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explainable artificial intelligence twin for metaheuristic optimization: Double-skin facade with energy storage in buildings

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第3期12卷 16-35页

作者： Tariq, Rasikh Recio-Garcia, Juan A. Cetina-Quinones, Armando J. Orozco-del-Castillo, Mauricio G. Bassam, Ali Tecnol Monterrey Inst Future Educ Ave Eugenio Garza Sada 2501 Monterrey 64849 NL Mexico Univ Complutense Madrid Dept Software Engn & Artificial Intelligence Inst Tecnol Conocimiento Madrid 28040 Spain Univ Autonoma Yucatan Fac Ingn Lab Modelado & Optimizac Proc Energet & Ambientale Ave Ind Contaminantes Merida 97302 Yucatan Mexico Tecnol Nacl Mexico Inst Tecnol Merida Ave Tecnol Km 4-5 S-N Merida 97118 Yucatan Mexico

The double facade solar chimney along with energy storage components is a sustainable building technology that harnesses the power of the sun to regulate indoor temperatures. Extensive research has been conducted on the theoretical simulation of such systems. The novelty of this work is to explore the potential of explainable artificial intelligence in improving the design and optimization of the double-skin solar chimney. The need for this research is owing to the high computational limitations of the physical model of such systems, thus the application of explainable artificial intelligence based upon artificial neural network can address this research gap. The paper solved a validated physical model and demonstrates the suitability of artificial neural network twins as computational-efficient subrogates that can be later used by a multiobjective optimization function to find the optimal design values for the facades of double-skin buildings. The results of the comparison between the physical and the artificial neural network model show the practical advantage of utilizing the digital twin model without compromising accuracy. The results have indicated that the artificial neural network can achieve a high coefficient of determination ranging from 0.921 to 0.999 on different performance indicators that implies a high goodness of fit. Accordingly, the optimization study based upon a nonsorting genetic algorithm (NSGA-II) has indicated a high ventilation rate of 2.86 1/h and an efficiency of 37.21%. The insights of this work have reflected that exploring the societal implications of sustainable building technologies such as the double facade solar chimney through educational initiatives can cultivate a new generation of society who not only understand the technical aspects but also appreciate the broader social and environmental contexts of their work, eventually to have future buildings with integrated passive systems.

关键词： explainable artificial intelligence metaheuristic optimization solar chimney energy and buildings

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explainable artificial intelligence-Based Search Space Reduction for Optimal Sensor Placement in the Pipeline Systems of Naval Ships

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STRUCTURAL CONTROL & HEALTH MONITORING 2025年第1期2025卷

作者： Kim, Chungeon Oh, Hyunseok Jung, Byung Chang Moon, Seok Jun Han, Bongtae Gwangju Inst Sci & Technol Sch Mech Engn Gwangju South Korea Korea Inst Machinery & Mat Dept Syst Dynam Daejeon South Korea Univ Maryland Dept Mech Engn College Pk MD USA

Pipeline damage in mission-critical systems, such as pipelines within naval ships, can result in substantial consequences. Compared to manual inspection of pipeline damage by crew members onboard, structural health monitoring of pipeline systems offers prompt identification of damage sites, enabling efficient damage mitigation. However, one challenge of this approach is deriving an optimal sensor placement (OSP) strategy, given the large and complex pipelines found in real-scale naval vessels. To address this issue, a search space reduction method is proposed for OSP suitable for the large and complex pipeline systems found in naval ships. In the proposed method, the original search space for sensor placement is reduced to a manageable scale using an explainable artificial intelligence (XAI) technique, namely, a gradient-weighted class activation map (Grad-CAM). Grad-CAM enables quantification and visualization of the contribution of individual pipeline nodes to classify damage scenarios. Noncritical sensor locations can be excluded from the candidate search space. Furthermore, a peak-finding algorithm is devised to select only a limited number of nodes with the highest Grad-CAM values;in this research, the algorithm is proven effective in reconstructing the search space. As a result, the original OSP problem-which has an extremely large search space-is reconstructed into a new OSP problem with a computationally manageable search space. The new OSP problem can be solved using either meta-heuristic methods or exhaustive search methods. The effectiveness of the proposed method is validated through a case study on a real-scale naval combat vessel, measuring 102 m in length and carrying a full load of 2300 tons. The results show that the proposed XAI-based search space reduction approach efficiently designs an optimal pipeline sensor network in real-scale naval combat vessels.

