Indexing Techniques Research Articles

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

Analysis of Growth Trends, Regional Variations and Productivity Metrics of Apple Cultivation in Himachal Pradesh, India

India has seen a spatial escalation on apple growth and it fostered the country’s economy and employment opportunities of the apple growing states. States of Jammu & Kashmir, Himachal Pradesh, Uttarakhand and Arunachal Pradesh are primarily known for apple production. Therefore, we worked on the secondary data on apple growth trends, its production from the different sources and analyzed it through growth rate, decomposition analysis and instability index techniques to quantify the production metrics and trend shift. Our findings reported that between1973–1974 to 2021–2022, there was an increase found in the global area, productivity and production of apples in Asia and India. The output of apples worldwide has found to be escalated at a compound annual growth rate of 2.51%, whereas, India has experienced a notable rise of 3.40 percent annually. The majority of the apple's area, output and productivity came from the Asian region, with growth rates of 3.38, 5.97, and 2.59 %, respectively. Furthermore, Worldwide scenario on apple cultivation reported that Poland had the highest relative instability in apple productivity (21.44%), followed by India (15.96%), China (12.53%), Turkey (11.18%) and USA (8.61%). Area yield effect was used to explain the rise in apple output in Asia, however, area expansion was found as the primary cause of increase in area under apple production. In Himachal Pradesh, Zone III has the greatest average yearly productivity (4.78%), followed by Zone II (3.35 %) and Zone IV (0.57%). In zones III and IV, the expansion of area (96.93%) and yields (85.13%) was the reason for the increase in apple production, however, in zone II, the yield effect (102.05%) was the only cause found for the increase in apple’s production.

Transipedia.org: k-mer-based exploration of large RNA sequencing datasets and application to cancer data

Indexing techniques relying on k-mers have proven effective in searching for RNA sequences across thousands of RNA-seq libraries, but without enabling direct RNA quantification. We show here that arbitrary RNA sequences can be quantified in seconds through their decomposition into k-mers, with a precision akin to that of conventional RNA quantification methods. Using an index of the Cancer Cell Line Encyclopedia (CCLE) collection consisting of 1019 RNA-seq samples, we show that k-mer indexing offers a powerful means to reveal non-reference sequences, and variant RNAs induced by specific gene alterations, for instance in splicing factors.

EASL: Enhanced append-only skip list index for agile block data retrieval on blockchain

The weakness of blockchain is widely recognised as the linear, temporal cost required for retrieving data due to the sequential structure of data blocks. To address this, conventional approaches have relied on database indexing techniques applied to each individual replica copy of a blockchain. However, this only partially addresses the problem, because if the index is not distributed it is not available for devices in the blockchain network. If an index is to be incorporated and distributed within blockchain, the unique attribute of immutability necessitates a more innovative approach. To that end, we propose an Enhanced Append-only Skip List (EASL). This specialised indexing technique utilises binary search with skip lists in blockchain, resulting in a sublinear cost for data retrieval. The EASL indexing technique is maintained by each newly appended blockchain block and offers enhanced readability and robustness using an explicitly recorded index structure. Our proposed technique is 42% more efficient in computing and 60% more efficient in storage consumption than its predecessor, the Deterministic Append-only Skip List (DASL) indexing technique. This is achieved through agile data retrieval, resulting in energy cost savings from less computational effort to maintain the index, and less network bandwidth to retrieve blockchain data. The code for the proposed technique is publicly available on GitHub {https://github.com/jarednewell/EASL/}, to expedite future research and encourage the practical application of this effectual data index.

