Publications
Comparison between Candidates for the Position of Manager based on Fuzzy Logic
Apr 15, 2025Journal 5th International Conference on Communication Engineering and Computer Science (CIC-COCOS’2024)
Publisher Cihan University-Erbil
DOI https://eprints.cihanuniversity.edu.iq/id/eprint/3449/
The process of selecting a manager is a vital and important process in the management of any organization or institution, as the manager is considered the person who bears responsibility for making important decisions and directing the team towards achieving the organization’s goals. Fuzzy logic is one of the artificial intelligence systems that helps make decisions that contain uncertain or unreliable information. In this research, four fuzzy inference systems were used to choose the appropriate manager based on four qualities (personality, scientific, leadership, administrative). Through the results applied to the group of candidates to whom the test questions were distributed, we found that there is a variation in the characteristics. One of the conclusions of this research is that the proposed method of selecting the appropriate manager can be applied in different organization.
A comparative study on the performance of Gray Wolves Optimization and Multi-Free Dynamic Schema
Apr 6, 2024Journal NTU Journal of Pure Sciences
Publisher Northern Technical University
DOI https://doi.org/10.56286/ntujps.v3i1.710
Issue 1
Volume 3
The most difficult step is to create new optimization algorithms and examine them using test functions. In this work, we present a comparison study on the performance of Gray Wolves Optimization (GWO) and Multi-free dynamic schema (MFDS) algorithms. The (MFDS) algorithm is a sophisticated optimization method created for solving optimization problems. It contains different operators (dynamic schema operator, dissimilarity operator, similarity operator and free dynamic schema operator). Where, The (GWO) is a meta-heuristic optimization algorithm inspired by the social behavior of grey wolves in a pack. This study focused on the run time and the number of iterations to reach the optimal solutions. The sample of this comparison was on ten functions. The results showed the superiority of an algorithm (MFDS) on (GWO) algorithm in most test functions, especially at the run time. The performance of any single-objective optimization algorithm is a tool to measures the effectiveness of any algorithm for determining out the best solution for a specific problem.
Shadow Image Enlargement Distortion Removal
Feb 22, 2021Journal Computer Science - Computer Vision and Pattern Recognition
Publisher Cornell University
DOI https://arxiv.org/abs/2102.11356
This project aims to adopt preprocessing operations to get less distortions for shadow image enlargement. The preprocessing operations consists of three main steps: first enlarge the original shadow image by using any kind of interpolation methods, second apply average filter to the enlargement image and finally apply the unsharp filter to the previous averaged image. These preprocessing operations leads to get an enlargement image very close to the original enlarge image for the same shadow image. Then comparisons established between the adopted image and original image by using different types of interpolation and different alfa values for unsharp filter to reach the best way which have less different errors between the two images.
An Optimization Algorithm Based on Multi-free Dynamic Schema of Chromosomes
Sep 5, 2019Journal Advances in Intelligent Systems and Computing
Publisher Springer Nature Link
DOI https://link.springer.com/chapter/10.1007/978-3-030-30604-5_13
Issue AISC
Volume 1051
In this work, continuing the line of research from our previous papers [1,2,3,4], we further explore the notion of a schema in evolutionary algorithms and its role in finding global optima in numerical optimization problems. We present another optimization algorithm called Multi-Free Dynamic Schema (MFDS) which differs from the Free Dynamic Schema (FDS) algorithm introduced in [6] because in the MFDS the free dynamic schema operator is applied six times to different groups of chromosomes. The results of numerical experiments show that this change speeds up the search for a global optimum for most of the test problems.
An Optimization Algorithm Based on Multi-free Dynamic Schema of Chromosomes
Sep 5, 2019Journal Advances in Intelligent Systems and Computing
Publisher Springer Nature Link
DOI https://link.springer.com/chapter/10.1007/978-3-030-30604-5_13
Issue AISC
Volume 1051
In this work, continuing the line of research from our previous papers [1,2,3,4], we further explore the notion of a schema in evolutionary algorithms and its role in finding global optima in numerical optimization problems. We present another optimization algorithm called Multi-Free Dynamic Schema (MFDS) which differs from the Free Dynamic Schema (FDS) algorithm introduced in [6] because in the MFDS the free dynamic schema operator is applied six times to different groups of chromosomes. The results of numerical experiments show that this change speeds up the search for a global optimum for most of the test problems.