关键词： explainable artificial intelligence genetic algorithm optimal sensor placement sensor network design structural health monitoring

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explainable artificial intelligence to explore the intrinsic characteristics of climatic parameters governing meteorological drought forecasting: opening the black box

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2025年 1-22页

作者： Ozupek, Ertugrul Teke, Alihan Celik, Nuri Kavzoglu, Taskin Erzincan Binali Yildirim Univ Dept Math TR-24100 Erzincan Turkiye Gebze Tech Univ Dept Geomat Engn TR-41400 Gebze Kocaeli Turkiye Gebze Tech Univ Dept Math TR-41400 Gebze Kocaeli Turkiye

Meteorological drought, exacerbated by global climate change, is a recurrent and complex catastrophe that disrupts water resources, agriculture, and economies worldwide. Forecasting future drought conditions is therefore of utmost importance in facilitating effective intervention and adaptation strategies. artificial intelligence and machine learning techniques have gained attention owing to their potential in forecasting drought severity. However, they are often criticized for their black-box nature and lack of explainability and interpretability. To address this issue, this study explores the potential of local and global explainable artificial intelligence (XAI) tools, including SHAP and LIME, to extract intrinsic characteristics from different drought indices, including Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) and climatic parameters (temperature, relative humidity, and wind speed). These techniques were applied to understand the driving forces behind drought events under different scenarios. SHAP was used to provide global model explanations, revealing how climatic parameters varied in importance across stations and scenarios, while LIME was employed to offer local explanations for specific predictions, highlighting the role of lag time and the significance of each parameter in individual forecasting scenarios. Using data from four meteorological stations in the Konya province of T & uuml;rkiye, spanning 1965 to 2023, six ensemble-based machine learning models were employed to forecast SPI and SPEI values at 6-month and 12-month scales, resulting in a total of 768 scenarios. Widely used evaluation metrics, namely MSE, RMSE, MAE, MAPE and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{R}<^>{2},$$\end{document} were empl

关键词： Meteorological drought SPI SPEI Machine learning explainable artificial intelligence

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explainable artificial intelligence for Drug Discovery and Development: A Comprehensive Survey

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IEEE ACCESS 2024年 12卷 35796-35812页

作者： Alizadehsani, Roohallah Oyelere, Solomon Sunday Hussain, Sadiq Jagatheesaperumal, Senthil Kumar Calixto, Rene Ripardo Rahouti, Mohamed Roshanzamir, Mohamad De Albuquerque, Victor Hugo C. Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Geelong Vic 3216 Australia Lulea Univ Technol Dept Comp Sci Elect & Space Engn Lulea 93177 Sweden Dibrugarh Univ Examinat Branch Dibrugarh Assam India Mepco Schlenk Engn Coll Dept Elect & Commun Engn Sivakasi India Univ Fed Ceara Dept Teleinformat Engn BR-60455970 Fortaleza Brazil Fordham Univ Dept Comp & Informat Sci Bronx NY USA Fasa Univ Dept Comp Engn Fac Engn Fasa *** Iran

The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. explainable artificial intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

关键词： Drug discovery explainable artificial intelligence machine learning big data

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explainable artificial intelligence models in intrusion detection systems

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ENGINEERING APPLICATIONS OF artificial intelligence 2025年 144卷

作者： Samed, A. L. Sagiroglu, Seref Grad Sch Nat & Appl Sci Gazi Univ Informat Secur Engn Ankara Turkiye

This study introduces the growing necessity of explainable artificial intelligence (XAI) in the context of cybersecurity, particularly in Intrusion Detection Systems (IDSs). As artificial intelligence (AI) technologies, especially complex neural networks, become more prevalent and difficult to understand, the opacity of these models poses increasing risks in critical firald like cybersecurity. XAI aims to address these concerns by making AI models and their outputs more interpretable and transparent, thus fostering trust and secure use. Despite the rising interest and application of XAI in various domains, its integration into IDSs remains insufficient. This research provides a comprehensive overview of XAI, examining its definitions, terminologies, and the evolution of the field. It delves into the opportunities, challenges, and research needs within XAI, as well as the latest developments, tools, and technologies for implementing XAI in AI-based IDSs, alongside associated risks. The study also summarizes and demonstrates a gap in the literature regarding comprehensive reviews of IDS solutions that incorporate XAI, aiming to fill this gap by detailing the application of XAI in IDS research. The findings are expected to guide researchers, experts, users, and policymakers in the domains of AI, XAI, and IDS.

