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Artigos de revistas sobre o tema "Interpretable By Design Architectures"

Autor: Grafiati

Publicado: 7 de dezembro de 2024

Última modificação: 19 de julho de 2025

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1

Pasupuleti, Murali Krishna. "Supersymmetric Quantum Neural Networks: Bridging Superalgebras and AI Architectures." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 48–59. https://doi.org/10.62311/nesx/rp0425.

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Abstract: This paper proposes a novel paradigm for quantum artificial intelligence: the design and implementation of Supersymmetric Quantum Neural Networks (S-QNNs) that explicitly integrate superalgebraic structures into quantum circuit-based AI architectures. By embedding the symmetry principles of supersymmetry—captured by quantum superalgebras such as osp(1∣2) and sl(1∣1)—into the computational fabric of quantum neural networks, we aim to create models that exhibit both structural elegance and computational efficiency. The resulting framework allows for interpretable, energy-efficient, and

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Zhang, Xinyu, Vincent C. S. Lee, Jia Rong, Feng Liu, and Haoyu Kong. "Multi-channel convolutional neural network architectures for thyroid cancer detection." PLOS ONE 17, no. 1 (2022): e0262128. http://dx.doi.org/10.1371/journal.pone.0262128.

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Early detection of malignant thyroid nodules leading to patient-specific treatments can reduce morbidity and mortality rates. Currently, thyroid specialists use medical images to diagnose then follow the treatment protocols, which have limitations due to unreliable human false-positive diagnostic rates. With the emergence of deep learning, advances in computer-aided diagnosis techniques have yielded promising earlier detection and prediction accuracy; however, clinicians’ adoption is far lacking. The present study adopts Xception neural network as the base structure and designs a practical fra

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Xie, Nan, and Yuexian Hou. "MMIM: An Interpretable Regularization Method for Neural Networks (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15933–34. http://dx.doi.org/10.1609/aaai.v35i18.17963.

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In deep learning models, most of network architectures are designed artificially and empirically. Although adding new structures such as convolution kernels in CNN is widely used, there are few methods to design new structures and mathematical tools to evaluate feature representation capabilities of new structures. Inspired by ensemble learning, we propose an interpretable regularization method named Minimize Mutual Information Method(MMIM), which minimize the generalization error by minimizing the mutual information of hidden neurons. The experimental results also verify the effectiveness of

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Di Gioacchino, Andrea, Jonah Procyk, Marco Molari, et al. "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection." PLOS Computational Biology 18, no. 9 (2022): e1010561. http://dx.doi.org/10.1371/journal.pcbi.1010561.

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Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequenc

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Feinauer, Christoph, Barthelemy Meynard-Piganeau, and Carlo Lucibello. "Interpretable pairwise distillations for generative protein sequence models." PLOS Computational Biology 18, no. 6 (2022): e1010219. http://dx.doi.org/10.1371/journal.pcbi.1010219.

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Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures have shown great performances, commonly attributed to the capacity to extract non-trivial higher-order interactions from the data. In this work, we analyze two different NN models and assess how close they are to simple pairwise distributions, which have been used in the past for similar problems. We present an approach for extracting pairwise model

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Geng, Xinyu, Jiaming Wang, Xiaolin Huang, Fanglin Chen, and Jun Xu. "ParseCaps: An Interpretable Parsing Capsule Network for Medical Image Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 3122–30. https://doi.org/10.1609/aaai.v39i3.32321.

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Deep learning has excelled in medical image classification, but its clinical application is limited by poor interpretability. Capsule networks, known for encoding hierarchical relationships and spatial features, show potential in addressing this issue. Nevertheless, traditional capsule networks often underperform due to their shallow structures, and deeper variants lack hierarchical architectures, thereby compromising interpretability. This paper introduces a novel capsule network, ParseCaps, which utilizes the sparse axial attention routing and parse convolutional capsule layer to form a pars

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Zhang, Zizhao, Han Zhang, Long Zhao, Ting Chen, Sercan Ö. Arik, and Tomas Pfister. "Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3417–25. http://dx.doi.org/10.1609/aaai.v36i3.20252.

