NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Inference.")'

Knowing the adversary : leaders, intelligence, and assessment of intentions in international relations /

By Yarhi-Milo, Keren,. . xi, 355 pages : 25 cm.. 9780691159157 (hardcover : acidfree paper) | 9780691159164 (softcover)

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Learning data analysis with Data Desk student version 5.0 /

By Velleman, Paul F.,. Reading, Mass. : Addison-Wesley, 1997 . xiv, 363 p. : 28 cm. +. 0201571153

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Biomedical Engineering (E-Book)

By Carlos Alexandre Barros de Mello. India In-Teh 2009 . x,658 P;

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Bayesian Network (E-Book)

By Ahmed Rebai. India Sciyo 2010 . x,432p;

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Statistics for modern business decisions / MCSStudy guide

By Lapin, Lawrence L.. New York, 1978 Harcourt Brace . viii,318 ,p.; 0-15-583775-3 (hela verket) | 0-15-583776-1

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Statistical decision theory : kendall's library of statistics 9 /

By French, Simon.. [S.l.] : Wiley, 2000 . 320 p. ; , Hardcover. 24 cm.. 0340614609 | 9780340614600

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Stochastic Processes and Applications : diffusion processes, the Fokker-Planck and Langevin equations /

By Pavliotis, Grigorios A.,. . xiii, 339 pages : , Textbook for graduates. 25 cm.. 9781493913220 | 1493913220

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Through Wall Image Enhancement /

By Riaz, Muhammad Mohsin. Rawalpindi, MCS (NUST), 2013 . xxii,100 -p,

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Gordis epidemiology /

By Celentano, David D.,. . xi, 420 pages : , Preceded by Epidemiology / Leon Gordis. Sixth edition. 2019. 24 cm. 9780323877756

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Comparison of Different Machine Learning Models for Quality Control in Biscuit Manufacturing Industry of Pakistan /

By Awais, Muhammad . . 113p. , Ensuring consistent product quality is a critical challenge in the biscuit manufacturing industry, particularly in developing economies like Pakistan, where manual inspection remains the norm. This approach, however, is prone to human error, fatigue, and inconsistency, leading to variable quality control outcomes. This research proposes an intelligent, machine learning–based automated quality inspection system tailored for real-world biscuit production lines. A comprehensive dataset of 40,000 high-resolution images was curated, representing four popular biscuit varieties Rio, Sooper, Candy, and Marie captured under actual factory lighting conditions. The dataset includes both defective and non-defective samples, covering a wide range of defect types such as breakage, charring, deformation, and missing pieces. To ensure high-quality annotations with scalable efficiency, a semi-automated iterative annotation framework was developed, combining initial manual labeling with model-assisted annotation and human-in-the-loop refinement across multiple cycles. Multiple state-ofthe-art machine learning models were implemented, fine-tuned, and rigorously evaluated, including YOLOv8, YOLOv11, YOLOv12, Faster R-CNN (via Detectron2), and Vision Transformer (ViT-B/16). Models were assessed using key performance metrics such as mAP@0.5:0.95, precision, recall, F1-score, inference speed (FPS), and computational efficiency. The results demonstrate that modern deep learning models, particularly YOLOv12 and Vision Transformers, achieve high detection accuracy (mAP > 90%) while maintaining feasibility for real-time deployment when optimized. This study provides a comparative analysis of accuracy-speed trade-offs, offering actionable insights for manufacturers seeking cost-effective, scalable solutions. The research concludes with practical recommendations for integrating AI-driven inspection systems into existing production infrastructure in resource-constrained environments, balancing performance, hardware requirements, and long-term maintainability. 30cm.

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