VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…K-fold cross-validation ensured the robustness of the models, and optimization techniques were applied to fine-tune parameters for maximum accuracy. …”
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A Hybrid VMD-BO-GRU Method for Landslide Displacement Prediction in the High-Mountain Canyon Area of China by Bao Liu, Jiahuan Xu, Jiangbo Xi, Chaoying Zhao, Xiaosong Feng, Chaofeng Ren, Haixing Shang

“…Therefore, developing effective landslide displacement prediction models is essential. The paper introduces a model designed to forecast the landslide displacement using Variational Mode Decomposition (VMD), Bayesian Optimization (BO), and Gated Recurrent Units (GRU). …”
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Production Planning of a Failure-Prone Manufacturing System under Different Setup Scenarios by Guy-Richard Kibouka, Donatien Nganga-Kouya, Jean-Pierre Kenne, Victor Songmene, Vladimir Polotski

“…The objective of the study is to find the production and setup policies which minimize the setup and inventory costs, as well as those associated with shortages. A modeling approach based on stochastic optimal control theory and a numerical algorithm used to solve the obtained optimality conditions are presented. …”
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Research Status and Development Trends of Deep Reinforcement Learning in the Intelligent Transformation of Agricultural Machinery by Jiamuyang Zhao, Shuxiang Fan, Baohua Zhang, Aichen Wang, Liyuan Zhang, Qingzhen Zhu

“…It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. …”
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Spectral Fingerprinting of Tencha Processing: Optimising the Detection of Total Free Amino Acid Content in Processing Lines by Hyperspectral Analysis by Qinghai He, Yihang Guo, Xiaoli Li, Yong He, Zhi Lin, Hui Zeng

“…This study employs VNIR-HSI combined with machine learning algorithms to develop a model for visualizing the total free amino acid content in Tencha samples that have undergone different processing steps on the production line. …”
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Enhancing Air Conditioning System Efficiency Through Load Prediction and Deep Reinforcement Learning: A Case Study of Ground Source Heat Pumps by Zhitao Wang, Yubin Qiu, Shiyu Zhou, Yanfa Tian, Xiangyuan Zhu, Jiying Liu, Shengze Lu

“…This study proposes a control method that integrates deep reinforcement learning with load forecasting, to enhance the energy efficiency of ground source heat pump systems. Eight machine learning models are first developed to predict future cooling loads, and the optimal one is then incorporated into deep reinforcement learning. …”
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A Comprehensive Review of Deep Learning Applications in Cotton Industry: From Field Monitoring to Smart Processing by Zhi-Yu Yang, Wan-Ke Xia, Hao-Qi Chu, Wen-Hao Su, Rui-Feng Wang, Haihua Wang

“…Future research should prioritize lightweight, robust models, standardized multi-source datasets, and real-time performance optimization. …”
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Metformin improves HPRT1-targeted purine metabolism and repairs NR4A1-mediated autophagic flux by modulating FoxO1 nucleocytoplasmic shuttling to treat postmenopausal osteoporosis by Keda Yang, Xiaochuan Wang, Chi Zhang, Dian Liu, Lin Tao

“…Through energy metabolism-targeted metabolomics, we revealed that purine metabolism disorder is the main mechanism involved in inducing oxidative damage in bone tissue, which was verified via the use of machine-learning data from human databases. Xanthine and xanthine oxidase were used to treat osteoblasts to construct a purine metabolism disorder model. …”
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Privacy-sensitive federated learning for cross-domain adaptation: The Mamba-MoE approach by Muhammad Kashif Jabbar, Huang Jianjun, Ayesha Jabbar, Zaka Ur Rehman

“…The results highlight the model's superiority in addressing domain heterogeneity while maintaining privacy, making it a robust solution for decentralized machine learning applications in privacy-sensitive domains such as healthcare and internet of things (IoT). …”
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Enhancing Incentive Schemes in Edge Computing through Hierarchical Reinforcement Learning by Gowtham R, Vatsala Anand, Yadati Vijaya Suresh, Kasetty Lakshmi Narasimha, R. Anil Kumar, V. Saraswathi

“… Edge learning is a distributed approach for training machine learning models using data from edge devices. …”
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Heart disease prediction using ECG-based lightweight system in IoT based on meta-heuristic approach by Amin Abbaszadeh, Mahdi Bazargani

