Iraqi Journal of Intelligent Computing and Informatics

In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

These days, it is not easy to get the correct information after typing a keyword into a search engine because so many results are returned. Classification of Web pages is a technique that helps us locate the wanted information quickly and effectively. In addition, website categorization is crucial for businesses that provide marketing and analytical solutions because it enables them to create a well-balanced mix of search engine and directory listings. This will give marketers a better idea of where their local company listings appear online, allowing them to have more judgment about initiative and strategy. Therefore, the research aimed to construct a classification system based on a dataset of English web pages. This information has been acquired from the Kaggle website and consisted of 1408 distinct rows organized into 16 categories.The research has employed mixed strategies to determine which strategy for Web page categorization would yield the best results. The first strategy puts into practice a collection of machine-learning algorithms. It assesses how well they accomplish the given classification task. Ensemble stacking is the second strategy, and it is employed to enhance the classification of websites.Comparing the results of the two strategies reveals that Ensemble stacking, the second strategy, was the more influential architecture for classifying web pages this approach had 0.95 F1-score, 0.95 accuracy, 0.95 precision, and 0.95 recall achieved by this method. The first approach, which made use of machine learning techniques, on the other hand, received an F1-score of 0.93, 0.94 for precision, 0.93 for recall, and 0.93 for accuracy

The Sidr tree is an important fruit tree in many parts of the world, but it is often affected by various diseases and pests that can impact the quality and quantity of its fruit production. This paper proposes the use of ontology and semantic web technologies with Python programming language to program a diagnosis service for the Sidr tree diseases. Programming a Semantic Web application system and services requires knowledge of many technologies such as Resource Description Framework (RDF), Web Ontology Language (OWL), and SPARQL Query. The underline Web service(Sidr tree disease Web service),has to be programmed in connection to a pre-existing ontology and knowledge base.The service program is made of three main components. These components are the user interface, diagnosis service-related functions, Knowledge base, and query engine. The Python 3.10 code listing in this paper represents only the code related to disease diagnosing service and the Sidr tree ontology (SidrTreeonto). In addition to the listed statements and functions, the complete diagnosing service code contains some other unlisted HTML static and dynamic Web pages. The code is run and tested and found to be simple and easy to use even by unexpert users. Overall, this application will provide a useful tool for Sidr tree farmers and researchers to diagnose and manage diseases affecting their trees

Our research examined two of unsupervised Data Mining algorithms for both comparison and prediction to predict the High-Power Hearing Aid for audiology patients who suffer from hearing impairment. These Data Mining techniques are Manifold Learning and Multidimensional Scaling.Both algorithms define specific rules to choose the linear projection of the data. These methods can be effective, but sometimes miss the structure of non-linear data. Our research deals with new specific data set which collects and analyses depends on Audiology information and Patient's diagnosis. Note that the data set selected must be subject to accurate data analysis with pre-processing of data. It should be applicable and authoritative because these factors are very important to obtain the highest degree of prediction possible, as long, some data types are not appropriate for decision tree or some methods of classification. The data set we created consists of seventy-two fields distributed on seventy-one fields for data details and one further for class. All data set fields are categorical, and it contains some of missing values. The fact that our data was subjected to a very accurate analysis (before cleaning) based on the correct medical diagnosis and comprehensive information of the most important points that directly affect the selection of appropriate hearing aid for audiology patient, and via applying data mining techniques, we obtained a prediction of 100% for hearing aid selection, and 98% to determine which power type of hearing aid that those patients should use. To reach our goal, we examined Data Mining techniques utilizing Python for coding and modelling.

In recent days, our world has entered the age of advanced digital systems, which means humans has to interact with technology in all scopes of live, this leads too as a result to another main concept, security. There are many security technologies that was developed to keep user information save. for example, there are several technologies used to let just the accurate user to log into devices like fingerprint, face recognition, or any another type of these systems. These technologies are in continuous development and are still suffering from accuracy, so several studies were performed to get the best accuracy for any of these systems.Computer vision was developed to give the devices the ability to see and recognize objects in an image or a video scene. It is a science that aims to build an intelligent application that are able to understand the content inside images as humans can do. To create a system like this, first step is to make Data acquisition, this task is done bycameras which gives us a sequence of frames when we record a video. After that, this data is analyzed and studied to extract important features from it. This feature will allow us to re-construct the description of the outer world in a manner that can understood by computer system.Face recognition systems are used on a wide range in mobile applications, banks, and military sites, police stations to make a recognition of the criminals, even in covid19 situations the face recognition was used too.Our proposed method aims to increase the accuracy a face recognition system using deep learning models, the system proposed is able to work on any platform compiled with python which is the application was used in our study. The model performed gives a high accuracy with a very speed result.