An Evolutionary Optimization Method Based on Scalarization for Multi-objective Problems
Sep 2, 2019Journal Advances in Intelligent Systems and Computing
Publisher Springer Nature Link
DOI https://link.springer.com/chapter/10.1007/978-3-319-67220-5_5
Issue AISC
Volume 655
In this paper, we perform some computational experiments on the new global scalarization method for multi-objective optimization problems. Its main idea is to construct, for a given multi-objective optimization problem, a global scalarization function whose values are non-negative real numbers. The points where the scalarization function attains the zero value are exactly weak Pareto stationary points for the original multi-objective problem. We apply two different evolutionary algorithms to minimize the scalarization function; both of them are designed for solving scalar optimization problems. The first one is the classical Genetic Algorithm (GA). The second one is a new algorithm called Dissimilarity and Similarity of Chromosomes (DSC), which has been designed by the authors. The computational results presented in this paper show that the DSC algorithm can find more minimizers of the scalarization function than the classical GA.
New optimization algorithm based on free dynamic schema
Aug 9, 2019Journal International Conference on Computational Collective Intelligence
Publisher Springer International Publishing
DOI https://link.springer.com/chapter/10.1007/978-3-030-28377-3_45
Issue LNAI
Volume 11683
In this paper, we describe and test a new evolutionary algorithm based on the notion of a schema, which is designed to solve global optimization problems. We call it Free Dynamic Schema (FDS). It is a more refined variant of our previous DSC, DSDSC and MDSDSC algorithms. FDS processes two populations which are partially composed of the same chromosomes. The algorithm divides each population into several groups to which various genetic operators are applied: free dynamic schema, dissimilarity, similarity, and dynamic dissimilarity. Also, some new chromosomes are regenerated randomly. The FDS algorithm is applied to 22 test functions in 2, 4 and 10 dimensions. It is also compared with the classical GA, CMA-ES and DE algorithms. Moreover, the FDS algorithm is compared with the BA and PSA algorithms for some functions. In most cases, we have found the FDS algorithm to be superior to the …
An Optimization Algorithm Based on Multi-Dynamic Schema of Chromosomes
May 11, 2018Journal International Conference on Artificial Intelligence and Soft Computing
Publisher Springer Nature
DOI https://www.uotechnology.edu.iq/tec_magaz/2014/volum322014/No.09.A.2014/Text(14).pdf
Issue (ICAISC 2018)
Volume (ICAISC 2018)
In this work, a new efficient evolutionary algorithm to enhance the global optimization search is presented, which applies double populations, each population divided into several groups. The first population is original and the second one is a copy of the first one but with different operators are applied to it. The operators used in this paper are dynamic schema, dynamic dissimilarity, dissimilarity, similarity and a random generation of new chromosomes. This algorithm is called Multi-Dynamic Schema with Dissimilarity and Similarity of Chromosomes (MDSDSC) which is a more elaborate version of our previous DSC and DSDSC algorithms. We have applied this algorithm to 20 test functions in 2 and 10 dimensions. Comparing the MDSDSC with the classical GA, DSC, DSDSC and, for some functions, BA and PSO algorithms, we have found that, in most cases, our method is better than the GA, BA and DSC.
