Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images).
One of the most efficient methods to observe the impact of geographical, environmental, and geological changes is remote sensing. Nowadays, nanosatellites are being used to observe climate change using remote sensing technology. Communication between a remote sensing nanosatellite and Earth significantly depends upon antenna systems. Body-mounted solar panels are the main source of satellite operating power unless deployable solar panels are used. Lower ultra-high frequency (UHF) nanosatellite antenna design is a crucial challenge due to the physical size constraint and the need for solar panel integration. Moreover, nanosatellite space missions are vulnerable because of antenna and solar panel deployment complexity. This paper proposes a solar panel-integrated modified planner inverted F antenna (PIFA) to mitigate these crucial limitations. The antenna consists of a slotted rectangular radiating patch with coaxial probe feeding and a rectangular ground plane. The proposed antenna has achieved a -10 dB impedance bandwidth of 6.0 MHz (447.5 MHz⁻453.5 MHz) with a small-sized (80 mm× 90 mm× 0.5 mm) radiating element. In addition, the antenna achieved a maximum realized gain of 0.6 dB and a total efficiency of 67.45% with the nanosatellite structure and a solar panel. The challenges addressed by the proposed antenna are to ensure solar panel placement between the radiating element and the ground plane, and provide approximately 55% open space to allow solar irradiance into the solar panel.
A number of primate census techniques have been developed over the past half-century, each of which have advantages and disadvantages in terms of resources required by researchers (e.g., time and costs), availability of technologies, and effectiveness in different habitat types. This study aims to explore the effectiveness of a thermal imaging technique to estimate the group size of different primate species populations in a degraded riparian forest in the Lower Kinabatangan Wildlife Sanctuary (LKWS), Sabah. We compared this survey technique to the conventional visual counting method along the riverbank. For 38 days, a total of 138 primate groups were observed by thermal camera and visually throughout the study. Optimal conditions for the thermal camera were clear weather, not more than 100 m distance from the observer to the targeted area, boat speed ranging between 5 and 12 km/h, and early morning between 04:30 and 05:30 am. The limitations of the thermal cameras include the inability to identify individual species, sexes, age classes, and also to discern between animals closely aggregated (i.e., mothers with attached infants). Despite these limitations with the thermal camera technique, 1.78 times more primates were detected than counting by eye (p
Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.
The aim of this paper is to review the potentialities and major methodological challenges
of integrating remote sensing (RS) and geographic information system (GIS) with socioeconomic data
from published articles or book chapters. RS and GIS combined with social science (SS)(termed as
geoinformation technology) serve many applications for sustainable management and monitoring of
the environment. This combined approach gives more accurate results than the single one. It makes
information available about the trend and pattern of land use and land cover change (LUCC) with
socioeconomic variables like population, demographic or income. This combined study which links
RS and GIS with socioeconomic data can also be used successfully for monitoring transmission rate
of disease and mapping or preparing vulnerability index. For impact assessment and modelling, this
combined technology provides better results than the single one. There are some methodological
problems for the researchers to link completely two different disciplines as the object of study and
observational unit is completely different. However, this interdisciplinary study is gaining popularity
day by day to researchers from different disciplines as well as decision makers.
In the backdrop of prompt advancement, information and communication technology (ICT) has become an inevitable part to plan and design of modern solid waste management (SWM) systems. This study presents a critical review of the existing ICTs and their usage in SWM systems to unfold the issues and challenges towards using integrated technologies based system. To plan, monitor, collect and manage solid waste, the ICTs are divided into four categories such as spatial technologies, identification technologies, data acquisition technologies and data communication technologies. The ICT based SWM systems classified in this paper are based on the first three technologies while the forth one is employed by almost every systems. This review may guide the reader about the basics of available ICTs and their application in SWM to facilitate the search for planning and design of a sustainable new system.
Malaysia is in the process of modernizing its oil palm plantation management, by implementing geo-information technologies which include Remote Sensing (RS), Geographic Information System (GIS), and Spatial Decision Support System (DSS). Agencies with large oil palm plantations such as the Federal Land Development Authority (FELDA), Federal Land Consolidation and Rehabilitation Authority (FELCRA), Guthrie Sdn. Bhd., and Golden Hope Sdn. Bhd. have already incorporated GIS in their plantation management, with limited use of RS and DSS. In 2005, FELCRA, Universiti Putra Malaysia (UPM) and Espatial Resources Sdn. Bhd. (ESR) collaborated in a research project to explore the potentials of geo-informatics for oil palm plantation management. The research was conducted in FELCRA located in Seberang Perak Oil Palm Scheme. In that research, a tool integrating RS, GIS and Analytical Hierarchy Process (AHP) was developed to support decision making for replanting of the existing old palms. RS was used to extract productive stand per hectare; AHP was used to compute the criteria weights for the development of a suitable model; and GIS was used for spatial modelling so as to generate the decision support layer for replanting. This paper highlights the approach adopted in developing the tool with special emphasis on the AHP computation.
Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able - for the first time - to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed - specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the world's forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large-scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.
Body Area Networks (BANs) consist of various sensors which gather patient's vital signs and deliver them to doctors. One of the most significant challenges faced, is the design of an energy-efficient next hop selection algorithm to satisfy Quality of Service (QoS) requirements for different healthcare applications. In this paper, a novel efficient next hop selection algorithm is proposed in multi-hop BANs. This algorithm uses the minimum hop count and a link cost function jointly in each node to choose the best next hop node. The link cost function includes the residual energy, free buffer size, and the link reliability of the neighboring nodes, which is used to balance the energy consumption and to satisfy QoS requirements in terms of end to end delay and reliability. Extensive simulation experiments were performed to evaluate the efficiency of the proposed algorithm using the NS-2 simulator. Simulation results show that our proposed algorithm provides significant improvement in terms of energy consumption, number of packets forwarded, end to end delay and packet delivery ratio compared to the existing routing protocol.
Geographic Information System (GIS) and remote sensing are geospatial technologies that have been used for many years in environmental studies, including gathering and analysing of information on the physical parameters of wildlife habitats and modelling of habitat assessments. The home range estimation provided in a GIS environment offers a viable method of quantifying habitat use and facilitating a better understanding of species and habitat relationships. This study used remote sensing, GIS and Analytic Hierarchy Process (AHP) application tools as methods to assess the habitat parameters preference of Asian elephant. Satellite images and topographical maps were used for the environmental and topographical habitat parameter generation encompassing land use-land cover (LULC), Normalized Digital Vegetation Index (NDVI), water sources, Digital Elevation Model (DEM), slope and aspect. The kernel home range was determined using elephant distribution data from satellite tracking, which were then analysed using habitat parameters to investigate any possible relationship. Subsequently, the frequency of the utilization distribution of elephants was further analysed using spatial and geostatistical analyses. This was followed by the use of AHP for identifying habitat preference, selection of significant habitat parameters and classification of criterion. The habitats occupied by the elephants showed that the conservation of these animals would require good management practices within and outside of protected areas so as to ensure the level of suitability of the habitat, particularly in translocation areas.
Basal stem rot (BSR), caused by the Ganoderma fungus, is an infectious disease that affects oil palm (Elaeis guineensis) plantations. BSR leads to a significant economic loss and reductions in yields of up to Malaysian Ringgit (RM) 1.5 billion (US$400 million) yearly. By 2020, the disease may affect ∼1.7 million tonnes of fresh fruit bunches. The plants appear symptomless in the early stages of infection, although most plants die after they are infected. Thus, early, accurate, and nondestructive disease detection is crucial to control the impact of the disease on yields. Terrestrial laser scanning (TLS) is an active remote-sensing, noncontact, cost-effective, precise, and user-friendly method. Through high-resolution scanning of a tree's dimension and morphology, TLS offers an accurate indicator for health and development. This study proposes an efficient image processing technique using point clouds obtained from TLS ground input data. A total of 40 samples (10 samples for each severity level) of oil palm trees were collected from 9-year-old trees using a ground-based laser scanner. Each tree was scanned four times at a distance of 1.5 m. The recorded laser scans were synched and merged to create a cluster of point clouds. An overhead two-dimensional image of the oil palm tree canopy was used to analyze three canopy architectures in terms of the number of pixels inside the crown (crown pixel), the degree of angle between fronds (frond angle), and the number of fronds (frond number). The results show that the crown pixel, frond angle, and frond number are significantly related and that the BSR severity levels are highly correlated (R2 = 0.76, P < 0.0001; R2 = 0.96, P < 0.0001; and R2 = 0.97, P < 0.0001, respectively). Analysis of variance followed post hoc tests by Student-Newman-Keuls (Newman-Keuls) and Dunnett for frond number presented the best results and showed that all levels were significantly different at a 5% significance level. Therefore, the earliest stage that a Ganoderma infection could be detected was mildly infected (T1). For frond angle, all post hoc tests showed consistent results, and all levels were significantly separated except for T0 and T1. By using the crown pixel parameter, healthy trees (T0) were separated from unhealthy trees (moderate infection [T2] and severe infection [T3]), although there was still some overlap with T1. Thus, Ganoderma infection could be detected as early as the T2 level by using the crown pixel and the frond angle parameters. It is hard to differentiate between T0 and T1, because during mild infection, the symptoms are highly similar. Meanwhile, T2 and T3 were placed in the same group, because they showed the same trend. This study demonstrates that the TLS is useful for detecting low-level infection as early as T1 (mild severity). TLS proved beneficial in managing oil palm plantation disease.