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Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical way. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simplified architecture that requires minor code changes upon the original vision transformer. The benefits of the proposed judiciously-select

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Benfaress, Ilyass, Afaf Bouhoute, and Ahmed Zinedine. "Advancing Traffic Sign Recognition: Explainable Deep CNN for Enhanced Robustness in Adverse Environments." Computers 14, no. 3 (2025): 88. https://doi.org/10.3390/computers14030088.

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This paper presents a traffic sign recognition (TSR) system based on the deep convolutional neural network (CNN) architecture, which proves to be extremely accurate in recognizing traffic signs under challenging conditions such as bad weather, low-resolution images, and various environmental-impact factors. The proposed CNN is compared with other architectures, including GoogLeNet, AlexNet, DarkNet-53, ResNet-34, VGG-16, and MicronNet-BF. Experimental results confirm that the proposed CNN significantly improves recognition accuracy compared to existing models. In order to make our model interp

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Gao, Xinjian, Tingting Mu, John Yannis Goulermas, Jeyarajan Thiyagalingam, and Meng Wang. "An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts." IEEE Transactions on Image Processing 29 (2020): 3911–26. http://dx.doi.org/10.1109/tip.2020.2965275.

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Liu, Hao, Youchao Sun, Xiaoyu Wang, Honglan Wu, and Hao Wang. "NPFormer: Interpretable rotating machinery fault diagnosis architecture design under heavy noise operating scenarios." Mechanical Systems and Signal Processing 223 (January 2025): 111878. http://dx.doi.org/10.1016/j.ymssp.2024.111878.

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Sturm, Patrick Obin, and Anthony S. Wexler. "Conservation laws in a neural network architecture: enforcing the atom balance of a Julia-based photochemical model (v0.2.0)." Geoscientific Model Development 15, no. 8 (2022): 3417–31. http://dx.doi.org/10.5194/gmd-15-3417-2022.

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Abstract. Models of atmospheric phenomena provide insight into climate, air quality, and meteorology and provide a mechanism for understanding the effect of future emissions scenarios. To accurately represent atmospheric phenomena, these models consume vast quantities of computational resources. Machine learning (ML) techniques such as neural networks have the potential to emulate computationally intensive components of these models to reduce their computational burden. However, such ML surrogate models may lead to nonphysical predictions that are difficult to uncover. Here we present a neural

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Jacob, Stefan, and Christian Koch. "Unveiling weak auditory evoked potentials using data-driven filtering." Journal of the Acoustical Society of America 154, no. 4_supplement (2023): A141. http://dx.doi.org/10.1121/10.0023054.

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Auditory evoked potentials (AEPs) are commonly used to objectively evaluate sound perception in humans. Close to the hearing threshold and for low frequencies, efficient filtering of AEP from other brain activities is of major concern due to weak potentials and the requirement of long averaging times. Filtered AEP data are well-interpretable and useful, especially in medical and psychological diagnostics. Here, we present two data-driven approaches for efficient AEP filtering. First, neural networks of different architectures trained for EEG denoising are used to extract weak late-response AEP

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Zhou, Shuhui. "An exploration of KANs and CKANs for more efficient deep learning architecture." Applied and Computational Engineering 83, no. 1 (2024): 20–25. http://dx.doi.org/10.54254/2755-2721/83/2024glg0060.

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Deep learning has revolutionized the field of machine learning with its ability to discern complex patterns from voluminous data. Despite the success of Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), there is an ongoing quest for architectures that offer higher expressiveness with fewer parameters. This paper focuses on the Kolmogorov-Arnold Networks (KANs) and Convolutional Kolmogorov-Arnold Networks (CKANs), which integrate learnable spline functions for enhanced expressiveness and efficiency. This study designs a range of networks to compare KANs with MLPs and CKAN

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Koriakina, Nadezhda, Nataša Sladoje, Vladimir Bašić, and Joakim Lindblad. "Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection." PLOS ONE 19, no. 4 (2024): e0302169. http://dx.doi.org/10.1371/journal.pone.0302169.

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The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interest

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Diaz-Gomez, Liliana, Andres E. Gutierrez-Rodriguez, Alejandra Martinez-Maldonado, Jose Luna-Muñoz, Jose A. Cantoral-Ceballos, and Miguel A. Ontiveros-Torres. "Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer’s Disease and Progressive Supranuclear Palsy." Current Issues in Molecular Biology 44, no. 12 (2022): 5963–85. http://dx.doi.org/10.3390/cimb44120406.

