Kurdistan Institution for Strategic Studies and Scientific Research
Recent publications
One of the popular metaheuristic search algorithms is Harmony Search (HS). It has been verified that HS can find solutions to optimization problems due to its balanced exploratory and convergence behavior and its simple and flexible structure. This capability makes the algorithm preferable to be applied in several real-world applications in various fields, including healthcare systems, different engineering fields, and computer science. The popularity of HS urges us to provide a comprehensive survey of the literature on HS and its variants on health systems, analyze its strengths and weaknesses, and suggest future research directions. In this review paper, the current studies and uses of harmony search are studied in four main domains. (i) The variants of HS, including its modifications and hybridization. (ii) Summary of the previous review works. (iii) Applications of HS in healthcare systems. (iv) And finally, an operational framework is proposed for the applications of HS in healthcare systems. The main contribution of this review is intended to provide a thorough examination of HS in healthcare systems while also serving as a valuable resource for prospective scholars who want to investigate or implement this method.
Severe droughts and mismanagement of water resources during the last decades have pro�pelled authorities in the Kurdistan Region to be con�cerned about better management of precipitation which is considered the primary source of recharg�ing surface and groundwater in the area of interest. The drought cycles in the last decades have stimu�lated water stakeholders to drill more wells and store uncontrolled runof in suitable structures during rainy times to fulfll the increased water demands. The optimum sites for rainwater harvesting sites in the Qaradaqh basin, which is considered a water-scarce area, were determined using the analytical hierar�chy process (AHP), sum average weighted method (SAWM), and fuzzy-based index (FBI) techniques. The essential thematic layers within the natural and artifcial factors were rated, weighted, and ntegrated the northern and around the basin’s boundary, while unsuitable areas cover northeastern and some scat�ter zones in the middle due to restrictions of geology, distance to stream with the villages, and slope crite�ria. The total harvested runof was 377,260 m3 from all the suggested structures. The proposed sites may provide a scientifc and reasonable basis for utiliz�ing this natural resource and minimize the impacts of future drought cycles.
Machine Learning (ML) is a part of Artificial intelligence (AI) that designs and produces systems, which is capable of developing and learning from experiences automatically without making them programmable. ML concentrates on the computer program improvement, which has the ability to access and utilize data for learning from itself. There are different algorithms in ML field, but the most important questions that arise are: Which technique should be utilized on a dataset? and How to investigate ML algorithm? This paper presents the answer for the mentioned questions. Besides, investigation and checking algorithms for a data set will be addressed. In addition, it illustrates choosing the provided test options and metrics assessment. Finally, researchers will be able to conduct this research work on their datasets to select an appropriate model for their datasets.
The competitive advantage of aspect oriented programming (AOP) is that it improves the maintainability and understandability of software systems by modularizing crosscutting concerns. However, some concerns, such as logging or debugging, may be overlooked and should be entangled and distributed across the code base. AOP is a software development paradigm that enables developers to capture crosscutting concerns in split-aspect modes. Additionally, it is a novel notion that has the potential to improve the quality of software programs by removing the complexity involved with the production of code tangles via the usage of separation of concerns. As a result, it provides more modularity. Throughout its early development, some believed that AOP was easier to build and maintain than other implementations since it was based on an existing one. The statements are predicated on the premise that local improvements are easier to implement. Additionally, without appropriate visualization tools for both static and dynamic structures, cross-cutting challenges may be difficult for developers and researchers to appreciate. In recent years, AspectJ has begun to enable the depiction of crosscutting concerns via the release of IDE plugins. This article explains aspect oriented programming and how it may be used to improve the readability and maintainability of software projects. Additionally, it will evaluate the challenges it presents to application developers and academics.
O-PTIR was used for simultaneous collection of infrared and Raman spectra from clinical pathogens associated with bloodstream infections.
