A hybrid model using decision tree and neural network for credit scoring problem

Nowadays credit scoring is an important issue for financial and monetary organizations that has substantial impact on reduction of customer attraction risks. Identification of high risk customer can reduce finished cost. An accurate classification of customer and low type 1 and type 2 errors have been investigated in many studies. The primary objective of this paper is to develop a new method, which chooses the best neural network architecture based on one column hidden layer MLP, multiple columns hidden layers MLP, RBFN and decision trees and ensembling them with voting methods. The proposed method of this paper is run on an Australian credit data and a private bank in Iran called Export Development Bank of Iran and the results are used for making solution in low customer attraction risks

A Critical Review of Khaleghi Motlagh’s Correction in the Story of Rostam and Sohrab

Text correction is one of the most difficult and delicate areas of research in the Persian language and literature. In the field of Shahnameh, since the first correction of Lamzend's incomplete correction, there have been many useful efforts. Undoubtedly, one of the best efforts in this field is Khaleghi Motlagh’s work. This work is greatly used as a guide for those who are researching and working on text correction. In this correction, despite some merits, there are also such weaknesses as fully relying on Florence's version, neglecting some of the mistakes, misunderstanding the meanings of the verses, and ignoring the narrative context of the story by the wrong choice. The sample of the weaknesses in the story of Rostam and Sohrab has been examined and corrected by mentioning the reasons and examining other works and publications. In this research, it became clear that the old versions and the majority of editions can not be completely relied on, and sometimes a new version can solve text problems

Automatically Harnessing Sparse Acceleration

Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. High-performance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendor-specific software and hardware ecosystems, creating non-portable code. In this paper, we develop a new approach based on our specification Language for implementers of Linear Algebra Computations (LiLAC). Rather than requiring the application developer to (re)write every program for a given library, the burden is shifted to a one-off description by the library implementer. The LiLAC-enabled compiler uses this to insert appropriate library routines without source code changes. LiLAC provides automatic data marshaling, maintaining state between calls and minimizing data transfers. Appropriate places for library insertion are detected in compiler intermediate representation, independent of source languages. We evaluated on large-scale scientific applications written in FORTRAN; standard C/C++ and FORTRAN benchmarks; and C++ graph analytics kernels. Across heterogeneous platforms, applications and data sets we show speedups of 1.1$\times$ to over 10$\times$ without user intervention.Comment: Accepted to CC 202

Automatic Sparse Computation Parallelization by Utilizing Domain-Specific Knowledge in Data Dependence Analysis

Sparse vectors, matrices, and tensors are commonly used to compress nonzero values of big data manipulated in data analytics, scientific simulations, and machine learning computations. As with general computations, parallelization of loops in sparse computations, codes manipulating sparse structures, is essential to efficiently utilize available parallel architectures. The sparse computations often exhibit partial parallelism in loops that are sequential in the corresponding dense computation due to sparsity of data dependencies coming from indirect memory access through index arrays, e.g. {\tt col} in $val[col[j]]$. Such dependencies can only be discovered at runtime when content of index arrays are available. Consequently, performance programmers typically use the inspector/executor strategy to take advantage of partial parallelism in sparse computation. There, programmers implement an inspector code that creates iteration dependency graph at runtime from which wavefronts of iterations are extracted and fed into a parallel version of the computation called an executor. The executor executes iteration waves sequentially to respect sparse dependencies while executing iterations inside each wavefront in parallel. To automate the generation of the inspector and executor code, compiler-based loop-carried data dependency analysis is needed. However, straightforward automatically generated inspectors typically have significantly higher overhead than hand written optimized ones. Consequently, the specific problem that I am addressing in this dissertation is how can we automate the strategies used by expert programmers to generate efficient runtime inspectors for parallelizing sparse computation. The overarching contribution of this dissertation is an approach for encoding index array properties for individual index arrays and relationships between index arrays as universally quantified constraints and using them in compiler-based data dependence analysis. The dependence analysis is then evaluated in the context of finding wavefront parallelism in sparse computations. More specifically, one contribution is an approach to automatically use index array properties to prove more data dependencies unsatisfiable, removing the need for inspecting them at runtime. Other contributions are methods to use the same properties to simplify compile-time-satisfiable dependences by finding equalities and subset relationships enabling generation of faster runtime inspectors. The last contribution includes compile-time methods for expanding opportunities for array privatization in sparse computations by defining an array as private if its contents start and end each iteration with the same value. Evaluation results show my approach is able to find seven fully parallel loops in seven sparse computations where previous compiler-based approach could not, and efficiently extract partial parallelism from outer most loops of five out of six sparse computations

Supervising abnormal (remarkable) behaviors of moving objects from tracking and analyzing their trajectories

