This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH₂) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

Actinopolyspora iraqiensis IQ-H1 is a novel strain of actinobacteria isolated from extremely halophilic soil samples in Iraq. The whole-genome sequence of this strain is deposited in the National Center for Biotechnology Information (NCBI) GenBank under the accession number NZ_AICW01000000. In this study, the genome features and the molecular phylogenetic tree of Act. iraqiensis IQ-H1are analyzed. The RAST tool was used for genome annotation. The genomic features were elucidated using QUAST tool. The circular genome map, and the core and pan-genome map of Act. iraqiensis IQ-H1 was generated using CGView and the GView tools respectively. The JSpeciesWS server was used for the tetranucleotide signature analysis a

Learning the vocabulary of a language has great impact on acquiring that language. Many scholars in the field of language learning emphasize the importance of vocabulary as part of the learner's communicative competence, considering it the heart of language. One of the best methods of learning vocabulary is to focus on those words of high frequency. The present article is a corpus based approach to the study of vocabulary whereby the research data are analyzed quantitatively using the software program "AntWordprofiler". This program analyses new input research data in terms of already stored reliable corpora. The aim of this article is to find out whether the vocabularies used in the English textbook for Intermediate Schools in Iraq are con

     Cadastral map environment is directed, more than ever before, towards Artificial Intelligence use to produce fast and accurate maps and keep up with the huge population growth. The traditional approach currently in production of maps is expensive and effort-intensive in addition to be considered as highly time-consuming process.  UAV-based cadastral mapping imagery that use automatic techniques are newly being exploited to accelerate the process of production or updating. The state-of-the-art intelligent algorithms are capable to extract land boundaries from images better than conventional techniques. This paper presents an automatic workflow of cadastral map production based on land boundary and automatic f

Diabetic retinopathy (DR) is a diabetes- caused disease that is associated with  leakage of fluid from the blood vessels into the retina, leading to its damage. It is one of the most common diseases that can lead to weak vision and even blindness. Exudates is a clear indication of diabetic retinopathy, which is the main cause of blindness in people with diabetes. Therefore, early detection of exudates is a crucial and essential step to prevent blindness and vision loss is in the analysis of digital diabetic retinopathy systems. This paper presents an improved approach for detection of exudates in retina image using supervised-unsupervised Minimum Distance (MD) segmentation method. The suggested system includes three stages; First, a

     Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust E

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s