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Saima Jabeen

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Lahore College For Women University Lahore Pakistan

Islamic University of Technology

Computer Science & Information Technology (CS & IT) Computer Science Conference Proceedings (CSCP)

Rubén Fuentes-fernández

ABDULLAH ALSAEEDI

hariman bahtiar

Universitas Hamzanwadi

ahmad hasanpour

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Papers by Saima Jabeen

Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects

Complexity, 2020

Software defects prediction at the initial period of the software development life cycle remains ... more Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets...

Aggregated provenance and its implications in clouds

Future Generation Computer Systems, 2018

h i g h l i g h t s • This paper discusses the importance of aggregated provenance in the domain ... more h i g h l i g h t s • This paper discusses the importance of aggregated provenance in the domain of cloud computing. • The paper presents a complete architecture for aggregated provenance regarding key cloud layers such as IaaS, PaaS, SaaS and Storage service. • The paper presents various scenarios (use cases) via a sample application where the significance and application of aggregated provenance is provided.

Multi-document summarization based on the Yago ontology

Expert Systems with Applications, 2013

Sentence-based multi-document summarization is the task of generating a succinct summary of a doc... more Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semanticsbased models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process. This paper proposes a novel summarizer, namely Yago-based Summarizer,

DNA Methylation Prediction Using Reduced Features Obtained via Gappy Pair Kernel and Partial Least Square

IEEE Access

It is critical to correctly identify DNA methylation because it has been linked to a variety of h... more It is critical to correctly identify DNA methylation because it has been linked to a variety of human disorders, particularly cancer. DNA methylation is an epigenetic process that allows cells to alter gene expression. This work deals with a type of DNA methylation called 5-methyl cytosine (m5c), in which the methyl group (CH 3) is attached to the 5 th carbon of cytosine. The performances of different machine learning algorithms used for methylation identification are greatly degraded due to poor representation of input sequential data. In the current work, we have proposed a classification model that is based on the extraction of high differentiating features from the sample sequences using gappy pair kernel. Increasing the number of features to better represent a sequence leads to the curse of dimensionality, which is handled by a dimensionality reduction technique called PLS (Partial Least Square). The obtained features are then subjected to multiple classifiers to test the discriminating power of these features. Results are computed for cross species i.e human and mouse, to check the robustness of our proposed model. Finally, the obtained results are compared in terms of sensitivity, specificity, and accuracy with the state-of-theart approaches. Our proposed approach has outperformed state-of-the-art techniques in all three metrics for both datasets. For research community to test our technique, we have uploaded our code on github (https://github.com/sajidshahbs/gappypairKernel_Rcode).

Multi-document summarization exploiting frequent itemsets

Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012

A summary is a succinct and informative description of a data collection. In the context of multi... more A summary is a succinct and informative description of a data collection. In the context of multi-document summarization, the selection of the most relevant and not redundant sentences belonging to a collection of textual documents is definitely a challenging task. Frequent itemset mining is a well-established data mining technique to discover correlations among data. Although it has been widely used

Combining Semantics and Social Knowledge for News Article Summarization

Data Mining and Analysis in the Engineering Field

With the diffusion of online newspapers and social media, users are becoming capable of retrievin... more With the diffusion of online newspapers and social media, users are becoming capable of retrieving dozens of news articles covering the same topic in a short time. News article summarization is the task of automatically selecting a worthwhile subset of news' sentences that users could easily explore. Promising research directions in this field are the use of semantics-based models (e.g., ontologies and taxonomies) to identify key document topics and the integration of social data analysis to also consider the current user's interests during summary generation. The chapter overviews the most recent research advances in document summarization and presents a novel strategy to combine ontology-based and social knowledge for addressing the problem of generic (not query-based) multi-document summarization of news articles. To identify the most salient news articles' sentences, an ontology-based text analysis is performed during the summarization process. Furthermore, the socia...

PatTexSum : A pattern-based text summarizer

In the last decade the growth of the Internet has made a huge amount of textual documents availab... more In the last decade the growth of the Internet has made a huge amount of textual documents available in the electronic form. Text summarization is commonly based on clustering or graph-based methods and usually considers the bag-of-word sentence representation. Frequent itemset mining is a widely exploratory technique to discover relevant correlations among data. The well-established application of frequent itemsets to large transactional datasets prompts their usage in the context of document summarization as well. This paper proposes a novel multi-document summarizer, namely PatTexSum (Pattern-based Text Summarizer), that is mainly based on a patternbased model, i.e., a model composed of frequent itemsets. Unlike previously proposed approaches, PatTexSum selects most representative and not redundant sentences to include in the summary by considering both (i) the most informative and non-redundant itemsets extracted from document collections tailored to the transactional data format...

Document analysis by means of data mining techniques

This chapter presents a suite of analysis tools used for mining text of document collections. Int... more This chapter presents a suite of analysis tools used for mining text of document collections. Introduction of text mining is presented in section 2.1. An overview of some established and extensively used text mining approaches is presented in section 2.2 along with their merits and demerits. Section 2.3 and 2.4 briefly discuss some categories of summarization models and some types of document collections respectively.

Multi-document summarization based on the Yago ontology

Sentence-based multi-document summarization is the task of generating a succinct summary of a doc... more Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semanticsbased models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process. This paper proposes a novel summarizer, namely Yago-based Summarizer,

Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects

Complexity, 2020

Software defects prediction at the initial period of the software development life cycle remains ... more Software defects prediction at the initial period of the software development life cycle remains a critical and important assignment. Defect prediction and correctness leads to the assurance of the quality of software systems and has remained integral to study in the previous years. The quick forecast of imperfect or defective modules in software development can serve the development squad to use the existing assets competently and effectively to provide remarkable software products in a given short timeline. Hitherto, several researchers have industrialized defect prediction models by utilizing statistical and machine learning techniques that are operative and effective approaches to pinpoint the defective modules. Tree family machine learning techniques are well-thought-out to be one of the finest and ordinarily used supervised learning methods. In this study, different tree family machine learning techniques are employed for software defect prediction using ten benchmark datasets...