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Neurodegenerative diseases, tauopathies, constitute a serious global health problem. The etiology of these diseases is unclear and an increase in their incidence has been projected in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using Machine and Deep Learning algorithms has been widely studied for medical imaging such as Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the brains of patients have not yet been used for thi

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Sridhar, Uthra. "Demystifying Deep Learning and Neural Networks: A Technical Overview." European Journal of Computer Science and Information Technology 13, no. 8 (2025): 1–23. https://doi.org/10.37745/ejcsit.2013/vol13n8123.

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Deep learning has revolutionized artificial intelligence by enabling machines to learn hierarchically from data with minimal human intervention. Neural networks, inspired by the human brain's structure, form the foundation of this paradigm shift, processing information through interconnected layers of artificial neurons to extract complex patterns from data. These architectures have transformed numerous domains including computer vision, natural language processing, and specialized applications such as autonomous vehicles and drug discovery. Despite remarkable achievements, significant challen

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Wang, Xingyu, Rui Ma, Jinyuan He, Taisi Zhang, Xiajing Wang, and Jingfeng Xue. "INNT: Restricting Activation Distance to Enhance Consistency of Visual Interpretation in Neighborhood Noise Training." Electronics 12, no. 23 (2023): 4751. http://dx.doi.org/10.3390/electronics12234751.

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In this paper, we propose an end-to-end interpretable neighborhood noise training framework (INNT) to address the issue of inconsistent interpretations between clean and noisy samples in noise training. Noise training conventionally involves incorporating noisy samples into the training set, followed by generalization training. However, visual interpretations suggest that models may be learning the noise distribution rather than the desired robust target features. To mitigate this problem, we reformulate the noise training objective to minimize the visual interpretation consistency of images i

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Pasupuleti, Murali Krishna. "Quantum Supersymmetry Meets AI: An Algebraic Framework for Generalized Intelligence." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 04 (2025): 118–32. https://doi.org/10.62311/nesx/rp0925.

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This paper presents a novel interdisciplinary framework that integrates quantum supersymmetry with artificial intelligence to develop an algebraically structured foundation for generalized intelligence. Drawing from the mathematical rigor of supersymmetric quantum mechanics and Lie superalgebras such as osp(1∣2) and sl(2∣1), we propose AI architectures where learning dynamics are governed by graded algebraic transformations and symmetry-preserving constraints. By modeling intelligent agents as evolving quantum systems under the action of supercharges, the framework introduces dual representati

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Awolesi Abolanle Ogunboyo. "Neuro-Symbolic Generative AI for Explainable Reasoning." International Journal of Science and Research Archive 16, no. 1 (2025): 121–25. https://doi.org/10.30574/ijsra.2025.16.1.2019.

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The integration of neural and symbolic systems termed neuro-symbolic AI presents a compelling path toward explainable reasoning in Artificial Intelligence (AI). While deep learning models excel at pattern recognition and generative capabilities, their opaque decision-making process has raised concerns about transparency, interpretability, and trustworthiness. This research investigates the convergence of generative AI and neuro-symbolic architectures to enhance explainable reasoning. Employing a mixed-methods methodology grounded in empirical evaluation, knowledge representation, and symbolic

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Zhang, Ting-He, Md Musaddaqul Hasib, Yu-Chiao Chiu, et al. "Transformer for Gene Expression Modeling (T-GEM): An Interpretable Deep Learning Model for Gene Expression-Based Phenotype Predictions." Cancers 14, no. 19 (2022): 4763. http://dx.doi.org/10.3390/cancers14194763.

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Deep learning has been applied in precision oncology to address a variety of gene expression-based phenotype predictions. However, gene expression data’s unique characteristics challenge the computer vision-inspired design of popular Deep Learning (DL) models such as Convolutional Neural Network (CNN) and ask for the need to develop interpretable DL models tailored for transcriptomics study. To address the current challenges in developing an interpretable DL model for modeling gene expression data, we propose a novel interpretable deep learning architecture called T-GEM, or Transformer for Gen

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Jandoubi, Bassem, and Moulay A. Akhloufi. "Multimodal Artificial Intelligence in Medical Diagnostics." Information 16, no. 7 (2025): 591. https://doi.org/10.3390/info16070591.

