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Artificial Intelligence in Quality Assurance: A New Paradigm for Total Quality Management

Operations Research Forum 2025年第2期6卷 1-23页

作者： R, Jothikumar S, Mohan Raju Y.C, Mohan S, Susi Kumar, Jayendra Department of Computer Science and Engineering School of Engineering Anurag University Hyderabad India MBA Department Er. Perumal Manimekalai College of Engineering Hosur India Full time Faculty USDC Global 6th Phase J P Nagar Bangalore India Department of MBA Shadan Women’s College of Engineering and Technology Hyderabad India

Quality Assurance is a fundamental process where organisations ensure that their products and services are met with established standards and customer needs. Integrating artificial intelligence (AI) and machine learning (ML) into quality assurance (QA) processes can significantly enhance efficiency and accuracy. In this paper, the researcher perform a bibliometric analysis (2013–2024) of the role of AI in quality assurance, evaluate its effect on Total Quality Management (TQM), and recognize important research developments. A bibliometric analysis was accomplished based on explorations in the Scopus database to identify and collect peer-reviewed research articles related to AI in QA. Bibliometric indicators were employed to assess the trends in publications, distribution of disciplines, geographic contributions, institutional collaborations, funding organizations, and journal metrics [9]. Through bibliometric analysis, utilizing data obtained from the Scopus database, this study systematically identified and assessed peer-reviewed research regarding the use of artificial intelligence (AI) in quality assurance (QA). The findings showed a significant increase in the research output concerning AI-driven QA systems in the past decade, as well as the countries, universities, and funding agencies driving specific research trends. AI-driven QA approaches such as defect detection using ML, predictive maintenance frameworks, and automated quality control systems were key themes dominating the literature. Moreover, the analysis revealed ongoing challenges related to AI integration in quality assurance (QA), covering topics such as algorithm bias, ethical concerns, and data governance issues. By facilitating more accurate, speedy and flexible quality management systems—artificial intelligence (AI) is changing the way that quality assurance (QA) is done. Yet harnessing its power requires a well-thought-out deployment strategy, sound governance frameworks, and integration betwee

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Integrating Regression Models and Deep Learning for Lung Cancer Risk Prediction

Integrating Regression Models and Deep Learning for Lung Can...

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Automation and Computation (AUTOCOM), International Conference on

作者： Divya Sharma Sunil. MP Dhananjay Kumar Yadav Raghunathan S Preetjot Singh Soumen Nayak Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Early diagnosis is vital for successful treatment of lung cancer, as the disease remains one of the most common causes of death by cancer worldwide. Historically, the prediction of lung cancer has been based on regression models and disemboweled by the fact that it cannot optimally handle complex data. In contrast, deep learning techniques have exhibited great potential in developing precise predictive models of disease based on more complicated forms of data; nevertheless, these methods have been less rigorously applied specifically for predicting lung cancer risks. This project aims to combine conventional regression models and deep learning techniques to achieve better performance with more generalizable accuracy for lung cancer risk prediction. A hybrid framework will be developed, exploiting the strengths of regression models in being interpretable and deep learning for its superior prediction. Our proposed methodology will also tackle the issue of high-dimensional imaging data needed for lung cancer risk prediction. These advances in notably conjunctional regression models and deep learning may contribute to better prediction models for lung cancer risk by enhancing the accuracy and translation ability of the developed scores. It will help improve the identification of people at high risk for lung cancer and, on that note, also target the population likely to benefit, thereby ensuring early controls with better outcomes.

关键词： Deep learning Translation Accuracy High dimensional data Computational modeling Noise Neural networks Lung cancer Predictive models Reliability

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SleepGMUformer: A gated multimodal temporal neural network for sleep staging

arXiv

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arXiv 2025年

作者： Zhao, Chenjun Niu, Xuesen Yu, Xinglin Chen, Long Lv, Na Zhou, Huiyu Zhao, Aite College of Computer Science and Technology Qingdao University Qingdao China Institute of Clinical Sciences Faculty of Medicine Imperial College London United Kingdom School of Information Science and Engineering University of Jinan Jinan China School of Computing and Mathematical Sciences University of Leicester United Kingdom

Sleep staging is a key method for assessing sleep quality and diagnosing sleep disorders. However, current deep learning methods face challenges: 1) post-fusion techniques ignore the varying contributions of different modalities;2) unprocessed sleep data can interfere with frequency-domain information. To tackle these issues, this paper proposes a gated multimodal temporal neural network for multidomain sleep data, including heart rate, motion, steps, EEG (Fpz-Cz, Pz-Oz), and EOG from WristHR-Motion-Sleep and SleepEDF-78. The model integrates: 1) a pre-processing module for feature alignment, missing value handling, and EEG de-trending;2) a feature extraction module for complex sleep features in the time dimension;and 3) a dynamic fusion module for real-time modality *** show classification accuracies of 85.03% on SleepEDF-78 and 94.54% on WristHR-Motion-Sleep datasets. The model handles heterogeneous datasets and outperforms state-of-the-art models by 1.00%-4.00%. Copyright © 2025, The Authors. All rights reserved.

