Search Results (273)

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges. Full article

A vast proportion of scientific data remains locked behind dynamic web interfaces, often called the deep web—inaccessible to conventional search engines and standard crawlers. This gap between data availability and machine usability hampers the goals of open science and automation. While registries like FAIRsharing offer structured metadata describing data standards, repositories, and policies aligned with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, they do not enable seamless, programmatic access to the underlying datasets. We present FAIRFind, a system designed to bridge this accessibility gap. FAIRFind autonomously discovers, interprets, and operationalizes access paths to biological databases on the deep web, regardless of their FAIR compliance. Central to our approach is the Deep Web Communication Protocol (DWCP), a resource description language that represents web forms, HyperText Markup Language (HTML) tables, and file-based data interfaces in a machine-actionable format. Leveraging large language models (LLMs), FAIRFind combines a specialized deep web crawler and web-form comprehension engine to transform passive web metadata into executable workflows. By indexing and embedding these workflows, FAIRFind enables natural language querying over diverse biological data sources and returns structured, source-resolved results. Evaluation across multiple open-source LLMs and database types demonstrates over 90% success in structured data extraction and high semantic retrieval accuracy. FAIRFind advances existing registries by turning linked resources from static references into actionable endpoints, laying a foundation for intelligent, autonomous data discovery across scientific domains. Full article

Nested named entity recognition (NER) is a task that identifies hierarchically structured entities, where one entity can contain other entities within its span. This study introduces a nested NER model for few-shot learning environments, addressing the difficulty of building extensive datasets for general named entities. We enhance the Biaffine nested NER model by modifying its output layer to incorporate label semantic information through a novel label description embedding (LDE) approach, improving performance with limited training data. Our method replaces the traditional biaffine classifier with a label attention mechanism that leverages comprehensive natural language descriptions of entity types, encoded using BERT to capture rich semantic relationships between labels and input spans. We conducted comprehensive experiments on four benchmark datasets: GENIA (nested NER), ACE 2004 (nested NER), ACE 2005 (nested NER), and CoNLL 2003 English (flat NER). Performance was evaluated across multiple few-shot scenarios (1-shot, 5-shot, 10-shot, and 20-shot) using F1-measure as the primary metric, with five different random seeds to ensure robust evaluation. We compared our approach against strong baselines including BERT-LSTM-CRF with nested tags, the original Biaffine model, and recent few-shot NER methods (FewNER, FIT, LPNER, SpanNER). Results demonstrate significant improvements across all few-shot scenarios. On GENIA, our LDE model achieves 45.07% F1 in five-shot learning compared to 30.74% for the baseline Biaffine model (46.4% relative improvement). On ACE 2005, we obtain 44.24% vs. 32.38% F1 in five-shot scenarios (36.6% relative improvement). The model shows consistent gains in 10-shot (57.19% vs. 49.50% on ACE 2005) and 20-shot settings (64.50% vs. 58.21% on ACE 2005). Ablation studies confirm that semantic information from label descriptions is the key factor enabling robust few-shot performance. Transfer learning experiments demonstrate the model’s ability to leverage knowledge from related domains. Our findings suggest that incorporating label semantic information can substantially enhance NER models in low-resource settings, opening new possibilities for applying NER in specialized domains or languages with limited annotated data. Full article

The use of intelligent crop recommendation systems has become crucial in the era of smart agriculture to increase yield and enhance resource utilization. In this study, we compared different machine learning (ML), and deep learning (DL) models utilizing structured tabular data for crop recommendation. During our experimentation, both ML and DL models achieved decent performance. However, their architectures are not suited for setting up conversational systems. To overcome this limitation, we converted the structured tabular data to descriptive textual data and utilized it to fine-tune Large Language Models (LLMs), including BERT and GPT-2. In comprehensive experiments, we demonstrated that GPT-2 achieved a higher accuracy of 99.55% than the best-performing ML and DL models, while maintaining precision of 99.58% and recall of 99.55%. We also demonstrated that GPT-2 not only keeps up competitive accuracy but also offers natural language interaction capabilities. Due to this capability, it is a viable option to be used for real-time agricultural decision support systems. Full article

