Comparative Analysis of Vanilla CNN and Transfer Learning Models for Glaucoma Detection by Brendan Ubochi, Abayomi E. Olawumi, John Macaulay, Oyawoye I. Ayomide, Kayode F. Akingbade

“…The obtained results demonstrate the peculiarity of the dataset, its selectiveness of the most appropriate model, and the potential of deep neural networks (DNNs) as an effective screening tool for glaucoma, enabling prompt interventions, reducing healthcare costs, and helping optometrists make swift decisions.…”
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Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub by Thirunavukarasu Balasubramaniam, Wathsala Anupama Mohotti, Kenneth Sabir, Richi Nayak

“…Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. Our results show that incorporating averaging-based feature-engineered climate attributes significantly improves pasture yield predictions, with enhancements of up to 20.28%, 31.81%, and 31.11% across the three evaluation measures, RMSE, MAE, and R2, respectively. …”
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Estimation Model for Bread Quality Proficiency Using Fuzzy Weighted Relevance Vector Machine Classifier by Zainab N. Ali, Iman Askerzade, Saddam Abdulwahab

“…The results indicate that the proposed FWRVM-based classifier estimates the quality of the breads with 96.67% accuracy, 96.687% precision, 96.6% recall, and 96.6% F-measure within 8.96726 seconds processing time which is better than the compared Support vector machine (SVM), RVM, and Deep Neural Networks (DNN) classifiers.…”
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A convolutional autoencoder framework for ECG signal analysis by Ugo Lomoio, Patrizia Vizza, Raffaele Giancotti, Salvatore Petrolo, Sergio Flesca, Fabiola Boccuto, Pietro Hiram Guzzi, Pierangelo Veltri, Giuseppe Tradigo

“…Analysis of time varying signals may be done by using autoencoders (AEs) deep neural networks. AE specialized for signal data, named Convolutional Autoencoder (CAE), showed the best performances in the analysis of ECG signals.This paper presents a CAE-based framework for ECG signal analysis and anomaly identification. …”
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Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum by Mengze Li, Jiaqi Cai, Li Deng, Xiaopeng Li, Francesca Iacopi, Yang Yang

“…The integration of deep neural networks helps reduce the phase coupling between the frequency bands, enabling independent control of the OAM states. …”
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Multi-label classification with deep learning techniques applied to the B-Scan images of GPR by El Karakhi, Soukayna, Reineix, Alain, Guiffaut, Christophe

“…With the emergence of deep neural networks and with a learning phase on a large number of Bscan, it becomes possible to extract almost instantaneously the characteristics of GPR radar data. …”
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Impact of stain variation and color normalization for prognostic predictions in pathology by Siyu Lin, Haowen Zhou, Mark Watson, Ramaswamy Govindan, Richard J. Cote, Changhuei Yang

“…Abstract In recent years, deep neural networks (DNNs) have demonstrated remarkable performance in pathology applications, potentially even outperforming expert pathologists due to their ability to learn subtle features from large datasets. …”
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A two‐stage reactive power optimization method for distribution networks based on a hybrid model and data‐driven approach by Ghulam Abbas, Wu Zhi, Aamir Ali

“…Compared to traditional deep neural networks (DNNs) and convolutional neural networks (CNNs), the transformer network provides superior reactive power optimization results.…”
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Forecasting the Applied Deep Learning Tools in Enhancing Food Quality for Heart Related Diseases Effectively: A Study Using Structural Equation Model Analysis by Sunil L. Bangare, Deepali Virmani, Girija Rani Karetla, Pankaj Chaudhary, Harveen Kaur, Syed Nisar Hussain Bukhari, Shahajan Miah

“…The researchers have identified that critical algorithms like CART support the predictability of the disease by 93.3% whereas the conventional models possess vert less specificity. Furthermore, deep neural networks can be applied for analyzing and detecting heart failures effectively and supporting medical practitioners in making better and more critical clinical decisions making. …”
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Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols by Jinhwan Jang

“…In this context, the present study investigates machine learning techniques, including Random Forest, CatBoost, and Deep Neural Networks, for forecasting nighttime icing on rural highways in Korea. …”
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Time Complexity of Training DNNs With Parallel Computing for Wireless Communications by Pengyu Cong, Chenyang Yang, Shengqian Han, Shuangfeng Han, Xiaoyun Wang

