Explainable Artificial Intelligence Methods Applied to Unmanned Aerial Vehicles: A Conceptual Framework

Electrocardiogram signal analysis can be time-consuming, tedious, and error-prone. Therefore, automated analysis is need of time that will assist clinicians in detecting cardiac abnormalities accurately and efficiently. Recently, deep learning models have shown unprecedented progress and strong arrhythmia classification capabilities, but their deployment in the medical sector is constrained due to their “black-box” nature. This paper proposes a robust explainability method to assist in explaining the underlying decision-making process in deep neural networks (DNN) and help provide feedback on biases that would benefit in improving DNN models. To achieve these objectives, initially, a deep learning model is trained on the MIT-BIH Arrhythmia Database and their classification performance is evaluated. The classification findings are then interpreted using the post-hoc explanation methods such as the SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM) that interpret the decision rationale. As these methods are initially proposed for image applications therefore a new masking approach is proposed to cater these post-hoc explainability methods for ECG time-series data. After evaluating these methods for ECG arrhythmia classification, several drawbacks are drawn such as they fail to locate a feature’s importance if there are multiple occurrences of the same feature in a signal and also, SHAP and LIME perform random perturbations that sometimes produce unreliable explanations. Therefore, to overcome the drawbacks associated with these post-hoc explainability methods on time-series data, a novel K-GradCam method is proposed. The proposed K-GradCam method ensemble the benefits of these gradient based and perturbation based approaches and has demonstrated advantages over SHAP, Grad-CAM and LIME in terms of interpreting the models’ decisions. To compare the the proposed technique with post-hoc explainability methods quantitatively, the confidence index of the proposed method is evaluated using dice loss. The proposed method shares 71% similarity with SHAP and 81% similarity with Grad-CAM methods; however, it shares the benefits of both methods and is computationally faster than SHAP and LIME.

Introduction: The integration of Artificial Intelligence (AI) into personalized banking has enhanced service delivery in areas such as loan processing, credit assessment, and fraud detection. Despite these advancements, ethical concerns, especially algorithmic bias and lack of fairness, pose significant challenges. This study addresses the need for equitable AI systems that promote transparency, fairness, and regulatory compliance in the banking sector. Objective: This study aims to develop and implement a comprehensive framework for integrating ethical principles into AI-driven banking systems, with a focus on mitigating algorithmic bias, enhancing fairness, and improving transparency in personalized banking services. Methods: A comprehensive methodology is proposed that integrates bias-aware data collection, fairness-constrained machine-learning models, and explainable AI (XAI) techniques. Tools such as Shapley Additive Explanations (SHAPs) and Local Interpretable Model-Agnostic Explanations (LIMEs) are applied to interpret model outputs. Adversarial debiasing and fairness-aware learning algorithms were employed to identify and mitigate systemic biases in financial data. Alternative data sources, including utility and rental payment histories, were incorporated to enhance inclusivity. Results: The implementation of the proposed framework demonstrates improved fairness in decision-making without significantly compromising model accuracy. Bias metrics show measurable reductions in disparate impacts across the demographic groups. Explainability tools enhance transparency, enabling a more transparent communication of AI decisions to both users and regulators. Conclusions: Embedding ethical principles into AI-driven banking systems is critical to ensuring fairness, regulatory alignment, and public trust. The structured framework presented in this study supports the development of responsible AI systems to mitigate bias, enhance explainability, and foster financial inclusion. This approach serves as the foundation for building equitable and accountable AI applications in modern banking.

<p dir="ltr">Objective: We aim to develop a multimodal model to predict chronic kidney disease (CKD) in patients with type 2 diabetes mellitus (T2DM), given the limited research on this integrative approach.</p><p dir="ltr">Research Design and Methods: We obtained multimodal datasets from Kyung Hee University Medical Center (South Korea; n=7028; discovery cohort) for training and internal validation and UK Biobank (n=1544; validation cohort) for external validation. CKD was defined based on the international classification of disease-9/10 codes and/or estimated glomerular filtration rate (eGFR) ≤60 mL/min/1.73m². We ensembled various deep learning models and interpreted their predictions using explainable artificial intelligence (AI) methods, including shapley additive explanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM). Subsequently, we investigated the potential association between the model probability and vascular complications.</p><p dir="ltr">Results: The multimodal model, which ensembles visual geometry group 16 (VGG16) and deep neural network (DNN), presented high performance in predicting CKD, with area under the receiver operating characteristic curve (AUROC) of 0.880 (95% CI, 0.806–0.954) in the discovery cohort and 0.722 in the validation cohort. SHAP and Grad-CAM highlighted key predictors, including eGFR and optic disc, respectively. The model probability was associated with an increased risk of macrovascular complications (T1: adjusted hazard ratio, 1.42 [95% CI, 1.06–1.90]; T2: 1.59 [1.17–2.16]; and T3: 1.64 [1.20–2.26]) and microvascular complications (T3: 1.30 [1.02–1.67]).</p><p dir="ltr">Conclusions: Our multimodal AI model integrates fundus images and clinical data from binational cohorts to predict the risk of new-onset CKD within five years and associated vascular complications in patients with T2DM.</p>

Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers.

The rising incidence of internet addiction (IA) among university students has raised concerns about its impact on academic performance, mental health, and social relationships. This study aims to model internet addiction in 1,180 university students using Explainable Artificial Intelligence (XAI) methods, including SHapley Additive Explanations (SHAP). Unlike traditional statistical approaches, such as linear regression, and opaque AI models like deep neural networks, XAI offers interpretable outputs that help reveal underlying factors contributing to IA. Student demographics, internet use patterns, and psychological variables were analyzed by machine learning algorithms, and SHAP was used to interpret the models. The findings reveal key predictors of IA, such as fear of missing out (FoMO), social anxiety, and social-emotional loneliness. By using XAI, the study shows how specific traits and behaviors elevate IA risk and offers actionable insights for educators and policymakers. It also supports early detection and intervention strategies in educational and psychological contexts.

Species distribution models (SDMs) remotely guide conservation programs for endangered species by estimating potential reserve areas based on a set of environmental features. Most SDM research only explains their predictions across the study area (global), effectively disregarding the predictions for specific sites (local) where conservation-related activities are confined. This study aims to address this spatial gap in explainability by applying model-agnostic post-hoc methods in explainable artificial intelligence for SDM at two scopes. These methods explain the importance, effects, and interactions of bioclimatic features on the SDM for Mindoro warty pigs (Sus oliveri), an emblematic yet endangered endemic fauna in Mindoro Island, Philippines. Areas with a high predicted probability of presence coincide with higher elevation, spanning the Mindoro Mountain Range. Global explainability methods-Permutation Feature Importance, Shapley Additive Explanations (SHAP), and Accumulated Local Effect-reveal that annual precipitation mostly accounts for this island-wide trend, with more rain corresponding to higher probabilities. This is also observed using local explainability methods-SHAP, Local Interpretable Model-agnostic Explanations, and Break Down-for the respective predictions on three potential conservation sites. The cumulative effect of bioclimatic features in these ~ 1km2 sites-within Mts. Iglit-Baco National Park, Upper Amnay Watershed, and Mt. Calavite Wildlife Sanctuary-is a decrease in the predicted probability of presence. This calls for improved local monitoring of Mindoro warty pig populations. While building upon our ongoing efforts for its conservation in Mindoro Island, this study also extends the pipeline for SDM using explainability methods, thereby opening a new axis for interpreting SDM predictions.

The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy.

The transportation industry contributes significantly to climate change through carbon dioxide (CO2) emissions, intensifying global warming and leading to more frequent and severe weather phenomena such as flooding, drought, heat waves, glacier melting, and rising sea levels. This study proposes a comprehensive approach for predicting CO2 emissions from vehicles using deep learning techniques enhanced by eXplainable Artificial Intelligence (XAI) methods. Utilizing a dataset from the Canadian government’s official open data portal, we explored the impact of various vehicle attributes on CO2 emissions. Our analysis reveals that not only do high-performance engines emit more pollutants, but fuel consumption under both city and highway conditions also contributes significantly to higher emissions. We identified skewed distributions in the number of vehicles produced by different manufacturers and trends in fuel consumption across fuel types. This study used deep learning techniques to construct a CO2 emission prediction model, specifically a light multilayer perceptron (MLP) architecture called CarbonMLP. The proposed model was optimized by hyperparameter tuning and achieved excellent performance metrics, such as a high R-squared value of 0.9938 and a low Mean Squared Error (MSE) of 0.0002. This study employs XAI approaches, particularly SHapley Additive exPlanations (SHAP), to improve the model interpretation ability and provide information about the importance of features. The findings of this study show that the proposed methodology accurately predicts CO2 emissions from vehicles. Additionally, the analysis suggests areas for further research, such as increasing the dataset, integrating additional pollutants, improving interpretability, and investigating real-world applications. Overall, this study contributes to the design of effective strategies for reducing vehicle CO2 emissions and promoting environmental sustainability.