Automated brain tumor detection is becoming a highly considerable medical diagnosis research. In recent medical diagnoses, detection and classification are highly considered to employ machine learning and deep learning techniques. Nevertheless, the accuracy and performance of current models need to be improved for suitable treatments. In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms, Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks Optimization (HHO), called G-HHO has been considered. This hybridization features Grey Wolf Optimization (GWO) and HHO to give better results, limiting the convergence rate and enhancing performance. Moreover, Otsu thresholding is adopted to segment the tumor portion that emphasizes brain tumor detection. Experimental studies are conducted to validate the performance of the suggested method on a total number of 2073 augmented MRI images. The technique’s performance was ensured by comparing it with the nine existing algorithms on huge augmented MRI images in terms of accuracy, precision, recall, f-measure, execution time, and memory usage. The performance comparison shows that the DCNN-G-HHO is much more successful than existing methods, especially on a scoring accuracy of 97%. Additionally, the statistical performance analysis indicates that the suggested approach is faster and utilizes less memory at identifying and categorizing brain tumor cancers on the MR images. The implementation of this validation is conducted on the Python platform. The relevant codes for the proposed approach are available at: https://github.com/bryarahassan/DCNN-G-HHO.
Background The Kurds as an ethnic group are believed to be a combination of earlier Indo-European tribes who migrated and inhabited a mountainous area thousands of years ago. However, as it is difficult to describe the precise history of their origin, it is necessary to investigate their population relationship with other geographical and ethnic groups. Results Seventeen Y-STR markers included in the AmpFLSTR™ Yfiler™ PCR Amplification Kit (Thermo Fisher Scientific, USA) were used to type DNA samples from the Sorani (Central) Kurdish population in Sulaymaniyah province. 157 haplotypes were obtained from 162 unrelated male individuals. The highest and lowest gene diversities were DYS385a/b (GD = 0.848) and DYS392 (GD = 0.392), respectively. The haplotypes were used to predict the most likely haplogroups in the Sulaymaniyah population. Conclusion Haplogroup prediction indicated predominance (28%) of subclade J2 (44/157) in the Sorani Kurds, northeast of Iraq. The pairwise genetic distance results showed that the Kurdish group clustered along with Asian populations, whereas the furthest countries were Europeans and Africans.
In this manuscript, we report the proteins macrophage infectivity potentiator ( mip , CAB080), major outer membrane protein ( momp , CAB048), and polymorphic outer membrane protein ( pmp18D , CAB776) that are expressed in different times of pregnancy in mice infected with Chlamydia abortus . Enzootic abortion of ewes (EAE) by C. abortus , an obligate intracellular pathogen, is a critical zoonotic disease-causing significant economic loss to livestock farming globally. This study was carried out for the detection and characterization of macrophage infectivity potentiator ( mip , CAB080), major outer membrane protein ( momp , CAB048), and polymorphic outer membrane protein ( pmp18D , CAB776) using RT-qPCR. These proteins are believed to be expressed as virulence factors in C . abortus isolated from aborted ewes. BALB/c mice (pregnant and nonpregnant) were used as an animal model to be injected intraperitoneally with C. abortus culture in Vero cells since the endometrial lymphoid tissues of these animals resembles that of ewes. Also, the short duration of pregnancy in mice makes them a suitable animal model for obstetric studies. Tissue samples were taken from the mice after 10, 15, and 20 days of pregnancy to compare the expression of the genes mip, pmp18D , and ompA . Transcription level was quantified using RT-qPCR, the GAPDH transcription quantification, as a normalization signal. Abortion occurred in pregnant mice, and apparent differences between the transcriptional levels of the mip, pmp18D , and ompA genes in the samples taken during different time intervals of pregnancy were not observed ( p > 0.05). The result indicated that the three bacterial genes, mip , pmp18D , and ompA , play a role as virulence factors in abortion and are differentially expressed in pregnant and nonpregnant animals. Inactivation of the genes is suggested to confirm the hypothesis.
