Enhancing parkinson disease detection through feature based deep learning with autoencoders and neural networks by P. Valarmathi, Y. Suganya, K. R. Saranya, S. Shanmuga Priya

“…The present study introduces a potentially effective approach for the automated and non-intrusive identification of Parkinson’s disease through the analysis of audio data. The integration of Autoencoder-based feature extraction with Deep Neural Networks (DNN) presents a dependable and easily accessible solution for the early detection and continuous monitoring of Parkinson’s disease. …”
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A complex roadside object detection model based on multi-scale feature pyramid network by Zhihao Zheng, Jianguang Zhao, Jingjing Fan, Ruirui Bai, Jiana Zhao, Jianan Liu

“…Combining the weighted Bi-directional Feature Pyramid Network (BCFPN) for feature fusion incorporates deep, shallow, and original features, reinforces feature integration, minimizes information loss during convolution processes, and enhances target detection accuracy. …”
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Echoes From the Void: Detecting DNS Tunneling With Blackhole Features in Encrypted Scenarios With High Accuracy by Wafa S. Alorainy

“…A five-fold cross-validation with a random forest classifier achieved 99.9% classification accuracy, a 99.9% F1 score, and 100% precision and recall. Feature importance analysis indicates that blackhole-related features contribute to the detection query-to-blackhole ratios (Gini importance of 0.35) and inter-blackhole intervals (0.20), which help focus on stealthy tunneling behavior. …”
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SFG-Net: A Scattering Feature Guidance Network for Oriented Aircraft Detection in SAR Images by Qingyang Ke, Youming Wu, Wenchao Zhao, Qingbiao Meng, Tian Miao, Xin Gao

“…Benefiting from the powerful capacity of feature extraction and analysis of deep learning, aircraft detection performance has been improved by most traditional general-purpose visual intelligence methods. …”
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UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism by Shaodan Lin, Deyao Huang, Libin Wu, Zuxin Cheng, Dapeng Ye, Haiyong Weng

“…This design aims to enhance feature extraction and focus on critical image regions, addressing the challenges of detecting small and complex disease patterns in UAV-captured imagery. …”
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Utility of FET-PET in detecting high-grade gliomas presenting with equivocal MR imaging features by Ameya D. Puranik, Mathew Boon, Nilendu Purandare, Venkatesh Rangarajan, Tejpal Gupta, Aliasgar Moiyadi, Prakash Shetty, Epari Sridhar, Archi Agrawal, Indraja Dev, Sneha Shah

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A Comparative Analysis of Artificial Intelligence Techniques for Single Open-Circuit Fault Detection in a Packed E-Cell Inverter by Bushra Masri, Hiba Al Sheikh, Nabil Karami, Hadi Y. Kanaan, Nazih Moubayed

Subjects: “…wavelet analysis…”
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Automated Earthwork Detection Using Topological Persistence by Dana A. Lapides, Gillian Grindstaff, Mary H. Nichols

“…Breached berms and berms with significant upslope sedimentation are most likely not to be detected using persistence. These results indicate that persistence can be useful for terrain analysis, and it has the potential to substantially reduce manual effort in feature detection by identifying regions where berms may be found.…”
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Multi-Frame Joint Detection Approach for Foreign Object Detection in Large-Volume Parenterals by Ziqi Li, Dongyao Jia, Zihao He, Nengkai Wu

“…Current methods for detecting foreign particles in LVP solutions through image analysis primarily rely on single-frame detection or simple temporal smoothing strategies, which fail to effectively utilize spatiotemporal correlations across multiple frames. …”
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On-line Identification Method of Pantograph Anomaly Based on Feature Analysis by WANG Junping, MAO Huihua, SHEN Yunbo, LI Miaocheng, CHEN Xin'an

“…Therefore, this paper proposes an on-line recognition method based on feature analysis and independent of the number of fault samples. …”
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Temporal Community Detection and Analysis with Network Embeddings by Limengzi Yuan, Xuanming Zhang, Yuxian Ke, Zhexuan Lu, Xiaoming Li, Changzheng Liu

“…In order to understand their dynamic characteristics, it is essential to investigate temporal community detection (TCD), which poses significant challenges compared to static network analysis. …”
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Machine learning models and dimensionality reduction for improving the Android malware detection by Pablo Morán, Antonio Robles-Gómez, Andres Duque, Llanos Tobarra, Rafael Pastor-Vargas