“…This method includes demarcation of classes with the help of optimized non-linear support vector machine technique in the first step and determining the modified fuzzy class in the second step. …”
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A Quality Control Method based on Combination Deep Learning for Measurement Data of Complex Mountain Wind Farm by Runjin YAO, Shuaibing CHENG, Qianqian ZHAO, Wenlong LI, Dong QIAN

“…Mountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a quality control method （VCG） based on variational mode decomposition， convolutional neural networks， and deep learning of gated cyclic units is constructed， and a particle swarm optimization strategy and wind power reconstruction model are introduced to comprehensively improve the quality of observation data.To verify the effectiveness of this method， 10 minute wind speed and direction data of target wind turbines in six complex mountainous wind farms in Jiangxi Ganzhou， Sichuan Guangyuan， Anhui Wuhu， Hubei Huangshi， Henan Pingdingshan， and Guangxi Hezhou in 2016 was quality controlled by VCG and compared with single machine learning method， spatial regression method （SRT）， and inverse distance weighting method （IDW）.The results indicate that VCG method is suitable for quality control of observed wind data in mountainous wind farms， and has a higher error detection rate for suspicious data compared to conventional methods； The controlled data can better restore the observed background field and have a lower error rate when applied to the power generation evaluation business of wind farms； And it has the characteristics of strong terrain adaptability.…”
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Graph Neural Network for In-Network Placement of Real-Time Metaverse Tasks in Next-Generation Networks by Sulaiman Muhammad Rashid, Ibrahim Aliyu, Il-Kwon Jeong, Tai-Won Um, Jinsul Kim

“…Although optimal solutions can be derived using standard optimization solvers, their high computational overhead makes them unsuitable for real-time deployment. …”
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SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes from hardwood by Marie Alopaeus, Matthias Stosiek, Daryna Diment, Joakim Löfgren, MiJung Cho, Jarl Hemming, Teija Tirri, Andrey Pranovich, Patrik C. Eklund, Davide Rigo, Mikhail Balakshin, Chunlin Xu, Patrick Rinke

“…Furthermore, SP-LCC provides valuable data for training machine learning models for further optimization of biorefineries outside the scope of AqSO.…”
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Analisis Kinerja Intrusion Detection System Berbasis Algoritma Random Forest Menggunakan Dataset Unbalanced Honeynet BSSN by Kuni Inayah, Kalamullah Ramli

“…The analysis results show that IDS modeling based on machine learning has an average accuracy value of more than 90%, a precision value of 91%, a recall value of 90%, and an F1 score of 90%. …”
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Learning to trade autonomously in stocks and shares: integrating uncertainty into trading strategies by Yuyang Li, Minghui Liwang, Li Li

“…Three trading strategies were implemented in this model; namely, a Price Model Strategy, a Probabilistic Model Strategy, and a Bayesian Gated Recurrent Unit Strategy, each leveraging the respective model’s outputs to optimize trading decisions. …”
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Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment by Dimple Dimple, Jitendra Rajput, Nadhir Al-Ansari, Ahmed Elbeltagi

“…Therefore, constructing precise and adequate models may be beneficial in resolving this problem in agricultural water management to determine the suitable water quality classes for optimal crop yield production. …”
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A data-driven PCA-RF-VIM method to identify key factors driving post-fracturing gas production of tight reservoirs by Yifan Zhao, Xiaofan Li, Lei Zuo, Zhongtai Hu, Liangbin Dou, Huagui Yu, Tiantai Li, Jun Lu

“…In order to verify the validity of the PCA-RF-VIM model, a consolidation model that uses three other independent data-driven methods (Pearson correlation coefficient, RF feature significance analysis method, and XGboost feature significance analysis method) are applied to compare with the PCA-RF-VIM model. …”
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Research on future trends of electricity consumption based on conditional generative adversarial network considering dual‐carbon target by Jinghua Li, Zibei Qin, Yichen Luo, Jianfeng Chen, Shanyang Wei

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Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts by Tinofirei Museba, Fulufhelo Nelwamondo, Khmaies Ouahada, Ayokunle Akinola

“…A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. …”
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