Explainable Artificial Intelligence (XAI) has aided machine learning (ML) researchers with the ability to scrutinize the decisions of the black-box models. XAI methods enable looking deep inside the models' behavior, eventually generating explanations along with a perceived trust and transparency. However, the degree of trustworthiness varies across XAI methods. It is evident that XAI methods can themselves be a victim of post-hoc adversarial attacks that manipulate the expected outcome from the explanation module. Among such attack tactics, fairwashing explanation (FE), manipulation explanation (ME), and backdoor-enabled manipulation attacks (BD) are the notable ones. In this paper, we try to understand these adversarial attack techniques, tactics, and procedures (TTPs) on explanation alteration and thus the effect on the model's decisions. We have explored six distinct attack procedures on post-hoc explanation methods such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanation), and IG (Integrated Gradients), and investigated those adversarial attacks in cybersecurity applications scenarios such as phishing, malware, intrusion, and fraudulent website detection. Our experimental study reveals the actual effectiveness of these attacks, thus providing an urgency for immediate attention to enhance the resiliency of XAI methods and their applications.

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

BackgroundMonkeypox, a viral zoonotic disease, is an emerging global health concern, with rising incidence and outbreaks extending beyond its endemic regions in Central and, West Africa and the world. The disease transmits through contact with infected animals and humans, leading to fever, rash, and lymphadenopathy symptoms. Control efforts include surveillance, contact tracing, and vaccination campaigns; however, the increasing number of cases underscores the necessity for a coordinated global response to mitigate its impact. Since monkeypox has become a public health issue, new methods for efficiently identifying cases are required. The control of monkeypox infections depends on early detection and prediction. This study aimed to utilize Symptom-Based Detection of Monkeypox using a machine-learning approach.MethodsThis research presents a machine learning approach that integrates various Explainable Artificial Intelligence (XAI) to enhance the detection of monkeypox cases based on clinical symptoms, addressing the limitations of image-based diagnostic systems. In this study, we used a publicly available dataset from GitHub containing clinical features about monkeypox disease. The data have been analysed using Random Forest, Bagging, Gradient Boosting, CatBoost, XGBoost, and LGBMClassifier to develop a robust predictive model.ResultsThe study shows that machine learning models can accurately diagnose monkeypox based on symptoms like fever, rash, lymphadenopathy and other clinical symptoms. By using XAI techniques for feature importance, the approach not only achieved high accuracy but also provided transparency in decision-making. This integration of explainable Artificial intelligence (AI) enhances trust and allows healthcare professionals to understand predictions, leading to timely interventions and improved public health responses to monkeypox outbreaks. All Machine learning methods have been compared with the evaluation matrix. The best performance was for the LGBMClassifier, with an accuracy of 89.3%. In addition, multiple Explainable Techniques tools were used to help in examining and explaining the output of the LGBMClassifier model.ConclusionsOur research shows that combining explainable techniques with AI models greatly enhances the accuracy of case detection and boosts the trust of medical professionals. These models result in directly involving the reader and health care professional in the decision-making process, making informed decisions, and efficiently allocating resources by providing insight into the decision-making process. In addition, this study underscores the potential of AI in public health surveillance, particularly in enhancing responses to emerging infectious diseases such as monkeypox.

