An overview of artificial intelligence based automated diagnosis in paediatric dentistry by Suba B. Rajinikanth, Densingh Samuel Raj Rajkumar, Akshay Rajinikanth, Ponsekar Abraham Anandhapandian, Bhuvaneswarri J.

“…The field of AI, deep machine learning and CNN's is an upcoming and newer area, with new developments this will open up areas for more sophisticated algorithms in multiple layers to predict accurately, when compared to experienced Paediatric dentists.…”
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Identifying and Validating an Acidosis-Related Signature Associated with Prognosis and Tumor Immune Infiltration Characteristics in Pancreatic Carcinoma by Pingfei Tang, Weiming Qu, Dajun Wu, Shihua Chen, Minji Liu, Weishun Chen, Qiongjia Ai, Haijuan Tang, Hongbing Zhou

“…The tumor immune infiltrating pattern was characterized by the single-sample gene set enrichment analysis (ssGSEA) method, and the prediction of immunotherapy responsiveness was conducted using the tumor immune dysfunction and exclusion (TIDE) algorithm. …”
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Addressing human–elephant conflicts in Taita Taveta County, Kenya: Integrating species distribution modeling into targeted conservation strategies by Tino Johansson, Martha Munyao, Petri K.E. Pellikka, Sakari Äärilä, Patrick Omondi, Mika Siljander

“…This study assesses multiple distribution model algorithms and ensemble models, using Kenya Wildlife Service incident data and ten geospatial variables, to predict human–elephant conflicts in the county. …”
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Big Data Cleaning Based on Improved CLOF and Random Forest for Distribution Networks by Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng

“…First, the Local Outlier Factor (LOF) algorithm based on DBSCAN clustering is used to detect outliers. …”
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Life cycle assessment and multicriteria decision making analysis of additive manufacturing processes towards optimal performance and sustainability by Jayant M. Raut, Prashant B. Pande, Kamlesh V. Madurwar, Rajesh M. Bhagat, Satyajit S. Uparkar, Nilesh Shelke, Haytham F. Isleem, Arpita, Vikrant S. Vairagade

“…In this study, GPR-based predictive LCA is conducted using historical and real-time environmental data for modeling impact categories of CO2 and energy use. …”
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Diagnosis and Severity Assessment of COPD Using a Novel Fast-Response Capnometer and Interpretable Machine Learning by Leeran Talker, Cihan Dogan, Daniel Neville, Rui Hen Lim, Henry Broomfield, Gabriel Lambert, Ahmed Selim, Thomas Brown, Laura Wiffen, Julian Carter, Helen F. Ashdown, Gail Hayward, Elango Vijaykumar, Scott T. Weiss, Anoop Chauhan, Ameera X. Patel

“…Output probabilities from the severity determination model produced a correlation of 0.71 with percentage predicted FEV1.Conclusion The N-TidalTM device could be used alongside interpretable machine learning as an accurate, point-of-care diagnostic test for COPD, particularly in primary care as a rapid rule-in or rule-out test. …”
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A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints by Tianli Ma, Rong Zhang, Song Gao, Hong Li, Yang Zhang

“…By choosing the skew-t and inverse Wishart distributions as the prior information of the measurement noise and predicted error covariance matrix, the state vector, the predicted error covariance matrix, and noise parameters are inferred and approximated by using the VB method. …”
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The block wave region growing of the image based on quadra-grid pixels by O. M. Almiahi, V. YU. Tsviatkou, V. K. Kanapelka

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MODERN METHODS OF AUTOMATIC RECTANGLE OBJECTS DETECTION by E. S. Matusevich, I. E. Kheidorov

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SECTOR LOCALIZATION, PARAMETERIZATION AND IDENTIFICATION OF REFERENCE POINTS BASED ON CORNER COEFFICIENTS FOR ALIGNING THE OVERLAPPING IMAGES by O. J. Al-Furaiji, V. K. Kanapelka, V. YU. Tsviatkou

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Classification of behaviour with low-frequency accelerometers in female wild boar. by Thomas Ruf, Jennifer Krämer, Claudia Bieber, Sebastian G Vetter

