Deep-learning-based detection of underwater fluids in multiple multibeam echosounder data by Tyméa Perret, Gilles Le Chenadec, Arnaud Gaillot, Yoann Ladroit, Stéphanie Dupré

“…Fluids can be detected in the water column using multibeam echosounder data. …”
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Electrocardiogram Abnormality Detection Using Machine Learning on Summary Data and Biometric Features by Kennette James Basco, Alana Singh, Daniel Nasef, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

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Qseek: A data-driven Framework for Automated Earthquake Detection, Localization and Characterization by Marius Isken, Sebastian Heimann, Peter Niemz, Jannes Münchmeyer, Simone Cesca, Hannes Vasyura-Bathke, Torsten Dahm

“… We introduce a data-driven method and software for detecting and locating earthquakes in large seismic datasets. …”
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Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data by H. T. Rüdisser, A. Windisch, U. V. Amerstorfer, C. Möstl, T. Amerstorfer, R. L. Bailey, M. A. Reiss

“…However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. …”
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Lightweight hybrid transformers-based dyslexia detection using cross-modality data by Abdul Rahaman Wahab Sait, Yazeed Alkhurayyif

“…Traditional dyslexia detection (DD) relies on lengthy, subjective, restricted behavioral evaluations and interviews. …”
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Enhancing Bee Mite Detection with YOLO: The Role of Data Augmentation and Stratified Sampling by Hong-Gu Lee, Jeong-Yong Shin, Su-Bae Kim, Min-Jee Kim, Moon S. Kim, Hoyoung Lee, Changyeun Mo

“…The model applied data augmentation, and stratified sampling achieved the highest performance, with F1 scores of 97.4% and 96.4% for the detection of bee mites and seven beekeeping-related objects, respectively. …”
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Vision-Based Activity Recognition for Unobtrusive Monitoring of the Elderly in Care Settings by Rahmat Ullah, Ikram Asghar, Saeed Akbar, Gareth Evans, Justus Vermaak, Abdulaziz Alblwi, Amna Bamaqa

“…The system integrates a frame differencing algorithm with adjustable sensitivity parameters and an anomaly detection model tailored to identify deviations from individual behavior patterns without relying on large volumes of labeled data. …”
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Risk Management of Import and Export Products Based on Big Data Analysis: Assessment Model with IndetermSoft Set by Ling Chen, Chunpeng Liu

“…The integration of big data analytics into risk evaluation processes has revolutionized the ability to detect, assess, and mitigate potential threats across international trade operations. …”
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Constraining the Mass of a Hypothetical Secondary Black Hole in M87 with the NANOGrav 15 yr Data Set by Motoki Kino, Masahiro Nagashima, Hyunwook Ro, Yuzhu Cui, Kazuhiro Hada, Jongho Park

“…To constrain the mass ratio between the primary SMBH ( M _1 ) and the secondary black hole ( M _2 ) defined as q ≡ M _2 / M _1 ≤ 1, and the length of the semimajor axis of the binary system ( a ), we impose the following three constraints: (i) the lower limit of a , below which the SMBH binary is expected to merge; (ii) the strain amplitude of the gravitational-wave background at nanohertz frequencies shown in the NANOGrav 15 yr data set; and (iii) a finite length of the semimajor axis of M _1 , which can induce periodic behavior in the jet. …”
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Improved model for intrusion detection in the Internet of Things by Marina S. Amine, Fayza A. Nada, Khalid M. Hosny

“…Abstract The Internet of Things (IoT) includes many devices generating vast amounts of data that need extensive computation. IoT has several definitions, but the most popular refers to multiple devices, objects, and sensors all connecting via a network to exchange data. …”
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Comparative Analysis of Transformers to Support Fine-Grained Emotion Detection in Short-Text Data by Robert H. Frye, David C. Wilson

“…To understand the applicability and tradeoffs among common transformers within such contexts, our research investigates accuracy and efficiency considerations in fine-tuning transformers for granular emotion detection in short-text data. This paper presents a comparative study investigating the performance of five common transformers as applied in the specific context of multi-category emotion detection in short-text Twitter data. …”
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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification by Mohamed Refaat Abdellah, Hasan H. Oudah, Ahmed Mohamed Ahmed Badawy, Mohamed AbdElFattah AbdElFattah M Hassan, Shady Ahmed Bedier, Ahmed A. Metwaly, Mohamed eassa, Ahmed Abdelhafeez

“…Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. …”
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A Parameter-Free Topological Disassembly-Guided Method for Hyperspectral Target Detection by Xiaotong Sun, Lina Zhuang, Lianru Gao, Hongmin Gao, Xu Sun, Bing Zhang

“…Additionally, properties of point sets in the image-level topology are exploited to accurately quantify feature differences among various objects, further improving detection accuracy and reliability. …”
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Multi-Modal Feature Set-Based Detection of Freezing of Gait in Parkinson’s Disease Patients Using SVM by Ghulam Murtaza, Mohammed Hammoud, Andrey Somov

“…This work aims to detect FoG using a multi-modal feature set. A publicly available multi-modal dataset was used, comprising data collected from 12 patients using various sensors, including Electroencephalogram (EEG), Electromyography (EMG), tri-axial ACC (accelerometers and gyroscopes), and Skin Conductance (SC) sensors. …”
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Evaluating model generalization for cow detection in free-stall barn settings: Insights from the COw LOcalization (COLO) dataset by Mautushi Das, Gonzalo Ferreira, C.P. James Chen

“…This study investigates the generalization capabilities of object detection models for cow detection in indoor free-stall barn settings, focusing on varying training data characteristics such as view angles and lighting, and model complexities. …”
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Application research of sample data generation based on improved Cycle-GAN in intrusion detection by ZENG Qingpeng, GUO Hangkai

“…The issue of slow data updates, insufficient data samples for certain intrusion categories, and imbalanced distributions between normal and abnormal data sets in standard intrusion detection data sets have been addressed through both data sample augmentation and detection model optimization. …”
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Cyber intrusion detection using ensemble of deep learning with prediction scoring based optimized feature sets for IOT networks by Deepesh M. Dhanvijay, Mrinai M. Dhanvijay, Vaishali H. Kamble

“…To address these issues, we propose the Ensemble of Deep Learning Models with Prediction Scoring-based Optimized Feature Sets (EDLM-PSOFS). Our approach begins with data preprocessing utilizing MissForest imputation and label one-hot encoding, effectively managing incomplete and categorical data.For feature selection, we employ the Median-based Shapiro-Wilk test alongside Correlation-Adaptive LASSO Regression (CALR) to ensure robust feature extraction. …”
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Detection and prioritization of COVID-19 infected patients from CXR images: Analysis of AI-assisted diagnosis in clinical settings by Carlo Alberto Barbano, Luca Berton, Riccardo Renzulli, Davide Tricarico, Osvaldo Rampado, Domenico Basile, Marco Busso, Marco Grosso, Marco Grangetto

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Leveraging Gradient Noise for Detection and Filtering of Byzantine Clients by Latifa Errami, Vyacheslav Kungurtsev, El Houcine Bergou

“…Distributed Learning enables multiple clients to collaboratively train large models on private, decentralized data. However, this setting faces a significant challenge: real-world datasets are inherently heterogeneous, and the distributed nature of the system makes it vulnerable to Byzantine attacks. …”
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Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system by Parminder Singh, Sujatha Krishnamoorthy, Anand Nayyar, Ashish Kr Luhach, Avinash Kaur

“…The recurrent neural network model is applied to classify the data set of intrusion detection system and normal instances for various subclasses. …”
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