Stability indicators in network reconstruction. by Michele Filosi, Roberto Visintainer, Samantha Riccadonna, Giuseppe Jurman, Cesare Furlanello

“…The number of available algorithms to infer a biological network from a dataset of high-throughput measurements is overwhelming and keeps growing. …”
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Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study by Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee

“…Objective: We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. …”
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Smart city planning focused on the US cities in need of policing innovations and public health safety technologies and strategies by Delores Springs

“…In particular, the article proposes the following for implementation: identifying crime hotspots by means of advanced geographic information systems, applying machine learning algorithms to predict future crime trends, using advanced real-time surveillance technologies and firearms detection systems, applying geospatial analysis to optimise patrol routes, dynamically adjusting patrol routes depending on real-time data analytics, deploying mobile command centres. …”
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Artificial Intelligence in Pathology: Past, Present, and Future Perspectives by Isma Balaj, Sibora Bërdica, Teona Bushati

“…Conclusion: There has been a significant increase in the development and application of AI tools, including image-based algorithms, in pathology services, and they are expected to dominate the field in the coming years. …”
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Mir-494-3p enhances aggressive phenotype of non-small cell lung cancer cells by regulating SET/I2PP2A by Yuwen Du, Taisuke Kajino, Yukako Shimada, Takashi Takahashi, Ayumu Taguchi

“…Integration of RNA sequencing analysis of NSCLC cells with miR-494-3p inhibition and a bioinformatic search of miRNA target prediction algorithms resulted in identification of SET/I2PP2A as a direct target of miR-494-3p. …”
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Advancing invasive species monitoring: A free tool for detecting invasive cane toads using continental-scale data by Franco Ka Wah Leung, Lin Schwarzkopf, Slade Allen-Ankins

“…Here, we present the development and assessment of a user-friendly and transferable monitoring tool for the invasive cane toad (Rhinella marina) using passive acoustic monitoring (PAM) and machine learning algorithms. Leveraging a continental-scale PAM dataset (Australian Acoustic Observatory), we trained a cane toad classifier using the BirdNET algorithm, a convolutional neural network architecture capable of identifying acoustic events. …”
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Myoelectric Control in Rehabilitative and Assistive Soft Exoskeletons: A Comprehensive Review of Trends, Challenges, and Integration with Soft Robotic Devices by Alejandro Toro-Ossaba, Juan C. Tejada, Daniel Sanin-Villa

“…Key contributions include critically evaluating machine learning-based motion prediction, model-free adaptive control methods, and real-time validation strategies to enhance rehabilitation outcomes. …”
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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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Combination of Transformed-means Clustering and Neural Networks for Short-Term Solar Radiation Forecasting by M. Ghayekhloo, M. B. Menhaj

“…The proposed Transformed-Means clustering method is based on inverse data transformation and K-means algorithm that presents moreaccurate clustering results when compared to the K-Means algorithm; its improved version and also otherpopular clustering algorithms. …”
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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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A systematic review on artificial intelligence approaches for smart health devices by Lerina Aversano, Martina Iammarino, Ilaria Mancino, Debora Montano

“…We aim to provide a comprehensive overview of the AI methodologies used, the neural network architectures adopted, and the algorithms employed, as well as examine the privacy and security issues related to the management of health data collected by wearable IoT devices. …”
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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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Identification of the immune infiltration and biomarkers in ulcerative colitis based on liquid–liquid phase separation-related genes by Zhixing Hong, Shilin Fang, Haihang Nie, Jingkai Zhou, Yuntian Hong, Lan Liu, Qiu Zhao

“…We identified the hub LLPS-RGs (DE-LLPS-RGs) (HSPB3, SLC16A1, TRIM22, SRI, PLEKHG6, GBP1, PADI2) by machine learning algorithms. Hub genes were screened that displayed high prediction accuracy of UC patients. …”
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Assessment of seasonal variations in the air quality index (2019–2022) in Al-Jahra city, Kuwait by Alsowaidan S., Al-Hurban A., Alsaber A.

“…An assessment of air quality indices was carried out by using different algorithms models, including random forest (RF), an artificial neural network (ANN), and extreme gradient boosting (XGBoost). …”
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Deep Learning-Driven Geospatial Modeling of Elderly Care Accessibility: Disparities Across the Urban-Rural Continuum in Central China by Yi Yu, Tian Dong

“…Spatial weighting characteristics of elderly care facility locations were analyzed through machine learning algorithms, and service coverage disparities between urban districts and suburban towns were assessed under 5-, 10-, and 15-min walking thresholds. …”
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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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