Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. The aim of this work is to develop machine learning models for determining the rheological properties of polymers from experimental stress relaxation curves. The paper presents an overview of the main directions of metaheuristic approaches (local search, evolutionary algorithms) to solving combinatorial optimization problems. Metaheuristic algorithms for solving some important combinatorial optimization problems are described, with special emphasis on the construction of decision trees. A comparative analysis of algorithms for solving the regression problem in CatBoost Regressor has been carried out. The object of

"The aim of the research is to identify the availability of the dimensions of the research variables represented by organizational symmetry and the quality of work-life at the University of Information and Communications Technology, which is one of the formations of the Ministry of Higher Education and Scientific Research in Baghdad, in addition to knowing the relationship and influence between them. The research relied on the descriptive analytical approach based on peer description. The research was analyzed and the research sample consisted of (148) individuals, the sample was chosen using the comprehensive inventory method, data was obtained by relying on the questionnaire which was prepared from ready-made m

Coagulation is the most important process in drinking water treatment. Alum coagulant increases the aluminum residuals, which have been linked in many studies to Alzheimer's disease. Therefore, it is very important to use it with the very optimal dose. In this paper, four sets of experiments were done to determine the relationship between raw water characteristics: turbidity, pH, alkalinity, temperature, and optimum doses of alum [   .14 O] to form a mathematical equation that could replace the need for jar test experiments. The experiments were performed under different conditions and under different seasonal circumstances. The optimal dose in every set was determined, and used to build a gene expression model (GEP). The models were co

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol

The Video Assistant Referee (VAR) is a technology designed to review on- eld decisions through video footage in order to correct clear and critical refereeing errors. It enables the replay of key moments in slow motion to determine the correct naldecision,withcommunicationbetweenthevideoof cialsandtherefereeconductedviaheadset.Thesystem operates under the principle of "minimal interference, maximum bene t," intervening only in essential situations. This study aimedtoassessthecurrent implementationofVARintheIraqStarsFootballLeagueduringthe2023–2024season. To achieve this objective, the researchers employed a descriptive survey method involving a sample of 220 participants, including referees, coaches, players, assessors, academics, a

Any design subject to a set of forces contributing to the establishment of relations working to strengthen the internal elements of the design; any imbalance in these elements can make a fragmented and weak design, thus preventing it from achieving the goal or performance. Poor performance can be attributed to various factors: the extent and function of the elements and principles in the design, realization of the idea, especially in fashion design.
Moreover, there are many aspects of a design that go into achieving the realization of the designer’s idea. The design utilizes a lot of stimulants by drawing attention to its design, which is consistent with the need for psychological and material individuals. In this research, we will

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has