Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning by Jiaju Qi, Lei Lei, Thorsteinn Jonsson, Lajos Hanzo

“…As a result, the high-level agent learns an effective policy for prescribing the charging targets for every charging period, while the low-level agent learns an optimal policy for setting the charging power of every time step within a single charging period, with the aim of minimizing the charging costs while meeting the charging target. …”
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A cooperative jamming resource allocation method based on PSO-SSNOA by Wei Liu, You Chen, Xiaohong Zhao, Dejiang Lu, Tianjian Yang

“…This approach innovatively integrates Particle Swarm Optimization (PSO) with the Nutcracker Optimization Algorithm (NOA), while enhancing the nutcracker’s behavioral patterns through seasonal adaptation strategies. …”
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A comparison of tools used for tuberculosis diagnosis in resource-limited settings: a case study at Mubende referral hospital, Uganda. by Adrian Muwonge, Sydney Malama, Barend M de C Bronsvoort, Demelash Biffa, Willy Ssengooba, Eystein Skjerve

“…Clinical variables from a questionnaire and DZM were used to predict TB status in multivariable logistic and Cox proportional hazard models, while optimization and visualization was done with receiver operating characteristics curve and algorithm-charts in Stata, R and Lucid-Charts respectively.…”
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Fractional-Order Adaptive P-Laplace Equation-Based Art Image Edge Detection by Ming Chen

“…The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. …”
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Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images by Weerach Charerntantanakul, Marta Yebra, Hilary Rose Dawson, Adrienne Beth Nicotra, Saul Alan Cunningham, Matthew Theodore Brookhouse

“…We tested random-forest (RF), support vector machine (SVM), and multiple linear regression (MLR) to find the algorithm that provides the best accuracy. Cross-validation experiments were undertaken to optimize the model configurations. …”
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Predicting the Attraction and Retention of Customers in Sports Pools in Isfahan City Using a Decision Tree: Presenting a Data Mining-Based Model by Seyed Masoud Mirsaeidi, Davood Nasr Esfahani

“…To evaluate the features, two algorithms—Grasshopper Optimization Algorithm (GOA) and Simulated Annealing (SA)—were used for feature selection, while classification algorithms, specifically Decision Tree (DT) and K-Nearest Neighbors (KNN), were employed to classify and recognize customer behavior. …”
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Basketball teaching methods based on 3D-Convolutional neural network by Chao Huang, Xian Wu

“…The optimized algorithm is then used for video skilled movement detection processing to obtain cropped frame images. …”
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Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks by Dongsup Jin, Minho Jang, Ji-Woong Jang, Gyuyeol Kong

“…Our proposed RL-based algorithm leverages the actor-critic policy gradient method to learn optimal channel selection strategies by dynamically adapting to the environment and minimizing TTR. …”
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Comparing MEG and EEG measurement set-ups for a brain-computer interface based on selective auditory attention. by Dovilė Kurmanavičiūtė, Hanna Kataja, Lauri Parkkonen

“…Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. …”
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Modeling of train flow handling through a limiting single-track section of the route at the organization of high-speed operation using the existing infrastructure by S. V. Karasev, A. D. Kalidova

“…The article examines the problem of studying the process of handling the opposing train flows through a single track section of the route by modeling for the conditions of organizing high-speed operation while minimizing capital investments in infrastructure. …”
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Hybrid precoding method for mmWave massive MIMO systems based on LFM by Haoyi CHEN, Yifan DING, Ying CHENG

“…Analog-digital hybrid precoding is a key technology for millimeter wave massive MIMO systems that reduce hardware costs while balancing system performance.However,the traditional hybrid precoding scheme often needed to find a suitable codebook for precoding,and some codebooks were not easy to obtain or had deviations in actual situations.An analog-digital hybrid precoding method based on latent factor model (LFM) in machine learning without codebook was proposed for this problem.The LFM decomposition and stochastic gradient descent method were used to approximate the designed precoding matrix to the optimal full digital precoding matrix for good performance.The simulation results show that compared with the hybrid precoding design method based on orthogonal matching pursuit (OMP) algorithm,this method not only does not need a codebook,but also has better performance than the hybrid precoding algorithm based on OMP algorithm,which is closer to optimal full digital precoding method.…”
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Comparative Analysis of Bio-Inspired Enhancement Techniques for Localization in 2D Wireless Sensor Networks by Rabhi Seddik

