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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 interest (ROI) segmentation/labeling classifier, and (2) a treatment plan acceptance classifier. For each classifier, a local interpretable model-agnostic explanation (LIME) framework and a novel adaption of team-based Shapley values framework are constructed. We test these methods in datasets for two radiotherapy treatment sites (prostate and breast), and demonstrate the importance of evaluating QA classifiers using interpretable machine learning approaches. We additionally develop a notion of explanation consistency to assess classifier performance. Our explanation method allows for easy visualization and human expert assessment of classifier decisions in radiotherapy QA. Notably, we find that our team-based Shapley approach is more consistent than LIME. The ability to explain and validate automated decision-making is critical in medical treatments. This analysis allows us to conclude that both QA classifiers are moderately trustworthy and can be used to confirm expert decisions, though the current QA classifiers should not be viewed as a replacement for the human QA process.
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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 software quality and expedite testing procedures, hence redefining defect prevention for American businesses. This study uses a systematic methodology that combines machine learning-based defect prediction with real-world case studies, analyzing defect trends and evaluating the effectiveness of predictive models. The results show that enterprises leveraging predictive analytics in QA automation experience higher defect detection rates reduced testing overhead, and faster release cycles. The study identifies key machine learning models, such as Random Forests, Support Vector Machines (SVM), and Neural Networks, which have demonstrated significant accuracy in defect prediction. It also discusses the integration of predictive analytics within DevOps and CI/CD pipelines, enabling continuous monitoring and proactive defect prevention. Defect prediction skills will be significantly improved in the future by developments in Explainable AI (XAI), deep learning models, and Natural Language Processing (NLP). In addition to supporting data-driven decision-making, model transparency, and continuous learning frameworks, this article offers important advice for businesses looking to integrate predictive analytics into their QA procedures. U.S. businesses may go from reactive to proactive QA approaches by adopting predictive analytics, which will guarantee better software quality, lower expenses, and an enhanced user experience
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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. Radiotherapy must have machine- and patient-specific quality assurance (QA) to ensure safety and accuracy. High-precision radiotherapy, including IMRT and VMAT, has become increasingly difficult to manage on the QA level. This paper will explore the role of Artificial Intelligence in software testing.' Quality Assurance in the new age will be greatly influenced by Artificial Intelligence, as it can significantly reduce time and increase efficiency for developing advanced software.
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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 empirical results. The review concludes by outlining future directions, including explainable QA systems, continual learning agents, and QA-AIOps integration. This paper serves as a guide for researchers and practitioners striving to deliver high-quality software at scale.
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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 prospects and present a comparative study on the basis of question types.
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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 frameworks. Secondly, the paper reviews existing methodologies and solutions proposed in scholarly literature to enhance the assessment of ML programs, ensuring they perform as intended and meet ethical standards.This comprehensive manual serves as a guiding resource for professionals and scholars navigating the dynamic convergence of QA and ML. It underscores the need for continual learning and adaptation in an era where AI's potential is matched by the responsibilities of ethical and resilient model development. By offering profound insights and methodologies, the paper equips QA practitioners and AI enthusiasts alike to navigate the intricate terrain of quality assurance in the era of machine learning effectively.
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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 development is a time consuming, expensive, and extremely risky procedure. Up to 90% of drug concept are discard due to challenges such as toxicity safety and efficacy resulting significant loss of investor. AI's capabilities range from enhancing accuracy and minimizing error to enabling previously impossible new ideas. AI powered quality assurance framework, leveraging machine learning, computer vision, and predictive analytics to ensure unparalleled quality excellence.
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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 extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering
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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 locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance. Results. For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%. Conclusions. The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.
