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IEEE Transactions on Artificial Intelligence Publication Information
Publication Year: 2024,Page(s):C2 - C2
IEEE Transactions on Artificial Intelligence Publication Information
Year: 2024 | Volume: 5 | Issue: 2
Deep Learning Methods for Small Molecule Drug Discovery: A Survey
Wenhao Hu;Yingying Liu;Xuanyu Chen;Wenhao Chai;Hangyue Chen;Hongwei Wang;Gaoang Wang
Publication Year: 2024,Page(s):459 - 479
Cited by: Papers (4)
With the development of computer-assisted techniques, research communities, including biochemistry and deep learning, have been devoted into the drug discovery field for over a decade. Various applications of deep learning have drawn great attention in drug discovery, such as molecule generation, molecular property prediction, retrosynthesis prediction, and reaction prediction. While most of the e...Show More
How Artificial Intelligence Helped the Humanity During the COVID-19 Pandemic: A Review
Publication Year: 2024,Page(s):480 - 489
Cited by: Papers (1)
The unprecedented circumstances encountered during the COVID-19 pandemic in a variety of life aspects such as health, economy, and environment have urged the humanity to devise new solutions to control and mitigate the impact of the pandemic. Motivated by the high-accuracy decision-making capabilities of artificial intelligence, this article aims to highlight the effectiveness of artificial intell...Show More
A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry
Shreyas J. Upasane;Hani Hagras;Mohammad Hossein Anisi;Stuart Savill;Ian Taylor;Kostas Manousakis
Publication Year: 2024,Page(s):490 - 504
Cited by: Papers (5)
Industrial maintenance has undergone a paradigm shift due to the emergence of artificial intelligence (AI), the Internet of things, and cloud computing. Rather than accepting the drawbacks of reactive maintenance, leading firms worldwide are embracing “predict-and-prevent” maintenance. However, opaque box AI models are sophisticated and complex for the average user to comprehend and explain. This ...Show More
Portfolio Selection via Graph-Aware Gaussian Processes With Generalized Gaussian Likelihood
Naiqi Li;Zhikang Xia;Yiming Li;Ercan E. Kuruoğlu;Yong Jiang;Shu-Tao Xia
Publication Year: 2024,Page(s):505 - 515
Cited by: Papers (3)
Portfolio selection aims to manage the allocation of wealth among different assets, which remains to be a fundamental and challenging financial task. Markowitz's mean-variance analysis is one of the most well-known and widely adopted techniques for this problem. However, it requires accurate estimations of both the mean and variance of the return, while predicting the variance is particularly diff...Show More
Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets
Qirui Hua;Martin Hedegaard Nielsen;Zeliang An;Jian Ren;Rafal Wisniewski;Søren Kold;Ole Rahbek;Ming Shen
Publication Year: 2024,Page(s):516 - 529
Cited by: Papers (1)
Ultra-wideband (UWB) wireless indoor positioning systems rely on time-of-flight to estimate distances but can be biased and miscalculated due to non-line-of-sight (NLOS) transmission channels in complex environments. Therefore, to remove errors, several machine learning (ML) techniques have been proposed for identifying NLOS signals from channel impulse responses (CIRs). However, as CIR signals co...Show More
A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices
Publication Year: 2024,Page(s):530 - 540
Cited by: Papers (1)
Intent detection and slot filling are two main tasks in natural language understanding and play an essential role in task-oriented dialogue systems. The joint learning of both tasks can improve inference accuracy and is popular in recent works. However, most joint models ignore the inference latency and cannot meet the need to deploy dialogue systems at the edge. In this article, we propose a fast...Show More
Hierarchical Patch Selection: An Improved Patch Sampling for No Reference Image Quality Assessment
Publication Year: 2024,Page(s):541 - 555
Cited by: Papers (4)
Quality degradation due to the compression and the transmission of images is a significant threat to multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion and dynamically set the optimal parameters for developing image compression standards, image restoration algorithms, etc. Insufficient training data with quality scores are a challenge ...Show More
A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation
Publication Year: 2024,Page(s):556 - 565
Cited by: Papers (8)
Deep neural networks have played a vital role in developing automated methods for addressing medical image segmentation. However, their reliance on labeled data impedes the practicability. Semi-Supervised learning is gaining attention for its intrinsic ability to extract valuable information from labeled and unlabeled data with improved performance. Recently, consistency regularization methods hav...Show More
Datum-Wise Inference in Structured Environments
Publication Year: 2024,Page(s):566 - 577
Cited by: Papers (1)
In various application domains (e.g., health psychology), experts use Bayesian networks to represent relationships among variables. However, these variables are not, in practice, directly observable but can be instead inferred via noisy but costly features. Herein, we study the problem of datum-wise feature selection and classification in the case where the label of each data instance is described...Show More
Online Aware Synapse Weighted Autoencoder for Recovering Random Missing Data in Wastewater Treatment Process
Publication Year: 2024,Page(s):578 - 589
Cited by: Papers (2)
Missing values in wastewater treatment process (WWTP) data hinder the monitoring and prediction of operational status. Therefore, various online imputation methods have been proposed to recover missing values from streaming data collected from WWTP from real time. However, existing methods tend to ignore previous learned knowledge. In this article, an online aware synapse weighted autoencoder impu...Show More
Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation
Publication Year: 2024,Page(s):590 - 601
Cited by: Papers (4)
