Deep Learning Methods Used in Precision Agriculture by Wang Yuchu

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Deep Learning Method for Bearing Fault Diagnosis by LIU Xiu, MA Shan-tao, XIE Yi-ning, HE Yong-jun

“…In recent years, deep learning technology has shown great potential in bearing fault diagnosis based on vibration signals.However, in the fault diagnosis method based on deep learning, the traditional single network topology feature extraction has weak discrimination and low noise robustness, and the accuracy of fault diagnosis is not high.In addition, most of the current research methods have a low fault recognition rate in a variable load environment.In response to the above problems, this paper proposes an improved neural network end-to-end fault diagnosis model.The model combines convolutional neural networks (CNN) and the attention long short-term memory (ALSTM) based on the attention mechanism, and uses ALSTM to capture long-distance correlations in time series data , Effectively suppress the high frequency noise in the input signal.At the same time, a multi-scale and attention mechanism is introduced to broaden the range of the convolution kernel to capture high and low frequency features, and highlight the key features of the fault. …”
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Deep dive into deep learning methods for cervical cancer detection and classification by Pooja Patre, Dipti Verma

“…This review presents a thorough analysis of deep learning methods utilized for cervical cancer diagnosis, with an emphasis on critical approaches, evaluation metrics, and the ongoing challenges faced in the field. …”
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Deep Learning Methods in Soft Robotics: Architectures and Applications by Tomáš Čakurda, Monika Trojanová, Pavlo Pomin, Alexander Hošovský

“…Specific properties of recent deep learning architectures and the usefulness of their features in addressing various types of issues found in soft robotics are analyzed. …”
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Deep Reinforcement Learning: A Chronological Overview and Methods by Juan Terven

Subjects: “…deep reinforcement learning…”
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Introduction to deep learning methods for multi‐species predictions by Yuqing Hu, Sara Si‐Moussi, Wilfried Thuiller

“…This paper explores the potential of deep learning methods to overcome these challenges and provide more accurate multi‐species predictions. …”
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Survey of text classification methods based on deep learning by Sijia DU, Haining YU, Hongli ZHANG

“…Text classification is a research hot spot in the field of natural language processing,which is mainly used in public opinion detection,news classification and other fields.In recent years,artificial neural networks has good performance in many tasks of natural language processing,the application of neural network technology to text classification has also made many achievements.In the field of text classification based on deep learning,numerical representation of text and deep-learning-based text classification are two main research directions.The important technology of word embedding in text representation and the implementation principle and research status of deep learning method applied in text classification were systematically analyzed and summarized.And the shortcomings and the development trend of text classification methods in view of the current technology development were analyzed.…”
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Deep graph representation learning: methods, applications, and challenges by ZHANG Xulong, QU Xiaoyang, XIAO Chunguang, WANG Jianzong

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Editorial: Machine-learning/deep-learning methods in neuromarketing and consumer neuroscience by Marco Bilucaglia, Marco Bilucaglia, Luca Mainardi, Thomas Zoëga Ramsøy, Thomas Zoëga Ramsøy, Paul J. Zak, Paul J. Zak, Margherita Zito, Margherita Zito, Vincenzo Russo, Vincenzo Russo

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GNSS interference mitigation method based on deep learning by Feiqiang Chen, Feiqiang Chen, Zhe Liu, Zhe Liu, Long Huang, Long Huang, Yuchen Xie, Yuchen Xie, Binbin Ren, Qin Zhou

“…By leveraging a deep learning network model, our method automatically selects the optimal interference mitigation technique based on the specific characteristics of the interference. …”
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A Gesture Recognition Method Based on Deep Learning by DING Chi, LIN Jun, YOU Jun, YUAN Hao

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Sign language recognition method based on deep learning by He Mu

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Deep learning for predicting myopia severity classification method by WangMeiYu Xing, XiaoNa Li, JingShu Ni, YuanZhi Zhang, ZhongSheng Li, Yong Liu, YiKun Wang, Yao Huang

“…To improve the efficiency of myopia screening, this paper proposes a deep learning model, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to classify different severities of myopia. …”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Node selection method in federated learning based on deep reinforcement learning by Wenchen HE, Shaoyong GUO, Xuesong QIU, Liandong CHEN, Suxiang ZHANG

“…To cope with the impact of different device computing capabilities and non-independent uniformly distributed data on federated learning performance, and to efficiently schedule terminal devices to complete model aggregation, a method of node selection based on deep reinforcement learning was proposed.It considered training quality and efficiency of heterogeneous terminal devices, and filtrate malicious nodes to guarantee higher model accuracy and shorter training delay of federated learning.Firstly, according to characteristics of model distributed training in federated learning, a node selection system model based on deep reinforcement learning was constructed.Secondly, considering such factors as device training delay, model transmission delay and accuracy, an optimization model of accuracy for node selection was proposed.Finally, the problem model was constructed as a Markov decision process and a node selection algorithm based on distributed proximal strategy optimization was designed to obtain a reasonable set of devices before each training iteration to complete model aggregation.Simulation results demonstrate that the proposed method significantly improves the accuracy and training speed of federated learning, and its convergence and robustness are also well.…”
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Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping by Yue‐Lin Dong, Zhen‐Jie Zhang

“…The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing outcomes. …”
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Advanced Methods for Identifying Counterfeit Currency: Using Deep Learning and Machine Learning by Nama'a Hamed, Fadwa Al Azzo

“…In this work, we offer a thorough investigation of sophisticated methods for detecting counterfeit money that make use of deep learning and machine learning approaches. …”
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Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning by Jean Max T. Habib, A. A. Poguda

“…Several different models in ML and CNN with the LSTM model, but SVM with the TF-IDF vectorizer proved most effective for this unbalanced data set. In general, both deep classification algorithm. A combination of both approaches can also learning and feature-based selection methods can be used to solve be used to further improve the efficiency of the algorithm. some of the most pressing problems. …”
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Paraphrase Identification With Deep Learning: A Review of Datasets and Methods by Chao Zhou, Cheng Qiu, Lizhen Liang, Daniel E. Acuna

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Breast Cancer Classification with Various Optimized Deep Learning Methods by Mustafa Güler, Gamze Sart, Ömer Algorabi, Ayse Nur Adıguzel Tuylu, Yusuf Sait Türkan

“…<b>Methods:</b> In this study, 11 different deep learning algorithms (Vanilla, ResNet50, ResNet152, VGG16, DenseNet152, MobileNetv2, EfficientB1, NasNet, DenseNet201, ensemble, and Tuned Model) were used. …”
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