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Automatic voice activity detection using fuzzy-neuro classifier
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Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, autocorrelation, and log energy. A modified version of fuzzy C-Means is then used to cluster speech segments into three clusters; two clusters for voice and one for unvoiced. After that, three feed forward neural networks are trained to adjust their weights, in which each network represents one cluster. To make the final decision regarding the class type of a given speech segment, the membership degrees of this segment in all clusters along with neural networks' decisions are given to a defuzzification step which finally gives the class type of that segment. The proposed FN-AVAD is tested on the public multimodal emotion database, Surrey AudioVisual Expressed Emotion (SAVEE), and the error rate was 2.08%. The achieved results are comparable to the results achieved by the current published works in the literature.

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Publication Date
Wed Mar 18 2020
Journal Name
Baghdad Science Journal
A Software Defined Network of Video Surveillance System Based on Enhanced Routing Algorithms
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Software Defined Network (SDN) is a new technology that separate the ‎control plane from the data plane. SDN provides a choice in automation and ‎programmability faster than traditional network. It supports the ‎Quality of Service (QoS) for video surveillance application. One of most ‎significant issues in video surveillance is how to find the best path for routing the packets ‎between the source (IP cameras) and destination (monitoring center). The ‎video surveillance system requires fast transmission and reliable delivery ‎and high QoS. To improve the QoS and to achieve the optimal path, the ‎SDN architecture is used in this paper. In addition, different routing algorithms are ‎used with different steps. First, we eva

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Planner And Development
The "actor network theory" approach in dealing with landscapes in historical centers
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The historical center's landscape suffers from neglect, despite their importance and broad capabilities in enhancing the cultural value of the historical center, as landscape includes many heterogeneous human and non-human components, material and immaterial, natural and manufactured, also different historical layers, ancient, modern and contemporary. Due to the difference in these components and layers, it has become difficult for the designer to deal with it. Therefore, the research was directed by following a methodology of actor-network theory as it deals with such a complex system and concerned with an advanced method to connect the various components of considering landscape as a ground that can include various elements and deal wi

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Publication Date
Mon Mar 23 2020
Journal Name
Journal Of Engineering
Survey Study of Factional Order Controllers
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This is a survey study that presents recent researches concerning factional controllers. It presents several types of fractional order controllers, which are extensions to their integer order counterparts. The fractional order PID controller has a dominant importance, so thirty-one paper are presented for this controller. The remaining types of controllers are presented according to the number of papers that handle them; they are fractional order sliding mode controller (nine papers), fuzzy fractional order sliding mode controller (five papers), fractional order lag-lead compensator (three papers), fractional order state feedback controller (three papers), fractional order fuzzy logic controller (three papers). Finally,

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Publication Date
Sun Oct 05 2025
Journal Name
Mesopotamian Journal Of Computer Science
DGEN: A Dynamic Generative Encryption Network for Adaptive and Secure Image Processing
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Cyber-attacks keep growing. Because of that, we need stronger ways to protect pictures. This paper talks about DGEN, a Dynamic Generative Encryption Network. It mixes Generative Adversarial Networks with a key system that can change with context. The method may potentially mean it can adjust itself when new threats appear, instead of a fixed lock like AES. It tries to block brute‑force, statistical tricks, or quantum attacks. The design adds randomness, uses learning, and makes keys that depend on each image. That should give very good security, some flexibility, and keep compute cost low. Tests still ran on several public image sets. Results show DGEN beats AES, chaos tricks, and other GAN ideas. Entropy reached 7.99 bits per pix

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Publication Date
Wed Jul 31 2019
Journal Name
Journal Of Engineering
A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot
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In this paper, a cognitive system based on a nonlinear neural controller and intelligent algorithm that will guide an autonomous mobile robot during continuous path-tracking and navigate over solid obstacles with avoidance was proposed. The goal of the proposed structure is to plan and track the reference path equation for the autonomous mobile robot in the mining environment to avoid the obstacles and reach to the target position by using intelligent optimization algorithms. Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) Algorithms are used to finding the solutions of the mobile robot navigation problems in the mine by searching the optimal paths and finding the reference path equation of the optimal

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Publication Date
Thu Mar 31 2022
Journal Name
Iraqi Geological Journal
Development of Artificial Intelligence Models for Estimating Rate of Penetration in East Baghdad Field, Middle Iraq
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It is well known that the rate of penetration is a key function for drilling engineers since it is directly related to the final well cost, thus reducing the non-productive time is a target of interest for all oil companies by optimizing the drilling processes or drilling parameters. These drilling parameters include mechanical (RPM, WOB, flow rate, SPP, torque and hook load) and travel transit time. The big challenge prediction is the complex interconnection between the drilling parameters so artificial intelligence techniques have been conducted in this study to predict ROP using operational drilling parameters and formation characteristics. In the current study, three AI techniques have been used which are neural network, fuzzy i

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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Optimized Artificial Neural network models to time series
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        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

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Publication Date
Fri Jan 01 2021
Journal Name
Prepodavatel Xxi Vek
Enrichment of Vocabulary and the Formation of Grammatically Correct Speech of Foreign Students When Studying the Text of a Literary Work
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Publication Date
Fri Nov 03 2023
Journal Name
Iraqi Journal Of Pharmaceutical Sciences( P-issn 1683 - 3597 E-issn 2521 - 3512)
Synthesis, Characterization, and Preliminary Antimicrobial Evaluation of New Schiff Bases and Mannich Bases of Isatin
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Till now, isatin derivatives have received a lot of interest in organic and medicinal chemistry due to their significant biological and pharmacological activities. Schiff’s and Mannich bases of isatins are an effective group of heterocyclic derivatives that play a significant role in medicinal chemistry as antimicrobial agents. In light of these facts, new Schiff bases and Mannich bases of isatin were synthesized. The monomer Mannich bases; 3(a-e) have been synthesized by reacting isatin with different secondary amines, piperidine, morpholine, and pyrrolidine, dimethylamine, diphenylamine, separately, and formaldehyde, while the dimer (5) formed by using piperazine and formaldehyde which then react separately with Phenylhydrazine

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Publication Date
Sun Mar 02 2014
Journal Name
Baghdad Science Journal
Determination of Advanced Oxidation Protein Products, E3 SUMO-Protein Ligase NSE2[NSMCE2], as a Marker to Predict Child Acute Lymphoblastic Leukemia
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Acute lymphoblastic leukemia (ALL) is a cancer of the blood and bone marrow (spongy tissue in the center of bone). In ALL, too many bone marrow stem cells develop into a type of white blood cell called lymphocytes. These abnormal lymphocytes are not able to fight infection very well. The aim of this study was to investigate possible links between E3 SUMO-Protein Ligase NSE2 [NSMCE2] and increase DNA damage in the childhood patients with Acute lymphoblastic leukemia (ALL). Laboratory investigations including hemoglobin(Hb) ,white blood cell (WBC) , serum total protein , albumin ,globulin , in addition to serum total antioxidant activity (TAA) , Advanced oxidation protein products(AOPP) and E3 SUMO-Protein Ligase NSE2[NSMCE2]. Blood samples

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