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

“…This paper proposes a skip-connected surrogate model based on convolutional neural network (CNN) and Bayesian neural network (BNN). …”
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Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders by Anjali S, Don S

“…Through the reconstruction of normal patterns and the computation of reconstruction error in relation to ground truth, convolutional autoencoders detect anomalies. Frames with errors above a threshold are flagged as abnormal. …”
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LPFFNet: Lightweight Prior Feature Fusion Network for SAR Ship Detection by Xiaozhen Ren, Peiyuan Zhou, Xiaqiong Fan, Chengguo Feng, Peng Li

“…In addition, the enhanced ghost convolution (EGConv) is used to generate more reliable gradient information. …”
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U-shape-based network for left ventricular segmentation in echocardiograms with contrastive pretraining by Zhengkun Qian, Tao Hu, Jianming Wang, Zizhong Yang

“…Additionally, we incorporate the Spatial and Channel reconstruction Convolution (SCConv) module through spatial and channel reconstruction during downsampling and replace the Binary Cross Entropy Loss (BCELoss) with Polynomial Loss (PolyLoss) to achieve superior segmentation performance. …”
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A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8 by Ye Zheng, Jun Yan, Junxia Meng, Ming Liang

“…Because of their low cost and ease of deployment, side-scan sonars is one of the most widely used underwater survey instruments. …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…Firstly, the FPC-F (Fast Parallel Cross-Fusion) module, which incorporates PConv, and the S-EMCP (Space-efficient Merging Convolution Pooling) module are designed in the backbone network to replace C2F and SPPF at various scale branches. …”
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Tuberculosis detection with customized CNN and oversampling techniques: a deep learning approach by B. H. Shekar, Shazia Mannan

“…In our work, we propose a customized CNN architecture having three convolution layers and three max pooling layers for accurately classifying the CXRs into TB-infected and normal classes. …”
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4D trajectory lightweight prediction algorithm based on knowledge distillation technique by Weizhen Tang, Jie Dai, Zhousheng Huang, Boyang Hao, Weizheng Xie

“…The student network adopts a Temporal Convolutional Network–LSTM (TCN–LSTM) design, integrating dilated causal convolutions and two LSTM layers for efficient temporal modeling. …”
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Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery by Lei Chen, TingTing Yang, ZhiQiang Wu, XinLong Li, YanZhen Lin, Yi Lian

“…This study integrates multispectral imagery with enhanced U-Net models (U-Net, U-Net++, U-Net3+) to achieve cost-effective large-scale CC estimation. These models are optimized by reordering the network output layers and enhancing feature fusion between convolutional and pooling operations. …”
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Multi-headed ensemble residual CNN: A powerful tool for fibroblast growth factor prediction by Naif Almusallam, Farman Ali, Harish Kumar, Tamim Alkhalifah, Fahad Alturise, Abdullah Almuhaimeed

“…These features were combined and analyzed using several deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), Gated Recurrent Units (GRU), and Multiheaded Ensemble Residual CNN (MERCNN). …”
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DS4NN: Direct training of deep spiking neural networks with single spike-based temporal coding by Maryam Mirsadeghi, Majid Shalchian, Saeed Reza Kheradpisheh

“…This shows that the proposed approach can make fast decisions with low-cost computation and high accuracy.…”
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Contaminant Transport Modeling and Source Attribution With Attention‐Based Graph Neural Network by Min Pang, Erhu Du, Chunmiao Zheng

“…In five synthetic case studies that involve varying monitoring networks in heterogeneous aquifers, aGNN is shown to outperform LSTM‐based (long‐short term memory) and CNN‐ based (convolutional neural network) methods in multistep predictions (i.e., transductive learning). …”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…This work aims to overcome the issues by proposing a lightweight, homogenous stacked deep ensemble model, termed Homogenous Stacking Temporal Convolutional Network with Nu-Support Vector Classifier (HSTCN-NuSVC), for activity classification. …”
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AI-Based Point Cloud Upsampling for Autonomous Driving Systems by Nicolás Salomón, Claudio A. Delrieux, Damián A. Morero, Leandro E. Borgnino

“…This highlights the viability and scalability of our approach in realizing cost-effective yet high-performance autonomous driving systems. …”
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Evaluating the performance of automated detection systems for long-term monitoring of delphinids in diverse marine soundscapes. by Ellen L White, Paul R White, Jonathan M Bull, Denise Risch, Susanna Quer, Suzanne Beck

“…There is an increasing reliance on passive acoustic monitoring (PAM) as a cost-effective method for monitoring cetaceans, necessitating robust and efficient automated tools for extracting species presence. …”
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A Systematic Review and Evaluation of Sustainable AI Algorithms and Techniques in Healthcare by Yehia Ibrahim Alzoubi, Ahmet E. Topcu, Ersin Elbasi

“…A comprehensive performance analysis is presented across five dimensions: energy consumption, latency, accuracy, complexity, and cost. The review highlights mLZW as promising for energy efficiency, complexity, and cost, OFA for low-latency deployment, and Hybrid Quantum Classical Optimization for diagnostic accuracy. …”
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EmotionNet-X: An Optimized CNN Architecture for Robust Facial Emotion Analysis by Syed Muhammad Aqleem Abbas, Qaisar Abbas, Syed Muhammad Naqi

“…Existing pretrained models suffer from high computational costs, limiting real-time IoT deployment. Deep Neural Networks (DNNs), particularly Convolutional Neural Networks (CNNs), are widely used for facial expression recognition (FER). …”
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