关键词： explainable artificial intelligence Interpretable artificial intelligence Intrusion detection System Local interpretable model-agnostic explana-tions Shapley additive explanations

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explainable artificial intelligence-based framework for efficient content placement inelastic optical networks

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 262卷

作者： Goscien, Roza Wroclaw Univ Sci & Technol Fac Informat & Commun Technol Dept Syst & Comp Networks Wybrzeze Wyspianskiego 27 PL-50370 Wroclaw Poland

The rapid development of telecommunication networks brings new optimization problems and the urgent need for dedicated and highly efficient solution methods. Recently, the idea of aiding network optimization with machine learning (ML) algorithms has gained more and more attention in the research society. Despite numerous successful applications of these methods, their adaption in real networks and systems is hindered due to the lack of a full explainability of their decisions and, in turn - the lack of trust. Hopefully, these aspects maybe addressed by explainable artificial intelligence methods (xAI). In this paper, we study an essential problem of the anycast content placement. Having a set of physical data centers (acs) located in selected network nodes and a set of different contents (services), the task is to decide in which ac s place each of the contents in order to improve the optical network performance (measured as a bandwidth blocking probability (BBP)). To this end, we propose a dedicated ML-based framework, which approaches the placement problem as a supervised learning task of predicting network's BBP fora content placement configuration. We perform extensive numerical experiments to tune the framework, considering five supervised learning algorithms and three comparison metrics. We also use explainable artificial intelligence methods to interpret the models' decisions and draw general conclusions regarding beneficial content placement in a real network. Lastly, we compare the performance of the proposed ML-based placement framework with three reference methods. The results prove our approach's extremely high efficiency, which reduced the BBP significantly compared to the best reference approach. Depending on the network settings and the offered traffic volume, the framework allowed to serve up to 47% of the traffic that would be rejected by the best reference method (corresponding to 3.76 Tbps of data).

关键词： Content placement Anycasting Elastic optical network Supervised learning explainable artificial intelligence

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explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor

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BIOSENSORS & BIOELECTRONICS 2025年 267卷 116773页

作者： Choi, Jae Yi Park, Sungwook Shim, Ji Sung Park, Hyung Joon Kuh, Sung Uk Jeong, Youngdo Park, Min Gu Il Noh, Tae Yoon, Sung Goo Park, Yoo Min Lee, Seok Jae Kim, Hojun Kang, Seok Ho Lee, Kwan Hyi Korea Inst Sci & Technol Ctr Adv Biomol Recognit Biomed Res Div Seoul 02792 South Korea Yonsei Univ Coll Med Dept Med Device Engn & Management Seoul 06229 South Korea Korea Univ Coll Med Dept Urol Seoul 02841 South Korea Korea Univ KU KIST Grad Sch Converging Sci & Technol Seoul 02841 South Korea Yonsei Univ Coll Med Dept Neurosurg Seoul 03722 South Korea Hanyang Univ Dept HY KIST Bioconvergence Seoul 04763 South Korea Natl NanoFab Ctr NNFC Ctr Nano Bio Dev Daejeon 34141 South Korea Univ Sci & Technol UST KIST Sch Div Biomed Sci & Technol Daejeon South Korea

Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases, has become a standard prostate cancer (PCa) screening method due to exceptional diagnosis performance. However, PI-RADS 3 lesions are an unmet medical need because PI-RADS provides diagnosis accuracy of only 30-40% at most, accompanied by a high false-positive rate. Here, we propose an explainable artificial intelligence (XAI) based PCa screening system integrating a highly sensitive dual-gate field-effect transistor (DGFET) based multi- marker biosensor for ambiguous lesions identification. This system produces interpretable results by analyzing sensing patterns of three urinary exosomal biomarkers, providing a possibility of an evidence-based prediction from clinicians. In our results, XAI-based PCa screening system showed a high accuracy with an AUC of 0.93 using 102 blinded samples with the non-invasive method. Remarkably, the PCa diagnosis accuracy of patients with PI-RADS 3 was more than twice that of conventional PI-RADS scoring. Our system also provided a reasonable explanation of its decision that TMEM256 biomarker is the leading factor for screening those with PIRADS 3. Our study implies that XAI can facilitate informed decisions, guided by insights into the significance of visualized multi-biomarkers and clinical factors. The XAI-based sensor system can assist healthcare professionals in providing practical and evidence-based PCa diagnoses.

