Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss by Zhengpeng Yang, Suyu Yan, Chao Ming, Xiaoming Wang

“…Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. …”
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A feature selection method utilizing path accumulation cost, redundancy minimization, and interaction maximization for the diagnosis of coronary heart disease by Jiayao Jiang, Zheng Yue, Hongling Zhu, Yan Wang, Hongsen Cai, Wenguang Hou

“…Results Experimental outcomes demonstrate that our algorithm outperforms several classical algorithms in terms of classification accuracy and feature dimension reduction, while also identifying highly correlated feature subsets. …”
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HybridFormer: a convolutional neural network-Transformer architecture for low dose computed tomography image denoising by Shanaz Sharmin Jui, Zhitao Guo, Rending Jiang, Jiale Liu, Bohua Li

“…Traditional techniques like sinogram domain filtration and iterative reconstruction algorithms require inaccessible raw data. Thus, this article introduces HybridFormer, a revolutionary image-denoising model utilizing the Residual Convolution-Swin Transformer Network, designed to enhance images while preserving vital details. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…By optimizing the GRU model with the grey wolf optimizer (GWO) algorithm, the framework enhances both prediction stability and adaptability, while the nonlinear integration approach effectively mitigates error accumulation. …”
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Optimizing Feature Selection for IOT Intrusion Detection Using RFE and PSO by zahraa mehssen agheeb Alhamdawee

“…In this paper, An approach the defense algorithm of IDS for IoT networks’ security development contrary to attacks of DoS applying unusual ML and diagnosis has been presented. …”
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Federated Collaborative Learning with Sparse Gradients for Heterogeneous Data on Resource-Constrained Devices by Mengmeng Li, Xin He, Jinhua Chen

“…Federated learning enables devices to train models collaboratively while protecting data privacy. However, the computing power, memory, and communication capabilities of IoT devices are limited, making it difficult to train large-scale models on these devices. …”
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Multi-objective optimization of 200 kW air centripetal turbine based on artificial neural networks by Zideng Wang, Yan Zhang, Yuyang Leng, Jiabao Lai, Zhenzhen Wang, Weixiong Chen

“…Then, after optimizing the artificial neural network model with a genetic algorithm, the total -total efficiency of the air centrifugal turbine was improved by 1.479 %, while the axial thrust was reduced by 1.07 %.…”
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A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field by Bo Ma, Yi Ji, Liyong Fang

“…The traditional artificial potential field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. …”
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Computation Offloading and Resource Allocation for Energy-Harvested MEC in an Ultra-Dense Network by Dedi Triyanto, I Wayan Mustika, Widyawan

“…Overall, the results of simulation studies demonstrate that the LYMOC algorithm may considerably decrease the overall cost of system execution when compared with the Lyapunov–MILP-based short-distance complete local execution algorithm and the full offloading-computation method.…”
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Feature Extraction using Histogram of Oriented Gradients and Moments with Random Forest Classification for Batik Pattern Detection by Wafiq Azizah, Soffiana Agustin

“…These findings underscore the efficacy of integrating HOG and Texture Moments with the Random Forest algorithm for automated batik pattern recognition.…”
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Classification and spectrum optimization method of grease based on infrared spectrum by Xin Feng, Yanqiu Xia, Peiyuan Xie, Xiaohe Li

“…Based on the different IR absorption spectrum bands produced by each kind of lubricating grease, the three-dimensional spatial distribution map of the lubricating grease drawn also verifies the accuracy of classification while recognizing the accuracy. This paper demonstrates fast recognition speed and high accuracy, proving that the Kohonen neural network algorithm has an efficient recognition ability for identifying the types of the lubricating grease.…”
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Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks by Lidong Fu, Guoxian Ma, Zengfa Dou, Yun Bai, Xi Zhao

“…The Kendall and Jaccard coefficients measure the consistency of node ranking results with SIR results, as well as the number of matching nodes, while monotonicity evaluates the algorithm’s capacity to distinguish nodes and prevent identical values for distinct nodes. …”
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Protein structural domain-disease association prediction based on heterogeneous networks by Jingpu Zhang, Lianping Deng, Lei Deng

“…Small proteins are often found to be composed of only a single domain, while most large proteins consist of multiple domains for achieving various composite cellular functions. …”
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Three-Dimensional Temperature Field Reconstruction Based on Tucker Decomposition and Acoustic Thermometry by Jidong Yan, Liansuo An, Pengbo Yao, Guoqing Shen, Shiping Zhang

“…In this work, we propose a novel 3D temperature field reconstruction algorithm based on Tucker decomposition and acoustic thermometry. …”
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Measuring the Effectiveness of Carbon-Aware AI Training Strategies in Cloud Instances: A Confirmation Study by Roberto Vergallo, Luca Mainetti

“…While such strategies have proved to achieve interesting results on a benchmark of modern models covering Natural Language Processing (NLP) and computer vision applications and a wide range of model sizes (up to 6.1B parameters), it is still unclear whether such results may hold also with different algorithms and in different geographical regions. …”
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Network function parallelism configuration with segment routing over IPv6 based on deep reinforcement learning by Seokwon Jang, Namseok Ko, Yeunwoong Kyung, Haneul Ko, Jaewook Lee, Sangheon Pack

“…To reduce the SFC completion time while maintaining a low network overhead, we propose a deep-reinforcement-learning-based NFP algorithm (DeepNFP) that provides an SFC processing policy to determine the sequential or parallel processing of every service function. …”
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Efficient Resources Provisioning Based on Load Forecasting in Cloud by Rongdong Hu, Jingfei Jiang, Guangming Liu, Lixin Wang

“…Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. …”
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Fine-Tuning Parameters for Emergent Environments in Games Using Artificial Intelligence by Vishnu Kotrajaras, Tanawat Kumnoonsate

“…Adjusting properties for a scenario allows a specific scene to take place while still enables players to meddle with emergent maps. …”
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Extracting of Fault Features for Rotating Machinery of High-speed Train Based on EEMD Energy Entropy-LPP by Chen ZHANG, Tao FANG, Kaiqi YAN

“…By combining EEMD, energy entropy and LPP, the vibration signal was subjected to EEMD adaptive decomposition, and the energy entropy of the high-frequency IMF component was calculated to obtain a high-dimensional feature vector set to complete the preliminary feature extraction; then the high-dimensional feature vector was converted by the LPP algorithm set projection to low-dimensional space to further extract features to form a low-dimensional sample set, which reduced the complexity of feature data while preserving the local geometric information of fault features, while improved the classification performance of fault pattern recognition; finally, the low-dimensional sample set was input to the KNN classifier for fault recognition. …”
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From Incoherence to Sustainability: Performance, Activism, and Social Media in the Most Recent Russian Poetry by Kirill Korchagin

“…The central hypothesis of this article is that all of these poets react differently to the methods of discursive organization provided (and enforced) by social networks and strive in different ways to liberate themselves from the censorship of the algorithm: some emphasize the discursive incoherence of the platform, while others, on the contrary, seek to develop a sustainable manner of uniting private discourses into a new totality. …”
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