Voter Authentication Using Enhanced ResNet50 for Facial Recognition by Aminou Halidou, Daniel Georges Olle Olle, Arnaud Nguembang Fadja, Daramy Vandi Von Kallon, Tchana Ngninkeu Gil Thibault

“…Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. …”
Get full text

Visual explainable artificial intelligence for graph-based visual question answering and scene graph curation by Sebastian Künzel, Tanja Munz-Körner, Pascal Tilli, Noel Schäfer, Sandeep Vidyapu, Ngoc Thang Vu, Daniel Weiskopf

“…The decision-making process of the model is demonstrated by highlighting certain internal states of a graph neural network (GNN). The proposed system is built on top of a GraphVQA framework that implements various GNN-based models for VQA trained on the GQA dataset. …”
Get full text

Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device by Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park

“…This study was conducted using a widely used publicly accessible database following a benchmark training and evaluation procedure. Compared to a standard convolutional neural network-based model which exhibited 81.5% overall accuracy, the proposed lightweight model achieved more precise arrhythmia classification with achieving 87.1% overall accuracy. …”
Get full text

Modeling Traders’ Behavior with Deep Learning and Machine Learning Methods: Evidence from BIST 100 Index by Afan Hasan, Oya Kalıpsız, Selim Akyokuş

“…To predict the direction of the index, Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) classification techniques are used. …”
Get full text

Leveraging neuroinformatics to understand cognitive phenotypes in elite athletes through systems neuroscience by Yubin Huang, Jun Liu, Qi Yu

“…The model features a specialized embedding mechanism for disentangling latent factors and a tailored optimization strategy incorporating domain-specific priors and regularization techniques.ResultsExperimental evaluations demonstrate LCEN's superiority in predicting and interpreting cognitive phenotypes across diverse datasets, providing deeper insights into the neural underpinnings of elite performance.DiscussionThis work bridges computational modeling, neuroscience, and psychology, contributing to the broader understanding of cognitive variability in specialized populations.…”
Get full text

Performance of Mathematical Indices in Transformer Condition Monitoring Using k-NN Based Frequency Response Analysis by Mehdi Bigdeli

“…In addition, in order to prove the ability of k-NN, a comparison is made with the results of the artificial neural network (ANN).…”
Get full text

Classification of lung cancer severity using gene expression data based on deep learning by Ali Bou Nassif, Nour Ayman Abujabal, Aya Alchikh Omar

“…Evaluating and validating the performance of the proposed model required addressing some common challenges in gene datasets, such as class imbalance and overfitting, due to the low number of samples and the high number of features. …”
Get full text

USE OF AN INFRA-LOW FREQUENCY EEG BIOLOGICAL FEEDBACK TECHNIQUE IN THE COMPREHENSIVE REHABILITATION OF PATIENTS WITH A DECREASED LEVEL OF CONSCIOUSNESS by M. V. Shendyapina, S. A. Kazymaev, T. V. Shapovalenko, K. V. Lyadov

“…A protocol for EEG biological feedback (BFB) at infra-low frequencies (<0.01 Hz) corresponding to those of the brain default mode network has been devised in recent years.Objective: to evaluate the therapeutic features of an EEG BFB technique in the rehabilitation of patients with a decreased level of consciousness.Patients and methods. …”
Get full text

On the effect of sampling frequency on the electricity theft detection performance by Fatemeh Soleimani Nasab, Foad Ghaderi

“…We also proposed a bagging network that could improve the overall detection performance at different sampling frequencies.…”
Get full text

A Novel Deeply-Learned Image Quality Analysis Algorithm for Clustering by Zhongzhe Chen, Xing Gao

“…This model’s simplified structure allows for more efficient end-to-end training and shows enhanced stability and resilience against variations in initial network parameter settings. Experimental evaluations on the MNIST (Modified National Institute of Standards and Technology database) and STL-10 (Self-Taught Learning 10) indicate that our model surpasses other leading clustering architectures in terms of clustering efficacy. …”
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

“…Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. …”
Get full text

A multi-modal multi-branch framework for retinal vessel segmentation using ultra-widefield fundus photographs by Qihang Xie, Qihang Xie, Xuefei Li, Yuanyuan Li, Yuanyuan Li, Jiayi Lu, Jiayi Lu, Shaodong Ma, Yitian Zhao, Yitian Zhao, Jiong Zhang, Jiong Zhang

