Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…The study focuses on predicting the ultimate moment capacity of Ultra-High-Performance Concrete (UHPC) beams and U-shaped girders. …”
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QSAR, Molecular Docking, and Pharmacokinetic Studies of 1,8-Naphthyridine Derivatives as Potential Anticancer Agents Targeting DNA Topoisomerase II by Garba Suleiman, Auwal Salisu Isa, Adamu Uzairu, Nabil El Brahmi, Saïd El Kazzouli

“…External validation demonstrated high predictive ability, with Q2 (F1) and Q2 (F2) scores of 0.8683 and 0.8670, respectively, indicating substantial reliability in predicting the biological activity of new compounds. …”
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The BRCA1 variant p.Ser36Tyr abrogates BRCA1 protein function and potentially confers a moderate risk of breast cancer. by Charita M Christou, Andreas Hadjisavvas, Maria Kyratzi, Christina Flouri, Ioanna Neophytou, Violetta Anastasiadou, Maria A Loizidou, Kyriacos Kyriacou

“…PolyPhen algorithm predicted that the BRCA1 p.Ser36Tyr VUS identified in the Cypriot population was damaging, whereas Align-GVGD predicted that it was possibly of no significance. …”
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Development of a Short-Range Multispectral Camera Calibration Method for Geometric Image Correction and Health Assessment of Baby Crops in Greenhouses by Sabina Laveglia, Giuseppe Altieri, Francesco Genovese, Attilio Matera, Luciano Scarano, Giovanni Carlo Di Renzo

“…The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. …”
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Development of an Intelligent Tablet Press Machine for the In-Line Detection of Defective Tablets Using Machine Learning and Deep Learning Models by Sun Ho Kim, Su Hyeon Han

“…The TPM was verified by sorting defective tablets in-line using a pretrained defect-detection algorithm. <b>Results:</b> The RF model demonstrated the highest predictive accuracy at 93.7% with an Area Under the Curve (AUC) of 0.895, while the ANN model achieved an accuracy of 92.6% with an AUC of 0.878. …”
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Experimental assessment of communication delay's impact on connected automated vehicle speed volatility and energy consumption by Wan Li, Jackeline Rios-Torres, Boyu Wang, Zulqarnain H. Khattak

“…To fill the research gap, this study leverages the facilities at America Center of Mobility (ACM) Smart City Test Center to implement and evaluate a CAV merging control algorithm through vehicle-in-the-loop testing. This study aims at achieving three main objectives: (1) develop and implement a CAV merging control strategy in the experimental test bed through vehicle-in-the-loop testing, (2) propose analytical models to quantify the impacts of communication delay on the variability of CAV speed and energy consumption based on field experiment data, and (3) create a predictive model for energy usage considering various CAV attributes and dynamics, e.g., speed, acceleration, yaw rate, and communication delays. …”
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Inflow Forecast of Iranamadu Reservoir, Sri Lanka, under Projected Climate Scenarios Using Artificial Neural Networks by Chamaka Karunanayake, Miyuru B. Gunathilake, Upaka Rathnayake

“…Results revealed that the LM training algorithm outperforms the other tests algorithm in developing the prediction model. …”
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Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…Pearson correlation and statistical multivariate linear regression (MLR) were applied to model the removal% and pHfinal of both antibiotics, along with the three machine learning algorithms, Artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF), to make the same predictions. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”
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Assessing perioperative risks in a mixed elderly surgical population using machine learning: A multi-objective symbolic regression approach to cardiorespiratory fitness derived fro... by Pietro Arina, Davide Ferrari, Maciej R Kaczorek, Nicholas Tetlow, Amy Dewar, Robert Stephens, Daniel Martin, Ramani Moonesinghe, Mervyn Singer, John Whittle, Evangelos B Mazomenos

“…Preoperative cardiorespiratory fitness data from cardiopulmonary exercise testing (CPET), demographic and clinical data were extracted and integrated into advanced machine learning (ML) algorithms. Multi-Objective-Symbolic-Regression (MOSR), a novel algorithm utilizing Genetic Programming to generate mathematical formulae for learning tasks, was employed to predict patient morbidity at Postoperative Day 3, as defined by the PostOperative Morbidity Survey (POMS). …”
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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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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

“…In contrast, the RF analysis identified 21 important variables predicting a higher probability of having diabetes. …”
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Artificial Intelligence in Revolutionizing Kidney Care and Beyond: Kid-AI Revolution by Kounaina Khan, Farhan Zameer, Pratheek Jain, Ravi KR, Vidya Niranjan, Manoj S, Ravish H, Subrahmanya Padyana

“…This review provides a comprehensive overview of AI applications in renal pathology, focusing on computer vision algorithms for kidney structure segmentation, specific pathological changes, diagnosis, treatment, and prognosis prediction based on images along with the role of machine learning (ML) and deep learning (DL) in addressing global public health issues related to various nephrological conditions. …”
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Use of artificial intelligence in the diagnosis, treatment and surveillance of patients with kidney cancer by E. Yu. Timofeeva, С. R. Azilgareeva, A. O. Morozov, M. S. Taratkin, D. V. Enikeev

“…AI finds its application in histopathological evaluation: the AI model reaches 100.0% sensitivity and 97.1% specificity in the differential diagnosis of normal tissue from RCC. AI model algorithms may be used to identify patients at high risk of relapse requiring long-term follow-up, as well as to develop individual treatment and follow-up strategies. …”
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Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning by Jian Huang, Lu Wang, Jiangfei Zhou, Tianming Dai, Weicong Zhu, Tianrui Wang, Hongde Wang, Yingze Zhang

“…By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. …”
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Automated Recognition of Conidia of Nematode-Trapping Fungi Based on Improved YOLOv8 by Enming Zhao, Hongyi Zhao, Guangyu Liu, Jianbo Jiang, Fa Zhang, Jilei Zhang, Chuang Luo, Bobo Chen, Xiaoyan Yang

“…Our approach integrates an efficient channel attention (ECA) mechanism within the backbone layer of YOLOv8l, improving the algorithm&#x2019;s capability to detect global information effectively. …”
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Engineering Synthetic Microbial Communities: Diversity and Applications in Soil for Plant Resilience by Arneeb Tariq, Shengzhi Guo, Fozia Farhat, Xihui Shen

“…Artificial intelligence-driven models can predict complex microbial interactions, while machine learning algorithms can analyze vast datasets to identify key microbial taxa and their functions. …”
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Replicating PET Hydrolytic Activity by Positioning Active Sites with Smaller Synthetic Protein Scaffolds by Yujing Ding, Shanshan Zhang, Xian Kong, Henry Hess, Yifei Zhang

“…Recent breakthroughs in protein structure prediction and de novo design, powered by artificial intelligence, now enable to create enzymes with desired functions without solely relying on traditional genome mining. …”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…Moreover, its emphasis the importance of computed and estimated indices obtained through UPINN for predicting voltage collapse occurrences within the system.…”
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State of Health Estimation of Li-Ion Battery via Incremental Capacity Analysis and Internal Resistance Identification Based on Kolmogorov–Arnold Networks by Jun Peng, Xuan Zhao, Jian Ma, Dean Meng, Shuhai Jia, Kai Zhang, Chenyan Gu, Wenhao Ding

“…Three commonly used machine learning methods (BP, LSTM, TCN) and two hybrid algorithms (LSTM-KAN and TCN-KAN) were used to establish the SOH estimation model. …”
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