scholarly journals Editorial for Vol.28, No.3

2021 ◽  
Vol 28 (3) ◽  
pp. i-ii
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Natural Language Processing ◽  
Natural Language ◽  
Software Testing ◽  
Language Processing ◽  
Business Management ◽  
Testing And Debugging ◽  
Regular Section

This third issue (September 2020) of CIT. Journal of Computing and Information Technology comprises four papers from the regular section, tackling topics from the areas of software testing and debugging, machine learning, natural language processing and business management processing.

scholarly journals Perancangan Chatbot Menggunakan Dialogflow Natural Language Processing (Studi Kasus: Sistem Pemesanan pada Coffee Shop)

2020 ◽  
Vol 4 (1) ◽  
pp. 208
Author(s):  
Albert Yakobus Chandra ◽  
Didik Kurniawan ◽  
Rahmat Musa
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Services ◽  
Human Language ◽  
Coffee Shop ◽  
The Impact

Some cases that are often experienced at a particular institution such as Micro Enterprise are often a staff / employee in providing information services and transactions that are carried out manually to customers related to these business activities. This cycle always repeats from one customer to another. The impact if there are conditions where the queue of customer that is quite crowded than the workload of staff/employees will be higher and the risk of error in transactions will be high too. The development of information technology in artificial intelligence on 4.0 industry era is moving forward. One of them is Machine Learning - Natural Language Processing (NLP) which is one of the sciences that focuses on how computers can understand the human language and response to it. Therefor in this research a chatbot system will be builtin providing information and conducting transaction with the customers. This chatbot will be develop using the Dialogflow tools provided by Google. This Chatbot that was build expected to be an alternative that can be implemented in various bussines to provide better service for customers

scholarly journals Metamorphic Testing and Certified Mitigation of Fairness Violations in NLP Models

2020 ◽  
Author(s):  
Pingchuan Ma ◽  
Shuai Wang ◽  
Jin Liu
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Software Testing ◽  
Language Processing ◽  
Credit Scoring ◽  
Metamorphic Testing ◽  
Testing Scheme ◽  
Model Predictions

Natural language processing (NLP) models have been increasingly used in sensitive application domains including credit scoring, insurance, and loan assessment. Hence, it is critical to know that the decisions made by NLP models are free of unfair bias toward certain subpopulation groups. In this paper, we propose a novel framework employing metamorphic testing, a well-established software testing scheme, to test NLP models and find discriminatory inputs that provoke fairness violations. Furthermore, inspired by recent breakthroughs in the certified robustness of machine learning, we formulate NLP model fairness in a practical setting as (ε, k)-fairness and accordingly smooth the model predictions to mitigate fairness violations. We demonstrate our technique using popular (commercial) NLP models, and successfully flag thousands of discriminatory inputs that can cause fairness violations. We further enhance the evaluated models by adding certified fairness guarantee at a modest cost.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

scholarly journals Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

2021 ◽  
Vol 28 (1) ◽  
pp. e100262
Author(s):  
Mustafa Khanbhai ◽  
Patrick Anyadi ◽  
Joshua Symons ◽  
Kelsey Flott ◽  
Ara Darzi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Patient Experience ◽  
Language Processing ◽  
Performance Metrics ◽  
Free Text ◽  
Patient Feedback

ObjectivesUnstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.MethodsDatabases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.ResultsNineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.ConclusionNLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

scholarly journals Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics

2019 ◽  
pp. 1-8 ◽  
Author(s):  
Tomasz Oliwa ◽  
Steven B. Maron ◽  
Leah M. Chase ◽  
Samantha Lomnicki ◽  
Daniel V.T. Catenacci ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Named Entity ◽  
Pathology Reports

PURPOSE Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology notes, which complicate data abstraction. Our motivation is to develop a natural language processing method that dynamically identifies existing pathology specimen elements necessary for locating specimens for future use in a manner that can be re-implemented by other institutions. PATIENTS AND METHODS Pathology reports from patients with gastroesophageal cancer enrolled in The University of Chicago GI oncology tumor bank were used to train and validate a novel composite natural language processing-based pipeline with a supervised machine learning classification step to separate notes into internal (primary review) and external (consultation) reports; a named-entity recognition step to obtain label (accession number), location, date, and sublabels (block identifiers); and a results proofreading step. RESULTS We analyzed 188 pathology reports, including 82 internal reports and 106 external consult reports, and successfully extracted named entities grouped as sample information (label, date, location). Our approach identified up to 24 additional unique samples in external consult notes that could have been overlooked. Our classification model obtained 100% accuracy on the basis of 10-fold cross-validation. Precision, recall, and F1 for class-specific named-entity recognition models show strong performance. CONCLUSION Through a combination of natural language processing and machine learning, we devised a re-implementable and automated approach that can accurately extract specimen attributes from semistructured pathology notes to dynamically populate a tumor registry.

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