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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 (ML) has emerged as a transformative solution, introducing predictive and adaptive capabilities that optimize test case selection, automate defect detection, and enhance overall software quality assurance (QA). This study explores the integration of ML in software testing, addressing the challenges of traditional QA methodologies and demonstrating how AI-driven frameworks improve testing efficiency. Methodology - To investigate the impact of ML in software testing, this research adopts a systematic approach by analyzing ML-driven test automation techniques, including predictive testing, adaptive test execution, and automated test case generation. Research reviews how Google Microsoft Facebook IBM and Deep Code put ML-based quality assurance frameworks into operation. The study leverages supervised learning, reinforcement learning, deep learning, and NLP-based techniques to demonstrate how ML models predict software defects, dynamically adapt test cases, and optimize testing resources. The research tests how ML-based testing models operate within CI/CD pipelines to improve ongoing testing and deployment flow. Analysis & Results - The analysis of ML-driven software testing reveals that predictive analytics improves early defect detection rates. It helps developers spend 37% less time debugging their work. Adaptive testing models, including self-healing test scripts, minimize maintenance costs by 50% and enhance test reliability in agile environments. The integration of NLP-based test case generation increases test coverage. NLP technology enables automatic connection between requirements and test cases at 89% success rate. Additionally, reinforcement learning techniques improve test case selection, reducing redundant test executions by 43%. Our research shows different ML methods work well to lessen incorrect error alerts. ML integration for QA surely increasing defect prediction accuracy and optimizing test execution time. Findings & Contributions - This research contributes to the field of AI-driven software testing by providing a comprehensive framework for ML-based QA methodologies. Our study shows that machine learning helps find more software problems better adapts test cases and lowers testing expenses to solve present software development needs. The study also identifies critical challenges, including data availability, model interpretability, and computational overhead, suggesting future research directions in Explainable AI (XAI), hybrid AI-ML testing models, and AI-driven security testing. As the industry moves toward AI-first software testing, this research paves the way for fully autonomous QA frameworks, enabling intelligent, scalable, and cost- effective software validation techniques. Keywords - Machine Learning, Software Testing, Quality Assurance, Predictive Testing, Adaptive Testing, Test Automation, Defect Prediction, Self- Healing Test Scripts, AI-Driven QA, Reinforcement
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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, End-user feedback, and Machine learning. Also, industry standards and future outlooks in the VR QA area are discussed in the context of today, and the idea of improvement and evolution of methods of measuring VR effectiveness is emphasized. For this reason, implementing these KPIs and measurement methods will enhance developers’ ability to ensure that the provision of VR applications meets the users, stakeholders, and industry standards’ expectations.
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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, multidimensional understanding of context. Identifying the intent of the question helps to extract expected answer from a given passage. Pretrained language models (LMs) have demonstrated excellent results on many language tasks. The paper proposes model of deep learning architecture in hierarchical pattern to learn the semantic of question and extracting appropriate answer. The proposed method converts the given context to fine grained embedding to capture semantic and positional representation, identifies user intent and employs a encoder model to concentrate on answer span. The proposed methods show a remarkable improvement over existing system
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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 January 2024 to January 2025. Methodology: QA data (pass/fail results, defect logs, code coverage) were collected from 1,200 test cases spread across 15 microservices. An ensemble machine learning (ML) model (Random Forest + Gradient Boosting) was trained to predict modules with high defect probability. I integrated the AI-driven test prioritization algorithm into a Jenkins-based CI/CD pipeline. A series of 12 iterative production releases were monitored, capturing metrics like regression test time, defect detection, concurrency handling, and QA engineer feedback. The proposed ensemble machine learning model achieved an F1-score of 0.92, reducing missed defect rates by 32% and test execution time by 45%. Results: Test execution time reduced by 45% on average (from 110 minutes to ~60 minutes per full regression cycle). Escaped defect rate decreased by 32%, indicating more thorough coverage of high-risk areas. QA professionals reported a 35% increase in test efficiency and 20% fewer redundant test scripts. Concurrency issues (e.g., thread safety, race conditions) were detected 25% earlier in the QA cycle thanks to dynamic risk-based scheduling. Conclusion: AI-driven automation can significantly improve the speed and efficacy of QA for complex distributed systems, resulting in lower operational costs and more rapid release cycles. The proposed approach can serve as a blueprint for organizations seeking to modernize their QA pipelines with intelligent test orchestration.