New genetic algorithm based on dissimilarities and similarities
Jan 2, 2018Journal Computer Science
Publisher AGH university
DOI https://journals.agh.edu.pl/csci/article/view/2522
Issue 19
Optimization is essential for nding suitable answers to real life problems. In particular, genetic (or more generally, evolutionary) algorithms can provide satisfactory approximate solutions to many problems to which exact analytcal results are not accessible. In this paper we present both theoretical and experimental results on a new genetic algorithm called Dissimilarity and Simlarity of Chromosomes (DSC). This methodology constructs new chromosomes starting with the pairs of existing ones by exploring their dissimilarities and similarities. To demonstrate the performance of the algorithm, it is run on 17 two-dimensional, one four-dimensional and two ten-dimensional optimization problems described in the literature, and compared with the well-known GA, CMA-ES and DE algorithms. The results of tests show the superiority of our strategy in the majority of cases.
An optimization algorithm based on dynamic schema with dissimilarities and similarities of chromosomes
Aug 9, 2016Journal International Journal of Computer, Electrical, Automation, Control and Information Engineering
Publisher World Academy of Science, Engineering and Technology
DOI https://www.researchgate.net/profile/Radhwan-Al-Jawadi/publication/306034015_An_Optimization_Algorithm_Based_on_Dynamic_Schema_with_Dissimilarities_and_Similarities_of_Chromosomes/links/57abb54708ae3765c3b793da/An-Optimization-Algorithm-Based-on-Dynamic-Schema-with-Dissimilarities-and-Similarities-of-Chromosomes.pdf
Issue 8
Volume 10
Optimization is necessary for finding appropriate solutions to a range of real-life problems. In particular, genetic (or more generally, evolutionary) algorithms have proved very useful in solving many problems for which analytical solutions are not available. In this paper, we present an optimization algorithm called Dynamic Schema with Dissimilarity and Similarity of Chromosomes (DSDSC) which is a variant of the classical genetic algorithm. This approach constructs new chromosomes from a schema and pairs of existing ones by exploring their dissimilarities and similarities. To show the effectiveness of the algorithm, it is tested and compared with the classical GA, on 15 two-dimensional optimization problems taNen from literature. We have found that, in most cases, our method is better than the classical genetic algorithm.
A Comparison Between Support Vector Machine for Regression (SVR) and Neural Network Back- Propagation (BPNN) :Applied Study for Tigris Water Monthly Flow in Mosul City
Jun 2, 2016Journal المجلة العراقیة للعلوم الاحصائیة
Publisher جامعة الموصل
DOI https://stats.uomosul.edu.iq/index.php/stats/article/view/29295/29137
Issue 2
Volume 13
The idea of this research is based on adopting Support Vector Machine for Regression (SVR) in estimating and predicting regression models and comparing it with one of the multi layer neural network that has backpropagation((MLP). Therefore ,this will lead to demonstrate the advantages and possibility to use it in scientific application by illustrating how it is accurate in comparison with the ((MLP) by using Mean Square Errors(MSe). The time sequence data are that used for month flow for the water from Tigris that incomes in to Mosul city for the period between (1950-1995) to accomplish the comparison process by using the employment of two computers softwares.
Estimating Reference Evapo- transpiration in Mosul (Iraq) Using Cascade Neural Networks
Apr 6, 2014Journal Engineering and Technology Journal
Publisher Technical University
DOI https://www.uotechnology.edu.iq/tec_magaz/2014/volum322014/No.09.A.2014/Text(14).pdf
Issue 9
Volume 32
Recently artificial neural network (ANN) has been applied for estimating reference evapo-transpiration (ETₒ). In this study a mathematical model was built by application the cascade forward network technique (CCANN) to estimate the daily reference evapo-transpiration in the city of Mosul, north of Iraq. The input parameters for the CCANN were the: temperature, solar radiation, wind speed at 2m height, and relative humidity. A check for the accuracy of the performance of the network was made using values of reference evapo-transpiration obtained from pan evaporation method. The results revealed linear correlation between the network output and the data of the measured pan evapo-transpiration with correlation coefficient of (0.9679). This indicates the possibility of use of CCANN to determine the daily reference evapo-transpiration. The results also show that the CCANN model performs better more accurate compared to other models.
