A New Associative Classification Algorithm for Predicting Groundwater Locations

Aburub, Faisal; Hadi, Wael

doi:10.1142/s0219649218500430

Cited by 6 publications

(5 citation statements)

References 32 publications

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“…Approximately 60% of the references collected contribute to seven out of nine goals related to water supply; goal 2.a., which focuses on developing a secure and safe water supply in the area, is included in six studies; four focus on allocating new water sources [56][57][58][59], and two studies focus on sustainable management [52,60]. While six studies were found to be aligned with goal 2.b., which focuses on using desalinated water as a major source for water supply, four focused on saline water intrusion [43,[61][62][63], one on hydrochemistry [42], and one on salinization scenarios [64].…”

Section: Goals Related To Water Supplymentioning

confidence: 99%

“…About 35% of the collected documents focused on modeling in terms of: (a) estimating the recharge rate [50,106], (b) enhancing the recharge amount [78,80,81,84,85,[87][88][89][90]92], (c) studying the impact of climate change on water resources [53], (d) assessing surface water and drought [51,55,107], (e) locating potential areas for groundwater abstraction [58,59], (f) analyzing time series [32,108], (g) building water quality models [70], (h) building groundwater models [29][30][31]54,[96][97][98], (i) delineating isohyetal maps for rainfall [93], (j) creating vulnerability maps [65][66][67], and (k) proposing sustainable water management plans [52,60].…”

Section: Research Focus Areas Analysismentioning

confidence: 99%

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A Novel Method to Assess the Impact of a Government’s Water Strategy on Research: A Case Study of Azraq Basin, Jordan

Alqadi

Dwairi

Dehnavi

et al. 2021

Water

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Water scarcity drives governments in arid and semi-arid regions to promote strategies for improving water use efficiency. Water-related research generally also plays an important role in the same countries and for the same reason. However, it remains unclear how to link the implementation of new government strategies and water-related research. This article’s principal objective is to present a novel approach that defines water-related research gaps from the point of view of a government strategy. The proposed methodology is based on an extensive literature review, followed by a systematic evaluation of the topics covered both in grey and peer-reviewed literature. Finally, we assess if and how the different literature sources contribute to the goals of the water strategy. The methodology was tested by investigating the impact of the water strategy of Jordan’s government (2008–2022) on the research conducted in the Azraq Basin, considering 99 grey and peer-reviewed documents. The results showed an increase in the number of water-related research documents from 37 published between 1985 and 2007 to 62 published between 2008 and 2018. This increase should not, however, be seen as a positive impact of increased research activity from the development of Jordan’s water strategy. In fact, the increase in water-related research activity matches the increasing trend in research production in Jordan generally. Moreover, the results showed that only about 80% of the documents align with the goals identified in the water strategy. In addition, the distribution of the documents among the different goals of the strategy is heterogeneous; hence, research gaps can be identified, i.e., goals of the water-strategy that are not addressed by any of the documents sourced. To foster innovative and demand-based research in the future, a matrix was developed that linked basin-specific research focus areas (RFAs) with the MWI strategy topics. In doing so, the goals that are not covered by a particular RFA are highlighted. This analysis can inspire researchers to develop and apply new topics in the Azraq Basin to address the research gaps and strengthen the connection between the RFAs and the strategy topics and goals. Moreover, the application of the proposed methodology can motivate future research to become demand-driven, innovative, and contribute to solving societal challenges.

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Section: Goals Related To Water Supplymentioning

confidence: 99%

Section: Research Focus Areas Analysismentioning

confidence: 99%

A Novel Method to Assess the Impact of a Government’s Water Strategy on Research: A Case Study of Azraq Basin, Jordan

Alqadi

Dwairi

Dehnavi

et al. 2021

Water

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“… Aburub & Hadi (2018) developed an associative classification algorithm for prediction of existence of underground water at a given place. Again this algorithm has been developed for associative classification of fully-labeled data.…”

Section: Related Workmentioning

confidence: 99%

“…The frequency of patterns is also called the support and the associativity of a pattern is called the confidence . The difference between associative classification and ARM is that in an associative classification rule consequent is always a class label ( Aburub & Hadi, 2018 ). Associative classification has shown better performance than non-associative classifiers ( Shahzad & Baig, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised associative classification using ant colony optimization algorithm

Awan¹,

Shahzad²

2021

PeerJ Computer Science

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Labeled data is the main ingredient for classification tasks. Labeled data is not always available and free. Semi-supervised learning solves the problem of labeling the unlabeled instances through heuristics. Self-training is one of the most widely-used comprehensible approaches for labeling data. Traditional self-training approaches tend to show low classification accuracy when the majority of the data is unlabeled. A novel approach named Self-Training using Associative Classification using Ant Colony Optimization (ST-AC-ACO) has been proposed in this article to label and classify the unlabeled data instances to improve self-training classification accuracy by exploiting the association among attribute values (terms) and between a set of terms and class labels of the labeled instances. Ant Colony Optimization (ACO) has been employed to construct associative classification rules based on labeled and pseudo-labeled instances. Experiments demonstrate the superiority of the proposed associative self-training approach to its competing traditional self-training approaches.

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“…PBC4cip has been shown to outperform at least eight state-of-the-art algorithms designed for class imbalance problems [26,30].…”

mentioning

confidence: 99%

An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication

2022

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Empathy is a necessary component of human communication. However, it has been largely ignored in favor of other concepts such as emotion and feeling in Affective computing. Research that has been carried out regarding empathy in computer science lacks a method of measuring empathy based on psychological research. Likewise, it does not present an avenue for expanding knowledge regarding this concept. We provide a comprehensive study on the nature of empathy and a method for detecting it in textual communication. We measured empathy present in conversations from a database through volunteers and psychological research. Subsequently, we made use of a pattern-based classification algorithm to predict the Empathy levels in each conversation. Our research contributions are: the Empathy score, a metric for measuring empathy in texts; Empathetic Conversations, a database containing conversations with their respective Empathy score; and our results. We show that an explicative pattern-based approach (PBC4cip) is, to date, the best approach for detecting empathy in texts. This is by measuring performance in both nominal and ordinal metrics. We found a statistically significant difference in performance for our approach and other algorithms with lower performance. In addition, we show the advantages of interpretability by our model in contrast to other approaches. This is one of the first approaches to measuring empathy in texts, and we expect it to be useful for future research.

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A New Associative Classification Algorithm for Predicting Groundwater Locations

Cited by 6 publications

References 32 publications

A Novel Method to Assess the Impact of a Government’s Water Strategy on Research: A Case Study of Azraq Basin, Jordan

A Novel Method to Assess the Impact of a Government’s Water Strategy on Research: A Case Study of Azraq Basin, Jordan

Semi-supervised associative classification using ant colony optimization algorithm

An Explainable Artificial Intelligence Approach for Detecting Empathy in Textual Communication

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