Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. by Lin Lin, Elena Dacal, Nuria Díez, Claudia Carmona, Alexandra Martin Ramirez, Lourdes Barón Argos, David Bermejo-Peláez, Carla Caballero, Daniel Cuadrado, Oscar Darias-Plasencia, Jaime García-Villena, Alexander Bakardjiev, Maria Postigo, Ethan Recalde-Jaramillo, Maria Flores-Chavez, Andrés Santos, María Jesús Ledesma-Carbayo, José M Rubio, Miguel Luengo-Oroz

“…It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. …”
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A Three-Dimensional Phenotype Extraction Method Based on Point Cloud Segmentation for All-Period Cotton Multiple Organs by Pengyu Chu, Bo Han, Qiang Guo, Yiping Wan, Jingjing Zhang

“…Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. …”
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EDITORIAL: ARTIFICIAL INTELLIGENCE AND ITS TRANSFORMATIVE IMPACT ON SCIENTIFIC PUBLISHING by Abubakar Munir

“…Conventional software such as Turnitin and iThenticate have come a long way, using deep learning algorithms to identify more evolved instances of academic fraud, including paraphrasing plagiarism and AI-generated content. …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. …”
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Comparative performance of deep learning architectures for diabetic peripheral neuropathy detection using corneal confocal microscopy: a retrospective single-centre study by Yuyang Deng, Wenqu Chen, Weihuang Xu, Jianzhang Hu

“…For single-image predictions in the three-class classification task of CCM images, the InceptionV3 model achieved a precision of 0.8385, a recall of 0.9083, an F1 score of 0.8720 and an AUC of 0.8769 for predicting DPN+.Conclusions The InceptionV3-based DLA model achieved superior performance compared with traditional convolutional neural network architectures like ResNet and DenseNet, and the Swin transformer model, highlighting its potential for effective DPN screening.…”
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A Fuzzy Supplier Selection Application Using Large Survey Datasets of Delivery Performance by Jonathan Davis, Margaret F. Shipley, Gary Stading

“…A model is developed using fuzzy probability to screen survey data across relevant criteria for selecting suppliers based on fuzzy expected values. …”
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Autonomic nervous system development-related signature as a novel predictive biomarker for immunotherapy in pan-cancers by Cunen Wu, Cunen Wu, Cunen Wu, Cunen Wu, Weiwei Xue, Yuwen Zhuang, Dayue Darrel Duan, Dayue Darrel Duan, Zhou Zhou, Zhou Zhou, Xiaoxiao Wang, Zhenfeng Wu, Jin-yong Zhou, Xiangkun Huan, Ruiping Wang, Haibo Cheng, Haibo Cheng

“…This approach also aims to develop more accurate prediction models and therapeutic interventions, thereby helping more patients benefit from immunotherapy.…”
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The application of compressed sensing on tumor mutation burden calculation from overlapped pooling sequencing data by Yue Cui, Yi Qiao, Rongming An, Xuan Pan, Jing Tu

“…Additionally, we performed an assessment of the reconstruction efficiency of both the BP model and the OMP model.…”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Then, the decomposed sta-tionary subsequence is combined with carefully extracted optimal features and input into the TCN‒BiGRU combination model for prediction. This combination model aims to use the advantages of TCN and BiGRU to process data and make more accurate predictions. …”
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Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma by Shiyan Song, Wenfei Ge, Xiaochen Qi, Xiangyu Che, Qifei Wang, Guangzhen Wu

“…Radiomics features were screened using LASSO analysis. Eight ML algorithms were selected for diagnostic analysis of the test set. …”
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The Place of Local Field Potentials in Deep Brain Stimulation Programming for Parkinson’s Disease: A Review by Chun Him Shelton Leung, Hugh D. Simpson, Dominic Thyagarajan

“…Results: Analyzing LFPs clearly has the potential to assist or streamline DBS programming in clinical practice, but there are knowledge gaps and challenges to overcome, especially in the utilization of intraoperative LFPs. Conclusions: More research is required to compare different algorithms that utilize LFPs in DBS programming to identify a simple, practical and time-saving algorithm incorporating reliable LFP biomarkers that will enhance the DBS programming experience for both patients and clinicians.…”
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Application of GPR Underground Pipeline Detection Technology in Urban Complex Geological Environments by Xiaoqiang Liang, Da Hu, Yongsuo Li, Yunyi Zhang, Xian Yang

“…To address different kinds of complex conditions, this experiment in the present paper takes ground penetrating radar as the research basis and uses a self-correction and screening algorithm to innovatively detect underground pipelines. …”
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A comparative study between Near-Infrared (NIR) spectrometer and High-Performance Liquid Chromatography (HPLC) on the sensitivity and specificity. by Elisa M Maffioli, Chimezie Anyakora

“…While these devices hold great potential, regulators should require more independent evaluations of various drug formulations before implementing them in real-world settings. …”
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Perceived age estimation from facial image and demographic data in young and middle-aged South Korean adults by Ilkoo Ahn, Younghwa Baek, Bok-Nam Seo, Su Eun Lim, Kyoungsik Jung, Ho Seok Kim, Jeongkyun Kim, Sukyung Lee, Siwoo Lee

“…The averaging models of Lasso, XGBoost, and CatBoost showed a mean absolute error of 2.2944, indicating that this algorithm can be used as a screening method for general health status in the population.…”
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A clinical scoring system to prioritise investigation for tuberculosis among adults attending HIV clinics in South Africa. by Yasmeen Hanifa, Katherine L Fielding, Violet N Chihota, Lungiswa Adonis, Salome Charalambous, Nicola Foster, Alan Karstaedt, Kerrigan McCarthy, Mark P Nicol, Nontobeko T Ndlovu, Edina Sinanovic, Faieza Sahid, Wendy Stevens, Anna Vassall, Gavin J Churchyard, Alison D Grant

“…<h4>Participants</h4>Representative sample of adult HIV clinic attendees; data from participants reporting ≥1 symptom on the WHO screening tool were split 50:50 to derive, then internally validate, a prediction model.…”
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Sweetener identification using transfer learning and attention mechanism by Fanchao Lin, Yuan Ji, Shoujiang Xu

“…Accurate identification of the taste of compounds has helped in the screening and development of new sweeteners. This study proposes a deep learning model for sweetener identification based on transfer learning and attention mechanism. …”
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YOLOv5-DTW: Gesture recognition based on YOLOv5 and dynamic time warping for digital media design by Lu Zhao, Jing Yu

“…Dynamic time warping (DTW) algorithm is used to fuse different surface EMG signals, calculate the similarity between samples and models, and realize gesture recognition. …”
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The Effect of Arctic Sea‐Ice Loss on Extratropical Cyclones by Stephanie Hay, Matthew D. K. Priestley, Hao Yu, Jennifer L. Catto, James A. Screen

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The Value of Subclinical Carotid Atherosclerosis for Primary Prevention of Cardiovascular Diseases. Review of the Main International Studies by E. K. Butina, E. V. Bochkareva

“…Measures for the prevention of cardiovascular diseases (CVD) are more effective if they are performed taking into account the risk factors of their development. …”
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Ways to predict interstitial lung disease in patients with systemic sclerosis: results of an observational study by D. V. Khorolsky, A. A. Klimenko, E. S. Pershina, N. M. Babadeva, A. A. Kondrashov, N. A. Shostak, E. P. Mikheeva, E. V. Zhilyaev

“…It is advisable to include these indicators in the algorithm for screening and monitoring patients with SSc.…”
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