Real-Time Detection of Milk Adulteration with a Portable Multispectral Analysis Device: A Multispectral Sensor and Optimized Logistic Regression Approach by Mahmut Durgun

“…Utilizing an AS7265x multispectral sensor and Arduino Nano 33 BLE Sense microcontroller, this system employs an optimized logistic regression model to identify starch adulteration in milk samples with near-perfect accuracy. Unlike complex neural network models, the logistic regression model offers simplicity, low power consumption, and efficient operation on microcontrollers. …”
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Text Knowledge Acquisition Method of Collaborative Product Design Based on Genetic Algorithm by Chongyuan Li, Mengmeng Yang

“…After the text knowledge clustering is completed, the text knowledge data of collaborative product design are obtained in an all-around way by using the method of rough set and neural network. The experimental results show that compared with the traditional text knowledge acquisition methods, the clustering effect of the proposed method is better and the text knowledge error is reduced up to 0.02.…”
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Research on upper limb rehabilitation assessment model based on belief rule base by Dawei Jiang, Zixu Zhao, Lijun Wang, Chao Zhang, Meixuan He, Tiejun Ji

“…We then optimized the BRB model’s evaluation accuracy using the Fmincon algorithm and compared its result with commonly used methods such as the Back Propagation (BP) neural network and Support Vector Machine (SVM). This comparison validated the effectiveness and advancement of our BRB approach. …”
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Crack detection based on attention mechanism with YOLOv5 by Min‐Li Lan, Dan Yang, Shuang‐Xi Zhou, Yang Ding

“…Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. …”
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A Lightweight Laser Chip Defect Detection Algorithm Based on Improved YOLOv7-Tiny by HU Wei, ZHAO Jumin, LI Dengao

“…[Methods] By employing a lightweight convolutional neural network as the feature extraction backbone and integrating multi-branch reparameterized convolution blocks, this algorithm not only significantly reduces resource consumption but also enhances feature representation capabilities. …”
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Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests by Juan Cheng, Gen Li, Xianhua Chen

“…Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.…”
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Stage-Based Remaining Useful Life Prediction for Bearings Using GNN and Correlation-Driven Feature Extraction by Guangzhong Huang, Wenping Lei, Xinmin Dong, Dongliang Zou, Shijin Chen, Xing Dong

“…This paper presents a model that combines correlation analysis feature extraction with a Graph Neural Network (GNN)-based approach for bearing degradation stage classification and RUL prediction, aiming to achieve accurate bearing life prediction. …”
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Downscaling Global Weather Forecast Outputs Using ANN for Flood Prediction by Nam Do Hoai, Keiko Udo, Akira Mano

“…This paper presents an empirical-statistical downscaling method for precipitation prediction which uses a feed-forward multilayer perceptron (MLP) neural network. The MLP architecture was optimized by considering physical bases that determine the circulation of atmospheric variables. …”
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Probabilistic Prediction of Mine Dynamic Disaster Risk Based on Multiple Factor Pattern Recognition by Tianwei Lan, Chaojun Fan, Sheng Li, Hongwei Zhang, Adrian Sergaevich Batujin

“…The risk probability prediction criteria of mine dynamic disasters and the risk probability values of each unit in the prediction area are determined by using the method of neural network and fuzzy mathematics. The dangerous area, threat area, and safety area of mine dynamic disasters are divided to evaluate the dangerous degree. …”
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Complexity Analysis of a Four-Dimensional Energy-Economy-Environment Dynamic System by Shuai Jin, Liuwei Zhao

“…Based on the official data, the Levenberg–Marquardt backpropagation neural network method was optimized by genetic algorithm to effectively identify the parameters in the 3E system. …”
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Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning by Shuo Jia, Fei Hui, Cheng Wei, Xiangmo Zhao, Jianbei Liu

“…For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. …”
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Implementation and Testing of V2I Communication Strategies for Emergency Vehicle Priority and Pedestrian Safety in Urban Environments by Federica Oliva, Enrico Landolfi, Giovanni Salzillo, Alfredo Massa, Simone Mario D’Onghia, Alfredo Troiano

“…The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities. Both scenarios were tested at two distinct intelligent intersections in Lioni, Avellino, Italy, and demonstrated notable effectiveness. …”
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Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models by Pei Jing Low, Bo Yan Ng, Nur Insyirah Mahzan, Jing Tian, Cheung-Chi Leung

