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Journal articles on the topic 'Machine Learning in QA'

Author: Grafiati

Published: 7 June 2025

Last updated: 17 July 2025

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1

Chen, Yunsheng, Dionne M. Aleman, Thomas G. Purdie, and Chris McIntosh. "Understanding machine learning classifier decisions in automated radiotherapy quality assurance." Physics in Medicine & Biology 67, no. 2 (2022): 025001. http://dx.doi.org/10.1088/1361-6560/ac3e0e.

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Abstract The complexity of generating radiotherapy treatments demands a rigorous quality assurance (QA) process to ensure patient safety and to avoid clinically significant errors. Machine learning classifiers have been explored to augment the scope and efficiency of the traditional radiotherapy treatment planning QA process. However, one important gap in relying on classifiers for QA of radiotherapy treatment plans is the lack of understanding behind a specific classifier prediction. We develop explanation methods to understand the decisions of two automated QA classifiers: (1) a region of in

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Alam, Gazi Touhidul, Mohammed Majid Bakhsh, Nusrat Yasmin Nadia, and S. A. Mohaiminul Islam. "Predictive Analytics in QA Automation:." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 4, no. 2 (2025): 55–66. https://doi.org/10.60087/jklst.v4.n2.005.

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An essential component of contemporary software development is quality assurance (QA) automation, which guarantees program dependability, effectiveness, and user pleasure. Traditional QA techniques, on the other hand, frequently have trouble finding flaws early in the software development lifecycle, which raises expenses and delays releases. By predicting possible flaws before they appear, predictive analytics which is fueled by machine learning (ML) and artificial intelligence (AI) offers a revolutionary approach to QA automation. This study examines how predictive analytics might improve sof

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Momin, Anam Rafik* Chavan Shraddha Mitthu Dr. Datkhile Sachin Vitthal Dr. Lokhande Rahul Prakash. "Application of Artificial Intelligence and Machine Learning in Quality Assurance." International Journal of Pharmaceutical Sciences 3, no. 3 (2025): 18–25. https://doi.org/10.5281/zenodo.14950226.

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Artificial intelligence (AI) technology is experiencing rapid growth in various fields due to advancements in computers and technology. AI has also led to the development of several techniques for automated segmentation and planning in the radiotherapy treatment process, greatly improving overall treatment effectiveness.[A]. There have been numerous reports of AI-based applications in machine and patient-specific QA, including predictions for machine beam data or gamma passing rates on IMRT or VMAT plans. Moreover, the development of these technologies is being pursued for multicenter studies.

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Chemlal, Y., and M. Azouazi. "Implementing quality assurance practices in teaching machine learning in higher education." Mathematical Modeling and Computing 10, no. 3 (2023): 660–67. http://dx.doi.org/10.23939/mmc2023.03.660.

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The development of machine learning and deep learning (ML/DL) change the skills expected by society and the form of ML/DL teaching in higher education. This article proposes a formal system to improve ML/DL teaching and, subsequently, the graduates' skills. Our proposed system is based on the quality assurance (QA) system adapted to teaching and learning ML/DL and implemented on the model suggested by Deming to continuously improve the QA processes.

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Kunal Parekh. "Next-Gen Quality Assurance: Leveraging AI, Automation, and DevOps for Scalable Software Excellence." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 06 (2025): 2345–51. https://doi.org/10.47392/irjaem.2025.0370.

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As software delivery accelerates in scope and scale, traditional Quality Assurance (QA) methods are proving insufficient. This review explores the evolution and future of QA through the integration of Artificial Intelligence (AI), automation, and DevOps practices—collectively termed Next-Gen QA. We synthesize findings from key research and industry implementations to highlight how AI-driven test generation, machine learning-based anomaly detection, and continuous testing pipelines have transformed the QA landscape. We also present a conceptual model for scalable QA and validate it through empi

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Singh, Vaishali, and Sanjay K. Dwivedi. "Question Answering." International Journal of Information Retrieval Research 4, no. 3 (2014): 14–33. http://dx.doi.org/10.4018/ijirr.2014070102.

