Traffic adversarial example attack and defense method based on explainable artificial intelligence by MA Bowen, GUO Yuanbo, TIAN Jiwei, MA Jun, HU Yongjin

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Automated detection of sea cucumbers in turbid subtidal marine habitats: An explainable approach by Cheryl Chu, Yi-Fei Gu, Adrian Wong, Bayden D. Russell

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Explaining consumers’ channel-switching behavior in the post-COVID-19 pandemic era by Anh Tram Nguyen Tran, Uyen Hoang Dinh Nguyen, Vu Minh Ngo, Huan Huu Nguyen

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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

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How Valid Is Bandura’s Social Cognitive Theory to Explain Physical Activity Behavior? by Viktoria Sophie Egele, Eric Klopp, Robin Stark

“…The present study addresses these two research gaps and tests the tenability of the theoretical assumptions of SCT to explain physical activity behavior in the general population. (2) Methods: A total of 194 German adults (109 male, 85 female) with a mean age of 26.03 years (SD = 10.33) completed two validated questionnaires concerning their expressions on SCT components (t1) and their physical activity (t2). …”
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Explaining and Evaluating the Understanding of Divine Attributes from the Perspective of Mirza Mahdi Esfahani by Mahdi Azadparvar

“…The intellect of human beings cannot also know the divine attributes due to the inconsistency between the Creator and the creature, and the description of God to human notions, concepts and knowledge is not correct, but it is God who introduces his attributes to human beings and for this reason, this quotation of this theory does not lead to the cessation of the knowledge of God’s attributes. By explaining and analyzing this theory, it becomes clear that this theory actually leads to the cessation of the intellect in understanding the knowledge of divine attributes.…”
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Fuzzy C-Means and Explainable AI for Quantum Entanglement Classification and Noise Analysis by Gabriel Marín Díaz

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Minimization of proteome reallocation explains metabolic transition in hierarchical utilization of carbon sources by Zhihao Liu, Minghao Chen, Jingmin Hu, Yonghong Wang, Yu Chen

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Explaining Gender Differences in Caries: A Multifactorial Approach to a Multifactorial Disease by Maria Ferraro, Alexandre R. Vieira

“…Further research in these areas could be done in the future to explain their contribution, or lack thereof, to a higher caries rate in women.…”
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Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI by Salman Muneer, Sagheer Abbas, Asghar Ali Shah, Meshal Alharbi, Haya Aldossary, Areej Fatima, Taher M. Ghazal, Khan Muhammad Adnan

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Prediction of Metastasis in Paragangliomas and Pheochromocytomas Using Machine Learning Models: Explainability Challenges by Carmen García-Barceló, David Gil, David Tomás, David Bernabeu

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Network self-organization explains the statistics and dynamics of synaptic connection strengths in cortex. by Pengsheng Zheng, Christos Dimitrakakis, Jochen Triesch

“…In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. …”
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On the black-box explainability of object detection models for safe and trustworthy industrial applications by Alain Andres, Aitor Martinez-Seras, Ibai Laña, Javier Del Ser

Subjects: “…Explainable Artificial Intelligence…”
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Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture by Lefteris Benos, Dimitrios Tsaopoulos, Aristotelis C. Tagarakis, Dimitrios Kateris, Patrizia Busato, Dionysis Bochtis

“…This study addresses a critical gap in human activity recognition (HAR) research by enhancing both the explainability and efficiency of activity classification in collaborative human–robot systems, particularly in agricultural environments. …”
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A novel explainable deep learning framework for reconstructing South Asian palaeomonsoons by K. M. R. Hunt, K. M. R. Hunt, S. P. Harrison

“…<p>We present novel explainable deep learning techniques for reconstructing South Asian palaeomonsoon rainfall over the last 500 years, leveraging long instrumental precipitation records and palaeoenvironmental datasets from South and East Asia to build two types of models: dense neural networks (“regional models”) and convolutional neural networks (CNNs). …”
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Explaining the classroom behavior process of medical sciences students: A grounded theory by Yeganeh Moradi, Zhila Fereidouni, Abbas Abdollahi, Ali Khani Jeihooni, Nazanin Poornasiri, Zahra Jokar, Samaneh Dehghani, Shahnaz Karimi, Mohammad Hasan Keshavarzi

“…The aim of this study was to explain the classroom behavior process of medical sciences students. …”
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Explain the behavior change and maintenance in diabetic patients using MTM-HAPA framework by Yibo Wu, Zhenjie Yu, Xiaoqiu Yin, Yimiao Li, Yang Jiang, Gongli Liu, Xinying Sun

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Explaining basketball game performance with SHAP: insights from Chinese Basketball Association by Yan Ou-Yang, Wei Hong, Liming Peng, Cheng-Xi Mao, Wen-Jia Zhou, Wei-Tao Zheng, Quan Wang, Feng Qi, Xue-Wei Li, Shi-Huan Chen, Ce Xu, Yu-Fan Wang

“…The SHapley Additive exPlanation (SHAP) method is applied to explain the optimal prediction model and analyze the KPIs. …”
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Improved CKD classification based on explainable artificial intelligence with extra trees and BBFS by Ahmed M. Elshewey, Enas Selem, Amira Hassan Abed

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Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway by Esmaeil S. Nadimi, Jan-Matthias Braun, Benedicte Schelde-Olesen, Smith Khare, Vinay C. Gogineni, Victoria Blanes-Vidal, Gunnar Baatrup

“…We developed a family of algorithms based on explainable deep neural networks (DNN) that detect polyps within a sequence of images, feed only those images containing polyps into two parallel independent networks to characterize, and estimate the size of important findings. …”
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