关键词： Sleep research

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Enhancing Image Security With a Novel Chaotic System: A Focus on Multi-Face Image Encryption in Smart Applications

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IEEE Internet of Things Journal 2025年

作者： Liu, Pengbo Teng, Lin Liu, Huipeng Iu, Herbert Ho-Ching Wang, Mingxu Yan, Xiaopeng Fu, Xianping Dalian Maritime University School of Information Science and Technology Dalian116026 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Key Laboratory of Ministry of Fisheries and Education Dalian Ocean University College of Information Engineering Dalian Key Laboratory of Smart Fishery Liaoning Key Laboratory of Marine Information Technology Dalian116023 China

To ensure stringent security strategies for image information involving personal privacy during conveyance and storage, we propose an innovative multi-face privacy protection scheme based on chaos theory. Compared to single-face encryption algorithms, the proposed scheme has broader potential applications in fields such as smart cities and smart transportation. Specifically, a new spatiotemporal chaotic system named the sine-cosine coupled mapping lattice system (SCCML) is designed. It features a larger parameter domain, complexity, and profound unpredictability, yet maintains a simpler construction aimed at providing potential benefits and implementations in the field of information security. In the proposed multi-face privacy protection scheme, multiple faces within an image are rapidly and accurately identified and then encrypted using the proposed SCCML-based digital separation loop encryption algorithm. The encryption algorithm exhibits a synchronous scrambling diffusion mechanism. Additionally, the introduction of mixed multi-base cascade diffusion offers multiple layers of security for facial data, prevents diffusion singularity, and enhances diversity, making it significantly more challenging to crack. Experimental verification on a real multi-face image dataset shows that the algorithm is superior, practical, safe, and efficient. © 2014 IEEE.

关键词： Differential privacy

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Evaluating Deep Learning Models for Brain Tumor Detection Utilizing Transfer Learning and Fine-Tuning Approach 5

Evaluating Deep Learning Models for Brain Tumor Detection Ut...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Chauhan, Nitin Kumar Kumar, Dharmendra Gupta, Shiv Narain Kumar, Amit Kumar, Mukesh Chatterjee, Pinaki Sankar Indore Institute of Science and Technology Department of Ece Indore453331 India Thapar Institute of Engineering and Technology Department of Eied Punjab 147004 India Greater Noida Institute of Technology Department of Ece Greater Noida India CNRS@Create 138602 Singapore Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneshwar751024 India

ISBN: (纸本)9798331522100

Brain tumor identification is a significant task in medical imaging, necessitating precise and fast diagnostic methods to enhance patient outcomes. Despite advances in medical imaging technologies, prompt and precise diagnosis of brain tumors remains a face-off. Specifically, Deep Learning networks that make utilization of transfer learning and fine-tuning have demonstrated that they have the potential to be effective in resolving this issue. This investigation compares the performance of some prevalent progressive Deep Learning networks for brain tumor diagnostics, using Transfer Learning and Fine-Tuning approaches. Ten Deep Learning models namely Inception, Xception, ResNet-50, VGG-16, DenseNet-121, ResNet-152, DenseNet-169, VGG-19, EfficientNetB0, and MobileNetV2 are used for performance evaluation in the 4-class classification of brain tumor images. To improve detection capabilities, the models are pre-trained on a huge 'ImageNet' database and then fine-tuned using an annotated brain tumor image dataset. The assessment demonstrates that VGG-16 surpasses others with an accuracy of 99.24% for the 4-class classification of brain tumor data. © 2025 IEEE.

关键词： Deep learning

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Dual-Network Cross-Learning for Metabolite-Disease Association Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第2期22卷 545-556页

作者： Yanxin Chen Qiao Ning Yitong Zhang Hui Li Shikai Guo Department of Information Science and Technology Dalian Maritime University Dalian China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Dalian Key Laboratory of Artificial Intelligence Dalian China

In recent years, increasing evidence has demonstrated a close association between metabolites and various complex human diseases, providing valuable insights for disease diagnosis, treatment, and prevention. Although deep learning-based approaches have achieved certain success in predicting metabolic disease associations, challenges remain in enriching graph information and effectively integrating metabolic and disease features. To address these issues, this paper proposes a model named DCMDA, which extracts deep features of both metabolites and diseases using Dual-network Cross-learning for Metabolite- Disease Association prediction. DCMDA consists of three parts. The data processing module integrates similarity networks with association networks to construct a heterogeneous network. The feature extraction module extracts features from the metabolite-disease association network based on the non-negative matrix factorization method and from the heterogeneous network using graph autoencoder techniques. The feature fusion module combines the association matrix feature with the heterogeneous network feature through a Cross-Attention mechanism, thereby obtaining deep representations of metabolites and diseases. These features are then used to train the model to predict association scores between metabolites and diseases. Experimental results demonstrate that in 5-fold cross-validation, DCMDA achieves an area under the receiver operating characteristic curve (AUC) of 97.8% and an area under the precision-recall curve (AUPR) of 97.9%, outperforming state-of-the-art prediction methods.