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With the huge amount of data available on web, it has turned out to be a fertile area for Question Answering (QA) research. Question answering, an instance of information retrieval research is at the cross road from several research communities such as, machine learning, statistical learning, natural language processing and pattern learning. In this paper, the authors survey the research in area of question answering with respect to different prospects of NLP, machine learning, statistical learning and pattern learning. Then they situate some of the prominent QA systems concerning these prospe

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Parameshwar Reddy Kothamali, Vinod Kumar Karne, and Sai Surya Mounika Dandyala. "Integrating AI and Machine Learning in Quality Assurance for Automation Engineering." International Journal for Research Publication and Seminar 15, no. 3 (2024): 93–102. http://dx.doi.org/10.36676/jrps.v15.i3.1445.

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The integration of AI and Machine Learning (ML) into Quality Assurance (QA) for Automation Engineering represents a transformative shift, leveraging data-driven decision-making and automation across industries. Despite their promising benefits, the reliability, fairness, and generalizability of ML models remain significant concerns. This paper addresses these challenges by exploring the complexities inherent in assessing and validating ML programs. Firstly, it identifies obstacles such as bias, model robustness, and adaptability to new data, emphasizing the necessity for rigorous testing frame

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Sadiya, Inamdar Tejashree Kedar Harshada Gujar Prasad Tanpure Sachin Sapkal. "Harnessing AI And Machine Learning in Pharmaceutical Quality Assurance." International Journal of Scientific Research and Technology 1, no. 11 (2024): 145–50. https://doi.org/10.5281/zenodo.14186540.

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The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical quality assurance (QA) presents transformative opportunities for improving the accuracy, efficiency, and consistency of quality control processes. This review explores the key applications of AI/ML in QA, including data analysis, predictive modelling, automation of routine tasks, and real-time quality monitoring. By harnessing AI, pharmaceutical companies can enhance regulatory compliance, streamline documentation, and improve decision-making through intelligent decision support systems. Drug developmen

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Valdes, Gilmer, Maria F. Chan, Seng Boh Lim, Ryan Scheuermann, Joseph O. Deasy, and Timothy D. Solberg. "IMRT QA using machine learning: A multi‐institutional validation." Journal of Applied Clinical Medical Physics 18, no. 5 (2017): 279–84. http://dx.doi.org/10.1002/acm2.12161.

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Abdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, and Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.

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Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label

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Moreau, Noémie, Laurine Bonnor, Cyril Jaudet, et al. "Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine." Diagnostics 13, no. 5 (2023): 943. http://dx.doi.org/10.3390/diagnostics13050943.

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Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload. Methods. Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locati

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Jawalkar, Santosh Kumar. "Machine Learning in QA: A Vision for Predictive and Adaptive Software Testing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 07 (2021): 1–7. https://doi.org/10.55041/ijsrem9725.

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Background & Problem Statement - Software testing is a critical phase in the software development lifecycle (SDLC), ensuring that applications function correctly, meet user requirements, and maintain high- quality standards. Traditional software testing approaches, including manual testing and rule-based automation, often face challenges in scalability, efficiency, and adaptability to dynamic software environments. Traditional testing methods are overwhelmed by complex software systems which slows down defect detection and extends both testing costs and release schedules. Machine Learning

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Valdes, G., R. Scheuermann, C. Y. Hung, A. Olszanski, M. Bellerive, and T. D. Solberg. "A mathematical framework for virtual IMRT QA using machine learning." Medical Physics 43, no. 7 (2016): 4323–34. http://dx.doi.org/10.1118/1.4953835.

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Komal, Jasani. "Measuring VR QA Success with Key Performance Indicators (KPIs)." Journal of Advances in Developmental Research 13, no. 2 (2022): 1–8. https://doi.org/10.5281/zenodo.14988577.

