Timetable Design of Metro Train Based on RegenerativeBraking Energy Utilization by HE Deqiang, WANG Heliang, TAN Wenju, MIAO Jian

“…The multiple vehicles energy-saving optimization model wasbuilt based on the use of regenerative braking energy and the design of timetable. …”
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A comparison study and multi-objective optimization of a DICI engine fueled with petroleum diesel and Fischer-Tropsch diesel by Panpan Cai, Zheng Jing, Chunhua Zhang, Bing Han, Gang Li, Wenlong Song

“…Subsequently, non-dominated sorting genetic algorithm (NSGA-II) is applied to realize multi-objective optimization of fuel economy and emissions. …”
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Optimizing travel time reliability with XAI: A Virginia interstate network case using machine learning and meta-heuristics by Navid Khorshidi, Shahriar Afandizadeh Zargari, Soheil Rezashoar, Hamid Mirzahossein

“…This paper applies machine learning models to predict travel time reliability in transportation networks, using XGBoost, LightGBM, and CatBoost optimized with seven metaheuristic algorithms. …”
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Complex Working Condition Bearing Fault Diagnosis Based on Multi-Feature Fusion and Improved Weighted Balance Distribution Adaptive Approach by Jing Yang, Yanping Bai, Ting Xu, Rong Cheng, Wendong Zhang, Guojun Zhang

“…In order to improve the accuracy and generalization ability of fault diagnosis for rotating machinery bearings under complex working conditions, a new model based on multi-feature fusion and improved weighted balance distribution adaptation is proposed. …”
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Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization by Sivakumar Palanıswamy, Vijayakumar Vaıthyam Rengarajan, Sandhya Devi Ramıah Subburaj

“…Computer Vision systems and advancements in deep learning-based modeling methodologies gained significant attention in smart farming. …”
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Discovering hierarchical process models: an approach based on events partitioning by Antonina K. Begicheva, Irina A. Lomazova, Roman A. Nesterov

“…Currently, many companies are using this technology to optimize and improve their business processes. However, a discovered process model may be too detailed, sophisticated, and difficult for experts to understand. …”
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Medical laboratory data-based models: opportunities, obstacles, and solutions by Jiaojiao Meng, Moxin Wu, Fangmin Shi, Ying Xie, Hui Wang, You Guo

“…Proposed solutions include establishing standardized data formats, utilizing deep learning frameworks, employing distributed computing, improving interpretability, and implementing techniques like federated learning and algorithm optimization to address bias and safeguard privacy. …”
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Optimal Design of a Sensor Network for Guided Wave-Based Structural Health Monitoring Using Acoustically Coupled Optical Fibers by Rohan Soman, Jee Myung Kim, Alex Boyer, Kara Peters

“…The analytical implementation of the cost function is validated experimentally. The results show that the optimization does indeed have the potential to improve the quality of SHM while reducing the instrumentation costs significantly.…”
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A novel deep learning-based spider wasp optimization approach for enhancing brain tumor detection and physical therapy prediction by Suleiman Daoud, Ahmad Nasayreh, Khalid M.O. Nahar, Wlla k. Abedalaziz, Salem M. Alayasreh, Hasan Gharaibeh, Ayah Bashkami, Amer Jaradat, Sultan Jarrar, Hammam Al-Hawamdeh, Absalom E. Ezugwu, Raed Abu Zitar, Aseel Smerat, Vaclav Snasel, Laith Abualigah

“…We also present the Spider Wasp Optimization (SWO) algorithm, which aims to improve performance by feature selection. …”
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Refining efficiency in standalone proton exchange membrane fuel cell systems through gross hopper optimization-based maximum power point tracking control by Nethra K., Reddy K. Jyotheeswara, Dash Ritesh, Parida Prasanta Kumar, Swain Sarat Chandra, Dhanamjayulu C., Mahapatro Abinash

“…To further improve prediction accuracy, the GHO algorithm incorporates a natural cubic-spline prediction model within its iterative mechanism, which enhances power generation predictions under dynamic conditions such as abrupt changes in fuel cell temperature and reactant partial pressure. …”
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Parametric Model for Coaxial Cavity Filter with Combined KCCA and MLSSVR by Shengbiao Wu, Huaning Li, Xianpeng Chen