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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 regression test optimization, which eliminate human error and shortens time to market. As part of this research, this thesis studies integration of AI into QA processes with a framework designed to encompass data collection, preprocessing, model training and deployment. The system we have proposed exploits a feedback loop for its continuous improvement and thus it is adaptable to changing software environment. The system was able to detect faults with relatively high precision and recall by using supervised learning, deep learning, and reinforcement learning techniques. The case studies show the system works effectively in discovering critical faults of large scale and mobile application projects, thereby validating its scalability and real-world application. AI driven QA is found to increase fault detection accuracy and along with it increase system reliability and development efficiency. This study concludes that testing using AI driven QA is a paradigm shift in software testing and AI driven and intelligent fault management will be the norms of the future.
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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 parameters on the air entrainment rate, Qa, were investigated experimentally in a confined plunging liquid jet reactor (CPLJR). Various downcomer diameters (Dc), jet lengths (Lj), liquid volumetric flow rates (Qj), nozzle diameters (dn), and jet velocities (Vj) were used to measure the air entrainment rate, Qa. The non-linear relationship between the air entrainment ratio and confined plunging jet reactor parameters suggests that applying unconventional regression algorithms to predict the air entrainment ratio is appropriate. In addition to the experimental work, machine learning (ML) algorithms were applied to the confined plunging jet reactor parameters to determine the parameter that predicts Qa the best. The results obtained from ML showed that K-Nearest Neighbour (KNN) gave the best prediction abilities, the proportion of variance in the Qa that can be explained by the CPLJR parameter was 90%, the root mean square error (RMSE) = 0.069, and the mean absolute error (MAE) = 0.052. Sensitivity analysis was applied to determine the most effective predictor in predicting Qa. The Qj and Vj were the most influential among all the input variables. The sensitivity analysis shows that the lasso algorithm can create an effective air entrainment rate model with just two of the most crucial variables, Qj and Vj. The coefficient of determination (R2) was 82%. The present findings support using machine learning algorithms to accurately forecast the CPLJR system’s experimental results.
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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 machine learning models. Sequential backward elimination algorithm was used for feature selection. Six machine learning approaches including linear regression, ridge regression, support vector regression (SVR) with linear kernel, quadratic kernel and radial basis function (RBF) kernel and feedforward neural network were used to predict age using FA and QA features respectively. Main results. Age predictions using FA features were more accurate than predictions using QA features for all the six machine learning algorithms. Post-hoc analysis revealed that FA was more sensitive to age-related white matter alterations in the brain. In addition, SVR with RBF kernel based on FA features achieved better performances than the competing algorithms with mean absolute error ranging from 7.74 to 10.54, mean square error (MSE) ranging from 87.79 to 150.86, and normalized MSE ranging from 0.05 to 0.14. Significance. FA from DTI model was more suitable than QA from GQI model in age prediction. FA metric was more sensitive to age-related white matter changes in the brain and FA of several brain regions could be used as white matter biomarkers in aging.
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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 defects, and enhancement of user experience as well as challenges and limitation that is encountered while implementing AI within QA. A glimpse of emerging trends illustrates the dynamic landscape of AI-driven QA.
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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 the possibility of using AI technologies to enhance quality assurance processes in the manufacturing of medical devices. Manufacturers may improve product quality and streamline production workflows by utilising AI techniques like machine learning, computer vision, and natural language processing to automate and optimize important QA procedures. Artificial intelligence systems can analyse large amounts of data to find abnormalities, uncover flaws, and anticipate any problems in real-time. This allows for proactive intervention and reduces the chances of non-compliance hazards. In addition, AI-powered QA systems provide adaptive learning capabilities, constantly enhancing performance through feedback and adapting to changing regulatory needs. The incorporation of artificial intelligence (AI) into current quality management systems enables smooth and efficient sharing of data and compatibility, promoting a comprehensive approach to quality control throughout the whole production process.
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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 authoring (and simple querying) with very high accuracy that at present is unachievable via machine learning approaches. The present paper expands on the question answering aspect of KALM and introducesKALM-QA(KALM for Question Answering) that is capable of answering much more complex English questions. We show that KALM-QA achieves 100% accuracy on an extensive suite of movie-related questions, calledMetaQA, which contains almost 29,000 test questions and over 260,000 training questions. We contrast this with a published machine learning approach, which falls far short of this high mark.
