Neural Network for Underwater Fish Image Segmentation Using an Enhanced Feature Pyramid Convolutional Architecture by Guang Yang, Junyi Yang, Wenyao Fan, Donghe Yang

“…Unlike conventional methods, the multi-scale feature extraction module that we designed not only improves the extraction of high-level semantic features but also optimizes the distribution of low-level shape feature weights through the synergistic interactions of the branches, all while preserving the inherent properties of the image. …”
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Cost-Effective Multitask Active Learning in Wearable Sensor Systems by Asiful Arefeen, Hassan Ghasemzadeh

“…When employed alongside rank-based query selection, our proposed CSS algorithm demonstrates up to 9% improvement in accuracy over traditional querying approaches for a 2000-query budget.…”
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Integrated Thermal Management Strategy Using High-Value Prioritized Experience Replay Deep Reinforcement Learning for Battery Electric Vehicles by Yongqiang Jia, Guanghui Cao, Jinrui Gao

“…In low-temperature environments, electric vehicles rely on effective thermal management strategies (TMS) to rapidly heat the battery to its optimal operating temperature while maintaining cabin temperature within a comfortable range. …”
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The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease by Mohammed A. Chowdhury, Rodrigue Rizk, Conroy Chiu, Jing J. Zhang, Jamie L. Scholl, Taylor J. Bosch, Arun Singh, Lee A. Baugh, Jeffrey S. McGough, KC Santosh, William C.W. Chen

“…We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. …”
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GAB-YOLO: a lightweight deep learning model for real-time detection of abnormal behaviors in juvenile greater amberjack fish by Mingxin Liu, Mingxin Liu, Chun Zhang, Cong Lin, Cong Lin

“…Finally, the introduction of the Wise-ShapeIoU loss function further accelerates the model’s convergence and optimization process. Experimental results demonstrate that, compared to the original model, the number of parameters and FLOPs are reduced by 36.7% and 28.4%, respectively, while the Precision is increased by 5.1%. …”
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Overhead line path planning based on deep reinforcement learning and geographical information system by Jiahui Chen, Yi Yang, Ling Peng, Lina Yang, Yinhui Han, Xingtong Ge

“…Experimental verification of real data shows that compared with existing algorithms, the DSOP method is not only more consistent with the manual line selection effect (improved by more than 3%), but also has a high success rate. …”
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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

“…Special attention is given to de novo drug design using generative adversarial networks (GANs) and variational autoencoders (VAEs), as well as AI-driven high-throughput virtual screening that reduces computational costs while improving hit identification. The review also discusses platforms like Deep-PK and DeepTox for pharmacokinetics and toxicity prediction using graph-based descriptors and multitask learning. …”
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Perceptual-Preference-Based Touring Routes in Xishu Gardens Using Panoramic Digital-Twin Modeling by Xueqian Gong, Zhanyuan Zhu, Li Guo, Yong Zhong, Deshun Zhang, Jing Li, Manqin Yao, Wei Yong, Mengjia Li, Yujie Huang

“…This study adopted an improved Ward–K-medoids hybrid clustering algorithm to analyze 885 trajectory samples and 34,384 synchronized data points capturing emotional valence, cognitive evaluations, and dwell time behaviors via panoramic digital twins across three heritage sites (Du Fu Thatched Cottage, San Su Shrine, and Wangjiang Tower Park). …”
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Computational Linguistics Applications in AI-Based Investment and Cost Structuring Models by Miralieva Dilafruz, Ramatov Jumaniyoz, Azimova Lola, Khusamiddinova Malika, Alimbaeva Shahlo, Omonova Laylo

“…AI-enabled linguistics modeling promotes scalable optimization and context-aware applications of financial analytics, and realizes cost transparency improvements in automated investment systems. …”
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Model‐Free Deep Reinforcement Learning with Multiple Line‐of‐Sight Guidance Laws for Autonomous Underwater Vehicles Full‐Attitude and Velocity Control by Chengren Yuan, Changgeng Shuai, Zhanshuo Zhang, Jianguo Ma, Yuan Fang, YuChen Sun

