Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation by Zhiguo Wang, Shuai Ma, Yongguang Zhai, Pingping Huang, Xiangli Yang, Jianhao Cui, Qimuge Eridun

“…This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). …”
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Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework by Ziyin Zeng, Qingyong Hu, Zhong Xie, Bijun Li, Jian Zhou, Yongyang Xu

“…Specifically, we construct the U-Next by stacking multiple U-Net L1 sub-networks in a dense arrangement to diminish the semantic gap. Concurrently, it integrates feature maps across various scales to proficiently restore intricate fine-grained details. …”
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Moisture prediction in chicken litter using hyperspectral data and machine learning by Ahmad Tulsi, Abdul Momin, Victoria Ayres

“…This study addresses that gap by evaluating the feasibility of combining HSI with machine learning models to predict moisture content in chicken litter. …”
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Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis by Agostino Marengo, Vito Santamato

“…This study emphasizes the importance of hybrid quantum-classical models and cross-disciplinary research to bridge the gap between cutting-edge quantum computing theory and its practical applications in healthcare.…”
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A Risk-Optimized Framework for Data-Driven IPO Underperformance Prediction in Complex Financial Systems by Mazin Alahmadi

“…The current research landscape lacks modern models that address the needs of small and imbalanced datasets relevant to emerging markets, as well as the risk preferences of investors. To fill this gap, we present a practical framework utilizing tree-based ensemble learning, including Bagging Classifier (BC), Random Forest (RF), AdaBoost (Ada), Gradient Boosting (GB), XGBoost (XG), Stacking Classifier (SC), and Extra Trees (ET), with Decision Tree (DT) as a base estimator. …”
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Triple-layered security system: reliable and secured image communications over 5G and beyond networks by Tarek Srour, Mohsen A. M. El-Bendary, Mostafa Eltokhy, Atef E. Abouelazm

“…This paper presents the proposed vision of 5G and beyond security to build a research gap of existing and related technique that lack the adaptation, boosting gradient and complexity analysis, through design and evaluate the adapted and graded security system. …”
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Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach by Kingsley Friday Attai, Constance Amannah, Moses Ekpenyong, Said Baadel, Okure Obot, Daniel Asuquo, Ekerette Attai, Faith-Valentine Uzoka, Emem Dan, Christie Akwaowo, Faith-Michael Uzoka

“…Most studies have focused on vector control measures, such as insecticide-treated nets and time series analysis, often neglecting emerging yet critical risk factors vital for effectively preventing febrile diseases. We address this gap by investigating the use of machine learning (ML) models, specifically extreme gradient boost and random forest, in predicting adult females’ susceptibility to these diseases based on biological, environmental, and socioeconomic factors. …”
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Desertification Monitoring Using Machine Learning Techniques with Multiple Indicators Derived from Sentinel-2 in Turkmenistan by Arslan Berdyyev, Yousef A. Al-Masnay, Mukhiddin Juliev, Jilili Abuduwaili

“…Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). …”
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A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. …”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…This study addresses the gap in disease-specific binding affinity prediction by integrating PDAC-derived targets with diverse molecular descriptors and artificial intelligence (AI) models, enabling more accurate therapeutic profiling. …”
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A Comprehensive Review of Fused Filament Fabrication: Numerical Modeling Approaches and Emerging Trends by Maria Enriconi, Rocío Rodriguez, Márcia Araújo, João Rocha, Roberto García-Martín, João Ribeiro, Javier Pisonero, Manuel Rodríguez-Martín

“…This review highlights the need for unified testing protocols, more accurate multi-physics simulations, and the integration of AI-based process monitoring to bridge the gap between numerical predictions and real-world performance. …”
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A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization by Dongjie Pang, Cristina Moliner, Tao Wang, Jin Sun, Xinyan Zhang, Yingping Pang, Xiqiang Zhao, Zhanlong Song, Ziliang Wang, Yanpeng Mao, Wenlong Wang

“…A comprehensive understanding of future technologies will necessitate a synthesis of knowledge and data-oriented approaches, the design of autonomous control systems, and the integration of digital twin technologies to bridge the gap between theory and practice.…”
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pH-Sensitive TRPC5 Is Differentially Expressed in Various Common Skin Tumors by Lara Hopmann, Judith Heider, Dennis Niebel, Katja Evert, Florian Zeman, Christoph M. Hammers, Tobias Ettl, Christoph Brochhausen, Stephan Schreml

“…Transient receptor potential classical or cation channels (TRPCs) are integral to tumor biology, particularly in maintaining Ca²⁺ homeostasis within cancer cells. …”
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Evaluating livelihood resilience in the Qinghai-Tibet Plateau's pastoral communities: Insights from an entropy-TOPSIS and geospatial analysis approach by Tong Li, Lizhen Cui, Zhihong Xu, Xiaoyong Cui, Yanfen Wang

“…To address the gap in evaluating herders' livelihood resilience with a comprehensive methodology, this research harnessed survey data from 758 pastoralists within the Three River Headwater Region (TRHR) on the Qinghai-Tibet Plateau (QTP). …”
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CFD analysis of air flows and temperatures based on radiant heating in industrial environments by E. S. Aralov, B. M. Kumitsky

“…The findings demonstrate the potential for significant progress in energy savings and improved worker comfort in industrial environments using radiant heating. The integrated research approach fills a critical gap in existing research, highlighting the need and potential for further exploration of sustainable heating technologies in challenging industrial environments.…”
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Application of machine learning for predicting the incubation period of water droplet erosion in metals by Khaled AlHammad, Mamoun Medraj, Moussa Tembely

“…A range of ML models—linear regression (LR), decision tree regressor (DT), random forest regressor (RF), gradient boosting regressor (GBR), and artificial neural networks (ANN)—were trained and validated using experimental data from five different alloys under various impact conditions. …”
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Establishment of a low-temperature immersion method for extracting high-activity and high-purity mitochondria from Syntrichia caninervis Mitt. by Wenting Huo, Xiaohua Lin, Mengyu Gao, Xiang Shi, Hongbin Li, Lu Zhuo

“…This study pioneered the use of low-temperature immersion combined with differential centrifugation and discontinuous percoll density gradient centrifugation to isolate mitochondria from Syntrichia caninervis, a model desiccation-tolerant moss. …”
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Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age by Han Chen, Zhi Cao, Jing Zhang, Dun Li, Yaogang Wang, Chenjie Xu

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A Transformer-LSTM-SVR hybrid model for AI-driven emotional optimization in NEV embedded interior systems by Zongming Liu, Xuhui Chen, Xinan Liang, Zhicheng Sun, Fengqi Yang, Wenwen Ou, Linwei Li, Xiayan Qin

“…Current research on emotion optimization in new energy vehicle interiors lacks efficient nonlinear modeling methods. To address this gap, a hybrid Transformer-LSTM-SVR model is proposed, which significantly enhances emotion prediction accuracy by integrating attention mechanisms and temporal modeling. …”
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Developing a Transparent Anaemia Prediction Model Empowered With Explainable Artificial Intelligence by Muhammad Sajid Farooq, Muhammad Hassan Ghulam Muhammad, Oualid Ali, Zahid Zeeshan, Muhammad Saleem, Munir Ahmad, Sagheer Abbas, Muhammad Adnan Khan, Taher M. Ghazal

“…The proposed model utilizes machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, K-Neighbors Classifier, and Gradient Boosting Classifier, enhanced with Explainable AI (XAI) techniques like SHAP and LIME. …”
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