Savindi Thathsarani¹, Ashan Lakshitha², Pasindu Pramodya², Praveen Perera², Rasanjali Samarakoon^1,*, Shagufta Henna³, Upaka Rathnayake^4,*

Phyton-International Journal of Experimental Botany, Vol.95, No.4, 2026, DOI:10.32604/phyton.2026.078657 - 28 April 2026

Abstract Accurately determining the optimal post-harvest storage period is still a major challenge in mango processing, especially for the Tom EJC (TEJC) variety, due to reliance on subjective visual evaluations, leading to inconsistent product quality and increased post-harvest losses. This study presents an artificial intelligence-based framework combining computer vision and physicochemical analysis to objectively predict the optimal post-harvest storage period of TEJC mango before processing. TEJC mangoes of grade one were stored for eight days at 24–28°C temperature and 66.4–80% relative humidity. Daily measurements of pH, Total Soluble Solids (TSS), firmness, and peel color parameters (L*,… More > Graphic Abstract

Mamoona Jabbar, Uzma Jamil^*, Muhammad Younas, Bushra Zafar

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080241 - 27 April 2026

Abstract Magnetic Resonance Imaging is one of the most commonly used neuro-oncology imaging modalities, which is a non-invasive mode of imaging and helps in detecting brain abnormalities in an effective way. Earlier researchers have demonstrated that brain tumor segmentation and classification can be effectively performed using deep learning techniques. Existing studies are primarily aimed at increasing prediction accuracy and provide insignificant consideration to model interpretability, limiting their practical application in clinical practice. To address this limitation, this research presents a two-stage explainable deep learning model, which combines transformer-based segmentation with an ensemble classification model that is… More >

Ghada Atteia¹, Abdulaziz Altamimi², Nihal Abuzinadah³, Khaled Alnowaiser⁴, Muhammad Umer^5,*, Yunyoung Nam⁶, Yongwon Cho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078510 - 27 April 2026

Abstract Cardiovascular diseases (CVD) remain the leading cause of global mortality, making early and accurate diagnosis essential for improving patient outcomes. However, most existing deep learning approaches address cardiac image segmentation or disease classification independently, limiting their effectiveness in complex clinical decision-making scenarios. In this study, we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection. The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D (SAM-Med2D) to achieve accurate cardiac structure segmentation, followed by Mamba-based temporal feature extraction and Transformer-driven spatial… More >

Chun-I Fan^1,2, Sheng-Feng Lu¹, Cheng-Han Shie¹, Ming-Feng Tsai¹, Tomohiro Morikawa^3,*, Takeshi Takahashi⁴, Tao Ban⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077799 - 27 April 2026

Abstract Due to its partly open-source architecture, which allows for application analysis and repackaging, along with its large market share, the Android operating system is a main target for malware. In recent years, researchers have widely adopted neural network-based methods for detecting Android malware, achieving impressive results but without interpretability. Interpretability is crucial for showing how models behave and identifying biases in their predictions, which helps in validating and improving them. Additionally, in urgent malware analysis situations, interpretability lets analysts quickly assess harmful behaviors and aids in future malware development and investigation. Therefore, interpretability is vital… More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.076888 - 27 April 2026

Abstract The engine serves as the primary component that generates power and drives vehicle movement. Given its critical role, accurately diagnosing engine faults is essential for ensuring vehicle safety and reliability. Recent advances in machine learning (ML) have enabled the development of artificial intelligence (AI)-based diagnostic models with strong predictive performance. However, the lack of transparency in these models constrains user confidence in their diagnostic outcomes. While explainable AI (XAI) methods such as local interpretable model-agnostic explanations (LIME) and Shapley additive explanations (SHAP) have been introduced to improve interpretability, their reliance on visual outputs requires manual… More >

Thatsamaphon Boonchuntuk¹, Thanyapisit Buaprakhong¹, Varintorn Sithisint¹, Awirut Phusaensaart¹, Sinthon Wilke¹, Thittaporn Ganokratanaa^1,*, Mahasak Ketcham²

