Tootfinder

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo
https://arxiv.org/abs/2508.06078

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning.Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems.The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this de…

Audio-Visual Speech Enhancement: Architectural Design and Deployment Strategies
Anis Hamadouche, Haifeng Luo, Mathini Sellathurai, Tharm Ratnarajah
https://arxiv.org/abs/2508.08468

Audio-Visual Speech Enhancement: Architectural Design and Deployment Strategies
This paper introduces a new AI-based Audio-Visual Speech Enhancement (AVSE) system and presents a comparative performance analysis of different deployment architectures. The proposed AVSE system employs convolutional neural networks (CNNs) for spectral feature extraction and long short-term memory (LSTM) networks for temporal modeling, enabling robust speech enhancement through multimodal fusion of audio and visual cues. Multiple deployment scenarios are investigated, including cloud-based, edg…

Short-Term Gaze Prediction: Analysis of Individual Differences, Typical and Extreme-Case Errors
Kateryna Melnyk, Lee Friedman, Oleg Komogortsev
https://arxiv.org/abs/2509.07126 …

Short-Term Gaze Prediction: Analysis of Individual Differences, Typical and Extreme-Case Errors
Gaze prediction is a diverse field of study with multiple research focuses and practical applications. This article investigates how recurrent neural networks and transformers perform short-term gaze prediction. We used three models: a three-layer long-short-term memory (LSTM) network, a simple transformer-encoder model (TF), and a classification-predictor network (ClPr), which simultaneously classifies the signal into eye movement events and predicts the positions of gaze. The performance of t…

Solar Flare Prediction Using LSTM and DLSTM with Sliding Window Pattern Recognition
Zeinab Hassani, Davud Mohammadpur, Hossein Safari
https://arxiv.org/abs/2507.05313

Solar Flare Prediction Using LSTM and DLSTM with Sliding Window Pattern Recognition
We investigate the use of Long Short-Term Memory (LSTM) and Decomposition-LSTM (DLSTM) networks, combined with an ensemble algorithm, to predict solar flare occurrences using time-series data from the GOES catalog. The dataset spans from 2003 to 2023 and includes 151,071 flare events. Among approximately possible patterns, 7,552 yearly pattern windows are identified, highlighting the challenge of long-term forecasting due to the Sun's complex, self-organized criticality-driven behavior. A slidi…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
Jonathan Teagan
https://arxiv.org/abs/2509.07813 https://arxiv.org/pdf/2509…

Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses during the ongoing war in Ukraine. Drawing on daily and monthly open-source intelligence (OSINT) data from WarSpotting, we aim to assess trends in attrition, evaluate model performance, and estimate future loss patterns through the end of 2025. Our findings show that deep learning …

EmoAugNet: A Signal-Augmented Hybrid CNN-LSTM Framework for Speech Emotion Recognition
Durjoy Chandra Paul, Gaurob Saha, Md Amjad Hossain
https://arxiv.org/abs/2508.06321 https:…

EmoAugNet: A Signal-Augmented Hybrid CNN-LSTM Framework for Speech Emotion Recognition
Recognizing emotional signals in speech has a significant impact on enhancing the effectiveness of human-computer interaction (HCI). This study introduces EmoAugNet, a hybrid deep learning framework, that incorporates Long Short-Term Memory (LSTM) layers with one-dimensional Convolutional Neural Networks (1D-CNN) to enable reliable Speech Emotion Recognition (SER). The quality and variety of the features that are taken from speech signals have a significant impact on how well SER systems perfor…

Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
Kong Mun Yeen, Rafidah Md Noor, Wahidah Md Shah, Aslinda Hassan, Muhammad Umair Munir
https://arxiv.org/abs/2509.02076

Forecasting Future DDoS Attacks Using Long Short Term Memory (LSTM) Model
This paper forecasts future Distributed Denial of Service (DDoS) attacks using deep learning models. Although several studies address forecasting DDoS attacks, they remain relatively limited compared to detection-focused research. By studying the current trends and forecasting based on newer and updated datasets, mitigation plans against the attacks can be planned and formulated. The methodology used in this research work conforms to the Cross Industry Standard Process for Data Mining (CRISP-DM…

Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves
Taiki Kawamuro, Shinya Yamada, Shigehiro Nagataki, Shunji Matsuura, Yusuke Sakai, Satoshi Yamada
https://arxiv.org/abs/2507.05589

Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves
We investigate whether a novel method of quantum machine learning (QML) can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we adopt is a quantum version of the long-short term memory (LSTM) where some fully connected layers are replaced with quantum circuits. The LSTM, making predictions based on preceding data, allows identification of anomalies by comparing predicted and actual time…

Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
Adebola K. Ojo, Ifechukwude Jude Okafor
https://arxiv.org/abs/2507.01964 h…

Forecasting Nigerian Equity Stock Returns Using Long Short-Term Memory Technique
Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. Th…

Deep Learning-Enhanced for Amine Emission Monitoring and Performance Analysis in Industrial Carbon Capture Plants
Lokendra Poudel, David Tincher, Duy-Nhat Phan, Rahul Bhowmik
https://arxiv.org/abs/2509.05241

Deep Learning-Enhanced for Amine Emission Monitoring and Performance Analysis in Industrial Carbon Capture Plants
We present data driven deep learning models for forecasting and monitoring amine emissions and key performance parameters in amine-based post-combustion carbon capture systems. Using operational data from the CESAR1 solvent campaign at Technology Center Mongstad, four DL architectures such as Basic Long Short-Term Memory (LSTM), Stacked LSTM, Bi-directional LSTM, and Convolutional LSTM were developed to capture time-dependent process behavior. For emission prediction, models were designed for 2…

Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media
Khalid Hasan, Jamil Saquer, Mukulika Ghosh
https://arxiv.org/abs/2507.19511

Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media
The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Processing (NLP), particularly transformer-based architectures, have demonstrated significant potential in text analysis. This study provides a comprehensive evaluation of state-of-the-art transformer models (BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA) against Long Short-Term Memory (LSTM) based approaches using diff…

Emulating Global 21 cm Cosmology Observations from the Lunar Far Side to Achieve Quick and Reliable Physical Constraints
J. Dorigo Jones, J. O. Burns, D. Rapetti, Shah Mohammad Bahauddin, B. Reyes, D. W. Barker
https://arxiv.org/abs/2509.00976

Emulating Global 21 cm Cosmology Observations from the Lunar Far Side to Achieve Quick and Reliable Physical Constraints
Efforts are underway to measure the global 21 cm signal from neutral hydrogen, which is a powerful probe of the early universe, using NASA radio telescopes on the far side of the Moon. Physics-based models of the signal are computationally expensive to perform Bayesian multi-parameter inferences, for which we have developed novel, publicly-available neural network emulators utilizing a Long Short-Term Memory (LSTM) network and a Kolmogorov-Arnold Network (KAN). $\texttt{21cmLSTM}$ is currently …

Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control
Ruisi Li, Xinhui Gu
https://arxiv.org/abs/2507.00332 https://…

Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control
Propose a deep learning driven multi factor investment model optimization method for risk control. By constructing a deep learning model based on Long Short Term Memory (LSTM) and combining it with a multi factor investment model, we optimize factor selection and weight determination to enhance the model's adaptability and robustness to market changes. Empirical analysis shows that the LSTM model is significantly superior to the benchmark model in risk control indicators such as maximum retrace…

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Abhiram Bhupatiraju, Sung Bum Ahn
https://arxiv.org/abs/2507.22220

Explainability-Driven Feature Engineering for Mid-Term Electricity Load Forecasting in ERCOT's SCENT Region
Accurate load forecasting is essential to the operation of modern electric power systems. Given the sensitivity of electricity demand to weather variability and temporal dynamics, capturing non-linear patterns is essential for long-term planning. This paper presents a comparative analysis of machine learning models, Linear Regression, XGBoost, LightGBM, and Long Short-Term Memory (LSTM), for forecasting system-wide electricity load up to one year in advance. Midterm forecasting has shown to be …

Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials
Yannick Hollenweger, Dennis M. Kochman, Burigede Liu
https://arxiv.org/abs/2508.18806

Temperature-Aware Recurrent Neural Operator for Temperature-Dependent Anisotropic Plasticity in HCP Materials
Neural network surrogate models for constitutive laws in computational mechanics have been in use for some time. In plasticity, these models often rely on gated recurrent units (GRUs) or long short-term memory (LSTM) cells, which excel at capturing path-dependent phenomena. However, they suffer from long training times and time-resolution-dependent predictions that extrapolate poorly. Moreover, most existing surrogates for macro- or mesoscopic plasticity handle only relatively simple material b…

IoT Malware Network Traffic Detection using Deep Learning and GraphSAGE Models
Nikesh Prajapati, Bimal Karki, Saroj Gopali, Akbar Siami Namin
https://arxiv.org/abs/2507.10758

IoT Malware Network Traffic Detection using Deep Learning and GraphSAGE Models
This paper intends to detect IoT malicious attacks through deep learning models and demonstrates a comprehensive evaluation of the deep learning and graph-based models regarding malicious network traffic detection. The models particularly are based on GraphSAGE, Bidirectional encoder representations from transformers (BERT), Temporal Convolutional Network (TCN) as well as Multi-Head Attention, together with Bidirectional Long Short-Term Memory (BI-LSTM) Multi-Head Attention and BI-LSTM and LSTM…

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
Jihao Li, Keqi Zhu, Guodong Lu, I-Ming Chen, Huixu Dong
https://arxiv.org/abs/2506.11570

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning
We present a novel under-actuated gripper with two 3-joint fingers, which realizes force feedback control by the deep learning technique- Long Short-Term Memory (LSTM) model, without any force sensor. First, a five-linkage mechanism stacked by double four-linkages is designed as a finger to automatically achieve the transformation between parallel and enveloping grasping modes. This enables the creation of a low-cost under-actuated gripper comprising a single actuator and two 3-phalange fingers…

Efficacy of Temporal Fusion Transformers for Runoff Simulation
Sinan Rasiya Koya, Tirthankar Roy
https://arxiv.org/abs/2506.20831 https://

Efficacy of Temporal Fusion Transformers for Runoff Simulation
Combining attention with recurrence has shown to be valuable in sequence modeling, including hydrological predictions. Here, we explore the strength of Temporal Fusion Transformers (TFTs) over Long Short-Term Memory (LSTM) networks in rainfall-runoff modeling. We train ten randomly initialized models, TFT and LSTM, for 531 CAMELS catchments in the US. We repeat the experiment with five subsets of the Caravan dataset, each representing catchments in the US, Australia, Brazil, Great Britain, and …

Detection of Adverse Drug Events in Dutch clinical free text documents using Transformer Models: benchmark study
Rachel M. Murphy (Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands), Nishant Mishra (Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, The Netherlands), Nicolette F. de Keizer (Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Amsterdam, T…

Detection of Adverse Drug Events in Dutch clinical free text documents using Transformer Models: benchmark study
In this study, we set a benchmark for adverse drug event (ADE) detection in Dutch clinical free text documents using several transformer models, clinical scenarios and fit-for-purpose performance measures. We trained a Bidirectional Long Short-Term Memory (Bi-LSTM) model and four transformer-based Dutch and/or multilingual encoder models (BERTje, RobBERT, MedRoBERTa.nl, and NuNER) for the tasks of named entity recognition (NER) and relation classification (RC) using 102 richly annotated Dutch I…

Forecasting Probability Distributions of Financial Returns with Deep Neural Networks
Jakub Micha\'nk\'ow
https://arxiv.org/abs/2508.18921 https://a…

Forecasting Probability Distributions of Financial Returns with Deep Neural Networks
This study evaluates deep neural networks for forecasting probability distributions of financial returns. 1D convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) architectures are used to forecast parameters of three probability distributions: Normal, Student's t, and skewed Student's t. Using custom negative log-likelihood loss functions, distribution parameters are optimized directly. The models are tested on six major equity indices (S\&P 500, BOVESPA, DAX, WIG, Nikkei 225, …

