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Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

World statistics declare that aging has direct correlations with more and more health problems with comorbid conditions. As healthcare communities evolve with a massive amount of data at a faster pace, it is essential to predict, assist, and prevent diseases at the right time, especially for elders. Similarly, many researchers have discussed that elders suffer extensively due to chronic health conditions. This work was performed to review literature studies on prediction systems for various chronic illnesses of elderly people. Most of the reviewed papers proposed machine learning prediction models combined with, or without, other related intelligence techniques for chronic disease detection of elderly patients at an early stage to avoid emergency situations. This method provides a promising approach in the analysis of either structured or unstructured datasets to produce very substantial pattern discoveries. By defining the generic architecture for the prediction model, we reviewed various papers involved in similar fields, based on suggested methodologies and their associated outcomes. The study discussed the pros and cons of different prediction models using traditional and modern machine learning techniques. [ABSTRACT FROM AUTHOR]

Getting knowledge from raw data has delivered beneficial information in several domains. The prevalent utilizing of social media produced extraordinary quantities of social information. Simply, social media delivers an available podium for employers for sharing information. Data Mining has ability to present applicable designs that can be useful for employers, commercial, and customers. Data of social media are strident, massive, formless, and dynamic in the natural case, so modern encounters grow. Investigation methods of data mining utilized via social networks is the purpose of the study, accepting investigation plans on the basis of criteria, and by selecting a number of papers to serve as the foundation for this article. Afterward a watchful evaluation of these papers, it has been discovered that numerous data extraction approaches were utilized with social media data to report a number of various research goals in several fields of industrial and service. Though, implementations of data mining are still raw and require more work via industry and academic world to prepare the work sufficiently. Bring this analysis to a close. Data mining is the most important rule for uncovering hidden data in large datasets, especially in social network analysis, and it demonstrates the most important social media technology. [ABSTRACT FROM AUTHOR]

The last two decades have seen a marked increase in the illegal activities on the Dark Web. Prompt evolvement and use of sophisticated protocols make it difficult for security agencies to identify and investigate these activities by conventional methods. Moreover, tracing criminals and terrorists poses a great challenge keeping in mind that cybercrimes are no less serious than real life crimes. At the same time, computer security societies and law enforcement pay a great deal of attention on detecting and monitoring illegal sites on the Dark Web. Retrieval of relevant information is not an easy task because of vastness and ever-changing nature of the Dark Web; as a result, web crawlers play a vital role in achieving this task. Thereafter, data mining techniques are applied to extract useful patterns that would help security agencies to limit and get rid of cybercrimes. The aim of this paper is to present a survey for those researchers who are interested in this topic. We started by discussing the internet layers and the properties of the Deep Web, followed by explaining the technical characters of The Onion Routing (TOR) network, and finally describing the approaches of accessing, extracting and processing Dark Web data. Understanding the Dark Web, its properties and its threats is vital for internet servers; we do hope this paper be of help in that goal. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A rapid growth has occurred for the act of plagiarism with the aid of Internet explosive growth wherein a massive volume of information offered with effortless use and access makes plagiarism the process of taking someone else’s work (represented by ideas, or even words) and representing it as other's own work easy to be performed. For ensuring originality, detecting plagiarism has been massively necessitated in various areas so that the people who aim to plagiarize ought to offer considerable effort for introducing works centered on their research. In this paper, work has been proposed for improving the detection of textual plagiarism through proposing a model for candidate retrieval phase. The model proposed for retrieving candidates has adopted the vector space method VSM as a retrieval model and centered on representing documents as vectors consisting of average term weights and considering them as queries for retrieval instead of representing them as vectors of term weight. The detailed comparison task comes as the second phase wherein fuzzy semantic based string similarity has been applied. Experiments have been conducted using PAN-PC-10 as an evaluation dataset for evaluating the proposed system. As the problem statement in this paper is restricted to detect extrinsic plagiarism and works on English documents, experiments have been performed on the portion dedicated to extrinsic detection and on documents in English language only. For evaluating performance of the proposed model for retrieving candidates, Precision, Recall, and F-measure have been used as an evaluation metrics. The overall performance of the proposed system has been assessed through the use of the five standard PAN measures Precision, Recall, F-measure, Granularity and . The experimental results have clarified that the proposed model for retrieving candidates has a positive impact on the overall performance of the system and the system outperforms the other state-of-the-art methods. They clarified that the proposed model has detected about 80% of the plagiarism cases and about 90% of the detections were correct. The proposed model has the ability to detect literal plagiarism in addition to cases containing paraphrasing. Performance comparison has clarified that the proposed system is either comparable or outperforms the other baseline systems in terms of the five evaluation metrics. [ABSTRACT FROM AUTHOR]

