Contextual Relationships Research Articles

NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning

BackgroundsThe single-pass long reads generated by third-generation sequencing technology exhibit a higher error rate. However, the circular consensus sequencing (CCS) produces shorter reads. Thus, it is effective to manage the error rate of long reads algorithmically with the help of the homologous high-precision and low-cost short reads from the Next Generation Sequencing (NGS) technology.MethodsIn this work, a hybrid error correction method (NmTHC) based on a generative neural machine translation model is proposed to automatically capture discrepancies within the aligned regions of long reads and short reads, as well as the contextual relationships within the long reads themselves for error correction. Akin to natural language sequences, the long read can be regarded as a special “genetic language” and be processed with the idea of generative neural networks. The algorithm builds a sequence-to-sequence(seq2seq) framework with Recurrent Neural Network (RNN) as the core layer. The before and post-corrected long reads are regarded as the sentences in the source and target language of translation, and the alignment information of long reads with short reads is used to create the special corpus for training. The well-trained model can be used to predict the corrected long read.ResultsNmTHC outperforms the latest mainstream hybrid error correction methods on real-world datasets from two mainstream platforms, including PacBio and Nanopore. Our experimental evaluation results demonstrate that NmTHC can align more bases with the reference genome without any segmenting in the six benchmark datasets, proving that it enhances alignment identity without sacrificing any length advantages of long reads.ConclusionConsequently, NmTHC reasonably adopts the generative Neural Machine Translation (NMT) model to transform hybrid error correction tasks into machine translation problems and provides a novel perspective for solving long-read error correction problems with the ideas of Natural Language Processing (NLP). More remarkably, the proposed methodology is sequencing-technology-independent and can produce more precise reads.

Landslide extraction from aerial imagery considering context association characteristics

Accurate extraction of landslide information is crucial for timely disaster emergency response, yet this process faces significant challenges due to the interference of bare land and vegetation cover as well as the variability in landslide scales. Furthermore, the scarcity of comprehensive landslide datasets has hindered the in-depth exploration of deep learning techniques in this domain. Addressing these issues, this study introduces a novel framework for landslide extraction that leverages the characteristics of context association. In the encoding phase, a two-branch multiscale context feature extraction module (TMCFM) is established, which captures the contextual relationships across different scales through an attention mechanism while concurrently extracting context information within the same scale. To refine the interplay between features at different levels, self-attention is utilized, allowing for the effective fusion of context information at both ends of the same scale through feature fusion exploiting static and dynamic context feature modules (FSDC). Building upon this, this study develops a deeply supervised classifier (DSC) that enhances the network’s discriminative capabilities in the prediction phase via six auxiliary branches. Additionally, this study contributes a new aerial image dataset for landslide extraction created through meticulous visual interpretation. The proposed method is thoroughly compared with 17 contemporary deep learning methods, demonstrating an increase in the intersection-over-union (IoU) metric of 0.92%-16.94% over these methods. The robustness and superiority of the proposed approach are further validated through various discussions and analyses.

An investigation of the interrelationship among circular supply chain management indicators in small and medium enterprises

Circular Supply Chain Management (CSCM) is gaining prominence among diverse stakeholders, practitioners, and scholars. However, its adoption remains limited, particularly within Small and Medium Enterprises (SMEs). This study employs Interpretative Structural Modeling (ISM), specifically tailored for SMEs, to elucidate the contextual relationships among CSCM indicators. Furthermore, it employs the Matrice d’Impacts Croisés Multiplication Appliqué à un Classement (MICMAC) analysis to categorize these indicators into driving- dependence power quadrants. Thirteen CSCM indicators are identified and classified into three sustainability dimensions: economic, environmental, and social. The ISM model comprises four levels, with employees’ exposure to hazardous materials at level one, followed by ten indicators at level two, one at level three (reuse, remanufacturing, recycling complexity), and one at level four (eco-material). MICMAC analysis reveals that none of the indicators falls into the autonomous quadrant. Employees’ exposure to hazardous materials is categorized in the dependent indicators’ quadrant, while ten indicators belong to the linkage quadrant. The independent quadrant includes two indicators: eco-material and reuse, remanufacturing, and recycling complexity. SMEs can utilize these CSCM indicators as an initial step toward circularity implementation. The recommended implementation sequence follows the ISM model hierarchy, starting with level four indicators and progressing through levels three, two, and one, acknowledging the influence of higher-level indicators on lower-level ones.

