UAV Spiral Maneuvering Trajectory Intelligent Generation Method Based on Virtual Trajectory by Tao Chen, Shaopeng Li, Yong Xian, Leliang Ren, Zhenyu Liu

“…A deep reinforcement learning (DRL) approach, employing the proximal policy optimization (PPO) algorithm, is implemented to train a policy network capable of end-to-end virtual trajectory generation. …”
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ISAC Receiver Design: Joint DoA and Data Estimation in the Presence of Incomplete Signal Observations by Iman Valiulahi, Christos Masouros, Mahmoud Alaaeldin, Emad Alsusa

“…We propose a convex optimization based on the dual of atomic norm minimization (ANM). …”
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Acoustic Source Localization Based on the Two-Level Data Aggregation Technology in a Wireless Sensor Network by Yuwu Feng, Guohua Hu, Lei Hong

“…The inherent energy constraints of sensor nodes render energy efficiency optimization a critical challenge in wireless sensor network deployments. …”
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GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries by Peixiong He, Libo Sun, Xian Gao, Yi Zhou, Xiao Qin

“…However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. …”
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PID Control and Input Shaping for Quadrotor UAV Stabilization and Payload Swing Reduction by Liyana Ramli, Izzuddin M. Lazim, Amalin Aisya Awi, Aina Syazwin Shukor

“…To enhance swing suppression, the input shaper parameters are optimized using the Particle Swarm Optimization (PSO) algorithm. …”
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Hybrid Damping Mode MR Damper: Development and Experimental Validation with Semi-Active Control by Jeongwoo Lee, Kwangseok Oh

“…This configuration supports four damping modes—Soft/Soft, Hard/Soft, Soft/Hard, and Hard/Hard—allowing adaptability to varying driving conditions. Magnetic circuit optimization ensures rapid damping force adjustments (≈10 ms), while a semi-active control algorithm incorporating skyhook logic, roll, dive, and squat control strategies was implemented. …”
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Deep knowledge tracing and cognitive load estimation for personalized learning path generation using neural network architecture by Chunyan Tong, Changhong Ren

“…A dual-objective optimization algorithm balances knowledge acquisition with cognitive load management to generate paths that maintain optimal challenge levels. …”
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Large-Scale Transmission Expansion Planning with Network Synthesis Methods for Renewable-Heavy Synthetic Grids by Adam B. Birchfield, Jong-oh Baek, Joshua Xia

“…The transmission expansion planning (TEP) problem is particularly challenging because of the combinatorial, integer optimization nature of the problem and the complexity of engineering analysis for any one possible solution. …”
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Artificial Neural Network Prediction on negative and positive activation energy of magnetohydrodynamic nanofluid flow with multiple slips by Shovan Sarkar, Hiranmoy Mondal, Prabir Kumar Kundu

“…Another important matter to discuss here that we have used feed-forward back-propagation multilayer perceptron artificial neural network with Levenberg-Marquard algorithm as the training algorithm to predict the Sherwood number for both activation energy values 1 and -1. …”
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Scalable Hyperspectral Enhancement via Patch-Wise Sparse Residual Learning: Insights from Super-Resolved EnMAP Data by Parth Naik, Rupsa Chakraborty, Sam Thiele, Richard Gloaguen

“…The spectral and spatial characteristics of the scene encoded in the dictionary enable reconstruction through a first-order optimization algorithm to ensure an efficient sparse representation. …”
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Detection of soluble solid content in table grapes during storage based on visible-near-infrared spectroscopy by Yuan Su, Ke He, Wenzheng Liu, Jin Li, Keying Hou, Shengyun Lv, Xiaowei He

“…Comparing the modelling results derived from whole-band spectral data with those obtained from selected spectral variables, the optimal spectral prediction model was formulated utilizing PLSR. …”
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Embedded Hardware-Efficient FPGA Architecture for SVM Learning and Inference by B. B. Shabarinath, Muralidhar Pullakandam

“…While Sequential Minimal Optimization (SMO) has enhanced the efficiency of SVM training, traditional implementations still suffer from high computational cost. …”
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Learning super-resolution and pyramidal convolution residual network for vehicle re-identification by Mengxue Liu, Weidong Min, Qing Han, Hongyue Xiang, Meng Zhu

“…This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. …”
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Anomaly detection of large scale network based on data streams by ZHENG Jun, HU Ming-zeng, YUN Xiao-chun, ZHENG Zhong

“…The anomaly detection algorithms of the large scale network（LSN） were required to analysis the vast network traffic of G bit level in real-time and on-the-fly.A novel monitoring mechanism of LSN anomaly detection based on the data stream approach was proposed.The main contributions included: the sketch data structure and the frequent sketch algorithm of data streams were designed for anomaly detection of LSN.Optimized query methods were designed for customizing the security monitoring and detection policy with the correlations of multi data streams.The data reduction was proposed to make it possible that the whole network traffic character could be got using a few of special data streams.The experiments of the real networking environments validate the effectivity of LSN anomaly detection methods.…”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. …”
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Assigning Priorities for Fixed Priority Preemption Threshold Scheduling by Saehwa Kim

“…This paper analyzes the inefficiency or nonoptimality of the previously proposed optimal priority assignment algorithms for PTS. We develop theorems for exhaustively but safely pruning infeasible priority orderings while assigning priorities to tasks for PTS. …”
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EDT-MCFEF: a multi-channel feature fusion model for emergency department triage of medical texts by Tao Lin, Shiming Yi

“…However, traditional triage methods frequently depend on human judgment, which is susceptible to high subjectivity and low efficiency.MethodsTo address these issues, this paper presents a novel emergency department triage algorithm. The proposed EDT-MCFEF (Emergency Triage Algorithm Based on Multi-Channel Feature Extraction and Fusion) addresses numerous shortcomings of conventional triage methodologies. …”
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Analysis and Research on Intelligent Logistics Data under Internet of Things and Blockchain by Yige Li

“…Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. …”
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Machine learning frameworks to accurately predict coke reactivity index by Ayat Hussein Adhab, Morug Salih Mahdi, Krunal Vaghela, Anupam Yadav, Jayaprakash B, Mayank Kundlas, Ankur Srivastava, Jayant Jagtap, Aseel Salah Mansoor, Usama Kadem Radi, Nasr Saadoun Abd, Samim Sherzod

“…To minimize overfitting in each algorithm, K-fold cross-validation methodology is employed during the training phase. …”
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