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VR, an industry that has grown rapidly in recent years, has many applications in different sectors, and to address these complex applications, proper QA is necessary for appropriate usability. However, it is challenging to determine the level of success of VR QA since it is a new concept that presents some challenges. This paper seeks to discuss the impact of KPIs in evaluating the success of VR QA. It includes technical efficiency, usefulness, features, and usage rates of the learning system's KPIs. The article also describes ways the VR QA can be assessed, including Automation Testing tools,

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Et. al., K. P. Moholkar ,. "Question Classification for Efficient QA System." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1876–84. http://dx.doi.org/10.17762/turcomat.v12i2.1526.

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Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), supports the machine to understand and manipulate the human languages in different sectors. Subsequently, the Question and answering scheme using Machine learning is a challengeable task. For an efficient QA system, understanding the category of a question plays a pivot role in extracting suitable answer. Computers can answer questions requiring single, verifiable answers but fail to answer subjective question demanding deeper understanding of question. Subjective questions can take different forms entailing deeper,

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Anatolii, Husakovskyi. "Enhancing Automation in QA Engineering with Advanced AI Techniques in Complex Distributed Systems." Asian Journal of Research in Computer Science 18, no. 4 (2025): 410–15. https://doi.org/10.9734/ajrcos/2025/v18i4628.

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Aims: This study explores integrating artificial intelligence (AI) into automated quality assurance (QA) workflows for complex distributed systems. Study Design: A multi-phase empirical approach was adopted. First, I developed a novel AI-driven test framework. Next, I deployed it in a real-world microservices environment and compared key metrics (defect detection rates, test coverage, execution time) against a conventional, manually-maintained QA suite. Place and Duration of Study: This work was conducted at the Department of Computer Science and Engineering, «Kharkiv Aviation Institute», from

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Nagaraj Bhadurgatte Revanasiddappa. "AI-powered quality assurance: Enhancing software infrastructure through intelligent fault detection." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 3199–213. https://doi.org/10.30574/wjarr.2024.23.3.1392.

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Recently artificial intelligence (AI) came into software quality assurance (QA), helping us overcome the shortfalls of the traditional fault detection techniques. Manual and semi-automated QA approaches become rapidly hard to scale, in terms of accuracy and efficiency, as software systems are becoming increasingly complex and interdependent. AI driven QA takes advantage of advanced machine learning (ML) models and smart algorithms to optimize fault detection, predictive analysis, and automated decision making. The key innovations are automated test case generation, anomaly detection, and regre

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Alazmi, Asmaa, and Bader S. Al-Anzi. "Assessment of Machine Learning Algorithms for Predicting Air Entrainment Rates in a Confined Plunging Liquid Jet Reactor." Sustainability 15, no. 18 (2023): 13802. http://dx.doi.org/10.3390/su151813802.

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A confined plunging liquid jet reactor (CPLJR) is an unconventional efficient and feasible aerator, mixer and brine dispenser that operates under many operating conditions. Such operating conditions could be challenging, and hence, utilizing prediction models built on machine learning (ML) approaches could be very helpful in giving reliable tools to manage highly non-linear problems related to experimental hydrodynamics such as CPLJRs. CPLJRs are vital in protecting the environment through preserving and sustaining the quality of water resources. In the current study, the effects of the main p

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Guo, Yingying, Xi Yang, Zilong Yuan, Jianfeng Qiu, and Weizhao Lu. "A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches." Journal of Neural Engineering 19, no. 1 (2022): 016013. http://dx.doi.org/10.1088/1741-2552/ac4bfe.

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Abstract Objective. Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain. Approach. Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of

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Bussa, Santhosh. "Artificial Intelligence in Quality Assurance for Software Systems." Stallion Journal for Multidisciplinary Associated Research Studies 2, no. 2 (2023): 15–26. https://doi.org/10.55544/sjmars.2.2.2.

