Extreme heat prediction through deep learning and explainable AI. by Fatima Shafiq, Amna Zafar, Muhammad Usman Ghani Khan, Sajid Iqbal, Abdulmohsen Saud Albesher, Muhammad Nabeel Asghar

“…This study investigates the efficacy of deep learning (DL) models, including Artificial Neural Network (ANN), Conolutional Neural Network (CNN) and Long-Short Term Memory (LSTM), using five years of meteorological data from Pakistan Meteorological Department (PMD), by integrating Explainable AI (XAI) techniques to enhance the interpretability of models. …”
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Explaining the Concept of Urbanisation based on the Distinction between the City and the Urban by Amir Fathi Farzaneh, Mohammad Hossein Sharifzadegan

“…Based on the intellectual principles of qualitative content analysis, this research explains the conceptual framework of the distinct components of the city and the urban. …”
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The Role of Parental Self-Efficacy in Explaining Children’s Academic Outcomes by Andreja Bubić, Antonela Tošić, Irena Mišetić

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Explaining the photometric and spectroscopic properties of The Sip-39 galaxy pair by Haydar R. Al-baqir, Abdullah K. Ahmed

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Explaining the Performance Improvement Model and Competencies of Managers of Agricultural Cooperatives by Naser Seifollahi, Mohammadreza Keshavarz

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An explainable Bi-LSTM model for winter wheat yield prediction by Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, Subrata Chakraborty, Renuganth Varatharajoo, Abdullah Alamri, Shilpa Gite, Chang-Wook Lee

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Ethnoracial Transformations? Linking Administrative Data to Explain Changes in Identification by John Anders, Mary E. Campbell, Craig Wesley Carpenter, Luna Chandna

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME by Ahmed M. Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, Petia Radeva, Steffen E. Petersen, Karim Lekadir, Gloria Menegaz

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Drug discovery and mechanism prediction with explainable graph neural networks by Conghao Wang, Gaurav Asok Kumar, Jagath C. Rajapakse

“…However, existing methods mainly focus on forward encoding of drugs, which is to obtain an accurate prediction of the response levels, but omitted to decipher the reaction mechanism between drug molecules and genes. We propose the eXplainable Graph-based Drug response Prediction (XGDP) approach that achieves a precise drug response prediction and reveals the comprehensive mechanism of action between drugs and their targets. …”
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Genetic effects on molecular network states explain complex traits by Matthias Weith, Jan Großbach, Mathieu Clement‐Ziza, Ludovic Gillet, María Rodríguez‐López, Samuel Marguerat, Christopher T Workman, Paola Picotti, Jürg Bähler, Ruedi Aebersold, Andreas Beyer

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TATPat based explainable EEG model for neonatal seizure detection by Turker Tuncer, Sengul Dogan, Irem Tasci, Burak Tasci, Rena Hajiyeva

“…The primary objective of this research is to detect neonatal seizures and explain these seizures using the new version of Directed Lobish. …”
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The interplay between firms’ capabilities and ownership in explaining environmental performance by Beatriz Forés, César Camisón-Zornoza, José María Fernández-Yáñez

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Deep supervised, but not unsupervised, models may explain IT cortical representation. by Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte

“…The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. …”
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Explainable artificial intelligence for sustainable urban water systems engineering by Shofia Saghya Infant, Sundaram Vickram, A Saravanan, C M Mathan Muthu, Devarajan Yuarajan

Subjects: “…Explainable artificial intelligence (XAI)…”
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Using Segmentation to Boost Classification Performance and Explainability in CapsNets by Dominik Vranay, Maroš Hliboký, László Kovács, Peter Sinčák

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Explainable AI for Enhancing Efficiency of DL-Based Channel Estimation by Abdul Karim Gizzini, Yahia Medjahdi, Ali J. Ghandour, Laurent Clavier

“…Tackling this issue can be achieved by developing explainable AI (XAI) schemes that aim to explain the logic behind the black-box model behavior, and thus, ensure its efficient and safe deployment. …”
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Activity dependent degeneration explains hub vulnerability in Alzheimer's disease. by Willem de Haan, Katherine Mott, Elisabeth C W van Straaten, Philip Scheltens, Cornelis J Stam

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Can Differences in Characteristics Explain Ethnic Wage Gap in Latvia? by Vilerts Kārlis, Krasnopjorovs Oļegs

“…We used anonymized micro data from Labour Force Survey to estimate the ethnic wage gap in Latvia and find the factors that explain it. We found that a notable ethnic wage gap still exists in Latvia with non-Latvians earning 10 % less than Latvians in 2015. …”
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Identifying and Explaining the Facilitating and Inhibiting Factors of Research Self-Efficacy by Ali Ebrahimi Ghale Ghazi, Kolsum Nami, Mokhtar Zakeri, Mahmoud Sabahizadeh

“…The present study aimed to identify and explain the facilitating and inhibiting factors of research self-efficacy among graduate students at Islamic Azad University in Hormozgan. …”
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A Review of Explainable AI for Android Malware Detection and Analysis by Maryam Tanha, Somayeh Kafaie

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