In the present paper we have developed a new method for constructing magic cube by using the folded magic square technique. The proposed method considers a new step towards the magic cube construction that applied a good insight and provides an easy generalized technique. This method generalized the design of magic cube with N order regardless the type of magic square whether odd order, singly even order or doubly even order. The proposed method is fairly easy, since it have depended mainly on the magic square construction methods, and all what the designer need is just how to builds six magic square sequentially or with constant difference value between each pair of the numbers in the square matrix, whereby each one of this magic square will represents the surface or dimension for magic cube configuration. The next step for the designer will be how to arrange each square in the proper order to constitute the regular cube in order to maintain the properties of magic cube, where the sum of rows, columns and the diagonals from all directions are the same.

Inspection task is traditionally carried out by human. However, Automated Visual Inspection (AVI) has gradually become more popular than human inspection due to the advantageous in the aspect of high precision and short processing time. Therefore, this paper proposed a system which identifies the object’s position for industrial robot based on colors and shapes where, red, green, blue and circle, square, triangle are recognizable. The proposed system is capable to identify the object’s position in three modes, either based on color, shape or both color and shape of the desired objects. During the image processing, RGB color space is utilized by the proposed system while winner take all approach is used to classify the color of the object through the evaluation of the pixel’s intensity value of the R, G and B channel. Meanwhile, the shapes and position of the objects are determined based on the compactness and the centroid of the region respectively. Camera settings, such as brightness, contrast and exposure is another important factor which can affect the performance of the proposed system. Lastly, a Graphical User Interface was developed. The experimental result shows that the developed system is highly efficient when implemented in the selected database.

Rheumatoid joint inflammation is characterized as a perpetual incendiary issue which influences the joints by hurting body tissues Therefore, there is an urgent need for an effective intelligent identification system of knee Rheumatoid arthritis especially in its early stages. This paper is to develop a new intelligent system for the identification of Rheumatoid arthritis of the knee utilizing image processing techniques and neural classifier. The system involves two principle stages. The first one is the image processing stage in which the images are processed using some techniques such as RGB to grayscale conversion, rescaling, median filtering, background extracting, images subtracting, segmentation using canny edge detection, and features extraction using pattern averaging. The extracted features are used then as inputs for the neural network which classifies the X-ray knee images as normal or abnormal (arthritic) based on a backpropagation learning algorithm which involves training of the network on 400 X-ray normal and abnormal knee images. The system was tested on 400 x-ray images and the network shows good performance during that phase, resulting in a good identification rate 95.5 %.

The precise modeling of average air temperature is a significant and much essential parameter in frame of reference for decision-making in agriculture field, drought detection and environmental related issues. The aim of this research is to construct an accurate model to modeling average air temperature using hybrid Wavelet-ANFIS techniques. Being cognizant of the fact, uncertainty handling capability is achieved with ANFIS technique; a cognitive approach to integrate ANFIS technique along with pre-processed data by using Wavelet transformation. Detailing on approach, in this work utilized Discrete Wavelet transform under Daubechies mother Wavelet up to 3rd level of decomposition. This study extends up to seven station’s meteorological data records. The following developed hybrid model’s performance is compared with single ANFIS models for all seven stations. The obtained results were evaluated using correlation coefficient, root mean square error and scatter index These results confirmed that the proposed hybridized Wavelet- ANFIS model has estimable potential in terms of modeling temperature than ANFIS model alone.

Intuitionistic Fuzzy Numbers (IFNs) transfer more information than fuzzy numbers do in uncertain situations. It is caused that many others tried to define methods for ranking of IFNs and arithmetic operations on them, which are used in practical applications of IFNs such as decision making. Arithmetic operators on IFNs changed membership and non-membership degrees. The resulted degrees have important interpretations in real application of IFNs. In this paper, we will first review the existing methods for ranking and arithmetic operations on several representations of IFNs. Then, we will propose a new method based on arithmetic mean and geometric mean to compute membership and non-membership degrees of resulted IFN from arithmetic operations on IFNs. It is caused that the resulted degrees don't change monotonousness and be closer to reality. Furthermore, a new method for ranking of IFNs will be proposed. Finally, the proposed methods are used in the numerical examples, compared to some other existing methods.

Diabetes is a condition in which the amount of sugar in the blood is higher than normal. Classification systems have been widely used in medical domain to explore patient’s data and extract a predictive model or set of rules. The prime objective of this research work is to facilitate a better diagnosis (classification) of diabetes disease. There are already several methodology which have been implemented on classification for the diabetes disease. The proposed methodology implemented work in 2 stages: (a) In the first stage Genetic Algorithm (GA) has been used as a feature selection on Pima Indian Diabetes Dataset. (b) In the second stage, Multilayer Perceptron Neural Network (MLP NN) has been used for the classification on the selected feature. GA is noted to reduce not only the cost and computation time of the diagnostic process, but the proposed approach also improved the accuracy of classification. The experimental results obtained classification accuracy (79.1304%) and ROC (0.842) show that GA and MLP NN can be successfully used for the diagnosing of diabetes disease.

Software maintenance is one of the main quality characteristics of the software product. The maintainability of a system is a measure of the ability of the system to undergo maintenance or to return to normal operation after a failure. In this paper, a new model to improve the maintainability of object-oriented software has been proposed. The proposed model is based on newer versions of software quality standard and it is according to the measurement of several new metric. This model has been evaluated on famous PHP framework and the results showed that the proposed model is effective compared with the previous models.

The technological growth generates the massive data in all the fields. Classifying these high-dimensional data is a challenging task among the researchers. The high-dimensionality is reduced by a technique is known as attribute reduction or feature selection. This paper proposes a genetic algorithm (GA)-based features selection to improve the accuracy of medical data classification. The main purpose of the proposed method is to select the significant feature subset which gives the higher classification accuracy with the different classifiers. The proposed genetic algorithm-based feature selection removes the irrelevant features and selects the relevant features from original dataset in order to improve the performance of the classifiers in terms of time to build the model, reduced dimension and increased accuracy. The proposed method is implemented using MATLAB and tested using the medical dataset with various classifiers namely Na?ve Bayes, J48, and k-NN and it is evident that the proposed method outperforms other methods compared.