On the Efficient Representation of Datasets as Graphs to Mine Maximal Frequent Itemsets

Halim, Zahid; Ali, Omer; Khan, Muhammad Ghufran

doi:10.1109/tkde.2019.2945573

Cited by 36 publications

(11 citation statements)

References 54 publications

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“…A popular Frequent Itemsets (FIs) mining is a task to find itemsets in a transactional database. The graph-based approach is used for the representation of a complete transactional database ( Halim, Ali & Khan, 2019 ).…”

Section: Review Overviewmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

Gite

Aluvalu³

2021

PeerJ Computer Science

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Background Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment. Methodology A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study. Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made. Conclusion There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.

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Section: Review Overviewmentioning

confidence: 99%

A review of microscopic analysis of blood cells for disease detection with AI perspective

Deshpande

Gite

Aluvalu³

2021

PeerJ Computer Science

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“…Various works [15][16][17][18][19][20][21] have been done on detecting sentence-level polarity with handcrafted features. Recent advances introduced neural network-based approaches [1,2,[22][23][24][25][26][27][28]. Despite such approaches yielding promising results, these do not cater to sentences expressing emotions on multiple topics.…”

Section: Related Workmentioning

confidence: 99%

Improving Aspect-Level Sentiment Analysis with Aspect Extraction

Majumder,

Bhardwaj,

Poria

et al. 2020

Preprint

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Aspect-based sentiment analysis (ABSA), a popular research area in NLP has two distinct partsaspect extraction (AE) and labeling the aspects with sentiment polarity (ALSA). Although distinct, these two tasks are highly correlated. The work primarily hypothesize that transferring knowledge from a pretrained AE model can benefit the performance of ALSA models. Based on this hypothesis, word embeddings are obtained during AE and subsequently, feed that to the ALSA model. Empirically, this work show that the added information significantly improves the performance of three different baseline ALSA models on two distinct domains. This improvement also translates well across domains between AE and ALSA tasks.

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“…Apart from these, there exist some linguistic‐based works on judicial documents, as in Halim, Ali, and Khan (2020), Halim and Rehan (2020) Metsker, Trofimov, Sikorsky, and Kovalchuk (2018), Tang and Kageura (2019) and Yuan, Lan, Hao, and Zhao (2019). These practical applications ensured strong motivation for research in the judicial text.…”

Section: Introductionmentioning

confidence: 99%

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Thomas

Sangeetha

2021

Expert Systems

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Tremendous growth in the availability of judicial documents has demanded the rise of information extraction (IE) techniques that support the automatic extraction of relevant concepts or data from judicial texts. Among various approaches available for IE, ontology‐based IE has proven to be the most appropriate for extracting domain‐specific information from natural language text. Through this article, we propose a knowledge‐driven, semi‐supervised pattern‐based learning (bootstrapping) approach to extract domain‐specific facts from judicial text, starting with a small set of seed facts. In the semantic analysis of legal text, fact extraction is the next step to entity identification, which involves the identification of roles played by each entity in the judicial text. The proposed methodology learns extraction patterns for 12 classes of facts from the judicial text through the integration of the domain ontology called judicial case ontology (JCO). The experimental results were evaluated by human judges and found to be quite promising. One main feature of the proposed methodology is its portability across various domains (such as medical, banking, insurance, etc.) which in turn helps build expert systems in various sectors.

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On the Efficient Representation of Datasets as Graphs to Mine Maximal Frequent Itemsets

Cited by 36 publications

References 54 publications

A review of microscopic analysis of blood cells for disease detection with AI perspective

A review of microscopic analysis of blood cells for disease detection with AI perspective

Improving Aspect-Level Sentiment Analysis with Aspect Extraction

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

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