DSpace Collection:http://umt-ir.umt.edu.my:8080/handle/123456789/233972026-06-07T16:18:52Z2026-06-07T16:18:52ZAN IMPROVED MLP-RFE MODEL FOR ENHANCED ACCURACY IN HEART DISEASE PREDICTION USING DEEP LEARNINGPSNZhttp://umt-ir.umt.edu.my:8080/handle/123456789/234022026-06-07T11:31:15Z2026-04-01T00:00:00ZTitle: AN IMPROVED MLP-RFE MODEL FOR ENHANCED ACCURACY IN HEART DISEASE PREDICTION USING DEEP LEARNING
Authors: PSNZ2026-04-01T00:00:00ZSUSTAINABLE PROCESSING OF RUBBER (HEVEA BRASILIENSIS) SEED OIL: PHYSICOCHEMICAL INSIGHTS INTO EXTRACTION AND ANTIOXIDANT PRESERVATIONPSNZhttp://umt-ir.umt.edu.my:8080/handle/123456789/234012026-06-07T11:58:29Z2026-05-01T00:00:00ZTitle: SUSTAINABLE PROCESSING OF RUBBER (HEVEA BRASILIENSIS) SEED OIL: PHYSICOCHEMICAL INSIGHTS INTO EXTRACTION AND ANTIOXIDANT PRESERVATION
Authors: PSNZ2026-05-01T00:00:00ZBONE-DERIVED HYDROXYAPATITE TOOTHPASTE FOR SUSTAINABLE PHARMACEUTICAL AND BIOMEDICAL APPLICATIONS BONE-DERIVED HYDROXYAPATITE TOOTHPASTE FOR SUSTAINABLE PHARMACEUTICAL AND BIOMEDICAL APPLICATIONSPSNZhttp://umt-ir.umt.edu.my:8080/handle/123456789/234002026-06-07T11:30:05Z2026-03-01T00:00:00ZTitle: BONE-DERIVED HYDROXYAPATITE TOOTHPASTE FOR SUSTAINABLE PHARMACEUTICAL AND BIOMEDICAL APPLICATIONS BONE-DERIVED HYDROXYAPATITE TOOTHPASTE FOR SUSTAINABLE PHARMACEUTICAL AND BIOMEDICAL APPLICATIONS
Authors: PSNZ2026-03-01T00:00:00ZA COST-EFFECTIVE IOT-BASED SOIL MOISTURE MONITORING COST-EFFECTIVE IOT-BASED SOIL MOISTURE MONITORING SYSTEM FOR WILDFIRE-PRONE COASTAL SOILSYSTEM FOR WILDFIRE-PRONE COASTAL SOILSPSNZhttp://umt-ir.umt.edu.my:8080/handle/123456789/233992026-02-16T15:58:07Z2026-02-24T00:00:00ZTitle: A COST-EFFECTIVE IOT-BASED SOIL MOISTURE MONITORING COST-EFFECTIVE IOT-BASED SOIL MOISTURE MONITORING SYSTEM FOR WILDFIRE-PRONE COASTAL SOILSYSTEM FOR WILDFIRE-PRONE COASTAL SOILS
Authors: PSNZ
Abstract: Abstract: The accuracy and unit cost of sensors are important factors for a continuous soil moisture monitoring system. This study compares the accuracy of four soil moisture sensors differing in unit costs in coarse-, fine- and medium-textured soils. The sensor outputs were recorded for the VWC, ranging from 0% to 50%. Low-cost capacitive and resistive sensors were evaluated with and without the external 16-bit analog-to-digital converter ADS1115 to improve their performances without adding much cost. Without ADS1115, using only Arduino’s built-in analog-to-digital converter, the low-cost sensors had a maximum RMSE of 4.79% (v/v) for resistive sensors and 3.78% for capacitive sensors in medium-textured soil. The addition of ADS1115 showed improved performance of the
low-cost sensors, with a maximum RMSE of 2.64% for resistive sensors and 1.87% for capacitive sensors. The higher-end sensors had an RMSE of up to 1.8% for VH400 and up to 0.95% for the 5TM sensor. The RMSE differences between higher-end and low-cost sensors with the use of ADS1115 were not statistically significant.2026-02-24T00:00:00Z