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The integration of artificial intelligence into healthcare has advanced rapidly in recent years, with multimodal approaches emerging as promising tools for improving diagnostic accuracy and clinical decision making. These approaches combine heterogeneous data sources such as medical images, electronic health records, physiological signals, and clinical notes to better capture the complexity of disease processes. Despite this progress, only a limited number of studies offer a unified view of multimodal AI applications in medicine. In this review, we provide a comprehensive and up-to-date analys

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De Santi, Lisa Anita, Franco Italo Piparo, Filippo Bargagna, Maria Filomena Santarelli, Simona Celi, and Vincenzo Positano. "Part-Prototype Models in Medical Imaging: Applications and Current Challenges." BioMedInformatics 4, no. 4 (2024): 2149–72. http://dx.doi.org/10.3390/biomedinformatics4040115.

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Recent developments in Artificial Intelligence have increasingly focused on explainability research. The potential of Explainable Artificial Intelligence (XAI) in producing trustworthy computer-aided diagnosis systems and its usage for knowledge discovery are gaining interest in the medical imaging (MI) community to support the diagnostic process and the discovery of image biomarkers. Most of the existing XAI applications in MI are focused on interpreting the predictions made using deep neural networks, typically including attribution techniques with saliency map approaches and other feature v

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Jiang, Xuejie, Siti Norlizaiha Harun, and Linyu Liu. "Explainable Artificial Intelligence for Ancient Architecture and Lacquer Art." Buildings 13, no. 5 (2023): 1213. http://dx.doi.org/10.3390/buildings13051213.

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This research investigates the use of explainable artificial intelligence (XAI) in ancient architecture and lacquer art. The aim is to create accurate and interpretable models to reveal these cultural artefacts’ underlying design principles and techniques. To achieve this, machine learning and data-driven techniques are employed, which provide new insights into their construction and preservation. The study emphasises the importance of transparent and trustworthy AI systems, which can enhance the reliability and credibility of the results. The developed model outperforms CNN-based emotion reco

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Hu, Ziye. "Research on the Impact of Social Media Algorithmic on User Decision-making: Focus on Algorithmic Transparent and Ethical Design." Applied and Computational Engineering 174, no. 1 (2025): 18–22. https://doi.org/10.54254/2755-2721/2025.po24665.

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Social media algorithms, as the invisible architects of user decision-making in the digital age, construct a new paradigm of human-computer interaction through behavior prediction and content curation. Using a combination of computational behavioral analysis and psychological experiments, this study systematically reveals the dual effects of algorithmic recommendation systems between enhancing user engagement and eroding mental health. Data analysis showed that the engagement prioritization mechanism of platforms such as Instagram increased the exposure of negative emotional content by 23%, le

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Asaad, Ayman, A. M. Azizul Hassan Chy, Anzam Shahriar Kabir, Amrun Nakib, and Nazifa Tabassum. "Navigating the Labyrinth: A Review of Explainability and Trustworthiness in Large Language Model-Powered Systems for Sensitive Decision-Making." Scientia. Technology, Science and Society 2, no. 7 (2025): 5–19. https://doi.org/10.59324/stss.2025.2(7).02.

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Large Language Models (LLMs) have proven to be game-changers in sectors that require high-stakes decision-making, such as healthcare, finance, and law. Despite this, LLMs are opaque, black boxes that present significant challenges in terms of explainability and trust, particularly when ethical, legal, and regulatory requirements necessitate transparency. This review examines the state-of-the-art in the intersection of Large Language Models (LLMs), Explainable Artificial Intelligence (XAI), and trustworthy AI. It addresses four research questions: the faithfulness of LLMs' explanations, the cur

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Criel, Bjorn, Steff Taelman, Wim Van Criekinge, Michiel Stock, and Yves Briers. "PhaLP: A Database for the Study of Phage Lytic Proteins and Their Evolution." Viruses 13, no. 7 (2021): 1240. http://dx.doi.org/10.3390/v13071240.

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Phage lytic proteins are a clinically advanced class of novel enzyme-based antibiotics, so-called enzybiotics. A growing community of researchers develops phage lytic proteins with the perspective of their use as enzybiotics. A successful translation of enzybiotics to the market requires well-considered selections of phage lytic proteins in early research stages. Here, we introduce PhaLP, a database of phage lytic proteins, which serves as an open portal to facilitate the development of phage lytic proteins. PhaLP is a comprehensive, easily accessible and automatically updated database (curren

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Chen, Hua, Yong Zhou, and Ping Zhang. "Value Perception and Willingness to Pay for Architectural Heritage Conservation: Evidence from Kumbum Monastery in China." Buildings 15, no. 8 (2025): 1295. https://doi.org/10.3390/buildings15081295.

