Identification of Specific Abnormal Brain Functional Activity and Connectivity in Cancer Pain Patients: A Preliminary Resting-State fMRI Study by Hua Y, Geng Y, Liu S, Xia S, Liu Y, Cheng S, Chen C, Pang C, Zhao Z, Peng B, Dai Y, Ji J, Wu D

“…Using the statistically different brain areas as seed points to construct FC networks and performing statistical analysis, it was found that the regions with decreased FC connection strength between the cancer pain group and the normal group were mainly in the prefrontal cortex (PFC), the postcentral gyrus of the parietal lobe, and the cerebellum. …”
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Occupational stress among Namibian diagnostic radiographers during the COVID-19 pandemic by Festus Shidolo, Aladdin Speelman, Valdiela Daries

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A PSA SNP associates with cellular function and clinical outcome in men with prostate cancer by Srilakshmi Srinivasan, Thomas Kryza, Nathalie Bock, Brian W. C. Tse, Kamil A. Sokolowski, Panchadsaram Janaththani, Achala Fernando, Leire Moya, Carson Stephens, Ying Dong, Joan Röhl, Saeid Alinezhad, Ian Vela, Joanna L. Perry-Keene, Katie Buzacott, Robert Nica, The IMPACT Study, Manuela Gago-Dominguez, The PROFILE Study Steering Committee, Johanna Schleutker, Christiane Maier, Kenneth Muir, Catherine M. Tangen, Henrik Gronberg, Nora Pashayan, Demetrius Albanes, Alicja Wolk, Janet L. Stanford, Sonja I. Berndt, Lorelei A. Mucci, Stella Koutros, Olivier Cussenot, Karina Dalsgaard Sorensen, Eli Marie Grindedal, Ruth C. Travis, Christopher A. Haiman, Robert J. MacInnis, Ana Vega, Fredrik Wiklund, David E. Neal, Manolis Kogevinas, Kathryn L. Penney, Børge G. Nordestgaard, Hermann Brenner, Esther M. John, Marija Gamulin, Frank Claessens, Olle Melander, Anders Dahlin, Pär Stattin, Göran Hallmans, Christel Häggström, Robert Johansson, Elin Thysell, Ann-Charlotte Rönn, Weiqiang Li, Nigel Brown, Goce Dimeski, Benjamin Shepherd, Tokhir Dadaev, Mark N. Brook, Amanda B. Spurdle, Ulf-Håkan Stenman, Hannu Koistinen, Zsofia Kote-Jarai, Robert J. Klein, Hans Lilja, Rupert C. Ecker, Rosalind Eeles, The Practical Consortium, The Australian Prostate Cancer BioResource, Judith Clements, Jyotsna Batra

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On Usage of Artificial Intelligence for Predicting Neonatal Diseases, Conditions, and Mortality: A Bibliometric Review by Flavio Leandro de Morais, Raysa Carla Leal da Silva, Anna Beatriz Silva, Estefani Pontes Simao, Maria Eduarda Ferro de Mello, Stephany Paula da Silva Canejo, Katia Maria Mendes, Waldemar Brandao Neto, Jackson Raniel Florencio da Silva, Maicon Herverton Lino Ferreira da Silva Barros, Patricia Takako Endo

“…Results: The results show that the United States, China and the United Kingdom lead scientific production and international collaborations. 12 neonatal diseases were identified, with emphasis on “retinopathy of prematurity”, “necrotizing enterocolitis” and “bronchopulmonary dysplasia”; 7 clinical conditions, including “prematurity”, “perinatal asphyxia” and “jaundice”; and 5 neonatal outcomes, mainly “sepsis”, “mortality” and “cerebral palsy.” Cluster analysis revealed that studies predominantly use clinical, laboratory, genetic and imaging data, with Logistic Regression, Random Forest and Convolutional. …”
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Uncertainty-aware deep learning in healthcare: A scoping review. by Tyler J Loftus, Benjamin Shickel, Matthew M Ruppert, Jeremy A Balch, Tezcan Ozrazgat-Baslanti, Patrick J Tighe, Philip A Efron, William R Hogan, Parisa Rashidi, Gilbert R Upchurch, Azra Bihorac

“…All imaging model architectures used convolutional neural networks or a variation thereof. …”
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Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye) by Oktar Osman

“…Both ascending and descending synthetic aperture radar (SAR) images covering the same period (30 images between February 2022 and February 2023) were analyzed. …”
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A nomogram for predicting post-stroke cognitive impairment no dementia in patients with first-ever mild ischemic stroke by Zhenjie Teng, Zhenjie Teng, Zhenjie Teng, Zhenjie Teng, Jing Feng, Xiaohua Xie, Tianyuan Guan, Jing Xu, Xin Jiang, Yanzhong Chang, Yanzhong Chang, Yanzhong Chang, Peiyuan Lv, Peiyuan Lv, Peiyuan Lv

