Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation by N.S. Davydov, V.V. Evdokimova, P.G. Serafimovich, V.I. Protsenko, A.G. Khramov, A.V. Nikonorov

“…Subject head motion remains the main source of artifacts - even the tiniest head movement can perturb the structural and functional data derived from the fMRI. In this paper, we propose an end-to-end neural network technology for detecting step anomalies with training on partially synthetic data with adaptation to a specific small set of real data. …”
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Multi-sensor Fault Detection for Natural Gas Combined Cycle Power Plants Based on Multiple Robust Input Training Neural Network Models by Zheng HUANG, Hongxing WANG, Haiquan YU, Dou LI, Fengqi SI

“…The influence of numerous failure data with significant errors can be effectively inhibited by virtue of reliable sensor data calculated from cooperative multi-model, such that the accuracy and reliability of fault detection is greatly improved. …”
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Microvascular Metrics on Diabetic Retinopathy Severity: Analysis of Diabetic Eye Images from Real-World Data by Cristina Cuscó, Pau Esteve-Bricullé, Ana Almazán-Moga, Jimena Fernández-Carneado, Berta Ponsati

“…<b>Objective:</b> To quantify microvascular lesions in a large real-world data (RWD) set, based on single central retinal fundus images of diabetic eyes from different origins, with the aim of validating its use as a precision tool for classifying diabetic retinopathy (DR) severity. …”
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PhenoVision: A framework for automating and delivering research‐ready plant phenology data from field images by Russell Dinnage, Erin Grady, Nevyn Neal, Jonn Deck, Ellen Denny, Ramona Walls, Carrie Seltzer, Robert Guralnick, Daijiang Li

“…One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global‐scale research‐grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist—data spanning all angiosperms and drawn from across the globe—to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. …”
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Socio-demographic inequalities in the prevalence, diagnosis and management of hypertension in India: analysis of nationally-representative survey data. by Kath A Moser, Sutapa Agrawal, George Davey Smith, Shah Ebrahim

“…Small studies suggest high, and increasing, prevalence especially in urban areas, with poor detection and management, but national data has been lacking. …”
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A multi-factor integration-based semi-supervised learning for address resolution protocol attack detection in SDIIoT by Zhong Li, Huimin Zhuang

“…We conduct experiments based on a real data set collected from a deepwater port and a simulated data set. …”
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Face Recognition for Personal Data Collection using Eigenface, Support Vector Machine, and Viola Jones Method by Lalu Zazuli Azhar Mardedi, Muhammad Zulfikri, Moch. Syahrir, Kurniadin Abd. Latif, Apriani Apriani

“…This study examines the implementation of machine learning as a solution, utilizing video and photo data for face detection and recognition. The study’s goal is to evaluate the effectiveness of facial image recognition by combining several methods, aiming for practical application across diverse settings, such as offices and schools. …”
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A Unified Approach to Video Anomaly Detection: Advancements in Feature Extraction, Weak Supervision, and Strategies for Class Imbalance by Rui Z. Barbosa, Hugo S. Oliveira

“…This paper explores advancements in Video Anomaly Detection (VAD), combining theoretical insights with practical solutions to address model limitations. …”
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A multi-tiered feature selection model for android malware detection based on Feature discrimination and Information Gain by Parnika Bhat, Kamlesh Dutta

“…There are several malware detection approaches available to fortify the Android operating system from such attacks. …”
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Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data by Sajad Amouei Sheshkal, Morten Gundersen, Michael Alexander Riegler, Øygunn Aass Utheim, Kjell Gunnar Gundersen, Helge Rootwelt, Katja Benedikte Prestø Elgstøen, Hugo Lewi Hammer

“…To assess the performance of these models, we selected a set of suitable evaluation metrics tailored to the data set’s challenges. …”
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Real-Time Damage Detection in an Airplane Wing During Wind Tunnel Testing Under Realistic Flight Conditions by Yoav Ofir, Uri Ben-Simon, Shay Shoham, Iddo Kressel, Bernardino Galasso, Umberto Mercurio, Antonio Concilio, Gianvito Apuleo, Jonathan Bohbot, Moshe Tur

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FakeMusicCaps: A Dataset for Detection and Attribution of Synthetic Music Generated via Text-to-Music Models by Luca Comanducci, Paolo Bestagini, Stefano Tubaro

“…We evaluate the proposed dataset by performing initial experiments regarding the detection and attribution of TTM-generated audio considering both closed-set and open-set classification.…”
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A novel framework for identifying fishing grounds from AIS data containing vessels of unknown types by Changhai Huang, Jialong Kong, Jian Zheng, Yuli Chen, Jingen Zhou

“…Based on this algorithm, a fishing behavior detection model is developed to identify fishing activity trajectories from AIS data that encompasses vessels of unknown types. …”
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IoT and ML-driven framework for managing infectious disease risks in communal spaces: a post-COVID perspective by Dhruv Parikh, Avaneesh Karthikeyan, V. Ravi, Merin Shibu, Riya Singh, Reka S. Sofana

“…These systems help us to efficiently monitor, authenticate, track health parameters, and process data in real-time. The face mask and face shield detection subsystems leverage a hybrid model that combines the capabilities of MobileNetV2 and VGG19, enabling more robust and accurate detection by leveraging MobileNetV2′s efficiency and VGG19′s depth in feature extraction, which has an overall accuracy of 97% and notably the face shield detection component obtains an efficiency of 99%. …”
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An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID‐19 by Zhe Chen, Nicholas W. Russo, Matthew M. Miller, Robert X. Murphy, David B. Burmeister

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Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition by Faheed A.F. Alrslani, Manal Abdullah Alohali, Mohammed Aljebreen, Hamed Alqahtani, Asma Alshuhail, Menwa Alshammeri, Wafa Sulaiman Almukadi

“…The primary goal of the ARDL-FDIAR technique is to improve security via the FDIA detection process. The ARDL-FDIAR technique uses Z-score normalization to scale the input data. …”
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Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study by Hongbo Chen, Myrtede Alfred, Eldan Cohen

“…ObjectiveWe aimed to investigate the efficacy of ICL in detecting stigmatizing language within EHRs under data-scarce conditions. …”
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High‐speed serializer timing‐error detection circuit with enhanced verification reliability through floating state prevention by Jongchan Lee, Joo‐Hyung Chae

“…A timing mismatch in the SER leads to bit errors during data transmission. To detect the timing error, the proposed design employs a set‐reset latch to self‐latch the detection result, improving the error detection accuracy, unlike the previously reported timing‐error detection design using a D flip‐flop. …”
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The adaptive constant false alarm rate for sonar target detection based on back propagation neural network access by Zhou Chen, Xianwen Zhao, Ziqi Zhou, Xuefei Ma, Qi Cheng, Xuan Cai, Bowang Jiang, Rahim Khan, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba

“…Abstract With oceanic reverberation and a large amount of data being the main sources of interference for underwater acoustic target detection, it is difficult to obtain a more robust detection performance by relying on the traditional constant false alarm rate (CFAR) detection method. …”
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Ultraviolet-Visible Spectroscopy and Chemometric Strategy Enable the Classification and Detection of Expired Antimalarial Herbal Medicinal Product in Ghana by Jacob N. Mensah, Abena A. Brobbey, John N. Addotey, Isaac Ayensu, Samuel Asare-Nkansah, Kwabena F. M. Opuni, Lawrence A. Adutwum

“…Support vector machine classification gave specificity and accuracy of 1.00 (100%) for training set data for all the products. The validation set HMP1, HMP2, and HMP3 had sensitivity, specificity, and accuracy of 1.00. …”
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