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The rapid advancement in software development has taken place with the invention of a new quality assurance (QA) process for producing robust, reliable, and efficient systems. Artificial Intelligence is a "force of change" that promises automating most QA activities with promising predictive insight into the generation of dynamic test cases and intelligent detection of defects. This paper covers the theme of integrating AI with SQA through techniques such as Machine Learning, Natural Language Processing, and Neural Networks. The paper covers automation of testing, AI-driven management of defec

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Lizar, Jéssica Caroline, Carolina Cariolatto Yaly, Alexandre Colello Bruno, Gustavo Arruda Viani, and Juliana Fernandes Pavoni. "Patient-specific IMRT QA verification using machine learning and gamma radiomics." Physica Medica 82 (February 2021): 100–108. http://dx.doi.org/10.1016/j.ejmp.2021.01.071.

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Chan, M. "SP-0150: Integration of AI and Machine Learning in Radiotherapy QA." Radiotherapy and Oncology 152 (November 2020): S70—S71. http://dx.doi.org/10.1016/s0167-8140(21)00174-2.

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Kodey, Naga Harini. "Optimizing AdTech Campaigns with Machine Learning: Techniques and QA Validation Methods." International Journal of Computer Applications 186, no. 57 (2024): 25–29. https://doi.org/10.5120/ijca2024924309.

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Khinvasara, Tushar, Stephanie Ness, and Abhishek Shankar. "Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing." Asian Journal of Research in Computer Science 17, no. 6 (2024): 13–35. http://dx.doi.org/10.9734/ajrcos/2024/v17i6454.

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The medical device sector adheres to strict regulatory frameworks, requiring precise adherence to quality assurance (QA) processes during the production process. Conventional quality assurance (QA) approaches, although successful, sometimes require substantial time and resource allocations, resulting in possible obstacles and higher expenses. The emergence of Artificial Intelligence (AI) in recent years has completely transformed quality assurance (QA) methods in different sectors, providing unparalleled prospects for improved productivity, precision, and scalability. This research examines th

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GAO, TIANTIAN, PAUL FODOR, and MICHAEL KIFER. "Querying Knowledge via Multi-Hop English Questions." Theory and Practice of Logic Programming 19, no. 5-6 (2019): 636–53. http://dx.doi.org/10.1017/s1471068419000103.

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AbstractThe inherent difficulty of knowledge specification and the lack of trained specialists are some of the key obstacles on the way to making intelligent systems based on the knowledge representation and reasoning (KRR) paradigm commonplace.Knowledge and query authoringusing natural language, especiallycontrollednatural language (CNL), is one of the promising approaches that could enable domain experts, who are not trained logicians, to both create formal knowledge and query it. In previous work, we introduced theKALMsystem (Knowledge Authoring Logic Machine) that supports knowledge author

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Moholkar, Kavita, and S. H. Patil. "Lioness Adapted GWO-Based Deep Belief Network Enabled with Multiple Features for a Novel Question Answering System." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 30, no. 01 (2022): 93–114. http://dx.doi.org/10.1142/s0218488522500052.

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Recently, the researches on Question Answering (QA) systems attract progressive attention with the enlargement of data and the advances on machine learning. Selection of answers from QA system is a significant task for enhancing the automatic QA systems. However, the major complexity relies in the designing of contextual factors and semantic matching. Motivation: Question Answering is a specialized form of Information Retrieval which seeks knowledge. We are not only interested in getting the relevant pages but we are interested in getting specific answer to queries. Question Answering is in it

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Suryanto, Tri Lathif Mardi, Aji Prasetya Wibawa, Hariyono Hariyono, and Andrew Nafalski. "Comparative Performance of Transformer Models for Cultural Heritage in NLP Tasks." Advance Sustainable Science Engineering and Technology 7, no. 1 (2025): 0250115. https://doi.org/10.26877/asset.v7i1.1211.

