•Comparative analysis of Dual Antiplatelet Therapy (DAPT) and Intravenous Tissue Plasminogen Activator (t-PA) in Acute Ischemic Minor Stroke (AIS).•Comprehensive meta-analysis of 5 studies involving ...3,978 DAPT-treated and 2,224 t-PA-treated patients.•No significant differences in achieving favorable modified Rankin Scale (mRS) scores (0-1 and 0-2) or combined mRS scores between DAPT and IV t-PA groups.•Similar outcomes in NIHSS score change from baseline and mortality rates for both treatment groups.•Significant reduction in symptomatic intracranial hemorrhage (sICH) and overall bleeding risk with DAPT compared to IV t-PA.•Conclusively, DAPT shows a favorable safety profile without compromising efficacy in treating AIS.
To compare the safety and efficacy of Dual Antiplatelet Therapy (DAPT) and Intravenous (IV) Tissue Plasminogen Activator (t-PA) in minor Acute Ischemic Stroke (AIS).
Following Cochrane and PRISMA guidelines, we analyzed observational studies and clinical trials comparing DAPT and IV t-PA in patients with minor AIS. Databases included PubMed, Scopus, and Web of Science. Data extraction included study characteristics, patient demographics, and analyzed outcomes. RevMan 5.3 and OpenMetaAnalyst 2021 were used to analyze the data and assess heterogeneity, respectively. The risk of bias was determined using RoB 2.0 and the Newcastle-Ottawa scale.
This meta-analysis included five studies with 3,978 DAPT-treated patients and 2,224 IV t-PA-treated patients. We found no significant differences in achieving modified Rankin scale (mRS) scores of 0-1 (OR 1.11, 95 % CI: 0.79, 1.55, p = 0.56) and 0-2 (OR 0.90, 95 % CI: 0.61, 1.31, p = 0.57), as well as combined mRS scores (OR 1.05, 95 % CI: 0.82, 1.34, p = 0.72). Similarly, there were no significant disparities between the two treatment groups in NIHSS score change from baseline (MD 0.32, 95 % CI: -0.35, 0.98, p = 0.35) and in mortality rates (OR 0.87, 95 % CI: 0.26, 2.93, p = 0.83). Notably, in comparison to the IV t-PA group, the DAPT group exhibited a significantly lower incidence of bleeding (OR 0.31, 95 % CI: 0.14, 0.69, p = 0.004) and symptomatic intracranial hemorrhage (sICH) (OR 0.10, 95 % CI: 0.04, 0.26, p < 0.00001).
Our meta-analysis found no significant differences in efficacy between DAPT and IV t-PA. However, DAPT demonstrated a significantly lower risk of sICH and bleeding compared with IV t-PA.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Semantic-enhanced recommendation systems are promising approaches to overcome the sparsity and cold-start problems, which are hard to handle using the conventional collaborative filtering (CF) ...approaches. Further research is needed to effectively integrate ontologies into collaborative filtering recommender systems. This paper proposes an ontology-based semantic similarity measure to evaluate similarities between items and eventually generate accurate recommendations. The proposed semantic similarity measure termed fusion-based semantic similarity takes into account the semantics of ontological instances (i.e. items) inferred from a specific domain ontology, which is determined by analyzing the hierarchical relationships among the instances, as well as the features of the instances and their relationships to other instances. The new measure comprehensively captures the semantic knowledge associated with instances by exploiting all possible shared semantics between instances in a given domain ontology. Furthermore, this paper proposes a new semantic-enhanced hybrid recommendation approach as a result of combining the new semantic similarity measure with the standard item-based CF to enhance the quality of generated recommendations. In order to assess the effectiveness of our semantic-enhanced hybrid collaborative filtering method, a series of experiments were conducted to compare the performance of the proposed approach against well-established benchmark techniques. The reported experimental results consistently emphasize its superiority, demonstrating enhanced predictive abilities and a notable improvement in the quality of recommendations. More specifically, the proposed approach achieved notable 6% reduction in Mean Absolute Error (MAE) in certain cases, outperforming other benchmark techniques. Additionally, this study highlights the potential of using semantic-based similarity to enhance the performance of recommendation systems. Such enhancements address challenges within collaborative filtering, potentially leading to advancements in recommendation system design and optimization.
