An experiment was carried out to reveal the precipitation pattern and to find out the interrelationship between precipitation and production of rice in Rangpur district. Rangpur division is more favorable for rice production. Monthly and yearly precipitation data (1983-2013) were collected from Bangladesh Meteorological department (BMD), Agargaon, Dhaka; Bangladesh Agricultural Research Council (BARC), Farmgate, Dhaka and rice production data were collected from Bangladesh Rice research Institute (BRRI); Department of Agricultural Extension (DAE), Rangpur, Bangladesh. From the analyzed precipitation data, it was clearly found that in 1984, 1985, 1987, 1999 and 2005, there were heavy precipitations and resulting flash flood. The average precipitation of Rangpur was higher (1344 mm) in 1987 than 1984, 1985, 1999 and 2005. In Rangpur district, rice production was the highest in the year of 1983, 1986, 1988, 1989, 1990, 1991, 1992, 1993, 1995, 1996, 1997, 1998, 2001, 2002, 2003, 2004, 2007, 2008, 2009, 2010, 2011, 2012 and 2013 and lower in the year of 1984, 1985, 1987, 1994, 1999, 2000, 2005, and 2006 on the basis of total annual precipitation. Rice production reduced main two reasons such as, heavy precipitation causes flash flood and lower precipitation causes drought stress condition. Both are threat-full for higher rice production. The results show that more precipitation in the years of lowest rice production period, heavy precipitation responsible for deduction of rice production area because of flooding and drought and also shows that normal/ minimum precipitation favorable for rice production at Rangpur district. From this study, it is concluded that the irregular precipitation of period (1984, 1985, 1987, 1994, 1999, 2000, 2005, and 2006) was not satisfactory for rice production due to heavy and/or excessive lower precipitation that resulting flood and/or in part of Rangpur district of Bangladesh. The analysis exposed that precipitation was one of the most important factors for higher amount of rice production in Rangpur district.Progressive Agriculture 29 (1): 10-21, 2018

The present study was conducted to investigate peoples’ perception level and awareness of air pollution in some selected areas of Mymensingh sadar upazila. The relationship of independent variables (age, educational qualification, family size, residence and communication exposure) with the peoples’ perception level and awareness of air pollution (dependent variable) was done to understand the objectives of the study. Six Hundreds (600) respondents were selected randomly from six study sites under Mymensingh sadar upazila for collecting data during the period of Jan 2016-April, 2017. Pearson’s product-moment correlation coefficients were computed to examine the relationship between the concerned variables. The findings revealed that about half (46.67 percent) of the peoples had medium perception and awareness, 31.67 percent had low and 21.67 percent had high perception and awareness about air pollution. In rural areas, 43.33 percent respondents had low, 50.00 percent had medium and only 6.67 percent had high perception and awareness of air pollution. In urban areas, 20.00 percent respondents had low, 43.33 percent had medium and 36.67 percent had high perception and awareness of air pollution. Majority of the respondents (93.33 percent) were lacking of proper awareness of air pollution in rural areas while 63.33 percent in urban areas. Out of five independent variables, three variables such as educational qualification, residence and communication exposure had positive and significant relationship, age had negative and significant relationship and family size had no relationship with their perception and awareness of air pollution.Progressive Agriculture 29 (1): 22-32, 2018

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0%, while the rest of the algorithms were over 90.0% accurate.

The objective of this study was to clarify the inside of socio-economic condition and cattle production scenario in few areas of Pabna district. Data of socio-economic condition of farmers, cattle population, genotype and overall management were collected from three villages at Bera upazila of Pabna district during August to September in 2017. The collected data were tabulated and analyzed following one-way ANOVA including descriptive statistics. It was observed that the age of farmers were average 42 years with 19 years of average farming experiences. Agriculture was found as primary occupation (47.37%) followed by house wife (15.79%) and animal husbandry (10.53%). Among the total livestock population, 52% (n=286) was cattle with different genotypes but the frequency of local Pabna cattle was highest (70%). About 79% cattle were reared intensively at home because the areas were surrounded by water. The average peak day milk yield (4.56 liters) of local Pabna cattle was significantly (p <0.001) lower than Holstein crossbred (7.43 liters). The cattle feeding system was mostly intensive (77%) followed by semi-extensive (23%) with 60%, 49% and 98% restricted feeding for straw, green grass and concentrate feeds in the studied households. The capacity building training of farmers including different farm oriented facilities would enhance a dairy development programme in those areas from local Pabna cattle which could contribute their socio-economic condition as well.

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0%, while the rest of the algorithms were over 90.0% accurate.<br>

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0$\%$, while the rest of the algorithms were over 90.0$\%$ accurate.<br>

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

Diabetes is a disease that affects how your body processes blood sugar and is often referred to as diabetes mellitus. Insulin insufficiency and ineffective insulin use coincide when the pancreas cannot produce enough insulin or the human body cannot use the insulin that is produced. Insulin is a hormone produced by the pancreas that helps in the transport of glucose from food into cells for use as energy. The common effect of uncontrolled diabetes is hyper-glycemia, or high blood sugar, which plus other health concerns, raises serious health issues, majorly towards the nerves and blood vessels. According to 2014 statistics, people aged 18 or older had diabetes and, according to 2019 statistics, diabetes alone caused 1.5 million deaths. However, because of the rapid growth of machine learning(ML) and deep learning (DL) classification algorithms. indifferent sectors, like health science, it is now remarkably easy to detect diabetes in its early stages. In this experiment, we have conducted a comparative analysis of several ML and DL techniques for early diabetes disease prediction. Additionally, we used a diabetes dataset from the UCI repository that has 17 attributes, including class, and evaluated the performance of all proposed machine learning and deep learning classification algorithms using a variety of performance metrics. According to our experiments, the XGBoost classifier outperformed the rest of the algorithms by approximately 100.0%, while the rest of the algorithms were over 90.0% accurate.<br>