Detection of hypertension from pharyngeal images using deep learning algorithm in primary care settings in Japan by Takeo Nakayama, Yusuke Tsugawa, Hiroshi Yoshihara, Memori Fukuda, Sho Okiyama

Get full text

Collecting and Analyzing Eye-Tracking Data in Outdoor Environments by Karen M. Evans, Robert A. Jacobs, John A. Tarduno, Jeff B. Pelz

“…During analyses of a large set of eye-tracking data collected on geologists examining outdoor scenes, we have found that the nature of calibration, pupil identification, fixation detection, and gaze analysis all require procedures different from those typically used for indoor studies. …”
Get full text

Stochastic algorithm for HDFS data theft detection based on MapReduce by Yuanzhao GAO, Binglong LI, Xingyuan CHEN

“…To address the problems of big data efficient analysis and insider theft detection in the data theft detection of distributed cloud computing storage,taking HDFS (hadoop distributed file system) as a case study,a stochastic algorithm for HDFS data theft detection based on MapReduce was proposed.By analyzing the MAC timestamp features of HDFS generated by folder replication,the replication behavior’s detection and measurement method was established to detect all data theft modes including insider theft.The data set which is suitable for MapReduce task partition and maintains the HDFS hierarchy was designed to achieve efficient analysis of large-volume timestamps.The experimental results show that the missed rate and the number of mislabeled folders could be kept at a low level by adopting segment detection strategy.The algorithm was proved to be efficient and had good scalability under the MapReduce framework.…”
Get full text

Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment by Sikha S. Bagui, Germano Correa Silva De Carvalho, Asmi Mishra, Dustin Mink, Subhash C. Bagui, Stephanie Eager

“…If the objective is to detect every single attack, the results indicate that 325 clusters with a seed of 200, using an optimal set of features, would be able to correctly place 99% of attacks.…”
Get full text

Assessing Fracture Detection: A Comparison of Minimal-Resource and Standard-Resource Plain Radiographic Interpretations by Iskandar Zakaria, Teuku Muhammad Yus, Safrizal Rahman, Azhari Gani, Muhammad Ariq Ersan

“…In resource-limited settings (minimal-resource settings), imaging quality is often lower than in standard-resource facilities, potentially affecting diagnostic accuracy. …”
Get full text

Optimization of acousto-ultrasonic sensor networks using genetic algorithms based on experimental and numerical data sets by Ryan Marks, Alastair Clarke, Carol A Featherston, Rhys Pullin

“…Experimental data sets were acquired using three-dimensional scanning laser vibrometry enabling in-plane and out-of-plane Lamb wave components to be considered. …”
Get full text

A data fusion based approach for damage detection in linear systems by Ernesto Grande, Maura Imbimbo

“…The aim of the present paper is to propose innovative approaches able to improve the capability of classical damage indicators in detecting the damage position in linear systems. In particular, starting from classical indicators based on the change of the flexibility matrix and on the change of the modal strain energy, the proposed approaches consider two data fusion procedures both based on the Dempster-Shafer theory. …”
Get full text

Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. by Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke

“…This paper introduces a novel feature engineering method that circumvents these limitations by amalgamating two feature sets derived from input data to generate a new dataset, which is then subjected to feature selection. …”
Get full text

scFocus: Detecting branching probabilities in single-cell data with SAC by Chunlin Chen, Zeyu Fu, Jiajia Yang, Huaqing Chen, Jiabao Huang, Shitian Qin, Chuhuai Wang, Xiaoqian Hu

Get full text

Detecting small seamounts in multibeam data using convolutional neural networks by Tobias Ziolkowski, Colin W. Devey, Agnes Koschmider

“…Seamounts play a crucial role in marine ecosystems, ocean circulation, and plate tectonics, yet most remain unmapped due to limitations in detection methods. While satellite altimetry provides large-scale coverage, its resolution is insufficient for detecting smaller seamounts, necessitating high-resolution multibeam bathymetry. …”
Get full text

Anomaly Detection Algorithms for Real-Time Log Data Analysis at Scale by Andras Horvath, Andras Olah, Attila Pinter, Balint Siklosi, Gergely Lukacs, Istvan Z. Reguly, Kalman Tornai, Tamas Zsedrovits, Zoltan Mathe

