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Additional file 1. Original dataset file.

Additional file 2. Online Resource 2_DataSet_Preprocessed.

International audience; Islamic banks and Islamic financial institutes was established to develop the Islamic economic, where one of its main tools from the financial and monetary perspective, but under a provisional system to ensure the legitimacy and correct path as well as the right goals and tools. According to that, a Legitimate (Sharia) Council do these active functions to achieve the obligation of bank and the establishment in the provisions and legal principles. The legitimate Oversight Council protects the banking functions through its system which includes Sharia rules that regulate the various banking activities with accordance of Islamic law and prohibiting banking activities which found opposing the provision of Sharia and Islamic law. It is recommended that to ensure full compliance with the provision of Islamic law and Sharia in financial activities, the Legitimate Oversight Council should exist in any Islamic bank to ensure the compliance of Islamic banking products and activities, and provide full integrity of financial transactions in terms of their Islamic character.

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. Anemia is a lack of RBCs is characterized by its level compared to the normal hemoglobin level. In this study, a system based image processing methodology was developed to localize and extract RBCs from microscopic images. Also, the machine learning approach is adopted to classify the localized anemic RBCs images. Several textural and geometrical features are calculated for each extracted RBCs. The training set of features was analyzed using principal component analysis (PCA). With the proposed method, RBCs were isolated in 4.3secondsfrom an image containing 18 to 27 cells. The reasons behind using PCA are its low computation complexity and suitability to find the most discriminating features which can lead to accurate classification decisions. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network RBFNN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained within short time period, and the results became better when PCA was used., {"references":["Organization (WHO), World Health. \"Global Database on Anemia\". The\ndatabase on Anemia includes data by country on prevalence of anemia\nand mean hemoglobin concentration, (2010).","Turgeon, Mary Louise, \"Clinical Hematology: Theory and Procedures\",\nLippincott Williams & Wilkins, P. 100, (2012).","Suzuki, Kenji. \"A Review of Computer-Aided Diagnosis in Thoracic\nand Colonic Imaging\". Quantitative imaging in medicine and surgery,\nVol. 2, No. 3, P. 163, (2012).","Adollah, R., Mashor, M.Y., Mohd Nasir, N.F., Rosline, H., Mahsin, H.,\nAdilah, H. \"Blood Cell Image Segmentation: A Review\", In 4th Kuala\nLumpur Internationals Conference on Biomedical Engineering, (2008).","Hiremath, P. S., ParashuramBannigidad, and SaiGeeta, \"Automated\nIdentification and Classification of WhiteBlood Cells (Leukocytes) in\nDigital Microscopic Images\", IJCA special issue on \"Recent Trends in\nImage Processing and Pattern Recognition: RTIPPR, (2010).","Chen, Hung-Ming, Ya-Ting Tsao, and Shin-Ni Tsai, \"Automatic Image\nSegmentation and Classification Based on Direction Texton Technique\nfor Hemolytic Anemia in","Thin Blood Smears\", Machine Vision and Applications, Vol. 25, No. 2,\n(2014).","Aerkewar, Prafulla N., and G. H. Agrawal, \"Image Segmentation\nMethods for Dermatitis Disease: A Survey\", Image, Vol 2, Issue 1, Pp.\n01-06, (2013).","Baghli, Ismahan,\"Hybrid framework Based on Evidence Theory for\nBlood Cell Image Segmentation\", Medical Imaging, (2014).\n[10] Roussev, Vassil, and Candice Quates, \"File Fragment Encoding\nClassification - An Empirical Approach\", Digital Investigation, Vol. 10,\nS69-S77, (2013).\n[11] Yılmaz, Z. and M.R. Bozkurt, \"Determination of Women Iron\nDeficiency Anemia Using Neural Networks\", Journal of Medical\nSystems, Vol. 36, No. 5, Pp. 2941-2945, (2012).\n[12] Jolliffe, Ian, \"Principal Component Analysis\", John Wiley & Sons, Ltd,.\n2005.\n[13] WEKA Available from: http://www.cs.waikato.ac.nz/ ~ml/weka\n[14] Savkare, S. and S. Narote, \"Automatic System for Classification of\nErythrocytes Infected with Malaria and Identification of Parasite's Life\nStage\", Procedia Technology, Vol. 6, Pp. 405-410, (2012).\n[15] Lavesson, Niklas, \"Evaluation and Analysis of Supervised Learning\nAlgorithms and Classifiers\", Blekinge Institute of Technology, 2006\n[16] Rajendran, P. and M. Madheswaran, \"An Improved Brain Image\nClassification Technique with Mining and Shape Prior Segmentation\nProcedure\", Journal of Medical Systems, Vol. 36, No. 2, Pp. 747-764,\n(2012)."]}

This paper proposes a novel methodology for facilities layout planning and optimization in which fitness evaluation of layout alternatives is done in an automated manner using an artificial neural network trained to user preferences. The intrinsically uncertain, unstructured, and often tacit nature of facilities layout design preferences and constraints necessitates the use of domain experts in the fitness evaluation of layout alternatives. However, unavailability of domain experts in a timely or economical fashion highlights the need for resorting to the use of automation in this important area. To test the key novel component of the proposed approach, a variety of artificial neural networks are trained on a large data set containing both qualitative and quantitative fitness of layout alternatives and subjective ranking by a domain expert utilizing the knowledge of the application domain. Simulation results strongly support the viability of the proposed concept. Such an automated approach to fitness evaluations of layout alternatives is expected to significantly improve the efficacy and efficiency of the facilities layout planning process. Furthermore, such an approach would spur the much sought for research in decision support and expert systems in layout planning. Scopus