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Terahertz Communication: State-of-the-Art and Future Directions by Sanjeev Sharma, Praveen K. Singya, Kuntal Deka, Cedric Adjih, Mohit Sharma

“…Terahertz (THz) communication is a cutting-edge technology poised to address the increasing demand for ultra-high data rates in next-generation wireless networks. …”
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Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries by Azeez Okikiola Lawal, Tolutope Joseph Ogunniyi, Oriire Idunnuoluwa Oludele, Oluwaloseyi Ayomipo Olorunfemi, Olalekan John Okesanya, Jerico Bautista Ogaya, Emery Manirambona, Mohamed Mustaf Ahmed, Don Eliseo Lucero-Prisno

“…The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. …”
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THEORETICAL AND METHODOLOGICAL ASPECTS OF USING INFORMATION ANDCOGNITIVE TECHNOLOGIES IN THE TRAINING OF TRANSPORT SPECIALISTS by Lavrentieva Olena, Krupskyi Oleksandr

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Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence by Marco Zeppieri, Lorenzo Gardini, Carola Culiersi, Luigi Fontana, Mutali Musa, Fabiana D’Esposito, Pier Luigi Surico, Caterina Gagliano, Francesco Saverio Sorrentino

“…Through the fast and accurate analysis of massive amounts of imaging data, artificial intelligence (AI), in particular machine learning (ML) and deep learning (DL), has emerged as a promising method to improve the early detection and management of glaucoma. …”
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FACS-Proteomics strategy toward extracellular vesicles single-phenotype characterization in biological fluids: exploring the role of leukocyte-derived EVs in multiple sclerosis by Maria Concetta Cufaro, Paola Lanuti, Domenico De Bellis, Serena Veschi, Anna Piro, Antonella Fontana, Alice Di Sebastiano, Davide Brocco, Pasquale Simeone, Serena Pilato, Reza M. H. Khorooshi, Valentina Tomassini, Marianna Gabriella Rispoli, Luca Federici, Ilaria Cicalini, Damiana Pieragostino, Piero Del Boccio

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Adaptive weighted dual MAML: Proposing a novel method for the automated diagnosis of partial sleep deprivation. by Soraya Khanmohmmadi, Toktam Khatibi, Golnaz Tajeddin, Elham Akhondzadeh, Amir Shojaee

“…Although convolutional neural networks (CNNs) are commonly used for detecting these conditions, their performance is inadequate when applied to our heterogeneous and limited-scale EEG data. …”
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Tourism product consumers clustering for developing the tailored marketing mix by Nataliya Chukhray, Kateryna Yarmola, Andrii Chukhrai

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Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol by Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella

“…The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. …”
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The Use of Artificial Intelligence in Sturgeon Aquaculture by Dragoș Sebastian Cristea, Alexandru Adrian Gavrilă, Ștefan Mihai Petrea, Dan Munteanu, Sofia David, Cătălin Octavian Mănescu

“…Both YOLACT technology and a computer vision context were tested using LAB and HSV colour spaces to estimate fish biomass based on imaging data. It was found that the LAB colour space provided superior results in terms of precision and efficiency, but maximum accuracy was achieved using convolutional neural networks (YOLACT). …”
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Plant leaf classification using the multiscale entropy of curvature and feature aggregation by Raphael G. Pinheiro, José G.F. Lopes, Marcelo M.S. Souza, Fátima N.S. Medeiros

“…Moreover, the handcrafted descriptors achieved better results with 40 features than LeNet’s 50 features. The qualitative analysis of the multidimensional data visualization results prove that combining different shape features and texture details improved the description of the leaf images, as it provided better intraclass compactness and interclass separation in these datasets.…”
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ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases by Sheng Yu, Li Xie, Liang Dai

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Advances in Remote Sensing and Deep Learning in Coastal Boundary Extraction for Erosion Monitoring by Marc-André Blais, Moulay A. Akhloufi

“…Coastal boundaries, a critical factor in coastal erosion monitoring, are typically extracted from remote sensing images. Due to the extensive scale of coastal areas and the complexity of the data, manually extracting coastal boundaries is challenging. …”
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Near Real‐Time Mapping of All‐Sky Land Surface Temperature From GOES‐R Using Machine Learning by Sadegh Ranjbar, Danielle Losos, Sophie Hoffman, Shiva Arabi, Ankur Desai, Paul C. Stoy

“…We compared two machine learning (ML) models, Long Short‐Term Memory (LSTM) networks and Gradient Boosting Regressor (GBR), using top‐of‐atmosphere observations from the Advanced Baseline Imager (ABI) on the GOES‐16 satellite against observations from hundreds of observation sites for a five‐year period. …”
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