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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 itself intersection of Natural Language Processing, Information Retrieval, Machine Learning, Knowledge Representation, Logic and Inference and Semantic Search. Contribution: Feature extraction plays a major role for accurate classification, where the learned features get extracted for enhancing the capability of sequence learning. Optimized Deep Belief network model is adopted for the precise question answering system, which could handle both objective and subjective questions. A new hybrid optimization algorithm known as Lioness Adapted GWO (LA-GWO) algorithm is introduced, which mainly concentrates on high reliability and convergence rate. This paper intends to formulate a novel QA system, and the process starts with word embedding. From the embedded results, some of the features get extracted, and subsequently, the classification is carried out using the hybrid optimization enabled Deep Belief Network (DBN). Specifically, the hidden neurons in DBN will be optimally tuned using a new Lioness Adapted GWO (LA-GWO) algorithm, which is the hybridization of both Lion Algorithm (LA) and Grey Wolf optimization (GWO) models. Finally, the performance of proposed work is compared over other conventional methods with respect to accuracy, sensitivity, specificity, and precision, respectively.
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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 culturally specific QA tasks. The study involved data preparation, initial model training, ROUGE metric evaluation (ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum), and result analysis. Findings show that FlanT5 outperforms mT5 on multiple metrics, making it better at preserving cultural context. These results are impactful for NLP applications that rely on cultural insight, such as cultural preservation QA systems and context-based educational platforms.
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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 SQA sometimes outperformed SA by an exponential time factor. On the other hand, it remains unclear whether SQA can work efficiently on a wide range of other problems. In this study, we experimentally compared SA and SQA on instances of the restricted Boltzmann machine RBM, known as a fundamental building block in deep learning, and 3SAT, a fundamental combinatorial optimization problem. The results show that SQA gives slightly better solutions than SA as the problem size increases for RBM in terms of both accuracy and computation time in our setting, but the opposite trend is observed for 3SAT, indicating that there is no significant difference between the two methods. From the viewpoint of artificial intelligence research, it is necessary to further examine whether deep learning can be made more efficient by applying QA and SQA to RBM.
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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 improve medical device quality assurance procedures. Artificial intelligence (AI) methods such as computer vision, machine learning, and natural language processing can automate and optimize critical QA operations, allowing manufacturers to expedite production workflows, while improving product quality. Systems powered by artificial intelligence can sift through mountains of data in search of irregularities, defects, and faults, and they can do it in real-time. This lessens the likelihood of non-compliance problems and enables proactive response. Furthermore, QA systems driven by AI offer the ability to learn and adapt, which allows them to continuously improve performance by analyzing input and meeting evolving regulatory requirements.
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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 addition, we propose two novel strategies, language adversarial training and language arbitration framework, which significantly improve the (zero-resource) cross-lingual transfer performance and result in LM embeddings that are less language-variant. Empirically, we show that the proposed models outperform the previous zero-shot baseline on the recently introduced multilingual MLQA and TyDiQA datasets.
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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 insights, and offers scalable and adaptive QA processes. The implementation of advanced technologies like automated testing tools, real-time bug reporting systems, and data analytics significantly strengthens QA practices. Furthermore, case studies such as "The Legend of Zelda: Breath of the Wild" and "Anthem" exemplify the profound impact of rigorous or neglected error tracking on game success and player engagement. This paper advocates for integrating cutting-edge AI and machine learning techniques to predict bugs, automate testing, and enhance playtesting, ensuring robust game quality in an evolving industry.
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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 bring out the high priority job first. According to our method, the jobs are re-prioritized the schedules so that prioritized jobs can get theavailable system resources. Furthermore, the characterization, analysis, and visualization of the reasons for a manual change in the schedule are to be considered. This work requires extensive data preprocessing and building machine learning models for the causal relationship between various system variables and the time of manual changes.
