Effective Context-Aware File Path Embeddings for Anomaly Detection by Ra-Kyung Lee, Hyun-Min Song, Taek-Young Youn

“…Unlike static embeddings, the proposed method processes file paths as structured sequences to enhance anomaly detection accuracy. …”
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An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks by Zhen Wang, Anazida Zainal, Maheyzah Md Siraj, Fuad A. Ghaleb, Xue Hao, Shaoyong Han

“…Abstract The application of artificial neural networks (ANNs) can be found in numerous fields, including image and speech recognition, natural language processing, and autonomous vehicles. As well, intrusion detection, the subject of this paper, relies heavily on it. …”
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Liquid Biopsy for Colorectal Cancer: Advancing Detection and Clinical Application by Yan Li, Qiong Zhang, Shelly Cook

“…While current diagnostic methods, such as colonoscopy and tissue biopsy, are widely employed in clinical practice, they are invasive, expensive, and limited in assessing tumor heterogeneity and monitoring disease processes, including therapy response. Therefore, early and accurate detection, coupled with minimal invasion and cost-effective strategies, are critical for improving patient outcomes. …”
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Mixture-of-experts graph transformers for interpretable particle collision detection by Donatella Genovese, Alessandro Sgroi, Alessio Devoto, Samuel Valentine, Lennox Wood, Cristiano Sebastiani, Simone Scardapane, Monica D’Onofrio, Stefano Giagu

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Energy-Efficient Deep Learning for Cloud Detection Onboard Nanosatellite by Imane Khalil, Amina Daghouri, Mohammed Alae Chanoui, Zouhair Guennoun, Adnane Addaim

“…Deep learning has been increasingly utilized for cloud detection in Earth observation nanosatellites, offering effective solutions to enhance mission performance. …”
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Detection of Deuterated Hydrocarbon Nanoparticles in the Whirlpool Galaxy, M51 by B. T. Draine, Karin Sandstrom, Daniel A. Dale, J.-D. T. Smith, Ryan Chown, Grant P. Donnelly, Sara E. Duval, Cory M. Whitcomb, Angela Adamo, L. Armus, Danielle A. Berg, Torsten Böker, Alberto D. Bolatto, Martha L. Boyer, Daniela Calzetti, B. G. Elmegreen, Brandt A. L. Gaches, Karl D. Gordon, L. K. Hunt, R. C. Kennicutt, Ralf S. Klessen, Thomas S.-Y. Lai, Adam K. Leroy, Sean T. Linden, Alex Pedrini, Noah S. J. Rogers, Julia C. Roman-Duval, Eva Schinnerer, Evan B. Skillman, Fabian Walter, Tony D. Weinbeck, Benjamin F. Williams

“…If so, the 4.65 μ m feature may be detectable in absorption.…”
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Optimizing Digital Image Quality for Improved Skin Cancer Detection by Bogdan Dugonik, Marjan Golob, Marko Marhl, Aleksandra Dugonik

“…Accurate imaging plays a crucial role in early detection, yet challenges related to color accuracy, image distortion, and resolution persist, leading to diagnostic errors. …”
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Pig Detection Algorithm Based on Sliding Windows and PCA Convolution by Longqing Sun, Yan Liu, Shuaihua Chen, Bing Luo, Yiyang Li, Chunhong Liu

“…An accurate and rapid pig detection algorithm based on video image processing technology can be helpful to identify abnormal pigs and take timely measures to reduce the incidence of diseases. …”
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Deep learning-based edge detection for random natural images by Kanija Muntarina, Rafid Mostafiz, Sumaita Binte Shorif, Mohammad Shorif Uddin

“…It serves as a fundamental step in tasks such as image segmentation, shape extraction, pattern recognition, auto-navigation, and motion analysis, with applications spanning various domains including radar and sonar image processing. The edge detection model attempts to identify points in digital images where significant intensity changes occur, known as edges or region boundaries. …”
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Explainable Graph Neural Networks for Power Grid Fault Detection by Richard Bosso, Corey Chang, Mahdi Zarif, Yufei Tang

“…This research presents a comprehensive framework that systematically evaluates state-of-the-art explanation strategies, representing the first use of such a framework for Graph Neural Network models for defect location detection. By assessing the strengths and weaknesses of different explanatory methods, it identifies and recommends the most effective strategies for clarifying the decision-making processes of GNN models. …”
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Emerging Biomarkers in Atherosclerosis: Advancing Early Detection and Risk Stratification by Aleksandra Sojka, Julia Sosin, Anna Pilarz, Julia Stachowiak, Maria Zwierzchowska, Dariusz Salamon, Wojciech Domagała