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AI and Machine Learning are crucial in advancing technology, especially for processing large, complex datasets. The transformer model, a primary approach in natural language processing (NLP), enables applications like translation, text summarization, and question-answer (QA) systems. This study compares two popular transformer models, FlanT5 and mT5, which are widely used yet often struggle to capture the specific context of the reference text. Using a unique Goddess Durga QA dataset with specialized cultural knowledge about Indonesia, this research tests how effectively each model can handle

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Yao, Yunkai. "Quantum computation of Restricted Boltzmann Machines by Monte Carlo Methods." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 227–32. http://dx.doi.org/10.54097/hset.v9i.1780.

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In recent years, the diversification of problems that require computers to solve has attracted attention to the construction of meta-heuristics that can be applied to a wide range of problems, and to specialized computers that implement these meta-heuristics in their devices. The representative meta-heuristics are Simulated Annealing (SA) and its extension to quantum computation, Quantum Annealing (QA), and its path-integral Monte Carlo method for classical simulation Crosson and Harrow showed that for certain problems where QA outperformed SA, SQA achieved performance close to that of QA, and

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Barla, Phani Chandra, and Dr Laina Karthikeyan. "HARNESSING ARTIFICIAL INTELLIGENCE FOR REAL-TIME QUALITY ASSURANCE IN MEDICAL DEVICE MANUFACTURING." American Journal of Engineering and Technology 6, no. 6 (2024): 24–31. http://dx.doi.org/10.37547/tajet/volume06issue06-04.

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The production process for medical devices must precisely follow quality assurance (QA) procedures to comply with the sector's stringent regulatory requirements. Although conventional QA procedures are generally effective, they can be time-consuming and resource-intensive, which can lead to problems and increased costs. With its unprecedented potential for increased productivity, accuracy, and scalability, Artificial Intelligence (AI) has revolutionized quality assurance (QA) approaches across industries since its inception. In this study, we look at how artificial intelligence (AI) could impr

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Buzzi, S., M. Bianchi, A. Bresolin, et al. "PP07.01 MACHINE LEARNING MODEL FOR PATIENT-SPECIFIC QA PREDICTION IN STEREOTACTIC RADIOSURGERY." Physica Medica 125 (September 2024): 103676. http://dx.doi.org/10.1016/j.ejmp.2024.103676.

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Buzzi, Simone A., Monica Bianchi, Caterina Zaccone, et al. "2729: Machine learning model for patient-specific QA prediction in stereotactic radiosurgery." Radiotherapy and Oncology 194 (May 2024): S4557—S4559. http://dx.doi.org/10.1016/s0167-8140(24)02887-1.

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Bornea, Mihaela, Lin Pan, Sara Rosenthal, Radu Florian, and Avirup Sil. "Multilingual Transfer Learning for QA using Translation as Data Augmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12583–91. http://dx.doi.org/10.1609/aaai.v35i14.17491.

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Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a QA model on English and test on other languages. In this work, we explore strategies that improve cross-lingual transfer by bringing the multilingual embeddings closer in the semantic space. Our first strategy augments the original English training data with machine translation-generated data. This results in a corpus of multilingual silver-labeled QA pairs that is 14 times larger than the original training set. In ad

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Hagverdiyev, F. "LEVERAGING SYSTEMATIC ERROR TRACKING TO ENHANCE GAME QUALITY ASSURANCE: A SCIENTIFIC APPROACH." Scientific heritage, no. 137 (May 24, 2024): 49–54. https://doi.org/10.5281/zenodo.11278779.

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Quality Assurance (QA) is a fundamental component of game development that ensures a high-quality player experience and maintains the reputation of gaming titles. Systematic error tracking is a crucial aspect of QA, focusing on a methodical approach to identifying, analyzing, and resolving bugs throughout the game development process. This paper highlights the importance of systematic error tracking in enhancing game quality and customer satisfaction. It delineates how systematic error tracking increases efficiency, improves product quality, boosts customer satisfaction, provides data-driven i

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Lam, Dao, Xizhe Zhang, Harold Li, et al. "Predicting gamma passing rates for portal dosimetry‐based IMRT QA using machine learning." Medical Physics 46, no. 10 (2019): 4666–75. http://dx.doi.org/10.1002/mp.13752.