“…In recent years, Artificial Intelligence for IT Operations (AIOps) has gained popularity as a solution to various challenges in IT operations, particularly in anomaly detection. Although numerous studies have focused on anomaly detection, they often overlook cloud-based systems and the vast amount of data they generate. …”
Get full text

Collaborative Surveillance: Using a Minimum Set of Key Data Parameters for One Health Participatory Surveillance by Mark Smolinski, Nomita Divi, Onicio Leal Neto

“…To enable this vision, we propose a minimum set of key data parameters for One Health participatory surveillance that could be collected in any system through self-reporting by the public. …”
Get full text

Deep learning for enhanced prediction of diabetic retinopathy: a comparative study on the diabetes complications data set by Weijun Gong, You Pu, Tiao Ning, Yan Zhu, Gui Mu, Jing Li

“…While existing studies predominantly focus on image-based AI diagnosis, there is a pressing need for accurate risk prediction using structured clinical data. The purpose of this study was to develop, compare, and validate models for predicting retinopathy in diabetic patients via five traditional statistical models and deep learning models.MethodsOn the basis of 3,000 data points from the Diabetes Complications Data Set of the National Center for Population Health Sciences Data, the differences in the characteristics of patients with diabetes mellitus and diabetes combined with retinopathy were statistically analyzed using SPSS software. …”
Get full text

Real‐World Data of Comprehensive Cancer Genomic Profiling Tests Performed in the Routine Clinical Setting in Sarcoma by Eiji Nakata, Daisuke Ennishi, Tatsunori Osone, Kiichiro Ninomiya, Shuta Tomida, Takuto Itano, Tomohiro Fujiwara, Toshiyuki Kunisada, Naoyuki Ida, Hideki Yamamoto, Mashu Futagawa, Tatsunori Shimoi, Hiroyuki Yanai, Akira Hirasawa, Shinichi Toyooka, Masahiro Tabata, Toshifumi Ozaki

“…However, the clinical impact of CGP, as covered by public health insurance in the management of sarcomas, remains unknown. Especially, the data on the utility of the newly emerging dual DNA–RNA panel compared to the conventional DNA‐only panel in clinical settings is lacking. …”
Get full text

Foundation Models and Fine-Tuning: A Benchmark for Out of Distribution Detection by Francesco Cappio Borlino, Lorenzo Lu, Tatiana Tommasi

Get full text

Unsupervised Attribute Reduction Algorithms for Multiset-Valued Data Based on Uncertainty Measurement by Xiaoyan Guo, Yichun Peng, Yu Li, Hai Lin

“…Missing data introduce uncertainty in data mining, but existing set-valued approaches ignore frequency information. …”
Get full text

Research on Detection Technology of Wind Turbine Blade AnomalyBased on Audio Data by HU Kaikai, CHEN Yanan, CHEN Gang, SHU Hui, LI Ziyuan

“…By installing a pickup on the wind turbine, analyzing and mining the collected wind turbine audio data, and based on the multi classification machine learning model, it explores a set of audio data feature analysis and pattern recognition methods. …”
Get full text

Application of weighted low rank approximations: outlier detection in a data matrix by Marisol García-Peña, Sergio Arciniegas-Alarcón, Kaye E. Basford

“…This paper presents strategies to identify outliers in any data set using weighted approximations of a matrix. …”
Get full text

Subspace‐based distributed target detection method with small training data samples by Guangfen Wei, Zhan Zhou, Yuan Luo, Tao Jian, Xiaoming Tang

“…Generally, distributed targets are often modelled with subspace models of unknown coordinates, and clutter is modelled as the complex Gaussian distribution with zero mean and unknown covariance matrix, while covariance matrix is estimated with a set of training data without the target signal. …”
Get full text

Genotyping from targeted NGS data based on a small set of SNPs correctly matches patient samples by Deyan Yordanov Yosifov, Christof Schneider, Stephan Stilgenbauer, Daniel Mertens, Eugen Tausch

“…Results We compiled a custom list of 28 SNPs and with its help we demonstrated the practicability of using only tNGS data to cost-effectively detect mislabelled samples. …”
Get full text