Detecting Alzheimer’s Disease by Exploiting Linguistic Information from Nepali Transcript

Thapa, Surendrabikram; Adhikari, Surabhi; Naseem, Usman; Singh, Priyanka; Bharathy, Gnana; Prasad, Mukesh

doi:10.1007/978-3-030-63820-7_20

Cited by 26 publications

(7 citation statements)

References 13 publications

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“…Rule-based methods contain manually created rules to extract biomedical information from the corpus. Various techniques are used to define these rules, which are used to capture patterns within natural language [25].…”

Section: Rule-based Methodsmentioning

confidence: 99%

A Comparative Analysis of Active Learning for Biomedical Text Mining

Naseem

Khushi

Khan

et al. 2021

ASI

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An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called Active Learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL) based strategies on three unique biomedical datasets. We investigated Random Sampling (RS), Least confidence (LC), Informative diversity and density (IDD), Margin and Maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Additionally, AL-assisted pre-annotations accelerates the de novo annotation process with less annotation time required.

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Section: Rule-based Methodsmentioning

confidence: 99%

A Comparative Analysis of Active Learning for Biomedical Text Mining

Naseem

Khushi

Khan

et al. 2021

ASI

Self Cite

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“…Artificial intelligence (AI) and the internet of things (IoT) have both played a vital role since the beginning of the COVID-19 crisis, demonstrating how they can be quite helpful in coping with this sort of pandemic [2]. With the rapid development of Artificial Intelligence, the healthcare industry has been completely improved [3]. Various state-of-the-art technologies such as Artificial Neural Networks, Convolutional Neural Networks, and others are being used in various health care fields such as ophthalmology, radiology, dermatology, and genome interpretation [4].…”

Section: Applications Of Ai and Iot In Covid-19mentioning

confidence: 99%

Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life

Almars

Gad

Atlam

2021

Studies in Computational Intelligence

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COVID-19 illness has been recognized as an International Public Health Emergency (PHEIC). According to the World Health Organization (WHO), more than 200 countries worldwide have been impacted by the COVID-19. More than 4.7 M people in the globe have lost their lives due to the coronavirus pandemic. Governments and hospitals have taken several actions and measures to prevent the further spread of infectious diseases, protect all people, and minimize illness and death rates. Artificial Intelligence (AI) and the Internet of Things (IoT) have great potential to create prevailing tools for battling COVID-19. Several technologies such as deep learning, machine learning, natural language processing, and the Internet of Things have been used to properly address healthcare concerns, including diagnosis, drug and vaccine development, vaccine distribution, sentiment analysis, and fake news identification regarding COVID-19 reviews. The primary goal of this chapter is to focus on the potential of Artificial Intelligence (AI) and the Internet of Things (IoT). This chapter aims to provide a comprehensive overview of different approaches of AI and IoT related to combating COVID-19. In addition, the chapter discusses several actions and measurements that governments have used to prevent the spread of COVID-19. This chapter covers all methodologies applied to forecast the acceptance and the demand of the COVID-19 vaccine.

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“…The complex and monographically rich nature of the Nepali language makes Natural Language Processing (NLP) tasks particularly challenging for this language [12]. Unlike languages such as English, which has ample resources and a plethora of NLP studies [13], [14], research in the field of low-resource languages such as Nepali is scarce [15], [16]. There have been some works involving sentimental analysis in Nepali [12], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

Rauniyar,

Poudel,

Shiwakoti

et al. 2023

IEEE Access

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In today's social media-dominated landscape, digital platforms wield substantial influence over public opinion, particularly during crucial political events such as electoral processes. These platforms become hubs for diverse discussions, encompassing topics, reforms, and desired changes. Notably, in times of government dissatisfaction, they serve as arenas for anti-establishment discourse, highlighting the need to analyze public sentiment in these conversations. However, the analysis of such discourse is notably scarce, even in high-resource languages, and entirely non-existent in the context of the Nepali language. To address this critical gap, we present Nepal Anti Establishment discourse Tweets (NAET), a novel dataset comprising 4,445 multi-aspect annotated Nepali tweets, facilitating a comprehensive understanding of political conversations. Our contributions encompass evaluating tweet relevance, sentiment, and satire, while also exploring the presence of hate speech, identifying its targets, and distinguishing directed and non-directed expressions. Additionally, we investigate hope speech, an underexplored aspect crucial in the context of antiestablishment discourse, as it reflects the aspirations and expectations from new political figures and parties. Furthermore, we set NLP-based baselines for all these tasks. To ensure a holistic analysis, we also employ topic modeling, a powerful technique that helps us identify and understand the prevalent themes and patterns emerging from the discourse. Our research thus presents a comprehensive and multi-faceted perspective on anti-establishment election discourse in a low-resource language setting. The dataset is publicly available, facilitating in-depth analysis of political tweets in Nepali discourse and further advancing NLP research for the Nepali language through labeled data and baselines for various NLP tasks. The dataset for this work is made available at https://github.com/rkritesh210/NAET.

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Detecting Alzheimer’s Disease by Exploiting Linguistic Information from Nepali Transcript

Cited by 26 publications

References 13 publications

A Comparative Analysis of Active Learning for Biomedical Text Mining

A Comparative Analysis of Active Learning for Biomedical Text Mining

Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life

Multi-Aspect Annotation and Analysis of Nepali Tweets on Anti-Establishment Election Discourse

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