It takes a lot of time to classify the banana slices by sweetness level using traditional methods. By assessing the quality of fruits more focus is placed on its sweetness as well as the color since they affect the taste. The reason for sorting banana slices by their sweetness is to estimate the ripeness of bananas using the sweetness and color values of the slices. This classifying system assists in establishing the degree of ripeness of bananas needed for processing and consumption. The purpose of this article is to compare the efficiency of the SVM-linear, SVM-polynomial, and LDA classification of the sweetness of banana slices by their LRV level. The result of the experiment showed that the highest accuracy of 96.66% was achieved by the SVM-polynomial algorithm, while the lowest 86.66% by LDA algorithm. The SVM-linear also has an accuracy of 90%. The study showed how machine learning algorithms can be used to classify banana slices according to their sweetness.
CNC machines are widely used in production fields since they produce similar parts in a minimum time, at higher speed and with possibly minimum error. A control system is designed, implemented and tested to control the operation of a laboratory CNC milling machine having three axes that are moved by using a stepper motor attached to each axis. The control system includes two parts, hardware part and software part, the hardware part used a PC (works as controller) connected to the CNC machine through its parallel port by using designed interface circuit. The software part includes the algorithms needed to control the CNC. The sample needs to be machined is drawn by using one of the drawing software like AUTOCAD or 3D MAX and is saved in a we
... Show MoreCodes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de
... Show MoreIn general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As t
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Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob
... Show MoreHS Saeed, SS Abdul-Jabbar, SG Mohammed, EA Abed, HS Ibrahem, Solid State Technology, 2020
Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor
... Show MoreVehicular ad hoc network (VANET) is a distinctive form of Mobile Ad hoc Network (MANET) that has attracted increasing research attention recently. The purpose of this study is to comprehensively investigate the elements constituting a VANET system and to address several challenges that have to be overcome to enable a reliable wireless communications within a vehicular environment. Furthermore, the study undertakes a survey of the taxonomy of existing VANET routing protocols, with particular emphasis on the strengths and limitations of these protocols in order to help solve VANET routing issues. Moreover, as mobile users demand constant network access regardless of their location, this study seeks to evaluate various mobility models for vehi
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