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Academic Articles & Research Papers
Showing 20 of 54 academic articles
Olusola Bamisile, Dongsheng Cai, Humphrey Adun, Chukwuebuka Ejiyi et al. • Energy Reports
(2023)
The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic ...
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Open Access (DOAJ)
DOI: 10.1016/j.egyr.2023.05.229
Zhewei Zhang, Huzi Cheng, Tianming Yang • PLoS Computational Biology
(2020)
The brain makes flexible and adaptive responses in a complicated and ever-changing environment for an organism's survival. To achieve this, the brain needs to understand the contingencies between its sensory inputs, actions, and rewards. Th...
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Open Access (DOAJ)
DOI: 10.1371/journal.pcbi.1008342
Aditya Malte, Pratik Ratadiya • arXiv (Cornell University)
(2019)
In this paper, we present a study of the recent advancements which have helped bring Transfer Learning to NLP through the use of semi-supervised training. We discuss cutting-edge methods and architectures such as BERT, GPT, ELMo, ULMFit amo...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1910.07370
Dheya Mustafa, Safaa M. Khabour, Mousa Al-kfairy, Ahmed Shatnawi • PeerJ Computer Science
(2025)
Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a cru...
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Open Access (DOAJ)
DOI: 10.7717/peerj-cs.2669
Wenpeng Yin, Katharina Kann, Mo Yu, Hinrich Schütze • arXiv (Cornell University)
(2017)
Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle var...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1702.01923
Dejun Zhang, Mingbo Hong, Lu Zou, Fei Han et al. • International Journal of Computational Intelligence Systems
Recurrent neural network (RNN) is one of the most popular architectures for addressing variable sequence text, and it shows outstanding results in many natural language processing (NLP) tasks and remarkable performance in capturing long-ter...
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Open Access (DOAJ)
DOI: 10.2991/ijcis.d.190710.001
Palash Goyal, Sumit Kumar Pandey, Karan Jain • Apress eBooks
(2018)
Discover the concepts of deep learning used for natural language processing (NLP) in this practical book, with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2...
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DOI: https://doi.org/10.1007/978-1-4842-3685-7
Hang Li • National Science Review
(2017)
Deep learning refers to machine learning technologies for learning and utilizing ‘deep’ artificial neural networks, such as deep neural networks (DNN), convolutional neural networks (CNN) and recurrent neural networks (RNN). Recently, deep ...
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DOI: https://doi.org/10.1093/nsr/nwx110
Aditya Jain, Gandhar Kulkarni, Vraj Shah • International Journal of Computer Sciences and Engineering
(2018)
Natural language processing is widely discussed and researched topic nowadays. As it is one of the oldest area of research in machine learning it is used in major fields such as machine translation speech recognition and text processing. Na...
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DOI: https://doi.org/10.26438/ijcse/v6i1.161167
Yumin Zhang, Yanyong Wang • EURASIP Journal on Image and Video Processing
(2025)
Abstract Due to the rapid advances in computer vision and deep learning, human action recognition has become one of the most important representative tasks for video understanding. Especially for human action recognition based on RGB-D data...
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Open Access (DOAJ)
DOI: 10.1186/s13640-025-00677-0
Muhammad Salihin Saealal, Mohd Zamri Ibrahim, David. J. Mulvaney, Mohd Ibrahim Shapiai et al. • PLoS ONE
(2022)
Deep learning is notably successful in data analysis, computer vision, and human control. Nevertheless, this approach has inevitably allowed the development of DeepFake video sequences and images that could be altered so that the changes ar...
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Open Access (DOAJ)
Xinkun Tang, Ying Xu, Feng Ouyang, Ligu Zhu • Applied Sciences
(2023)
Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details...
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Open Access (DOAJ)
DOI: 10.3390/app131810165
Alex Warstadt, Amanpreet Singh, Samuel R. Bowman • Transactions of the Association for Computational Linguistics
(2019)
This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the Corpus of Linguistic Acceptability (CoLA), a set ...
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DOI: https://doi.org/10.1162/tacl_a_00290
Ya Zhang, Ravie Chandren Muniyandi, Faizan Qamar • Applied Sciences
(2025)
In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. T...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/app15031552
Haohan Wang, Bhiksha Raj • arXiv (Cornell University)
(2017)
This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1702.07800
Fuyong Xing, Yuanpu Xie, Hai Su, Fujun Liu et al. • IEEE Transactions on Neural Networks and Learning Systems
(2017)
Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...
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DOI: https://doi.org/10.1109/tnnls.2017.2766168
Jong-Min Kim • Axioms
(2025)
This paper presents the use of Copula-based deep learning with Horvitz–Thompson (HT) weights and inverse probability of treatment weighting (IPTW) for estimating propensity scores in causal inference problems. This study compares the perfor...
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Open Access (DOAJ)
DOI: 10.3390/axioms14060458
Senzhang Wang, Jiannong Cao, Philip S. Yu • IEEE Transactions on Knowledge and Data Engineering
(2020)
With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temp...
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Open Access
DOI: https://doi.org/10.1109/tkde.2020.3025580
Junjie Ji, Yongzhang Zhou, Qiuming Cheng, Shoujun Jiang et al. • Land
(2023)
Selecting samples with non-landslide attributes significantly impacts the deep-learning modeling of landslide susceptibility mapping. This study presents a method of information value analysis in order to optimize the selection of negative ...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/land12061125
Liyan Luo, Liujun Zhang, Mei Wang, Zhenghong Liu et al. • IEEE Access
(2021)
In recent decades, surveillance and home security systems based on video analysis have been proposed for the automatic detection of abnormal situations. Nevertheless, in several real applications, it may be easier to detect a given event fr...
DOAJ
Open Access (DOAJ)
DOI: 10.1109/ACCESS.2021.3123970