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P, Priyanka, and Deivanai K. "A SURVEY ON MACHINE LEARNING APPROACH TO MAINFRAME ANALYSIS." Asian Journal of Pharmaceutical and Clinical Research 10, no. 13 (2017): 36. http://dx.doi.org/10.22159/ajpcr.2017.v10s1.19542.

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Mainframe system processing includes a “Batch Cycle” that approximately spans in regular interval on a daily basis. The core part of the cycle completes in the middle of the regular interval with key client deliverables associated with the end times of certain jobs are tracked by service delivery. There are single and multi-client batch streams, a QA stream which includes all clients, and about huge batch jobs per day that execute. Despite a sophisticated job scheduling software and automated system workload management, operator intervention is required. The outcome of our proposed work is to

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Garg, Shally. "AI-Driven Innovations in Storage Quality Assurance and Manufacturing Optimization." International Journal of Multidisciplinary Research and Growth Evaluation 6, no. 2 (2025): 1083–87. https://doi.org/10.54660/.ijmrge.2025.6.2.1083-1087.

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Artificial intelligence (AI) and machine learning (ML) are very vital in changing hardware manufacture and storage software quality assurance (QA). Tools like FIO and SMART monitoring let automated performance testing, predictive failure analysis, and anomaly detection in software QA, so enhancing storage system dependability. By improving fault tolerance, data integrity, and workload optimization—which reduces downtime and improves efficiency—AI also improves error tolerance. In hardware manufacturing, AI-driven wafer inspection systems enhance defect identification, while predictive maintena

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Garg, Shally. "AI-Driven Innovations in Storage Quality Assurance and Manufacturing Optimization." International Journal of Multidisciplinary Research and Growth Evaluation 1, no. 1 (2020): 143–47. https://doi.org/10.54660/.ijmrge.2020.1.1.143-147.

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Artificial intelligence (AI) and machine learning (ML) are very vital in changing hardware manufacture and storage software quality assurance (QA). Tools like FIO and SMART monitoring let automated performance testing, predictive failure analysis, and anomaly detection in software QA, so enhancing storage system dependability. By improving fault tolerance, data integrity, and workload optimization—which reduces downtime and improves efficiency—AI also improves error tolerance. In hardware manufacturing, AI-driven wafer inspection systems enhance defect identification, while predictive maintena

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Mohamed, Abdulrahman, and Kennedy Hadullo. "Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such as Kiswahili." Open Journal for Information Technology 7, no. 2 (2024): 71–78. https://doi.org/10.32591/coas.ojit.0702.02071m.

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Processing low-resource languages, such as Kiswahili, using machine learning is difficult due to lack of adequate training data. However, such low-resource languages are still important for human communication and are already in daily use and users need practical machine processing tasks such as summarization, disambiguation and even question answering (QA). One method of processing such languages, while bypassing the need for training data, is the use semantic networks. Some low resource languages, such as Kiswahili, are of the subject-verb-object (SVO) structure, and similarly semantic netwo

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Wamkaya Wanjawa, Barack, Lawrence Muchemi, and Evans Miriti. "Algorithm for Semantic Network Generation from Texts of Low Resource Languages Such as Kiswahil." Open Journal for Information Technology 7, no. 2 (2024): 55–70. https://doi.org/10.32591/coas.ojit.0702.01055w.