“…Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. …”
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Oriented R-CNN With Disentangled Representations for Product Packaging Detection by Jiangyi Pan, Jianjun Yang, Yinhao Liu, Yijie Lv

“…In recent years, with the rise of deep neural networks, there has been significant progress in improving object detection accuracy. …”
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Using machine learning-based models for personality recognition by Fatemeh Mohades Deilami, Hossein Sadr, Mozhdeh Nazari

“…Among various deep neural networks, Convolutional Neural Networks (CNN) have demonstrated profound efficiency in natural language processing and especially personality detection. …”
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Improving Health Through Indoor Environmental Quality Monitoring: A Review of Data-Driven Models and Smart Sensor Innovations by Kidari Rachid, Tilioua Amine

“…Numerous cutting-edge deep learning techniques, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), and deep neural networks (DNNs), are incorporated into the hybrid framework. …”
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Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach by İlker Özgür Koska, Çağan Koska

“…Integrating the information in different MRI sequences and leveraging the high entropic capacity of deep neural networks, we built a 3D ROI-based custom CNN classifier for the automatic prediction of MGMT methylation status of glioblastoma in multi-parametric MRI. …”
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Cross‐ethnicity face anti‐spoofing recognition challenge: A review by Ajian Liu, Xuan Li, Jun Wan, Yanyan Liang, Sergio Escalera, Hugo Jair Escalante, Meysam Madadi, Yi Jin, Zhuoyuan Wu, Xiaogang Yu, Zichang Tan, Qi Yuan, Ruikun Yang, Benjia Zhou, Guodong Guo, Stan Z. Li

“…The biometrics community has achieved impressive progress recently due to the excellent performance of deep neural networks and the availability of large datasets. …”
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Automatic etiological classification of stroke thrombus digital photographs using a deep learning model by Álvaro Lucero-Garófano, Álvaro Lucero-Garófano, Alicia Aliena-Valero, Isabel Vielba-Gómez, Isabel Vielba-Gómez, Irene Escudero-Martínez, Irene Escudero-Martínez, Lluís Morales-Caba, Lluís Morales-Caba, Fernando Aparici-Robles, Fernando Aparici-Robles, Diana L. Tarruella Hernández, Diana L. Tarruella Hernández, Gerardo Fortea, Gerardo Fortea, José I. Tembl, José I. Tembl, Juan B. Salom, Juan B. Salom, José V. Manjón

“…A DL method was designed based on two deep neural networks for: (1) image segmentation and (2) image classification including clinical characteristics. …”
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Fully automatic reconstruction of prostate high-dose-rate brachytherapy interstitial needles using two-phase deep learning-based segmentation and object tracking algorithms by Mohammad Mahdi Moradi, Zahra Siavashpour, Soheib Takhtardeshir, Eman Showkatian, Ramin Jaberi, Reza Ghaderi, Bahram Mofid, Farzad Taghizadeh-Hesary

“…The whole process is divided into two phases using two different deep neural networks. First, BT needles segmentation was accomplished through a pix2pix Generative Adversarial Neural network (pix2pix GAN). …”
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Automated skin melanoma diagnostics based on mathematical model of artificial convolutional neural network by D. A. Gavrilov, E. I. Zakirov, E. V. Gameeva, V. Yu. Semenov, O. Yu. Aleksandrova

“…The development of computer vision technology has allowed the development of technical vision systems that allow detec on and classifi ca on of skin diseases with a quality that is comparable and in some cases exceeds the values a ained by man.In this paper, the authors propose an algorithm for the primary diagnosis of skin melanoma based on deep neural networks, achieving an accuracy of 91% for melanoma in dermatoscopic images. …”
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Few-shot Remote Sensing Imagery Recognition with Compositionality Inductive Bias in Hierarchical Representation Space by Shichao Zhou, Zhuowei Wang, Zekai Zhang, Wenzheng Wang, Yingrui Zhao, Yunpu Zhang

“…Different from the naive data-driven strategies mentioned above, we alternatively devote to delicate feature modeling by constraining the mapping behavior of deep neural networks. Specifically, we embed inductive bias of compositionality into hierarchical latent representation space, which operates on two aspects: 1) disentangled and reusable representation. …”
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