Aggregated provenance and its implications in clouds

Future Generation Computer Systems, 2018

h i g h l i g h t s • This paper discusses the importance of aggregated provenance in the domain ... more h i g h l i g h t s • This paper discusses the importance of aggregated provenance in the domain of cloud computing. • The paper presents a complete architecture for aggregated provenance regarding key cloud layers such as IaaS, PaaS, SaaS and Storage service. • The paper presents various scenarios (use cases) via a sample application where the significance and application of aggregated provenance is provided.

Multi-document summarization based on the Yago ontology

Expert Systems with Applications, 2013

Sentence-based multi-document summarization is the task of generating a succinct summary of a doc... more Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semanticsbased models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process. This paper proposes a novel summarizer, namely Yago-based Summarizer,

DNA Methylation Prediction Using Reduced Features Obtained via Gappy Pair Kernel and Partial Least Square

IEEE Access

It is critical to correctly identify DNA methylation because it has been linked to a variety of h... more It is critical to correctly identify DNA methylation because it has been linked to a variety of human disorders, particularly cancer. DNA methylation is an epigenetic process that allows cells to alter gene expression. This work deals with a type of DNA methylation called 5-methyl cytosine (m5c), in which the methyl group (CH 3) is attached to the 5 th carbon of cytosine. The performances of different machine learning algorithms used for methylation identification are greatly degraded due to poor representation of input sequential data. In the current work, we have proposed a classification model that is based on the extraction of high differentiating features from the sample sequences using gappy pair kernel. Increasing the number of features to better represent a sequence leads to the curse of dimensionality, which is handled by a dimensionality reduction technique called PLS (Partial Least Square). The obtained features are then subjected to multiple classifiers to test the discriminating power of these features. Results are computed for cross species i.e human and mouse, to check the robustness of our proposed model. Finally, the obtained results are compared in terms of sensitivity, specificity, and accuracy with the state-of-theart approaches. Our proposed approach has outperformed state-of-the-art techniques in all three metrics for both datasets. For research community to test our technique, we have uploaded our code on github (https://github.com/sajidshahbs/gappypairKernel_Rcode).

Multi-document summarization exploiting frequent itemsets

Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012

A summary is a succinct and informative description of a data collection. In the context of multi... more A summary is a succinct and informative description of a data collection. In the context of multi-document summarization, the selection of the most relevant and not redundant sentences belonging to a collection of textual documents is definitely a challenging task. Frequent itemset mining is a well-established data mining technique to discover correlations among data. Although it has been widely used

Combining Semantics and Social Knowledge for News Article Summarization

Data Mining and Analysis in the Engineering Field

With the diffusion of online newspapers and social media, users are becoming capable of retrievin... more With the diffusion of online newspapers and social media, users are becoming capable of retrieving dozens of news articles covering the same topic in a short time. News article summarization is the task of automatically selecting a worthwhile subset of news' sentences that users could easily explore. Promising research directions in this field are the use of semantics-based models (e.g., ontologies and taxonomies) to identify key document topics and the integration of social data analysis to also consider the current user's interests during summary generation. The chapter overviews the most recent research advances in document summarization and presents a novel strategy to combine ontology-based and social knowledge for addressing the problem of generic (not query-based) multi-document summarization of news articles. To identify the most salient news articles' sentences, an ontology-based text analysis is performed during the summarization process. Furthermore, the socia...

PatTexSum : A pattern-based text summarizer

In the last decade the growth of the Internet has made a huge amount of textual documents availab... more In the last decade the growth of the Internet has made a huge amount of textual documents available in the electronic form. Text summarization is commonly based on clustering or graph-based methods and usually considers the bag-of-word sentence representation. Frequent itemset mining is a widely exploratory technique to discover relevant correlations among data. The well-established application of frequent itemsets to large transactional datasets prompts their usage in the context of document summarization as well. This paper proposes a novel multi-document summarizer, namely PatTexSum (Pattern-based Text Summarizer), that is mainly based on a patternbased model, i.e., a model composed of frequent itemsets. Unlike previously proposed approaches, PatTexSum selects most representative and not redundant sentences to include in the summary by considering both (i) the most informative and non-redundant itemsets extracted from document collections tailored to the transactional data format...

Document analysis by means of data mining techniques

This chapter presents a suite of analysis tools used for mining text of document collections. Int... more This chapter presents a suite of analysis tools used for mining text of document collections. Introduction of text mining is presented in section 2.1. An overview of some established and extensively used text mining approaches is presented in section 2.2 along with their merits and demerits. Section 2.3 and 2.4 briefly discuss some categories of summarization models and some types of document collections respectively.

Multi-document summarization based on the Yago ontology

Sentence-based multi-document summarization is the task of generating a succinct summary of a doc... more Sentence-based multi-document summarization is the task of generating a succinct summary of a document collection, which consists of the most salient document sentences. In recent years, the increasing availability of semanticsbased models (e.g., ontologies and taxonomies) has prompted researchers to investigate their usefulness for improving summarizer performance. However, semantics-based document analysis is often applied as a preprocessing step, rather than integrating the discovered knowledge into the summarization process. This paper proposes a novel summarizer, namely Yago-based Summarizer,