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

An introduction to counselling /

By McLeod, John.. [S.l.] : Open University Press, 2003 . 613 p. ; 24 cm.. 0335211895 (paperback) | 9780335211890 (paperback)

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Manual of engineering drawing technical product specification and documentation to British and international standards /

By Simmons, C. H.. Burlington : Butterworth Heinemann, 2012 . 1 online resource (ix, 369 pages) , Includes index. 9789382291572

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Study Guide and Solutions Manual Organic Chemistry

By McMurry,Susan. Califormia Brooks/Cole publishing 1988 . 532p. , P.B 0534079695

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Principles of water treatment /

Hoboken : Wiley, 2012 . xvii, 654 pages : , Includes index. 25 cm.. 9780470405383 (hardback)

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Financial theory and corporate policy /

By Copeland, Thomas E.. [S.l.] : Addison Wesley, 1988 . 946 p. ; , Hardcover. 25 cm.. 0201106485 | 9780201106480

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Smart Logistics System (SLS) /

By Dua, Fakhrunissa . . 125 p

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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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