DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…The objective of the DTHC method is to improve detection accuracy while minimizing false alarm rates. …”
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A Novel Energy Replenishment Algorithm to Increase the Network Performance of Rechargeable Wireless Sensor Networks by Tariq, Vishwanath Eswarakrishnan, Adil Hussain, Zhu Wei, Muhammad Uzair

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Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms by Yuki Hanawa, Tomohiro Harada, Yukiya Miura

“…Surrogate-assisted evolutionary algorithms (SAEAs) are widely used to solve expensive optimization problems where evaluating candidate solutions is computationally intensive. …”
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VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

“…Additionally, a lightweight Optimized Shared Detection Head (OSDH-Head) is introduced, reducing computational complexity while improving detection efficiency. …”
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Resource-Efficient Context-Aware Dynamical Decoupling Embedding for Arbitrary Large-Scale Quantum Algorithms by Paul Coote, Roman Dimov, Smarak Maity, Gavin S. Hartnett, Michael J. Biercuk, Yuval Baum

“…We introduce and implement GraphDD: an efficient method for real-time, circuit-specific, optimal embedding of dynamical decoupling (DD) into executable quantum algorithms. …”
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BED-YOLO: An Enhanced YOLOv10n-Based Tomato Leaf Disease Detection Algorithm by Qing Wang, Ning Yan, Yasen Qin, Xuedong Zhang, Xu Li

“…In this paper, we propose an improved tomato leaf disease detection method based on the YOLOv10n algorithm, named BED-YOLO. …”
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Resource allocation strategy based on optimal matching auction in the enterprise network by Xin CONG, Lingling ZI, Xueli SHEN

“…To address the issue that the owners of computer are selfish in the enterprise networks,which caused the low available number of resource nodes and low efficiency of resource allocation,an optimized matching resource allocation strategy OMRA was proposed and its core was the auction mechanism.Selfishness was restrained and the number of available resources was increased by OMRA,so as the operating efficiency of the whole auction market was improved.First,the initial prices were determined by normalizing the costs of different type of resources on the beginning of auction.Secondly,an optimal matching auction algorithm was designed to maximize the interests of the auction markets.Then,service perfecting algorithm was performed such that the sellers could get more services at the current transaction value,thus ensuring the benefits of resource providers.At last,a request price updating algorithm was adopted to assurance that both sellers and buyers could get priorities in the next auction processing.Compared with the cloud resource allocating algorithm via fitness-enabled auction (CRAA/FA),the experiment results indicate that the efficiency of resource allocation improves by 10% and the benefits of market increase by 11.4%.…”
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Research on prediction algorithm of effluent quality and development of integrated control system for waste-water treatment by JianWun Lai

“…The ICS is superior to standard WWTCS by a vital error boundary, minimizing energy consumption by 17% and boosting chemical-based consumption optimization by 24%. With an average removal rate of 94.23% for Chemical Oxygen Demand (COD) compared to 88.76% for standard systems, the findings from experiments exhibited significant performance improvements.…”
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Investigating the performance of random oversampling and genetic algorithm integration in meteorological drought forecasting with machine learning by Tahsin Baykal, Özlem Terzi, Gülsün Yıldırım, Emine Dilek Taylan

“…Therefore, this study aims to evaluate the effectiveness of machine learning methods for meteorological drought estimation and to integrate Random Oversampling (ROS) and Genetic Algorithm (GA) methods to improve estimation accuracy. …”
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Estimation of the Ultimate Bearing Capacity of the Rocks via Utilization of the AI-Based Frameworks by Bianca Damico, Matteo Conti

“…The approach adopted here is new and solves the problem using KNN combined with two modern nature-inspired optimization frameworks, namely the Honey Badger Algorithm (HBA) and Equilibrium Slime Mould Algorithm (ESMA). …”
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Heuristic Algorithms for the Heterogeneous Vehicle Routing Problem With Time Windows, Customers Priority, Pickup and Delivery by Moayad Tanash, Rami As'Ad