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The protection and utilization of architectural heritage requires the synergistic cooperation of multiple interest groups. Tourists’ perception of the value of architectural heritage will determine how much attention and support they pay to heritage protection, which in turn affects their participation and willingness to pay. This paper establishes the value perception evaluation system of architectural heritage for Kumbum Monastery in China, quantifies tourists’ willingness to pay for heritage protection, and applies an interpretable machine learning model to analyze the causal relationship b

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Wang, Jing. "Construction and Empirical Analysis of Corporate Financial Crisis Early Warning Model under the Perspective of Multimodal Data Fusion." Frontiers in Business, Economics and Management 20, no. 1 (2025): 118–22. https://doi.org/10.54097/f485xn92.

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Corporate financial risks in the current economic environment are becoming more and more complex, and it is difficult to meet the early warning needs with single modal data analysis. In this study, we use deep learning technology to design a multimodal data fusion architecture and construct a corporate financial crisis early warning model that includes financial data, text information and market transaction data. The study innovatively introduces a two-way attention mechanism to achieve adaptive fusion of features, develops a multi-level interpretable analysis framework, and improves the accur

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Ma, Xinghua, Xinyan Fang, Mingye Zou, et al. "A Trusted Lesion-assessment Network for Interpretable Diagnosis of Coronary Artery Disease in Coronary CT Angiography." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6009–17. https://doi.org/10.1609/aaai.v39i6.32642.

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Coronary Artery Disease (CAD) poses a significant threat to cardiovascular patients worldwide, underscoring the critical importance of automated CAD diagnostic technologies in clinical practice. Previous technologies for lesion assessment in Coronary CT Angiography (CCTA) images have been insufficient in terms of interpretability, resulting in solutions that lack clinical reliability in both network architecture and prediction outcomes, even when diagnoses are accurate. To address the limitation of interpretability, we introduce the Trusted Lesion-Assessment Network (TLA-Net), which provides a

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Tian, Jinkai, and Wenjing Yang. "Mapping Data to Concepts: Enhancing Quantum Neural Network Transparency with Concept-Driven Quantum Neural Networks." Entropy 26, no. 11 (2024): 902. http://dx.doi.org/10.3390/e26110902.

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We introduce the concept-driven quantum neural network (CD-QNN), an innovative architecture designed to enhance the interpretability of quantum neural networks (QNNs). CD-QNN merges the representational capabilities of QNNs with the transparency of self-explanatory models by mapping input data into a human-understandable concept space and making decisions based on these concepts. The algorithmic design of CD-QNN is comprehensively analyzed, detailing the roles of the concept generator, feature extractor, and feature integrator in improving and balancing model expressivity and interpretability.

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Zhang, Zhiyuan, Zhan Wang, and Inwhee Joe. "CAM-NAS: An Efficient and Interpretable Neural Architecture Search Model Based on Class Activation Mapping." Applied Sciences 13, no. 17 (2023): 9686. http://dx.doi.org/10.3390/app13179686.

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Artificial intelligence (AI) has made rapid progress in recent years, but as the complexity of AI models and the need to deploy them on multiple platforms gradually increases, the design of network model structures for specific platforms becomes more difficult. A neural network architecture search (NAS) serves as a solution to help experts discover new network structures that are suitable for different tasks and platforms. However, traditional NAS algorithms often consume time and many computational resources, especially when dealing with complex tasks and large-scale models, and the search pr

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Athanasopoulou, Konstantina, Vasiliki-Ioanna Michalopoulou, Andreas Scorilas, and Panagiotis G. Adamopoulos. "Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions." Current Issues in Molecular Biology 47, no. 6 (2025): 470. https://doi.org/10.3390/cimb47060470.