Community Engagement, The Context for Teachers’ Work-Efficiency? A Tism Approach

Purpose: The main purpose of this research work is to find the scope of enhancing the teachers’ work-efficiency through the logical linkages of emotional exhaustion, attitude, knowledge management (KM), and community-engagement. Methods: With a qualitative research approach, i.e.: Total Interpretive Structural Modeling (TISM), this study tried to explore the intricate relationships between the aforesaid components in Indian context. With the overview of community engagement having its significant importance in educational settings, research problem is defined, which guided for extensive literature review, the core part of TISM. The study utilizes fundaments of ISM along with the contextual relationships to reach the hierarchical model of TISM to visually represent the relationships between the identified factors, reflecting their driving power and dependency power. Results & discussions: This study established the complete interpretations of the linkages among variables through developed model of TISM. Further, this research-work shows how the identified factors collectively contribute to teachers’ overall work-efficiency, where online customer-engagement and offline customer-engagement forms the context for the attitude and KM, and emotional exhaustion that are in turn leads to work-efficiency of teachers. Implications of the study: This study highlights the scopes for comprehensive strategies to enhance work-efficiency of teachers through fostering positive attitudes and effective KM on the ground of community engagement, where emotional exhaustion is managed adequately.

Cognitive skills and economic growth in the twenty-first century: Evidence from PISA and cognitive ability studies

In recent years, some educational researchers have questioned the theory of a robust relationship between test scores in international large-scale assessments such as PISA and lagged economic growth. However, both the proponents and opponents of such a human capital approach have mainly focused on earlier growth patterns during the twentieth century. Hence, the current article analysed the relationship between PISA 2000 and economic growth in 2010–2019 for 31 countries, and composite cognitive skills for 80 countries and their relationships with economic growth in 2010–2019. The results indicate that PISA scores or other cognitive skill measures could to some extent predict economic growth between 2010 and 2019, even when controlling for a significant number of variables. In a consecutive analysis, the relations were substantially associated with Middle Eastern countries because many countries from this region experienced political instability and decreasing oil revenues throughout this period. Hence, it is possible that regional and contextual relationships affect these patterns.

CONTEXTS.py (CS.py): A supervised contextual post-classification method to access multiple dimensions of complex geospatial objects

The qualitative dimensions of visible features in space can be captured by connecting spatial configurations arranged in a variety of different ways to diverse conceptual spaces. By conceptual spaces, we intend mental concepts describing specific spatial configurations present in a geographical area, defined by the contextual relationships among their constitutive elements.This paper presents a new supervised post-classification method allowing the extraction of semantically complex spatial objects from a single image of the Earth as, for instance, diverse conceptual spaces referring to multiple dimensions of land use (temporal, cultural, social, etc.).Computationally, our method is operationalised by CONTEXTS.py (CS.py), a plugin written in Python for QGIS. CS.py relies on training areas, defined by the user at diverse scales, to identify and extract in the input image conceptual spaces whose spatial contexts have the same spatial features present in the training areas.Applied to a case study on the island of Sicily, where millennial land use dynamics have resulted in a mosaic landscape, CS.py could detect from an orthophoto diverse conceptual spaces of land use in an area ordinarily classified as one land cover, thus expanding the capabilities of geospatial analysis to reach additional qualitative dimensions of information from image data.•CS.py simplifies a supervised contextual post-classification routine in an easy-to-use, practical and accessible QGIS plugin;•CS.py joins a family of tools for supervised object-based classification (e.g. OTB, GRASS), providing, additionally, the possibility to include contextual information as spatial criteria to train the classification routine.•CS.py has broad applications in different disciplines investigating landscape from quantitative and qualitative perspectives, allowing both, as in multiple environments.

