Protein structural domain-disease association prediction based on heterogeneous networks by Jingpu Zhang, Lianping Deng, Lei Deng

“…Finally, we train a binary classifier based on the XGBOOST (eXtreme Gradient Boosting) algorithm to predict the potential associations between domains and diseases. …”
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Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data by Trushal Sardhara, Christian J Cyron, Martin Ritter, Roland C Aydin

“…To achieve valid 3D reconstructions from FIB tomography data, semantic segmentation of these images using machine learning-based methods is often beneficial. However, supervised machine learning requires a large amount of training data and ground truth, which is challenging because FIB tomography is a destructive technique. …”
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Monitoring pilot trainees’ cognitive control under a simulator-based training process with EEG microstate analysis by Mengting Zhao, Wenjun Jia, Sion Jennings, Andrew Law, Alain Bourgon, Chang Su, Marie-Hélène Larose, Hugh Grenier, David Bowness, Yong Zeng

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Fitting Method of Optimal Energy-Running Time Curve Based on Train Operation Data of an Urban Rail Section by Lianbo Deng, Hongda Mei, Wenliang Zhou, Enwei Jing

“…Due to the complexity of the operation control of urban rail transit and diversity requirements for section running time standards, based on actual train operation data, this paper proposes a curve fitting method to find the interrelation between running time and energy consumption. …”
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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

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VILLAGE WEBSITE DEVELOPMENT TRAINING AS A DIGITAL-BASED SERVICE OPTIMIZATION IN ALANG-ALANG VILLAGE, TRAGAH, BANGKALAN by Erna Setijaningrum, Muhammad Ghofiqi, Luqman Purwanto, Rendy Billiyanto, Faried Effendy

“…The event was held in person at the Alang-Alang Village Office, with the main focus on introducing website features designed to improve transparency, efficiency, and digital-based services. …”
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Application Value of an AI-based Imaging Feature Parameter Model 
for Predicting the Malignancy of Part-solid Pulmonary Nodule by Mingzhi LIN, Yiming HUI, Bin LI, Peilin ZHAO, Zhizhong ZHENG, Zhuowen YANG, Zhipeng SU, Yuqi MENG, Tieniu SONG

“…This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). …”
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Dual-Path Adversarial Denoising Network Based on UNet by Jinchi Yu, Yu Zhou, Mingchen Sun, Dadong Wang

“…We propose a novel three-module architecture for image denoising, comprising a generator, a dual-path-UNet-based denoiser, and a discriminator. The generator creates synthetic noise patterns to augment training data, while the dual-path-UNet denoiser uses multiple receptive field modules to preserve fine details and dense feature fusion to maintain global structural integrity. …”
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Improvement of signal detection based on using machine learning by Bassam Abd

“…The deep learning algorithm has become a very attractive tool for distinguishing between signal and noise. Learning and training are the two important steps in designing any deep learning system. …”
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CNN‐based off‐angle iris segmentation and recognition by Ehsaneddin Jalilian, Mahmut Karakaya, Andreas Uhl

“…Within the framework of these approaches, a series of experiments is carried out to determine whether (i) improving the segmentation outputs and/or correcting the output iris images before or after the segmentation can compensate for some off‐angle distortions, (ii) a CNN trained on frontal eye images is capable of detecting and extracting the learnt features on the corrected images, or (iii) the generalisation capability of the network can be improved by training it on iris images of different gaze angles. …”
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FedeAMR-CFF: A Federated Automatic Modulation Recognition Method Based on Characteristic Feature Fine-Tuning by Meng Zhang, Jiankun Ma, Zhenxi Zhang, Feng Zhou

“…Moreover, when terminal devices independently train models solely based on their local data, the model performance often suffers due to issues like data distribution disparities and insufficient training samples. …”
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Research on Binary Mixed VOCs Gas Identification Method Based on Multi-Task Learning by Haixia Mei, Ruiming Yang, Jingyi Peng, Keyu Meng, Tao Wang, Lijie Wang

“…Traditional volatile organic compounds (VOCs) detection models separate component identification and concentration prediction, leading to low feature utilization and limited learning in small-sample scenarios. …”
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Crack Detection, Classification, and Segmentation on Road Pavement Material Using Multi-Scale Feature Aggregation and Transformer-Based Attention Mechanisms by Arselan Ashraf, Ali Sophian, Ali Aryo Bawono

“…This paper introduces a novel approach to pavement material crack detection, classification, and segmentation using advanced deep learning techniques, including multi-scale feature aggregation and transformer-based attention mechanisms. …”
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Research on Modulation Format Recognition for Underwater Visible Light Communication based on BiGRU by GAO Wendi, WANG Yunkai, XU Chi, XU Zengyi, CHI Nan

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Enhancing sound-based classification of birds and anurans with spectrogram representations and acoustic indices in neural network architectures by Fábio Felix Dias, Moacir Antonelli Ponti, Rosane Minghim

“…To leverage the best from these models, this work investigates different audio input representations, particularly spectrogram-based and acoustic indices, which are pre-processed features extracted from audio sources. …”
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Min3GISG: A Synergistic Feature Selection Framework for Industrial Control System Security with the Integrating Genetic Algorithm and Filter Methods by Saiprasad Potharaju, Swapnali N. Tambe, G. Madhukar Rao, M. V. V. Prasad Kantipudi, Kalyan Devappa Bamane, Mininath Bendre

“…These features were used to train classification models (Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM)) with a 70:30 train-test split and tenfold cross-validation. …”
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Extraction and Recognition of Color Feature in true Color Images Using Neural Network Based on Colored Histogram Technique by Orjuwan Aljawadi

“…In this research, a neural network using backpropagation (BPNN) algorithm was trained and learned to work as the cone cells in human eyes to recognize the three fundamental cells’ colors and hues, as the neural network showed good results in training and testing the color feature  it was trained and learned again to recognize two nature scenes images ; Red sunset and Blue sky images where both scenes images contain color interaction and different hues such as red-orange and blue-violet. …”
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A Dual-Perspective Self-Supervised IoT Intrusion Detection Method Based on Topology Reconstruction and Feature Perturbation by Ruisheng Li, Huimin Shen, Qilong Zhang

“…As a critical technology for securing IoT, intrusion detection systems aim to identify potential threats by analyzing network traffic features. Yet, traditional models struggle to capture the complex topological structures in IoT environments, and their training often relies heavily on large amounts of labeled data, making them unsuitable for IoT settings where massive data is continually generated. …”
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A Principal Component Analysis-Based Feature Optimization Network for Few-Shot Fine-Grained Image Classification by Meijia Wang, Boyuan Zheng, Guochao Wang, Junpo Yang, Jin Lu, Weichuan Zhang

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Short-term load forecasting based on multi-frequency sequence feature analysis and multi-point modified FEDformer by Kaiyuan Hou, Xiaotian Zhang, Junjie Yang, Jiyun Hu, Guangzhi Yao, Jiannan Zhang

“…Finally, a point-by-point forecasting method based on a tree model generates multi-point load prediction results by training multiple LightGBM models. …”
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