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@article{206681,
        author = {Prof. Daya Naik and Dr Parvathraj K M M and Prof. Nivin K S and Likhitha and Jayaprada and Bhavya and Nagendra G},
        title = {Investigating The Prevalence and Identifying Impact of Tuberculosis Disease in Cattle},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {183-187},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206681},
        abstract = {The agricultural economy and food security rely heavily on the productivity and health of cattle. This study looks at the prevalence and effects of certain diseases in cattle populations using an Artificial Intelligence and Machine Learning (AIML) approach. We developed predictive models to spot disease outbreaks and assess their severity. This involved examining a dataset that included environmental factors, animal health metrics, and veterinary records. We used methods like supervised learning, clustering, and anomaly detection to identify trends and links between disease incidence and its contributing factors. The findings illustrate how AIML can improve early disease detection, help farmers and veterinarians make informed decisions, and optimize livestock management. Our results show that using AIML tools in veterinary care can boost animal welfare and significantly reduce financial losses. Tuberculosis (TB) in cattle is a serious zoonotic disease that affects livestock productivity, food safety, and global agricultural sustainability. This study explores the patterns of prevalence and economic impact of bovine tuberculosis by merging AIML methods with veterinary surveillance data.},
        keywords = {.},
        month = {July},
        }