An Integration of Deep Neural Network-Based Extended Kalman Filter (DNN-EKF) Method in Ultra-Wideband (UWB) Localization for Distance Loss Optimization by Chanthol Eang, Seungjae Lee

“…The proposed approach combines a DNN with the EKF to significantly improve indoor localization accuracy for mobile robots. …”
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A Review of Generative Design Using Machine Learning for Additive Manufacturing by Parankush Koul

“…This paper ends with what generative design and ML hold for the future of AM and how designing continues to be improved and modified to match changing industry requirements.…”
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Design Optimization of Compliant Mechanisms for Vibration- Assisted Machining Applications Using a Hybrid Six Sigma, RSM-FEM, and NSGA-II Approach by Huy-Tuan Pham, Van-Khien Nguyen, Quang-Khoa Dang, Thi Van Anh Duong, Duc-Thong Nguyen, Thanh-Vu Phan

“…This paper proposes the design of a new 2-DOF high-precision compliant positioning mechanism using an optimization process combining the response surface method, finite element method, and Six Sigma analysis into a multi-objective genetic algorithm. …”
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An Inter-Regional Lateral Transshipment Model to Massive Relief Supplies with Deprivation Costs by Shuanglin Li, Na Zhang, Jin Qin

“…A case study based on a typhoon disaster in the Chinese region of Bohai Rim demonstrates and verifies the effectiveness and applicability of the proposed model and algorithm. The results and sensitivity analysis indicate that reducing loading and unloading times and improving transshipment efficiency can effectively decrease transfer time. …”
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BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION by YE Ling, JIANG HongKang, ZOU YuQing, CHEN HuaPeng, WANG LiCheng

“…The traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain population competition was proposed.First，the differential evolution algorithm was introduced in the traditional method of Metropolis-Hastings algorithm.Based on the interaction of different information carried by Markov chains in the population，optimization suggestions were obtained to approach the objective function quickly.It solves the defect of sampling retention in the updating process of high-dimensional parameter model.Then，the competition algorithm was introduced，which has constant competitive incentives and a built-in mechanism for losers to learn from winners.Higher precision was obtained by using fewer Markov chains，which improves the efficiency and precision of model updating.Finally，a numerical example of finite element model updating of a truss structure was used to verify the proposed algorithm in this paper.Compared with the results of standard MH algorithm，the proposed algorithm can quickly update the high-dimensional parameter model with high accuracy and good robustness to random noise.It provides a stable and effective method for finite element model updating of large-scale structure considering uncertainty.…”
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Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier by Ali Saeed Almuflih, Ilyos Abdullayev, Sergey Bakhvalov, Rustem Shichiyakh, Bibhuti Bhusan Dash, K. B. V. Brahma Rao, Kritika Bansal

“…Since the hyperparameters play a vital part in the classification performance, the BSODL-ZDADC technique employs an improved sparrow search algorithm (ISSA). The experimental validation of the BSODL-ZDADC technique is verified by utilizing the ToN-IoT dataset. …”
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Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning by I Nyoman Mahayasa Adiputra, Paweena Wanchai, Pei-Chun Lin

“…Additionally, this study provided a robustness measurement for algorithms, demonstrating that CostLearnGAN outperforms other sampling methods in improving the performance of classical machine learning models with a 5.68 robustness value on average.…”
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A prior information-based multi-population multi-objective optimization for estimating 18F-FDG PET/CT pharmacokinetics of hepatocellular carcinoma by Yiwei Xiong, Siming Li, Jianfeng He, Shaobo Wang

“…Abstract Background 18F fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) pharmacokinetics is an approach for efficiently quantifying perfusion and metabolic processes in the liver, but the conventional single-individual optimization algorithms and single-population optimization algorithms have difficulty obtaining reasonable physiological characteristics from estimated parameters. …”
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Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques by Shoayee Dlaim Alotaibi, Abeer A. K. Alharbi