“…However, considering that atherosclerosis is one of the leading causes of mortality worldwide and its etiology still remains insufficiently elucidated, there is a pressing need for new, more specific biomarkers for its earlier detection. Aim of the study: The aim of this study is to summarize the knowledge concerning new potential biomarkers for the early detection of atherosclerosis through analyzing the biological processes associated with the initiation and progression of the disease, such as inflammation, oxidative stress, and endothelial dysfunction, to better understand its pathophysiology.  …”
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Blockchain smart contract vulnerability detection based on GR algorithm by WANG Deguang, SHAN Mengtao, LI Fengqi, TONG Ning

“…To address the current technical bottlenecks in smart contract vulnerability detection, such as limited coverage of vulnerability categories and detection efficiency, the study was conducted. …”
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A Comprehensive Review of EEG-Based Seizure Detection Techniques by Goldwyn Sudhakar Jebaraj, Konguvel Elango

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AN EVALUATION OF RETROSPECTIVE RESULTS DETECTED IN THE HOSPITAL INFECTION RESEARCH LABORATORY by Niyousha Matloobi Aghdam, Gülseren Aktaş

“…It is thought that the data we obtain will contribute to the infection control processes of the hospitals.…”
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Copy-move forgery detection using binary discriminant features by Priya Mariam Raju, Madhu S. Nair

“…The neighbouring blocks are then merged with the suspected regions based on color similarity, using Color Histogram Matching and a final morphological close operation extracts the detected forged regions. Experimental results show that the proposed method has better detection accuracy, in terms of precision, recall and F1 Score, when compared with the state-of-the-art methods of copy-move forgery detection, under conditions of plain copy-move forgery as well as post-processing conditions like brightness changes, contrast adjustments, color reduction and blurring.…”
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Edge AI for Real-Time Anomaly Detection in Smart Homes by Manuel J. C. S. Reis, Carlos Serôdio

“…Traditional cloud-based approaches introduce latency, privacy concerns, and network dependency, making Edge AI a compelling alternative for low-latency, on-device processing. This paper presents an Edge AI-based anomaly detection framework that combines Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) models to identify anomalies in IoT sensor data. …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…It focuses on evaluating deep learning techniques and unmanned aerial vehicles for crop disease detection. (Research purpose) The study aims to review and systemize scientific literature on the application of unmanned aerial vehicles, remote sensing technologies and deep learning 24 methods for the early detection and prediction of crop diseases. …”
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Road-Type Detection Based on Traffic Sign and Lane Data by Zoltán Fazekas, Gábor Balázs, Csaba Gyulai, Péter Potyondi, Péter Gáspár

“…The road is used by an ego vehicle with an on-board smart camera looking ahead and with a number of vehicular sensors. A complex processing step—not detailed in the study—detects TCDs with reasonable probability and error rate and locates them with respect to a 3D coordinate frame fixed to the ego vehicle. …”
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AGILE Gamma-Ray Detection of the Exceptional GRB 221009A by Marco Tavani, Giovanni Piano, Andrea Bulgarelli, Luca Foffano, Alessandro Ursi, Francesco Verrecchia, Carlotta Pittori, Claudio Casentini, Andrea Giuliani, Francesco Longo, Gabriele Panebianco, Ambra Di Piano, Leonardo Baroncelli, Valentina Fioretti, Nicolò Parmiggiani, Andrea Argan, Alessio Trois, Stefano Vercellone, Martina Cardillo, Lucio Angelo Antonelli, Guido Barbiellini, Patrizia Caraveo, Paolo W. Cattaneo, Andrew W. Chen, Enrico Costa, Ettore Del Monte, Guido Di Cocco, Immacolata Donnarumma, Yuri Evangelista, Marco Feroci, Fulvio Gianotti, Claudio Labanti, Francesco Lazzarotto, Paolo Lipari, Fabrizio Lucarelli, Martino Marisaldi, Sandro Mereghetti, Aldo Morselli, Luigi Pacciani, Alberto Pellizzoni, Francesco Perotti, Piergiorgio Picozza, Maura Pilia, Massimo Rapisarda, Andrea Rappoldi, Alda Rubini, Paolo Soffitta, Massimo Trifoglio, Valerio Vittorini, Fabio D’Amico

“…Gamma-ray emission in the MeV–GeV range from explosive cosmic events is of invaluable relevance to understanding physical processes related to the formation of neutron stars and black holes. …”
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Detecting Intermediate-mass Black Holes Using Quasar Microlensing by Zihao Wu, Luis C. Ho

“…These IMBHs largely preserve their birth masses, offering critical insights into the formation of heavy black hole seeds and the dynamical processes driving their evolution. We propose that such IMBHs could produce detectable microlensing effects on quasars. …”
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