Enhancing practicality and efficiency of deepfake detection by Ismael Balafrej, Mohamed Dahmane

“…Furthermore, some key considerations were identified to significantly reduce the size of the core convolutional neural network. The experiment yielded competitive results when evaluated on two second-generation deepfake datasets, namely Celeb-DFv2 and DFDC, while requiring only a fraction of the typical computational cost and resources.…”
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Fast QTMT partition decision based on deep learning by Shuang PENG, Xiaodong WANG, Zongju PENG, Fen CHEN

“…Compared with the predecessor standards, versatile video coding (VVC) significantly improves compression efficiency by a quadtree with nested multi-type tree (QTMT) structure but at the expense of extremely high coding complexity.To reduce the coding complexity of VVC, a fast QTMT partition method was proposed based on deep learning.Firstly, an attention-asymmetric convolutional neural network was proposed to predict the probability of partition modes.Then, the fast decision of partition modes based on the threshold was proposed.Finally, the cost of coding performance and time was proposed to obtain the optimal threshold, and the threshold decision method was proposed.Experimental results at different levels show that the proposed method achieves an average time saving of 48.62%/52.93%/62.01% with the negligible BDBR of 1.05%/1.33%/2.38%.Such results demonstrate that the proposed method significantly outperforms other state-of-the-art methods.…”
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Machine Learning-Based Quantification of Lateral Flow Assay Using Smartphone-Captured Images by Anne M. Davis, Asahi Tomitaka

“…Despite its advantages as convenient and low-cost testing, it suffers from poor quantification capacity where only yes/no or positive/negative diagnostics are achieved. …”
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Effective Identification of Variety and Origin of Chenpi Using Hyperspectral Imaging Assisted with Chemometric Models by Hangxiu Liu, Youyou Wang, Yiheng Wang, Jingyi Wang, Hanqing Hu, Xinyi Zhong, Qingjun Yuan, Jian Yang

“…To overcome the inefficiency and high cost of conventional detection methods, this study proposed a nondestructive approach that integrates hyperspectral imaging (HSI) with deep learning to classify Chenpi varieties and their geographical origins. …”
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Comparisons of different deep learning-based methods on fault diagnosis for geared system by Bing Han, Xiaohui Yang, Yafeng Ren, Wanggui Lan

“…It will greatly reduce the maintenance cost of a mechanical system to identify the faulty state of the geared transmission system. …”
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A Deep Learning and Transfer Learning Approach for Vehicle Damage Detection by Lin Li, Koshin Ono, Chun-Kit Ngan

“…Then a convolutional neural network (CNN) model is built to classify whether or not the vehicles have damages. …”
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Design and Development of an Application for the Generation of Garment Patterns Based on Body Measurements Using CNN by Geraldine Curipaco, Jeiel Tarazona, Daniel Subauste

“…Ateliers specialize in making garments to the customer's measurements, so this process requires a high level of time, cost and personnel specialized in taking body measurements and pattern making. …”
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Adaptive clustering federated learning via similarity acceleration by Suxia ZHU, Binke GU, Guanglu SUN

“…In order to solve the problem of model performance degradation caused by data heterogeneity in the federated learning process, it is necessary to consider personalizing in the federated model.A new adaptively clustering federated learning (ACFL) algorithm via similarity acceleration was proposed, achieving adaptive acceleration clustering based on geometric properties of local updates and the positive feedback mechanism during clients federated training.By dividing clients into different task clusters, clients with similar data distribution in the same cluster was cooperated to improve the performance of federated model.It did not need to determine the number of clusters in advance and iteratively divide the clients, so as to avoid the problems of high computational cost and slow convergence speed in the existing clustering federation methods while ensuring the performance of models.The effectiveness of ACFL was verified by using deep convolutional neural networks on commonly used datasets.The results show that the performance of ACFL is comparable to the clustered federated learning (CFL) algorithm, it is better than the traditional iterative federated cluster algorithm (IFCA), and has faster convergence speed.…”
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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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Skip-Connected CNN Exploiting BNN Surrogate for Antenna Modelling by Yubo Tian, Jinlong Sun, Zhiwei Zhu

“…However, the high computational cost of calling the full-wave EM simulation hundreds or thousands of times makes it unacceptable in terms of design time. …”
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Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders by Anjali S, Don S

“…Our adaptive thresholding technique leverages reconstruction cost, peak signal-to-noise ratio (PSNR) and frame brightness for optimal threshold identification, enhancing adaptability to different scenarios. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…Multiple ML models were constructed and optimized, with the convolutional neural network (CNN) model achieving the highest prediction accuracy at 99%. …”
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A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials by Shayan Dodge, Sami Barmada, Alessandro Formisano

“…The key advantage of this new architecture is the ability to refine predictions through multiple lightweight PINN blocks to achieve accurate results with lower computational cost and less architectural complexity than more advanced neural networks like Recurrent Neural Networks or Convolutional Neural Networks. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…But most of them had the time cost exceeding 80ms, making them could not perform real-time calculations. …”
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Advanced investing with deep learning for risk-aligned portfolio optimization. by Minh Duc Nguyen

“…Future work may include other asset classes, transaction cost modeling, and dynamic rebalancing. Combining deep learning with macroeconomic or alternative data could also improve forecasting and portfolio outcomes.…”
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An attack detection method based on deep learning for internet of things by Yihan Yu, Yu Fu, Taotao Liu, Kun Wang, Yishuai An

“…Firstly, a genetic algorithm is used for feature selection; secondly, a cost-sensitive function is employed to address the scarcity of attack traffic in IoT; and finally, a combination of Convolutional Neural Networks and Long Short Term Memory Network is utilized to extract spatiotemporal information from the network. …”
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Advancements and Challenges in Character Recognition: A Comparative Analysis of CNN and Deep Learning Approaches by Yang Ximin

“…This paper provides a comprehensive review of character recognition technologies, focusing on the application of Convolutional Neural Networks (CNN) and deep learning methodologies. …”
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Optimizing Sensor Placement for Event Detection: A Case Study in Gaseous Chemical Detection by Priscile Fogou Suawa, Christian Herglotz

“…Effective algorithms and well-planned sensor locations are required for reliable results. Using deep convolutional neural networks (DCNNs) and decision tree (DT) methods, we implemented and tested detection models on a public dataset of chemical substances collected at five locations. …”
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Flow Visualization In Closed Loop Pulsating Heat Pipe (CLPHP) Using Deep Learning Techniques by N Santhi Sree, P Bhramara, Medidi Veerabhadra Rao, Anthani Kamala Priya, G Avinash, K Sindhu

“…YOLOv5 based Convolutional Neural Network (CNN) is used to identify and classify the flow in CLPHP. …”
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An integrated approach for advanced vehicle classification. by Rui Liu, Shiyuan Wen, Yufei Xing

“…The DWAN introduces a four-level discrete wavelet transform in the convolutional neural network architecture and combines it with Convolutional Block Attention Module (CBAM) to efficiently capture multiscale feature information. …”
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