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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 maintenance models lower HDD and SSD production failures. Higher product quality, lower running expenses, and better problem diagnostics follow from these developments. AI and ML clears the path for intelligent storage systems by automating storage optimization and failure prediction, hence enabling self-healing. Emphasizing important tools, trends, and difficulties that molded contemporary storage technology, this article investigates the influence of AI/ML-driven advancements in storage QA and hardware manufacturing.
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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 maintenance models lower HDD and SSD production failures. Higher product quality, lower running expenses, and better problem diagnostics follow from these developments. AI and ML clears the path for intelligent storage systems by automating storage optimization and failure prediction, hence enabling self-healing. Emphasizing important tools, trends, and difficulties that molded contemporary storage technology, this article investigates the influence of AI/ML-driven advancements in storage QA and hardware manufacturing.
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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 networks are a triple of subject-predicate-object, hence SVO parts of speech tags can map into a semantic network triple. An algorithm to process raw natural language text and map it into a semantic network is therefore necessary and desirable in structuring low resource languages texts. This algorithm tested on the Kiswahili QA task with up to 78.6% exact match.
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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 networks are a triple of subject-predicate-object, hence SVO parts of speech tags can map into a semantic network triple. An algorithm to process raw natural language text and map it into a semantic network is therefore necessary and desirable in structuring low resource languages texts. This algorithm tested on the Kiswahili QA task with up to 78.6% exact match.
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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 paper proposes a novel Extended Generative Pretrained Transformers-based Question Answering (ExtGPT-QA) model to generate precise and relevant answers to questions about a given context. The proposed architecture comprises two modified forms of encoder and decoder as compared to GPT. The encoder uses a positional encoder to assign a unique representation with each word in the sentence for reference to address the textual ambiguities. Subsequently, the decoder module involves a multi-head attention mechanism along with affine and aggregation layers to mitigate semantic vagueness with MRC systems. Additionally, we applied syntax and semantic feature engineering techniques to enhance the effectiveness of the proposed model. To validate the proposed model’s effectiveness, a comprehensive empirical analysis is carried out using three benchmark datasets including SQuAD, Wiki-QA, and News-QA. The results of the proposed ExtGPT-QA outperformed state of art MRC techniques and obtained 93.25% and 90.52% F1-score and exact match, respectively. The results confirm the effectiveness of the ExtGPT-QA model to address textual ambiguities and semantic vagueness issues in MRC systems.
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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 processes by enhancing efficiency and accuracy in peer review and plagiarism detection. Concurrently, there is a growing focus on ethical considerations, with accreditation systems incorporating ethical reviews and compliance with privacy regulations like GDPR. Additionally, the expansion of accreditation criteria reflects the multifaceted nature of research quality, including process evaluation and societal impact. The globalization of research has led to efforts to standardize accreditation practices, facilitating international collaboration and consistency. Challenges remain, such as balancing rigid standards with flexibility, addressing inequities in access to accreditation resources, and ensuring data security and privacy. To address these issues, ongoing dialogue and adaptation within the QA and accreditation community are essential--QA and accreditation practices can continue to support high-quality research and advance knowledge across diverse disciplines.
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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 acceptable or not. The complexity of RT treatment plans, coupled with the aforementioned class imbalance issue, introduces a generalization problem that significantly hinders the efficacy of traditional binary classification approaches. We introduce a novel one-class classification framework, using an adaptive neural network architecture, that outperforms both traditional binary and standard one-class classification methods in this imbalanced and complex context, despite the inherent disadvantage of not learning from unacceptable plans. Unlike its predecessors, our method enhances anomaly detection for RT plan QA without compromising on interpretability—a critical feature in healthcare applications, where understanding and trust in automated decisions are paramount. By offering clear insights into decision-making processes, our method allows healthcare professionals to quickly identify and address specific deficiencies in RT plans deemed unacceptable, thereby streamlining the QA process and enhancing patient care efficiency and safety.