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The integration of artificial intelligence (AI) into next-generation sequencing (NGS) has revolutionized genomics, offering unprecedented advancements in data analysis, accuracy, and scalability. This review explores the synergistic relationship between AI and NGS, highlighting its transformative impact across genomic research and clinical applications. AI-driven tools, including machine learning and deep learning, enhance every aspect of NGS workflows—from experimental design and wet-lab automation to bioinformatics analysis of the generated raw data. Key applications of AI integration in NGS

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Xie, Falian, Haihong Song, and Huina Zhang. "Research on Light Comfort of Waiting Hall of High-Speed Railway Station in Cold Region Based on Interpretable Machine Learning." Buildings 13, no. 4 (2023): 1105. http://dx.doi.org/10.3390/buildings13041105.

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Upon the need for sustainability and natural lighting performance simulation for high-speed railway station waiting halls in cold regions, a new prediction method was proposed for the quantitative analysis of their natural lighting performance in the early design stage. Taking the waiting hall of Harbin West Railway Station as the prototype, the authors explore the optimization design of green performance-oriented waiting halls in this paper. To maximize daylight and minimize visual discomfort, and with the help of Rhinoceros and Grasshopper and Ladybug, and Honeybee platform simulation progra

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Wang, Sixuan, Cailong Ma, Wenhu Wang, et al. "Prediction of Failure Modes and Minimum Characteristic Value of Transverse Reinforcement of RC Beams Based on Interpretable Machine Learning." Buildings 13, no. 2 (2023): 469. http://dx.doi.org/10.3390/buildings13020469.

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Shear failure of reinforced concrete (RC) beams is a form of brittle failure and has always been a concern. This study adopted the interpretable machine-learning technique to predict failure modes and identify the boundary value between different failure modes to avoid diagonal splitting failure. An experimental database consisting of 295 RC beams with or without transverse reinforcements was established. Two features were constructed to reflect the design characteristics of RC beams, namely, the shear–span ratio and the characteristic value of transverse reinforcement. The characteristic valu

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Li, Rui. "DBSCAN-based line density clustering algorithm for CAD architectural drawings." Applied and Computational Engineering 19, no. 1 (2023): 109–15. http://dx.doi.org/10.54254/2755-2721/19/20231018.

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In the traditional architecture industry, architects often need to convert CAD drawings into BIM in order to express the final effect of the building more concretely and to understand whether the design of each profession is reasonable after the drawings are completed. The whole modeling process is boring and tedious, and due to human fatigue, the final result is prone to problems, which eventually leads to failure to meet expectations. In order to free architects from the tedious task of model transformation, companies have designed software for automatic model transformation using computers.

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Zeng, Pengyu, Jun Yin, Yan Gao, Jizhizi Li, Zhanxiang Jin, and Shuai Lu. "Comprehensive and Dedicated Metrics for Evaluating AI-Generated Residential Floor Plans." Buildings 15, no. 10 (2025): 1674. https://doi.org/10.3390/buildings15101674.

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In response to the growing importance of AI-driven residential design and the lack of dedicated evaluation metrics, we propose the Residential Floor Plan Assessment (RFP-A), a comprehensive framework tailored to architectural evaluation. RFP-A consists of multiple metrics that assess key aspects of floor plans, including room count compliance, spatial connectivity, room locations, and geometric features. It incorporates both rule-based comparisons and graph-based analysis to ensure design requirements are met. A comparison of RFP-A and existing metrics was conducted both qualitatively and quan

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Leung, Eman, Albert Lee, Yilin Liu, et al. "Impact of Environment on Pain among the Working Poor: Making Use of Random Forest-Based Stratification Tool to Study the Socioecology of Pain Interference." International Journal of Environmental Research and Public Health 21, no. 2 (2024): 179. http://dx.doi.org/10.3390/ijerph21020179.

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Pain interferes with one’s work and social life and, at a personal level, daily activities, mood, and sleep quality. However, little research has been conducted on pain interference and its socioecological determinants among the working poor. Noting the clinical/policy decision needs and the technical challenges of isolating the intricately interrelated socioecological factors’ unique contributions to pain interference and quantifying the relative contributions of each factor in an interpretable manner to inform clinical and policy decision-making, we deployed a novel random forest algorithm t

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Zhang, Jing, and Juan Chen. "Research on grading detection methods for diabetic retinopathy based on deep learning." Pakistan Journal of Medical Sciences 41, no. 1 (2024): 225–29. https://doi.org/10.12669/pjms.41.1.9171.