In data mining and machine learning methods, it is traditionally assumed that training data, test data, and the data that will be processed in the future, should have the same feature space distribution. This is a condition that will not happen in the real world. In order to overcome this challenge, domain adaptation-based methods are used. One of the existing challenges in domain adaptation-based methods is to select the most efficient features so that they can also show the most efficiency in the destination database. In this paper, a new feature selection method based on deep reinforcement learning is proposed. In the proposed method, in order to select the best and most appropriate features, the essential policies in deep reinforcement learning are defined, and then the selection features are applied for training random forest, k-nearest neighborhood and support vector machine classifiers. The trained classifiers with the considered features are evaluated on the target database. The results are evaluated with the criteria of accuracy, sensitivity, positive and negative predictive rates in the classifiers. The achieved results show the superiority of the proposed method of feature selection when used in domain adaptation. By implementing the RF classifier on the VisDA-2018 database and the Syn2Real database, the classification accuracy in the feature selection of the proposed deep learning reinforcement has increased compared to the two-feature selection of Laplace monitoring and feature selection states. The classification sensitivity with the help of SVM classifier on the Syn2Real databases had the highest values in the feature selection state of the proposed deep learning reinforcement. The obtained number 100 is a positive predictive rate in the Syn2Real database with the help of SVM classifier and in the case of selecting the proposed feature, it indicates its superiority. The negative predictive rate in the Syn2Real database in the state of feature selection of the proposed deep reinforcement learning was 100%, which showed its superiority in comparison with 90.1% in the state of selecting the Laplace monitoring feature. Gmean in KNN classifier on the Syn2Real database has improved in the feature selection state of the proposed deep learning reinforcement in comparison to without feature selection state. [ABSTRACT FROM AUTHOR]

Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments achieving both semantic web and SNA tools. Its contribution clearly appears on the community productions and skills developments. Python 3.9.0 platform was used for data pre-processing, feature extraction and clustering via Naïve Bayesian and support vector machine. Two case studies were conducted to test the accuracy rate of the proposed system. The accuracy rate for the case studies was (90.771%) and (90.1149 %) respectively, which is considered an affective precision when it is compared with the related scenario with the same data set. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

E-mail is an efficient and reliable data exchange service. Spams are undesired email messages which are randomly sent in bulk usually for commercial aims. Obfuscated image spamming is one of the new tricks to bypass text-based and Optical Character Recognition (OCR)-based spam filters. Image spam detection based on image visual features has the advantage of efficiency in terms of reducing the computational cost and improving the performance. In this paper, an image spam detection schema is presented. Suitable image processing techniques were used to capture the image features that can differentiate spam images from non-spam ones. Weighted k-nearest neighbor, which is a simple, yet powerful, machine learning algorithm, was used as a classifier. The results confirm the effectiveness of the proposed schema as it is evaluated over two datasets. The first dataset is a real and benchmark dataset while the other is a real-like, modern, and more challenging dataset collected from social media and many public available image spam datasets. The obtained accuracy was 99.36% and 91% on benchmark and the proposed dataset, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The majority of systems dealing with natural language processing (NLP) and artificial intelligence (AI) can assist in making automated and automaticallysupported decisions. However, these systems may face challenges and difficulties or find it confusing to identify the required information (characterization) for eliciting a decision by extracting or summarizing relevant information from large text documents or colossal content. When obtaining these documents online, for instance from social networking or social media, these sites undergo a remarkable increase in the textual content. The main objective of the present study is to conduct a survey and show the latest developments about the implementation of text-mining techniques in humanities when summarizing and eliciting automated decisions. This process relies on technological advancement and considers (1) the automateddecision support-techniques commonly used in humanities, (2) the performance evolution and the use of the stylometric approach in text-mining, and (3) the comparisons of the results of chunking text by using different attributes in Burrows' Delta method. This study also provides an overview of the efficiency of applying some selected data-mining (DM) methods with various text-mining techniques to support the critics' decision in artistry ‒ one field of humanities. The automatic choice of criticism in this field was supported by a hybrid approach to these procedures. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)