Nonnegative matrix factorization (NMF) algorithms are a series of dimensional reduction techniques widely used in data preprocessing. To improve the performance of clustering and the discrimination of the low-dimensional representation in NMF, we proposed a novel semisupervised constrained nonnegative matrix factorization based on label propagation (LpCNMF). Specifically, the proposed LpCNMF adopt...Show More
This work introduces novel architecture components and training procedures to create augmented neural networks with the ability to process data bidirectionally via an end-to-end approximate inverse. We develop pseudoinvertible neural network (PsI-NN) layers which function as drop-in replacements for corresponding convolutional and fully connected layers; by using these, existing architectures gain...Show More
Video-Based Depth Estimation Autoencoder With Weighted Temporal Feature and Spatial Edge Guided Modules
Publication Year: 2024,Page(s):613 - 623
Convolutional neural networks with encoder and decoder structures, generally referred to as autoencoders, are used in many pixelwise transformation, detection, segmentation, and estimation applications, for example, which can be applied for face swapping, lane detection, semantic segmentation, and depth estimation, respectively. However, traditional autoencoders, which are based on single-frame in...Show More
Masked Siamese Prompt Tuning for Few-Shot Natural Language Understanding
Publication Year: 2024,Page(s):624 - 633
Cited by: Papers (5)
Recently, prompt-based learning has shown excellent performance on few-shot scenarios. Using frozen language models to tune trainable continuous prompt embeddings has become a popular and powerful methodology. For few-shot natural language understanding, even if we freeze the parameters of the pretrained language model, the learned pseudo-prompts might still be overfitted. In this article, we prop...Show More
Masked Siamese Prompt Tuning for Few-Shot Natural Language Understanding
Year: 2024 | Volume: 5 | Issue: 2
Deep Q-Learning-Based Molecular Graph Generation for Chemical Structure Prediction From Infrared Spectra
Publication Year: 2024,Page(s):634 - 646
Cited by: Papers (4)
In this article, we present a novel approach to predicting chemical structures from their infrared (IR) spectra using deep Q-learning. IR spectra measurements are widely used in chemical analysis because they provide information on the types and characteristics of chemical bonds present within compounds. However, there are currently no algorithms to predict the entire chemical structure of a broad...Show More
Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost
Jiao Tian;Pei-Wei Tsai;Kai Zhang;Xinyi Cai;Hongwang Xiao;Ke Yu;Wenyu Zhao;Jinjun Chen
Publication Year: 2024,Page(s):647 - 660
Cited by: Papers (6)
Applying sparsity- and overfitting-aware eXtreme Gradient Boosting (XGBoost) for classification in federated learning allows many participants to train a series of trees collaboratively. Since various local multiclass distributions and global aggregation diversity, model performance plummets as convergence slowly and accuracy decreases. Worse still, neither the participants nor the server can dete...Show More
Pyramid Dynamic Bayesian Networks for Key Performance Indicator Prediction in Long Time-Delay Industrial Processes
Publication Year: 2024,Page(s):661 - 671
Building dynamic Bayesian networks (DBNs) for time-delay industrial processes has always been tough, since the structure learning of the DBN is a NP-hard problem. In this article, a pyramid DBN (PDBN) framework is proposed to speed up modeling and improve feature learning for industrial processes with large time delays. In the PDBN framework, a sequence of small-sized DBNs are established, each of...Show More
$\log$-Sigmoid Activation-Based Long Short-Term Memory for Time-Series Data Classification
Publication Year: 2024,Page(s):672 - 683
Cited by: Papers (4)
With the enhanced usage of artificial-intelligence-driven applications, the researchers often face challenges in improving the accuracy of data classification models, while trading off the complexity. In this article, we address the classification of time-series data using the long short-term memory (LSTM) network while focusing on the activation functions. While the existing activation functions,...Show More
Query Attack by Multi-Identity Surrogates
Publication Year: 2024,Page(s):684 - 697
Cited by: Papers (3)
Deep neural networks (DNNs) are acknowledged as vulnerable to adversarial attacks while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates. For query-efficiency, surrogate models of the victim are used to generate transferable adversarial examples (AEs) because of their gradient similarity (GS), i.e., surrogates' attack gradients are similar to...Show More
A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework
H Pallab Jyoti Dutta;M. K. Bhuyan;Debanga Raj Neog;Karl F. MacDorman;R. H. Laskar
Publication Year: 2024,Page(s):698 - 708
Cited by: Papers (5)
In this article, we propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requir...Show More
Pseudo-shot Learning for Soil Classification With Laser-Induced Breakdown Spectroscopy
Publication Year: 2024,Page(s):709 - 723
Cited by: Papers (2)
Laser-induced breakdown spectroscopy (LIBS) has become an emerging analytical technique for soil analysis. The application of machine learning for quantitative and qualitative analysis has made LIBS more promising. However, the emission line distribution can be highly variable due to the soil samples' varied physical properties and/or chemical composition. It may cause spectra distribution change ...Show More
A Radial Basis Function-Based Graph Attention Network With Squeeze Loss Optimization for Link Prediction
Publication Year: 2024,Page(s):724 - 736
Graph attention networks are a popular method to deal with link prediction tasks, but the weight assigned to each sample is not focusing on the sample's own performance in training. Moreover, since the number of links is much larger than nodes in a graph, mapping functions are usually used to map the learned node features to link features, whose expression of node similarity determines the quality...Show More
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