[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116922-en":3,"doc-seo-116922-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116922,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","MEAT QUALITY PREDICTION USING MACHINE LEARNING - Master of Science Project","Meat quality is a critical food-industry requirement, and conventional meat freshness prediction techniques often face constraints related to accuracy, cost, and time efficiency. This project develops machine learning and deep learning models using image data to classify meat as fresh or spoiled, with emphasis on both predictive accuracy and processing speed. A Kaggle dataset is used to evaluate algorithms including Support Vector Machines, Decision Trees, and Random Forests combined with convolutional neural networks. Results indicate hybrid CNN-based neural network pipelines can improve freshness determination. The study also supports faster automation by reducing human input, while suggesting future work integrating multimodal data such as genetics and feeding or processing practices.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 5-2023\u003Cbr>MEAT QUALITY PREDICTION USING MACHINE LEARNING Rohit Buddiga\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Computer Engineering Commons |  |\n\nRecommended Citation  \nBuddiga, Rohit, \"MEAT QUALITY PREDICTION USING MACHINE LEARNING\" (2023) . Electronic Theses, Projects, and Dissertations. 1665.  \n[https://scholarworks.lib.csusb.edu/etd/1665](https://scholarworks.lib.csusb.edu/etd/1665)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nMEAT QUALITY PREDICTION USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in  \nInformation Systems and Technology  \nby  \nRohit Buddiga May 2023  \nMEAT QUALITY PREDICTION USING MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nRohit Buddiga  \nMay 2023  \nApproved by:  \nDr. Conrad Shayo, Committee Chair Dr. Kamvar Farahbod, Committee Co-Chair Dr. Conrad Shayo, Department Chair, Information and Decision Sciences  \n© 2023 Rohit Buddiga  \nABSTRACT  \nMeat quality is an essential aspect of the food industry. However, traditional methods of meat quality prediction have limitations in terms of accuracy, cost, and time efficiency. This project focused on utilizing advanced Deep learning and Machine learning algorithms to develop-machine learning models that could predict the freshness (or spoilage) of meat with a 100% accuracy, based on image data. In addition to accuracy, this study emphasizes the significance of speed and time in selecting the optimal machine learning model. The research questions are: Q1 . What hybrid neural networks should be used to predict freshness? Q2 . How do hybrid neural networks determine the freshness of the meat based on the image? Q3 . How can accuracy and performance speed be improved? A dataset from the Kaggle repository was used to explore various machine learning algorithms such as Support Vector Machines, Decision Trees, and Random Forests with a combination of Convolutional Neural Network, a deep learning network. The findings are: Q1 . A combination of Support Vector Machines-Convolutional Neural Network, Decision Trees-Convolutional Neural Network, and Random Forests-Convolutional Neural Network were used to predict freshness. 2) The hybrid neural networks were trained using the tensorflow. keras. models, a high-level neural networks API of the TensorFlow library, which allowed the creation and training of complex machine learning models in a simple and straightforward manner. 3) The accuracy and performance speed of the model can be improved by utilizing a  \ndistributed computing environment for training, which involves the collaboration of multiple machines to carry out computations. The conclusion from our project is that Utilizing the hybrid neural networks developed, it is possible to classify meat products as either fresh or spoiled using image analysis. This approach not only reduces the reliance on human input for meat classification but also decreases the time taken to complete the classification process. Furthermore, emerging areas for future research that emerged from this study is to develop machine learning models that can integrate and fuse multi-modal data such as genetics, feeding and processing techniques to make more accurate predictions of meat quality.  \nDEDICATION  \nDedicated to my beloved mother.  \nTABLE OF C","cbCaikTzCWDrJgPE","https://ap.wps.com/l/cbCaikTzCWDrJgPE","pdf",1581931,1,58,"English","en",105,"# ABSTRACT\n# CHAPTER ONE: INTRODUCTION\n## Problem Statement\n## Objectives\n## Organization of Project\n# CHAPTER TWO: LITERATURE REVIEW\n## Literature Review\n# CHAPTER THREE: RESEARCH METHODS\n## Hybrid Neural Networks for Meat Quality Prediction\n## Python Libraries\n# CHAPTER FOUR: DATA ANALYSIS\n## Dataset Analysis & Results\n# CHAPTER FIVE: DISCUSSION, CONCLUSION, AND AREAS FOR FURTHER STUDY\n## Discussion\n## Conclusion\n## Areas of Further Study\n# APPENDIX : CODE\n# REFERENCES","[{\"question\":\"What problem does the project address in meat quality prediction?\",\"answer\":\"It targets limitations of traditional freshness prediction methods, especially issues with accuracy, cost, and time efficiency.\"},{\"question\":\"Which data source and model types are used for freshness classification?\",\"answer\":\"The project uses an image dataset from the Kaggle repository and trains hybrid machine learning/deep learning models combining classical methods with convolutional neural networks.\"},{\"question\":\"How is model accuracy and performance speed intended to be improved?\",\"answer\":\"The project proposes using a distributed computing environment for training so computations can be carried out collaboratively across multiple machines.\"}]","MEAT QUALITY PREDICTION USING MACHINE LEARNING - Master of Science Project | PDF",1785672543,146,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"meat-quality-prediction-using-machine-learning-master-of-science-project","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/meat-quality-prediction-using-machine-learning-master-of-science-project/116922/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the project address in meat quality prediction?","Question",{"text":75,"@type":76},"It targets limitations of traditional freshness prediction methods, especially issues with accuracy, cost, and time efficiency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data source and model types are used for freshness classification?",{"text":80,"@type":76},"The project uses an image dataset from the Kaggle repository and trains hybrid machine learning/deep learning models combining classical methods with convolutional neural networks.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model accuracy and performance speed intended to be improved?",{"text":84,"@type":76},"The project proposes using a distributed computing environment for training so computations can be carried out collaboratively across multiple machines.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]