Search

Floods and rain significantly impact the economy of many agricultural countries in the world. Early prediction of rain and floods can dramatically help prevent natural disaster damage. This paper presents a machine learning and data-driven method that can accurately predict short-term rainfall. Various machine learning classification algorithms have been implemented on an Australian weather dataset to train and develop an accurate and reliable model. To choose the best suitable prediction model, diverse machine learning algorithms have been applied for classification as well. Eventually, the performance of the models has been compared based on standard performance measurement metrics. The finding shows that the hist gradient boosting classifier has given the highest accuracy of 91%, with a good F1 value and receiver operating characteristic, the area under the curve score.

The parsing technique based on associate grammar rules as well as probability is called stochastic parsing. This paper suggested a probabilistic method to eliminate the uncertainty from the sentences of Bangla. The technique of Binarization is applied to increase the precision of the parsing. CYK algorithm is used in this paper. The work mainly focused on intonation-based sentences, for these reasons PCFGs (Probabilistic Context-Free Grammars) is based on proposed. About 30324 words are used to test the proposed system; average 93% accuracy is achieved. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 51-56

Object: To isolate and identify the microorganisms from the burn patients admitted to the National Institute of Burn and Plastic Surgery Unit in Tertiary Medical College Hospital, Bangladesh. A total number of fifty wound surface swab samples of first and second-degree burn patients were collected and the microbial analysis as well as the study of antibacterial susceptibility was conducted. The study showed the bacterial isolates were found. 45 (90%) of wound swab were positive among 50 and only 5 samples (10%) were negative in bacterial growth, which presented invasive burn wound infection from both sex age groups marked 12 - 60 years. The total viable count TVC-11651 CFU/plate was found and the highest amount in the second-degree burn patients. The results showed that Pseudomonas aeruginosa was common in all positive samples 6636 CFU/plate (57%) followed by Staphylococcus aureus 4070 CFU/plate (35%), Klebsiella spp. 450 CFU/plate (5%), Proteus spp. 243 CFU/plate (2%), and E. coli 162 CFU/plate (1%). Most of the pathogens were found to be drug-resistant while several isolates were noted to be multi-drug resistant. The growth of multidrug-resistant organisms should be considered as a serious risk factor in a burn unit. Aggressive infection control measures should be applied to limit the emergence and spread of multidrug-resistant pathogens.