Use of Artificial Intelligence in Imaging Dementia by Manal Aljuhani, Azhaar Ashraf, Paul Edison

“…Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. …”
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Diagnosing Autoimmune Bullous Diseases—An Indian Perspective by Adhyatm Bhandari, Dipankar De, Shikha Shah, Debajyoti Chatterjee, Vinod Kumar, Rahul Mahajan, Sanjeev Handa

“…Accurate diagnosis of the specific subtype of AIBD is crucial for effective management and predicting prognosis, especially in cases with an increased risk of malignancy. …”
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Artificial Intelligence Driven Human Identification by Purushottam Sharma, Aazar Imran Khan, Samyak Jain, Abhishek Srivastava

“…Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. …”
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Cross-Network User Identity Linkage Method with Deep Learning Based on SDNE Embedding Representation by CHENG Jialin, YUAN Deyu, SUN Zeyu, CHEN Ziyan

“…Experimental verification is conducted on the real social network dataset and synthetic network datasets, and the experimental results are more than 8 percentage points higher than the baseline algorithms PALE (predict anchor links via embedding), CLF (collective link fusion) and Deeplink in accuracy and F1 value, indicating that the eSUIL algorithm proposed in this paper has excellent performance in user identity linkage. …”
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Harnessing AI and Quantum Computing for Revolutionizing Drug Discovery and Approval Processes: Case Example for Collagen Toxicity by David Melvin Braga, Bharat Rawal

“…By generating computational data, predicting the efficacy of pharmaceuticals, and assessing their safety, AI and quantum computing can accelerate and optimize the process of identifying potential drug candidates. …”
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Optimizing Small Wind Turbine Blades: A BEMT Approach Optimizing Small Wind Turbine Blades: A BEMT Approach by Tshepo Sithole, Lukas W. Snyman, Vasudeva R. Veeredhi, Thembelani Sithebe

“…This paper explores the optimization of small wind turbine blades, focusing on the design and utilization of theoretical algorithms such as computational fluid dynamics (CFD), blade elementary method (BEM) theory, and the vortex wake system (VWS). …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
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Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques by Muyiwa Lawrence Adedara, Ridwan Taiwo, Olusola Olaitan Ayeleru, Hans-Rudolf Bork

“…It evaluates the recycling diversion rate (RDR) of household recyclables (HSRs) across local government areas using field surveys and population data. Machine learning algorithms (logistic regression, random forest, XGBoost, and CatBoost) refined with Bayesian optimisation were employed to predict household recycling motivation. …”
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Contrastive cross-domain sequential recommendation with attention-aware mechanism by Wei Zhao, Bo Li, Xian Mo

“…Abstract Cross-domain sequential recommendation (CDSR) aims to predict future sequential interactions in a target domain by analyzing historical sequence data from different domains. …”
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Assessing Climate Change Impacts on Cropland and Greenhouse Gas Emissions Using Remote Sensing and Machine Learning by Nehir Uyar, Azize Uyar

“…The results revealed a strong correlation between agricultural expansion and increased C and N₂O emissions, with RF and GBT models demonstrating superior predictive accuracy. Specifically, GBT and RF achieved the highest R² value (0.71, 0.59) and the lowest error metrics in modeling emissions, whereas SVM performed poorly across all cases. …”
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Mantle Flow and Anisotropy in Subduction Zones: Modeling and Clustering of Olivine Textures by Yijun Wang, Ágnes Király, Clinton P. Conrad, Valerie Maupin

“…We compare olivine texture evolution predicted using different methods in both retreating and stationary‐trench settings. …”
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Machine learning and deep learning in medicine and neuroimaging by Iván Sánchez Fernández, Jurriaan M. Peters

“…Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. …”
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Stability of the Subaxial Spine after Penetrating Trauma: Do Classification Systems Apply? by Jackson Rucker Staggers, Thomas Elliot Niemeier, William E. Neway, Steven Michael Theiss

“…Blunt spinal trauma classification systems are well established and provide reliable treatment algorithms. To date, stability of the spine after civilian gunshot wounds (CGSWS) is poorly understood. …”
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Identification of Fake Comments in E-Commerce Based on Triplet Convolutional Twin Network and CatBoost Model by Juanjuan Peng

“…At the same time, the accuracy of the four features was 0.8931, 0.9012, 0.8846, and 0.8961, respectively, which verified the excellent predictive recognition performance. Compared with the other two comparison algorithms, the detection time required by the proposed model was the shortest at 115us and consumed the least amount of resources. …”
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Chemoreactomic study of fonturacetam effects: molecular mechanisms of influence on adipose tissue metabolism by O. A. Gromova, I. Yu. Torshin

“…The lipolytic effect is predicted specifically for fonturacetam as a result of activation by this molecule of β3-adrenoceptors, adenosine receptors, glucagon-like peptide, sphingosine phosphate and peroxisome proliferators, as well as specific inhibition of cannabinoid, opioid, histamine, glutamate, nociceptin, orexin and neuropeptide Y receptors. …”
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A Hybrid Machine Learning Approach for Estimating Aboveground Biomass and Carbon Stock in Tanzania’s Miombo Woodlands by Emmanuel F. Chifunda, Ramkumar T. Balan, Peter J. Kirigiti

“…A model (ANN-RF) was developed using a combination of ANN and RF models. Initially, the RF algorithm combined the predictions from the ANN models. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…Furthermore, these workflows lay the foundation for implementing long-term learning algorithms, a pivotal increment for monitoring initiatives. …”
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Collaborative Optimization Planning Method for Distribution Network Considering “Hydropower, Photovoltaic, Storage, and Charging” by Jinlin Liao, Jia Lin, Guilian Wu, Sudan Lai

“…Secondly, the trip chain method is used to predict the charging demand of electric vehicles. …”
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RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

“…Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. …”
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Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning. by Maggy T Sikulu-Lord, Michael D Edstein, Brendon Goh, Anton R Lord, Jye A Travis, Floyd E Dowell, Geoffrey W Birrell, Marina Chavchich

“…The spectra were analysed using machine learning to develop predictive models for infection.<h4>Findings</h4>Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). …”
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