“…This study conducts a comparative evaluation of three bio-inspired optimization algorithms for node localization: Particle Swarm Optimization (PSO), Fruit Fly Optimization Algorithm (FOA), and Drop Mongoose Optimization Algorithm (DMOA). …”
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Stability Study of Distributed Drive Vehicles Based on Estimation of Road Adhesion Coefficient and Multi-Parameter Control by Peng Ji, Fengrui Han, Yifan Zhao

“…First, a seven-degree-of-freedom (7-DOF) vehicle dynamics model is established and optimized with a layered control strategy. The upper-level control module calculates the desired yaw rate and sideslip angle using the two-degree-of-freedom (2-DOF) vehicle model and estimates the road adhesion coefficient by using the singular-value optimized cubature Kalman filtering (CKF) algorithm; the middle-level utilizes the second-order sliding mode controller (SOSMC) as a direct yaw moment controller in order to track the desired yaw rate and sideslip angle while also employing a joint distribution algorithm to control the torque distribution based on vehicle stability parameters, thereby enhancing system robustness; and the lower-level controller performs optimal torque allocation based on the optimal tire loading rate as the objective. …”
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New design paradigm for federated edge learning towards 6G:task-oriented resource management strategies by Zhiqin WANG, Jiamo JIANG, Peixi LIU, Xiaowen CAO, Yang LI, Kaifeng HAN, Ying DU, Guangxu ZHU

“…To this end, it is necessary to model the total training time minimization problem, optimize the quantization series and bandwidth allocation, and design an alternate optimization algorithm to solve this problem. 2. …”
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Research on Dynamic Storage Location Assignment of Picker-to-Parts Picking Systems under Traversing Routing Method by Xiangbin Xu, Chenhao Ren

“…Considering time-varying demand of online retail industry, the traditional static storage location assignment is converted into a multistage storage location assignment process based on the idea of gradual and small-step-forward optimization, which can respond to rapid changes in demand by adjusting the storage location of SKUs in the warehouse in real time and dynamically. …”
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GREEN ECONOMY AND HOLISTIC PLANNING BY ARTIFICIAL INTELLIGENCEAPPLICATION by STEVOVIC IVAN, HADROVIĆ SABAHUDIN, JOVANOVIC MIHAILO

“…On the one hand, IT such as machine learning, data analytics, and optimization algorithms offer immense potential to enhance resource efficiency, optimize energy systems, and facilitate sustainable decision-making processes. …”
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Sparse Feature-Weighted Double Laplacian Rank Constraint Non-Negative Matrix Factorization for Image Clustering by Hu Ma, Ziping Ma, Huirong Li, Jingyu Wang

“…We also propose an efficient alternating iterative update algorithm to optimize the proposed model and provide a theoretical analysis of its performance. …”
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Innovative Lightweight Detection for Airborne Remote Sensing: Integrating G-Shuffle and Dynamic Multiscale Pyramid Networks by Ruofei Liang, Yigang Cen, Linna Zhang, Fugui Zhang, Yansen Huang, Fei Gan

“…To address the challenges of significant scale variations and difficulty in extracting discriminative features for small targets in airborne remote sensing object detection, while considering the constraints and efficiency requirements of onboard systems, this article proposes a lightweight detection algorithm. …”
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A machine learning-based efficient anomaly detection system for enhanced security in compromised and maligned IoT Networks by Anita Punia, Manish Tiwari, Sourabh Singh Verma

“…We use HP Tuned Machine Learning Algorithms further for developing an anomaly detection model and optimization of them by implementing machine learning algorithms. …”
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ReliefSeq: a gene-wise adaptive-K nearest-neighbor feature selection tool for finding gene-gene interactions and main effects in mRNA-Seq gene expression data. by Brett A McKinney, Bill C White, Diane E Grill, Peter W Li, Richard B Kennedy, Gregory A Poland, Ann L Oberg

“…The ReliefSeq implementation adapts the number of nearest neighbors (k) for each gene to optimize the Relief-F test statistics (importance scores) for finding both main effects and interactions. …”
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