Conventionally, diagenesis has been studied by making qualitative morphological observations which have been organised into complex classification schemes. Petrophysics, with its many quantitative measurements, now gives us the ability to quantify the effects of the type, degree and timing of complex diagenetic process. The aim of this paper is to examine how different diagenetic processes affect the petrophysical properties of carbonate rocks and to develop quantitative methodologies to describe the results of diagenetic processes. A large number of petrophysical measurements have been made on a suite of 172 core plugs to provide a test data set. Diagenetic modification of the primary depositional fabric was observed in a wide range of measured petrophysical parameters, and that porosity and pore connectedness exert dominant control on all of the electrical and hydraulic rock parameters. This observation has been used to propose a new theoretical framework linking the effect of diagenetic process to petrophysical measureables. Cementation exponent was found to increase with permeability and pore size, especially in recrystallized rocks, and is explained by smaller porosity samples having a better connected pore network. Electrical connectedness was also found to correlate extremely well with hydraulic permeability, showing that these phenomena are linked closely in tight carbonate reservoir rocks. A method for calculating pre- and post-dolomitisation porosity and the degree of dolomitisation from the measured petrophysical and compositional data has also been developed and tested. All electrical and hydraulic properties are related to pore type, allowing cementation exponent to be obtained from optical microscopy/SEM studies or NMR measurements, providing a new approach to estimating cementation exponent in carbonate rocks. This paper also provides a powerful new approach allowing petrophysical changes associated with the type, degree and timing of different diagenetic processes to be tracked quantitatively using a method we have called ‘petrodiagenetic pathways’.
Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application.
The Upper Triassic Baluti Formation has been identified and mapped based on its log response in selected wells from the Zagros foldbelt in the Kurdistan Region of northern Iraq. A preliminary evaluation of the formation's source rock potential was made by Rock-Eval screening analysis in four wells along a NW-SE profile (Atrush-1, Shaikan-5B, Taq Taq-22 and Miran-2) with maturity determined from reflectance measurements in samples from well Taq Taq-22. The Baluti Formation consists of thinly interbedded shales, carbonates and anhydrite ranging in thickness from 48 m in well Atrush-1 to 118 m in well Miran-2. The Rock-Eval screening was conducted primarily on bulk cuttings samples plus selected picked cuttings. The TOC content is low to moderate (0.23 to 1.14 wt%). However, the shale content in many of the analysed bulk samples was relatively low, making assessment of the source potential problematic. The highest TOCs are recorded from the thickest analysed sections from wells Miran-2 and Taq Taq-22, where high-gamma bituminous shales are present. Rock-Eval Tmax values ranging from 295 to 438°C are not consistent with estimates of pre-Zagros burial to depths of between 4600 m (Atrush-1) and 6900 m (Miran-2). The relatively low Tmax values suggest that the S2 response does not reflect kerogen pyrolysis in these samples and may be due to the presence of solid bitumen, which is observed in the Baluti Formation in at least three of the study wells (Taq Taq-22, Miran-2 and Shaikan-5B). Little pyrolysable organic matter remains in the formation due to the interpreted deep pre-Zagros burial and the consequent high maturity in Taq Taq-22 (VR = 1.51%Ro) and Miran-2 (estimated VR >2%Ro), and the poor source character in Atrush-1 and Shaikan-5B. Organic petrography suggests the presence of vestiges of Types I and II kerogen in Taq Taq-22, with bitumen observed as stains in the matrix of the shales and also in the pores and fractures of interbedded dolostones. However, bitumen reflectance determinations for Taq Taq-22 indicate an equivalent vitrinite reflectance maturity of no more than 0.93%Ro, which is significantly less than that of the indigenous vitrinite, implying the solid bitumen in this well is derived primarily from migrated hydrocarbons. Further detailed analysis is required, but the results suggest that the Baluti Formation may have sourced hydrocarbons in its depocentre which is identified in this study as covering a NW-SE trending area between Bekhme and Sangaw.