Assessing sources of groundwater quality and health risks using graphical, multivariate, and index techniques from a part of Rajasthan, India

Groundwater provides sustainable potable water in developing countries like India. However, contaminated groundwater directly affects human health. The United Nation policy is to provide clean water for all by 2030. Therefore, groundwater samples collected from a part of Rajasthan, India were analysed for chemical parameters to identify the sources of groundwater quality variation and associated health risks. To achieve this objective, a comprehensive approach of entropy water quality index (EWQI), Piper and Gibbs diagrams, ionic ratios (IR), synthetic pollution index (SPI), principal component analysis (PCA), and non-carcinogenic health risk (NHR) methods were used. EWQI revealed that groundwater quality was not fit for drinking in 81.86% of the area due to higher TDS, Na+, , , , and contents, which was statistically significant by ANOVA test. Modified Piper’s diagram identified Ca2+ - , Ca2+ - , Na+- , and Na+ - types in 36.67%, 22.73%, 13.64%, and 27.27% of groundwater samples, respectively. Gibbs diagrams illustrated that 59.09% and 40.81% of groundwater samples fell in the rock and evaporation domains, respectively. Ionic ratios demonstrated that geogenic processes (mineral dissolution, ion exchange, and evaporation) and anthropogenic activities cause groundwater quality degradation. PCA indicated that PC1 has high loadings of TDS, Mg2+, Na+, , and , PC2 shows high loadings of pH and , and PC3 represents high loadings of , K+, and , which was further supported by t-test analysis. SPI revealed that 99.82% of the study area is polluted. NHR values (0.03 to 1.68, 0.02 to 1.29, and 0.03 to 1.30 for children, women, and men, respectively) showed that long-term exposure to and -containing drinking water poses a higher health risk to children compared to men and women. Regular water quality monitoring, sanitation facilities, limited application of agrochemicals, etc. were suggested. Such a study would aid environmental public health programs.

Improving Cloud Database Performance Using Storage Technologies

Cloud databases have become indispensable for modern applications due to their scalability, flexibility, and cost-effectiveness. However, optimizing performance in cloud environments can be challenging due to factors such as network latency, data distribution, and resource contention. This paper explores various storage technologies and techniques that can significantly enhance cloud database performance. We delve into the characteristics of different storage types, including solid-state drives (SSDs), hard disk drives (HDDs), and object storage, and discuss their suitability for various workloads. Additionally, we examine caching mechanisms, data compression, and deduplication strategies that can optimize data access and reduce storage costs. Furthermore, we explore the impact of database architecture choices, such as distributed databases and inmemory databases, on performance. We also discuss the importance of effective query optimization and indexing techniques. By understanding and implementing these storage technologies and techniques, organizations can achieve substantial improvements in cloud database performance, leading to better user experiences and increased application efficiency

Class IV composite restoration with direct silicon index technique

Class IV composite restoration with direct silicon index technique

Evaluation of Groundwater Quality for Drinking in Various Districts of Jammu and Kashmir with Special Emphasis on Kashmir

Abstract: In India, groundwater is a valuable resource for humans, yet human activity has consistently reduced its quality. For the management of groundwater resources to be sustainable, an assessment of the quality of the groundwater is required. With the use of integrated-weight water quality index and multivariate statistical techniques, this study sought to assess the groundwater quality in the Indian area of Kashmir for drinking purposes. Eight water quality measures were included in the data, which came from 50 observation wells and included pH, total hardness, nitrate (NO3−), iron (Fe), lead (Pb), mercury (Hg), arsenic (As), and coliforms. The integrated-weight water quality index (IWQI), principal component analysis (PCA), and cluster analysis (CA) were used to examine various districts. The majority of the metrics, according to the results, were within acceptable ranges, however some areas had excessive hardness and Fe contamination. More than 76% of samples had significant coliform contamination found in them. With four clusters of sample locations, the CA results showed that geological locations had a significant influence on groundwater quality. A PCA's three main components may account for 64.8% of the variation in groundwater quality. Groundwater quality was categorized as excellent (74.11%), good (45%), poor (6.4%), very poor (2.9), and undrinkable (9.4) based on IWQI values ranging from 4 to 2761. These classifications were linked to coliform contamination. These results have shed light on the region's groundwater quality state, which is helpful in creating a plan to safeguard the water supply.

Vector Databases

Vector databases represent a significant advancement in data management, tailored to efficiently handle high-dimensional vector embeddings. Unlike traditional databases, which face challenges with the complexity and scale of high-dimensional data, vector databases are engineered for optimal storage, indexing, and retrieval of vector data. This work explores the challenges posed by high-dimensional data, including the curse of dimensionality, and examines how vector databases address these issues through advanced indexing techniques such as Inverted File (IVF), Product Quantization (PQ), and Locality Sensitive Hashing (LSH). It highlights the importance of vector databases in facilitating rapid similarity searches, which are crucial for applications such as recommendation systems and search engines. The discussion also covers the evolution of vector databases and their impact on AI and machine learning.

