Implementation of the Human-Like Lane Changing Driver Model Based on Bi-LSTM by Junyu Cai, Haobin Jiang, Junyan Wang

“…This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. …”
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Real-world pharmacovigilance of ofatumumab in multiple sclerosis: a comprehensive FAERS data analysis by Hui Chen

“…Statistical approaches used included the Reporting Odds Ratio, Proportional Reporting Ratio, Bayesian Confidence Propagation Neural Network, and Multi-item Gamma Poisson Shrinker. …”
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GMTBLC: a deep learning-based bi-modal network traffic classification method by WEI Debin, JIANG Qinlong, WEN Jinglong, WANG Xinrui

“…Simultaneously, session images were processed by the spatio-temporal feature extraction (SFE) module, of which the spatial features of packets were extracted by a convolutional neural network with residual connections, and temporal features of packets were extracted by a bi-directional long short-term memory network. …”
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Utilising AI technique to identify depression risk among doctoral students by Changhong Teng, Chunmei Yang, Qiushi Liu

“…Despite this, studies specifically addressing depression risk in doctoral students are relatively scarce, and existing findings are not universally applicable. Using neural network feature extraction technology, this study aims to investigate the factors contributing to the high depression risk of doctoral students and effectively identify doctoral students at depression risk, so as to propose corresponding improvement strategies to prevent and intervene doctoral students with depression risk for universities. …”
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Wireless key generation system for internet of vehicles based on deep learning by Han WANG, Liquan CHEN, Zhongmin WANG, Tianyu LU

“…In recent years, the widespread application of internet of vehicles technology has garnered attention due to its complex nature and point-to-point communication characteristics.Critical and sensitive vehicle information is transmitted between different devices in internet of vehicles, necessitating the establishment of secure and reliable lightweight keys for encryption and decryption purposes in order to ensure communication security.Traditional key generation schemes have limitations in terms of flexibility and expandability within the vehicle network.A popular alternative is the physical layer key generation technology based on wireless channels, which offers lightweight characteristics and a theoretical basis of security in information theory.However, in the context of internet of vehicles, the movement speed of devices impacts the autocorrelation of generated keys, requiring improvements to traditional channel modeling methods.Additionally, the randomness and consistency of generated wireless keys are of higher importance in applications in internet of vehicles.This research focused on a key generation system based on the wireless physical layer, conducting channel modeling based on line-of-sight and multipath fading effects to reflect the impact of vehicle speed on autocorrelation.To enhance the randomness of key generation, a differential quantization method based on cumulative distribution function was proposed.Furthermore, an information reconciliation scheme based on neural network auto-encoder was introduced to achieve a dynamic balance between reliability and confidentiality.Compared to the implementation of Slepian-Wolf low-density parity-check codes, the proposed method reduces the bit disagreement rate by approximately 30%.…”
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A New Preprocessing Method for Diabetes and Biomedical Data Classification by Sarbast CHALO, İbrahim Berkan AYDİLEK

“…We present a method for the identification of diabetes that involves the training of the features of a deep neural network between five and 10 times using the cross-validation training mode. …”
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Analysis of College Students’ Public Opinion Based on Machine Learning and Evolutionary Algorithm by Jinqing Zhang, Pengchao Zhang, Bin Xu

“…To solve this problem, this paper proposes a new way by using a questionnaire which covers most aspects of a student’s life to collect comprehensive information and feed the information into a neural network. With reliable prediction on students’ state of mind and awareness of feature importance, colleges can give students guidance associated with their own experience and make macroscopic policies more effective. …”
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Unveiling the Complexity of Medical Imaging through Deep Learning Approaches by Javaid Iqbal Bhat, Novsheena Rasool

“…Specifically, an in-depth discussion is conducted on the Convolutional Neural Network (CNN) owing to its widespread adoption as a paramount tool in computer vision tasks. …”
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Robust Spike Sorting Using Dual Tree Complex Wavelet Transform: Overcoming Traditional Limitations by Gorkem Serbes