A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari, Mohamed Chahine Ghanem
https://arxiv.org/abs/2508.12470 …

A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security
The increased Internet of Medical Things IoMT and the Industrial Internet of Things IIoT interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces a novel transformer-based intrusion detection system IDS, termed BiGAT-ID a hybrid model that combines bidirectional gated recurrent units BiGRU, long short-term memory LSTM networks, and multi-head att…

Machine Learning-Based Estimation of Monthly GDP
Yonggeun Jung
https://arxiv.org/abs/2506.14078 https://arxiv.org/pdf/2506.14078

Machine Learning-Based Estimation of Monthly GDP
This paper proposes a scalable framework to estimate monthly GDP using machine learning methods. We apply Multi-Layer Perceptron (MLP), Long Short-Term Memory networks (LSTM), Extreme Gradient Boosting (XGBoost), and Elastic Net regression to map monthly indicators to quarterly GDP growth, and reconcile the outputs with actual aggregates. Using data from China, Germany, the UK, and the US, our method delivers robust performance across varied data environments. Benchmark comparisons with prior U…

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
Maciej Wysocki, Pawe{\l} Sakowski
https://arxiv.org/abs/2508.14999 https://

Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models
This paper investigates an important problem of an appropriate variance-covariance matrix estimation in the Modern Portfolio Theory. We propose a novel framework for variancecovariance matrix estimation for purposes of the portfolio optimization, which is based on deep learning models. We employ the long short-term memory (LSTM) recurrent neural networks (RNN) along with two probabilistic deep learning models: DeepVAR and GPVAR to the task of one-day ahead multivariate forecasting. We then use …

When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria
Habeeb Balogun, Yahaya Zakari
https://arxiv.org/abs/2508.16244 ht…

When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria
Air pollution forecasting is critical for proactive environmental management, yet data irregularities and scarcity remain major challenges in low-resource regions. Northern Nigeria faces high levels of air pollutants, but few studies have systematically compared the performance of advanced machine learning models under such constraints. This study evaluates Long Short-Term Memory (LSTM) networks and the Facebook Prophet model for forecasting multiple pollutants (CO, SO2, SO4) using monthly obse…

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Khalid Hasan, Jamil Saquer
https://arxiv.org/abs/2507.14231

Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media
Bipolar disorder is a chronic mental illness frequently underdiagnosed due to subtle early symptoms and social stigma. This paper explores the advanced natural language processing (NLP) models for recognizing signs of bipolar disorder based on user-generated social media text. We conduct a comprehensive evaluation of transformer-based models (BERT, RoBERTa, ALBERT, ELECTRA, DistilBERT) and Long Short Term Memory (LSTM) models based on contextualized (BERT) and static (GloVe, Word2Vec) word embe…

Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM
Zicheng Guo, Shuqi Wu, Meixing Zhu, He Guandi
https://arxiv.org/abs/2508.11215 https://arxiv.org…

Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM
With the intensification of global climate change, accurate prediction of air quality indicators, especially PM2.5 concentration, has become increasingly important in fields such as environmental protection, public health, and urban management. To address this, we propose an air quality PM2.5 index prediction model based on a hybrid CNN-LSTM architecture. The model effectively combines Convolutional Neural Networks (CNN) for local spatial feature extraction and Long Short-Term Memory (LSTM) net…

Explainable AI in Deep Learning-Based Prediction of Solar Storms
Adam O. Rawashdeh, Jason T. L. Wang, Katherine G. Herbert
https://arxiv.org/abs/2508.16543 https://

Explainable AI in Deep Learning-Based Prediction of Solar Storms
A deep learning model is often considered a black-box model, as its internal workings tend to be opaque to the user. Because of the lack of transparency, it is challenging to understand the reasoning behind the model's predictions. Here, we present an approach to making a deep learning-based solar storm prediction model interpretable, where solar storms include solar flares and coronal mass ejections (CMEs). This deep learning model, built based on a long short-term memory (LSTM) network with a…