Deep Multi-Modal Skin-Imaging-Based Information-Switching Network for Skin Lesion Recognition by Yingzhe Yu, Huiqiong Jia, Li Zhang, Suling Xu, Xiaoxia Zhu, Jiucun Wang, Fangfang Wang, Lianyi Han, Haoqiang Jiang, Qiongyan Zhou, Chao Xin

“…The diagnostic potential of recent multi-modal skin lesion detection algorithms is limited because they ignore dynamic interactions and information sharing across modalities at various feature scales. …”
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Predictive analysis of clinical features for HPV status in oropharynx squamous cell carcinoma: A machine learning approach with explainability by Emily Diaz Badilla, Ignasi Cos, Claudio Sampieri, Berta Alegre, Isabel Vilaseca, Simone Balocco, Petia Radeva

“…Finding reliable non-invasive detection methods of this prognostic entity is key to propose appropriate therapeutic decisions. …”
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Simultaneous non-contrast assessment of cardiac microstructure and perfusion in vivo in the human heart by Camila Munoz, Eunji Lim, Pedro F. Ferreira, Dudley J. Pennell, Sonia Nielles-Vallespin, Andrew D. Scott

“…A simulation study was performed to investigate the optimal fitting algorithms for the IVIM parameters, which was subsequently used to create pixel-wise IVIM parameter maps for the in vivo acquisitions. …”
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Application and prospect of artificial intelligence in empowering the operation and managment of oil and gas pipelines by Qi LIAO, Chunying LIU, Jian DU, Hao LAN, Yongtu LIANG, Haoran ZHANG

“…The evolution of AI-driven research has transitioned from conventional approaches like neural networks, expert systems, fuzzy logic, and wavelet analysis towards new-generation algorithms, including deep learning, transfer learning, and reinforcement learning. …”
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AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters by Oana-Maria Butnaru, Monica Tatarciuc, Ionut Luchian, Teona Tudorici, Carina Balcos, Dana Gabriela Budala, Ana Sirghe, Dragos Ioan Virvescu, Danisia Haba

“…Additionally, AI showed high consistency in detecting alveolar bone loss, with comparable performance to senior specialists. …”
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Improving Malaria diagnosis through interpretable customized CNNs architectures by Md. Faysal Ahamed, Md Nahiduzzaman, Golam Mahmud, Fariya Bintay Shafi, Mohamed Arselene Ayari, Amith Khandakar, M. Abdullah-Al-Wadud, S. M. Riazul Islam

“…The SPCNN achieved a precision of 99.38 $$\pm$$ 0.21%, recall of 99.37 $$\pm$$ 0.21%, F1 score of 99.37 $$\pm$$ 0.21%, accuracy of 99.37 ± 0.30%, and an area under the receiver operating characteristic curve (AUC) of 99.95 ± 0.01%, demonstrating its robustness in detecting malaria parasites. Furthermore, we employed various transfer learning (TL) algorithms, including VGG16, ResNet152, MobileNetV3Small, EfficientNetB6, EfficientNetB7, DenseNet201, Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), ImageIntern, and Swin Transformer (versions v1 and v2). …”
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Safety assessment of temozolomidee: real-world adverse event analysis from the FAERS database by Yu Liu, Lan Ma, Lan Ma, Xiaojia Fu, Xiaojia Fu, Yi Zhang, Yi Zhang, Jinyu Zheng, Zhongjun Chen

“…Methods for identifying temozolomide-related AEs signals include taking a case/non-case approach. Specific detection algorithms also include report Odds ratio (ROR), Proportional Report ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item Gamma-Poisson constrictor (MGPS).ResultsAmong 48,766,547 FAERS reports, 13,608 TMZ-related AEs were identified. …”
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Control Tolerante a Fallas Activo: Estimación y acomodación de fallas en sensores aplicado al modelo LPV de una bicicleta sin conductor by J.A. Brizuela-Mendoza, C.M. Astorga-Zaragoza, A. Zavala-Río, F. Canales-Abarca

“…The proposed algorithms, considered as the main contributions in this work, achieves noise-free estimations for the faults and state, in order to compute the fault indicator and control law, respectively. …”
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Temporal scalability analysis of spectral remote sensing and Machine Learning-Based models for forest mapping in the tropical region of Nigeria by K. Pawłuszek-Filipiak, D. Włodarczyk, K. Stuchły, F.E. Ikuemonisan

“…The integration of machine learning (ML) algorithms with remote sensing (RS) data offers significant potential for enhancing dynamic forest characterization and change detection through high temporal density mapping. …”
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ANALISIS PROSES BERPIKIR KOMPUTASI SISWA PADA MATERI BARISAN ARITMETIKA BERDASARKAN KECERDASAN MAJEMUK by Rosevita Melati, Toto Nusantara, Mochammad Hafiizh

“…Visual-spatial intelligence students excelled at pattern detection throughout the algorithmic thinking level. …”
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Different environmental factors predict the occurrence of tick-borne encephalitis virus (TBEV) and reveal new potential risk areas across Europe via geospatial models by Patrick H. Kelly, Rob Kwark, Harrison M. Marick, Julie Davis, James H. Stark, Harish Madhava, Gerhard Dobler, Jennifer C. Moïsi