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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 intelligence (AI), and real-time dashboards. Key performance indicators (KPIs) like Defect Removal Efficiency (DRE), Mean Time to Repair (MTTR), and automation coverage provide a granular understanding of the development pipeline. Predictive analytics models, integrated within CI/CD pipelines, leverage historical defect trends and code complexity metrics to forecast potential failure points, optimize resource allocation, and reduce time-to-market. Furthermore, prescriptive analytics equips QA teams with actionable insights, recommending remediation paths and improving decision-making agility. This paper underscores the transformative potential of QA analytics in driving efficiency and reliability across software ecosystems. It also highlights challenges, such as overcoming data silos, ensuring cross-platform compatibility, and addressing skill gaps in QA teams. The study presents a comprehensive metrics framework, explores state-of-the-art tools and methodologies, and includes a case study demonstrating a 40% reduction in production defects using advanced analytics. Finally, the paper proposes future directions, including ethical QA analytics, real-time quality dashboards, and deeper integration with DevSecOps workflows. By adopting these innovations, organizations can align QA objectives with business goals, achieving enhanced customer satisfaction, minimized defect leakage, and optimized development cycles. This shift represents not merely an enhancement of existing practices but a fundamental evolution of the QA discipline, positioning it as a critical driver of technological and organizational excellence.
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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 the task even harder. We introduce MMM, a Multi-stage Multi-task learning framework for Multi-choice reading comprehension. Our method involves two sequential stages: coarse-tuning stage using out-of-domain datasets and multi-task learning stage using a larger in-domain dataset to help model generalize better with limited data. Furthermore, we propose a novel multi-step attention network (MAN) as the top-level classifier for this task. We demonstrate MMM significantly advances the state-of-the-art on four representative MCQA datasets.
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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 compliance with regulatory standards. Emerging technologies, including artificial intelligence, machine learning, and blockchain, is enhancing the efficiency of adverse event detection and data integrity, while global harmonization efforts seek to unify regulatory frameworks for greater consistency across regions. Together, these fields address the complexities of modern drug development, such as personalized medicine and biologic therapies, supporting proactive risk management and fostering public trust in pharmaceuticals. This review highlights the integrated role of pharmacovigilance and QA in promoting safe, effective, and high-quality drug use, with a forward-looking perspective on digital transformation and global collaboration in advancing patient care.
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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), robotic process automation (RPA), augmented reality (AR), virtual reality (VR), big data, and cyber security are transforming quality assurance (QA) procedures. The problems, considerations, and integration of big data, AI, and cyber physical systems for manufacturing process optimization are discussed in the conclusion.</em>
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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 first is to use an answer-driven method to build a richer Chinese question classification dataset for the small-scale problems of the existing experimental dataset, which has a certain reference value for the expansion of the dataset, especially for the construction of those low-resource language datasets. The second is to propose a deep learning model of problem classification with a Transformer + Bi-GRU + Attention structure. Transformer has strong learning and coding ability, but it adopts the scheme of fixed coding length, which divides the long text into multiple segments, and each segment is coded separately; there is no interaction that occurs between segments. Here, we achieve the information interaction between segments through Bi-GRU so as to improve the coding effect of long sentences. Our purpose of adding the Attention mechanism is to highlight the key semantics in questions that contain answers. The experimental results show that the model proposed in this paper has significantly improved the accuracy of question classification.
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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 additional heuristic rule after conducting the machine learning technique. For the machine learning technique, we combined word based calculation, phrase based calculation and similarity dependency relation based calculation as the complete features. Using 203 data, we were able to enhance the accuracy for the Indonesian factoid QA system compared to related work by only using the phrase information. The best accuracy was 84.34% for the correct answer classification and the best MRR was 0.954.
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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 to identify ‘acceptable’ plans (plans that are similar to historically approved plans) and ‘unacceptable’ plans (plans that are dissimilar to historically approved plans). This algorithm is a supervised extension of projective adaptive resonance theory, called SuPART, that learns a set of distinctive features, and considers deviations from them indications of unacceptable plans. We test SuPART on breast and prostate radiotherapy datasets from our institution, and find that SuPART outperforms common classification algorithms in several measures of accuracy. When no falsely approved plans are allowed, SuPART can correctly auto-approve 34% of the acceptable breast and 32% of the acceptable prostate plans, and can also correctly reject 53% of the unacceptable breast and 56% of the unacceptable prostate plans. Thus, usage of SuPART to aid in QA could potentially yield significant time savings.