“…We classified the behaviour of female wild boar, kept under semi-natural conditions in a large outdoor enclosure, using acceleration data. Predictions were based on a machine learning algorithm, specifically a random forest model in the open software h2o. …”
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Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin by Pei Zhang, Qiong Chen, Jiahui Lao, Juan Shi, Jia Cao, Xiao Li, Xin Huang

“…The machine learning models were developed using five different algorithms, including logistic regression (LR), random forest (RF), gradient boosting machine (GBM), extreme gradient boosting model (XGBoost), and light gradient boosting machine (Light GBM).ResultsThe XGBoost model exhibited the most superior performance in predicting amikacin-associated AKI among the developed machine learning models. …”
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Label efficient phenotyping for Long COVID using electronic health records by Chuan Hong, Jun Wen, Harrison G. Zhang, Vidul Ayakulangara Panickan, Doris Y. Yang, Alicia W. Chen, Xin Xiong, Xuan Wang, Michele Morris, Sara Morini, Rahul Sangar, Andrew Dey, Malarkodi J. Samayamuthu, Katherine Liao, Clara-Lea Bonzel, Vidisha Tanukonda, Monika Maripuri, Jacqueline Honerlaw, Yuk-Lam Ho, Shyam Visweswaran, Isaac S. Kohane, Kelly Cho, Gabriel Brat, Zongqi Xia, Tianxi Cai

“…Developing efficient phenotyping algorithms is crucial to enabling risk prediction and effective management of Long COVID. …”
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Research on Road Crack Detection Based on RGB-LPC-GPR Data Fusion by Z. Wang, D. Qiu, R. Wu, R. Wu, Y. Shi, W. Niu

“…This study presents a multimodal data fusion framework for road damage detection and prediction, integrating RGB images, LiDAR point clouds, and GPR (Ground Penetrating Radar) data to enable high-precision detection of surface cracks, potholes, and underground voids, as well as dynamic trend forecasting. …”
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Uso de inteligencia artificial para predecir complicaciones en cirugías de columna toracolumbar degenerativa: revisión sistemática by G. Ricciardi, J.I. Cirillo Totera, R. Pons Belmonte, L. Romero Valverde, F. López Muñoz, A. Manríquez Díaz

“…Abstract: Objective: We aim to conduct a systematic review of the literature to evaluate the effectiveness of artificial intelligence prediction models in predicting complications in adult patients undergoing surgery for degenerative thoracolumbar pathology compared with other commonly used prediction techniques. …”
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Quantifying Uncertainties in Solar Wind Forecasting due to Incomplete Solar Magnetic Field Information by Stephan G. Heinemann, Jens Pomoell, Ronald M. Caplan, Mathew J. Owens, Shaela Jones, Lisa Upton, Bibhuti Kumar Jha, Charles N. Arge

“…Solar wind forecasting plays a crucial role in space weather prediction, yet significant uncertainties persist duet to incomplete magnetic field observations of the Sun. …”
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Unveiling psychobiological correlates in primary Sjögren’s syndrome: a machine learning approach to determinants of disease burden by László V. Módis, László V. Módis, András Matuz, András Matuz, Zsófia Aradi, Ildikó Fanny Horváth, Antónia Szántó, Antal Bugán

“…Three machine learning algorithms were trained to predict outcome variables, first by each measure category, then on the entire set of predictor variables. …”
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Remote sensing reveals the role of forage quality and quantity for summer habitat use in red deer by Thomas Rempfler, Christian Rossi, Jan Schweizer, Wibke Peters, Claudio Signer, Flurin Filli, Hannes Jenny, Klaus Hackländer, Sven Buchmann, Pia Anderwald

“…Results The combination of vegetation indices and optical traits greatly improved predictive power in both the biomass (R2 = 0.60, Root mean square error (RMSE) = 88.55 g/m2) and relative nitrogen models (R2 = 0.34, RMSE = 0.28%). …”
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RETRACTED ARTICLE: Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks by K. Krishna Kishore, Arun Sekar Rajasekaran, Ismail Keshta, Haewon Byeon, Mohammed Wasim Bhatt, Azeem Irshad, Isaac Ofori

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Attention-based hybrid contrastive learning for unsupervised person re-identification by Weihao Qin, Yongxia Li, Jianguang Zhang, Xianbin Wen, Jiajia Guo, Qi Guo

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