Ground-based LiDAR also known as Terrestrial Laser Scanning (TLS) technology is an active remote sensing imaging method said to be one of the latest advances and innovations for plant phenotyping. Basal Stem Rot (BSR) is the most destructive disease of oil palm in Malaysia that is caused by white-rot fungus Ganoderma boninense, the symptoms of which include flattening and hanging-down of the canopy, shorter leaves, wilting green fronds and smaller crown size. Therefore, until now there is no critical investigation on the characterisation of canopy architecture related to this disease using TLS method was carried out. This study proposed a novel technique of BSR classification at the oil palm canopy analysis using the point clouds data taken from the TLS. A total of 40 samples of oil palm trees at the age of nine-years-old were selected and 10 trees for each health level were randomly taken from the same plot. The trees were categorised into four health levels - T0, T1, T2 and T3, which represents the healthy, mildly infected, moderately infected and severely infected, respectively. The TLS scanner was mounted at a height of 1 m and each palm was scanned at four scan positions around the tree to get a full 3D image. Five parameters were analysed: S200 (canopy strata at 200 cm from the top), S850 (canopy strata at 850 cm from the top), crown pixel (number of pixels inside the crown), frond angle (degree of angle between fronds) and frond number. The results taken from statistical analysis revealed that frond number was the best single parameter to detect BSR disease as early as T1. In classification models, a linear model with a combination of parameters, ABD - A (frond number), B (frond angle) and D (S200), delivered the highest average accuracy for classification of healthy-unhealthy trees with an accuracy of 86.67 per cent. It also can classify the four severity levels of infection with an accuracy of 80 per cent. This model performed better when compared to the severity classification using frond number. The novelty of this research is therefore on the development of new approach to detect and classify BSR using point clouds data of TLS.
This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of groundwater resources in the area. The overall results proved the efficacy of the data mining model and the geospatial technology in groundwater potential mapping.
The use of remote sensing in detecting aerosol or air pollution is not widely applied in Malaysia. The large area of coverage provided by remote sensing satellite may well be the solution to the lack of spatial coverage by the local ground air quality monitoring stations. This article discusses the application of remote sensing instruments in air quality monitoring of Malaysia. The remote sensing data is validated using ground truths either from local ground air monitoring stations or the Aerosol Robotic Network (AERONET). The correlation between remote sensing is relatively good with R from 0.5 to 0.9 depending on the satellite used. The correlation is much improved using the mixed effects algorithm applied on MODIS Aerosol Optical Depth (AOD) data. Accuracy of predicted air quality data by remote sensing is generally tested using the Root Mean Squared Error (RMSE) against the ground truths data. Besides the Geographic Information System (GIS) tools are used in manipulating the data from both remote sensing and ground stations so as to produce meaningful results such as spatio-temporal pattern mapping of air pollution. Overall the results showed that the application of remote sensing instruments in air quality monitoring in Malaysia is very useful and can be improved further.
Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.
In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline-rise-decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type's RSEI values behave differently at different times. As the UANST is the core area of Xinjiang's urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST.
The spatial and temporal dynamics of daily ultraviolet index (UVI) for a period of 18 years (2004-2022) over the Indian state of Kerala were statistically characterised in the study. The UVI measurements used for the study were derived from the ultraviolet-B (UVB) irradiance measured by the Ozone Monitoring Instrument (OMI) of the AURA satellite and classified into different severity levels for analysis. Basic statistics of daily, monthly and seasonal UVI as well as Mann-Kendall (MK) statistical trend characteristics and the rate of change of daily UVI using Theil-Sen's slope test were also evaluated. A higher variability of UVI characteristics was observed in the Kerala region, and more than 79% of the measurements fell into the categories of very high and extreme UVI values, which suggests the need of implementation of appropriate measures to reduce health risks. Although the UVI measured during the study period shows a slight decrease, most of the data show a seasonal variation with undulating low and peak values. Higher UVI are observed during the months of March, April and September. The region also has higher UVI during the southwest monsoon (SWM) and summer seasons. Although Kerala region as a single whole unit, UVI show a non-significant decreasing trend (-0.83), the MK test revealed the increasing and decreasing trends of UVI ranging from -1.96 to 0.41 facilitated the delineation of areas (domains) where UVI are increasing or decreasing. The domain of UVI increase occupies the central and southern (S) parts, and the domains of decrease cover the northern (N) and S parts of the Kerala region. The rate of change of daily UVI in domain of increase and decrease shows an average rate of 0.34 × 10-5 day-1 and -2 × 10-5 day-1, respectively. The parameters (rainfall, air temperature, cloud optical depth (COD) and solar zenith angle (SZA)) that affect the strength of UV rays reaching the surface indicate that a cloud-free atmosphere or low thickness clouds prevails in the Kerala region. Overall, the study results indicate the need for regular monitoring of UVI in the study area and also suggest appropriate campaigns to disseminate information and precautions for prolonged UVI exposure to reduce the adverse health effects, since the study area has a high population density.
In medical systems for patient's authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient's authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework.
The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered 'big data'. To our knowledge, no study has highlighted the link between 'big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six 'Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.
This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.