3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology by Chongyang YAO, Yongxin CHOU, Zhiwei LIANG, Haiping YANG, Jicheng LIU, Dongmei LIN

“…Firstly, a 3D pulse image detection system based on binocular vision to obtain pulse image signals is developed as experimental data. …”
Get full text

Financial fraud detection using a hybrid deep belief network and quantum optimization approach by Gui Yu, Zhenlin Luo

“…To address this issue, this paper proposes a novel financial fraud detection algorithm that integrates deep belief networks (DBN) with quantum optimisation algorithms. …”
Get full text

Enhanced Deep Autoencoder-Based Reinforcement Learning Model with Improved Flamingo Search Policy Selection for Attack Classification by Dharani Kanta Roy, Hemanta Kumar Kalita

“…The proposed work introduces an intelligent semi-supervised intrusion detection system based on different algorithms to classify the network attacks accurately. …”
Get full text

An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…Thus, hybrid and optimized DL models are increasingly necessary to improve detection performance and address data imbalance and noise. …”
Get full text

Using Smartwatches in Stress Management, Mental Health, and Well-Being: A Systematic Review by Nikoletta-Anna Kapogianni, Angeliki Sideraki, Christos-Nikolaos Anagnostopoulos

“…Drawing from 61 peer-reviewed studies published between 2016 and 2025, this review synthesizes empirical findings across diverse methodologies, including biometric data collection, machine learning algorithms, and user-centered design evaluations. …”
Get full text

One-Class Anomaly Detection for Industrial Applications: A Comparative Survey and Experimental Study by Davide Paolini, Pierpaolo Dini, Ettore Soldaini, Sergio Saponara

“…To address the limitations posed by restricted access to proprietary data, the study explores OCC methods that learn solely from legitimate network traffic, without requiring labeled malicious samples. …”
Get full text

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study by Chengkun Sun, Erin Mobley, Michael Quillen, Max Parker, Meghan Daly, Rui Wang, Isabela Visintin, Ziad Awad, Jennifer Fishe, Alexander Parker, Thomas George, Jiang Bian, Jie Xu

“…ObjectiveOur study aims to predict EOCRC using machine learning (ML) and structured electronic health record data for individuals under the screening age of 45 years, with the aim of exploring potential risk and protective factors that could support early diagnosis. …”
Get full text

Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

“…The major drawback of using conventional methods for identifying breast cancer using the available data sets is that a single algorithm is not sufficient for accurate breast cancer diagnosis due to the heterogeneity of tumors, diverse data types, pattern complexity, feature engineering and dataset overfitting. …”
Get full text

A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection by Mohammad Mohebbi, Amirhossein Manzourolajdad, Ethan Bennett, Phillip Williams

“…These images are processed in parallel by the MINN algorithm, allowing it to learn a comprehensive and precise representation of the underlying biological mechanisms. (3) Results: Our method, on an experimentally validated test set, detects target sites with an AUPRC of 0.9373, Precision of 0.8725, and Recall of 0.8703 and outperforms several commonly used computational methods of microRNA target-site predictions. (4) Conclusions: Incorporating diverse biologically explainable features, such as duplex structure, substructures, their MFEs, and binding probabilities, enables our model to perform well on experimentally validated test data. …”
Get full text

An Adaptive Framework for Collective Anomaly Detection in Key Performance Indicators From Mobile Networks by Madalena Cilinio, Thaina Saraiva, Marco Sousa, Pedro Vieira, Antonio Rodrigues

“…By leveraging data mining and Machine Learning (ML) techniques, the framework enables timely anomaly detection without requiring expert-labeled data. …”
Get full text

SAR remote sensing for monitoring harmful algal blooms using deep learning models by Kritnipit Phetanan, Do Hyuck Kwon, Jinmyeong Lee, Heewon Jeong, Gibeom Nam, Euiho Hwang, JongCheol Pyo, Kyung Hwa Cho

Get full text

Anomaly detection with grid sentinel framework for electric vehicle charging stations in a smart grid environment by V. Thiruppathy Kesavan, Md. Jakir Hossen, R. Gopi, Emerson Raja Joseph

“…This technology can also detect and respond to suspicious movements dynamically using powerful machine learning algorithms (long short-term memory (LSTM), random forest, and autoencoder models), ensuring safety. …”
Get full text

Automated Indonesian Plate Recognition: YOLOv8 Detection and TensorFlow-CNN Character Classification by Windu Gata, Dwiza Riana, Muhammad Haris, Maria Irmina Prasetiyowati, Dika Putri Metalica

Get full text

Deep Learning-Based Fault Diagnosis via Multisensor-Aware Data for Incipient Inter-Turn Short Circuits (ITSC) in Wind Turbine Generators by Qinglong Wang, Shihao Cui, Entuo Li, Jianhua Du, Na Li, Jie Sun

“…The experimental results demonstrate that deep learning models outperform machine learning algorithms in terms of precision and stability, achieving an mAP of 99.25% in fault identification, with three-phase current signals emerging as the most reliable indicator of generator faults compared to vibration and electromagnetic data. …”
Get full text

Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques by Mohammed Qader Kheder, Aree Ali Mohammed

“…Additionally, an IoT-aided robotic (IoRT) model has been developed with a modern architecture that integrates IoT sensor nodes and cameras to gather real-time traffic data. The main contributions of this research work are to implement two deep learning techniques based on modified LeNet-5 for real-time traffic sign recognition and the transfer learning-based Inception-V3 model for detecting and recognizing traffic lights. …”
Get full text

Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion by Rocío Aznar-Gimeno, Jose Luis Perez-Lasierra, Pablo Pérez-Lázaro, Irene Bosque-López, Marina Azpíroz-Puente, Pilar Salvo-Ibáñez, Martin Morita-Hernandez, Ana Caren Hernández-Ruiz, Antonio Gómez-Bernal, María de la Vega Rodrigalvarez-Chamarro, José-Víctor Alfaro-Santafé, Rafael del Hoyo-Alonso, Javier Alfaro-Santafé

“…This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. …”
Get full text

AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring.MethodsThis paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies. …”
Get full text

Computer Vision for Substrate Detection in High‐Throughput Biomaterial Screens Using Bright‐Field Microscopy by Robert Owen, Aishah Nasir, Mahetab H. Amer, Chenxue Nie, Xuan Xue, Laurence Burroughs, Chris Denning, Ricky D. Wildman, Faraz A. Khan, Morgan R. Alexander, Felicity R. A. J. Rose

Get full text

Enhanced Heart Disease Classification Using Dual Attention Mechanisms and 3D-Echo Fusion Algorithm in Echocardiogram Videos by S Deepika, N. Jaisankar

Get full text

Deformable detection transformers for domain adaptable ultrasound localization microscopy with robustness to point spread function variations by Sepideh K. Gharamaleki, Brandon Helfield, Hassan Rivaz

“…However, MB localization faces challenges due to dynamic point spread functions (PSFs) caused by harmonic and sub-harmonic emissions, as well as depth-dependent PSF variations in ultrasound imaging. Additionally, deep learning models often struggle to generalize from simulated to in vivo data due to significant disparities between the two domains. …”
Get full text