Linguistic-based Mild Cognitive Impairment detection using Informative Loss

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.

Railway accident causation prediction with improved transformer model based on lexical information and contextual relationships

The railway system is a prime example of a safety-critical system. Predicting the causes of railway accidents holds immense significance in enhancing railway transportation safety. Previous approaches to railway causation analysis have encountered huge challenges regarding data processing and analytical capabilities. To address this concern, this paper proposes an innovative deep model framework based on the Transformer architecture that utilizes historical data on railway equipment accidents to predict the causes behind such incidents. Firstly, this paper proposes the utilization of Convolutional Block Attention in the domain of text processing, serving as a lexical encoder to augment word semantics acquisition in accident texts. Subsequently, in order to address the deficiency of traditional Transformers that lack positional representation information, we propose incorporating a BiGRU (Bidirectional Gated Recurrent Unit) as a contextual positional information encoder to capture contextual positional information in railway accident data effectively. Finally, considering that accident data reports are discrete tabular data, this study suggests employing cue word techniques for preprocessing accident data to alleviate the model's learning burden. We applied the proposed model to the FRA (Federal Railroad Administration) dataset. The results demonstrate that our model surpasses the current state-of-the-art language models, exhibiting superior performance compared to the optimal model with a notable improvement of 3.56%, 0.42%, and 0.76% in Precision, Recall, and F1-score, respectively. Furthermore, our model accurately predicts accident categories prone to misjudgment even when trained on limited data, outperforming existing language models. The study findings will contribute to the prevention and management of railway accidents.

Unveiling the circular procurement Nexus: A hierarchical examination of barriers, interrelationships, and strategic insights

The integration of circularity into various functions, including procurement, is essential and is embraced by organizations for sustainable and profitable operations. Existing studies have explored circular procurement (CP) and its influencing factors. However, existing literature calls for a deeper examination of these factors to provide a smooth implementation guide for practitioners and to foster a comprehensive understanding of cause-and-effect relationships for future research by scholars. As a result, this study aims to investigate the interrelationship between CP barriers. Firstly, this study explores barriers through a thorough review of relevant literature with input from academic and industrial experts. Utilizing a classical triple-level framework, the work identifies and categorizes 21 barriers. Secondly, the study employs interpretive structural modeling (ISM) to examine the contextual relationships among the barriers. Thirdly, these barriers are classified into different categories using the MICMAC technique, based on their driving and dependence power. The research findings develop a hierarchical model of barriers, offering insights into which barriers are the most crucial and need immediate attention and how different barriers could influence each other. The study's unique contributions lie in its identification of hierarchical relationships between barriers and its categorization (independent, linkage, and dependent), contributing to tailored strategies for effective CP implementation.