This article examines the theological trajectories of Watchman Nee (1903–1972) and Witness Lee (1905–1997) on sanctification, union with Christ, and deification, situating their contributions within recent reappraisals of the doctrine of theosis in the academy. Though deification was universally affirmed by the early church and retained in various forms in medieval and early Protestant theology, post-Reformation Western Christianity marginalized this theme in favor of juridical and forensic soteriological categories. Against this backdrop, Nee and Lee offer a theologically rich, biblically grounded, and experientially oriented articulation of deification that warrants greater scholarly attention. Drawing from the Keswick Holiness tradition, patristic sources, and Christian mysticism, Nee developed a soteriology that integrates justification, sanctification, and glorification within an organic model of progressive union with God. Though he does not explicitly use the term “deification”, the language he employs regarding union and participation closely mirrors classical expressions of Christian theosis. For Nee, sanctification is not merely moral improvement but the transformative increase of the divine life, culminating in conformity to Christ’s image. Lee builds upon and expands Nee’s participatory soteriology into a comprehensive theology of deification, explicitly referring to it as “the high peak of the divine revelation” in the Holy Scriptures. For Lee, humans become God “in life and nature but not in the Godhead”. By employing the phrase “not in the Godhead”, Lee upholds the Creator–creature distinction—i.e., humans never participate in the ontological Trinity or God’s incommunicable attributes. Yet, in the first portion of his description, he affirms that human beings undergo an organic, transformative process by which they become God in deeply significant ways. His framework structures sanctification as a seven-stage process, culminating in the believer’s transformation and incorporation into the Body of Christ to become a constituent of a corporate God-man. This corporate dimension—often overlooked in Western accounts—lies at the heart of Lee’s ecclesiology, which he sees as being consummated in the eschatological New Jerusalem. Ultimately, this study argues that Nee and Lee provide a coherent, non-speculative model of deification that integrates biblical exegesis, theological tradition, and practical spirituality, and thus, present a compelling alternative to individualistic and forensic soteriologies while also highlighting the need for deeper engagement across global theological discourse on sanctification, union with Christ, and the Triune God. Full article

Dementia is a neurodegenerative disorder characterized by progressive cognitive impairment that significantly affects daily living. Early detection of Alzheimer’s disease—the most common form of dementia—remains essential for prompt intervention and treatment, yet clinical diagnosis often requires extensive and resource-intensive procedures. This article explores the effectiveness of automated Natural Language Processing (NLP) methods for identifying Alzheimer’s indicators from audio transcriptions of the Cookie Theft picture description task in the PittCorpus dementia database. Five NLP approaches were compared: a classical Tf–Idf statistical representation and embeddings derived from large language models (GloVe, BERT, Gemma-2B, and Linq-Embed-Mistral), each integrated with a logistic regression classifier. Transcriptions were carefully preprocessed to preserve linguistically relevant features such as repetitions, self-corrections, and pauses. To compare the performance of the five approaches, a stratified 5-fold cross-validation was conducted; the best results were obtained with BERT embeddings (84.73% accuracy) closely followed by the simpler Tf–Idf approach (83.73% accuracy) and the state-of-the-art model Linq-Embed-Mistral (83.54% accuracy), while Gemma-2B and GloVe embeddings yielded slightly lower performances (80.91% and 78.11% accuracy, respectively). Contrary to initial expectations—that richer semantic and contextual embeddings would substantially outperform simpler frequency-based methods—the competitive accuracy of Tf–Idf suggests that the choice and frequency of the words used might be more important than semantic or contextual information in Alzheimer’s detection. This work represents an effort toward implementing user-friendly software capable of offering an initial indicator of Alzheimer’s risk, potentially reducing the need for an in-person clinical visit. Full article

The automation of smart building design processes remains a significant challenge, particularly in translating complex natural language requirements into structured design parameters within Computer-Aided Design (CAD) environments. Traditional design workflows rely heavily on manual input, which can be inefficient, error-prone, and time-consuming, limiting the integration of adaptive, real-time inputs. To address this issue, this study proposes an intelligent Natural Language Processing (NLP)-based workflow for automating the conversion of design briefs into CAD-readable parameters. This study proposes a five-step integration framework that utilizes NLP to extract key design requirements from unstructured inputs such as emails and textual descriptions. The framework then identifies optimal integration points—such as APIs, direct database connections, or plugin-based solutions—to ensure seamless adaptability across various CAD systems. The implementation of this workflow has the potential to enable the automation of routine design tasks, reducing the reliance on manual data entry and enhancing efficiency. The key findings demonstrate that the proposed NLP-based approach may significantly streamline the design process, minimize human intervention while maintaining accuracy and adaptability. By integrating NLP with CAD environments, this study contributes to advancing intelligent design automation, ultimately supporting more efficient, cost-effective, and scalable smart building development. These findings highlight the potential of NLP to bridge the gap between human input and machine-readable data, providing a transformative solution for the architectural and construction industries. Full article

