Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers from different categories, such as Bayes, functions, lazy, meta, rules, and trees, were trained for efficient heart disease risk prediction using the full set of attributes of the Cleveland heart dataset and the optimal attribute sets obtained from three attribute evaluators. The performance of the algorithms was appraised using a 10-fold cross-validation testing option. Finally, we performed tuning of the hyperparameter number of nearest neighbors, namely, ‘k’ in the instance-based (IBk) classifier. The sequential minimal optimization (SMO) achieved an accuracy of 85.148% using the full set of attributes and 86.468% was the highest accuracy value using the optimal attribute set obtained from the chi-squared attribute evaluator. Meanwhile, the meta classifier bagging with logistic regression (LR) provided the highest ROC area of 0.91 using both the full and optimal attribute sets obtained from the ReliefF attribute evaluator. Overall, the SMO classifier stood as the best prediction method compared to other techniques, and IBk achieved an 8.25% accuracy improvement by tuning the hyperparameter ‘k’ to 9 with the chi-squared attribute set.
The increasing spread of the coronavirus across countries and with no sight of vaccine uncovered soon has prompted affected countries to impose strict containment measures. In view to ease the enormous strain on health systems; disinfection, decontamination, contact tracking, and isolation are a few health protocols that are to be observed by companies that resumed their activities to protect their employees from being infected. Hence, against a backdrop of heightened uncertainty, this project leverages on the advancement of technology to design and built a smart Infrared thermal scanning with a camera (Thermovis-Mi-FRAHT-800). An Ultraviolet-C spectrum disinfection system and integration of blockchain technology for data sharing, managing health records, and access control. SketchUp used as a 3D design platform for this project. This system designed with a precautionary measure which includes 3 conditions to be met for the automated barrier to be open which include temperature measurement, disinfection, and sanitization processes. Overall, a person spends less than a minute in the walkthrough path chamber as the process takes 20 to 25 seconds each. By this calculation, we assume that 2 people would be able to get disinfected within a minute which comes up to 120 people per hour. Thus, reducing the number of monitoring staffs in direct contact with the stakeholders with potential infection issues. It is envisaged that developing this conceptual design would be the cornerstone in adhering to control measure through appropriate infection control and modification using current and future technologies.
Supplemental LEDs lighting technology has been used as the promising lighting source in hydroponic cultures for sustainable production in urban agriculture. It could be the solution to address the growing concern about food safety, environmental impacts, bad weather, and efficient energy usage in agricultural production. In this study, the response of loose head lettuce toward the irradiance of the supplemental red-blue LED light with different power (Watt [W]) was investigated by comparing the treated lettuce with the lettuce cultured under only natural light. The lettuce plants were treated with red LED (640-660 nm) + blue LED (440-450 nm). The power output of the LEDs was specified to 3, 6, 9, 15, and 20 W. The lettuce plants were hydroponically cultured with 8 h red-blue LEDs light exposure (from 12 to 8 am) and 16 h without the red-blue LEDs light exposure (from 8 pm to 12 am) at average air temperatures of 31/28 ºC (day/night) for 50 days (7 weeks). On the harvesting day, the average shoot heights of the lettuce that was treated with 3, 6, 9, 15, 20 LEDs and natural light were 25.00, 24.75, 20.75, 19.88, 17.63, and 12.63 cm, respectively. The lettuce that was exposed to the 3 W LEDs had the highest shoot height compared to those that were exposed to LEDs with other power outputs. The average fresh weights of the lettuce that was treated with 3, 6, 9, 15, 20 W LEDs and natural light were 27.25, 24.75, 21.25, 19.88, 18.38, and 15.75 g, respectively. The results showed that the fresh weight of the lettuce that was irradiated with 3 W LED light was significantly higher compared to the lettuce that was exposed to LEDs with other power outputs. Hence, it can be concluded that supplementary LEDs lighting technology can be used as an alternative lighting source to improve the growth of lettuce in hydroponic systems. Moreover, the use of 3 W LEDs in hydroponic systems could yield a higher shoot weight and fresh weight.
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