MOLUNGN: a multi-omics graph neural network for biomarker discovery and accurate lung cancer classification by Daifeng Zhang, Daifeng Zhang, Daifeng Zhang, Guoqiang Bian, Guoqiang Bian, Guoqiang Bian, Yuanbin Zhang, Yuanbin Zhang, Jiadong Xie, Jiadong Xie, Chenjun Hu, Chenjun Hu, Chenjun Hu

“…We sought to investigate molecular mechanisms underlying stage-wise lung cancer progression and identify pivotal stage-specific biomarkers to support precise cancer staging classification.MethodsWe developed a novel multi-omics integrative model, named the Multi-Omics Lung Cancer Graph Network (MOLUNGN), based on Graph Attention Networks (GAT). …”
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Evaluation of stroke sequelae and rehabilitation effect on brain tumor by neuroimaging technique: A comparative study. by Xueliang Guo, Lin Sun

“…The Bra2020 dataset is utilized to assess the accuracy of the proposed deep learning neural network. By employing skip connections, the model effectively integrates the high-resolution features from the encoder with the up-sampling features from the decoder, thereby increasing the model's sensitivity to 3D spatial characteristics. …”
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Evaluation Method of Total Organic Carbon Content in Shale Based on Stacking Algorithm Ensemble Learning by SONG Yanjie, LIU Yingjie, TANG Xiaomin, ZHANG Zhaoqian

“…The results show that the ensemble learning model based on Stacking algorithm has the highest prediction accuracy for total organic carbon content compared with decision tree model, support vector regression machine model, BP neural network model, and improved ΔlgR method. Therefore, the ensemble learning model based on Stacking algorithm is the most effective method for calculating the total organic carbon content in the study area, which lays the foundation for accurately evaluating the hydrocarbon generation potential of shale oil reservoirs, ensuring efficient exploitation and resource utilization of shale oil reservoirs.…”
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Based on Regular Expression Matching of Evaluation of the Task Performance in WSN: A Queue Theory Approach by Jie Wang, Kai Cui, Kuanjiu Zhou, Yanshuo Yu

“…Finally, based on the queuing model, the sensor networks of task scheduling dynamic performance are evaluated. …”
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An Efficient Method for Lung Lesions Classification Using Automatic Vascularization Evaluation on Color Doppler Ultrasound by Roxana Rusu-Both, Adrian Satmari, Romeo-Ioan Chira, Alexandra Chira, Camelia Avram

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Digital Technology in Cultural Heritage: Construction and Evaluation Methods of AI-Based Ethnic Music Dataset by Dayang Chen, Na Sun, Jong-Hoon Lee, Changman Zou, Wang-Su Jeon

“…For music generation, a Generative Adversarial Network (GAN) model yielded a quality score of 7.8/10 and a Fréchet Audio Distance (FAD) of 0.32. …”
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Security Evaluation of Provably Secure ECC-Based Anonymous Authentication and Key Agreement Scheme for IoT by Kisung Park, Myeonghyun Kim, Youngho Park

“…We conducted a security analysis using the extended Canetti and Krawczyk (eCK) model, which is widely employed in security evaluations. This model considers scenarios where an attacker has complete control over the network, including the ability to intercept, modify, and delete messages, while also accounting for the potential exposure of ephemeral private keys. …”
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Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography. by MinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, InChan Hwang, Judy Wawira Gichoya, Aimilia Gastounioti, Imon Banerjee, Laleh Seyyed-Kalantari, Hari Trivedi

“…Full-field digital mammograms from women aged 18 years or older were used to create positive and negative patches with the patches matched based on size, location, patient demographics, and imaging features. Several convolutional neural network (CNN) architectures were tested, with ResNet152V2 demonstrating the best performance. …”
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Malaria Cell Image Classification Using Compact Deep Learning Architectures on Jetson TX2 by Adán-Antonio Alonso-Ramírez, Alejandro-Israel Barranco-Gutiérrez, Iris-Iddaly Méndez-Gurrola, Marcos Gutiérrez-López, Juan Prado-Olivarez, Francisco-Javier Pérez-Pinal, J. Jesús Villegas-Saucillo, Jorge-Alberto García-Muñoz, Carlos-Hugo García-Capulín

