In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

In aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we

This paper describes the transport of Alkaloids through Rotating Discs Contactor (RDC) using n-decane as a liquid membrane. The transport of Pelletierine Alkaloid from a source phase through bulk liquid membrane to the receiving phase has been investigated. The general behaviour of Pertraction process indicates that% Extraction of pelletierine Alkaloid increased with increase in the number of stages and the agitation speed but high agitation speed was not favoured due to the increased risk of droplet formation during the operation. The pH of source and receiving phases were also investigated. The effect of organic solvent membrane on the extraction of Pelletierine was evaluated using ndecane, n-hexane and methyl cyclohexane. The results sho

In this study Microwave and conventional methods have been used to extract and estimate pectin and its degree of esterification from dried grapefruit and orange peels. Acidified solution water with nitric acid in pH (1.5) was used. In conventional method, different temperature degrees for extraction pectin from grape fruit and orange(85 ,90 , 95 and 100?C) for 1 h were used The results showed grapefruit peels contained 12.82, 17.05, 18.47, 15.89% respectively, while the corresponding values were 5.96, 6.74, 7.41 and 8.00 %, respectively in orange peels. In microwave method, times were 90, 100, 110 and 120 seconds. Grapefruit peels contain 13.86, 16.57, 18.69, and 17.87%, respectively, while the corresponding values were of 6.53, 6.68, 7.2

The percentage of fatty acids, quantity of tocopherols, tocotrienols, carotens and physiochemical characteristics of crude red palm oil have been evaluated, in addition to specific chemical detection of active compounds unsaponifiable matters. Results of Gas Liquid Chromatography showed:- The major fatty acids in red palm oil is palmitic (44.36%) then oleic (39.65%), linolenic (10.55%), stearic (3.56%), myristic (1.22%), arachdonic (0.24%) and palmotic (0.19%). Red palm oil contains ? – ?- ?- ? – Tocopherols with concentration 258 , 121 , 259, 109 m/kg oil , ? – ?- ?- ? – Tocotrienol with concentration 462.77 , 571.03, 619.18, 509.07 m/kg oil respectively. Total tocopherols & tocotrienols 2909.05 m/kg oil and