The influence of corporate social responsibility on impulse buying

PurposeCorporate social responsibility (CSR) ISO standards have been noted as an essential marketing strategy by which firms can achieve consumer trust while improving environmental, social, and quality factors. This study discloses the contextual relationship between CSR ISO standards and sustainable impulse buying behavior. This study also looks to uncover the CSR ISO driving and linkage factors that motivate consumers to make sustainable impulsive purchases.Design/methodology/approachThree distinct research methods were employed in this research. First, a consumer expert opinion-based Interpretive Structural Modeling (ISM) approach was adopted to reveal the contextual relationship between CSR ISO factors and sustainable impulse buying behavior. Secondly, Matrice Impacts Croises Multiplication Appliques Classement (MICMAC) was used to examine these factors' driving and dependent power. In addition, Minitab package software was also used to check the statistical validation of ISM-MICMAC results.FindingsThe results indicate that although environmentally responsible CSR ISO 14001, socially responsible CSR ISO 26000, and consumer perception of product quality CSR ISO 9001 standards contain strong driving power, their dependent power was weak. All these CSR ISO factors (14,001, 26,000, and 9001) strongly impact each other and sustainable impulse buying. Therefore, these three CSR ISO factors have been placed at the bottom of the ISM model. The CSR ISO 14020 standard (labeling of the product), knowledge of CSR ISO standards, consumer trust, and advertising about CSR ISO standards have been placed in the middle. The mentioned factors have intense driving and dependent power and are classified as linkage factors for sustainable impulse buying. Impulse buying behavior has weak driving and strong dependent power, yet this factor strongly depends on other CSR ISO factors. Hence, this factor is placed at the top of the ISM model. In addition, the Minitab package software results indicate that ISM-MICMAC results are statistically valid.Originality/valueTo the best of our knowledge, this research is unique and examines the influence of CSR ISO factors on sustainable impulse buying in the context of Pakistani consumers. Secondly, our study has thoroughly investigated several CSR ISO factors and allied these factors in the context of consumer buying behavior. Third, several CSR ISO factors and impulse buying behavior were examined using a mix of ISM-MICAC and Minitab methods. Thus, including these steps in our study has led to the development of a novel technique.

An Adaptive Contextual Relation Model for Improving Response Generation

An Adaptive Contextual Relation Model for Improving Response Generation

Parallel feature enhancement and adaptive weighted feature fusion for semantic segmentation

To tackle the challenges posed by the insensitivity of current multi-scale networks to image detailed information and their limited capacity to model contextual relationships, our paper proposes a novel semantic segmentation network called LEFNet. It is based on Parallel Feature Enhancement (PFE) and Adaptive Weighted Feature Fusion (AWFF). In the coding stage, the PFE module performs posterior enhancement of multi-scale features by Detail Sharpening Attention (DSA) and High-level Dilation Fusion (HDF) methods. The DSA guides the learning of detailed information in low-level features. The HDF broadens the perceptual field, enabling the acquisition of richer high-level features. In the decoding stage, the AWFF module supersedes the conventional feature fusion methods. The AWFF module constructs perceptual factors for each multi-scale feature map, enabling weighted learning of features. It emphasizes features with stronger semantic information, making them more decisive in pixel classification. It integrates features more reasonably based on the relevance of global contextual information, fully releasing the expressive potential of encoded features. Our method achieves mIoU scores of 82.8%, 49.3%, and 45.4% on the Cityscapes, ADE 20 K, and COCO-Stuff 164 K datasets, respectively, reaching an advanced level on popular benchmarks. The experimental results show that LEFNet alleviates the challenge of insensitivity of multi-scale networks to image detailed information, improves its ability to model contextual relationships, and significantly improves segmentation performance.

Barriers for achieving sustainable agri supply chain: study in context to Indian MSMEs

ABSTRACT The micro, small, and medium enterprise (MSME) in the agriculture sector have become one of the most important pillars of the Indian economy as they provide employment opportunities with little capital outlay. This study identifies barriers to achieving sustainability in the Agri supply chain in India. The scope of the study was increased to determine the contextual relationships between the barriers and to rank them in relation to one another. The list of 11 barriers was enlisted after thorough literature and interaction with experts using the fuzzy-Delphi method. The causal relationship has been developed using Wings methodology. The results show that lack of information and mismanagement in handling the products after harvesting are crucial barriers to achieving sustainability in the Agri supply chain. The novelty of this work lies in identification of barriers achieving sustainability in Agri supply chain using integrated fuzzy-Delphi-Wings method. The stakeholders in the Agri supply chain will benefit from the research findings as they plan the strategic deployment of sustainability practices to achieve long-term competitive advantage.