Use of Machine Learning and Indexing Techniques for Identifying Industrial Pollutant Sources: A Case Study of the Lower Kelani River Basin, Sri Lanka

With the recent acceleration in urbanisation and industrialisation, industrial pollution has severely impacted inland water bodies and ecosystem services globally, causing significant restrains to freshwater availability and myriad damages to benthic species. The Kelani River Basin in Sri Lanka, covering only ~3.6% of the land but hosting over a quarter of its population and many industrial zones, is identified as the most polluted watershed in the country. This study used unsupervised learning (UL) and an indexing approach to identify potential industrial pollutant sources along the Kelani River. The UL results were compared with those obtained from a novel Industrial Pollution Index (IPI). Three latent variables related to industrial pollution were identified via Factor Analysis of monthly water quality data from 17 monitoring stations from 2016 to 2020. The developed IPI was validated using a Long Short-Term Memory Artificial Neural Network model (NSE = 0.98, RMSE = 0.81), identifying Cd, Zn, and Fe as the primary parameters influencing river pollution status. The UL method identified five stations with elevated concentrations for the developed latent variables, and the IPI confirmed four of them. Based on the findings from both methods, the industrial zones along the Kelani River have emerged as a likely source of pollution in the river’s water. The results suggest that the proposed method effectively identifies industrial pollution sources, offering a scalable methodology for other river basins to ensure sustainable water resource management.

Delineation of potential zones for groundwater recharge using integrated GIS-based AHP and CSI techniques in Ogun Waterside, Southwestern Nigeria

ABSTRACT Ogun Waterside region of southwestern Nigeria experiences seasonal water stress stemming from groundwater overdraft. Therefore, it became imperative to effectively manage the existing groundwater resources to fulfill the anticipated demand for potable water. To achieve this goal, an analysis involving seven spatial parameters: geomorphology, land use/land cover, slope, lineament density, drainage density, soil types, and geology was conducted. These parameters were examined using a combination of geographic information system (GIS)-based analytical hierarchy process (AHP) and cumulative score index (CSI) techniques to delineate potential zones for groundwater recharge. The AHP methodology was employed to assign relative weights to the thematic layers and their corresponding features, reflecting their importance. The AHP results were integrated using a weighted linear combination within a GIS environment to generate the groundwater recharge potential map. Further, the CSI was used to reclassify the composite groundwater recharge potential map. The resultant map revealed three zones which were classified into high, moderate, and low potential zones founded on the CSI approximation. About 98.56 km2 (8.28%) of the study area is categorized as having low potential, 803.77 km2 (67.54%) falls within the moderate potential zone and 287.71 km2 (24.18%) falls in the high potential zone.

Small-scale farmers perception, adaptation choices to climate change and existing barriers: Evidence from northern Ethiopia

Climate change continues to significantly impact agricultural production in Ethiopia that have encountered several environmental problems in recent decades. The study site is one of the areas vulnerable to climate change and influenced by climate variability. This study intends to identify small-scale farmers’ adaptation options, their perception and existing barriers in northern Ethiopia. It is based on an analysis of cross-sectional data collected from 193 randomly selected farm household heads. A multivariate probit model was employed to analyze the factors influencing farmers’ climate change adaptation choices. Constraint Facing Index (CFI) technique was also constructed to identify the existing barriers. The findings indicate that a significant number of farmers (91.2%) reported a trend toward rising temperatures. Similarly, 86.01% of the farmers indicated that the perceived rainfall trend has decreased. Adjusting planting dates (56.5%), integrated soil fertility management practices (47.7%), early maturing crop varieties (53.4%), terracing for soil and water conservation (54.9%), and income diversification (55.4%) were the most common climate change adaptation options practiced by farm households in the study area. Some of these adaptation options have significant and complementary relationships. The farmer’s choice of adaptation option was highly constrained by institutional factors and all these identified factors can be possibly addressed through a better institutional service provision system, for a better farm-level adaptation while considering demographic characteristics as well.