In this paper, an alternative approach to select base classifiers forming a parallel heterogeneous ensemble is proposed. The fundamental concept is to trim poorly performing classifiers; thus, a more ...effective heterogeneous ensemble can be generated. More specifically, the proposed trimming approach finds an optimal subset of classifiers to form the desired heterogeneous ensemble. The main challenge is how to detect poor performance classifiers. To address this issue, the differences in effectiveness between base classifiers forming the ensemble are utilized to spot weak classifiers. For evaluating the proposed approach, eighteen benchmark datasets are used for generating the heterogeneous ensemble classification and comparisons with the state-of-the-art methods are conducted. The experimental analysis demonstrated the effectiveness and superiority of the proposed approach when compared to other state-of-the-art approaches.
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GEOZS, IJS, NUK, OILJ, SBJE, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The lack of regulations and oversight on Online Social Networks (OSNs) has resulted in the rise of social spam, which is the dissemination of unsolicited and low-quality content that aims to deceive ...and manipulate users. Social spam can cause a range of negative consequences for individuals and businesses, such as the spread of malware, phishing scams, and reputational damage. While machine learning techniques can be used to detect social spammers by analysing patterns in data, they have limitations such as the potential for false positives and false negatives. In contrast, ontologies allow for the explicit modelling and representation of domain knowledge, which can be used to create a set of rules for identifying social spammers. However, the literature exposes a deficiency of ontologies that conceptualize domain-based social spam. This paper aims to address this gap by designing a domain-specific ontology called DSpamOnto to detect social spammers in microblogging that targes a specific domain. DSpamOnto can identify social spammers based on their domain-specific behaviour, such as posting repetitive or irrelevant content and using misleading information. The proposed model is compared and benchmarked against well-proven ML models using various evaluation metrics to verify and validate its utility in capturing social spammers.
Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their ...personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services.
•A hybrid semantic enhanced recommendation approach•A new Inferential Ontology-based Semantic Similarity (IOBSS) between two ontological instances•A few new concepts: Association, Associate Network and Common Associate Pair Set•A case study of Australian e-Government tourism services
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Online social networks have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as ...spammers and other untrustworthy users to publish and spread their content. Hence, it is vital to have an accurate understanding of the contextual content of social users, thus establishing grounds for measuring their social influence accordingly. In particular, there is the need for a better understanding of domain-based social trust to improve and expand the analysis process and determining the credibility of Social Big Data. The aim of this paper is to determine domain-based social influencers by means of a framework that incorporates semantic analysis and machine learning modules to measure and predict users’ credibility in numerous domains at different time periods. The evaluation of the experiment conducted herein validates the applicability of semantic analysis and machine learning techniques in detecting highly trustworthy domain-based influencers.
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CEKLJ, NUK, ODKLJ, UL, UM, UPUK
Information retrieval systems like web search engines can be used to meet the user’s information needs by searching and retrieving the relevant documents that match the user’s query. Firstly, the ...query is inputted to the web search engine and assumed to be a good representative for the user’s intention and reflecting specifically his information needs and thus it should be long enough, discriminative, specific and unambiguous. Secondly, the web search engine typically respond to the query by sending back a long flat list of web search results and each search result represents a relevant document. Typically, that list may contain thousands or millions of web search results and thus it is difficult to navigate and locate a specific document relevant to a specific topic. As a postretrieval process, web search results clustering may be a solution for this issue where web search results can be categorized as clusters. These clusters supposed to contain topically related documents and labelled by descriptive and concise labels. These labels supposed to correctly describe the contents of each cluster. Thus the users can easily choose a cluster representing the intended topic and navigate through relatively few documents inside that cluster. High-quality labelling for clusters is crucial for users who can now gain insight into that clusters’ contents, general structure, and distribution of the topics among documents in the clusters. This make the user able to preview and navigate easily and fast. To this end, the authors in this paper introduced a methodology to enhance labels for clusters of web search results. The proposed methodology is founded on the idea of using the existing labels nominated by the original Suffix Tree Clustering (STC) algorithm and adapting these labels and/or clusters so that it become more concise and descriptive. The propose methodology was conducted on the original STC algorithm to produce an enhanced version of the classical STC algorithm. The enhanced algorithm was experimented and the produced clusters and labels were evaluated and compared with respect to the classical STC algorithm. For evaluation, the authors used clusters labelling performance measure considered five parameters f1: Comprehensibility, f2: Descriptiveness, f3: Discriminative Power, f4: Uniqueness, and f5: Nonredundancy. The reported results shown that the new enhanced labels outperformed the original labels and the overall performance has been enhanced. The recorded results indicated that: (i) The proposed methodology achieved better performance and the overall average recorded values for the used performance measure (f6) was 0.921. (ii) Number of clusters was decreased from 15 to 9 clusters only. (iii) Number of duplicated results was decreased from 143 to 121 only, and (iv) average number of phrases per label was increased from 1.67 to 2.00 phrases.