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Processing low-resource languages, such as Kiswahili, using machine learning is difficult due to lack of adequate training data. However, such low-resource languages are still important for human communication and are already in daily use and users need practical machine processing tasks such as summarization, disambiguation and even question answering (QA). One method of processing such languages, while bypassing the need for training data, is the use semantic networks. Some low resource languages, such as Kiswahili, are of the subject-verb-object (SVO) structure, and similarly semantic netwo

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Ahmed, Muzamil, Hikmat Khan, Tassawar Iqbal, Fawaz Khaled Alarfaj, Abdullah Alomair, and Naif Almusallam. "On solving textual ambiguities and semantic vagueness in MRC based question answering using generative pre-trained transformers." PeerJ Computer Science 9 (July 24, 2023): e1422. http://dx.doi.org/10.7717/peerj-cs.1422.

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Machine reading comprehension (MRC) is one of the most challenging tasks and active fields in natural language processing (NLP). MRC systems aim to enable a machine to understand a given context in natural language and to answer a series of questions about it. With the advent of bi-directional deep learning algorithms and large-scale datasets, MRC achieved improved results. However, these models are still suffering from two research issues: textual ambiguities and semantic vagueness to comprehend the long passages and generate answers for abstractive MRC systems. To address these issues, this

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Sy, Anne Muller, and Abraham Gomez. "Quality Assurance and Accreditation in Research: Emerging Perspectives, Trends, and Issues." International Journal of Academic and Practical Research 2, no. 2 (2024): 55–58. https://doi.org/10.5281/zenodo.13165370.

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The evolving research landscape necessitates robust quality assurance (QA) and accreditation systems to uphold the integrity and excellence of research outputs. This perspective article examines the significant shifts in QA and accreditation practices driven by emerging challenges and opportunities. The replication crisis has highlighted the need for increased transparency and reproducibility, prompting practices such as preregistration and open data sharing to become more prevalent. Technological advancements, notably artificial intelligence (AI) and machine learning, are transforming QA proc

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Kamran, Hootan, Dionne Aleman, Chris McIntosh, and Tom Purdie. "Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance." PLOS One 20, no. 6 (2025): e0321968. https://doi.org/10.1371/journal.pone.0321968.

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In this study, we address the inherent challenges in radiotherapy (RT) plan quality assessment (QA). RT, a prevalent cancer treatment, utilizes high-energy beams to target tumors while sparing adjacent healthy tissues. Typically, an RT plan is refined through several QA cycles by experts to ensure it meets clinical and operational objectives before being considered safe for patient treatment. This iterative process tends to eliminate unacceptable plans, creating a significant class imbalance problem for machine learning efforts aimed at automating the classification of RT plans as either accep

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Chandra, Shekhar Pareek. "Advancing Software Quality: The Power of Predictive Metrics and Data-Driven QA Strategies." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 6, no. 6 (2020): 1–12. https://doi.org/10.5281/zenodo.15026981.

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In the dynamic landscape of modern software development, the integration of Quality Assurance (QA) with advanced analytics and metrics is redefining the paradigms of software quality engineering. This paper delves into the strategic role of QA metrics and analytics in enabling data-driven decisions, which foster a proactive and predictive approach to quality management. Traditional QA processes, often plagued by subjective assessments and reactive defect handling, are being replaced by evidence-based frameworks that utilize cutting-edge technologies such as machine learning (ML), artificial in

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Jin, Di, Shuyang Gao, Jiun-Yu Kao, Tagyoung Chung, and Dilek Hakkani-tur. "MMM: Multi-Stage Multi-Task Learning for Multi-Choice Reading Comprehension." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8010–17. http://dx.doi.org/10.1609/aaai.v34i05.6310.

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Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language. Multiple-Choice QA (MCQA) is one of the most difficult tasks in MRC because it often requires more advanced reading comprehension skills such as logical reasoning, summarization, and arithmetic operations, compared to the extractive counterpart where answers are usually spans of text within given passages. Moreover, most existing MCQA datasets are small in size, making

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Valdes, G., M. Chan, R. Scheuermann, J. Deasy, and T. Solberg. "MO-FG-202-09: Virtual IMRT QA Using Machine Learning: A Multi-Institutional Validation." Medical Physics 43, no. 6Part31 (2016): 3714. http://dx.doi.org/10.1118/1.4957313.