关键词： Prostate cancer explainable artificial intelligence Dual-gate field-effect-transistor sensor Cancer screening Urinary exosome PI-RADS

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explainable artificial intelligence in Alzheimer's Disease Classification: A Systematic Review

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COGNITIVE COMPUTATION 2024年第1期16卷 1-44页

作者： Viswan, Vimbi Shaffi, Noushath Mahmud, Mufti Subramanian, Karthikeyan Hajamohideen, Faizal Univ Technol & Appl Sci Coll Comp & Informat Sci Suhar Oman Nottingham Trent Univ Dept Comp Sci Nottingham NG11 8NS England Nottingham Trent Univ Med Technol Innovat Facil Nottingham NG11 8NS England Nottingham Trent Univ Comp & Informat Res Ctr Nottingham NG11 8NS England

The unprecedented growth of computational capabilities in recent years has allowed artificial intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number of Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption in the medical domain due to the typical blackbox nature of these AI models. Therefore, to facilitate the adoption of these AI models among the medical practitioners, the models' predictions must be explainable and interpretable. The emerging field of explainable AI (XAI) aims to justify the trustworthiness of these models' predictions. This work presents a systematic review of the literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during the last decade. Research questions were carefully formulated to categorise AI models into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) and frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations or SHAP, Gradient-weighted Class Activation Mapping or GradCAM, Layer-wise Relevance Propagation or LRP, etc.) of XAI. This categorisation provides broad coverage of the interpretation spectrum from intrinsic (e.g., Model-Specific, Ante-hoc models) to complex patterns (e.g., Model-Agnostic, Post-hoc models) and by taking local explanations to a global scope. Additionally, different forms of interpretations providing in-depth insight into the factors that support the clinical diagnosis of AD are also discussed. Finally, limitations, needs and open challenges of XAI research are outlined with possible prospects of their usage in AD detection.

关键词： Alzheimer's Disease Classification Ante-hoc Blackbox Models explainable artificial intelligence Intrepretable Machine Learning Model-Agnostic Model-Specific Post-hoc XAI

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explainable artificial intelligence in the design of selective carbonic anhydrase I-II inhibitors via molecular fingerprinting

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2024年第18期45卷 1530-1539页

作者： Kirboga, Kevser Kubra Isik, Mesut Bilecik Seyh Edebali Univ Fac Engn Dept Bioengn Bilecik Turkiye Suleyman Demirel Univ Bioengn Dept Isparta Turkiye Bilecik Seyh Edebali Univ Fac Engn Dept Bioengn TR-11100 Bilecik Turkiye

Inhibiting the enzymes carbonic anhydrase I (CA I) and carbonic anhydrase II (CA II) presents a potential avenue for addressing nervous system ailments such as glaucoma and Alzheimer's disease. Our study explored harnessing explainable artificial intelligence (XAI) to unveil the molecular traits inherent in CA I and CA II inhibitors. The PubChem molecular fingerprints of these inhibitors, sourced from the ChEMBL database, were subjected to detailed XAI analysis. The study encompassed training 10 regression models using IC50 values, and their efficacy was gauged using metrics including R-2, RMSE, and time taken. The Decision Tree Regressor algorithm emerged as the optimal performer (R-2: 0.93, RMSE: 0.43, time-taken: 0.07). Furthermore, the PFI method unveiled key molecular features for CA I inhibitors, notably PubChemFP432 (C(=O)N) and PubChemFP6978 (C(=O)O). The SHAP analysis highlighted the significance of attributes like PubChemFP539 (C(=O)NCC), PubChemFP601 (C(=O)OCC), and PubChemFP432 (C(=O)N) in CA I inhibitiotable n. Likewise, features for CA II inhibitors encompassed PubChemFP528(C(=O)OCCN), PubChemFP791 (C(=O)OCCC), PubChemFP696 (C(=O)OCCCC), PubChemFP335 (C(=O)NCCN), PubChemFP580 (C(=O)NCCCN), and PubChemFP180 (C(=O)NCCC), identified through SHAP analysis. The sulfonamide group (S), aromatic ring (A), and hydrogen bonding group (H) exert a substantial impact on CA I and CA II enzyme activities and IC50 values through the XAI approach. These insights into the CA I and CA II inhibitors are poised to guide future drug discovery efforts, serving as a beacon for innovative therapeutic interventions.

关键词： bioactivity carbonic anhydrase computational drug discovery explainable artificial intelligence

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explainable artificial intelligence(XAI)Model for Cancer Image Classification

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of explainable artificial intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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