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Objective: To design a deep learning-based model for early screening of diabetic retinopathy, predict the condition, and provide interpretable justifications. Methods: The experiment’s model structure is designed based on the Vision Transformer architecture which was initiated in March 2023 and the first version was produced in July 2023 at Affiliated Hospital of Hangzhou Normal University. We use the publicly available EyePACS dataset as input to train the model. Using the trained model, we predict whether a given patient’s fundus images indicate diabetic retinopathy and provide the relevant

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Perisic, Ana, and Branko Perisic. "Towards a Digital Transformation Hyper-Framework: The Essential Design Principles and Components of the Initial Prototype." Applied Sciences 15, no. 2 (2025): 611. https://doi.org/10.3390/app15020611.

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To cope with the complexity, the digital transformation of cyber-physical and socio-technology systems demands the utilization of heterogeneous tailorable development environments with dynamic configuring ability and transparent integration of independently developed dedicated frameworks. The essential design principles and component-based architecting of the initial prototype of the digital transformation hyper-framework represent this research target. These principles are derived from the broad scope analysis of digital transformation projects, methods, and tools and are glued to the propose

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Zhu, Guangxiang, Jianhao Wang, Zhizhou Ren, Zichuan Lin, and Chongjie Zhang. "Object-Oriented Dynamics Learning through Multi-Level Abstraction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6989–98. http://dx.doi.org/10.1609/aaai.v34i04.6183.

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Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reason

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R, Jain. "Transparency in AI Decision Making: A Survey of Explainable AI Methods and Applications." Advances in Robotic Technology 2, no. 1 (2024): 1–10. http://dx.doi.org/10.23880/art-16000110.

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Artificial Intelligence (AI) systems have become pervasive in numerous facets of modern life, wielding considerable influence in critical decision-making realms such as healthcare, finance, criminal justice, and beyond. Yet, the inherent opacity of many AI models presents significant hurdles concerning trust, accountability, and fairness. To address these challenges, Explainable AI (XAI) has emerged as a pivotal area of research, striving to augment the transparency and interpretability of AI systems. This survey paper serves as a comprehensive exploration of the state-of-the-art in XAI method

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Nair, Rajit. "Unraveling the Decision-making Process Interpretable Deep Learning IDS for Transportation Network Security." Journal of Cybersecurity and Information Management 12, no. 2 (2023): 69–82. http://dx.doi.org/10.54216/jcim.120205.

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The growing ubiquity of IoT-enabled devices in recent years emphasizes the critical need to strengthen transportation network safety and dependability. Intrusion detection systems (IDS) are crucial in preventing attacks on transport networks that rely on the Internet of Things (IoT). However, understanding the rationale behind deep learning-based IDS models may be challenging because they do not explain their findings. We offer an interpretable deep learning system that may be used to improve transportation network safety using IoT. To develop naturally accessible explanations for IDS projecti

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Zhang, Feng, Chenxin Wang, Xingxing Zou, et al. "Prediction of the Shear Resistance of Headed Studs Embedded in Precast Steel–Concrete Structures Based on an Interpretable Machine Learning Method." Buildings 13, no. 2 (2023): 496. http://dx.doi.org/10.3390/buildings13020496.

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Headed shear studs are an essential interfacial connection for precast steel–concrete structures to ensure composite action; hence, the accurate prediction of the shear capacity of headed studs is of pivotal significance. This study first established a worldwide dataset with 428 push-out tests of headed shear studs embedded in concrete with varied strengths from 26 MPa to 200 MPa. Five advanced machine learning (ML) models and three widely used equations from design codes were comparatively employed to predict the shear resistance of the headed studs. Considering the inevitable data variation

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Naresh Vurukonda. "A Novel Framework for Inherently Interpretable Deep Neural Networks Using Attention-Based Feature Attribution in High-Dimensional Tabular Data." Journal of Information Systems Engineering and Management 10, no. 50s (2025): 599–604. https://doi.org/10.52783/jisem.v10i50s.10290.

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Deep learning models for tabular data often lack interpretability, posing challenges in domains like healthcare and finance where trust is critical. We propose an attention-augmented neural network architecture that inherently highlights the most informative features, thus providing intrinsic explanations for its predictions. Drawing inspiration from TabNet and Transformer-based models, our model applies multi-head feature-wise attention to automatically weight each feature’s contribution. We incorporate an attention-weight regularization scheme (e.g. sparsemax) to encourage focused attributio

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Naresh Vurukonda. "A Novel Framework for Inherently Interpretable Deep Neural Networks Using Attention-Based Feature Attribution in High-Dimensional Tabular Data." Journal of Information Systems Engineering and Management 10, no. 51s (2025): 1076–81. https://doi.org/10.52783/jisem.v10i51s.10626.

