A review of explainable AI techniques and their evaluation in mammography for breast cancer screening by Noora Shifa (21392996)

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Explainable recommendation: when design meets trust calibration by Mohammad Naiseh (18513738)

“…Then, we conducted two co-design sessions with eight participants to identify design principles and techniques for explanations that help trust calibration. …”

Food fraud detection using explainable artificial intelligence by Okan Buyuktepe (17991493)

“…Although the use of AI has been rising in numerous industries, such as precision nutrition, self‐driving cars, precision agriculture, precision medicine and food safety, much of what AI systems do is a black box due to its poor explainability. This study covers numerous use cases of food fraud risk prediction using explainable artificial intelligence (XAI) techniques, such as LIME, SHAP and WIT. …”

Explainable persuasion for interactive design: The case of online gambling by Deniz Cemiloglu (17150980)

“…A total of 250 UK-based users of gambling platforms (age range 18–75, 127 female) completed our online survey based on principles of persuasion and explainability. Findings showed that players were aware of the use, persuasive intent, and potential harm of various persuasive design techniques used in online gambling platforms (e.g., the use of in-game rewards, reminders, and praise to encourage further gambling). …”

Explainable phishing website detection for secure and sustainable cyber infrastructure by Tanzila Kehkashan (20748842)

Explainable, trustworthy, and ethical machine learning for healthcare: A survey by Khansa Rasheed (17380573)

“…With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provided a comprehensive review of explainable and interpretable ML techniques for various healthcare applications. …”

Advancing explainable AI in healthcare: Necessity, progress, and future directions by Rashmita Kumari Mohapatra (17726427)

“…Over the years, various methods for liver image segmentation have emerged, with machine learning and computer vision techniques gaining rapid popularity due to their automation, suitability, and impressive results. …”

Plant Leaf Disease Detection Using Ensemble Learning and Explainable AI by Ammar Oad (21324182)

“…This study introduces an AI (Artificial Intelligence) model that detects and explains plant diseases through image analysis. The proposed system, distinct from existing detectors, identifies numerous diseases in vegetables and fruits by employing our proposed ensemble learning classifier involving four deep learning models: VGG16, VGG19, ResNet101 V2, and Inception V3, achieving an accuracy exceeding 90%. …”

Tamp-X: Attacking explainable natural language classifiers through tampered activations by Hassan Ali (3348749)

“…Recently Explainable Artificial Intelligence (XAI) methods have been proposed as a method for increasing DNN’s reliability and trustworthiness. …”

An End-to-End Concatenated CNN Attention Model for the Classification of Lung Cancer With XAI Techniques by Fariha Haque (21485518)

“…The model leverages explainable AI techniques, such as gradient-weighted class activation mapping (grad-CAM) and Shapley additive explanations (SHAP), to highlight critical regions within the images that influence the decision-making process. …”

A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti (21593819)

“…Although theoretical studies have demonstrated improved ML performance, they have not gained much interest among clinicians due to a lack of explainability. This study focuses on optimizing and comparing various machine learning models for ASD diagnosis, while incorporating explainable AI techniques to ensure model transparency and interpretability. …”

Reinforcement learning-based dynamic pruning for distributed inference via explainable AI in healthcare IoT systems by Emna Baccour (16896366)

“…The online pruning of DNN inferences without retraining is viable; however, it was not considered in the literature as most well-known techniques do not perform well without adjustment. In this paper, we propose a novel pruning strategy using Explainable AI (XAI) to enhance the performance of pruned DNNs without retraining, a necessity due to the scarcity and bias of local healthcare data. …”

MCDFN: supply chain demand forecasting via an explainable multi-channel data fusion network model by Md Abrar Jahin (20108252)

“…Although deep learning techniques have advanced, the lack of interpretable models hampers understanding and explaining predictions. …”

Explainable deep learning for rainfall prediction: A CNN-XGBoost hybrid approach in the northern region of Bangladesh by Md Safayet Islam (23073187)

“…This research highlights the potential of hybrid models in enhancing rainfall prediction accuracy while providing transparency through explainable AI techniques. Beyond hydrology, the predicted rainfall patterns provide essential inputs for urban planners to optimize land-use zoning in flood-prone areas, and guide resilient infrastructure development. …”

Forecasting techniques. (c1994) by Bahlu, Omar

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Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach by Shaikh Afnan Birahim (22303750)

“…By incorporating Synthetic Minority Oversampling Technique Tomek (SMOTE-Tomek) techniques to balance the dataset and employing explainable AI methods such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the proposed model achieves significant improvements in detection accuracy, precision, recall, and F1 score while providing clear, interpretable results. …”

Machine Learning Techniques for Pharmaceutical Bioinformatics by SULTAN, AHMED ATTA AHMED

“…The full features for each drug are not fully explained by researchers due to the incomplete drug profile description. …”
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Explainable Supervised and Semi-Supervised Learning for Breast Cancer Risk Prediction from Questionnaires: A Study on BCSC and UAE Datasets by Alsarookh, Omar Ahmad

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Prediction of CO₂ uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence by Mohd Azfar Shaida (19756971)

“…<p dir="ltr">This study introduces comprehensive research on the prediction of the carbon dioxide (CO₂) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (XAI) techniques. …”

Vocabulary Teaching Techniques in an Omani Government School by Al Jabri, Badr Abdullah

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