Flood-tech frontiers: smart but just? A systematic review of AI-driven urban flood adaptation and  associated governance challenges by Johannes Bhanye

“…Key limitations include the poor transferability of AI models across geographies, a lack of participatory design, and risks of algorithmic exclusion in already marginalized urban areas. …”
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Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine by B. Meerasha, M. Sagayam

“…Through experiments, we also discovered that employing time-series imagery generates substantially higher classification results than single-period images. …”
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The Emerging Role of Artificial Intelligence in Dermatology: A Systematic Review of Its Clinical Applications by Ernesto Martínez-Vargas, Jeaustin Mora-Jiménez, Sebastian Arguedas-Chacón, Josephine Hernández-López, Esteban Zavaleta-Monestel

“…However, further studies are required to address challenges such as algorithmic bias, data privacy, and regulatory oversight. …”
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Deep‐HH: A deep learning‐based high school student hidden hunger risk prediction system by Yang Yang, Zheng Zhang, Huake Cao, Yuchen Zhang, Minao Wang, Ning Zhang

“…Furthermore, the precision‐recall curve, calibration curve, and decision curve analysis also indicated that our model had high predictive accuracy. …”
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Methods of evaluation the quality of teaching in higher education institutions by Irina V. Barannikova, Elena N. Shaforostova

“…Such a system includes not only educational processes, but also the results of the quality of education. This problem is relevant because the quality of students’ training influences the formation of professional competencies and the demand on the labor market directly. …”
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Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang, Zhilong Huang

“…Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. …”
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A deep learning and IoT-driven framework for real-time adaptive resource allocation and grid optimization in smart energy systems by Arvind R. Singh, M. S. Sujatha, Akshay D. Kadu, Mohit Bajaj, Hailu Kendie Addis, Kota Sarada

“…Traditional energy management methods, based on static models or heuristic algorithms, often fail to handle real-time grid dynamics, leading to suboptimal energy distribution, high operational costs, and significant energy wastage. …”
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Comparative assessment of identification indicators of the mineral waters of the Nagutsky and Essentuksky deposits by the example of medical and table waters “Nagutskaya‑4” and “Es... by E. M. Sevostyanova, M. Yu. Ganin

“…Packaged mineral waters are not only recognized as a food product, but also are a valuable natural balneotherapeutic resource that has a therapeutic and prophylactic effect on the human body. …”
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Accurate and affordable multi-cancer early detection and localization via plasma cfDNA multi-omic profiling by Pin Cui, Weihuang He, Mingji Feng, Hanming Lai

“…Besides methylation patterns inferred from cfDNA fragmentation profile, we also combined other tissue specific cfDNA features including molecular end motif, CNV and transcription factor coverage to trace the organ origin of cancerous signals for cancer typing.…”
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In silico methods for immunogenicity risk assessment and human homology screening for therapeutic antibodies by Aimee E. Mattei, Andres H. Gutierrez, Soorya Seshadri, Jacob Tivin, Matt Ardito, Amy S. Rosenberg, William D. Martin, Anne S. De Groot

“…This toolkit has evolved and now contains an array of algorithms that can be used individually and/or consecutively for immunogenicity assessment and protein engineering. …”
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EXPRESSION OF MATRIX METALLOPROTEINASES IN LACRIMAL FLUID AND GENE POLYMORPHISM OF COMPLEMENT FACTOR H (CFH) IN PATIENTS WITH PRIMARY OPEN-ANGLE GLAUCOMA by V. A. Sokolov, V. G. Likhvantseva, O. N. Levanova, A. A. Nikiforov, V. A. Vygodin

“…., USA) employing standard algorithms of variation statistics, i.e., correlation  analysis, contingency tables, and various types of intergroup comparisons for the parameters studied. …”
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Oxygen isotope values of charred tree bark as an indicator of forest fire severity by Elizabeth McDonald, Elizabeth A. Webb, Jeffery P. Dech

“…We established a network of field plots, collected charred bark samples from standing white pine stems, and estimated burn severity based on a standard field assessment protocol known as the Composite Burn Index (CBI). We also analyzed pre- and post-fire Sentinel-2 imagery of the burn area to compute various Normalized Burn Ratio (NBR)-based change detection algorithms, which are known to produce reliable predictions of CBI. …”
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ARTIFICIAL INTELLIGENCE IN ACADEMIA: OPPORTUNITY OR CHALLENGE? by L.H. Grigoryan, M. V. Badalyan, A.Z. Grigoryan

“…Educators must not only adapt to technological innovations but also actively shape the discourse around the responsible and meaningful use of AI in education. …”
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The use of culturally adapted and translated depression screening questionnaires with South Asian haemodialysis patients in England. by Shivani Sharma, Sam Norton, Kamaldeep Bhui, Roisin Mooney, Emma Caton, Tarun Bansal, Clara Day, Andrew Davenport, Neill Duncan, Philip A Kalra, Maria Da Silva-Gane, Gurch Randhawa, Graham Warwick, David Wellsted, Magdi Yaqoob, Ken Farrington

“…Strategies to encourage recruitment of under-represented groups in renal research are also warranted, especially for in-depth discussions related to psychological care needs.…”
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Solar background radiation temperature calibration of a pure rotational Raman lidar by V. Jayaweera, R. J. Sica, G. Martucci, A. Haefele, A. Haefele

“…Furthermore, the technique works for temperature retrievals using both the optimal estimation method and the traditional temperature algorithms.</p>…”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…We also employed two benchmark datasets, DAVIS and BindingDB, to compare our models&#x2019; performance in binding affinity prediction. …”
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Fed-DPSDG-WGAN: Differentially Private Synthetic Data Generation for Loan Default Prediction via Federated Wasserstein GAN by Padmaja Ramachandra, Santhi Vaithiyanathan

“…However, creating robust deep-learning algorithms that classify loan defaulters requires abundant data, potentially compromising individuals&#x2019; privacy. …”
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Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar

“…The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. …”
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Modeling residue formation from crude oil oxidation using tree-based machine learning approaches by Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

“…Four advanced tree-based machine learning algorithms comprising gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to develop accurate predictive models. …”
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Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…It is common for these systems to be constructed using conventional machine learning algorithms. So many common signs of attacks are ignored. …”
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