This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of “premature convergence,” which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed.
Clustering is a commonly used method for exploring and analysing data where the primary objective is to categorise observations into similar clusters. In recent decades, several algorithms and methods have been developed for analysing clustered data. We notice that most of these techniques deterministically define a cluster based on the value of the attributes, distance, and density of homogenous and single-featured datasets. However, these definitions are not successful in adding clear semantic meaning to the clusters produced. Evolutionary operators and statistical and multidisciplinary techniques may help in generating meaningful clusters. Based on this premise, we propose a new evolutionary clustering algorithm (ECA*) based on social class ranking and meta-heuristic algorithms for stochastically analysing heterogeneous and multifeatured datasets. The ECA* is integrated with recombinational evolutionary operators, Levy flight optimisation, and some statistical techniques, such as quartiles and percentiles, as well as the Euclidean distance of the K-means algorithm. Experiments are conducted to evaluate the ECA* against five conventional approaches: K-means (KM), K-means++ (KM++), expectation maximisation (EM), learning vector quantisation (LVQ), and the genetic algorithm for clustering++ (GENCLUST++). That the end, 32 heterogeneous and multifeatured datasets are used to examine their performance using internal and external and basic statistical performance clustering measures and to measure how their performance is sensitive to five features of these datasets (cluster overlap, the number of clusters, cluster dimensionality, the cluster structure, and the cluster shape) in the form of an operational framework. The results indicate that the ECA* surpasses its counterpart techniques in terms of the ability to find the right clusters. Significantly, compared to its counterpart techniques, the ECA* is less sensitive to the five properties of the datasets mentioned above. Thus, the order of overall performance of these algorithms, from best performing to worst performing, is the ECA*, EM, KM++, KM, LVQ, and the GENCLUST++. Meanwhile, the overall performance rank of the ECA* is 1.1 (where the rank of 1 represents the best performing algorithm and the rank of 6 refers to the worst performing algorithm) for 32 datasets based on the five dataset features mentioned above.
With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically; however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters; (ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets; (iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms.
This article presents the data used to evaluate the performance of evolutionary clustering algorithm star (ECA*) compared to five traditional and modern clustering algorithms. Two experimental methods are employed to examine the performance of ECA* against genetic algorithm for clustering++ (GENCLUST++), learning vector quantisation (LVQ), expectation maximisation (EM), K-means++ (KM++) and K-means (KM). These algorithms are applied to 32 heterogenous and multi-featured datasets to determine which one performs well on the three tests. For one, ther paper examines the efficiency of ECA* in contradiction of its corresponding algorithms using clustering evaluation measures. These validation criteria are objective function and cluster quality measures. For another, it suggests a performance rating framework to measurethe the performance sensitivity of these algorithms on varos dataset features (cluster dimensionality, number of clusters, cluster overlap, cluster shape and cluster structure). The contributions of these experiments are two-folds: (i) ECA* exceeds its counterpart aloriths in ability to find out the right cluster number; (ii) ECA* is less sensitive towards dataset features compared to its competitive techniques. Nonetheless, the results of the experiments performed demonstrate some limitations in the ECA*: (i) ECA* is not fully applied based on the premise that no prior knowledge exists; (ii) Adapting and utilising ECA* on several real applications has not been achieved yet.
Fog computing is an interesting technology aimed at providing various processing and storage resources at the IoT networks’ edge. Energy consumption is one of the essential factors that can directly impact the maintenance cost and CO2 emissions of fog environments. Energy consumption can be mitigated by effective scheduling approaches, in which tasks are going to be mapped on the best possible resources regarding some conflicting objectives. To deal with these issues, we introduce an opposition-based hybrid discrete optimization algorithm, called DMFO-DE. For this purpose, first, a discrete and Opposition-Based Learning (OBL) version of the Moth–Flame Optimization (MFO) algorithm is provided, and it then is combined with the Differential Evolution (DE) algorithm to improve the convergence speed and prevent local optima problem. The DMFO-DE is then employed for scientific workflow scheduling in fog computing environments using the Dynamic Voltage and Frequency Scaling (DVFS) method. The Heterogeneous Earliest Finish Time (HEFT) algorithm is used to find the tasks execution order in the scientific workflows. Our workflow scheduling approach mainly tries to decrease the scheduling process’s energy consumption by minimizing the applied Virtual Machines (VMs), makespan, and communication between dependent tasks. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on the scientific workflows with four different sizes. The experimental results indicate that scheduling using the DMFO-DE algorithm can outperform other metrics such as the number of applied VMs, and energy consumption.
