A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition by Qingjun Song, Shirong Sun, Qinghui Song, Bingrui Wang, Zihao Liu, Haiyan Jiang

“…It combines traditional filtering methods and the idea of multi-scale learning, which can expand the breadth and depth of the feature learning process. the breadth and depth of the feature learning process. …”
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Use of information-fusion deep-learning techniques to detect possible electricity theft: A proposed method by Maria Chuwa, Daniel Ngondya, Rukia Mwifunyi

“…Such non-technical losses (NTLs) pose significant economic challenges to electricity grids, leading to the need for improved detection methods. This study tested an NTL detection method that transformed electricity consumption (EC) profiles into two-dimensional (2D) and one-dimensional (1D) representations, and utilised deep-learning techniques, specifically convolutional neural networks (CNN) and multi-layer perceptron (MLP), to extract features indicating NTLs. …”
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An Efficient and Reliable Routing Method for Hybrid Mobile Ad Hoc Networks Using Deep Reinforcement Learning by Murtadha M. A. Alkadhmi, Osman N. Uçan, Muhammad Ilyas

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Construction of a Surface Roughness and Burr Size Prediction Model Through the Ensemble Learning Regression Method by Ali Khosrozadeh, Seyed Ali Niknam, Fatemeh Hajizadeh

Subjects: “…ensemble learning regression method…”
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Impact of the Audio-Visual Learning Method on the Development of Foreign Language Communication Competence of Future Interpreters and Translator by Tetiana Sobol

“…Conclusion: the audiovisual method of learning a foreign language is one of the most effective for the foreign language communicative competence formation for future interpreters and translators. …”
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Modeling of Moisture Content of Subgrade Materials in High-Speed Railway Using a Deep Learning Method by LiLei Chen, Jing Chen, Chao Wang, Yanhua Dai, Rongyan Guo, Qian Huang

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Method for Preprocessing Video Data for Training Deep-Learning Models for Identifying Behavioral Events in Bio-Objects by Marina Barulina, Alexander Andreev, Ilya Kovalenko, Ilya Barmin, Eduard Titov, Danil Kirillov

Subjects: “…deep-learning models…”
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The VGG16 Method Is a Powerful Tool for Detecting Brain Tumors Using Deep Learning Techniques by Sarthak Raghuvanshi, Sumit Dhariwal

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Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method by Dianli Wang, Yu Zhang, Nasser Yousefi

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A deep learning short-term traffic flow prediction method considering spatial-temporal association by Yang ZHANG, Yue HU, Dongrong XIN

“…The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.…”
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Mutual-Energy Inner Product Optimization Method for Constructing Feature Coordinates and Image Classification in Machine Learning by Yuanxiu Wang

“…This paper proposes the mutual-energy inner product optimization method for constructing a feature coordinate system. …”
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Fast prediction method for fatigue life of pump truck boom structure based on ensemble learning model by DONG Qing, SU Youcheng, XU Gening, SHE Lingjuan, CHANG Yibin

“…ObjectiveTo rapidly and accurately assess the fatigue life of in-service concrete pump truck boom structures, a fatigue life prediction method based on an ensemble learning model is proposed, utilizing monitoring data and machine learning techniques.MethodsFirstly, a concrete pump truck information acquisition system was employed to obtain functional and performance characteristics during the operational phase of the pump truck. …”
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Study on the Compressive Strength Predicting of Steel Fiber Reinforced Concrete Based on an Interpretable Deep Learning Method by Huiming Wang, Jie Lin, Shengpin Guo

Subjects: “…SHAP interpretability method…”
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Innovations in postgraduate work integrated learning within the perioperative nursing environment: A mixed method review by Kylie Russell, Tracey Coventry

“… Purpose: To determine the impact of the Graduate Diploma of Perioperative Nursing on student learning and career progression. Participants and setting: A validated mixed methods descriptive survey was sent to participants (n=67). …”
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Non-Destructive Measurement of Rice Spikelet Size Based on Panicle Structure Using Deep Learning Method by Ruoling Deng, Weisen Liu, Haitao Liu, Qiang Liu, Jing Zhang, Mingxin Hou

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Online energy consumption forecast for battery electric buses using a learning-free algebraic method by Zejiang Wang, Guanhao Xu, Ruixiao Sun, Anye Zhou, Adian Cook, Yuche Chen

“…In contrast to the mainstream machine-learning-based methods, the proposed method does not require access to the historical energy consumption data. …”
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Resource Scheduling Method for Integration of TT＆C and Observation Based on Multi-Agent Deep Reinforcement Learning by Siyue CHENG, Haoran LI, Weigang BAI, Di ZHOU, Yan ZHU

“…With the development of satellite communication technology and the continuous expansion of the constellation scale, the integration of TT＆amp;C and observation technology has become the mainstream trend.The large constellation scale, many scheduling objects and complex operation joint control bring great challenges to the integrated resource scheduling of satellite network TT＆amp;C and observation.Subject to the low solution effi ciency and complex constraints of scheduling algorithms, the traditional TT＆amp;C resource scheduling technology adopts the advance injection TT＆amp;C instructions to perform tasks according to the fi xed deployment, which is diffi cult to meet the scheduling needs of emergencies and emergency tasks.Therefore, a kind of resource scheduling method based on multi-agent actor-Agent Actor-Critic Deterministic Policy Gradient Algorithms (MADDPG) was presented.With centralized training and distributed execution, the multi-agent model of integrated task of TT＆amp;C and observation was established.By analyzed the scheduling strategy of neighbor agent, the response speed of local information was improved.According to the model and constraints in the integrated resource scheduling problem of TT＆amp;C and observation, selected signifi cant and interpretable constraints, then established the multi-agent resource scheduling reinforcement learning model, and carried on the simulation test.The simulation results showed that the task benefi t of this method was 22% higher than the traditional method.…”
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Optimizing deep learning models for glaucoma screening with vision transformers for resource efficiency and the pie augmentation method. by Sirikorn Sangchocanonta, Pakinee Pooprasert, Nichapa Lerthirunvibul, Kanyarak Patchimnan, Phongphan Phienphanich, Adirek Munthuli, Sujittra Puangarom, Rath Itthipanichpong, Kitiya Ratanawongphaibul, Sunee Chansangpetch, Anita Manassakorn, Visanee Tantisevi, Prin Rojanapongpun, Charturong Tantibundhit

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A new learning rate based on Andrei method for training feed-forward artificial neural networks by Khalil K. Abbo, Hassan H. Abrahim, Firdos A. Abrahim

“… In this paper we developed a new method for computing learning rate for Back-propagation algorithm to train a feed-forward neural networks. …”
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A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

“…Spatially clustered samples are prevalent in geospatial machine learning (ML) predictions, especially in ecological mapping. …”
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