Engineering Spectro-Temporal Light States with Physics-Embedded Deep Learning by Shilong Liu, Stéphane Virally, Gabriel Demontigny, Patrick Cusson, Denis V. Seletskiy

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Mixed-Embeddings and Deep Learning Ensemble for DGA Classification With Limited Training Data by Christian Morbidoni, Alessandro Cucchiarelli, Luca Spalazzi

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Deep-Learning-Based Cognitive Assistance Embedded Systems for People with Visual Impairment by Huu-Huy Ngo, Hung Linh Le, Feng-Cheng Lin

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Spoof speech classification using deep speaker embeddings and machine learning models by Mohammed Hamzah Alsalihi, Dávid Sztahó

“…This paper examines the effectiveness of deep speaker embeddings combined with machine learning classifiers for spoof speech detection. …”
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Hate speech detection in Arabic social networks using deep learning and fine-tuned embeddings by Samar Al-Saqqa, Arafat Awajan, Bassam Hammo

“…This paper presents a suggested model to enhance the detection performance of hate speech using deep learning models with two types of word embedding models, the first model is Arabic models based on Wor2Vec including AraVec and Mazajak. …”
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A Hybrid Attention-Based Transformer Model for Arabic News Classification Using Text Embedding and Deep Learning by Md. Mithun Hossain, Md. Shakil Hossain, Mejdl Safran, Sultan Alfarhood, Meshal Alfarhood, M. F. Mridha

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Handling data sparsity via item metadata embedding into deep collaborative recommender system by Gopal Behera, Neeta Nain

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Weed detection on embedded systems using computer vision algorithms by D. Shadrin, S. Illarionova, R. Kasatov, M. Akimenkova, G. Rudensky, E. Erhan

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Effective defense against physically embedded backdoor attacks via clustering-based filtering by Mohammed Kutbi

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Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets by Yusuf Aliyu, Aliza Sarlan, Kamaluddeen Usman Danyaro, Abdullahi Sani abd Rahman, Aminu Aminu Muazu, Mustapha Yusuf Abubakar

“…The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. …”
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ArcticSoundsNET: BirdNET embeddings facilitate improved bioacoustic classification of Arctic species by Morgan A. Ziegenhorn, Richard B. Lanctot, Stephen C. Brown, Miles Brengle, Shiloh Schulte, Sarah T. Saalfeld, Christopher J. Latty, Paul A. Smith, Nicolas Lecomte

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Detecting Fake News in Urdu Language Using Machine Learning, Deep Learning, and Large Language Model-Based Approaches by Muhammad Shoaib Farooq, Syed Muhammad Asadullah Gilani, Muhammad Faraz Manzoor, Momina Shaheen

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Deep-CABPred: Deep learning model for predicting functional chlorophyll a-b binding proteins in trait-based plant ecology using hybrid embedding with semi-normalized temporal convolutional networks by Farman Ali, Raed Alsini, Tamim Alkhalifah, Fahad Alturise, Wajdi Alghamdi, Majdi Khalid

“…To overcome this, we developed Deep-CABPred, a novel deep learning model for efficient CAB prediction. …”
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Cword2vec: a novel morphological rule-based word embedding approach for Urdu text sentiment analysis by Saquib Khushhal, Abdul Majid, Syed Ali Abass, Rabia Riaz, Mohammad Babar, Shafiq Ahmad

“…The performance of self-trained morphological rule-based compound word embedding was then evaluated using four well-known deep learning models, i.e., long short-term memory (LSTM), bidirectional LSTM (BiLSTM), convolutional neural networks (CNN), and convolutional LSTM (C-LSTM) for sentiment analysis. …”
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Short term prediction of photovoltaic power with time embedding temporal convolutional networks by Jingxin Wang, Guohan Li, Jin Gu, Zhengyi Xu, Xinrong Chen, Jianming Wei

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Computing the Deep Semantics of Visual Communications by Trpimir Jeronim Ježić, Marko Maričević, Ivana Pavlović, Miroslav Mikota

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HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors by Duong Thanh Tran, Nhat Truong Pham, Nguyen Doan Hieu Nguyen, Leyi Wei, Balachandran Manavalan

“…While traditional experimental approaches are time-consuming and expensive, computational methods have emerged as viable alternatives, including similarity-based and machine learning (ML)-/deep learning (DL)-based methods. However, existing methods often struggle with robustness and generalizability. …”
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Triboelectric Nanogenerator-Embedded Intelligent Self-Aligning Roller Bearing with the Capability of Self-Sensing, Monitoring, and Fault Diagnosis by Hao Shen, Yufan Lv, Yun Kong, Qinkai Han, Ke Chen, Zhibo Geng, Mingming Dong, Fulei Chu

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A New Analysis of Web Customer Service Text Classification of Alexa Virtual Assistant Commands Using a Deep Learning Model by Seidakhmet Nurpatsha

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Fish Detection Using Deep Learning by Suxia Cui, Yu Zhou, Yonghui Wang, Lujun Zhai

“…The processing procedure can mimic human being’s learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. …”
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