Classification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE),  Border

In this paper, we proved that if R is a prime ring, U be a nonzero Lie ideal of R , d be a nonzero (?,?)-derivation of R. Then if Ua?Z(R) (or aU?Z(R)) for a?R, then either or U is commutative Also, we assumed that Uis a ring to prove that: (i) If Ua?Z(R) (or aU?Z(R)) for a?R, then either a=0 or U is commutative. (ii) If ad(U)=0 (or d(U)a=0) for a?R, then either a=0 or U is commutative. (iii) If d is a homomorphism on U such that ad(U) ?Z(R)(or d(U)a?Z(R), then a=0 or U is commutative.

Background: Acute myeloid leukemia (AML) is a genetically heterogeneous leukemia characterized by abnormal myeloid blast accumulation, disrupting normal hematopoiesis and leading to rapid progression. Objective: To investigate SNPs within the 3’UTR of the CCAAT/enhancer-binding protein alpha (CEBPA) gene and its association with AML in Iraqi patients. Methods: The study was carried out on 120 AML patients classified into newly diagnosed, induction chemotherapy, and consolidation chemotherapy stages (40 each), and 40 individuals as a control group. Genomic DNA was extracted from AML patients and controls, followed by PCR amplification and Sanger sequencing of the 3’UTR region of the CEBPA gene. The AML patients were characterized

Abstract.. Quantitative characteristics of the surrounding world’s entities are expressed by the specific words – logical quantifiers. In a language, quantifiers are represented by the so-called quantifier words. Representing different degrees of the concentration of quantity, quantifier words in natural languages are positioned in a strict sequence – the quantifying scale. In terms of logic, it starts with nothingness (quantifier of meaningful nothingness), terminating at universality while in terms of language quantifier words, it is represented by the complex scale of quantifying words’ intensity. Logically speaking, this scale should start with the quantifier word ‘nothing’ to move all the way through increasing intensity ov

Semi-active suspension systems have emerged as an attractive alternative to fully active suspensions because they offer a superior capacity to improve vehicle ride comfort and handling performance with significantly lower energy consumption. Conventional semi-active control strategies, however, such as skyhook damping, often cannot accommodate the nonlinear and time-varying dynamics of vehicles in operation under impulse or severe road disturbances. In this context, an intelligent smart-damper controller is proposed in this paper by incorporating a Modified Fuzzy Adaptive Fuzzy Logic Control framework in a half-car suspension model. In the developed controller, the effective damping force is adaptively tuned using real-time measurements of

In this paper, a numerical model for fluid-structure interaction (FSI) analysis is developed for investigating the aeroelastic response of a single wind turbine blade. The Blade Element Momentum (BEM) theory was adopted to calculate the aerodynamic forces considering the effects of wind shear and tower shadow. The wind turbine blade was modeled as a rotating cantilever beam discretized using Finite Element Method (FEM) to analyze the deformation and vibration of the blade. The aeroelastic response of the blade was obtained by coupling these aerodynamic and structural models using a coupled BEM-FEM program written in MATLAB. The governing FSI equations of motion are iteratively calculated at each time step, through exchanging data between