Remote Sensing Image Change Captioning (RSICC) aims to generate natural language descriptions of changes in bi-temporal remote sensing images, providing more semantically interpretable results than conventional pixel-level change detection methods. However, existing approaches often rely on stacked Transformer modules, leading to suboptimal feature discrimination. Moreover, direct difference computation after feature extraction tends to retain task-irrelevant noise, limiting the model’s ability to capture meaningful changes. This study proposes a novel cross-spatial Transformer and symmetric difference localization network (CTSD-Net) for RSICC to address these limitations. The proposed Cross-Spatial Transformer adaptively enhances spatial-aware feature representations by guiding the model to focus on key regions across temporal images. Additionally, a hierarchical difference feature integration strategy is introduced to suppress noise by fusing multi-level differential features, while residual-connected high-level features serve as query vectors to facilitate bidirectional change representation learning. Finally, a causal Transformer decoder creates accurate descriptions by linking visual information with text. CTSD-Net achieved BLEU-4 scores of 66.32 and 73.84 on the LEVIR-CC and WHU-CDC datasets, respectively, outperforming existing methods in accurately locating change areas and describing them semantically. This study provides a promising solution for enhancing interpretability in remote sensing change analysis. Full article

Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper proposes a fuzzy logic-based framework for incident categorization and prioritization, integrating natural language processing (NLP) with a formal system of fuzzy inference. The framework transforms semantic embeddings from incident reports into fuzzy sets, allowing incident severity and urgency to be represented as degrees of membership in multiple categories. A mathematical model based on Mamdani-type inference and triangular membership functions is developed to capture and process imprecise inputs. The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise. A comparative evaluation is conducted against two baseline models: a fine-tuned BERT classifier and a traditional support vector machine (SVM). Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%) for medium-severity incidents, where linguistic ambiguity is most prevalent. Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment. These findings suggest that fuzzy modeling offers a mathematically sound and operationally effective solution for managing uncertainty in text-based incident management, contributing to the broader understanding of mathematical modeling in enterprise-scale social phenomena. Full article

This paper presents a hybrid framework for dynamic incident prioritization in enterprise environments, combining fuzzy logic, natural language processing, and reinforcement learning. The proposed system models incident descriptions through semantic embeddings derived from advanced text analytics, which serve as state representations within a fuzzy Q-learning model. Severity and urgency are encoded as fuzzy variables, enabling the prioritization process to manage linguistic vagueness and operational uncertainty. A mathematical formulation of the fuzzy Q-learning algorithm is developed, including fuzzy state definition, reward function design, and convergence analysis. The system continuously updates its prioritization policy based on real-time feedback, adapting to evolving patterns in incident reports and resolution outcomes. Experimental evaluation on a dataset of 10,000 annotated incident descriptions demonstrates improved prioritization accuracy, particularly for ambiguous or borderline cases, and reveals a 19% performance gain over static fuzzy and deep learning-based baselines. The results validate the effectiveness of integrating fuzzy inference and reinforcement learning in incident management tasks requiring adaptability, transparency, and mathematical robustness. Full article

Constraint satisfaction problems (CSPs) are widely used in domains such as product configuration, scheduling, and resource allocation. However, formulating constraint models remains a significant challenge that often requires specialized expertise in constraint programming (CP). This study introduces the Automatic Constraint Model Generator (ACMG), a novel framework that leverages fine-tuned large language models (LLMs) to automate the translation of natural language problem descriptions into formal CSP models. The ACMG employs a multi-step process involving semantic entity extraction, constraint model generation, and iterative validation using the MiniZinc solver. Our approach achieves state-of-the-art (SOTA) or near-SOTA results, demonstrating the viability of LLMs in simplifying the adoption of CP. Its key contributions include a high-quality dataset for fine-tuning, a modular architecture with specialized LLM components, and empirical validation which shows its promising results for complex configuration tasks. By bridging the gap between natural language and formal constraint models, the ACMG significantly lowers the barrier to CP, making it more accessible to non-experts while maintaining a high level of robustness for industrial applications. Full article