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Chaitali, Kulkarni *. Pratiksha Shinde Aishwarya Shinde. "A Bird Eye View On Pharmacovigilance And Quality Assurance." International Journal of Pharmaceutical Sciences 2, no. 11 (2024): 123–37. https://doi.org/10.5281/zenodo.14029139.

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Pharmacovigilance and Quality Assurance (QA) are essential fields in pharmaceuticals dedicated to maintaining drug safety, efficacy, and regulatory compliance throughout a product's lifecycle. Pharmacovigilance emphasizes continuous monitoring, identification, and management of adverse drug reactions (ADRs), incorporating processes such as signal detection, causality assessment, and risk management to safeguard patient safety. QA complements these efforts by enforcing high standards in drug development and manufacturing through Good Manufacturing Practices (GMP), consistent quality checks, and

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Patil, Divyashree Kantilal, R. Dhankani Amitkumar, A. Dhankani Mansi, and P. Pawar S. "Revolutionizing Quality Assurance: A Deep Dive into Emerging Technologies." Journal of VLSI Design and its Advancement 7, no. 1 (2024): 26–32. https://doi.org/10.5281/zenodo.10791923.

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<em>A comprehensive review of quality assurance (QA) across a range of sectors, from developing technologies to national standards, is given in this paper. It explores the QA's historical background, highlighting how it changed from industrial norms to modern international standards. Good Manufacturing Practice (GMP) and Good Laboratory Practice (GLP) compliance in the pharmaceutical industry is scrutinized as a crucial aspect of quality assurance. After that, the focus of the story moves to how cutting-edge technologies like block chain, artificial intelligence (AI), machine learning (ML), ro

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Han, Dongfang, Turdi Tohti, and Askar Hamdulla. "Attention-Based Transformer-BiGRU for Question Classification." Information 13, no. 5 (2022): 214. http://dx.doi.org/10.3390/info13050214.

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A question answering (QA) system is a research direction in the field of artificial intelligence and natural language processing (NLP) that has attracted much attention and has broad development prospects. As one of the main components in the QA system, the accuracy of question classification plays a key role in the entire QA task. Therefore, not only the traditional machine learning methods but also today’s deep learning methods are widely used and deeply studied in question classification tasks. This paper mainly introduces our work on two aspects of Chinese question classification. The firs

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Afif, Irfan, and Ayu Purwarianti. "Employing Dependency Tree in Machine Learning Based Indonesian Factoid Question Answering." Jurnal Linguistik Komputasional (JLK) 2, no. 1 (2019): 28. http://dx.doi.org/10.26418/jlk.v2i1.9.

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We proposed the usage of dependency tree information to increase the accuracy of Indonesian factoid question answering. We employed MSTParser and Universal Dependency corpus to build the Indonesian dependency parser. The dependency tree information as the result of the Indonesian dependency parse is used in the answer finder component of Indonesian factoid question answering system. Here, we used dependency tree information in two ways: 1) as one of the features in machine learning based answer finder (classifying each term in the retrieved passage as part of a correct answer or not); 2) as an

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Kamran, Hootan, Dionne M. Aleman, Chris McIntosh, and Thomas G. Purdie. "SuPART: supervised projective adapted resonance theory for automatic quality assurance approval of radiotherapy treatment plans." Physics in Medicine & Biology 67, no. 6 (2022): 065004. http://dx.doi.org/10.1088/1361-6560/ac568f.

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Abstract Radiotherapy is a common treatment modality for the treatment of cancer, where treatments must be carefully designed to deliver appropriate dose to targets while avoiding healthy organs. The comprehensive multi-disciplinary quality assurance (QA) process in radiotherapy is designed to ensure safe and effective treatment plans are delivered to patients. However, the plan QA process is expensive, often time-intensive, and requires review of large quantities of complex data, potentially leading to human error in QA assessment. We therefore develop an automated machine learning algorithm

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