ABSTRACT
Fruit classification is required in many smart-farming and industrial applications. In the supermarket, a fruit classification system may be used to help cashiers and customer to identify the fruit species, origin, ripeness, and prices. Some methods, such as image processing and NIRS (near-infrared spectroscopy) are already used to classify fruit. In this paper, we propose a fast and cost-effective method based on a low-cost Vector Network Analyzer (VNA) device augmented by K-nearest neighbor (KNN) and Neural Network model. S-parameters features are selected, which take into account the information on signal amplitude or phase in the frequency domain, including reflection coefficient S11 and transmission coefficient S21. This approach was experimentally tested for two separate datasets of five types of fruits, including Apple, Avocado, Dragon Fruit, Guava, and Mango, for fruit recognition as well as their level of ripeness. The classification accuracy of the Neural Network model was higher than KNN with 98.75% and 99.75% on the first dataset, whereas the KNN was seen to be more effective in classifying ripeness with 98.4% as compared to 96.6% for neural network.
Subject(s)
Fruit , Neural Networks, Computer , Fruit/chemistry , Machine Learning , Spectroscopy, Near-Infrared/methods , Cluster Analysis , Support Vector MachineABSTRACT
BACKGROUND: Evidence on the effects of nutritional interventions on gastrointestinal cancer patients receiving chemotherapy is not well documented. This study aims to assess the effects of nutritional intervention in patients diagnosed with stomach and colon cancer receiving chemotherapy in Vietnam. METHODS: A quasi-experiment with intervention and control groups for pre- and post-intervention was carried out in cancer patients receiving chemotherapy in a university hospital in Vietnam. Patients in the intervention group were provided nutritional counseling, personalized specific dietary advice, and received oral nutrition supplements (ONSs) while patients in the control group only received nutrition counseling. RESULTS: The weight in the intervention and control group after 2 months increased significantly by 1.4 ± 2.6 kg and 0.4 ± 2.3 kg, respectively. Muscle mass increased by 1.2 ± 4.1 cm in the intervention group, while those in the control group decreased by 0.55 ± 2.77 cm. There was no statistical significance between two groups after intervention in terms of Mid-Upper Arm Circumference (MUAC) and percentage of fat. The percentage of malnutrition based on the Scored Patient-Generated Subjective Global Assessment (PG-SGA) and Body Mass Index (BMI) declined after the intervention in both groups. According to the average treatment effect on the treated (ATT) using the propensity score matching and DiD method, participants receiving the intervention were more likely to have a higher score of weight (Coef = 0.84; 95%CI = 0.47; 2.16) and muscle mass (Coef = 1.08; 95%CI = 0.09; 2.06) between pre- and post-intervention. By contrast, the PG-SGA scores on treated participants were more likely to decrease after the intervention (Coef = -1.28; 95%CI = -4.39; -0.84). After matching, being female, living in rural areas, or having stomach cancer were still positively related to being moderately/severely malnourished by the PG-SGA, and these findings were statistically significant (p < 0.05). CONCLUSION: The nutritional interventions had a positive effect on weight gain, muscle mass, and reduced malnutrition. Further studies with a longer follow-up duration are needed to confirm the effects of the intervention.
ABSTRACT
BACKGROUND: In recent times, scientists have found new treatments for colorectal cancer patients. AIM: The study is to evaluate the efficacy and toxicity of triplet combination chemotherapy of 5-fluorouracil/leucovorin, oxaliplatin, and irinotecan (FOLFOXIRI) for patients with metastatic colorectal cancer in stage IV. METHODS: Uncontrolled clinical trial carried on 39 stage IV colorectal cancer patients. RESULTS: The overall response rate of the treatment was 79.4%. The average progression-free survival was 13.4 ± 9 months. The overall survival rate at 12th month and 24th month were 90% and 76%, respectively. The proportion of granulocytopenia was 48.9%, no grade 3 or 4. Side effect beyond hematology was most seen in hepatic toxicity with 52.5%, mainly at grade 1. Vomiting was 18.3%, all at grade 1. Other adverse event was very low at percentage. CONCLUSIONS: The triplet combination FOLFOXIRI chemotherapy improves the outcome of patients with metastatic colorectal cancer regarding rate of response, overall survival rate and progression-free survival, and the level of toxicity was acceptable.