Handwriting recognition is one of the leading applications of pattern recognition and machine learning. Despite having some limitations, handwriting recognition systems have been used as an input method of many electronic devices and helps in the automation of many manual tasks requiring processing of handwriting images. In general, a handwriting recognition system comprises three functional components; preprocessing, recognition and post-processing. There have been improvements made within each component in the system. However, to further open the avenues of expanding its applications, specific improvements need to be made in the recognition capability of the system. Hidden Markov Model (HMM) has been the dominant methods of recognition in handwriting recognition in offline and online systems. However, the use of Gaussian observation densities in HMM and representational model for word modeling often does not lead to good classification. Hybrid of Neural Network (NN) and HMM later improves word recognition by taking advantage of NN discriminative property and HMM representational capability. However, the use of NN does not optimize recognition capability as the use of Empirical Risk minimization (ERM) principle in its training leads to poor generalization. In this thesis, we focus on improving the recognition capability of a cursive online handwritten word recognition system by using an emerging method in machine learning, the support vector machine (SVM). We first evaluated SVM in isolated character recognition environment using IRONOFF and UNIPEN character databases. SVM, by its use of principle of structural risk minimization (SRM) have allowed simultaneous optimization of representational and discriminative capability of the character recognizer. We finally demonstrate the various practical issues in using SVM within a hybrid setting with HMM. In addition, we tested the hybrid system on the IRONOFF word database and obtained favourable results.; Nos travaux concernent la reconnaissance de l'écriture manuscrite qui est l'un des domaines de prédilection pour la reconnaissance des formes et les algorithmes d'apprentissage. Dans le domaine de l'écriture en-ligne, les applications concernent tous les dispositifs de saisie permettant à un usager de communiquer de façon transparente avec les systèmes d'information. Dans ce cadre, nos travaux apportent une contribution pour proposer une nouvelle architecture de reconnaissance de mots manuscrits sans contrainte de style. Celle-ci se situe dans la famille des approches hybrides locale/globale où le paradigme de la segmentation/reconnaissance va se trouver résolu par la complémentarité d'un système de reconnaissance de type discriminant agissant au niveau caractère et d'un système par approche modèle pour superviser le niveau global. Nos choix se sont portés sur des Séparateurs à Vastes Marges (SVM) pour le classifieur de caractères et sur des algorithmes de programmation dynamique, issus d'une modélisation par Modèles de Markov Cachés (HMM). Cette combinaison SVM/HMM est unique dans le domaine de la reconnaissance de l'écriture manuscrite. Des expérimentations ont été menées, d'abord dans un cadre de reconnaissance de caractères isolés puis sur la base IRONOFF de mots cursifs. Elles ont montré la supériorité des approches SVM par rapport aux solutions à bases de réseaux de neurones à convolutions (Time Delay Neural Network) que nous avions développées précédemment, et leur bon comportement en situation de reconnaissance de mots.

Handwriting recognition is one of the leading applications of pattern recognition and machine learning. Despite having some limitations, handwriting recognition systems have been used as an input method of many electronic devices and helps in the automation of many manual tasks requiring processing of handwriting images. In general, a handwriting recognition system comprises three functional components; preprocessing, recognition and post-processing. There have been improvements made within each component in the system. However, to further open the avenues of expanding its applications, specific improvements need to be made in the recognition capability of the system. Hidden Markov Model (HMM) has been the dominant methods of recognition in handwriting recognition in offline and online systems. However, the use of Gaussian observation densities in HMM and representational model for word modeling often does not lead to good classification. Hybrid of Neural Network (NN) and HMM later improves word recognition by taking advantage of NN discriminative property and HMM representational capability. However, the use of NN does not optimize recognition capability as the use of Empirical Risk minimization (ERM) principle in its training leads to poor generalization. In this thesis, we focus on improving the recognition capability of a cursive online handwritten word recognition system by using an emerging method in machine learning, the support vector machine (SVM). We first evaluated SVM in isolated character recognition environment using IRONOFF and UNIPEN character databases. SVM, by its use of principle of structural risk minimization (SRM) have allowed simultaneous optimization of representational and discriminative capability of the character recognizer. We finally demonstrate the various practical issues in using SVM within a hybrid setting with HMM. In addition, we tested the hybrid system on the IRONOFF word database and obtained favourable results.; Nos travaux concernent la reconnaissance de l'écriture manuscrite qui est l'un des domaines de prédilection pour la reconnaissance des formes et les algorithmes d'apprentissage. Dans le domaine de l'écriture en-ligne, les applications concernent tous les dispositifs de saisie permettant à un usager de communiquer de façon transparente avec les systèmes d'information. Dans ce cadre, nos travaux apportent une contribution pour proposer une nouvelle architecture de reconnaissance de mots manuscrits sans contrainte de style. Celle-ci se situe dans la famille des approches hybrides locale/globale où le paradigme de la segmentation/reconnaissance va se trouver résolu par la complémentarité d'un système de reconnaissance de type discriminant agissant au niveau caractère et d'un système par approche modèle pour superviser le niveau global. Nos choix se sont portés sur des Séparateurs à Vastes Marges (SVM) pour le classifieur de caractères et sur des algorithmes de programmation dynamique, issus d'une modélisation par Modèles de Markov Cachés (HMM). Cette combinaison SVM/HMM est unique dans le domaine de la reconnaissance de l'écriture manuscrite. Des expérimentations ont été menées, d'abord dans un cadre de reconnaissance de caractères isolés puis sur la base IRONOFF de mots cursifs. Elles ont montré la supériorité des approches SVM par rapport aux solutions à bases de réseaux de neurones à convolutions (Time Delay Neural Network) que nous avions développées précédemment, et leur bon comportement en situation de reconnaissance de mots.