Introduction: In the current educational landscape, universities provide a vast array of diverse and obscure courses. However, the primary challenge students face is decision overload, as the abundance of options can make it difficult to choose courses that align with their interests, career aspirations, and strengths. The development and application of proper course suggestion systems might assist students in overcoming the challenges associated with choosing the right courses. However, these recommendation systems still require improvement to address explainability issues, security issues, and cold start. Our analysis reveals that there is limited research addressing how course recommendation systems can assist 12th-grade students in selecting courses for higher education. Therefore, this study presents a novel personalized recommendation system, namely "Interpretability-Driven Course Recommendation: A Random Forest Approach with Explainable AI", that chooses the top-3 courses for students based on their intermediate class grades/- marks. This research provides accurate and interpretable recommendations using the Random Forest algorithm with Explainable AI that ensures transparency by explaining why a particular course is recommended. Method: The system uses a Random Forest classifier with SHAP (SHapley Additive exPlanations) to forecast the top-3 courses with the greatest expected scores for appropriateness. The system uses SHAP (SHapley Additive exPlanations) values to integrate Explainable AI (XAI) to guarantee openness and trust. The proposed model solves the cold start problem and provides data security. Results: In terms of precision (0.90), recall (0.92), and F1-score (0.92), Random Forest fared better than any other classifier. By combining predictions from several decision trees, its ensemble learning technique increases stability, decreases overfitting, and improves generalization across a broad range of input data patterns. Discussions: A major drawback of conventional black-box machine learning models is their lack of transparency, which is addressed by the incorporation of SHAP into the course recommendation system. In this model, students get suggestions in addition to information on which topic scores had the biggest influence on their choice. Conclusion: This study presents a practical and interpretable course recommendation system designed for 12th-grade students transitioning into higher education. Through the use of Explainable AI-enhanced Random Forest, the model provides precise, transparent, and customized recommendations. It addresses the main drawbacks of conventional systems, such as their lack of explainability and their cold start problems. The model';s excellent precision, recall, and F1-score indicate its superior performance, which makes it a useful tool for student guidance and academic advice. For even more thorough recommendations, future research may consider combining long-term academic objectives, aptitude test results, and student interests.

With the rapid development and integration of artificial intelligence (AI) methods in next-generation networks (NextG), AI algorithms have provided significant advantages for NextG in terms of frequency spectrum usage, bandwidth, latency, and security. A key feature of NextG is the integration of AI, i.e., self-learning architecture based on self-supervised algorithms, to improve the performance of the network. A secure AI-powered structure is also expected to protect N extG networks against cyber-attacks. However, AI itself may be attacked, i.e., model poisoning targeted by attackers, and it results in cybersecurity violations. This paper proposes an AI trust platform using Streamlit for N extG networks that allows researchers to evaluate, defend, certify, and verify their AI models and applications against adversarial threats of evasion, poisoning, extraction, and interference.

The integration of artificial intelligence (AI) in cancer research has significantly advanced radiology, pathology, and multimodal approaches, offering unprecedented capabilities in image analysis, diagnosis, and treatment planning. AI techniques provide standardized assistance to clinicians, in which many diagnostic and predictive tasks are manually conducted, causing low reproducibility. These AI methods can additionally provide explainability to help clinicians make the best decisions for patient care. This review explores state-of-the-art AI methods, focusing on their application in image classification, image segmentation, multiple instance learning, generative models, and self-supervised learning. In radiology, AI enhances tumor detection, diagnosis, and treatment planning through advanced imaging modalities and real-time applications. In pathology, AI-driven image analysis improves cancer detection, biomarker discovery, and diagnostic consistency. Multimodal AI approaches can integrate data from radiology, pathology, and genomics to provide comprehensive diagnostic insights. Emerging trends, challenges, and future directions in AI-driven cancer research are discussed, emphasizing the transformative potential of these technologies in improving patient outcomes and advancing cancer care. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI.

Artificial Intelligence (AI) has demonstrated superhuman capabilities in solving a significant number of tasks, leading to widespread industrial adoption. For in-field network-management application, AI-based solutions, however, have often risen skepticism among practitioners as their internal reasoning is not exposed and their decisions cannot be easily explained, preventing humans from trusting and even understanding them. To address this shortcoming, a new area in AI, called Explainable AI (XAI), is attracting the attention of both academic and industrial researchers. XAI is concerned with explaining and interpreting the internal reasoning and the outcome of AI-based models to achieve more trustable and practical deployment. In this work, we investigate the application of XAI for network management, focusing on the problem of automated failure-cause identification in microwave networks. We first introduce the concept of XAI, highlighting its advantages in the context of network management, and we discuss in detail the concept behind Shapley Additive Explanations (SHAP), the XAI framework considered in our analysis. Then, we propose a framework for a XAI-assisted ML-based automated failure-cause identification in microwave networks, spanning model’s development and deployment phases. For the development phase, we show how to exploit SHAP for feature selection and how to leverage SHAP to inspect misclassified instances during model’s development process, and how to describe model’s global behavior based on SHAP’s global explanations. For the deployment phase, we propose a framework based on predictions uncertainty to detect possibly wrong predictions that will be inspected through XAI.