“…The segmentation network includes the Selective Fusion Module (SFM), which enhances feature extraction within the segmentation network by integrating features generated during the FFA imaging process. …”
Get full text

IoT BotScan: Ultra-Lightweight AI Defense Against Botnet Threats by Sapna Sadhwani, Urvi Kavan Modi, Raja Muthalagu, Pranav M. Pawar, Alavikunhu Panthakkan, Wathiq Mansoor

“…This research study examines the effectiveness of Deep Learning (DL) and Machine Learning (ML) algorithms in identifying BotNet attacks within network infrastructures. Various algorithms, including Random Forests (RF), Decision Trees (DT), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were evaluated using the N-BaIoT dataset, which encompasses multiple BotNet attack types. …”
Get full text

A Spatial Analysis of Atmospheric Ammonia and Ammonium in the U.K. by M.A. Sutton, Y.S. Tang, U. Dragosits, N. Fournier, A.J. Dore, R.I. Smith, K. J. Weston, D. Fowler

“…The national network is established with over 80 sampling locations. …”
Get full text

Research on Foreign Object Intrusion Detection for Railway Tracks Utilizing Risk Assessment and YOLO Detection by Shanping Ning, Rui Guo, Pengfei Guo, Lu Xiong, Bangbang Chen

“…This method integrates MobileNetv3 with Transformer to detect foreign objects on railway tracks, constructing a novel backbone feature extraction network, MobileNetV3-CATr, aimed at reducing model complexity. …”
Get full text

P-V-L Deep: A Big Data Analytics Solution for Now-casting in Monetary Policy by Maryam Hajipour Sarduie, Mohammadali Afshar Kazemi, Mahmood Alborzi, Adel Azar, Ali Kermanshah

“…The main scientific contribution of this article is presenting a new approach of policy-making for the now-casting of economic indicators in order to improve the performance of forecasting through the combination of deep nets and deep learning methods in the data and features representation. In this regard, a net under the title of P-V-L Deep: Predictive Variational Auto Encoders - Long Short-term Memory Deep Neural Network was designed in which the architecture of variational auto-encoder was used for unsupervised learning, data representation, and data reconstruction; moreover, long short-term memory was adopted in order to evaluate now-casting performance of deep nets in time-series of macro-econometric variations. …”
Get full text

Img2Neuro: brain-trained neural activity encoders for enhanced object recognition by Mona A Aboelnaga, Mohamed W El-Kharashi, Seif Eldawlatly

“…Therefore, rather than using the brain as an inspiration, in this paper, we introduce Img2Neuro; a convolutional neural network model feature extractor that predicts the visual brain’s response to images by encoding neural activity. …”
Get full text

Real-Time Detection and Instance Segmentation Models for the Growth Stages of <i>Pleurotus pulmonarius</i> for Environmental Control in Mushroom Houses by Can Wang, Xinhui Wu, Zhaoquan Wang, Han Shao, Dapeng Ye, Xiangzeng Kong

“…Additionally, it features an interactive attention mechanism between spatial and channel dimensions to build a cross-stage partial spatial group-wise enhance network (CSP-SGE), improving the feature fusion capability of the neck. …”
Get full text

Wearable IoT (w-IoT) artificial intelligence (AI) solution for sustainable smart-healthcare by Gurdeep Singh

“…This study will cover all stages of design science methodology, guidelines for w-IoT healthcare solution development, by presenting experimental prototype towards pipeline implementation to address healthcare needs, alleviating previously prevalent Body Area Networks (BANs) solutions precision with advancing w-IoT smart technologies or Wireless Body Sensor Networks (WBSNs).…”
Get full text

FARVNet: A Fast and Accurate Range-View-Based Method for Semantic Segmentation of Point Clouds by Chuang Chen, Lulu Zhao, Wenwu Guo, Xia Yuan, Shihan Tan, Jing Hu, Zhenyuan Yang, Shengjie Wang, Wenyi Ge

“…Second, the Intensity Reconstruction (IR) module is employed to update the “Intensity Vanishing State” for zero-intensity points, including those from LiDAR acquisition limitations, thus enhancing the learning ability and robustness of the network. Third, the Adaptive Multi-Scale Feature Fusion (AMSFF) is applied to balance high-frequency and low-frequency features, augmenting the model expressiveness and generalization ability. …”
Get full text