“…Predictive models based on Random Forest are found to achieve the most promising results. They can detect an average of 91.72% malware samples, with a very low false positive rate of 0.13%, and using only 5,000 features. …”
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Clinical features and prognostic analysis of AKI in the internal medicine departments by XUE Xian, TANG Xiao-jing, ZHANG Di, SHEN Ai-wen, MEI Chang-lin

“…Objective To explore the clinical features of patients with acute kidney injury(AKI)in the internal medicine departments.Methods The kidney function indexes of the internal medicine patients were collected in Shanghai Changzheng Hospital from 2011 to 2012.According to the AKI diagnostic criteria of KDIGO in 2012,we screened out the AKI patients who met the criteria.By retrospectively studying their clinical data,we analyzed and summarized their clinical characteristics and the factors affecting the prognosis.Results From January 2011 to December 2012,there were a total of 33 332 cases of internal medicine patients in our hospital and 245 of them met the criteria of AKI.The incidence of AKI was 0.74%in internal medicine departments while the detection rate of AKI was 35.92%.The proportions of patients with AKI stage 1 to 3 were 42.45%,20.41% and 37.14%,respectively.Among all the internal medicine departments,patients in the department of nephrology accounted for 33.47%,followed by the department of cardiology(20.00%),gastroenterology(11.84%)and infectious disease(10.61%).The incidence of AKI from high to low was infection disease department(1.70%),nephrology department(1.37%),and cardiology department(1.11%).Of all the hospitalized patients with AKI,71.84% discharged with a better health condition and the kidney function discovered in only 34.69% of the discharged patients;28.16% died or gave up treatment.The most common departments with high fatality rate were the department of infectious disease(21.74%),gastroenterology and cardiology.Infection was the main cause of the AKI in internal medicine departments.Logistic regression analysis showed that higher AKI stage,more AKI risk factors,combination with liver disease or tumor were risk factors for death and giving up treatment,while chronic kidney disease was the protective factor.ConclusionsAKI is common in internal medicine system,with a high mortality rate and a higher recognition rate than perioperative AKI.Except for the department of nephrology,cardiology,departments of infectious disease and gastroenterology have heavy burden of AKI.The advanced stage of AKI,more risk factors,and comorbidities such as liver disease or tumor were associated with poor prognosis.…”
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Clinical Features and Genetic Analysis of Drug-resistant Epilepsy in Children by ZUO Ranran, SUN Suzhen

“…Methods A retrospective analysis of 95 children with DRE who were hospitalized in Hebei Children's Hospital from 2020 to 2022. …”
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Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities by Nourhan Zayed, Heba A. Elnemr

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Large-scale candidate gene analysis of HDL particle features. by Bernhard M Kaess, Maciej Tomaszewski, Peter S Braund, Klaus Stark, Suzanne Rafelt, Marcus Fischer, Robert Hardwick, Christopher P Nelson, Radoslaw Debiec, Fritz Huber, Werner Kremer, Hans Robert Kalbitzer, Lynda M Rose, Daniel I Chasman, Jemma Hopewell, Robert Clarke, Paul R Burton, Martin D Tobin, Christian Hengstenberg, Nilesh J Samani

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Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection by Mishmala Sushith, Ajanthaa Lakkshmanan, M. Saravanan, S. Castro

“…Unlike traditional methods which evolved so far in DR analysis, the proposed model specifically processes the multi-scale spatial features through dual spatial transformer network and captures the temporal dependencies through adaptive temporal convolutional unit. …”
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Machine vision-based detection of key traits in shiitake mushroom caps by Jiuxiao Zhao, Jiuxiao Zhao, Wengang Zheng, Wengang Zheng, Yibo Wei, Yibo Wei, Qian Zhao, Qian Zhao, Jing Dong, Jing Dong, Xin Zhang, Xin Zhang, Mingfei Wang, Mingfei Wang

“…After conducting correlation analysis between phenotypic features and shiitake mushroom caps weight, four most correlated phenotypic features were identified: Area, Perimeter, External rectangular width, and Long axis; they were divided into four groups based on their correlation rankings. …”
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DETECTING POST!PRODUCTION TAMPERING ARTIFACTS IN VIDEO AND AUDIO RECORDINGS ON FAT16 AND FAT32 FORMATTED STORAGE DEVICES (METHODOLOGY RECOMMENDATIONS FOR FORENSIC PRACTITIONERS) by A. G. Boyarov

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Vision transformer embedded video anomaly detection using attention driven recurrence by Ummay Maria Muna, Shanta Biswas, Syed Abu Ammar Muhammad Zarif, Philip Jefferson Deori, Tauseef Tajwar, Swakkhar Shatabda

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