Wine production is an important sector of the food industry in Bulgaria, contributing to both economic development and cultural heritage. The present study aims to show how natural language processing (NLP) and machine learning methods can be applied to analyze expert-written Bulgarian wine descriptions and to extract patterns related to wine quality and style. Based on a bilingual dataset of reviews (in Bulgarian and English), semantic analysis, classification, regression and clustering models were used, which combine textual and structured data. The descriptions were transformed into numerical representations using a pre-trained language model (BERT), after which algorithms were used to predict style categories and ratings. Additional sentiment and segmentation analyses revealed differences between wine types, and clustering identified thematic structures in the expert language. The comparison between predefined styles and automatically derived clusters was evaluated using metrics such as Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). The resulting analysis shows that text descriptions contain valuable information that allows for automated wine profiling. These findings can be applied by a wide range of stakeholders—researchers, producers, retailers, and marketing specialists. Full article

The trajectory data collected by an Automatic Identification System (AIS) are an essential resource for various ships, and effective filtering and querying approaches are fundamental for managing these data. Natural language has become the preferred way to express complex query requirements and intents, due to its intuitiveness and universal applicability. In light of this, we propose a natural language-based AIS trajectory query approach using large language models. Firstly, trajectory textualization was designed to convert the time sequences of trajectories into semantic descriptions by segmenting AIS trajectories, extracting semantics, and constructing trajectory documents. Then, the semantic trajectory querying was completed by rewriting queries, retrieving AIS trajectories, and generating answers. Finally, comparative experiments were conducted to highlight the improvements in accuracy and relevance achieved by our proposed method over traditional approaches. Furthermore, a human study demonstrated the user-friendly interaction experience enabled by our approach. Additionally, we conducted an ablation study to illustrate the significant contributions of each module within our framework. The results demonstrate that our approach effectively bridges the gap between AIS trajectories and natural language query intents, offering an intuitive, user-friendly, and accessible solution for domain experts and novices. Full article

Identifying jewelry pieces presents a significant challenge due to the wide range of styles and designs. Currently, precise descriptions are typically limited to industry experts. However, translators and interpreters often require a comprehensive understanding of these items. In this study, we introduce an innovative approach to automatically identify and describe jewelry using neural networks. This method enables translators and interpreters to quickly access accurate information, aiding in resolving queries and gaining essential knowledge about jewelry. Our model operates at three distinct levels of description, employing computer vision techniques and image captioning to emulate expert analysis of accessories. The key innovation involves generating natural language descriptions of jewelry across three hierarchical levels, capturing nuanced details of each piece. Different image captioning architectures are utilized to detect jewels in images and generate descriptions with varying levels of detail. To demonstrate the effectiveness of our approach in recognizing diverse types of jewelry, we assembled a comprehensive database of accessory images. The evaluation process involved comparing various image captioning architectures, focusing particularly on the encoder–decoder model, crucial for generating descriptive captions. After thorough evaluation, our final model achieved a captioning accuracy exceeding 90%. Full article

As oil and gas exploration has deepened, the complexity and risk of well repair operations has increased, and the traditional description methods based on text and charts have limitations in accuracy and efficiency. Therefore, this study proposes a well repair scene description method based on visual language technology and a cross-modal coupling prompt enhancement mechanism. The research first analyzes the characteristics of well repair scenes and clarifies the key information requirements. Then, a set of prompt-enhanced visual language models is designed, which can automatically extract key information from well site images and generate structured natural language descriptions. Experiments show that this method significantly improves the accuracy of target recognition (from 0.7068 to 0.8002) and the quality of text generation (the perplexity drops from 3414.88 to 74.96). Moreover, this method is universal and scalable, and it can be applied to similar complex scene description tasks, providing new ideas for the application of well repair operations and visual language technology in the industrial field. In the future, the model performance will be further optimized, and application scenarios will be expanded to contribute to the development of oil and gas exploration. Full article