Hybrid Genetic Algorithm with Neural network in English Language Cipher
Oct 10, 2010Journal AL-Rafidain Journal of Computer Sciences and Mathematics (RJCSM)
Publisher Technical University
DOI https://stats.uomosul.edu.iq/index.php/csmj/article/view/37048
Issue 3
Volume 7
This research aims in the first stage to built a cipher system using hybrid Genetic Algorithm with single layer Neural network to prevent any data attack during the transition process , where the ASCII of the letters are used as inputs to the network and the random numbers are used as outputs to the network , then the weights will be constructed after the network training . In the second stage a decipher process is used to restore the ciphered data by using the inverse of the genetic neural network , where the inverse of weights is used as a key for the decryption process . Stream cipher method is used to input the data in the network during the ciphering stage. This suggested technique attained 100% success. All the ciphering and deciphering processes are built under MATLAB ver.(7) .
Multiple Data Type Encryption using genetic neural network
Jun 1, 2010Journal Tikrit Journal of Eng. Sciences
Publisher Tikrit University
DOI chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://iasj.rdd.edu.iq/journals/uploads/2025/01/13/d9b6ba7a0c89a7d0f2ec00e4eea393fb.pdf
Issue 2
Volume 17
The aim of this research is to build a ciphering system by using genetic neural network technique to protect data against unauthorized access to the data being transferred. The encryption data includes three stages: first Stage :- Using the genetic algorithm to train backpropagation neural network for obtaining weights. Second Stage:- Encryption data by using the weights obtained from first backpropagation layer and consider its weights as a encrypted key. third Stage:- Decryption data by using the weights obtained from second backpropagation layer and consider its weights as a decrypted key. This system is similar to coding asymmetric, and have the ability of coding a group of data such as:- pictures, waves and texts.
Evaluation of Clustering Validity
Mar 4, 2008Journal AL-Rafidain Journal of Computer Sciences and Mathematics
Publisher Mosul University
DOI chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://scispace.com/pdf/evaluation-of-clustering-validity-2wo4itb6kt.pdf
Issue 2
Volume 5
Clustering is a mostly unsupervised procedure and the majority of the clustering algorithms depend on certain assumptions in order to define the subgroups present in a data set. As a consequence, in most applications the resulting clustering scheme requires some sort of evaluation as regards its validity. In this paper, we present a clustering validity procedure, which evaluates the results of clustering algorithms on data sets. We define a validity indexes, S_Dbw & SD, based on well-defined clustering criteria enabling the selection of the optimal input parameters values for a clustering algorithm that result in the best partitioning of a data set. We evaluate the reliability of our indexes experimentally, considering clustering algorithm (K_Means) on real data sets. Our approach is performed favorably in finding the correct number of clusters fitting a data set.
تقليل التشويش الملازم عند الاخفاء في ملف صوتي
Jan 2, 2008Journal AL-Rafidain Journal of Computer Sciences and Mathematics (RJCSM)
Publisher Mosul University
DOI chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://iasj.rdd.edu.iq/journals/uploads/2024/12/08/1b5f8546dcb8e324ad7a89d71e35f980.pdf
Issue 1
Volume 5
The stereography which used to hide certain information using a host file is a science and an art. Such practice is usually done to keep away any thought in the presence of a hidden message in the host. Hiding is often done using sound, image or video files. The problem is that such hiding may cause some detectable changes in the host file .This study focuses on hiding in WAVE type sound file. Sound files are either one-byte sample or two- byte sample .The hiding idea involves the replacement of the original data by another data from a text file. The first four bits from each byte are used in wave file as a hiding domain. Accordingly, a hiding system was designed for the reduction of the accompanying noise. In this research, it was depended on the human herring for group of people to calculate the reduction in the noise of the host file .It was also shown that hiding in 8-bit sample (1 byte) results in a clearly detectable noise. This jumping 10 bytes in each replacement process led to eliminate the noise .It was also concluded, that when using first and second bit from each byte the noise was completely eliminated by jumping 30 byte. On the other hand, hiding in 16-bit sample (2byte) has led to complete disappearance of the noise when jumping 10 byte at each data replacement process.