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105. Krashen Revisited: Case Study of the Role of Input, Motivation and Identity in Second Language Learning

Author

Bailey, Francis and Fahad, Ahmed Kadhum

Abstract

Stephen Krashen has a long and enduring legacy in the field of second language acquisition. His "Input Hypothesis" was among the very first attempts to create a coherent theoretical account of second language learning. Krashen argued that learners can acquire language through the process of comprehending it. While elements of his model have been extensively critiqued, this idea has endured and offers teachers a clear mandate to provide learners with abundant opportunities to making meaning of the target language. Utilizing a case study of an English language learner, Krashen's model is challenged and enriched by considering the role that motivation and identity play in learning. Teachers tapping into an important source of learner motivation, role models drawn from the local community or broader society, can inspire and energize students' studies and help them visualize a life in which a second language plays a vital role. Building upon Krashen's idea of the importance of language teachers and programs creating robust reading programs for a sustained engagement with second language print resources, the authors propose to expand his vision and include all manner of multimedia and technologies. However, such a program can only succeed if teachers mediate their learners' social identities and motivations for sustained second language learning.

Published
2021

111. Antibody-directed evolution reveals a mechanism for enhanced neutralization at the HIV-1 fusion peptide site

Author

Banach, Bailey B., Pletnev, Sergei, Olia, Adam S., Xu, Kai, Zhang, Baoshan, Rawi, Reda, Bylund, Tatsiana, Doria-Rose, Nicole A., Nguyen, Thuy Duong, Fahad, Ahmed S., Lee, Myungjin, Lin, Bob C., Liu, Tracy, Louder, Mark K., Madan, Bharat, McKee, Krisha, O’Dell, Sijy, Sastry, Mallika, Schön, Arne, Bui, Natalie, Shen, Chen-Hsiang, Wolfe, Jacy R., Chuang, Gwo-Yu, Mascola, John R., Kwong, Peter D., and DeKosky, Brandon J.

Published
2023
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113. A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning

Author

Hussain, Musarrat, Satti, Fahad Ahmed, Hussain, Jamil, Ali, Taqdir, Ali, Syed Imran, Bilal, Hafiz Syed Muhammad, Park, Gwang Hoon, and Lee, Sungyoung

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Objective: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which can convert clinical text into causal knowledge. Methods: A practical approach based on term expansion, phrase generation, BERT based phrase embedding and semantic matching, semantic enrichment, expert verification, and model evolution has been used to construct a comprehensive causality mining framework. This active transfer learning based framework along with its supplementary services, is able to extract and enrich, causal relationships and their corresponding entities from clinical text. Results: The multi-model transfer learning technique when applied over multiple iterations, gains performance improvements in terms of its accuracy and recall while keeping the precision constant. We also present a comparative analysis of the presented techniques with their common alternatives, which demonstrate the correctness of our approach and its ability to capture most causal relationships. Conclusion: The presented framework has provided cutting-edge results in the healthcare domain. However, the framework can be tweaked to provide causality detection in other domains, as well. Significance: The presented framework is generic enough to be utilized in any domain, healthcare services can gain massive benefits due to the voluminous and various nature of its data. This causal knowledge extraction framework can be used to summarize clinical text, create personas, discover medical knowledge, and provide evidence to clinical decision making.

Published
2020
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114. AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into Practice

Author

Hussain, Musarrat, Hussain, Jamil, Ali, Taqdir, Satti, Fahad Ahmed, and Lee, Sungyoung

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

Background and Objectives: Clinical Practice Guidelines (CPGs) represent the foremost methodology for sharing state-of-the-art research findings in the healthcare domain with medical practitioners to limit practice variations, reduce clinical cost, improve the quality of care, and provide evidence based treatment. However, extracting relevant knowledge from the plethora of CPGs is not feasible for already burdened healthcare professionals, leading to large gaps between clinical findings and real practices. It is therefore imperative that state-of-the-art Computing research, especially machine learning is used to provide artificial intelligence based solution for extracting the knowledge from CPGs and reducing the gap between healthcare research/guidelines and practice. Methods: This research presents a novel methodology for knowledge extraction from CPGs to reduce the gap and turn the latest research findings into clinical practice. First, our system classifies the CPG sentences into four classes such as condition-action, condition-consequences, action, and not-applicable based on the information presented in a sentence. We use deep learning with state-of-the-art word embedding, improved word vectors technique in classification process. Second, it identifies qualifier terms in the classified sentences, which assist in recognizing the condition and action phrases in a sentence. Finally, the condition and action phrase are processed and transformed into plain rule If Condition(s) Then Action format. Results: We evaluate the methodology on three different domains guidelines including Hypertension, Rhinosinusitis, and Asthma. The deep learning model classifies the CPG sentences with an accuracy of 95%. While rule extraction was validated by user-centric approach, which achieved a Jaccard coefficient of 0.6, 0.7, and 0.4 with three human experts extracted rules, respectively.

