Encoding IoT Data: A Comprehensive Review of Image Transformation Techniques by Suat Özdemir, Feyza Yıldırım Okay, Duygu Altunkaya

“…Recent studies show that transforming IoT data into images improves the performance of the learning model. In this paper, we present a review of these studies which use image transformation/encoding techniques in IoT domain. …”
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Obfuscated malicious traffic detection based on data enhancement by Ke Ye, Tao Zeng, Yubing Duan, Jun Han, Guoxin Zhong, Zhi Chen, Yulong Wang

“…By introducing noise during the training phase, the robustness of the model is improved. To validate the effectiveness of RobustDetector, we conducted extensive experiments using public datasets. …”
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Enhancing Predictive Capabilities for Identifying At-Risk Stocks Using Multivariate Time-Series Classification: A Case Study of the Thai Stock Market by Katsamapol Petchpol, Laor Boongasame

“…Experimental results show deep learning models significantly outperform traditional baseline methods in capturing financial risk patterns. …”
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Artificial intelligence in drug development: reshaping the therapeutic landscape by Sarfaraz K. Niazi, Zamara Mariam

“…Although, to date, no AI-generated medicinal drug has been FDA-approved, HLX-0201 for fragile X syndrome and new molecules for idiopathic pulmonary fibrosis have entered clinical trials. However, AI models are generally considered “black boxes,” making their conclusions challenging to understand and limiting the potential due to a lack of model transparency and algorithmic bias. …”
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Synergistic Integration of LQR Control and PSO Optimization for Advanced Active Suspension Systems Utilizing Electro-Hydraulic Actuators and Electro-Servo Valves by Trong Tu

“…This research remarks the fundamentals of the experimental validation and further refinement of these control algorithms to adapt to various driving conditions and vehicle models, ultimately aiming to transition these optimized controllers from theoretical frameworks to practical, real-world applications. …”
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GastroEndoNet: Comprehensive endoscopy image dataset for GERD and polyp detectionMendeley Data by Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Kamrul Hassan Shakil, Aka Das, Khalid Been Badruzzaman Biplob, Imran Mahmud, Syed Md. Minhaz Hossain

“…It provides an invaluable resource for developing machine learning models aimed at the automatic diagnosis, classification, and detection of GERD and polyps, potentially improving the speed and accuracy of clinical decision-making. …”
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Generative design for architectural spatial layouts: a review of technical approaches by Shurui Yan, Chen Wu, Yixin Zhang

“…Additionally, improving computational efficiency makes knowledge-driven methods, such as heuristic algorithms, more user-friendly. …”
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Problems and perspectives of family doctors training on the undergraduate stage by Yu. M. Kolesnik, V. D. Syvolap, N. S. Mikhaylovskaya, T.O. Kulinich

“…Under healthcare reform activities of all medical schools and post-graduate education institutions are directed to improvement of the effectiveness of high-qualified family doctors training. …”
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Postmarketing safety evaluation of pemetrexed using FAERS and JADER databases by Luo Lv, Xiangyang Wu, Yubo Ren, Yuli Guo, Haixiong Wang, Xiaofang Li

“…Continuous pharmacovigilance is essential to optimize its clinical use and improve patient safety.…”
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The diagnostic and prognostic capability of artificial intelligence in spinal cord injury: A systematic review by Saran Singh Gill, Hariharan Subbiah Ponniah, Sho Giersztein, Rishi Miriyala Anantharaj, Srikar Reddy Namireddy, Joshua Killilea, DanieleS.C. Ramsay, Ahmed Salih, Ahkash Thavarajasingam, Daniel Scurtu, Dragan Jankovic, Salvatore Russo, Andreas Kramer, Santhosh G. Thavarajasingam

“…Method: ology: The primary aim was to evaluate the performance of AI algorithms in diagnosing and prognosticating tSCI. Subsequent systematic searching of seven databases identified studies evaluating AI models. …”
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Comparative Analysis of Facial Expression Recognition Methods by Denys - Florin COT

“…The research not only deepens the understanding of emotion classification algorithms but also provides valuable perspectives for their application in crucial areas such as mental health and personalized medical assistance. …”
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Comparison of Machine Learning Methods for Predicting Electrical Energy Consumption by Retno Wahyusari, Sunardi Sunardi, Abdul Fadlil

