Artificial intelligence driven innovations in biochemistry: A review of emerging research frontiers by Mohammed Abdul Lateef Junaid

“…The methodology involves an extensive analysis of existing literature, particularly peer-reviewed studies on AI applications in biochemistry. AI-driven tools like AlphaFold, which have significantly advanced protein structure prediction, are evaluated alongside AI’s role in expediting drug discovery. …”
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Accuracy measurement of automatic remeshing tools by Mattia Sullini

“…<br />Therefore, it is vital to evaluate the characteristic quality obtainable from different remeshing algorithms with simplification, but despite the importance of the matter, there is a lack of metrics to perform such comparative benchmarks. …”
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Artificial intelligence-assisted capsule endoscopy for detecting lesions in Crohn’s disease: a systematic review and meta-analysis by Yuling Bin, Rumei Peng, Yaqian Lee, Zhijie Lee, Yang Liu

“…However, given the limitations and heterogeneity of current research, more high-quality, large-sample studies are needed to comprehensively and thoroughly evaluate the practical application value of AI in CD diagnosis, thereby promoting its widespread adoption and optimization in clinical practice.…”
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Multiobjective Design Optimization of Grillage Systems according to LRFD-AISC by Tugrul Talaslioglu

“…For this purpose, multiobjective optimization algorithms (MOAs) employed are applied to the design optimization of three application examples and achieved to generate various optimal designations using different combinations of optimizer-related parameters. …”
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Determination of Flood Subsidy (2023/2024) Based on SAR Images for Agricultural Land in Lower Saxony, Germany by C.-H. Yang, C. Stemmler, C. Röttger, C. Büker

“…The additional datasets, such as Sentinel-2 imagery and local photos, validated our flood evaluations. Our operational workflow demonstrates the adaptability of SAR for diverse applications, from post-event flood evaluation to near real-time emergency response. …”
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Predictive models of sepsis-associated acute kidney injury based on machine learning: a scoping review by Jie Li, Manli Zhu, Li Yan

“…Then, we comprehensively extracted relevant data related to machine learning algorithms, predictors, and predicted objectives. We subsequently performed a critical evaluation of research quality, data aggregation, and analyses.Results We screened 25 studies on predictive models for sepsis-associated acute kidney injury from a total of originally identified 2898 studies. …”
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A novel prediction model for the prognosis of non-small cell lung cancer with clinical routine laboratory indicators: a machine learning approach by Yuli Wang, Na Mei, Ziyi Zhou, Yuan Fang, Jiacheng Lin, Fanchen Zhao, Zhihong Fang, Yan Li

“…Finally, critical variables in the optimal model were screened based on the interpretable algorithms to build a decision tree to facilitate clinical application. …”
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Research on Discrete Dynamic System Modeling of Vocal Performance Teaching Platform Based on Big Data Environment by Xiaohui Gong

“…Based on this, this study studies the innovation of the teaching model of national vocal music in colleges and universities based on the deep recurrent neural network algorithm and designs the teaching quality evaluation model based on the deep recurrent neural network algorithm. …”
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Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review by Veronica Atemnkeng Ntam, Tatjana Huebner, Michael Steffens, Catharina Scholl

“…The objective of the evaluation was to assess applicability of currently published ML-approaches for clinical settings in the EU on the basis of quality, ethical, social, and legal criteria.MethodsWe performed a bibliographic search on PubMed and Google Scholar for studies from January 2016 to December 2024 on ML-applications predicting treatment outcomes in MDD. …”
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Deep Learning Methods and UAV Technologies for Crop Disease Detection by S. G. Mudarisov, I. R. Miftakhov

“…The findings demonstrate that deep learning algorithms offer more accurate and earlier detection of diseases, highlighting their potential for application in plant growing. …”
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Assessing Lightweight Folding UAV Reliability Through a Photogrammetric Case Study: Extracting Urban Village’s Buildings Using Object-Based Image Analysis (OBIA) Method by Junyu Kuang, Yingbiao Chen, Zhenxiang Ling, Xianxin Meng, Wentao Chen, Zihao Zheng

“…With the rapid advancement of drone technology, modern drones have achieved high levels of functional integration, alongside structural improvements that include lightweight, compact designs with foldable features, greatly enhancing their flexibility and applicability in photogrammetric applications. Nevertheless, limited research currently explores data collected by such compact UAVs, and whether they can balance a small form factor with high data quality remains uncertain. …”
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Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest by M. Bagus Prayogi, Gustina Masitoh

“…User feedback on the Google Play Store shows mixed sentiments, including both positive and negative responses, which can be utilized by developers as material to improve the quality of the application. This study focuses on assessing the sentiment of Alfagift app user reviews through the application of the Random Forest algorithm. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…Furthermore, EEG signal preprocessing methods improved data quality, raising classification accuracy. This Model can be implemented for real-time monitoring using wearable devices, enabling continuous patient observation and remote healthcare applications.…”
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A Review on Trending Machine Learning Techniques for Type 2 Diabetes Mellitus Management by Panagiotis D. Petridis, Aleksandra S. Kristo, Angelos K. Sikalidis, Ilias K. Kitsas

“…Our work indicates that Gradient Boosting and tree-based models are the most successful ones, the SHAPley and Wrapper algorithms being quite popular feature interpretation and evaluation methods, highlighting urinary markers and dietary intake as emerging diabetes predictors besides the typical invasive ones. …”
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Chaotic Hénon–Logistic Map Integration: A Powerful Approach for Safeguarding Digital Images by Abeer Al-Hyari, Mua’ad Abu-Faraj, Charlie Obimbo, Moutaz Alazab

“…Performance evaluation in terms of encryption time, throughput, and image quality metrics validates the effectiveness of the proposed integrated approach. …”
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A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…Security analysis demonstrates that under the Canetti–Krawczyk (CK) model and the Decisional Diffie–Hellman (DDH) security assumption, the SEUS-VCS scheme satisfies the security of session keys. Performance evaluation indicates that compared to other key exchange schemes, the SEUS-VCS scheme shows a significant advantage in supporting national cryptographic algorithms and anonymity. …”
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Boosting Crowdsourced Annotation Accuracy: Small Loss Filtering and Augmentation-Driven Training by Yanming Fu, Weigeng Han, Jingsang Yang, Haodong Lu, Xin Yu

“…However, the labels provided by untrained crowdsourcing workers often contain a considerable amount of noise. Although the application of ground truth inference algorithms to deduce integrated labels effectively enhances label quality, a certain level of noise persists. …”
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A Review of Developments and Metrology in Machine Learning and Deep Learning for Wearable IoT Devices by Minh Long Hoang

“…It also examines how algorithm selection influences model performance metrics and computational efficiency, which are critical for real-time and resource-constrained applications. …”
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Data augmentation of time-series data in human movement biomechanics: A scoping review. by Christina Halmich, Lucas Höschler, Christoph Schranz, Christian Borgelt

“…Data augmentation can help address these limitations, but its use in biomechanical time-series data remains insufficiently evaluated.<h4>Objective</h4>This scoping review on data augmentation for biomechanical time-series data focuses on examining current techniques, evaluating their effectiveness, and offering recommendations for their application.…”
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Approaches for handling imbalanced data used in machine learning in the healthcare field: A case study on Chagas disease database prediction. by André G Coimbra, Cleiane G Oliveira, Matheus P Libório, Hasheem Mannan, Laercio I Santos, Elisa Fusco, Marcos F S V D'Angelo

“…This approach was applied to the real problem of Chagas disease. The application of these techniques aims to improve the quality of life for individuals, reduce healthcare costs, and allocate available resources more efficiently, making it a preventive action.…”
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