Assessment of Manual Abilities Using the Box and Block Test in Children with Bilateral Cerebral Palsy by Vanessa Zapata-Figueroa, Fernando Ortiz-Corredor

“…The BBT was applied to 162 children with cerebral palsy of bilateral distribution aged 6 to 13 years. The level of performance was evaluated according to the Manual Ability Classification System (MACS), Gross Motor Function Classification System (GMFCS), and Pediatric Evaluation of Disability Inventory (PEDI) in the domains of self-care, mobility, and social function. …”
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Self-perception of stuttering: association with self-perception of hearing, fluency profile, and contextual aspects by Camila Eduarda Elias Silva, Denise Brandão de Oliveira Britto, Stela Maris Aguiar Lemos

“…Methods Fifty-five adults who stutter (ages 18 to 58 years), speakers of Brazilian Portuguese speakers, participated in an observational study that included: (a) a clinical history survey to collect identification, sociodemographic, clinical, and assistance data; (b) the Brazil Economic Classification Criteria (CCEB); (c) a hearing self-perception questionnaire (Speech, Spatial and Qualities of Hearing Scale – SSQ, version 5.6); (d) self-perception of the impact of stuttering (Brazilian Portuguese version of the Overall Assessment of the Speaker's Experience of Stuttering – Adults – OASES-A); and (e) an assessment of speech fluency (Fluency Profile Assessment Protocol -- PAPF). …”
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Computed Tomography Features of Incidentally Detected Diffuse Thyroid Disease by Myung Ho Rho, Dong Wook Kim

“…When the “3 or more” CT classification was selected, the sensitivity, specificity, positive and negative predictive values, and accuracy of CT diagnosis for DTD were 55.8%, 95.5%, 80.6%, 86.7%, and 85.6%, respectively. …”
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Multidimensional analogs of A. Bicardze system by Eugenijus Paliokas

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Decoding substance use disorder severity from clinical notes using a large language model by Maria Mahbub, Gregory M. Dams, Sudarshan Srinivasan, Caitlin Rizy, Ioana Danciu, Jodie Trafton, Kathryn Knight

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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

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Epidemiological Characteristics and Prognostic Scoring in Toxic Epidermal Necrolysis and Stevens–Johnson Syndrome: Insights from a 17-Year Burn Center Experience by David Breidung, Sarina Delavari, Ioannis-Fivos Megas, Alexander Geierlehner, Wolfgang Hitzl, Karl J. Bodenschatz, Konrad Karcz, Denis Ehrl, Moritz Billner

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A Rare Anomaly of Biliary System: MRCP Evidence of a Cystic Duct Cyst by Cemil Goya, Mehmet Serif Arslan, Alpaslan Yavuz, Cihad Hamidi, Suzan Kuday, Mehmet Hanifi Okur, Bahattin Aydogdu

“…While the other bile duct cysts (choledochus and the intrahepatic bile ducts) are classified according to the classification described by Tadoni, there is no classification method described by the cystic duct cysts, although it is claimed that the cystic duct cysts may constitute a new “Type 6” category. …”
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Effectiveness of an E-Nose Based on Metal Oxide Semiconductor Sensors for Coffee Quality Assessment by Yhan S. Mutz, Samara Mafra Maroum, Leticia L. G. Tessaro, Natália de Oliveira Souza, Mikaela Martins de Bem, Loyane Silvestre Alves, Luisa Pereira Figueiredo, Denes K. A. do Rosario, Patricia C. Bernardes, Cleiton Antônio Nunes

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Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients by Jing Li, Xiantong Zhen, Xianzeng Liu, Gaoxiang Ouyang

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Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model by Hui Chen, Shenghua Xiong, Xuan Ren

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Training Neural Networks with a Procedure Guided by BNF Grammars by Ioannis G. Tsoulos , Vasileios Charilogis

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A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features by P. Amudha, S. Karthik, S. Sivakumari

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An Artificial Intelligence Method for Asphalt Pavement Pothole Detection Using Least Squares Support Vector Machine and Neural Network with Steerable Filter-Based Feature Extractio... by Nhat-Duc Hoang

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Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach. by Qiao Xiao, Chaofeng Wang

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Floer homology and non-fibered knot detection by John A. Baldwin, Steven Sivek

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A Malware Detection Scheme Based on Mining Format Information by Jinrong Bai, Junfeng Wang, Guozhong Zou

“…When the selected features were trained using classification algorithms, the results of our experiments indicate that the accuracy of the top classification algorithm is 99.1% and the value of the AUC is 0.998. …”
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Hierarchical Clustering for Anomalous Traffic Conditions Detection in Power Substations by Erwin Alexander Leal Piedrahita

“…The results obtained show that, using a hierarchical algorithm with Euclidean distance proximity criterion and simple link grouping method, a correct classification is achieved in the following operation scenarios: 1) Normal traffic, 2) IED disconnection, 3) Network discovery attack, 4) DoS attack, 5) IED spoofing attack and 6) Failure on the high voltage line. …”
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Citrus diseases detection using innovative deep learning approach and Hybrid Meta-Heuristic. by Nouman Butt, Muhammad Munwar Iqbal, Shabana Ramzan, Ali Raza, Laith Abualigah, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices.…”
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Combination of an E-Nose and an E-Tongue for Adulteration Detection of Minced Mutton Mixed with Pork by Xiaojing Tian, Jun Wang, Zhongren Ma, Mingsheng Li, Zhenbo Wei

“…It was shown that the capability of discrimination of the combined system (classification error 0%∼1.67%) was superior or equable to that obtained with the two instruments separately, and E-tongue system (classification error for E-tongue 0∼2.5%) obtained higher accuracy than E-nose (classification error 0.83%∼10.83% for E-nose). …”
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