“…Simulations demonstrate the efficacy of the proposed approach: A‐TD3 improves training speed by 30.8% while mitigating issues such as excessive rudder motion, poor operability, and high energy consumption across different missions. …”
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Toward edge-computing-enabled collision-free scheduling management for autonomous vehicles at unsignalized intersections by Ziyi Lu, Tianxiong Wu, Jinshan Su, Yunting Xu, Bo Qian, Tianqi Zhang, Haibo Zhou

“…Second, this problem is solved with low complexity by the Edge Intelligence Optimal Entry Time (EI-OET) algorithm based on edge-cloud computing power support. …”
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Acoustic-Based Machine Main State Monitoring for High-Speed CNC Drilling by Pimolkan Piankitrungreang, Kantawatchr Chaiprabha, Worathris Chungsangsatiporn, Chanat Ratanasumawong, Peemdej Chancharoen, Ratchatin Chancharoen

“…By integrating big data concepts and intelligent algorithms, the system supports continuous monitoring, anomaly detection, and process optimization. …”
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Leveraging petrophysical and geological constraints for AI-driven predictions of total organic carbon (TOC) and hardness in unconventional reservoir prospects by Nandito Davy, Ammar El-Husseiny, Umair bin Waheed, Korhan Ayranci, Manzar Fawad, Mohamed Mahmoud, Nicholas B. Harris

“…Petrophysical constraints were derived from triple combo well logs (gamma ray, bulk density, neutron porosity), while geological constraints included stratigraphic data or spatial distance between training and target wells—petrophysical constraints most improved predictions, while stratigraphic and spatial constraints had progressively less impact. …”
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A dynamic cobweb resistance network solution based on a structured zeroing neural network and its applications by Xiaoyu Jiang, Dexin Meng, Yanpeng Zheng, Zhaolin Jiang

“…By leveraging the hidden structural attributes of a Laplacian matrix, the study devises optimized algorithms for the neural network models, which markedly improve computational efficiency. …”
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Robust Drone Video Analysis for Occluded Urban Traffic Monitoring Based on Deep Learning by Carlos Gellida-Coutino, Reyes Rios-Cabrera, Alan Maldonado-Ramirez, Anand Sanchez-Orta

“…The results enable precise input for traffic simulators (e.g., PTV-Vissim), supporting data-driven UTM decisions while minimizing costly real-world experimentation.…”
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Handover Strategy for LEO Satellite Networks Using Bipartite Graph and Hysteresis Margin by Sahar Eydian, Maryam Hosseini, Gunes Karabulut Kurt

“…The numerical results demonstrate a significant reduction in handover rate and latency, while improving energy efficiency and achieving enhanced data rates. …”
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Precise application of water and fertilizer to crops: challenges and opportunities by Yingying Xing, Xiukang Wang

“…Advanced irrigation methods, such as subsurface drip irrigation and micro-sprinkling, improve water-use efficiency and reduce salinity levels, while precision fertilization techniques optimize nutrient uptake and minimize leaching. …”
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Three-Dimensional Imaging of High-Contrast Subsurface Anomalies: Composite Model-Constrained Dual-Parameter Full-Waveform Inversion for GPR by Siyuan Ding, Deshan Feng, Xun Wang, Tianxiao Yu, Shuo Liu, Mengchen Yang

“…Numerical experiments show that TV regularization can optimize permittivity reconstruction, while MSG regularization is more suitable for conductivity inversion. …”
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A Target Near-Field Scattering Measurement Technique Utilizing 3D Near-Field Imaging via Cylindrical Scanning by Zongkai Yang, Jingcheng Zhao, Weikang Si, Changyu Lou, Xin Zhao, Jungang Miao

“…To resolve this matter, we propose a hybrid 3D imaging algorithm that is optimized for cylindrical sampling and operates in both the wavenumber domain and time domain (WDTD). …”
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Prediction Model of Household Carbon Emission in Old Residential Areas in Drought and Cold Regions Based on Gene Expression Programming by Shiao Chen, Yaohui Gao, Zhaonian Dai, Wen Ren

“…Experimental results demonstrated that the model achieved an R² value of 0.81, reducing RMSE by 77.1% compared to conventional GEP models and by 60.4% compared to BP neural networks, while significantly improving stability. By combining data dimensionality reduction with adaptive evolutionary algorithms, this model overcomes the limitations of traditional methods in capturing complex nonlinear relationships. …”
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