“…The second approach leverages a multiple-frame <i>convolutional neural network</i> (CNN) to exploit temporal and spatial patterns in the video data. …”
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Radio frequency fingerprint data augmentation for indoor localization based on diffusion model by Haojun AI, Weike ZENG, Jingjie TAO, Jinying XU, Hanxiao CHANG

“…The radio frequency fingerprint indoor localization method ensures the accuracy by collecting a sufficient amount of fingerprints in the offline state to build a dense fingerprint database.A data augmentation method called FPDiffusion was proposed based on diffusion model to reduce the cost of fingerprint acquisition.Firstly, a temporal graph representation of the fingerprint sequence was constructed, the forward process of the diffusion model was accomplished by adding Gaussian noise, and a U-Net was utilized for the reverse process.The loss function of the network was designed according to the characteristics of radio frequency fingerprints.Finally, the computational process for generating dense fingerprints based on sparse fingerprints was presented.Experimental results demonstrate that FPDiffusion achieves 76% and 28% localization error reduction on K-nearest neighbor (KNN) and convolutional neural network (CNN) respectively, and significantly improves localization accuracy on KNN compared to Gaussian process regression (GPR) and GPR-GAN when only a small amount of labeled fingerprints is available.…”
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Model Calibration and Validation for the Fuzzy-EGARCH-ANN Model by Geleta T. Mohammed, Jane A. Aduda, Ananda O. Kube

“…This work shown as the fuzzy-EGARCH-ANN (fuzzy-exponential generalized autoregressive conditional heteroscedastic-artificial neural network) model does not require continuous model calibration if the corresponding DE algorithm is used appropriately, but other models such as GARCH, EGARCH, and EGARCH-ANN need continuous model calibration and validation so they fit the data and reality very well up to the desired accuracy. …”
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Application of Full Vector Deep Learning in Bearing Fault Diagnosis by Chen Chaoyu, Chen Lei, Zhang Wang, Han Jie

“…Then,a full-vector deep neural network is built on this basis,combining sparse coding and de-noising coding algorithm,the fault features can be extracted automatically. …”
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Scheduling framework based on reinforcement learning in online-offline colocated cloud environment by Ling MA, Qiliang FAN, Ting XU, Guanchen GUO, Shenglin ZHANG, Yongqian SUN, Yuzhi ZHANG

“…Some reinforcement learning-based scheduling algorithms for cloud computing platforms barely considered one scenario or ignored the resource constraints of jobs and treated all machines as the same type, which caused low resource utilization or insufficient scheduling efficiency.To address the scheduling problems in online-offline colocated cloud environment, a framework named JobFusion was proposed.Firstly, an efficient resource partitioning scheme was built in the cloud computing platform supporting virtualization technology by integrating the hierarchical clustering method with connectivity constraints.Secondly, a graph convolutional neural network was utilized to embed the attributes of elastic dimension with various constraints and the jobs with various numbers, to capture the critical path information of workflow.Finally, existing high-performance reinforcement learning methods were integrated for scheduling jobs.According to the results of evaluation experiments, JobFusion improves the resource utilization by 39.86% and reduces the average job completion time by up to 64.36% compared with baselines.…”
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A New Hybrid Model Predictive Controller Design for Adaptive Cruise of Autonomous Electric Vehicles by Yuanhang Chen, Guodong Feng, Shaofang Wu, Xiaojun Tan

“…The driving modes are divided into following and cruising, and the MPC algorithm based on simplified dual neural network (SDNN) and proportional-integral-derivative (PID) based on single neuron (SN) are applied to the following mode and the cruising mode, respectively. …”
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Event-Trigger Reinforcement Learning-Based Coordinate Control of Modular Unmanned System via Nonzero-Sum Game by Yebao Liu, Tianjiao An, Jianguo Chen, Luyang Zhong, Yuhan Qian

“…With the help of the ET mechanism, the periodic communication mechanism of the system is avoided. The ET-critic neural network (NN) is used to approximate the performance index function, thus obtaining the ETRL coordinate control policy. …”
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Building Arabic Speech Recognition System Using HuBERT Model and Studying the Sources of Errors [Arabic] by Rima Sbih, Assef Jafar, Ali Kazem

“…This paper presents the development of a speech recognition system for the Arabic language that can handle continuous speech and a large number of words, independent of the speaker, using deep neural network models trained by self-supervised learning. …”
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