The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound ...changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This article proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users’ behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers is compared and their effectiveness is measured via a number of robust evaluation measures. Furthermore, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
E-Government is becoming ever more active in terms of improving the provision of services to citizens from a citizen-centred perspective, in which online services and information are delivered to ...citizens on a personalised basis. Some developed governments have started to offer personalised services through their official portals. However, the personalised services that are offered are mostly limited to static customisation and are therefore far from achieving effective citizen-centred eGovernment services. Furthermore, delivering personalised online services that match the different needs and interests of government users is a challenge for e-Government, specifically in connection with the increasing information and services that are offered through the medium of government portals. Therefore, more advanced and intelligent eGovernment systems are desirable.Personalisation techniques, particularly in the form of recommender systems, are promising to provide better solutions to support the development of personalisation in eGovernment services. Furthermore, semantic enhanced recommender systems can better support citizen-centred e-Government services and enhance recommendation accuracy. The success of semantic enhanced hybrid recommendation approaches and the citizencentric initiative of e-Government have fostered the idea of developing personalised eGovernment recommendation service systems using semantic enhanced hybrid recommender systems. Accordingly, the effectiveness of utilising the semantic knowledge of e-Government services to enhance the recommendation quality of offered services is addressed in this thesis.This thesis makes five significant contributions to the area of e-Government personalised recommendation services. These contributions are summarised as follows: (i) the thesis first proposes a general framework for offering personalised e-Government services from a citizen-centred perspective based on the available user profiles information and semantic knowledge of a specific e-Government domain of interest; (ii) based on this general framework, a personalised e-Government tourism service recommendation framework is also proposed and considered as a target domain in this research study; (iii) new semantic enhanced hybrid recommendation approaches are developed to support the implementation of the recommendation generator engines of the proposed e-Government frameworks. The recommendation generator engines represent the core components of the proposed frameworks; (iv) new semantic similarity measures based on semantic knowledge of a target domain ontology are proposed to effectively evaluate the similarity between e-Government service items. The new semantic similarity measures are incorporated within the proposed hybrid approaches to improve the quality and accuracy of recommendations and to overcome the limitations of existing hybrid recommendation approaches; and (v) a switching semantic enhanced hybrid recommendation system is further proposed to enhance the overall quality of recommendation, address the sparsity, the cold-start user and item problems.Experimental evaluations of the proposed semantic enhanced hybrid recommendation approaches and switching system, on a real world tourism dataset, show promising results against state-of-the-art recommendation approaches in terms of the quality of recommendations, capacity to alleviate the sparsity, cold-start item and user problems.
The communication revolution has perpetually reshaped the means through which people send and receive information. Social media is an important pillar of this revolution and has brought profound ...changes to various aspects of our lives. However, the open environment and popularity of these platforms inaugurate windows of opportunities for various cyber threats, thus social networks have become a fertile venue for spammers and other illegitimate users to execute their malicious activities. These activities include phishing hot and trendy topics and posting a wide range of contents in many topics. Hence, it is crucial to continuously introduce new techniques and approaches to detect and stop this category of users. This paper proposes a novel and effective approach to detect social spammers. An investigation into several attributes to measure topic-dependent and topic-independent users' behaviours on Twitter is carried out. The experiments of this study are undertaken on various machine learning classifiers. The performance of these classifiers are compared and their effectiveness is measured via a number of robust evaluation measures. Further, the proposed approach is benchmarked against state-of-the-art social spam and anomalous detection techniques. These experiments report the effectiveness and utility of the proposed approach and embedded modules.
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