Multi-Timescale Voltage Control Method Using Limited Measurable Information with Explainable Deep Reinforcement Learning by Fumiya Matsushima, Mutsumi Aoki, Yuta Nakamura, Suresh Chand Verma, Katsuhisa Ueda, Yusuke Imanishi

“…This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. …”
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Application of Temporal Fusion Transformers to Run-Of-The-River Hydropower Scheduling by Rafael Francisco, José Pedro Matos, Rui Marinheiro, Nuno Lopes, Maria Manuela Portela, Pedro Barros

“…This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance.…”
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Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network by Xiangqun Li, Jiawen Liang, Jinyu Zhu, Shengping Shi, Fangyu Ding, Jianpeng Sun, Bo Liu

“…Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. …”
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An insightful analysis of CNN-based dietary medicine recognition by Mohammad Didarul Alam, Tanjir Ahmed Niloy, Aurnob Sarker Aurgho, Mahady Hasan, Md. Tarek Habib

“…The hybrid model uses an average ensemble approach. Nevertheless, our unwavering commitment to excellence continues to drive us to explore further refinements and optimizations to augment the resilience and precision of our seed classification models.…”
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Towards Effective Parkinson’s Monitoring: Movement Disorder Detection and Symptom Identification Using Wearable Inertial Sensors by Umar Khan, Qaiser Riaz, Mehdi Hussain, Muhammad Zeeshan, Björn Krüger

“…The proposed pipeline employs and evaluates manual feature crafting for classical machine learning algorithms, as well as an RNN-CNN-inspired deep learning model that does not require manual feature crafting. …”
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Collaborative filtering based on GNN with attribute fusion and broad attention by MingXue Liu, Min Wang, Baolei Li, Qi Zhong

“…In recent years, graph neural networks (GNN) based CF models have effectively addressed the limitations of nonlinearity and higher-order feature interactions in traditional recommendation methods, such as matrix decomposition-based methods and factorization machine approaches, achieving excellent recommendation performance. …”
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Rapid and Nondestructive Identification of Origin and Index Component Contents of Tiegun Yam Based on Hyperspectral Imaging and Chemometric Method by Yue Zhang, Yuan Li, Cong Zhou, Junhui Zhou, Tiegui Nan, Jian Yang, Luqi Huang

“…The optimal residual predictive deviation (RPD) values of starch, polysaccharide, and protein prediction models selected in this study were 5.21, 3.21, and 2.94, respectively. …”
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Comprehensive review of federated learning challenges: a data preparation viewpoint by Nawraz Saeed, Mohamed Ashour, Maggie Mashaly

“…Abstract Machine learning model accuracy, generalization, and reliability are greatly affected by the training data quality. …”
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Study On Parameters and Operating Modes of the Device for Deseeding Flax in the Field by V. G. Chernikov, R. A. Rostovtsev, S. V. Solov’ev

“…The authors proposed a model to determine them based on the physical and mechanical properties of flax and agrotechnical requirements for its harvesting.…”
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Head Horn Enhances Hydrodynamic Perception in Eyeless Cavefish by Zhiqiang Ma, Zheng Gong, Yonggang Jiang, Peng Wu, Changxin You, Zihao Dong, Hongchao Cao, Zhen Yang, Yahui Zhao, Huawei Chen, Deyuan Zhang

“…Furthermore, the eyeless cavefish model has ≈17% higher obstacle recognition accuracy and lower cost (time and sensor number) than eyed cavefish model is conceptually demonstrated, by incorporating with machine learning. …”
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Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments by Junqi Yin, Viktor Reshniak, Siyan Liu, Guannan Zhang, Xiaoping Wang, Zhongcan Xiao, Zachary Morgan, Sylwia Pawledzio, Thomas Proffen, Christina Hoffmann, Huibo Cao, Bryan C. Chakoumakos, Yaohua Liu

