Software refactoring prediction evaluation method based on deep learning models by Yichi ZHANG, Yang ZHANG, Yanlei LI, Kun ZHENG, Wei LIU

“…Secondly, convolutional neural network (CNN), long short-term memory (LSTM) network, gated recurrent unit (GRU) model, multilayer perceptron(MLP), and autoencoder(AE) were trained and tested on the datasets. …”
Get full text

Distribution network fault comprehensive identification method based on voltage–ampere curves and deep ensemble learning by Jian Wang, Bo Zhang, Dong Yin, Jinxin Ouyang

“…Moreover, the proposed method has significant advantages over the impedance method and artificial neural network method for fault section identification and fault distance estimation. …”
Get full text

Design, Sensing and Control of a Robotic Prosthetic Eye for Natural Eye Movement by J. J. Gu, M. Meng, A. Cook, P. X. Liu

“…Theoretical issues on sensor failure detection and recovery, and signal processing techniques used in sensor data fusion, are studied using statistical methods and artificial neural network based techniques. In addition, practical control system design and implementation using micro-controllers are studied and implemented to carry out the natural eye movement detection and artificial robotic eye control tasks. …”
Get full text

Digital-twin driven alignment control method for marine shafting with air spring vibration isolation system by Song Liu, Liang Shi, Wei Xu, ZeChao Hu

“…First, we design a digital twin prediction model based on the neural network to describe the data mapping relationship between the air spring pressures and shafting alignment state. …”
Get full text

Dueling Network Architecture for GNN in the Deep Reinforcement Learning for the Automated ICT System Design by Tianchen Zhou, Yutaka Yakuwa, Natsuki Okamura, Hiroyuki Hochigai, Takayuki Kuroda, Ikuko Eguchi Yairi

“…This paper presents an improved deep reinforcement learning-based (DRL) approach for end-to-end models using a Graph Neural Network(GNN). The proposed method aims to improve end-to-end deep Q learning with a GNN by decomposing the GNN-based Q-network structure into two sub-streams to separately estimate the global state value and the state-dependent action advantage instead. …”
Get full text

Detection and Attribution of a Spatial Heterogeneity in the Temporal Evolution of Bulgarian River Discharge by Natalya A. Kilifarska, Gergana I. Metodieva, Antonia Ch. Mokreva

“…., temperature, precipitation, and ozone at 70 hPa), combined with neural network analysis results, suggests ozone as a possible reason for the heterogeneous hydrological response. …”
Get full text

Utility of complexity analysis in electroencephalography and electromyography for automated classification of sleep-wake states in mice by Naoki Furutani, Yuki C. Saito, Yasutaka Niwa, Yu Katsuyama, Yuta Nariya, Mitsuru Kikuchi, Tetsuya Takahashi, Takeshi Sakurai

“…Based on these findings, we developed a sleep stage scoring model, termed Sleep Analyzer Complex (SAC), a convolutional neural network model that integrates these complexity features with conventional EEG spectrum and EMG amplitude analysis. …”
Get full text

WGAN-DL-IDS: An Efficient Framework for Intrusion Detection System Using WGAN, Random Forest, and Deep Learning Approaches by Shehla Gul, Sobia Arshad, Sanay Muhammad Umar Saeed, Adeel Akram, Muhammad Awais Azam

“…Then, we use three deep learning techniques, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), to classify the attacks. …”
Get full text

Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs by Hassen Louati, Ali Louati, Khalid Mansour, Elham Kariri

“…First, NAS is employed to automatically discover the optimal convolutional neural network (CNN) architecture tailored to the ChestX-Ray14 dataset, reducing the need for extensive manual tuning. …”
Get full text

BeatNet+: Real-Time Rhythm Analysis for Diverse Music Audio by Mojtaba Heydari, Zhiyao Duan

“…The main innovation of the proposed method is the auxiliary training strategy that helps the neural network model to learn a representation invariant to the amount of percussive components in the music. …”
Get full text

A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function by Tahsin Ali Mohammed Amin, Sabah Robitan Mahmood, Rebar Dara Mohammed, Pshtiwan Jabar Karim

