Edge detection of aerial images using artificial bee colony algorithm by Nurdan Akhan Baykan, Elif Deniz Yelmenoglu

Subjects: “…image processing…”
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Fusing Transformer-XL with bi-directional recurrent networks for cyberbullying detection by Md. Mithun Hossain, Md. Shakil Hossain, Md. Shakhawat Hossain, M. Firoz Mridha, Mejdl Safran, Sultan Alfarhood, Dunren Che

Subjects: “…Cyberbullying detection…”
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Advanced Color Edge Detection Using Clifford Algebra in Satellite Images by Uzair Aslam Bhatti, Zhou Ming-Quan, Qingsong Huo, Sajid Ali, Aamir Hussain, Yan Yuhuan, Zhaoyuan Yu, Linwang Yuan, Saqib Ali Nawaz

“…Edge detection is widely used for image processing to improve the detection and classification of objects, segmentation, and extraction of other features. …”
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A Graduate Level Personalized Learning Environment in the Field of f-NIRS Signal Processing by Dominique Persano Adorno, Giuseppe Costantino Giaconia

“…The study combines cutting-edge biosignal detection techniques with innovative educational strategies, offering substantial contributions to both bioengineering and educational research. …”
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Exploring Transformer-Based Learning for Negation Detection in Biomedical Texts by Ghadeer Althari, Mohammad Alsulmi

“…For both types of biomedical data, negation detection is an essential analytics task that can be applied to identify negated contexts in biomedical text (e.g., detecting the presence of a statement establishing that a patient does not have/fit a certain clinical condition or detecting statements that indicate the nonexistence of certain relations among biological entities). …”
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Enabling Model-Based Design for Real-Time Spike Detection by Mattia Di Florio, Yannick Bornat, Marta Care, Vinicius Rosa Cota, Stefano Buccelli, Michela Chiappalone

“…<italic>Results</italic>: The validation was conducted through in vivo experiments involving six animals and demonstrated the capability of our MBD-based real time processing (latency &lt;&#x003D; 100.37 &#x00B5;s) to achieve the same performances of offline spike detection. …”
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BibMon: An open source Python package for process monitoring, soft sensing, and fault diagnosis by Afrânio Melo, Tiago S.M. Lemos, Rafael M. Soares, Deris Spina, Nayher Clavijo, Luiz Felipe de O. Campos, Maurício Melo Câmara, Thiago Feital, Thiago K. Anzai, Pedro H. Thompson, Fábio C. Diehl, José Carlos Pinto

Subjects: “…Process monitoring…”
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Harnessing temporal dispersion for integrated pump filtering in spontaneous heralded single-photon generation processes by Julian Brockmeier, Timon Schapeler, Nina Amelie Lange, Jan Philipp Höpker, Harald Herrmann, Christine Silberhorn, Tim J Bartley

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Fault Classification in Diesel Engines Based on Time-Domain Responses through Signal Processing and Convolutional Neural Network by Gabriel Hasmann Freire Moraes, Ronny Francis Ribeiro Junior, Guilherme Ferreira Gomes

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Detection of Atrial Fibrillation in Holter ECG Recordings by ECHOView Images: A Deep Transfer Learning Study by Vessela Krasteva, Todor Stoyanov, Stefan Naydenov, Ramun Schmid, Irena Jekova

“…<b>Background/Objectives</b>: The timely and accurate detection of atrial fibrillation (AF) is critical from a clinical perspective. …”
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ATEX-Certified, FPGA-Based Three-Channel Quantum Cascade Laser Sensor for Sulfur Species Detection in Petrochemical Process Streams by Harald Moser, Johannes Paul Waclawek, Walter Pölz, Bernhard Lendl

“…In addition, field-programmable gate array (FPGA) hardware is used for real-time signal generation, laser driving, signal processing, and handling industrial communication protocols. …”
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Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images by M. Suriya Begum, S. Kalaivani

“…The proposed Cognitive Computing Process (CCP) detects and segments the nuclei using Deep U-Net with Spatial Attention Mechanisms (SAM) and microns-per-pixel measurements to accurately locate and assess nuclei density. …”
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Approaches to Extracting Patterns of Service Utilization for Patients with Complex Conditions: Graph Community Detection vs. Natural Language Processing Clustering by Jonas Bambi, Hanieh Sadri, Ken Moselle, Ernie Chang, Yudi Santoso, Joseph Howie, Abraham Rudnick, Lloyd T. Elliott, Alex Kuo

“…Method: Working with longitudinal cross-continuum encounter data from a regional health service system, various pattern detection analyses were conducted, employing (1) graph community detection algorithms, (2) natural language processing (NLP) clustering, and (3) a hybrid NLP–graph method. …”
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Understanding and detection of process instabilities in wire arc directed energy deposition additive manufacturing using meltpool imaging and machine learning by André Ramalho, Anis Assad, Benjamin Bevans, Fernando Deschamps, Telmo G. Santos, J.P. Oliveira, Prahalada Rao

“…This work concerns the wire arc directed energy deposition (WA-DED) additive manufacturing process. The objectives were two-fold: (1) observe and understand, through in-operando high-speed meltpool imaging, the causal dynamics of two common WA-DED process instabilities, namely, humping and humping-induced porosity; and (2) leverage the high-speed meltpool imaging data within machine learning algorithms for real-time detection of process instabilities. …”
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Computer Vision Algorithm for the detection of fracture cracks in Oil Hardening Non-Shrinking (OHNS) die steel after machining process by Akshansh Mishra, Vijaykumar S. Jatti, Nitin K. Khedkar, Rahul B. Dhabale, Ashwini V. Jatti

“…This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. …”
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Computer Vision Algorithm for the detection of fracture cracks in Oil Hardening Non-Shrinking (OHNS) die steel after machining process by Akshansh Mishra, Vijaykumar S Jatti, Nitin K Khedkar, Rahul B. Dhabale, Ashwini V Jatti

“…This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. …”
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Anomaly Detection and Business Process Orchestration for Low-Code Platform in Power System Based on Deep-Cross Model With Data Balancing by Jin Huang, Ming Sheng Xu, Chang Ming Zhu, Guo An Wang

“…Fourth, we develop an adaptive processing framework for asynchronous heterogeneous data streams, reducing detection latency by 62% compared to current synchronous methods. …”
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Detection and diagnostic process of liver abscess secondary to chemotherapy by mucor indicus in a patient with acute leukemic disease: case report by Hong Liu, Junjie Xia, Ming Guan

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Smart Power Systems Transformation: Advanced Fault Detection With Edge Computing and Signal Processing in LV Networks With EV Charging Stations by Partha Chakraborty, Pampa Sinha, Subhra Debdas, Kaushik Paul, Chidurala Saiprakash, Vasupalli Manoj, Taha Selim Ustun, Ahmet Onen

“…This research presents a novel framework for improving fault detection and grid resilience in modern power systems by leveraging edge computing, optimized infrastructure placement, and advanced signal processing. …”
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Application of Multiple Detection Data in the Analysis of Snowstorm Processes in Xinjiang during the Central Asia Extreme Precipitation Observation Test (CAEPOT) by Yong Zeng, Lianmei Yang, Zepeng Tong, Zuyi Zhang

“…To deepen the understanding of dynamics and microphysical processes and improve the level of forecasting and warning of snowstorms in northwestern China, the Institute of Desert Meteorology, China Meteorological Administration, Urumqi, carried out the Central Asia Extreme Precipitation Observation Test (CAEPOT) in Yili, Xinjiang, a typical arid region in China in February 2020. …”
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