Toward Memory-Efficient Analog Design Using Precomputed Lookup Tables

Analog design productivity remains a challenge in the digitally driven semiconductor chip design field. Knowledge-based and simulation-based analog automation approaches have not achieved widespread acceptance in the analog design community. Systematic analog design using precomputed lookup tables (LUTs) is a promising approach that can address the design productivity challenge. Although modern computing systems have powerful memory capabilities, which make the LUT approach viable, reducing the memory footprint of the LUTs remains a challenge. A memory-efficient design technique using LUTs is proposed by using an incomplete grid in the MOSFET degrees-of-freedom (DoFs) space. An efficient indexing technique for the incomplete grid is also proposed, using a precomputed offset array in various scenarios, such as two-sided constraints and three-dimensional LUTs. The results show that the proposed technique can achieve up to a 67% reduction in memory footprint, in addition to improving LUT generation time and query performance.

Early-stage soil organic carbon stabilization in conservation agriculture-based cereal systems

In South Asia, the sustainability of conventional tillage and input-intensive cereal-based cropping systems (such as rice-rice and rice-wheat) is under scrutiny due to soil organic carbon depletion, stagnant productivity, and adverse environmental impacts stemming from greenhouse gas emissions (N2O, CH4, and CO2). Against this backdrop, a long-term field experiment was initiated in 2009 at three sites in India (Karnal, Patna, and Aduthurai) and one site in Bangladesh (Gazipur) to assess four scenarios (S) reflecting current and future diversified crop rotations and conservation agriculture (CA) practices: S1 - double cereal rotation with conventional practices; S2 - double cereal plus legume rotation with partial CA; S3 - double cereal plus legume rotation with full CA; and S4 - futuristic diversified cereal-legume rotations with full CA. This study delved into the dynamics and stabilization of soil organic carbon across all scenarios and sites. Replicated soil samples were collected from depths of 0–15 cm and 15–30 cm after two crop cycles. We analyzed Walkley-Black carbon (WBC), total organic carbon (TOC), and various carbon pools with different oxidizability, determining the amount of carbon stabilized under CA-based scenarios and identifying optimal systems for carbon economy in South Asia. On average, active and passive carbon pools, TOC, and WBC stocks followed the order: S4 > S3 > S2 > S1 at all sites, except Gazipur, where the order was S3 > S4 > S2 > S1. CA practices stabilized applied carbon inputs to soil organic carbon at an annual rate of 1.7 %, with greater stabilization observed under S4 (11.7 %) > S3 (10.6 %) > S2 (7.8 %), regardless of location. Rice-rice sites exhibited a higher carbon stabilization rate (13.4 %) compared to rice-wheat sites (6.7 %). A significant proportion of stabilized carbon (63 %) was allocated to passive pools in soils under S2, S3, and S4, highlighting CA's potential to enhance carbon stability. Using a linear indexing technique, we identified that both S3 and S4 are conducive to better carbon stabilization, yield sustainability, and environmental co-benefits. Consequently, full CA systems with best management practices (S3) and best management practices with crop diversification (S4) are recommended for sustainable crop production in the major double cereal growing regions of South Asia, including India and Bangladesh.

A novel coal quality index and its estimation using diffuse reflectance spectroscopy