“…Both heuristics possess a two-phase structure, where the first phase yields highly prudent initial solutions employing a Greedy Randomized Adaptive Search Procedure (GRASP) in the first heuristic, and Priority-Based Ant Colony Optimization (PBACO) in the second heuristic. As for the second phase, both heuristics embrace a common Variable Neighborhood Search (VNS) algorithm that explores seven different neighborhoods to improve upon the initial solutions. …”
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Vehicular cache nodes selection algorithm under load constraint in C-V2X by Zhexin XU, Kaimeng GAO, Wenkang JIA, Yi WU

“…In order to solve the problem that the C-V2X vehicle topology in urban environment was highly dynamic and the load capacity of vehicle nodes was limited, and improve the utilization of vehicular cache resources and reduce the load of base station, a vehicle cache nodes selection algorithm under load constraints was proposed.Firstly, by defining the link stability metric, the predicted weight adjacency matrix was constructed to describe the vehicular micro-topology in essence.Next, the objective function was further constructed under the load constraints and non-overlapping coverage constraint, which maximized the average link weight of the clusters by using the least cache nodes.Finally, the greedy concept was then introduced and the node states were reasonably defined.As a result, the minimum dominating set of the vehicle topology was figured out under the load constraints.Besides, the serviced neighbor nodes were then determined preferentially.The simulation results show that the proposed algorithm is close to the global optimal results in terms of the number of cache nodes and the average weight of cluster links.Moreover, the repeated response ratio of the proposed algorithm is always zero while the request response ratio can achieve the theoretical upper bound.Furthermore, the response times of cache resources can be also effectively improved.…”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.…”
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Simulation of a scalable corridor waning radiance system using fuzzy PID and genetic algorithm by Abdallah Awamu, Jimmy Nabende Wanzala, Michael Robson Atim, Cosmos Dumba

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CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

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Diagnostic algorithm for the detection of carbapenemases and extended-spectrum β-lactamases in carbapenem-resistant Pseudomonas aeruginosa by Stefano Mancini, Laia Garcia-Verellen, Helena M. B. Seth-Smith, Peter M. Keller, Natalia Kolesnik-Goldmann, Muhammad Ali Syed, Irfan Ullah, Vladimira Hinic, Tim Roloff, Adrian Egli, Oliver Nolte

“…Incorporating Carba-5 into the phenotypic algorithm improved sensitivity for confirming MBL production to 100%. …”
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Comprehensive Comparison and Validation of Forest Disturbance Monitoring Algorithms Based on Landsat Time Series in China by Yunjian Liang, Rong Shang, Jing M. Chen, Xudong Lin, Peng Li, Ziyi Yang, Lingyun Fan, Shengwei Xu, Yingzheng Lin, Yao Chen

“…When considering different forest disturbance types, COLD achieved the highest accuracies for Fire, Harvest, and Other disturbances, while CCDC was most accurate for Forestation. These findings highlight the necessity of region-specific calibration and parameter optimization tailored to specific disturbance types to improve forest disturbance monitoring accuracy, and also provide a solid foundation for future studies on algorithm modifications and ensembles.…”
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Evaluating the generalisability of region-naïve machine learning algorithms for the identification of epilepsy in low-resource settings. by Ioana Duta, Symon M Kariuki, Anthony K Ngugi, Angelina Kakooza Mwesige, Honorati Masanja, Daniel M Mwanga, Seth Owusu-Agyei, Ryan Wagner, J Helen Cross, Josemir W Sander, Charles R Newton, Arjune Sen, Gabriel Davis Jones

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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…The ability to quickly and comprehensively extract common features of multiradar scheduling problems is essential for improving the efficiency of such AI scheduling algorithms. …”
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Optimal Modeling of Wireless LANs: A Decision-Making Multiobjective Approach by Tomás de Jesús Mateo Sanguino, Jhon Carlos Mendoza Betancourt

“…To this end, we implemented a multiobjective genetic algorithm (MOGA) with the purpose of meeting two simultaneous design objectives, namely, to minimize the number of APs while maximizing the coverage signal over a whole planning area. …”
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