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Cervical cancer growth is the fourth maximum of regular diseases in females. It is one of the sicknesses which is compromising ladies' wellbeing everywhere in the world and it is difficult to notice any sign in the beginning phase. But the screening process of cervical cancer sometimes is being hampered due to some social-behavioral factors. There is still a limited number of researches directed in cervical cancer identification dependent on the behavior and machine learning in the area of gynecology and computer science. In this research, we have proposed three machine learning models such as Decision Tree, Random Forest, and XGBoost to predict cervical cancer from behavior and its variables and we got significantly improved outcomes than the current methods with 93.33% accuracy. Moreover, we have shown the top features from the dataset according to the feature important scores to know their impacts on the development of the classification model.

The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model's feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality,

A B S T R A C TBrucellosis is a zoonotic bacterial disease of humans and animals caused by Gram negative bacteria of the genus Brucella. The study was conducted to determine the sero-prevalence of brucellosis and its associated risk factors in cattle and buffalo in greater Mymensingh district, Bangladesh. Blood samples were collected from cattle (n=150) and buffalo (n=60). Sera were tested for Brucella specific antibody by the rose Bengal plate test (RBPT). Overall prevalence of brucellosis was 15.33% in cattle and 13.33% in buffalo. Higher prevalence was recorded in cattle and buffalo of over 4 years age (18% and 15.68%, respectively). Female animals showed higher brucellosis prevalence (cattle 19.54%, buffalo 13.46%) than male ( cattle 8.21%, buffalo 12.5%). Pregnant cows showed higher prevalence of brucellosis (13.33%) as compare to non pregnant cows (10%). The prevalence of brucellosis was 10% in aborted cows, 4% in the case of retained placenta and 2.85% in repeat breeder cows. The study suggests that brucellosis is prevalent in the cattle and buffalo and its prevalence is affected by the animal's age, sex, pregnancy status and reproductive disorders.

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