Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer by Sandeep Dwarkanth Pande, Pala Kalyani, S Nagendram, Ala Saleh Alluhaidan, G Harish Babu, Sk Hasane Ahammad, Vivek Kumar Pandey, G Sridevi, Abhinav Kumar, Ebenezer Bonyah

“…This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchical Fusion Convolutional Neural Networks (HFCNN), to assess their effectiveness in liver cancer segmentation. …”
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Empirical analysis of control models for different converter topologies from a statistical perspective by Hole Shreyas Rajendra

“…A wide variety of such models are proposed by researchers, that include, but are not limited to, bioinspired techniques for rating selection, Neural Network based models for load-based component selection, etc. …”
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Effect of Molarity of Sodium Hydroxide on the Strength Behavior of Fiber-Reinforced Geopolymer Concrete Exposed to Elevated Temperature by Abbasali Saffar, Mohammad Ehsanifar, Seyed Mohammad Mirhoseini, Mohammad Javad Taheri Amiri

“…Beside, post-fire strength of FRGPC was predicted using artificial neural network (ANN) and support vector machines (SVM) with the integration of water cycle algorithm (WCA). …”
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Improving Road Semantic Segmentation Using Generative Adversarial Network by Arnick Abdollahi, Biswajeet Pradhan, Gaurav Sharma, Khairul Nizam Abdul Maulud, Abdullah Alamri

“…Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing provides. …”
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Analysis of Gas Pipeline Failure Factors Based on the Novel Bayesian Network by Machine Learning Optimization by Shuangqing Chen, Shun Zhou, Zhe Xu, Yongbin Liu, Bing Guan, Xiaoyu Jiang, Wencheng Li

“…Secondly, by establishing a neural network model, the value of the CPT of the Bayesian network is determined. …”
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Impact of lens autofluorescence and opacification on retinal imaging by Frank G Holz, Maximilian Pfau, Monika Fleckenstein, Raffael Liegl, Geena C Rennen, Marc Vaisband, Jan Hasenauer

“…A regression model for predicting image quality was developed using a convolutional neural network (CNN). Correlation analysis was conducted to assess the association of lens scores, with retinal image quality derived from human or CNN annotations.Results Retinal image quality was generally high across all imaging modalities (IR (8.25±1.99) >GAF >BAF (6.6±3.13)). …”
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Research on water and fertilizer irrigation system of tea plantation by Xuetao Jia, Ying Huang, Yanhua Wang, Daozong Sun

“…The principal component factor is extracted and input into the back propagation neural network to judge the quality of the tea. The number of principal component factors of image information and spectral information is set to six and three; the overall recognition rate reached 97.8%. …”
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Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection by Rong Pang, Yan Yang, Aiguo Huang, Yan Liu, Peng Zhang, Guangwu Tang

“…Although the Faster Region-based Convolutional Neural Network (Faster R-CNN) model has obvious advantages in defect recognition, it still cannot overcome challenging problems, such as time-consuming, small targets, irregular shapes, and strong noise interference in bridge defect detection. …”
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Enhanced Disc Herniation Classification Using Grey Wolf Optimization Based on Hybrid Feature Extraction and Deep Learning Methods by Yasemin Sarı, Nesrin Aydın Atasoy

“…The proposed approach begins with feature extraction using ResNet50, a deep convolutional neural network known for its robust feature representation capabilities. …”
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Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach by Junwei Ma, Xiaoxu Niu, Huiming Tang, Yankun Wang, Tao Wen, Junrong Zhang

“…The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. …”
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Analysis of Feature Extraction and Anti-Interference of Face Image under Deep Reconstruction Network Algorithm by Jin Yang, Yuxuan Zhao, Shihao Yang, Xinxin Kang, Xinyan Cao, Xixin Cao

“…To explore the anti-interference performance of convolutional neural network (CNN) reconstructed by deep learning (DL) framework in face image feature extraction (FE) and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the random gradient descent (SGD) and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild (LFW) face database. …”
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Violence Detection From Industrial Surveillance Videos Using Deep Learning by Hamza Khan, Xiaohong Yuan, Letu Qingge, Kaushik Roy

“…The lightweight convolutional neural network (CNN) model initially identifies individuals in the video stream to minimize the processing of irrelevant frames. …”
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Evaluating GRU Algorithm and Double Moving Average for Predicting USDT Prices: A Case Study 2017-2024 by RAHMAT, Munirul ula, Zara Yunizar

“…GRU, a deep learning-based recurrent neural network, processes sequential data using a gating mechanism, making it effective for capturing short-term price dynamics. …”
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Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches by Kamal Hossain Nahin, Jamal Hossain Nirob, Akil Ahmad Taki, Md Ashraful Haque, Narinderjit Sawaran SinghSingh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Hanaa A. Abdallah, Abdelhamied A. Ateya, Ahmed A. Abd El-Latif

“…Leveraging a meta learner-based stacked generalization ensemble strategy, this study integrates classical machine learning techniques with an optimized multi-feature stacked ensemble to predict antenna properties with greater accuracy. Specifically, a neural network is applied as a base learner for predicting antenna parameters, resulting in increased predictive performance, achieving R², EVS, MSE, RMSE, and MAE values of 0.96, 0.998, 0.00842, 0.00453, and 0.00999, respectively. …”
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CNN-Based Object Recognition and Tracking System to Assist Visually Impaired People by Fahad Ashiq, Muhammad Asif, Maaz Bin Ahmad, Sadia Zafar, Khalid Masood, Toqeer Mahmood, Muhammad Tariq Mahmood, Ik Hyun Lee

“…For object detection and recognition, a deep Convolution Neural Network (CNN) model is employed with an accuracy of 83.3%, whereas the dataset contains more than 1000 categories. …”
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Multitask Learning-Based Pipeline-Parallel Computation Offloading Architecture for Deep Face Analysis by Faris S. Alghareb, Balqees Talal Hasan

“…Deep Neural Networks (DNNs) have been widely adopted in several advanced artificial intelligence applications due to their competitive accuracy to the human brain. …”
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Correlation-guided decoding strategy for low-resource Uyghur scene text recognition by Miaomiao Xu, Jiang Zhang, Lianghui Xu, Wushour Silamu, Yanbing Li

“…Specifically, (1) CGDS employs a hybrid encoding strategy that combines Convolutional Neural Network (CNN) and Transformer. This hybrid encoding effectively leverages the advantages of both methods: On one hand, the convolutional properties and shared weight mechanism of CNN allow for efficient extraction of local features, reducing dependency on large datasets and minimizing errors caused by similar characters. …”
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Corrosion inhibition effects of eco-friendly clarithromycin molecules on aluminium in hydrochloric acid solution via experimental, theoretical and optimization approach by O.D. Onukwuli, I.A. Nnanwube, F.O. Ochili, M. Omotioma, J.I. Obibuenyi

“…Optimization by RSM gave an optimum IE of 85.43 %, from which artificial neural network (ANN) predicted improved inhibition efficiency. …”
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Characterization of G2/M checkpoint classifier for personalized treatment in uterine corpus endometrial carcinoma by Yiming Liu, Yusi Wang, Shu Tan, Xiaochen Shi, Jinglin Wen, Dejia Chen, Yue Zhao, Wenjing Pan, Zhaoyang Jia, Chunru Lu, Ge Lou

“…Ultimately, an artificial neural network (ANN) and machine learning were employed to develop the G2MC subtypes classifier. …”
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Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation by Zhen Wu, Runqing Ye, Jue Huang, Xiaolin Fu, Yao Chen

“…Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. …”
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