A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques. This study comprehensively analyzes different FS approaches based on optimization algorithms for TC. We begin by introducing the primary phases involved in implementing TC. Subsequently, we explore a wide range of FS approaches for categorizing text documents and attempt to organize the existing works into four fundamental approaches: filter, wrapper, hybrid, and embedded. Furthermore, we review four optimization algorithms utilized in solving text FS problems: swarm intelligence-based, evolutionary-based, physics-based, and human behavior-related algorithms. We discuss the advantages and disadvantages of state-of-the-art studies that employ optimization algorithms for text FS methods. Additionally, we consider several aspects of each proposed method and thoroughly discuss the challenges associated with datasets, FS approaches, optimization algorithms, machine learning classifiers, and evaluation criteria employed to assess new and existing techniques. Finally, by identifying research gaps and proposing future directions, our review provides valuable guidance to researchers in developing and situating further studies within the current body of literature.
The life insurance companies need a sound system to use it in selecting Insurable risks so they can avoid or reduce possible losses that may be insured to a minimum levels , But the application within IRAQI INSURANCE COMPANY reflects that it still depends on a traditional ways in the procedures used to select that risks .
This research represents an attempt to put acceptable suggestions about developing a system for selecting insurable risks which used now by iraqi insurance company by recognizing the risks of life insurance , determining kinds of risks which can difined as normal and upnormal risks , rectification of the
... Show MoreIn this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho
... Show MoreGenetic algorithms (GA) are a helpful instrument for planning and controlling the activities of a project. It is based on the technique of survival of the fittest and natural selection. GA has been used in different sectors of construction and building however that is rarely documented. This research aimed to examine the utilisation of genetic algorithms in construction project management. For this purpose, the research focused on the benefits and challenges of genetic algorithms, and the extent to which genetic algorithms is utilised in construction project management. Results showed that GA provides an ability of generating near optimal solutions which can be adopted to reduce complexity in project management and resolve difficult problem
... Show MoreAbstract The strategic performance of the United States depends on dealing with the Middle East countries and its variants on several bases and motives that enabled them to achieve American hegemony and invest its interests at the expense of the region countries. Within this performance, the administration of the United State President Donald Trump presented the Strategic Document on December 18, 2017, which focused on the principle of "America First", to determine the direction of future US strategic performance in the formulation of means of cooperation and intersections or hostility in addition to interests and threats.The future vision of the Arab region and the Middle East as a whole, this strategy is based on the fact that
... Show MoreIn this paper, a new method of selection variables is presented to select some essential variables from large datasets. The new model is a modified version of the Elastic Net model. The modified Elastic Net variable selection model has been summarized in an algorithm. It is applied for Leukemia dataset that has 3051 variables (genes) and 72 samples. In reality, working with this kind of dataset is not accessible due to its large size. The modified model is compared to some standard variable selection methods. Perfect classification is achieved by applying the modified Elastic Net model because it has the best performance. All the calculations that have been done for this paper are in
ST Alawi, NA Mustafa, Al-Mustansiriyah Journal of Science, 2013
The purpose of this work is to study the classification and construction of (k,3)-arcs in the projective plane PG(2,7). We found that there are two (5,3)-arcs, four (6,3)-arcs, six (7,3)arcs, six (8,3)-arcs, seven (9,3)-arcs, six (10,3)-arcs and six (11,3)-arcs. All of these arcs are incomplete. The number of distinct (12,3)-arcs are six, two of them are complete. There are four distinct (13,3)-arcs, two of them are complete and one (14,3)-arc which is incomplete. There exists one complete (15,3)-arc.
These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t
... Show MoreDrama is one of the means of transmitting human experiences, as it presents within it the life ideas and visions of the spectator, who is subject to their influence on him, robbed of the will in front of its charm and various display arts, which invade him with its dimensions and affect his references, and this art form is based on stories revolving around personalities involved in events that have grown As a result of the struggle of two conflicting opponents, or two opposing forces or emotions generated as a result of a voluntary conflict, as this dramatic conflict represents the most important elements of those events, as it is embodied in an inevitable scene that emerges from other scenes, and this scene is sometimes subject to the
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