“…The model was optimized for feature extraction and classification accuracy, achieving 97.72% accuracy, and evaluated using precision, recall, and F1-score metrics and execution time. …”
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Density Logging Curve Reconstruction Method Based on Bayesian-Optimized CNN-LSTM by LI Hongxi, CHEN Mingjiang, ZHANG Xiankun, YANG Bo, ZHAO Bin, LI Xiansheng, WANG Huanhuan

“…To address this issue, this study proposes a density logging curve reconstruction method based on Bayesian-optimized convolutional neural networks (CNN) combined with long short-term memory networks (LSTM). …”
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TAL-SRX: an intelligent typing evaluation method for KASP primers based on multi-model fusion by Xiaojing Chen, Xiaojing Chen, Jingchao Fan, Jingchao Fan, Shen Yan, Longyu Huang, Longyu Huang, Longyu Huang, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

“…Then, a hybrid neural network is constructed by combining ANN and LSTM to capture nonlinear relationships and extract complex features, while the Transformer algorithm is introduced to capture global dependencies in high-dimensional feature space. …”
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An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease by Steffi Rajan, Suresh Ponnan

“…The current study introduces the ESAE-ODNN, an improved stacked autoencoder-aided optimised deep neural network, as a new way to predict DED using feature selection (FS), feature extraction (FE), and classification. …”
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Product Image Generation Method Based on Morphological Optimization and Image Style Transfer by Aimin Zhou, Xinle Wang, Yujin Huang, Weitang Wang, Shutao Zhang, Jinyan Ouyang

“…Then, an automobile front-end image dataset is constructed, and a generative adversarial network model is trained. Using the aforementioned product form scheme as the content image and selecting automobile front-end images from the market as the target style image, the content features and style features are extracted by the encoder and input into the generator to generate style-transferred images. …”
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Super-resolution of 3D medical images by generative adversarial networks with long and short-term memory and attention by Qiong Zhang, Yiliu Hang, Fang Wu, Shentao Wang, Yue Hong

“…Abstract Since 3D medical imaging data is a string of sequential images, there is a strong correlation between consecutive images. Deep convolutional networks perform well in extracting spatial features, but are less capable for processing sequence data compared to recurrent convolutional networks. …”
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Evaluation on formation rate of Pleurotus eryngii primordium under different humidity conditions by computer vision by ZHOU Jun, DING Wenjie, ZHU Xuejun, CAO Junyi, NIU Xueming

“…A primordium quantity neural network prediction model was established based on back-propagation neural network in which matching quantity of primordium seeds was considered as input, the actual quantity of primordium as output. …”
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Identification and evaluation of metabolic mRNAs and key miRNAs in colorectal cancer liver metastasis by Guanxuan Chen, Shiwen Wang, Meng Zhang, Wenna Shi, Ruoyu Wang, Wanqi Zhu

“…We screened the featured genes using a machine-learning technique, followed by an evaluation of their diagnostic potential for CRC liver metastasis. …”
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Face Mesh for Dementia Detection: Evaluating Data Augmentation in Deep Learning and Traditional Machine Learning by Chuheng Zheng, Mondher Bouazizi, Taichi Okunishi, Tomoaki Ohtsuki, Momoko Kitazawa, Toshiro Horigome, Taishiro Kishimoto

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Evaluating the impact of deep learning approaches on solar and photovoltaic power forecasting: A systematic review by Oussama Khouili, Mohamed Hanine, Mohamed Louzazni, Miguel Angel López Flores, Eduardo García Villena, Imran Ashraf

“…This review specifically aims to identify the DL architectures employed, preprocessing and feature engineering techniques used, the input features leveraged, evaluation metrics applied, and the persistent challenges in this field. …”
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A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study by Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

“…Within a data-driven, clinical domain–guided framework to select the best set of features among cancer treatments, chronic health conditions, and socioenvironmental factors, we developed a 2-stage feature selection algorithm, that is, a multimetric, majority-voting filter and a deep dropout neural network, to dynamically and automatically select the best set of features for each behavioral outcome. …”
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Assessment of Scientific Creative-Potential by Near-Infrared Spectroscopy Using Brain-Network-Based Deep-Fuzzy Classifier by Sayantani Ghosh, Amit Konar, Atulya K. Nagar

“…The proposed method involves three key stages: i) construction of brain connectivity networks using Wavelet Transform Coherence (WTC), ii) abstraction and analysis of three node-based network features, and iii) classification of abstracted features into five degrees of creative potential by a novel Enhanced Graph Convolution Induced Type-2 Fuzzy Classifier (EGCIFC). …”
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