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Deep learning models for tabular data often lack interpretability, posing challenges in domains like healthcare and finance where trust is critical. We propose an attention-augmented neural network architecture that inherently highlights the most informative features, thus providing intrinsic explanations for its predictions. Drawing inspiration from TabNet and Transformer-based models, our model applies multi-head feature-wise attention to automatically weight each feature’s contribution. We incorporate an attention-weight regularization scheme (e.g. sparsemax) to encourage focused attributio

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Xiong, Ke, Guanghe Cao, Meizhizi Jin, and Biao Ye. "A Multi-modal Deep Learning Approach for Predicting Type 2 Diabetes Complications: Early Warning System Design and Implementation." World Journal of Innovation and Modern Technology 7, no. 6 (2024): 120–32. https://doi.org/10.53469/wjimt.2024.07(06).15.

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This paper presents a novel multi-modal deep learning framework for early prediction of Type 2 Diabetes (T2D) complications through an advanced early warning system. The proposed architecture integrates multiple data modalities including clinical measurements, laboratory results, and temporal patient data through a sophisticated attention-based fusion mechanism. The system implements specialized preprocessing techniques for different data modalities and employs an innovative feature extraction pipeline for comprehensive risk assessment. Experimental validation was conducted on a dataset compri

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Zhang, Benyuan, Xin Jin, Wenyu Liang, et al. "TabNet: Locally Interpretable Estimation and Prediction for Advanced Proton Exchange Membrane Fuel Cell Health Management." Electronics 13, no. 7 (2024): 1358. http://dx.doi.org/10.3390/electronics13071358.

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In the pursuit of advanced Predictive Health Management (PHM) for Proton Exchange Membrane Fuel Cells (PEMFCs), conventional data-driven models encounter considerable barriers due to data reconstruction resulting in poor data quality, and the complexity of models leading to insufficient interpretability. In addressing these challenges, this research introduces TabNet, a model aimed at augmenting predictive interpretability, and integrates it with an innovative data preprocessing technique to enhance the predictive performance of PEMFC health management. In traditional data processing approache

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Gim, Mogan, Junseok Choe, Seungheun Baek, et al. "ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction." Bioinformatics 39, Supplement_1 (2023): i448—i457. http://dx.doi.org/10.1093/bioinformatics/btad207.

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Abstract Motivation Protein–ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein–ligand attention mechanism for more explainable deep drug–target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guide

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Chauhan, Garima Goyal. "A Conceptual Framework for the Cooperation Of AI Algorithms in Intelligent Systems." International Journal of Advanced Information Technology 15, no. 1/2 (2025): 19–36. https://doi.org/10.5121/ijait.2025.15203.

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The Artificial Intelligence (AI) has progressed from operating as isolated algorithmic units to functioning as interconnected modules within complex intelligent systems. Today’s applications—such as autonomous vehicles, virtual assistants, and adaptive robotics—rely on the cooperation of multiple specialized algorithms, each handling distinct cognitive tasks like perception, learning, reasoning, and planning. This paper proposes a theoretical framework for understanding how these diverse algorithms interact to produce cohesive and intelligent behavior. It introduces a taxonomy of AI functions

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Zheng, Susheng, and Mary O’ Penetrante. "Design and Analysis of an AI-Driven Tax Avoidance Detection System in Big Data Environments for Public Sector Tax Administration." International Journal of Research and Innovation in Social Science IX, no. V (2025): 3118–30. https://doi.org/10.47772/ijriss.2025.905000241.

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This paper presents a descriptive design and conceptual analysis of an AI-driven Tax Avoidance Detection System (TADS), developed for deployment in big data environments to support tax compliance monitoring within public sector administrations. While tax avoidance remains legally permissible, its widespread and opaque application undermines public revenue generation, exacerbates socio-economic inequality, and diminishes state fiscal capacity. TADS is proposed as a hybrid analytical framework that integrates deterministic rule-based logic with deep learning techniques—specifically Long Short-Te

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