Facial Expression Recognition Using a Novel Modeling of Combined Gray Local Binary Pattern by An H. Ton-That, Nhan T. Cao

“…Facial Expression Recognition (FER) is an active research field at present. Deep learning is a good method that is widely used in this field but it has extreme hardware requirements and it is hard to apply in normal terminal devices. …”
Get full text

Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks by Huu-Thanh Duong, Tram-Anh Nguyen-Thi, Vinh Truong Hoang

“…Several experiments have been performed for both well-known machine learning-based classifiers and deep learning models. We compare, analyze, and evaluate the results to indicate the advantage and disadvantage points of the techniques for each approach. …”
Get full text

Intelligent Computer Technology-Driven Mural Pattern Recognition Method by Wenqing Wei, Lei Gao

“…In recent years, as a new image processing technology, deep learning based on a convolutional neural network is widely used in many fields. …”
Get full text

Automatic detection and prediction of COVID-19 in cough audio signals using coronavirus herd immunity optimizer algorithm by G. Ayappan, S. Anila

“…This study proposes an advanced framework to detect and predict COVID-19 using deep learning from cough audio signals. Audio data from the COUGHVID dataset undergo preprocessing through fuzzy gray level difference histogram equalization, followed by segmentation with a U-Net model. …”
Get full text

N2GNet tracks gait performance from subthalamic neural signals in Parkinson’s disease by Jin Woo Choi, Chuyi Cui, Kevin B. Wilkins, Helen M. Bronte-Stewart

“…In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs). …”
Get full text

Current Trends in Class Imbalance Learning for Software Defect Prediction by Somya R. Goyal

“…Among the available evaluation metrics, Area under the Curve is the most used one as it is insulated from the impact of imbalanced datasets. Deep learning models have potential and prospects to be explored for class imbalance handling in SDP in a full-fledged capacity.…”
Get full text

INVESTIGATING THE NEXUS BETWEEN GREEN ENERGY AND ARTIFICIAL INTELLIGENCE (AI) by MITHUN S. ULLAL, ADITYA ANAND, POPESCU VIRGIL, BIRAU RAMONA

“…The global shift towards green energy has to managed and optimized which is a major challenge. Deep learning can play a vital role in predictive maintenance and storage. …”
Get full text

Retinopathy Disease Detection and Classification Using a Coordinate Attention Module-Based Convolutional Neural Network with Leaky Rectified Linear Unit by Pravin Balaso Chopade, Prabhakar N. Kota, Bhagvat D. Jadhav, Pravin Marotrao Ghate, Shriram Sadashiv Kulkarni

“…To overcome these issues, this paper proposes a Deep Learning (DL)-based technique for early and accurate DR detection. …”
Get full text

Sistem Rekognisi Citra Digital Bahasa Isyarat Menggunakan Convolutional Neural Network dan Spatial Transformer by Mohammad Alfiano Rizky Mahardika, Novanto Yudistira, Achmad Ridok

“…Dengan menggunakan metode Convolutional Neural Network (CNN) yang merupakan bagian dari Deep Learning atau Machine Learning, sistem akan mengenali pose atau bentuk dari citra bahasa isyarat yang dimasukkan, dan memberikan luaran yang sesuai dengan maksud dari pose atau bentuk dari citra bahasa isyarat tersebut. …”
Get full text

A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM by Qiubo Zhong, Xiaoyi Fang

“…The majority of loop detection methods extract artificial features, which fall short of learning comprehensive data information, but unsupervised learning as a typical deep learning method excels in self-access learning and clustering to analyze the similarity without handling the data. …”
Get full text

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships. by Alexander Kroll, Sahasra Ranjan, Martin J Lercher

“…Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. …”
Get full text

A Detection Method Against Load Redistribution Attacks in the Interdependence of the Natural Gas Network and Power System Based on Entropy by Yaoying Wang

“…Instead of relying on deep learning algorithms, this study leverages entropy-based techniques for attack detection. …”
Get full text

A joint analysis of single cell transcriptomics and proteomics using transformer by Yuanyuan Chen, Xiaodan Fan, Chaowen Shi, Zhiyan Shi, Chaojie Wang

“…In this paper, we propose scTEL, a deep learning framework based on Transformer encoder layers, to establish a mapping from sequenced RNA expression to unobserved protein expression in the same cells. …”
Get full text

Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
Get full text

Enabling Fast AI-Driven Inverse Design of a Multifunctional Nanosurface by Parallel Evolution Strategies by Ashish Chapagain, Dima Abuoliem, In Ho Cho

“…For artificial intelligence (AI)-driven inverse design, earlier research integrates basic multiphysics principles such as dynamic viscosity, air diffusivity, surface tension, and electric potential with backward deep learning (DL) on the framework of ES. As a successful alternative to reinforcement learning, ES performed well for the AI-driven inverse design. …”
Get full text

A dataset of annotated African plum images from Cameroon for AI-based quality assessmentKaggle by Arnaud Nguembang Fadja, Armel Gabin Fameni Tagni, Sain Rigobert Che, Marcellin Atemkeng

“…This resource is valuable for developing and testing computer vision, deep learning-based recognition systems and object detection models in agriculture, enabling automated evaluation of plum quality for commercialization. …”
Get full text

Lightweight Photo-Response Non-Uniformity Fingerprint Extraction Algorithm Based on an Invertible Denoising Network by Zihang Yuan, Yanhui Xiao, Huawei Tian

“…During the PRNU fingerprint extraction, it is very important for source camera identification to estimate the natural noise from real-world images. Most existing deep learning-based neural networks have a large number of model parameters, and they may not be practical in realistic scenarios such as deploying the model on small devices like smartphones and remote forensics equipment. …”
Get full text

Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model. by Xiaoyi Wang, Zhen Jin

“…Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. …”
Get full text

DETECTING WEB-BASED BOTNETS USING A WEB PROXY AND A CONVOLUTIONAL NEURAL NETWORK by Trần Đắc Tốt, Phạm Tuấn Khiêm, Phạm Nguyễn Huy Phương

“…We experimented on the CTU-13 dataset using different configurations of the convolutional neural network to evaluate the potential of deep learning on the botnet detection problem. In particular, we propose a botnet detection system that uses a web proxy. …”
Get full text

A Scoping Review of the Smart Irrigation Literature Using Scientometric Analysis by Daraje Kaba Gurmessa, Shimelis G. Assefa

“…The leading main topics addressed in the field are IOT, smart irrigation, irrigation, water stress, energy, deep learning, soil moisture, and relations in the network. …”
Get full text