Medical image segmentation is one of the most actively studied fields in the past few decades, as the development of modern imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), physicians and technicians nowadays have to process the increasing number and size of medical images. Therefore, efficient and accurate computational segmentation algorithms become necessary to extract the desired information from these large data sets. Moreover, sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures presented in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning. Many of the proposed algorithms could perform well in certain medical image applications.The aim of this paper is to change the medical image into something that is more meaningful and easier to analyze and recognize features that helps the doctors to diagnoses the diseases .This paper views selected medical image and segmentation method that have been proposed, which are suitable for processing medical images by use the modification of the traditional interactive threshold technique. This method gave good results,and these results are testedaccordingto the measure of quality (PSNR).
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Pick and place system is one of the significant employments of modern robots utilized in industrial environments. The objective of this research is to make a comparison of time sequences by combining multiple axes of sequences. A pick-place system implemented with pneumatic linear double-acting cylinders to applicator in automated systems processes for manufacturing. The challenge of 3-axes movement control was achieved using the PLC (Programmable Logic Controller) controller such that the merging between two or three axes was achieved according to the selected sequence of the program. The outcomes show the contrasted sequences and the reference in a constant velocity. The main variable parameter is the number of steps for each sequ
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Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of
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This paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estima
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This research paper aimed to quantitively characterize the pore structure of shale reservoirs. Six samples of Silurian shale from the Ahnet basin were selected for nitrogen adsorption-desorption analysis. Experimental findings showed that all the samples are mainly composed of mesopores with slit-like shaped pores, as well as the Barrett-Joyner-Halenda pore volume ranging from 0.014 to 0.046 cm3/ 100 g, where the lowest value has recorded in the AHTT-1 sample, whereas the highest one in AHTT-6, while the rest samples (AHTT-2, AHTT-3, AHTT-4, AHTT-5) have a similar average value of 0.03 cm3/ 100 g. Meanwhile, the surface area and pore size distribution were in the range of 3.8 to 11.1 m2 / g and 1.7 to 40 nm, respectively.
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This article aims to estimate the partially linear model by using two methods, which are the Wavelet and Kernel Smoothers. Simulation experiments are used to study the small sample behavior depending on different functions, sample sizes, and variances. Results explained that the wavelet smoother is the best depending on the mean average squares error criterion for all cases that used.
This study was aimed to assess the efficiency of N.oleander to remove heavy metals such as Copper (Cu) from wastewater. A toxicity test was conducted outdoor for 65-day to estimate the ability of N.oleander to tolerate Cu in synthetic wastewater. Based on a previous range-finding test, five concentrations were used in this test (0, 50, 100, 300, 510 mg/l). The results showed that maximum values of removal efficiency was found 99.9% on day-49 for the treatment 50 mg/l. Minimum removal efficiency was 94% day-65 for the treatment of 510 mg/l. Water concentration was within the permissible limits of river conservation and were 0.164 at day-35 for the 50 mg/l treatment, decreased thereafter until the end of the observation, and 0.12 at d
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