“…Accurate spike sorting is vital for understanding the neural network dynamics of the brain through extracellular neural recordings. …”
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Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods by Haixiong Li, Fei Wang

“…In addition, a Convolutional Neural Network (CNN) regression model was constructed and optimized with the Northern Goshawk Optimization (NGO) algorithm, resulting in a more precise CNN-NGO prediction model. …”
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Evaluation of Efficacy of Artificial Intelligence in Orthopantomogram in Detecting and Classifying Radiolucent Lesions by Sheetal Singar, Ajay Parihar, Prashanthi Reddy

“…Aim and Objective: The objective of our study was to build a convolutional neural network (CNN) model and detection and classification of benign and malignant radiolucent lesions in orthopantomogram (OPG) by implementing CNN. …”
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A Small Target Detection Method Based on the Improved FCN Model by Guofeng Ma

“…This study proposes an improved FCN model based on the full convolutional neural network (FCN) and applies it to the STD. The following is the central concept of the proposed method. …”
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Prediction Interval of Interface Regions: Machine Learning Nowcasting Approach by Khaled Alielden, Enrico Camporeale, Marianna B. Korsós, Youra Taroyan

“…In this work, a 1D ensemble system comprised of a Long‐short‐term memory (LSTM) model and a Convolution Neural Network (CNN) model—LCNN is introduced to classify the observed IR time series and give the prediction interval nowcast of its transit time to the observer. …”
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Robust identification method of website fingerprinting against disturbance by ZHANG Jingxi, LI Tengyao, TU Yukuan, LUO Xiangyang

“…With the matrix as input, a robust flow classifier with convolutional neural network was established. Through extensive experimental analysis on the dataset provided by DF, the accuracy under RF Countermeasure is 95.4%, which is 21.2% higher than RF. …”
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Features of the restaurant market and consumer behavior in the Moscow restaurant business segment: study results by A. N. Timokhovich

“…At the second stage to identify the main trends of the Moscow restaurant market the method of content analysis of the Moscow restaurant business establishments sites with high rating indicators based on the Yandex neural network data was used. The main trends of the Moscow restaurant market are: restaurants’ focus on preparing healthy food and vegetarian cuisine; use of farm products and local ingredients in prepared dishes; technological innovations implementation that simplify consumer experience; focus on the principles of sustainable development and environmental friendliness in the business model; restaurant formats variety. …”
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Robust CNN for facial emotion recognition and real-time GUI by Imad Ali, Faisal Ghaffar

“…Utilizing a robust architecture of a convolutional neural network (CNN), we designed an efficacious framework for facial emotion recognition that predicts emotions and assigns corresponding probabilities to each fundamental human emotion. …”
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Hyperbolic Graph Convolutional Network Relation Extraction Model Combining Dependency Syntax and Contrastive Learning by Jinzhe Li

“…Based on the hyperbolic graph neural network, dependent syntactic information and information optimization strategies are introduced to solve the problem of word embedding concentration. …”
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On the Analysis and Assessment of First-Order Group Contribution Models for the Calculation of Normal Boiling Point and Critical Properties of Pure Compounds by Vanessa Villazón-León, Adrián Bonilla-Petriciolet, Juan Carlos Tapia-Picazo

“…The performance of these models was characterized and compared for several compound families using a standardized approach to determine their group contributions and parameters. An artificial neural network model was also applied and assessed to improve the estimations obtained with the best group contribution models. …”
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Enhancing Image-Based JPEG Compression: ML-Driven Quantization via DCT Feature Clustering by Shahrzad Sabzavi, Reza Ghaderi

“…In this study, an auto-encoder neural network is utilized to extract DCT features from images. …”
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The Reduced-Order Model for Droplet Drift of Aerial Spraying under Random Lateral Wind by Wencheng Li, Wenyun Wang, Xiaomao Huang, Chenyang Li

“…Based on the input and output dataset of CFD, the recursive algorithm including nonlinear autoregressive exogenous model and surrogate-based recurrence framework and the deep learning method for time-series prediction called long short-term memory neural network are used to build the efficient reduced-order model, respectively. …”
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