“…Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. …”
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Design of an Intelligent Energy Management Prototype for an Electric Lighting Network on a Raspberry Pi Board by Jouahri Mohammed Amine, Moukhtari Manal, Oulhaj Nabil, Khimouj Mounir, Tajer Abdelouahed, Boulghasoul Zakaria

“…These data are transmitted to the Raspberry Pi via the MQTT protocol, where a neural network model, trained beforehand, predicts the optimal operating cycle of the street lamps to adjust their brightness in real time. …”
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Method for Knowledge Transfer via Multi-Task Semi-Supervised Self-Paced by Yao Zhao, Hongying Liu, Huaxian Pan, Zhen Song, Chunting Liu, Anni Wei, Baoshuang Zhang, Wei Lu

“…Adequate labeled data is essential for learning a reliable and generalizable model in many machine learning tasks. However, labeled data is becoming scarce and costly to obtain, which has spurred consistent interest in knowledge transfer techniques. …”
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Impact of short-term soil disturbance on cadmium remobilization and associated risk in vulnerable regions by Zhong Zhuang, Hao Qi, Siyu Huang, Qiqi Wang, Yanan Wan, Huafen Li

“…This study highlights the potential of hybrid data-driven approaches, combining machine learning, mechanistic model and stochastic prediction to simplify the complex environmental process, allowing for integrated risk evaluations.…”
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Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT by Hafiyyan Putra Pratama, Dewi Indriati Hadi Putri, Hafiziani Eka Putri, Elysa Nensy Irawan, Makna A’raaf Kautsar

“…The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. …”
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Optimasi Algoritma Naive Bayes dengan Diskritisasi K-Means pada Diagnosis Penyakit Jantung by Nafa Fajriati, Budi Prasetiyo

“…Naïve Bayes merupakan algoritma klasifikasi yang memiliki kemampuan yang cukup baik untuk membangun model pengklasifikasi. Pada penelitian ini, dilakukan klasifikasi penyakit jantung menggunakan algoritma Naïve Bayes. …”
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Visualization of Moisture Distribution in Stacked Tea Leaves on Process Flow Line Using Hyperspectral Imaging by Yuying Zhang, Binhui Liao, Mostafa Gouda, Xuelun Luo, Xinbei Song, Yihang Guo, Yingjie Qi, Hui Zeng, Chuangchuang Zhou, Yujie Wang, Jingfei Zhang, Xiaoli Li

“…Visualizing this moisture distribution is crucial for optimizing processing parameters. In this study, we utilized hyperspectral imaging (HSI) technology combined with machine learning algorithms to evaluate the moisture content and its distribution in the stacked tea leaves in West Lake Longjing and Tencha green tea products during the processing flow line. …”
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A comprehensive analysis of molecular characteristics of hot and cold tumor of gastric cancer by Chenxi Lv, Tianwei Chen, Jiangtao Li, Yuqiang Shan, Hong Zhou

“…Weighted gene co-expression network analysis (WGCNA) and correlation analysis were applied to identify gene modules underlying the classification of immune “hot” and “cold” tumors. Subsequently, 101 machine learning algorithm combinations were employed to construct a prognostic model based on the identified gene modules. …”
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supDQN: Supervised Rewarding Strategy Driven Deep Q-Network for sEMG Signal Decontamination by Ashutosh Jena, Naveen Gehlot, Rajesh Kumar, Ankit Vijayvargiya, Mahipal Bukya

“…The identification accuracy is enhanced by using a local interpretable model-agnostic explanation. The deep Q-network is guided by this reward to select the filter optimally while decontaminating a signal. …”
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Temporal Backtracking and Multistep Delay of Traffic Speed Series Prediction by Licheng Qu, Minghao Zhang, Zhaolu Li, Wei Li

“…Besides, the performances were compared between three variants of RNNs (LSTM, GRU, and BiLSTM) and 6 frequently used models, which are decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), random forest (RF), gradient boosting decision tree (GBDT), and stacked autoencoder (SAE). …”
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