverage with the legion of health benefit and respond as a remedy for various disease like neurological disorder and cardiovascular. The identification of tea fermentation is onerous due to the heavy production of products. The manual investigation is expensive, laborious, and inconsistent. Thus, an automated machine learning-based algorithm is proposed for the grading of tea fermentation (fermented, over-fermented, under-fermented). Firstly, images are pre-processed by Gaussian filtering to enhance the quality of the image and removing of noise. Then various features namely, statistical, textural, color, geometrical, laws texture energy, the histogram of gradients, and discrete wavelet transform are extracted (150) and selected from feature vector by PCA. Lastly, k-NN, SRC, and SVM are used to make decisions for the detection of tea fermentation levels. The performance of the system has been validated by the k (10) fold cross-validation technique. The proposed algorithm achieves 87.39% (k-NN), 89.72% (SRC), and 98.75% (SVM) for tea fermentation level detection. The proper feature selection shows the enhanced performance of the system. Among three different classifiers, SVM shows more efficient results that are promising and comparable with the literature. This paper also includes the analytical comparison of distinct approaches proposed by the different researchers for tea fermentation level detection. This potential fermentation level detection may guide the detection of tea products which further promotes the development of the food industry. [ABSTRACT FROM AUTHOR]

Abstract The pandemic was announced by the world health organization coronavirus (COVID-19) universal health dilemma. Any scientific appliance which contributes expeditious detection of coronavirus with a huge recognition rate may be excessively fruitful to doctors. In this environment, innovative automation like deep learning, machine learning, image processing and medical image like chest radiography (CXR), computed tomography (CT) has been refined promising solution contrary to COVID-19. Currently, a reverse transcription-polymerase chain reaction (RT-PCR) test has been used to detect the coronavirus. Due to the moratorium period is high on results tested and huge false negative estimates, substitute solutions are desired. Thus, an automated machine learning-based algorithm is proposed for the detection of COVID-19 and the grading of nine different datasets. This research impacts the grant of image processing and machine learning to expeditious and definite coronavirus detection using CXR and CT medical imaging. This results in early detection, diagnosis, and cure for the accomplishment of COVID-19 as early as possible. Firstly, images are preprocessed by normalization to enhance the quality of the image and removing of noise. Secondly, segmentation of images is done by fuzzy c-means clustering. Then various features namely, statistical, textural, histogram of gradients, and discrete wavelet transform are extracted (92) and selected from the feature vector by principle component analysis. Lastly, k-NN, SRC, ANN, and SVM are used to make decisions for normal, pneumonia, COVID-19 positive patients. The performance of the system has been validated by the k (5) fold cross-validation technique. The proposed algorithm achieves 91.70% (k-Nearest Neighbor), 94.40% (Sparse Representation Classifier), 96.16% (Artificial Neural Network), and 99.14% (Support Vector Machine) for COVID detection. The proposed results show feature combination and selection improves the performance in 14.34 s with machine learning and image processing techniques. Among k-NN, SRC, ANN, and SVM classifiers, SVM shows more efficient results that are promising and comparable with the literature. The proposed approach results in an improved recognition rate as compared to the literature review. Therefore, the algorithm proposed shows immense potential to benefit the radiologist for their findings. Also, fruitful in prior virus diagnosis and discriminate pneumonia between COVID-19 and other pandemics. [ABSTRACT FROM AUTHOR]

Face Expression is the visible feature mask to concede person's behaviour, intension or reaction. The paper has dealt out a qualified data mining model for expression recognition called MPMFFT (Multiple Pattern Multiple Feature based Feature Transformation) integrated model. This proposed model transformed the facial information in 22 aggregative features. These features include textural, geometrical, mathematical and structural characteristics. The characteristics are captured in the entire facial region and expression sensitive selected facial segment. To acquire the features, three local region extraction models are used on both broad and responsive facial areas. This complete featured composition formed a set of 132 features. This transformed data form is treated under the danger theory. At First, DCA (Dendric Cell algorithm) is ready to generate feature patterns to identify safe and danger qualified features. After this, each cell group is processed under DBT (Dempster Belief Theory) for integrated feature weight assignment based on mapping to the expression class. This weighted and cell formed data set is finally processed under probabilistic implementation using Bayesian nets. The experiment is applied on JAFFE, CMU and CK datasets. The experimental observations are compiled for individual expressions against decision tree, SVM and KNN methods. The comparative results signify that the model improved the accuracy of each of distinctive facial expression.

Face Expression is the visible feature mask to concede person's behaviour, intension or reaction. The paper has dealt out a qualified data mining model for expression recognition called MPMFFT (Multiple Pattern Multiple Feature based Feature Transformation) integrated model. This proposed model transformed the facial information in 22 aggregative features. These features include textural, geometrical, mathematical and structural characteristics. The characteristics are captured in the entire facial region and expression sensitive selected facial segment. To acquire the features, three local region extraction models are used on both broad and responsive facial areas. This complete featured composition formed a set of 132 features. This transformed data form is treated under the danger theory. At First, DCA (Dendric Cell algorithm) is ready to generate feature patterns to identify safe and danger qualified features. After this, each cell group is processed under DBT (Dempster Belief Theory) for integrated feature weight assignment based on mapping to the expression class. This weighted and cell formed data set is finally processed under probabilistic implementation using Bayesian nets. The experiment is applied on JAFFE, CMU and CK datasets. The experimental observations are compiled for individual expressions against decision tree, SVM and KNN methods. The comparative results signify that the model improved the accuracy of each of distinctive facial expression. [ABSTRACT FROM AUTHOR]