“…The ant colony optimization algorithm is then used to train a recurrent neural network. Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). …”
Get full text

Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study. by Solange Amorim Nogueira, Fernanda Ambrogi B Luz, Thiago Fellipe O Camargo, Julio Cesar S Oliveira, Guilherme Carvalho Campos Neto, Felipe Brazao F Carvalhaes, Marcio Rodrigues C Reis, Paulo Victor Santos, Giovanna Souza Mendes, Rafael Maffei Loureiro, Daniel Tornieri, Viviane M Gomes Pacheco, Antonio Paulo Coimbra, Wesley Pacheco Calixto

“…This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. …”
Get full text

A Novel Approach of Label Construction for Predicting Remaining Useful Life of Machinery by Hailong Lin, Zihao Lei, Guangrui Wen, Xiaojun Tian, Xin Huang, Jinsong Liu, Haoxuan Zhou, Xuefeng Chen

“…Finally, the recurrent neural network (RNN) is used for prediction and verification. …”
Get full text

Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art Designing by Spoorthi Singh, Navya T. Hegde, Mohammad Zuber, Yuvraj Singh Nain, Vishnu G. Nair

“…The project faced challenges such as material selection, CAD design, and hardware-software interfacing, all of which were addressed through iterative design and validation processes. A convolutional neural network (CNN) was employed to process grayscale images, extracting and reconstructing features using pooling and deconvolution techniques, with the model achieving stable performance over multiple epochs. …”
Get full text

Sound recurrence analysis for acoustic scene classification by Jakob Abeßer, Zhiwei Liang, Bernhard Seeber

“…In the second part, we evaluate three strategies to incorporate self-similarity matrices as an additional input feature to a convolutional neural network architecture for ASC. We observe the characteristic repetition of transient sounds in recordings of “park” and “street traffic” as well as harmonic sound repetitions in acoustic scene classes related to public transportation. …”
Get full text

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images by Avigyan Roy, Priyam Saha, Nandita Gautam, Friedhelm Schwenker, Ram Sarkar

“…They are an essential tool in the study and understanding of diseases, aiding in research, education, and patient care. Convolutional neural network based pretrained deep learning models can be used successfully to detect lung cancer. …”
Get full text

Enhancing heat exchanger design using autoencoder model for predicting efficiency and cost in chemical processing by Manimegalai T, Anitha Gopalan, Vanmathi Murugesan, Jayant Giri, Praveen Barmavatu, Praveenkumar T R, Dinesh Mavaluru, Rafath Samrin

“…The autoencoder model, a type of artificial neural network, is trained on a comprehensive dataset encompassing various operating conditions, geometric configurations, and material properties of heat exchangers. …”
Get full text

Wind energy system fault classification using deep CNN and improved PSO‐tuned extreme gradient boosting by Chun‐Yao Lee, Edu Daryl C. Maceren

“…The methodology involves resampling the imbalanced SCADA dataset using synthetic minority oversampling technique (SMOTE) and near‐miss undersampling techniques, extracting deep learning features using deep convolutional neural network, and feeding them into an XGBoost (extreme gradient boosting) classifier with tuned parameters using adaptive elite‐particle swarm optimization (AEPSO). …”
Get full text

Cyber-Physical System Security for Manufacturing Industry 4.0 Using LSTM-CNN Parallel Orchestration by Salman Saeidlou, Nikdokht Ghadiminia, Kwadwo Oti-Sarpong

“…This paper presents an innovative cyber-physical system (CPS) security mechanism, using a long short-term memory (LSTM) network and a convolutional neural network (CNN) coordinated by a parallel orchestration (PLO) algorithm. …”
Get full text

The application of deep learning in early enamel demineralization detection by Ketai He, Rongxiu Zhang, Muchun Liang, Keyue Tian, Kaihui Luo, Ruoshi Chen, Jianpeng Ren, Jiajun Wang, Juan Li

“…A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN). The model was trained to automate the detection of enamel demineralization. …”
Get full text