A Novel Framework for Financial Cybersecurity and Fraud Detection Using XAI-RNN-SGRU by Smarajit Ghosh

“…The hybridization of Explainable Artificial Intelligence (XAI) with Ridgelet Neural Network (RNN) and Soft Gated Recurrent Unit (SGRU) (XAI-RNN-SGRU) is introduced to address these challenges. …”
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Linking structure and process in dendritic growth using persistent homology with energy analysis by Misato Tone, Shunsuke Sato, Sotaro Kunii, Ippei Obayashi, Yasuaki Hiraoka, Yui Ogawa, Hirokazu Fukidome, Alexandre Lira Foggiatto, Chiharu Mitsumata, Ryunosuke Nagaoka, Arpita Varadwaj, Iwao Matsuda, Masato Kotsugi

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DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images by Fatima Rauf, Muhammad Attique Khan, Ghassen Ben Brahim, Wardah Abrar, Areej Alasiry, Mehrez Marzougui, Seob Jeon, Yunyoung Nam

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Dual level dengue diagnosis using lightweight multilayer perceptron with XAI in fog computing environment and rule based inference by Deepika R., Pradeep Kumar T.S.

“…Since the suggested model is a “black box,” explainable artificial intelligence (XAI) tools such as SHAP and LIME have been used to help explain its predictions. …”
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Predicting mechanical ventilation duration in ICU patients: A data-driven machine learning approach for clinical decision-making by Shivi Mendiratta, Vinay Gandhi Mukkelli, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

“…Objective To develop explainable artificial intelligence (AI) models for predicting mechanical ventilation duration leveraging diverse clinical parameters from ICU patient data. …”
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Deep learning-based image classification for AI-assisted integration of pathology and radiology in medical imaging by Lanting He, Lan Luan, Dan Hu

“…IntroductionThe integration of pathology and radiology through artificial intelligence (AI) represents a groundbreaking advancement in medical imaging, providing a powerful tool for accurate diagnostics and the optimization of clinical workflows. …”
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Counterfactual and Prototypical Explanations for Tabular Data via Interpretable Latent Space by Simone Piaggesi, Francesco Bodria, Riccardo Guidotti, Fosca Giannotti, Dino Pedreschi

“…Artificial Intelligence decision-making systems have dramatically increased their predictive power in recent years, beating humans in many different specific tasks. …”
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Innovative machine learning approaches for complexity in economic forecasting and SME growth: A comprehensive review by Mustafa I. Al-Karkhi, Grzegorz Rza̧dkowski

“…By comparing existing surveys and identifying gaps, this review presents actionable insights and proposes future research directions that emphasize the need for integrative models that combine Explainable Artificial Intelligence (XAI) with cross-regional data fusion for more accurate and adaptable economic forecasts. …”
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STSA‐Based Early‐Stage Detection of Small Brain Tumors Using Neural Network by Nafiul Hasan, Md. Masud Rana, Md Mahmudul Hasan, AKM Azad, Dil Afroz, Md Mostafizur Rahman Komol, Mousumi Aktar, Mohammad Ali Moni

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Non-destructive detection of pre-incubated chicken egg fertility using hyperspectral imaging and machine learning by Md Wadud Ahmed, Asher Sprigler, Jason Lee Emmert, Ryan N Dilger, Girish Chowdhary, Mohammed Kamruzzaman

“…The Shapley explainable artificial intelligence (AI) method was used to interpret the robust CatBoost model, revealing that wavelength regions associated with yolk color, pre-incubation cellular activities related to embryonic development, changes in hydration levels, and variations in protein and lipid contents between fertile and infertile eggs are crucial for pre-incubation chicken egg fertility classification. …”
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Construction of a risk prediction model for pulmonary infection in patients with spontaneous intracerebral hemorrhage during the recovery phase based on machine learning by Jixiang Xu, Yuan Li, Fumin Zhu, Fumin Zhu, Xiaoxiao Han, Liang Chen, Yinliang Qi, Yinliang Qi, Xiaomei Zhou, Xiaomei Zhou

