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Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm
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Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over time. Here, we adopt a new perspective towards detecting the evolution of community structures. The proposed method realizes the decomposition of the problem into three essential components; searching in: intra-community connections, inter-community connections, and community evolution. A multi-objective optimization problem is defined to account for the different intra and inter community structures. Further, we formulate the community evolution problem as a Hidden Markov Model in an attempt to dexterously track the most likely sequence of communities. Then the new model, called Hidden Markov Model-based Multi-Objective evolutionary algorithm for Dynamic Community Detection (HMM-MODCD), uses a multi-objective evolutionary algorithm and Viterbi algorithm for formulating objective functions and providing temporal smoothness over time for clustering dynamic networks. The performance of the proposed algorithm is evaluated on synthetic and real-world dynamic networks and compared against several state-of-the-art algorithms. The results clearly demonstrate the effectiveness of the proposed algorithm to outperform other algorithms.

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Publication Date
Sun Dec 01 2019
Journal Name
Baghdad Science Journal
Symmetric- Based Steganography Technique Using Spiral-Searching Method for HSV Color Images
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Steganography is defined as hiding confidential information in some other chosen media without leaving any clear evidence of changing the media's features. Most traditional hiding methods hide the message directly in the covered media like (text, image, audio, and video). Some hiding techniques leave a negative effect on the cover image, so sometimes the change in the carrier medium can be detected by human and machine. The purpose of suggesting hiding information is to make this change undetectable. The current research focuses on using complex method to prevent the detection of hiding information by human and machine based on spiral search method, the Structural Similarity Index Metrics measures are used to get the accuracy and quality

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Publication Date
Sun Aug 01 2021
Journal Name
International Journal Of Mechanical Engineering And Robotics Research
Adaptive Approximation-Based Feedback Linearization Control for a Nonlinear Smart Thin Plate
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This paper proposes feedback linearization control (FBLC) based on function approximation technique (FAT) to regulate the vibrational motion of a smart thin plate considering the effect of axial stretching. The FBLC includes designing a nonlinear control law for the stabilization of the target dynamic system while the closedloop dynamics are linear with ensured stability. The objective of the FAT is to estimate the cubic nonlinear restoring force vector using the linear parameterization of weighting and orthogonal basis function matrices. Orthogonal Chebyshev polynomials are used as strong approximators for adaptive schemes. The proposed control architecture is applied to a thin plate with a large deflection that stimulates the axial loadin

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Publication Date
Mon Nov 01 2010
Journal Name
Journal Of Systems And Software
Development of Java based RFID application programmable interface for heterogeneous RFID system
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Publication Date
Sat Jun 29 2013
Journal Name
Wireless Personal Communications
A Low Cost Route Optimization Scheme for Cluster-Based Proxy MIPv6 Protocol
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Publication Date
Mon Feb 21 2022
Journal Name
Iraqi Journal For Computer Science And Mathematics
Fuzzy C means Based Evaluation Algorithms For Cancer Gene Expression Data Clustering
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The influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic

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Publication Date
Mon Dec 01 2014
Journal Name
2014 Ieee Student Conference On Research And Development
Feature extraction for co-occurrence-based cosine similarity score of text documents
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Publication Date
Thu Nov 22 2018
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
The Computer Based Tests: a digital substitution for the Iraqi postgraduate students
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The wide spread of Computer Based Test in the Iraqi universities raised an important concern for some scholars thinking about integrating computers as an academic assessment tool before making big change in the examination systems, that concern is: how the computerized test score is effected in comparison to old paper and pencil . This paper researches whether test scores of Iraqi students varies in the computer, Pencil based tests, sixty-four postgraduate graduates from Iraqi universities. Thirty five female and twenty nine male, all graduated from colleges of science in specializations other than computer science and applying for postgraduate studies in the Iraqi

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Publication Date
Mon Apr 26 2021
Journal Name
Journal Of Electrical Engineering & Technology
ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System
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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Feature - Based Approach to Automatic Fixturing System Planning For Uniform Polyhedra Workpiece
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This paper demonstrates the design of an algorithm to represent the design stages of fixturing system that serve in increasing the flexibility and automation of fixturing system planning for uniform polyhedral part. This system requires building a manufacturing feature recognition algorithm to present or describe inputs such as (configuration of workpiece) and built database system to represents (production plan and fixturing system exiting) to this algorithm. Also knowledge – base system was building or developed to find the best fixturing analysis (workpiece setup, constraints of workpiece and arrangement the contact on this workpiece) to workpiece.

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Publication Date
Sun Oct 01 2023
Journal Name
Ieee Transactions On Industrial Electronics
Singular Perturbation-Based Adaptive Integral Sliding Mode Control for Flexible Joint Robots
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The flexible joint robot (FJR) typically experiences parametric variations, nonlinearities, underactuation, noise propagation, and external disturbances which seriously degrade the FJR tracking. This article proposes an adaptive integral sliding mode controller (AISMC) based on a singular perturbation method and two state observers for the FJR to achieve high performance. First, the underactuated FJR is modeled into two simple second-order fast and slow subsystems by using Olfati transformation and singular perturbation method, which handles underactuation while reducing noise amplification. Then, the AISMC is proposed to effectively accomplish the desired tracking performance, in which the integral sliding surface is designed to reduce cha

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