“…Methods Geocoordinates that reported the detection of TBEV in ticks or rodents and anti-TBEV antibodies in rodent reservoirs in 2000 or later were extracted from published and grey literature. …”
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Automated classification of stress and relaxation responses in major depressive disorder, panic disorder, and healthy participants via heart rate variability by Sangwon Byun, Ah Young Kim, Min-Sup Shin, Hong Jin Jeon, Hong Jin Jeon, Chul-Hyun Cho, Chul-Hyun Cho

“…This study evaluated the feasibility of using machine-learning algorithms to detect stress automatically in MDD and PD patients, as well as healthy controls (HCs), based on HRV features.MethodsThe study included 147 participants (MDD: 41, PD: 47, HC: 59) who visited the laboratory up to five times over 12 weeks. …”
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Development of a Predictive Model for Metabolic Syndrome Using Noninvasive Data and its Cardiovascular Disease Risk Assessments: Multicohort Validation Study by Jin-Hyun Park, Inyong Jeong, Gang-Jee Ko, Seogsong Jeong, Hwamin Lee

“…Five machine learning algorithms were compared, and the best-performing model was selected based on the area under the receiver operating characteristic curve. …”
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Integrated Bioinformatics and Experimental Validation Reveal Macrophage Polarization-Related Biomarkers for Osteoarthritis Diagnosis by He Q, Liu L, Hu X, Lin L, Song Z, Xia Y, Lin Q, Wei J, Li S

“…Least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to identify hub genes and construct a diagnostic model validated through internal datasets and multiple external bulk RNA-seq and single-cell RNA-seq data. …”
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Accuracy Evaluation of Multi-Technique Combination Nonlinear Terrestrial Reference Frame and EOP Based on Singular Spectrum Analysis by Qiuxia Li, Xiaoya Wang, Yabo Li

“…The unlabeled jumps could be detected by the sequential <i>t</i>-test analysis of regime shifts (STARS) combined with the generalized extreme Studentized deviate (GESD) algorithms introduced in our earlier paper. …”
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DYNAMICS OF MMP-8, SRANKL, AND OSTEOCALCIN CONCENTRATIONS IN THE ORAL FLUID OF PATIENTS WITH SECONDARY EDENTULISM AND AFTER PROSTHETIC TREATMENT by R.V. Tsynkush, O.V. Voznyi

“…The results obtained underscore the feasibility of employing prosthetic treatment methods to reduce destructive effects on bone tissue and open up new prospects for creating personalized treatment and rehabilitation algorithms for patients who have lost their teeth. …”
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Carbapenemases Produced by Multidrug-Resistant Strains of <i>Klebsiella Pneumoniae</i> Isolated from Intensive Care Patients by T. V. Chernenkaya, L. A. Borisova, T. Yu. Vorobieva, M. A. Godkov, A. K. Shabanov

“…In 11.3% of carbapenem-resistant strains, the production of KPC, OXA-48, NDM, VIM and IMP genes was not detected. When developing algorithms for antibacterial therapy, it is necessary to take into account that from 25.7% to 60.6% of K. pneumoniae strains in different intensive care units are the producers of metallobetalactamases.…”
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Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recog... by Andrea P Garzón-Partida, Kimberly Magaña-Plascencia, Diana Emilia Martínez-Fernández, Joaquín García-Estrada, Sonia Luquin, David Fernández-Quezada

“…The collected data will then be used to train models with a neural network, which will then be validated against other models and compared with other algorithms. Demographic, psychological, digital biomarkers, and craving profiles will be created, correlations will be analyzed, and they will be compared with controls to generate a digital phenotype of SUD. …”
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Characterization, expression patterns of molt-inhibiting hormone gene of Macrobrachium nipponense and its roles in molting and growth. by Hui Qiao, Fengwei Jiang, Yiwei Xiong, Sufei Jiang, Hongtuo Fu, Fei Li, Wenyi Zhang, Shengming Sun, Shubo Jin, Yongsheng Gong, Yan Wu

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Platelet mass cytometry reveals dysregulation of prothrombotic pathways in essential thrombocythemia by Veronika Dill, Kilian Kirmes, Jiaying Han, Melissa Klug, Marc Rosenbaum, Giacomo Viggiani, Moritz von Scheidt, Markus List, Peter Herhaus, Jürgen Ruland, Florian Bassermann, Karl-Ludwig Laugwitz, Katharina S. Götze, Philipp J. Jost, Stefanie Jilg, Conor J. Bloxham, Philip W.J. Raake, Isabell Bernlochner, Dario Bongiovanni

“…In contrast, ET platelets had a lower expression of the integrin subunits of the fibrinogen receptor GPIIb/IIIa CD41 (p = .036) and CD61 (p = .044) and of the von Willebrand factor receptor CD42b (p = .044). Using the FlowSOM algorithm, we identified 2 subclusters of ET platelets with a prothrombotic expression profile, one of them (cluster 3) significantly overrepresented in ET (22.13% of the total platelets in ET, 2.94% in controls, p = .035). …”
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