“…First, the low-dimensional tuning data is mapped to the high-dimensional feature space by kernel canonical correlation analysis, and the nonlinear feature vectors are fused by the kernel function; second, the multioutput least squares support vector regression algorithm is used for parametric modeling to solve the problems of low accuracy and poor prediction performance; third, the support vector of the parameter model is optimized by the differential evolution whale algorithm (DWA) to improve the convergence and generalization ability of the model in actual tuning. …”
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A novel hybrid model for predicting the bearing capacity of piles by Li Tao, Xinhua Xue

“…The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. …”
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Optimal 5G Network Sub-Slicing Orchestration in a Fully Virtualised Smart Company Using Machine Learning by Abimbola Efunogbon, Enjie Liu, Renxie Qiu, Taiwo Efunogbon

“…The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms based on real-time network conditions and historical data. …”
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Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles by Zijia MO, Zhipeng GAO, Yang YANG, Yijing LIN, Shan SUN, Chen ZHAO

“…Aiming at the efficiency problem of privacy data sharing in the Internet of vehicles (IoV), an efficient distributed model sharing strategy based on blockchain was proposed.In response to the data sharing requirements among multiple entities and roles in the IoV, a master-slave chain architecture was built between vehicles, roadside units, and base stations to achieve secure sharing of distributed models.An asynchronous federated learning algorithm based on motivate mechanism was proposed to encourage vehicles and roadside units to participate in the optimization process.An improved DPoS consensus algorithm with hybrid PBFT was constructed to reduce communication costs and improve consensus efficiency.Experimental analysis shows that the proposed mechanism can improve the efficiency of data sharing and has certain scalability.…”
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Two-Stage Collaborative Power Optimization for Off-Grid Wind–Solar Hydrogen Production Systems Considering Reserved Energy of Storage by Yiwen Geng, Qi Liu, Hao Zheng, Shitong Yan

“…Stage II employs an improved multi-objective particle swarm optimization (IMOPSO) algorithm to optimize HESS power allocation, minimizing unit hydrogen production cost and reducing average battery charge–discharge depth. …”
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Implementation of YOLOv7 Model for Human Detection in Difficult Conditions by Arijal B, Andi Sunyoto, M. Hanafi

“…These findings not only demonstrate the relevance of YOLOv7 as a reliable human detection algorithm, but also provide a basis for further optimization of YOLOv7-based human detection systems, both through improving the model architecture and adapting to more specific datasets. …”
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Estimating the Aboveground Biomass of an Evergreen Broadleaf Forest in Xuan Lien Nature Reserve, Thanh Hoa, Vietnam, Using SPOT-6 Data and the Random Forest Algorithm by The Dung Nguyen, Martin Kappas

“…Adding texture features into the predictor variables did not improve the model performance. In addition, the SPOT-6 sensor has the potential to predict forest AGB using the RF algorithm.…”
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A multi task learning framework using DeBERTa and BWO optimization for enhancing long term english vocabulary memory by Jiasheng Zhu

“…This study shows that the BWO-optimized DeBERTa multi-task learning model can effectively improve the long-term memory retention of English words and has stronger anti-interference ability in complex memory environments.…”
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Diagnosis Method for High-Speed Train Axle Box Bearing Slight Faults Based on Improved SAE and Temperature-Vibration Fusion by XU Xiao, SONG Dongli, WANG Zifan

“…［Method］ First, an AE (auto encoder) driven bearing temperature feature extraction method is designed to obtain the abnormal bearing temperature features, and EMD (empirical modal decomposition) method is used to process the vibration signal，so as to obtain the statistical features of the effective vibration IMF (intrinsic modal function). Then, by optimizing the dimensionality reduction algorithm based on SAE (stacked auto encoder), an effective fusion method of temperature-vibration features is proposed to achieve nonlinear fusion and dimensionality reduction of temperature and vibration features. …”
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Detection of egg appreance based on Fasternet and YOLOv5 model by WEI Jingxin, CHEN Zhongju, XU Haoran

“…Compared with YOLOv7-tiny and YOLOv8 models, the improved model has significant advantages.ConclusionThe experimental method can optimize the detection percision and reduces the parameters in the detection of egg' appreance, so as to achieve the purpose of identifying defected eggs in the automatic production. …”
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