Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines by Hongbo Liu, Xiangzhao Meng

“…This approach resolves the issues of excessive iterations and high computational costs associated with conventional hyperparameter optimization methods, significantly enhancing the model’s predictive performance. …”
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FishAI: Automated hierarchical marine fish image classification with vision transformer by Chenghan Yang, Peng Zhou, Chun‐Sheng Wang, Ge‐Yi Fu, Xue‐Wei Xu, Zhibin Niu, Lin Zhu, Ye Yuan, Hong‐Bin Shen, Xiaoyong Pan

“…Abstract To address the issues of high demand for efficiently recognizing fish species in marine scientific research, such as impact assessments on biodiversity and monitoring, an automated hierarchical image classification web‐based platform, named FishAI, was developed. …”
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Deep linear matrix approximate reconstruction with integrated BOLD signal denoising reveals reproducible hierarchical brain connectivity networks from multiband multi-echo fMRI by Wei Zhang, Wei Zhang, Alexander Cohen, Michael McCrea, Pratik Mukherjee, Pratik Mukherjee, Yang Wang

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DeepGuard: real-time threat recognition using Golden Jackal optimization with deep learning model by Fatma S. Alrayes, Hamed Alqahtani, Wahida Mansouri, Asma Alshuhail, Wafa Sulaiman Almukadi, Ahmed Mahmud

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Advancing student outcome predictions through generative adversarial networks by Helia Farhood, Ibrahim Joudah, Amin Beheshti, Samuel Muller

“…Employing Feedforward Neural Networks, Convolutional Neural Networks, and Gradient-boosted Neural Networks, and using Bayesian optimisation for hyperparameter tuning, this research methodically examines the impact of synthetic data on prediction accuracy. …”
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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment by Faten Khalid Karim, José Varela-Aldás, Mohamad Khairi Ishak, Ayman Aljarbouh, Samih M. Mostafa

“…By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. …”
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PGTransNet: a physics-guided transformer network for 3D ocean temperature and salinity predicting in tropical Pacific by Song Wu, Senliang Bao, Wei Dong, Senzhang Wang, Xiaojiang Zhang, Chengcheng Shao, Junxing Zhu, Xiaoyong Li

“…Accurately predicting the spatio-temporal evolution trends and long-term dynamics of three-dimensional ocean temperature and salinity plays a crucial role in monitoring climate system changes and conducting fundamental oceanographic research. Numerical models are the most prevalent of the traditional approaches, which are often too complex and lack of generality. …”
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Scalable Detection of Underground Water Leaks in Dense Urban Environments Using L-Band SAR and Machine Learning by E. Ali, E. Ali, L. Xie, A. Sani-Mohammed, W. Xu, T. Zayed

“…Features extracted via Gray-Level Co-occurrence Matrix (GLCM) metrics and backscattering coefficients were used to train various machine learning, deep learning, and ensemble learning models, with hyperparameter optimization performed using a grid search algorithm. …”
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A hybrid object detection approach for visually impaired persons using pigeon-inspired optimization and deep learning models by Abdullah M. Alashjaee, Hussah Nasser AlEisa, Abdulbasit A. Darem, Radwa Marzouk

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Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets by Yusuf Aliyu, Aliza Sarlan, Kamaluddeen Usman Danyaro, Abdullahi Sani abd Rahman, Aminu Aminu Muazu, Mustapha Yusuf Abubakar

“…This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. …”
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Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach by Madiha Khalid, Muhammad Faheem Mushtaq, Urooj Akram, Mejdl Safran, Sultan Alfarhood, Imran Ashraf

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Deep‐HH: A deep learning‐based high school student hidden hunger risk prediction system by Yang Yang, Zheng Zhang, Huake Cao, Yuchen Zhang, Minao Wang, Ning Zhang

“…Abstract Background Hidden hunger (HH) refers to the deficiency of certain micronutrients. Current research suggests that approximately 70% of chronic diseases are linked to HH, which significantly affects public health. …”
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Prediction of copper contamination in soil across EU using spectroscopy and machine learning: Handling class imbalance problem by Chongchong Qi, Nana Zhou, Tao Hu, Mengting Wu, Qiusong Chen, Han Wang, Kejing Zhang, Zhang Lin

“…This study underscores the utility of the optimized model for managing soil Cu pollution and provides a valuable reference for addressing imbalanced learning challenges in soil pollution research.…”
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Survey on Replay-Based Continual Learning and Empirical Validation on Feasibility in Diverse Edge Devices Using a Representative Method by Heon-Sung Park, Hyeon-Chang Chu, Min-Kyung Sung, Chaewoon Kim, Jeongwon Lee, Dae-Won Kim, Jaesung Lee

“…The goal of on-device continual learning is to enable models to adapt to streaming data without forgetting previously acquired knowledge, even with limited computational resources and memory constraints. Recent research has demonstrated that weighted regularization-based methods are constrained by indirect knowledge preservation and sensitive hyperparameter settings, and dynamic architecture methods are ill-suited for on-device environments due to increased resource consumption as the structure scales. …”
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Beyond N-Grams: Enhancing String Kernels With Transformer-Guided Semantic Insights by Nazar Zaki, Reem Alderei, Mahra Alketbi, Alia Alkaabi, Fatima Alneyadi, Nadeen Zaki

“…The publicly available datasets and robust empirical evaluations contribute valuable benchmarks for future research. This work sets a new standard in AI-text detection methodologies, enhancing reliability, efficiency, and scalability for real-world applications.…”
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Machine learning analysis of CO2 and methane adsorption in tight reservoir rocks by Mehdi Maleki, Mohammad Rasool Dehghani, Moein Kafi, Ali Akbari, Yousef Kazemzadeh, Ali Ranjbar

“…This research provides valuable insights for optimizing gas composition and operational parameters in storage applications, serving as a foundation for future studies in gas sequestration and reservoir engineering.…”
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Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of... by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven, Rebecca Retzlaff

“…The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…The results provide a comprehensive approach to preventing and controlling gas disasters in fully mechanized mining operations. This research effectively promotes the transformation and upgrading of coal-mine-safety-monitoring systems to an active defense paradigm.…”
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Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit by Shuxing Wei, Hongmeng Dong, Weidong Yao, Ying Chen, Xiya Wang, Wenqing ji, Yongsheng Zhang, Shubin Guo

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Detection of Coffee Leaf Miner Using RGB Aerial Imagery and Machine Learning by Emerson Ferreira Vilela, Cileimar Aparecida da Silva, Jéssica Mayara Coffler Botti, Elem Fialho Martins, Charles Cardoso Santana, Diego Bedin Marin, Agnaldo Roberto de Jesus Freitas, Carolina Jaramillo-Giraldo, Iza Paula de Carvalho Lopes, Lucas de Paula Corrêdo, Daniel Marçal de Queiroz, Giuseppe Rossi, Gianluca Bambi, Leonardo Conti, Madelaine Venzon

“…A set of four machine learning algorithms was utilized: Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD). Following hyperparameter tuning, the test subset was employed for model validation. …”
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