Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence by Yushuang Dong, HuiPing Liao, Feiming Huang, YuSheng Bao, Wei Guo, Zhen Tan

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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…This paper proposes a hierarchical multi-scale decomposition and deep learning ensemble framework that addresses these limitations. …”
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Human as AI mentor: Enhanced human-in-the-loop reinforcement learning for safe and efficient autonomous driving by Zilin Huang, Zihao Sheng, Chengyuan Ma, Sikai Chen

“…Remarkably, we circumvent the intricate process of manually designing reward functions; instead, we directly derive proxy state-action values from partial human demonstrations to guide the agents’ policy learning. …”
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Evaluating machine learning-based intrusion detection systems with explainable AI: enhancing transparency and interpretability by Vincent Zibi Mohale, Ibidun Christiana Obagbuwa

“…Machine Learning (ML)-based Intrusion Detection Systems (IDS) are integral to securing modern IoT networks but often suffer from a lack of transparency, functioning as “black boxes” with opaque decision-making processes. …”
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Interdisciplinary framework for cyber-attacks and anomaly detection in industrial control systems using deep learning by Qawsar Gulzar, Khurram Mustafa

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Harnessing feature pruning with optimal deep learning based DDoS cyberattack detection on IoT environment by Eunmok Yang, Sooyong Jeong, Changho Seo

“…They are vulnerable to different cyber threats that can compromise the functionality and security of urban systems. Distributed Denial of Service (DDoS) attacks are among IoT networks’ most challenging and destructive cyber threats. …”
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Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation by Lin Sun, Suisui Chen, Jiucheng Xu, Yun Tian

“…To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). …”
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Hybrid Optimized Feature Selection and Deep Learning Method for Emotion Recognition That Uses EEG Data by asmaa Bashar Hmaza, Rajaa K. Hasoun

“…Optimization, machine learning, and deep learning algorithms are applied in this study. …”
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Mitigating riparian buffer zone degradation through policy interventions and learnings from best practices by Aditi Majumdar, Kirti Avishek

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Leveraging IoT-Enabled Sensor Networks and Machine Learning for Early Detection and Management of Wheat Rust by Adnan Myasar M., Almoussawi Zainab Abed, Anuradha Kodali

“…To augment exposure accuracy, the study integrates data fusion procedures that assimilate evidence from numerous sensors, cultivating the dependability of disease documentation. Machine learning demonstrations, counting Convolutional Neural Networks (CNNs), and Support Vector Machines (SVMs), are functional to analyze sensor and copy data for accurate recognition and organization of wheat rust symptoms. …”
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Adaptive Ensemble Learning Model-Based Binary White Shark Optimizer for Software Defect Classification by Jameel Saraireh, Mary Agoyi, Sofian Kassaymeh

“…Abstract Software dominates modern enterprises, affecting numerous functions. Software firms constantly experiment with new methodologies to define and assess software quality to stay competitive and ensure excellence. …”
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Chronic liver disease detection using ranking and projection-based feature optimization with deep learning by Sumaiya Noor, Salman A. AlQahtani, Salman Khan

“…The liver is a vital gland responsible for various essential functions such as digestion, metabolism, detoxification, and immunity. …”
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Enhanced Intrusion Detection in In-Vehicle Networks Using Advanced Feature Fusion and Stacking-Enriched Learning by Ali Altalbe

“…Modern vehicles rely heavily on interconnected electronic control units (ECUs) through in-vehicle networks to perform crucial functions such as braking and monitoring engine RPMs. …”
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Supervised Learning of Depth Estimation From Imperfect Rectified Stereo Laparoscopic Images of Liver Surface by Chongan Zhang, Ziyi Jin, Xiaoyue Liu, Yuelong Liang, Xiao Liang, Peng Wang, Xiujun Cai, Xuesong Ye

“…Using structured light 3D optical reconstruction and network training in porcine laparoscopic environments allows deep learning algorithms to achieve depth recovery in white light settings. …”
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Versatile machine learning algorithms for FTIR spectroscopy: differentiating crosslinked and non-crosslinked gelatin samples by Juliana Rincón-López, Eliana Álvarez-Valdés, Daniela Velez-Arango, Estefanía Rojas Zuleta, Leidy Yuliana Vargas Soto, Liliana Lellesch, Victor Alonso García Londoño, Milton Rosero-Moreano, Gonzalo Taborda-Ocampo

“…This is a mixture of amino acid polymers with a variable molecular weight and a diversity of secondary structures (α, β and γ chains). The functional and mechanical properties of the gelatin depend on the chemical and structural characteristics. …”
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Detection of Psychomotor Retardation in Youth Depression: A Machine Learning Approach to Kinematic Analysis of Handwriting by Vladimir Džepina, Nikola Ivančević, Sunčica Rosić, Blažo Nikolić, Dejan Stevanović, Jasna Jančić, Milica M. Janković

“…The feature selection process revealed that velocity-related features were most effective in distinguishing patients with depression from controls, expectedly reflecting a slowdown in psychomotor functioning among the patients. The findings demonstrate that kinematic handwriting analysis, when combined with machine learning techniques, offers a promising tool to support objective recognition of psychomotor speed, providing insight into psychomotor retardation in youth with depression.…”
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MSPO: A machine learning hyperparameter optimization method for enhanced breast cancer image classification by Haonan Li, Vijay Govindarajan, Tan Fong Ang, Zaffar Ahmed Shaikh, Amel Ksibi, Yen-Lin Chen, Chin Soon Ku, Ming Chern Leong, Fatiha Hana Shabaruddin, Wan Zamaniah Wan Ishak, Lip Yee Por

“…Tests using the CEC 2022 benchmark functions reveal that MSPO surpasses leading algorithms regarding optimization precision and convergence rate. …”
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GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification by Ebenezer Daniel, Anjalie Gulati, Shraya Saxena, Deniz Akay Urgun, Biraj Bista

“…Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. …”
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Advancing the Frontiers of Deep Learning for Low-Dose 3D Cone-Beam CT Reconstruction by Ander Biguri, Subhadip Mukherjee, Xuzhi Zhao, Xi Liu, Xinyi Wang, Rui Yang, Yi Du, Yahui Peng, Mikael Brudfors, Mark Graham, Hyungon Ryu, Oliver Kutter, Andreas Hauptmann, Mustafa Al-Rubaye, Miika T. Nieminen, Mikael A. K. Brix, Austin Yunker, Rajkumar Kettimuthu, John C. Roeske, Sasidhar Alavala, Subrahmanyam Gorthi, Carola-Bibiane Schonlieb

“…The classical variational approach for CT reconstruction minimizes an energy functional using an appropriate iterative algorithm. …”
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Univariate deep learning framework for short-term SST forecasting at high spatio-temporal scales by Jagdish Prajapati, Balaji Baduru, Athul C R, Biswamoy Paul, Vinod Daiya, Arya Paul

“…We present a univariate hybrid machine learning framework to predict daily high resolution Sea Surface Temperature (SST) near the Gulf of Kutch region at a resolution of ∼5.5 km. …”
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