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Register a new userA Synonym Based Approach of Data Mining in
أليات البحث في غوغل وسيو
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Damascus University حلقة بحث
Publication date
2019
fields
Informatics Engineering
and research's language is
العربية
Authors
Hussein Al khateb( طالب )
Created by
Hussein Khateb
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سنتحدث في هذه الحلقة عن أليات البحث في غوغل مستخدمين استكشاف المعرفة داتا وتحسين الطريقة باستخدام المترادافات لمجال سيو (البحث الأمثلي)
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A Synonym Based Approach of Data Mining in Search Engine Optimization
Making the road by searching – A search engine based on Swarm Information Foraging
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Data mining techniques have numerous applications in malware detection. Classification method is one of the most popular data mining techniques. In this paper we present a data mining classification approach to detect malware behavior.We suggested di
fferent classification methods in order to detect malware based on the feature and behavior of each malware. A dynamic analysis method has been presented for identifying the malware features.A suggested programhas been presented for converting a malware behavior executive history XML file to a suitable WEKA tool input. To illustrate the performance efficiency as well as training data and test, we apply the proposed approaches to a real case study data set using WEKA tool. The evaluation results demonstrated the availability of the proposed data mining approach. Also our proposed data mining approach is more efficient for detecting malware and behavioral classification of malware can be useful to detect malware in a behavioral antivirus.
Fine-grained opinion mining (OM) has achieved increasing attraction in the natural language processing (NLP) community, which aims to find the opinion structures of Who expressed what opinions towards what'' in one sentence. In this work, motivated b
y its span-based representations of opinion expressions and roles, we propose a unified span-based approach for the end-to-end OM setting. Furthermore, inspired by the unified span-based formalism of OM and constituent parsing, we explore two different methods (multi-task learning and graph convolutional neural network) to integrate syntactic constituents into the proposed model to help OM. We conduct experiments on the commonly used MPQA 2.0 dataset. The experimental results show that our proposed unified span-based approach achieves significant improvements over previous works in the exact F1 score and reduces the number of wrongly-predicted opinion expressions and roles, showing the effectiveness of our method. In addition, incorporating the syntactic constituents achieves promising improvements over the strong baseline enhanced by contextualized word representations.
The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been propos
ed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a roadmap from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.
Basic-level terms have been described as the most important to human categorisation. They are the earliest emerging words in children's language acquisition, and seem to be more frequently occurring in language in general. In this article, we explore
d the use of basic-level nouns in texts of different complexity, and hypothesise that hypernyms with characteristics of basic-level words could be useful for the task of lexical simplification. We conducted two corpus studies using four different corpora, two corpora of standard Swedish and two corpora of simple Swedish, and explored whether corpora of simple texts contain a higher proportion of basic-level nouns than corpora of standard Swedish. Based on insights from the corpus studies, we developed a novel algorithm for choosing the best synonym by rewarding high relative frequencies and monolexemity, and restricting the climb in the word hierarchy not to suggest synonyms of a too high level of inclusiveness.
Data mining is becoming a pervasive technology in activities as diverse as using historical data to predict the success of a marketing campaign looking for patterns in financial transactions to discover illegal activities. From this perspective it wa
s just a matter of time for the discipline to reach the important area of computer security This research presents a collection of research efforts on the use of data mining in computer security.
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