“…Accurate prediction of PI risk is crucial for early intervention and optimized clinical management. The aim of this study was to develop a machine learning (ML) model for predicting PI risk in patients during the recovery phase of deep SICH and to investigate the contributions of individual risk factors through explainable artificial intelligence techniques.MethodsWe conducted a retrospective study involving 649 patients diagnosed with PI during the recovery phase of deep SICH between 2021 and 2023. …”
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YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation by Uddin A Hasib, Raihan Md Abu, Jing Yang, Uzair Aslam Bhatti, Chin Soon Ku, Lip Yee Por

“…This study aims to develop a robust framework that integrates synthetic image augmentation with advanced deep learning (DL) models to address dataset imbalance, improve diagnostic accuracy, and enhance trust in artificial intelligence (AI)-driven diagnoses through Explainable AI (XAI) techniques. …”
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Tumor ViT-GRU-XAI: Advanced Brain Tumor Diagnosis Framework: Vision Transformer and GRU Integration for Improved MRI Analysis: A Case Study of Egypt by Mohammed Aly, Abdullatif Ghallab, Islam S. Fathi

“…Additionally, we applied Explainable Artificial Intelligence (XAI) techniques to clarify the model’s decision-making process, enhancing transparency and fostering clinician trust, which is essential for practical clinical use. …”
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Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study by Jingchao Lei, Jia Zhai, Yao Zhang, Jing Qi, Chuanzheng Sun

“…MethodsThis retrospective multicenter cohort study adhered to the TRIPOD+AI (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Extended for Artificial Intelligence) guidelines. Data from 8834 patients with sepsis in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were used for training and internal validation, while data from 4236 patients in the eICU-Collaborative Research Database (eICU-CRD) database were used for external validation. …”
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Eeg-based detection of epileptic seizures in patients with disabilities using a novel attention-driven deep learning framework with SHAP interpretability by Tawfeeq Shawly, Ahmed A. Alsheikhy

“…Recent developments in artificial intelligence (AI) and deep learning have facilitated the automation of seizure detection from EEG signals with improved accuracy. …”
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Toward a thermodynamic theory of evolution: a theoretical perspective on information entropy reduction and the emergence of complexity by Carlos Mendoza Montano

“…Together, these examples demonstrate that informational entropy reduction is a pervasive, measurable feature of evolving systems. While this article presents a theoretical perspective rather than empirical results, it offers a unifying explanation for major evolutionary transitions, the emergence of cognition and consciousness, the rise of artificial intelligence, and the potential universality of life. …”
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An IoT-enabled AI system for real-time crop prediction using soil and weather data in precision agriculture by MD Shaifullah Sharafat, Nilavro Das Kabya, Rahimul Islam Emu, Mehrab Uddin Ahmed, Jakaria Chowdhury Onik, Mohammad Aminul Islam, Riasat Khan

“…Context: Precision agriculture leverages advanced technologies such as the Internet of Things (IoT) and artificial intelligence (AI) to enhance crop productivity by providing data-driven insights. …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…The development of artificial intelligence provides a new approach for early disease identification. …”
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Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description-Based Code Selection by Sander Puts, Catharina M L Zegers, Andre Dekker, Iñigo Bermejo

“… Abstract BackgroundThe International Classification of Diseases (ICD), developed by the World Health Organization, standardizes health condition coding to support health care policy, research, and billing, but artificial intelligence automation, while promising, still underperforms compared with human accuracy and lacks the explainability needed for adoption in medical settings. …”
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Molecular dynamics-based explanation of the reinforcement geometry effects on CNT/graphene-reinforced Al0.3CoCrFeNi high-entropy alloys by Subrata Barman, Kritesh Kumar Gupta, Sudip Dey

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