ABSTRACT An essential component of coal’s efficient use and large-scale environmental impact management is evaluating its quality. This study introduces a novel quality indexing technique that incorporates multiple coal properties, providing a comprehensive measure of coal quality. Conventional methods for assessing coal quality, such as proximate and ultimate analyses, are often time-consuming, costly, and require extensive sample preparation, prompting the need for a more efficient approach. In this study, we used the minimum dataset (MDS) approach to estimate a comprehensive coal quality index (CQI) and show that the diffuse reflectance spectroscopy (DRS) may be used as a rapid, nondestructive technique for estimating CQI as a single quality indicator for coal. A total of 212 coal samples of different ranks and grades are collected from the Tertiary and Gondwana coal basins of India. Ten chemical properties of coal were measured following the conventional laboratory-based methods. The spectral responses of the coal samples were measured across the visible-NIR-SWIR (350–2500 nm) range. The MDS approach was adopted while estimating the CQI from the lab-based coal quality parameters. We also examined different chemometric models for estimating CQI values from spectroscopic data. Results showed that the feature selection-based partial-least-square regression (PLSRFS) model may be used to estimate CQI with coefficient of determination (R2) values as high as 0.7 with root-mean-squared error (RMSE) of 0.06. While CQI serves as a single coal quality parameter, the capability to use the DRS approach as a rapid and noninvasive sensing approach for coal quality assessment provides new opportunity to characterize coal quality in an instant and cost-effective manner, especially, when a large number of coal samples have to be tested. The integration of the CQI with DRS could facilitate better decision-making in coal utilization and management, leading to optimized performance and reduced environmental impact.

Integrated Geospatial and Geostatistical Multi-Criteria Evaluation of Urban Groundwater Quality Using Water Quality Indices

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (>150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities.

A Study of New Indexing Techniques for Multimodal Identification Using Iris, Fingerprint, and Face Biometrics

This paper explores innovative indexing techniques for multimodal biometric identification systems, focusing on iris, fingerprint, and facial recognition technologies. With the proliferation of digital identity verification needs, traditional single-modal biometric systems often fall short in terms of accuracy, speed, and security. Multimodal biometric systems, which integrate multiple biological characteristics, are becoming crucial for enhancing identification performance. This study aims to present a comprehensive analysis of new indexing methods that can significantly improve the efficiency, accuracy, and security of multimodal biometric identification systems.

Deep retino-network for automatic quantification of diabetic retinopathy

<p>Diabetic retinopathy (DR) is the ocular manifestation of the systemic disease. Since it is the most prevalent cause of blindness in the world, it demands a significant amount of therapeutic attention. As a result, a precise assessment of the DR condition as well as its evolution is very important for treatment. In this work, an automated quantification of diabetic retinopathy state (AQDRS) using fundus images is proposed. The state of DR is classified into 0 (low) to 3 (high) with the help of a deep retino-network (DRN). Before the classification by DRN, an image down-sampling scheme is employed. A DRN consists of convolution layer and max-pooling layers to extract the deep retina features and fully connected layer (FCL) for AQDRS where feed-forward neural network is employed for the classification. The performance of AQDRS by DRN for grading DR is evaluated using methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (MESSIDOR) database. Results show that the AQDRS by DRN can able to extract the relevant discriminative information for grading the fundus image. The average accuracy on normal images in MESSIDOR database is 97.9% and it is 95.3% for DR images.</p>

A Study on Exploring and Prioritizing Critical Risks in Construction Project Assessment

According to Mc Kinsey Report (2015) large construction projects take 20% large time and 80% more budget to get finished. This highlights the importance of Risk Assessment to ensure work safety, cost management and quality construction.The study aims to identify and investigate critical risks in the evaluation of construction projects by utilizing the Principal Component Analysis (PCA) and the Weighted Average Index method. The risk factors for project assessment were identified in the following domains: Budget and Cost Management Risk, Schedule and Time Management Risk, Scope and Planning Risk, Safety and Regulatory Compliance Risk, Resource Management Risk, Communication and Stakeholder Management Risk, and Environmental and Sustainability Risk, through an extensive literature review and expert interviews. A survey was distributed to building operators in Hyderabad, India. The Weighted Average Index technique was used to evaluate 180 completed responses and identify risk indicators. To find underlying patterns, Principal Component Analysis (PCA) was used to gain a better understanding of the connections between these variables. Results identified reliance on essential resources, insufficient risk assessment, cash flow restrictions, and safety issues as top considerations, with currency exchange rate swings and delayed information dissemination ranking lower but still substantial. These insights provide essential advise to stakeholders in order to successfully minimize risks and improve project results. By using systematic risk assessment and management methodologies, building projects in Hyderabad and elsewhere may handle hurdles more effectively, assuring long-term sustainability and resilience.

Analyzing the critical delay factors for construction projects in the public sector using relative importance index and machine learning techniques

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.