The oxidation desulphurization assisted by ultrasound waves was applied to the desulphurization of heavy naphtha. Hydrogen peroxide and acetic acid were used as oxidants, ultrasound waves as phase dispersion, and activated carbon as solid adsorbent. When the oxidation desulphurization (ODS) process was followed by a solid adsorption step, the performance of overall Sulphur removal was 89% for heavy naphtha at the normal condition of pressure and temperature. The process of (ODS) converts the compounds of Sulphur to sulfoxides/ sulfones, and these oxidizing compounds can be removed by activated carbon to produce fuel with low Sulphur content. The absence of any components (hydrogen peroxide, acetic acid, ultrasound waves and activated car

This paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that

In this paper, an efficient image segmentation scheme is proposed of boundary based & geometric region features as an alternative way of utilizing statistical base only. The test results vary according to partitioning control parameters values and image details or characteristics, with preserving the segmented image edges.

     This paper introduced a hybrid technique for lossless image compression of natural and medical images; it is based on integrating the bit plane slicing and Wavelet transform along with a mixed polynomial of linear and non linear base. The experiments showed high compression performance with fully grunted reconstruction.

Cuneiform symbols recognition represents a complicated task in pattern recognition  and image analysis as a result of problems that related to cuneiform symbols like distortion and unwanted objects that associated with applying Binrizetion process like spots and writing lines. This paper aims to present new proposed algorithms to solve these problems for reaching uniform results about cuneiform symbols recognition that related to (select appropriate Binerized method, erased writing lines and spots) based on statistical Skewness measure, image morphology and distance transform concepts. The experiment results show that our proposed algorithms have excellent result and can be adopted

     Today, the use of iris recognition is expanding globally as the most accurate and reliable biometric feature in terms of uniqueness and robustness. The motivation for the reduction or compression of the large databases of iris images becomes an urgent requirement. In general, image compression is the process to remove the insignificant or redundant information from the image details, that implicitly makes efficient use of redundancy embedded within the image itself. In addition, it may exploit human vision or perception limitations to reduce the imperceptible information.
     This paper deals with reducing the size of image, namely reducing the number of bits required in representing the

These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that.  The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the proce

In the last few decades, growing interest has been shown in the development of new solar selective coatings based on transition metal nitride and/or oxinitride for solar absorbing applications. Solar thermal collectors are well thought out to be the most effective process of converting and harvesting solar radiation. In this investigation, Cu/TiON/CrO2 multilayered solar selective absorber coatings have been coated onto Al substrates using the dip-coating process followed by an annealing process at (400, 450, 500, 550, and 600 °C. The XRD analysis showed excellent crystalline quality for the prepared thin films along with enhanced surface features as proved by FESEM images, and the grains are in the range of (27–81) nm. The optical in