“…In addition, the wavelet neural network (WNN) classifier is employed to detect and classify dementia. Finally, the improved salp swarm algorithm (ISSA) is implemented to choose the WNN technique’s hyperparameters optimally. …”
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Research on autonomous driving scenario modeling and application based on environmental perception data by Ming Cao, Wufeng Duan, Changqing Huo, Song Qiu, Mingchun Liu

“…Results indicated that the optimized autonomous driving algorithm significantly enhances vehicle performance in similar scenarios within highly realistic simulation scenarios, thereby improving the security of algorithm optimization and validation. …”
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Data-Driven Pavement Performance: Machine Learning-Based Predictive Models by Mohammad Fahad, Nurullah Bektas

“…A k-fold cross-validation technique was employed to optimize hyperparameters. Results indicate that LightGBM and CatBoost outperform other models, achieving the lowest mean squared error and highest R² values. …”
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Leveraging Agent-Based Modeling and IoT for Enhanced E-Commerce Strategies by Mohamed Shili, Sajid Anwar

“…This paper presents a novel approach for integrating e-commerce platforms with the Internet of Things (IoT) through the use of agent-based models. The key objective is to create a multi-agent system that optimizes interactions between IoT devices and e-commerce systems, thereby improving operational efficiency, adaptability, and user experience in online transactions. …”
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A Novel Separable Model and Decomposition Method for Sensor Locational Decision Problem by Linfeng Yang, Jie Li, Jin Ye, Zhigang Zhao

“…An improved interior point method based on optimal centering parameter is employed to solve the NLP subproblem. …”
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A Robust Heuristics for the Online Job Shop Scheduling Problem by Hugo Zupan, Niko Herakovič, Janez Žerovnik

“…The heuristics at the level of probabilistic rules for running the local queues is experimentally shown to provide the solutions of quality that is within acceptable approximation ratios to the best known solutions obtained by the best online algorithms. The probabilistic rule defines a model which is not unlike the spin glass models that are closely related to quantum computing. …”
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IWOA-LSTM based intrinsic structural identification of steel fiber concrete by Ping Li, Jie Feng, Shiwei Duan

“…In order to accurately identify the high-temperature constitutive model taking into account the damage evolution, a high-temperature constitutive identification model using the Improved Whale Algorithm (IWOA) optimised Long Short-Term Memory (LSTM) neural network is presented. …”
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Inversion and Fine Grading of Tidal Flat Soil Salinity Based on the CIWOABP Model by Jin Zhu, Shuowen Yang, Shuyan Li, Nan Zhou, Yi Shen, Jincheng Xing, Lixin Xu, Zhichao Hong, Yifei Yang

“…This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. …”
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An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance by Abhijeet Das

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HVS-based rate-control scheme for object-based embedded image coding by Dong-jie LI, Zhong-jie ZHU, Yu-er WANG

“…A new rate control algorithm for object-based embedded coding was proposed by incorporating the characteristics of human visual systems (HVS).Firstly,the importance and coding priority of each visual object were estimated.Then,bit-plane modeling and entropy coding were implemented for each object based on the coding priority and its corresponding bit stream was outputted.Finally,bit streams of visual objects were truncated and reassembled based on the rate-distortion optimization principle under the given bit rate.Experimental results reveal that the proposed algorithm can encode and transmit different important objects with different strategies.Compared with the PCRD algorithm,the proposed algorithm can improve the overall visual quality of the reconstructed image.…”
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ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages by Yan Mo, Shaowei Bai, Wei Chen

“…Quick and accurate identification of strawberry plants at different stages can provide important information for automated strawberry planting management. We propose an improved multistage identification model for strawberry based on the YOLOv9 algorithm—the ASHM-YOLOv9 model. …”
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Menstrual cycle inspired latent diffusion model for image augmentation in energy production by Gamal M. Mahmoud, Mostafa Elbaz, Wael Said, Amira A. Elsonbaty

“…First, a menstrual cycle-inspired metaheuristic algorithm is integrated to improve generated images’ pixel integrity and structural coherence. …”
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