Estimating chlorophyll content in tea leaves using spectral reflectance and deep learning methods by Yuta Tsuchiya, Yuhei Hirono, Rei Sonobe

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Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy by Kimji N. Pellano, Inga Strumke, Daniel Groos, Lars Adde, Espen F. Alexander Ihlen

“…This paper tests the reliability and applicability of Explainable AI (XAI) methods using a deep learning method that predicts CP by analyzing skeletal data extracted from video recordings of infant movements. …”
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A Comparative Study of Unsupervised Deep Learning Methods for Anomaly Detection in Flight Data by Sameer Kumar Jasra, Gianluca Valentino, Alan Muscat, Robert Camilleri

“…This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). …”
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Multistage deep learning methods for automating radiographic sharp score prediction in rheumatoid arthritis by Hajar Moradmand, Lei Ren

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Forecasting Shifts in Europe's Renewable and Fossil Fuel Markets Using Deep Learning Methods by Yonghong Liu, Muhammad S. Saleem, Javed Rashid, Sajjad Ahmad, Muhammad Faheem

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Plant Disease Detection and Classification Using Deep Learning Methods: A Comparison Study by Pei-Wern Chin, Kok-Why Ng, Naveen Palanichamy

“…Hence, challenges in choosing a suitable deep-learning technique to tackle the problem should be addressed. …”
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Comment on “Detection of Mucous Retention Cysts Using Deep Learning Methods on Panoramic Radiographs” by Manas Bajpai

“…Comment on “Detection of Mucous Retention Cysts Using Deep Learning Methods on Panoramic Radiographs”…”
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Mapping Wind Turbine Distribution in Forest Areas of China Using Deep Learning Methods by Pukaiyuan Yang, Zhigang Zou, Wu Yang

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Comparison of shallow and deep learning methods of ECG signals clas-sification for arrhythmia detection by Dodon Turianto Nugrahadi, Rudy Herteno, Dwi Kartini, Muhammad Haekal, Mohammad Reza Faisal

“… The research aimed to compare the classification performance of arrhythmia classification from the ECG signal dataset from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT-BIH) database. Shallow learning methods that were used in this study are Support Vector Machine,  Naïve Bayes, and Random Forest. 1D Convolutional Neural Network (1D CNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) were deep learning methods that were used for the study. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…Therefore, this paper reviews the literature on deep learning-based spatiotemporal fusion methods, analyzes and compares existing deep learning-based fusion algorithms, summarizes current challenges in this field, and proposes possible directions for future studies.…”
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Review of pedestrian trajectory prediction methods by Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN

“…With the breakthrough of deep learning technology and the proposal of large data sets, the accuracy of pedestrian trajectory prediction has become one of the research hotspots in the field of artificial intelligence.The technical classification and research status of pedestrian trajectory prediction were mainly reviewed.According to the different modeling methods, the existing methods were divided into shallow learning and deep learning based trajectory prediction algorithms, the advantages and disadvantages of representative algorithms in each type of method were analyzed and introduced.Then, the current mainstream public data sets were summarized, and the performance of mainstream trajectory prediction methods based on the data sets was compared.Finally, the challenges faced by the trajectory prediction technology and the development direction of future work were prospected.…”
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Aerial–Terrestrial Image Feature Matching: An Evaluation of Recent Deep Learning Methods by Hui Wang, Jiangxue Yu, San Jiang, Dejin Zhang, Qingquan Li

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Comprehensive analysis of supervised learning methods for electrical source imaging by Sarah Reynaud, Adrien Merlini, Douraied Ben Salem, François Rousseau

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Statistics release and privacy protection method of location big data based on deep learning by Yan YAN, Yiming CONG, Mahmood Adnan, Quanzheng SHENG

“…Aiming at the problems of the unreasonable structure and the low efficiency of the traditional statistical partition and publishing of location big data, a deep learning-based statistical partition structure prediction method and a differential publishing method were proposed to enhance the efficacy of the partition algorithm and improve the availability of the published location big data.Firstly, the two-dimensional space was intelligently partitioned and merged from the bottom to the top to construct a reasonable partition structure.Subsequently, the partition structure matrices were organized as a three-dimensional spatio-temporal sequence, and the spatio-temporal characteristics were extracted via the deep learning model in a bid to realize the prediction of the partition structure.Finally, the differential privacy budget allocation and Laplace noise addition were implemented on the prediction partition structure to realize the privacy protection of the statistical partition and publishing of location big data.Experimental comparison of the real location big data sets proves the advantages of the proposed method in improving the querying accuracy of the published location big data and the execution efficiency of the publishing algorithm.…”
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Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning by Dong Li, Panfei Yang

“…To solve this problem, a distributed method for UAV cluster testing, called UTDR (distributed UAV cluster Testing method by using Deep Reinforcement learning), based on the Deep Deterministic Policy Gradient (DDPG) is proposed in this work. …”
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A monocular distance measurement method for underground track obstacles based on deep learning by Yuanyuan XU, Qinghua CHEN, Yingsong CHENG

“…Aiming at the problem of anti-collision warning during underground electric locomotive running, a deep learning-based obstacle location method for underground track is proposed. …”
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Research on deep learning-based fracture network inversion method for shale gas reservoirs by CHEN Weiming, JIANG Lin, LUO Tongtong, LI Yue, WANG Jianhua

“…To address this, a shale gas reservoir fracture network inversion method based on deep learning was proposed. The core of this method is to quantitatively analyze the fracturing curve characteristic parameters based on the site fracturing curve data, using strongly correlated indicators of fracture network parameters as inputs and microseismic monitoring fracture network parameters (including length, width, height, and volume) as target outputs. …”
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Modelling of energy demand prediction system in potato farming using deep learning method by Riswanti Sigalingging, Nasha Putri Sebayang, Noverita Sprinse Vinolina, Lukman Adlin Harahap

“…In this comprehensive study, SPSS and Jupyter Notebook were used to model and predict the energy requirements of potato plants during cultivation. A system using deep learning methods, specifically the Convolutional Neural Network (CNN), was also developed to accurately predict the classification of potato plant growth phases using image data. …”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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A Secure Data Collection Method Based on Deep Reinforcement Learning and Lightweight Authentication by Yunlong Wang, Jie Zhang, Guangjie Han, Dugui Chen

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