Recently, numerous meta-heuristic-based approaches are deliberated to reduce the computational complexities of several existing approaches that include tricky derivations, very large memory space requirement, initial value sensitivity, etc. However, several optimization algorithms namely firefly algorithm, sine–cosine algorithm, and particle swarm optimization algorithm have few drawbacks such as computational complexity and convergence speed. So to overcome such shortcomings, this paper aims in developing a novel chaotic sine–cosine firefly (CSCF) algorithm with numerous variants to solve optimization problems. Here, the chaotic form of two algorithms namely the sine–cosine algorithm and the firefly algorithms is integrated to improve the convergence speed and efficiency thus minimizing several complexity issues. Moreover, the proposed CSCF approach is operated under various chaotic phases and the optimal chaotic variants containing the best chaotic mapping are selected. Then numerous chaotic benchmark functions are utilized to examine the system performance of the CSCF algorithm. Finally, the simulation results for the problems based on engineering design are demonstrated to prove the efficiency, robustness and effectiveness of the proposed algorithm.
The Governorate of Sulaymaniyah is located in the north of Iraq with a population of 856 990 in 2016. The process of selecting a landfill site is considered as a complicated task with several factors and regulations to take into account. Currently, there are no landfill sites in the Sulaymaniyah Governorate that Governorate that respect the prerequisites of the scientific and environmental criteria. Therefore, in this study, thirteen suitable criteria were selected. These criteria are: groundwater depth, urban area, rivers, villages, soil types, elevation, roads, slope, land use, archaeological sites, power lines, oil and gas field, and geology. These criteria were used in the GIS (Geographic Information System), due to its high ability to manage and analyze various data. In addition, the AHP (Analytical Hierarchy Process) method was used to derive the weightings of criteria, through a matrix of pairwise comparison. In this work, the study site was classified into four different areas according to the Suitability Index for landfill sites, where they all satisfied the scientific and environmental criteria.
As the world’s population has grown, waste generation has increased rapidly. Solid waste management requires a greater knowledge of the composition, generation quantity, physical properties, and impacts of economic aspects. This paper clarified the status of municipal solid waste management across Sulaimaniyah governorate and presented a comprehensive overview and implication of poor solid waste management in the study area. The core aspects covered were the future estimations of the cumulative solid waste amount with population growth by 2040 using brief calculations of the waste generation rate from 2016. The results revealed that the daily per capita waste generation in the Sulaimaniyah governorate is 1.32 kg by 2040, a cumulative solid waste of about 10,445,829 tons, and an estimated volume of 9,146,368 m³ which will be required for the disposal site in the future.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
60 members
Bryar Hassan
  • Department of Information Technology
Polla Khanaqa
  • Research Centre of Geology & Organic Petrology
Nawzad Jamal
  • Research Centre of Strategic Studies
Aram Mahmood Ahmed
  • International Academic Office
Rezhwan Majid
  • Directorate of Health and Safety
Information
Address
Building No. 10, Alley 60 Gullabax 335 Shorsh St. , Opposite Shoresh Hospital, As Sulaymānīyah, Kurdistan Region, Iraq
Head of institution
Professor Dr. Polla Khanaqa
Website
http://www.kissr.edu.krd/
Phone
+964 (0)748 010 4673; +964 (0)748 010 4674