L’évolution des référentiels spatio-temporels et les dernières avancées des systèmes d’information géographique ont favorisé l’apparition de nouveaux types de services et d’applications liés à la localisation et à la mobilité d’entités d’intérêt dont la supervision et le contrôle d’objets mobiles en temps réel.Ces constats nous ont conduit à nous intéresser en tout premier lieu aux évolutions des objets qui peuvent être envisagées sous forme de trajectoires et offrent de nouvelles perspectives quant à l’analyse en temps réel de leurs comportements particuliers individuels et/ou collectifs.Dans le contexte industriel de l’entreprise Intactile Design, un enjeu majeur émerge : il s’agit de mettre à la disposition de tout expert, amené à prendre une décision au cours d’opérations de surveillance, le plus d'informations possibles relatives au contexte environnant les objets mobiles afin d’en extraire celles permettant la détection de comportements remarquables.L’objectif est donc d'analyser et d'exploiter la masse de données acquises à partir du suivi d'objets mobiles de divers types, et plongés dans des contextes différents de supervision. Pour ce faire, nous proposons une approche générique déclinable sur divers cas de supervision partant de l'hypothèse qui consiste à envisager, pour tout objet mobile, une seule et même trajectoire tout au long de sa vie.L'une des problématiques principales de cette recherche relève des difficultés d'interprétation des données recueillies en temps réel issues de l'observation des objets. En effet celles-ci sont massives, de compositions variables et parfois incomplètes, possiblement redondantes, voire sémantiquement hétérogènes. L'idée est de s’affranchir du manque de sémantique contextuelle et de l’absence de maîtrise des informations liées à l’analyse et à l’exploitation de ces données.L’approche consiste à proposer le recours à une ontologie cadre à des fins d’enrichissements des observations et analyses, et ce pour aider à la détection de comportements d'objets mobiles. L’ontologie cadre représente l'objet mobile et sa trajectoire au sein de tout contexte de supervision et ce de manière générique. Ce modèle s'inspire de travaux existants autour de la modélisation de données spatiales comme temporelles et les étend pour répondre à la spécificité de l’analyse sémantique en temps réel de la mobilité des objets. Pour rendre compte de la spécificité des différents contextes de supervision, l'ontologie est complétée par des règles métiers construites avec l'aide des experts du domaine. L'idée est tout à la fois de disposer d'une représentation de connaissances la plus expressive possible sans augmenter pour autant le coût du raisonnement ; et de rendre l'approche adaptable à toute thématique liée à la supervision.L'approche modulaire spécifiée a ensuite été mise en application au sein d'un prototype logiciel général qui fonctionne comme un système à base de connaissances. Il assure la structuration, enrichissement, extraction et analyse spatio-temporelle des connaissances conformes à notre modèle ontologique et donc offre les éléments nécessaires à la compréhension de comportements remarquables définis par les experts des domaines ciblés.Nous illustrons notre approche au travers d’un cas d’étude concret relatif au domaine des systèmes de supervision des opérations de défense terrestre.The recent evolution of gazetteers and the latest advances in geographic information systems have promoted new types of services related to the location and mobility of entities of interest, including the real-time supervision and control of moving objects.On the strength of these facts, we are considering the motion of an object based on its trajectory (i.e., the path followed by this object in motion). At our sense, the modeling of trajectories of a number of moving objects offers new insights into real-time analysis of their individual and/or group specific behaviors.Within the industrial context of the company Intactile Design, enhancing decision making in real time for supervision purposes, turns out to be a major challenge. At the same time, much emphasis is being placed on making sure any accessible data, related to the context of the moving objects, are available to experts in order to fully support decision making processes. More importantly, the key idea is to find a strategy that enhances capabilities of detecting unusual behaviors whilst integrating some kind of valuable information related to the context.Consequently, the main objective is to collect and analyze all of the data acquired from the tracking of many different types of moving objects in a variety of supervision contexts.At this effect, we propose a generic and innovative approach that can be applied to any case of supervision based on the assumption that considers for every mobile object, a single and unique trajectory constantly changing over time.One of the main obstacles of this research is the difficulty of real-time interpreting of all of the collected data as these data are mostly complex, voluminous, semantically heterogeneous and incomplete.In this way, the idea is to overcome the lack of contextual semantics (i.e., semantics captured from the observations of the objects evolving within their contexts).To address these challenges, we propose a top domain ontology for moving objects and their trajectories, which is expressed in OWL 2 DL. The ontology attempts to describe the starting categories for the field of mobility and therefore is applicable to all supervision and control contexts.Additionally, this ontology is building upon a few number of existing ontologies that all refer to spatio-temporal knowledge, including GeoSPARQL and OWL Time.Moreover, the ontology and a set of business rules, provided by the experts on a domain of interest, are combined to fully capture the contextual semantics of the domain under consideration.The aim is double: on one side, to benefit from a knowledge representation as expressive as possible that offers a cost-effective reasoning, and on the other side to efficiently adapt the approach to any context related to supervision.Our modular approach is implemented through a general software prototype that runs as a knowledge-based system.The prototype ensures the sustainability, extraction and spatio-temporal reasoning of information that complies with our ontology, and therefore it offers the necessary elements to understand behaviors defined by the experts of the targeted areas.We illustrate our approach through a concrete case study of monitoring systems dedicated to land defense

Comparative evaluation of carbamazepine release from single and bi-polymeric based matrices

In the present study the release rate and kinetics of  carbamazepine as a model drug from various single and bi-polymeric matrices were studied. Matrices containing different percentages of hydroxylpropyl methylcellulose (HPMC), ethylcellulose (EC), Eudragit RS (EuRS) or various ratios of polymer blends based on HPMC were prepared. In vitro release studies were carried out in 1% sodium lauryl sulphate (SLS) aqueous solution. Mean dissolution times (MDT) were calculated and the release kinetics were evaluated using different mathematical models. The results showed that carbamazepine release was sustained in the presence of 15% HPMC for 6 hrs, but increasing the polymer content had no obvious effect. Application of EC or EuRS both in the percentages of 20% and 25% had more influence in retarding drug release rate (MDTs about 6 hrs). The results for bi-polymeric matrices revealed that overall, adding EC or EuRS as inert polymers to HPMC matrices resulted in a noticeable reduction in carbamazepine release rate compared to single polymer based matrices. Application of bi-polymeric matrices could be considered as a suitable approach for delivering the drug in a prolonged manner