A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering by Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He

“…To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. …”
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A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity by Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao

“…Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. …”
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Electricity Theft Detection Using Machine Learning in Traditional Meter Postpaid Residential Customers: A Case Study on State Electricity Company (PLN) Indonesia by Alief Pascal Taruna, Galih Arisona, Dwi Irwanto, Arif Bijak Bestari, Wildan Juniawan

“…Various classification models, including Decision Tree, Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, and Deep Neural Network, were evaluated, with Random Forest achieving the best performance across simulations. …”
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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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Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke by Ye Jin Jeon, Hong Gee Roh, Sumin Jung, Hyun Yang, Hee Jong Ki, Jeong Jin Park, Taek-Jun Lee, Na Il Shin, Ji Sung Lee, Jin Tae Kwak, Hyun Jeong Kim

“…We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. …”
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Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model by Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi

“…We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. …”
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Correction of CAMS PM₁₀ Reanalysis Improves AI-Based Dust Event Forecast by Ron Sarafian, Sagi Nathan, Dori Nissenbaum, Salman Khan, Yinon Rudich

“…To evaluate the contribution, we train a deep neural network to predict city-scale dust events (0–72 h) over the Balkans using PM₁₀ fields. …”
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Enhancing feature selection for multi-pose facial expression recognition using a hybrid of quantum inspired firefly algorithm and artificial bee colony algorithm by Mu Panliang, Sanjay Madaan, Siddiq Ahmed Babikir Ali, Gowrishankar J., Ali Khatibi, Anas Ratib Alsoud, Vikas Mittal, Lalit Kumar, A. Johnson Santhosh

“…The evaluated features are utilized for classifying face expressions by utilizing the deep neural network model, ResNet-50. The presented FER system has been tested using multi-pose facial expression benchmark datasets, including RaF (Radboud Faces) and KDEF (Karolinska Directed Emotional Faces). …”
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PCMMD: A Novel Dataset of Plasma Cells to Support the Diagnosis of Multiple Myeloma by Caio L. B. Andrade, Marcos V. Ferreira, Brenno M. Alencar, Jorge L. S. B. Filho, Matheus A. Guimaraes, Iarley Porto Cruz Moraes, Tiago J. S. Lopes, Allan S. dos Santos, Mariane M. dos Santos, Maria I. C. S. e Silva, Izabela M. D. R. P. Rosa, Gilson C. de Carvalho, Herbert H. M. Santos, Márcia M. L. Santos, Roberto Meyer, Luciana M. P. B. Knop, Songeli M. Freire, Ricardo A. Rios, Tatiane N. Rios

“…We also share a Deep Neural Network model, as a benchmark, trained to identify and count plasma and non-plasma cells automatically. …”
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Comparative study on deep and machine learning approaches for predicting wind pressures on tall buildings by Mosbeh R. Kaloop, Abidhan Bardhan, Pijush Samui, Jong Wan Hu, Mohamed Elsharawy

“…Two deep learning methods viz deep belief network (DBN) and deep neural network (DNN), and five machine learning methods namely feedforward neural network, extreme learning machine, weighted extreme learning machine, random forest, and gradient boosting machine were evaluated, and compared in predicting the design wind pressures on tall buildings. …”
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Sistem Identifikasi Pembicara Berbahasa Indonesia Menggunakan X-Vector Embedding by Alim Misbullah, Muhammad Saifullah Sani, Husaini, Laina Farsiah, Zahnur, Kikye Martiwi Sukiakhy

“…Selanjutnya, dibangun empat model dengan cara mengombinasikan dua jenis konfigurasi MFCC dan dua jenis arsitektur Deep Neural Network (DNN) yang memanfaatkan Time Delay Neural Network (TDNN). …”
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Monitoring changes of forest height in California by Samuel Favrichon, Jake Lee, Yan Yang, Yan Yang, Ricardo Dalagnol, Ricardo Dalagnol, Fabien Wagner, Le Bienfaiteur Sagang, Le Bienfaiteur Sagang, Sassan Saatchi, Sassan Saatchi, Sassan Saatchi

“…Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. …”
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A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification by Cheng-Jiang Wei, Yan Tang, Yang-Bai Sun, Tie-Long Yang, Cheng Yan, Hui Liu, Jun Liu, Jing-Ning Huang, Ming-Han Wang, Zhen-Wei Yao, Ji-Long Yang, Zhi-Chao Wang, Qing-Feng Li

“…To address privacy concerns, we utilized a lightweight deep neural network suitable for hospital deployment. The final model achieved an accuracy of 85.71% for MPNST diagnosis in the validation cohort and 84.75% accuracy in the independent test set, outperforming another classic two-step model. …”
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Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. …”
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A Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects by Ling Zhao, Xun Lv, Lili Zhu, Binyan Luo, Hang Cao, Jiahao Cui, Haifeng Li, Jian Peng

“…The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Objectives: This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. …”
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AI-assisted diagnosis of vulvovaginal candidiasis using cascaded neural networks by Zhongxiao Wang, Ruliang Wang, Haichun Guo, Qiannan Zhao, Huijun Ren, Jumin Niu, Ying Wang, Wei Wu, Bingbing Liang, Xin Yi, Xiaolei Zhang, Shiqi Xu, Xianling Dong, Liqun Wang, Qinping Liao

“…The microscope images collected from each slide effectively represent the slide.IMPORTANCEA cascaded deep neural network model was developed for slide-level diagnosis of vulvovaginal candidiasis (VVC), demonstrating superior diagnostic accuracy compared to experts. …”
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Presenting a Prediction Model for CEO Compensation Sensitivity using Meta-heuristic Algorithms (Genetics and Particle Swarm) by Saeed Khaljastani, Habib Piri, Reza Sotoudeh

“…Results The results demonstrate the superiority of the deep neural network model in terms of the coefficient of determination and MSE index. …”
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In-vivo high-resolution χ-separation at 7T by Jiye Kim, Minjun Kim, Sooyeon Ji, Kyeongseon Min, Hwihun Jeong, Hyeong-Geol Shin, Chungseok Oh, Robert J. Fox, Ken E. Sakaie, Mark J. Lowe, Se-Hong Oh, Sina Straub, Seong-Gi Kim, Jongho Lee

“…To address these challenges, we developed a novel deep neural network, R2PRIMEnet7T, designed to convert a 7T R2* map into a 3T R2′ map. …”
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Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy by Mingyu Yang, Jianli Ma, Chengcheng Zhang, Liming Zhang, Jianyu Xu, Shilong Liu, Jian Li, Jiabin Han, Songliu Hu

“…When both radiomic features and clinical information were combined in a model based on RF, the AUC improved slightly to 0.611, with a 95% confidence interval of 0.566-0.652.ConclusionsIn this study, a deep neural network-based multimodal fusion model improved the prediction performance compared to traditional radiomics. …”
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