“…Data pre-processing, specifically min-max normalization, is crucial for improving the accuracy of distance-based algorithms like KNN. …”
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Machine Learning-Based Seismic Damage Assessment of a Bridge Portfolio in Cohesive Soil by Burak Ozturk, Ahmed Fouad Hussein, Mohamed Hesham El Naggar

“…Results show that CatBoost and LightGBM outperform other methods in accuracy and robustness, particularly under imbalanced data conditions. Oversampling improves classification for specific targets but introduces overfitting risks in others, while undersampling generally degrades model performance. …”
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Hybrid Filtering Technique for Accurate GNSS State Estimation by Jahnvi Verma, Nischal Bhattarai, Thejesh N. Bandi

“…We demonstrate that this hybrid method outperforms classical algorithms both in terms of accuracy and flexibility for easier implementation, showing better than 30% improvement in the variance of the horizontal position for the simulated as well as the real-world dynamic receiver dataset. …”
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Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus by Wang K, Zhu R, Xu M, Zhu K, Li J, Li C, Meng D, Chen H, Sun L

“…Two key metabolites, tryptophan and beta-alanine, showed significantly decreased levels in SLE patients compared to healthy controls (both p< 0.05), while exhibiting opposite patterns in other autoimmune diseases. In the mouse model, tryptophan supplementation improved renal histology, reduced proteinuria, increased naïve T cells and central memory T cells, and decreased effector T cell frequencies in both peripheral blood and spleen.Conclusion: This study demonstrates the successful application of machine learning algorithms to metabolomics data for SLE classification and identifies tryptophan and beta-alanine as potential SLE-specific biomarkers. …”
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5G OFDM channel estimation method based on complex-valued generative adversarial network by LU Yuanzhi, WEI Xianglin, YU Long, YAO Changhua

“…Channel estimation methods based on least square (LS) and minimum mean square error (MMSE) effectively utilize the system’s sparsity, but LS algorithms face low computational precision, while MMSE algorithms suffer from high computational complexity. …”
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Metaheuristics in automated machine learning: Strategies for optimization by Francesco Zito, El-Ghazali Talbi, Claudia Cavallaro, Vincenzo Cutello, Mario Pavone

“…We examine various metaheuristic algorithms employed and, in particular, their effectiveness in improving model performance across diverse applications. …”
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Domain-Adaptive and Per-Fraction Guided Deep Learning Framework for Magnetic Resonance Imaging-Based Segmentation of Organs at Risk in Gynecologic Cancers by Reza Kalantar, PhD, Manasi Ingle, FRCR, Romelie Rieu, BA, BmBCh, Sebastian Curcean, MD, Jessica Mary Winfield, PhD, Gigin Lin, MD, PhD, Christina Messiou, MD, MRCP, FRCR, Susan Lalondrelle, FRCR, Dow-Mu Koh, MD, FRCP, FRCR, Matthew David Blackledge, PhD

“…Results: The synthetic MRL-assisted model improved organ at risk segmentation accuracy on MRL images, with fraction-adapted contours displaying high anatomical fidelity. …”
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Applying Neural Networks to Analyse Inflammatory, Sociodemographic, and Psychological Factors in Non-Melanoma Skin Cancer and Colon Cancer: A Statistical and Artificial Intelligenc... by Răzvan Mercuţ, Marius Eugen Ciurea, Emil Tiberius Traşcă, Mihaela Ionescu, Maria Filoftea Mercuţ, Patricia Mihaela Rădulescu, Cristina Călăraşu, Liliana Streba, Alin Gabriel Ionescu, Dumitru Rădulescu

“…This study aims to develop a comprehensive patient profiling model by comparing non-melanoma skin cancer (NMSC) and colorectal cancer (CRC). …”
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Transductive Radar Clutter Segmentation Network for Ionospheric Inhomogeneity Clutter Suppression by Yuanbiao Li, Lei Yu, Yinsheng Wei

“…Experiments demonstrate the effectiveness of TRCS-Net in improving clutter suppression performance compared to individual algorithms, leveraging transductive transfer learning and simulation data.…”
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