p-Norm Broad Learning for Negative Emotion Classification in Social Networks by Guanghao Chen, Sancheng Peng, Rong Zeng, Zhongwang Hu, Lihong Cao, Yongmei Zhou, Zhouhao Ouyang, Xiangyu Nie

“…Most existing methods are based on deep learning models, facing challenges such as complex structures and too many hyperparameters. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…Our results demonstrate that InceptionDTA outperforms various sequence-based, transformer-based, and graph-based deep learning approaches across warm-start, refined, and cold-start splitting settings. …”
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Research on Feature Extracted Method for Flutter Test Based on EMD and CNN by Hua Zheng, Zhenglong Wu, Shiqiang Duan, Jiangtao Zhou

“…The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. …”
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Multiple Morphological Constraints-Based Complex Gland Segmentation in Colorectal Cancer Pathology Image Analysis by Kun Zhang, JunHong Fu, Liang Hua, Peijian Zhang, Yeqin Shao, Sheng Xu, Huiyu Zhou, Li Chen, Jing Wang

“…In this project, we use deep learning to achieve stain separation by predicting the stain coefficient. …”
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Deep Temporal Clustering of Pathological Gait Patterns in Post-Stroke Patients Using Joint Angle Trajectories: A Cross-Sectional Study by Gyeongmin Kim, Hyungtai Kim, Yun-Hee Kim, Seung-Jong Kim, Mun-Taek Choi

“…The results demonstrate the effectiveness of end-to-end deep learning-based clustering, yielding significant performance improvements without the need for manual feature extraction. …”
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SC-ResNeXt: A Regression Prediction Model for Nitrogen Content in Sugarcane Leaves by Zihao Lu, Cuimin Sun, Junyang Dou, Biao He, Muchen Zhou, Hui You

“…Compared with four classical deep learning algorithms, SC-ResNeXt exhibited superior regression prediction performance. …”
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A comparative study on different machine learning approaches with periodic items for the forecasting of GPS satellites clock bias by Longjiang Song, Jiahao Liu, Leilei Wang, Ziyi Wang, Yibo Yuan

“…In recent applications, deep learning models have significantly improved handling time-series data. …”
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Foodborne Event Detection Based on Social Media Mining: A Systematic Review by Silvano Salaris, Honoria Ocagli, Alessandra Casamento, Corrado Lanera, Dario Gregori

“…Study variables included social media platforms, ML techniques (shallow and deep learning), and model performance, with a risk of bias assessed using the PROBAST tool. …”
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Evaluation of an enhanced ResNet-18 classification model for rapid On-site diagnosis in respiratory cytology by Wei Gong, Deep K. Vaishnani, Xuan-Chen Jin, Jing Zeng, Wei Chen, Huixia Huang, Yu-Qing Zhou, Khaing Wut Yi Hla, Chen Geng, Jun Ma

“…Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value. …”
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Attention-Based Multi-Learning Approach for Speech Emotion Recognition With Dilated Convolution by Samuel, Kakuba, Alwin, Poulose

“…The success of deep learning in speech emotion recognition has led to its application in resource-constrained devices. …”
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Rapid literature mapping on the recent use of machine learning for wildlife imagery by Nakagawa, Shinichi, Lagisz, Malgorzata, Francis, Roxane, Tam, Jessica, Li, Xun, Elphinstone, Andrew, Jordan, Neil R., O'Brien, Justine K., Pitcher, Benjamin J., Van Sluys, Monique, Sowmya, Arcot, Kingsford, Richard T.

“…Machine (especially deep) learning algorithms are changing the way wildlife imagery is processed. …”
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Telecom intelligent operation system based on big data grid by Yunfeng GUO, Heng CAI, Lei GE

“…Based on the advantages of telecom big data platform,the self-learning operation mode of artificial intelligence was utilized innovatively.The operational status of each IT system nodes were actively perceived by collecting and analyzing the massive log data of IT system.The influence,health and dependence of each IT system nodes were visualized by intelligent graph calculation and grid nebulae graph.The intelligent prediction of node failure was realized by Keras deep learning framework,and the big data grid intelligent operation system of telecom IT system was built.…”
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Wireless edge intelligence empowers smart society by Zhi WANG, Xing ZHANG, Wenbo WANG

“…To satisfy the technical requirements of eMBB,mMTC and uRLLC in 5G application scenarios,wireless edge intelligence sinks core capabilities of computing,caching and network to the edge of radio access network,as well as introduces technologies such as deep learning/artificial intelligence into the edge cloud,thereby enabling digital life,digital society and digital industry.Firstly,the concept,challenges and opportunities of wireless edge intelligence were clarified.Moreover,the platform deployment of wireless edge intelligence was analyzed.Finally,the possible future deployment directions of wireless edge intelligence were discussed.…”
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Effect of flipped classroom method on the reflection ability in nursing students in the professional ethics course; Solomon four-group design by Mohammad Rasool Khazaei, Eshagh Moradi, Azizeh Barry, Mohammad Hasan Keshavarzi, Akram Hashemi, Ghobad Ramezani, Atefeh Zabihi Zazoli, Farzaneh Farzadnia

“…Conclusion Considering that there are controversial issues in the course of professional ethics, this method can be effective in the field of deep learning of students.…”
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A pilot study on diabetes detection using handheld fundus camera and mobile app development by Hamada R. H. Al-Absi, Gilbert Njihia Muchori, Saleh Musleh, Syed Abdullah Basit, Mohammad Tariqul Islam, Younss Ait Mou, Tanvir Alam

“…Subsequently, we developed a deep learning model for early diagnosis of diabetes based on fundus image only. …”
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A robust adaptive meta-sample generation method for few-shot time series prediction by Chao Zhang, Defu Jiang, Kanghui Jiang, Jialin Yang, Yan Han, Ling Zhu, Libo Tao

“…Researchers can achieve effective TSP based on the deep learning model and a large amount of data. However, when sufficient high-quality data are not available, the performance of prediction models based on deep learning techniques may degrade. …”
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Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring by Fang Li, Shengguo Wang, Zhi Gao, Maofeng Qing, Shan Pan, Yingying Liu, Chengchen Hu

“…AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. …”
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Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images by Soichiro Kuwayama, Yuji Ayatsuka, Daisuke Yanagisono, Takaki Uta, Hideaki Usui, Aki Kato, Noriaki Takase, Yuichiro Ogura, Tsutomu Yasukawa

“…The purpose of this study is to test deep learning with image augmentation for automated detection of chorioretinal diseases. …”
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Cross-Shaped Heat Tensor Network for Morphometric Analysis Using Zebrafish Larvae Feature Keypoints by Xin Chai, Tan Sun, Zhaoxin Li, Yanqi Zhang, Qixin Sun, Ning Zhang, Jing Qiu, Xiujuan Chai

“…Deep learning-based morphometric analysis of zebrafish is widely utilized for non-destructively identifying abnormalities and diagnosing diseases. …”
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Intelligent model for forecasting fluctuations in the gold price by Mahdieh Tavassoli, Mahnaz Rabeei, Kiamars Fathi Hafshejani

“…The study also employed Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP) neural network models in deep learning mode to predict gold price fluctuations. …”
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