Prospects and challenges of achieving structural transformation in Africa through the African Continental Free Trade Area

The African Continental Free Trade Area (AfCFTA) agreement seeks to integrate markets in Africa into one large market. With the growing concerns about continental integration, this study identifies prospects and challenges of achieving structural transformation in Africa through the AfCFTA. The study employs the Interactive Structural Modelling technique, which helps to develop a contextual relationship between the factors in terms of their driving power or dependence power. The results indicate that unequal trade and investment opportunities, poor infrastructure, poor access to development finance, and the degree of industrialisation have a high driving power, hence a greater potential to create a ripple effect on other factors associated with African integration. Based on the findings, this study recommends that a platform be created within the agreement where these challenges can be tackled. In this view, the establishment of various development finance institutions monitored and facilitated by the AfCFTA affiliates, could be explored.

AUD-Net: A Unified Deep Detector for Multiple Hyperspectral Image Anomaly Detection via Relation and Few-Shot Learning.

This article addresses the problem of the building an out-of-the-box deep detector, motivated by the need to perform anomaly detection across multiple hyperspectral images (HSIs) without repeated training. To solve this challenging task, we propose a unified detector [anomaly detection network (AUD-Net)] inspired by few-shot learning. The crucial issues solved by AUD-Net include: how to improve the generalization of the model on various HSIs that contain different categories of land cover; and how to unify the different spectral sizes between HSIs. To achieve this, we first build a series of subtasks to classify the relations between the center and its surroundings in the dual window. Through relation learning, AUD-Net can be more easily generalized to unseen HSIs, as the relations of the pixel pairs are shared among different HSIs. Secondly, to handle different HSIs with various spectral sizes, we propose a pooling layer based on the vector of local aggregated descriptors, which maps the variable-sized features to the same space and acquires the fixed-sized relation embeddings. To determine whether the center of the dual window is an anomaly, we build a memory model by the transformer, which integrates the contextual relation embeddings in the dual window and estimates the relation embeddings of the center. By computing the feature difference between the estimated relation embeddings of the centers and the corresponding real ones, the centers with large differences will be detected as anomalies, as they are more difficult to be estimated by the corresponding surroundings. Extensive experiments on both the simulation dataset and 13 real HSIs demonstrate that this proposed AUD-Net has strong generalization for various HSIs and achieves significant advantages over the specific-trained detectors for each HSI.

Computational linguistics at the crossroads: A comprehensive review of NLP advancements

New NLP breakthroughs have put Computational Linguistics at a crossroads. NLP's past, present, and future are covered. This review explains computational linguistics' creation with a brief history of linguistics and computer science. Early solutions processed and understood natural language using rule-based systems using manually constructed linguistic rules. Over time, these tactics became increasingly problematic as language became more complex and obscure. Statistical approaches transformed operations. Neural network-based machine learning methods are leading the area because they can learn complicated patterns and representations from large text collections. A data-driven model revolution in natural language processing enhanced language modelling, machine translation, and sentiment analysis. Next, NLP improvements for several tasks and applications are evaluated. Language understanding models that capture semantic nuances and contextual relationships use deep learning frameworks. Word embeddings and transformer-based architectures like GPT and BERT perform well on benchmark datasets for text classification, question answering, and named item identification. The paper also shows how NLP interacts with computer vision, voice processing, and other domains to show the merits and cons of cross-disciplinary research. Multimodal techniques that combine text, graphics, and audio may increase natural language processing and interpretation. The review discusses NLP's effects on prejudice, justice, and privacy. Responsible development and implementation are needed when NLP technology becomes widespread due to algorithmic bias and data privacy concerns. NLP research directions and concerns are reviewed. Existing models may meet standards but fail in practice.

Enhancing Network Attack Detection Accuracy through the Integration of Large Language Models and Synchronized Attention Mechanism