Published
2020

118. PNRG: Knowledge Graph-Driven Methodology for Personalized Nutritional Recommendation Generation

Author

Lodhi, Aminah Bilal, Bilal, Muhammad Abdullah, Bilal, Hafiz Syed Muhammad, Khan, Kifayat Ullah, Satti, Fahad Ahmed, Khalid, Shah, Lee, Sungyoung, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jongbae, Kim, editor, Mokhtari, Mounir, editor, Aloulou, Hamdi, editor, Abdulrazak, Bessam, editor, and Seungbok, Lee, editor

Published
2023
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138. Mutational fitness landscapes reveal genetic and structural improvement pathways for a vaccine-elicited HIV-1 broadly neutralizing antibody

Author

Madan, Bharat, Zhang, Baoshan, Xu, Kai, Chao, Cara W., O’Dell, Sijy, Wolfe, Jacy R., Chuang, Gwo-Yu, Fahad, Ahmed S., Geng, Hui, Kong, Rui, Louder, Mark K., Nguyen, Thuy Duong, Rawi, Reda, Schön, Arne, Sheng, Zizhang, Nimrania, Rajani, Wang, Yiran, Zhou, Tongqing, Lin, Bob C., Doria-Rose, Nicole A., Shapiro, Lawrence, Kwong, Peter D., and DeKosky, Brandon J.

Published
2021

141. Conceptual Narratives in Translated Discourse: Ideology and social values at an intersection or crossroads.

Author

Otaif, Fahad Ahmed

Abstract

Discourse- language use as a social practice- (Fairclough, 1995) is an effective tool for communicating ideas and spreading values in social contexts. Given that discourse and ideology are indivisible, the narratives that a translated discourse may bring to the target culture may pass unnoticed and, in many cases, unquestionable. The current paper is a cognitive corpus-based critical discourse analysis of how the narratives of translated discourse translating the Western feminist expression 'single mother(s)' can contribute to the spread of particular 'socio-cognitive models' (van Dijk, 1998) in the target culture, e.g., the Arabic culture, to the extent that 'a faithful' translation of the source text may lead to a kind of an intellectual dominance of certain ideologies apart from the original purpose of translation. The findings revealed how a literal translation of such a foreign expression may lead to penetrating the socio-cognitive model of the target culture by localising its conceptual narratives in the target language i.e., Arabic public discourse. The current study concludes with a recommendation for further incorporation of cognitive critical discourse analysis in studying the narratives of translated discourse. [ABSTRACT FROM AUTHOR]

Published
2024

142. An internet of things-based healthcare system performing on a prediction approach based on random forest regression.

Author

Shaban, Fahad Ahmed and Golshannavaz, Sajjad

Subjects
RANDOM forest algorithms, RASPBERRY Pi, BODY temperature, INTERNET of things, HEART beat
Abstract

To predict physiological indicators, such as heart rate, blood pressure, and body heat sensors, this study develops an internet of things (IoT)-based healthcare approach performing on random forest regression models and mean square error (MSE). Machine learning approaches such as random forest design is trained to predict factors like age, heart rate, and recorded physiological measures using a dataset generated by sensors with Raspberry Pi. The precision and dependability of the models are assessed by contrasting the predictions with the physiological degrees produced by sensors. IoT-enabled models and sensors are useful for a variety of healthcare monitoring tasks, such as early anomaly detection and quick assistance for medical interventions. It is seen that the proposed model could provide appropriate predictions that are in line with common datasets demonstrated by the results. Moreover, there is strong agreement between the sensor readings and the predicted values for the considered parameters showcasing the outperformance of the proposed healthcare system. [ABSTRACT FROM AUTHOR]

Published
2024
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