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Kidney is a vital part of the body's metabolic process and a key toxic waste removal mechanism. The blood filtering procedure known as hemodialysis is performed in individuals with severe renal failure. By pushing a patient's blood against a fluid combination called dialysate, a dialysis machine performs hemodialysis. This study seeks to build a prototype design of a Hemodialysis Dialysate Temperature Control System to lessen the risk of patients experiencing temperature shock during hemodialysis treatment since the dialysis machine has problems maintaining the proper temperature of dialysate. To achieve a body temperature of (36⁰C) for patients, two different temperatures of the dialysate were combined with various flow rates. One temperature of the dialysate was maintained below body temperature (35⁰C), and the other was kept above human body temperature (37⁰C). The results show the desired output temperature of 36⁰C is achievable by using fuzzy logic-based controller. The system will give an approximate constant output of desired temperature with a time taken of 670 seconds where the output volume for each circulation is 50 ml/s. The hemodialysis dialysate temperature control system prototype with the implement of fuzzy logic shows that the system consumes time for heating but is able to give a close by to constant desired output temperature. [ABSTRACT FROM AUTHOR]

This study compares K Nearest Neighbor and random forests to increase accuracy in blockchain-based agriculture product supply chain management systems. K Nearest Neighbor and Random Forest algorithms are tested when the data sets are imported. Algorithms are run with varied training and testing splits to improve accuracy in blockchain-based farm product supply chain management systems. There are two groups for the two algorithms. There are 20 total samples, with 10 in each group, With G power setting parameters of (α=0.05 and power=0.80). Our research demonstrates a statistically significant difference between the K Nearest Neighbor algorithm's accuracy of 83.0% and the Random Forest algorithm's accuracy of 77.0%. Furthermore, the t-test for independent samples with statistically significant value of p=0.000, (p<0.05) was applied to estimate the mean, deviation, and standard error. According to the data obtained for this research, the Innovative K Nearest Neighbour Algorithm demonstrates superior performance in accuracy (83.0%) compared to the Random Forest Algorithm (77.0%). [ABSTRACT FROM AUTHOR]