In this study, we propose a novel method for detecting cyberattack behaviors by leveraging the combined strengths of large language models and a synchronized attention mechanism. Extensive experiments conducted on diverse datasets, including server logs, financial behaviors, and comment data, demonstrate the significant advantages of this method over existing models such as Transformer, BERT, OPT-175B, LLaMa, and ChatGLM3-6B in key performance metrics such as precision, recall, and accuracy. For instance, on the server log dataset, the method achieved a precision of 93%, a recall of 91%, and an accuracy of 92%; on the financial behavior dataset, it reached a precision of 90%, a recall of 87%, and an accuracy of 89%; and on the comment data dataset, it excelled with a precision of 95%, a recall of 93%, and an accuracy of 94%. The introduction of a synchronized attention mechanism and a newly designed synchronized loss function proved especially effective, enhancing the method’s ability to process multi-source data and providing superior performance in identifying complex cyberattack patterns. Ablation experiments further validated the crucial roles of these innovations in boosting model performance: the synchronous attention mechanism substantially improved the model’s precision, recall, and accuracy to 93%, 89%, and 91% respectively, far exceeding other attention mechanisms. Similarly, the synchronized loss showcased a significant advantage, achieving the best performance across all tested metrics compared to traditional cross-entropy loss, focal loss, and MSE. These results underscore the method’s ability to deeply mine and analyze semantic information and contextual relationships within text data as well as to effectively integrate and process multimodal data, thereby offering strong technical support for the accurate and efficient detection of cyberattack behaviors.

Improving Oriented Object Detection by Scene Classification and Task-Aligned Focal Loss

Oriented object detection (OOD) can precisely detect objects with arbitrary direction in remote sensing images (RSIs). Up to now, the two-stage OOD methods have attracted more attention because of their high detection accuracy. However, the two-stage methods only rely on the features of each proposal for object recognition, which leads to the misclassification problem because of the intra-class diversity, inter-class similarity and clutter backgrounds in RSIs. To address the above problem, an OOD model combining scene classification is proposed. Considering the fact that each foreground object has a strong contextual relationship with the scene of the RSI, a scene classification branch is added to the baseline OOD model, and the scene classification result of input RSI is used to exclude the impossible categories. To focus on the hard instances and enhance the consistency between classification and regression, a task-aligned focal loss (TFL) which combines the classification difficulty with the regression loss is proposed, and TFL assigns lager weights to the hard instances and optimizes the classification and regression branches simultaneously. The ablation study proves the effectiveness of scene classification branch, TFL and their combination. The comparisons with 15 and 14 OOD methods on the DOTA and DIOR-R datasets validate the superiority of our method.

Text Summarization Based on Semantic Similarity

In the contemporary information age, the sheer volume of textual data poses a significant challenge for efficient comprehension and utilization. This project endeavors to address this challenge by developing a Text Summarization System grounded in semantic similarities. The primary goal is to create a robust and intuitive tool that extracts key information from large textual datasets, offering users a concise and meaningful summary. The proposed system employs advanced Natural Language Processing (NLP) techniques to analyze the semantic relationships within the text. Rather than relying solely on syntactic structures, the model identifies and leverages semantic similarities, such as shared concepts, themes, and contextual relationships, to distill the essential content. This approach enhances the summarization process by ensuring that the generated summaries reflect a deeper understanding of the underlying semantics, thereby capturing the core meaning of the text. Throughout the development of this project, the B.Tech student will delve into the intricacies of semantic analysis, exploring techniques to recognize and prioritize key concepts. The system's effectiveness will be evaluated through rigorous testing on diverse textual datasets, assessing its ability to generate coherent and relevant summaries across various domains. This project not only contributes to the field of NLP but also has practical applications in information retrieval, document summarization, and content curation. By providing an innovative solution to the challenges of information overload, the Text Summarization System based on semantic similarities offers a valuable tool for enhancing efficiency in information processing and decision-making. Index terms Text Summarization, Semantic Similarities, Natural Language Processing (NLP), Semantic Analysis, Information Retrieval, Document Summarization, Content Curation, Information Overload, Decision Making, Textual Data Analysis, Key Concept Recognition, Conceptual Relationships, Syntactic Structures, Semantic Understanding, Textual Datasets Evalution.

Artificial intelligence-based pre-conception stage construction budget decision-making model and tool for residential buildings

PurposeDeveloping a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.Design/methodology/approachDue to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).FindingsThis research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.Research limitations/implicationsThe current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.Originality/valueThis research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.