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.069005 - 27 April 2026

Abstract Urban traffic congestion directly contributes to excessive energy consumption and urban air pollution, requiring adaptive traffic signal control strategies that incorporate sustainability objectives alongside mobility performance. This study proposes a Large Language Model (LLM) driven traffic signal optimization framework that transforms detailed intersection-level traffic states into structured natural-language prompts, enabling the LLM to reason over congestion patterns, queue asymmetry, phase history, and estimated energy emission impacts. Unlike reinforcement learning (RL) based controllers, the LLM requires no task-specific training and operates in a zero-shot manner through carefully designed structured prompts that encode traffic states, phase history,… More >

Edward Sutanto¹, Rinni Sutanto², Sara Velichkovikj³, Nikola Hadzi-Petrushev⁴, Mitko Mladenov⁴, Dimiter Avtanski^5,6,7, Radoslav Stojchevski^5,6,8,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.076157 - 22 April 2026

Abstract The rapid growth and accessibility of artificial intelligence (AI) and machine learning (ML) have opened many avenues to revolutionize biomedical research, particularly in oncogenesis. Oncogenesis is a hallmark process in the development of cancer, involving the amplification of proto-oncogenes and the subsequent dysregulation of molecular signaling networks. These pathways—including the RAS/RAF/MEK/ERK, PI3K-AKT, JAK-STAT, TGF-β/Smad, Wnt/β-Catenin, and Notch cascades—have been studied extensively in isolation, with major strides achieved in understanding how they drive cancer. However, there are still many considerations regarding how these networks interact. Ongoing studies show that crosstalk among these pathways occurs through feedback… More >

Abdul Manan^1,2, Sidra Ilyas^2,*

Oncology Research, Vol.34, No.5, 2026, DOI:10.32604/or.2026.074185 - 22 April 2026

Abstract Hepatocellular carcinoma (HCC) remains a significant global health challenge, with therapeutic efficacy in advanced stages often limited by underlying liver dysfunction and adaptive resistance. In this review, the evolving landscape of molecular targets and combinatorial strategies is critically examined, with a particular focus on the transition from preclinical discovery to clinical application. While traditional molecular heterogeneity is acknowledged, the aim is to elucidate how emerging computational paradigms are redefining target discovery and therapeutic stratification in HCC. The primary purpose is to evaluate the role of Artificial Intelligence (AI) and Machine Learning (ML) as integrative tools… More > Graphic Abstract

Yahya Ghazwani^1,2,3, Mohammad Alghafees^1,2,3,*, Mishari Alshasha^1,2,3, Fahad Brayan^1,2,3, Abdulrahman Alsayyari^1,2,3, Ali Alyami^1,2,3

Canadian Journal of Urology, Vol.33, No.2, pp. 291-308, 2026, DOI:10.32604/cju.2026.077411 - 20 April 2026

Abstract Objectives: The emergence of artificial intelligence (AI) and predictive modeling offers prospects for clinical, anatomical, and imaging factor combination, like radiomics, to help with stone-free status (SFS) estimation and peroperative decision-making. The goal of this study was, therefore, to define the present performance range, determine sources of heterogeneity, and determine methodological practices permitting reliable implementation by varied circumstances. Methods: We searched six bibliographic databases through 19 September 2025. Studies deriving or validating AI/predictive models for SFS after ureteroscopy were eligible. Independent dual screening, duplicate data extraction, and risk-of-bias consideration using QUADAS-AI were conducted. Results: Five retrospective… More >

Jaeseung Lee¹, Jehyeok Rew^2,*

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077691 - 09 April 2026

Abstract The accelerating transition toward electrified mobility has positioned electric vehicles (EVs) as a primary technology in modern transportation systems. In this context, ensuring the reliability of EV drive motors (EVDMs) becomes increasingly critical, given their central role in propulsion performance and operational safety. Accurate and interpretable fault diagnosis of EVDMs is therefore essential for enabling effective maintenance and supporting the broader sustainability and resilience of EVs. This study presents a novel framework that combines decision tree-based fault classification with a multi-agent large language model (LLM) interpretation architecture to deliver transparent and human-readable diagnostic explanations. The… More >