关键词： Diseases Feature extraction Data mining Semantics Deep learning Kernel Information entropy Prediction algorithms Noise Nearest neighbor methods

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Time-Series Analysis of Cancer Progression Using LSTM Network

Time-Series Analysis of Cancer Progression Using LSTM Networ...

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Automation and Computation (AUTOCOM), International Conference on

作者： Sheethal Komal Parashar Anukampa Behera Paramjit Baxi Koushik V Prasad Pavan Chaudhary Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Mechanical Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Cancer progression varies; in its simplest definition, it is the growth of abnormal cells anywhere on or within an organism. Time-series cancer data are high dimensional and non-linear, demanding a more advanced approach than traditional statistical tools. Long Short-Term Memory (LSTM) networks have become popular for time-series analysis as they can accommodate long-term dependencies well and model complex non-linear patterns. Here, we suggest using LSTM networks in cancer progression data that are often time-dependent and collected for each patient. To accurately identify discriminator signals in the time-series data, we present a stacked LSTM architecture using a deep learning framework generating adversarial examples and feature selection. We have shown that our proposed LSTM approach is superior to common statistical techniques in cancer progression prediction and feature selection. The new cancer progression LSTM network developed a time-series analysis, which has the possibility of being more effective and understandable in monitoring cancer progression for designing offer diagnosis along with treatment compared to the min max AI system.

关键词： Training Analytical models Statistical analysis Sensitivity analysis Time series analysis Feature extraction Organisms Long short term memory Monitoring Cancer

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Adaptive Mutation Strategies in Non-Convex Optimization for Big Data Processing

Adaptive Mutation Strategies in Non-Convex Optimization for ...

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Automation and Computation (AUTOCOM), International Conference on

作者： Takveer Singh Ashish Kumar Noopur Pandey Mamatha G N Daljeet Pal Singh Kartik N Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Electronics and Communication Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Applications (DCA) Presidency College Bengaluru India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

It has been argued that adaptive mutation mechanisms in nonconvex optimization can efficiently handle massive data sets, the curse of big data. Big data is high-dimensional and complex compared to traditional optimization strategies, which can be computationally slow and sub-optimal. Adaptive changes to these parameters in real-time are known as adaptive mutation strategies that adjust them throughout training based on data properties and where they are within the optimization process. This facilitates more accurate and stable solutions, even when the data has noise or uncertainty. In addition, the adaptively of these strategies prevents falling into local optima by exhibiting better global optimization performance. As a result, integrating adaptive mutation strategies into nonconvex optimization for big data processing improves computational efficiency and effectiveness in solving sophisticated, complex problems, thereby making them useful within an era where huge volume exists as well-known with big-data foes.

关键词： Training Industries Uncertainty Social networking (online) Noise Medical services Big Data Real-time systems Optimization Systematic literature review

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Level attraction from interference in a two-tone-driven cavity magnonics system

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Physical Review Applied 2025年第1期23卷 014048-014048页

作者： Alan Gardin Guillaume Bourcin Christian Person Christophe Fumeaux Romain Lebrun Isabella Boventer Giuseppe C. Tettamanzi Vincent Castel Quantum and Nano Technology Group School of Chemical Engineering The University of Adelaide Adelaide SA 5005 Australia IMT Atlantique Lab-STICC UMR CNRS 6285 29238 Brest France School of Electrical Engineering and Computer Science The University of Queensland Brisbane QLD 4072 Australia Unité Mixte de Physique CNRS Thales Université Paris-Saclay 91767 Palaiseau France

Coherent and dissipative couplings, respectively characterized by energy level repulsion and attraction, each have different applications for quantum information processing. Thus, a system in which both coherent and dissipative couplings are tunable on demand and in situ is tantalizing. A first step towards this goal is the two-tone driving of two bosonic modes, whose experimental signature was shown to exhibit controllable level repulsion and attraction by changing the phase and amplitude of one drive. However, whether the underlying physics is that of coherent and dissipative couplings has not been clarified, and cannot be concluded solely from the measured resonances (or antiresonances) of the system. Here, we show how the physics at play can be analyzed theoretically. Combining this theory with realistic finite-element simulations, we deduce that the observation of level attraction originates from interferences due to the measurement setup, and not dissipative coupling. Beyond the clarification of the origin of level attraction attributed to interference, our work demonstrates how effective Hamiltonians should be derived to appropriately describe the physics.

关键词： Hamiltonians

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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