Evaluating Packaging Design Relative Feature Importance Using an Artificial Neural Network (ANN) by Juan Gu, Euihark Lee

“…In this study, box compression strength (BCS) was used as a representative packaging property, and the relative importance of up to six BCS features (edge crush test (ECT), perimeter, thickness, depth, and flexural stiffness in both the machine and cross-machine directions) were evaluated. …”
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Prediction of pharmacokinetic/pharmacodynamic properties of aldosterone synthase inhibitors at drug discovery stage using an artificial intelligence-physiologically based pharmacok... by Mengjun Zhang, Keheng Wu, Sihui Long, Xiong Jin, Bo Liu

“…On a web-based platform, an AI-PBPK model, integrating machine learning and a classical PBPK model for the PK simulation of ASIs, was developed. …”
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Detecting soil mixing, grain size distribution, and clogging potential of tunnel excavation face by classification-regression algorithms using EPBM operational data by Sharmin Sarna, Marte Gutierrez

“…Earth pressure balance machine (EPBM) operation is sensitive to the properties of the excavated soil due to the requirements of proper soil conditioning and maintenance of necessary chamber pressure. …”
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Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case by Ivan Izonin, Roman Tkachenko, Nazarii Hovdysh, Oleh Berezsky, Kyrylo Yemets, Ivan Tsmots

“…The proposed method was tested on a small dataset within transplantology, focusing on binary classification. Optimal parameters for the method were identified using the Dual Annealing algorithm. …”
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Computational approaches in drug chemistry leveraging python powered QSPR study of antimalaria compounds by using artificial neural networks by Wakeel Ahmed, Tamseela Ashraf, Maliha Tehseen Saleem, Emad E. Mahmoud, Kashif Ali, Shahid Zaman, Melaku Berhe Belay

“…These models utilize several topological indices global variables quantifying the connectivity and geometric characteristics of molecules to estimate the ability of prospective antimalarial compounds to interact with the target enzyme and other physicochemical parameters. Molecular descriptors such as size, shape, and electronic structure indices are a way of mapping molecular properties into a set of quantitative data that can be analyzed by Machine Learning techniques. …”
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Unsupervised Learning for Reference Signals Overhead Reduction in 3GPP MIMO Systems by Omar M. Sleem, Mohamed Salah Ibrahim, Akshay Malhotra, Mihaela Beluri, Constantino M. Lagoa

“…Toward this end, this paper proposes a machine learning-based approach that enables reference signal-free data channel demodulation. …”
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Assessment of the Diagnostic Accuracy of Artificial Intelligence Software in Identifying Common Periodontal and Restorative Dental Conditions (Marginal Bone Loss, Periapical Lesion... by Wael I. Ibraheem, Saurabh Jain, Mohammed Naji Ayoub, Mohammed Ahmed Namazi, Amjad Ismail Alfaqih, Aparna Aggarwal, Abdullah A. Meshni, Ammar Almarghlani, Abdulkareem Abdullah Alhumaidan

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Dropout Kolmogorov–Arnold Networks: A Novel Data-Driven Impedance Modeling Approach for Voltage-Source Converters by Moetasem Ali, Yasser Abdel-Rady I. Mohamed

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A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering by Aryan Sharma, Tanmay Khade, Shashank Mouli Satapathy

“…Therefore, rapid diagnosis and prompt treatment of HCV is crucial. This study utilizes machine learning (ML) to precisely identify hepatitis C in patients by analyzing parameters obtained from a standard biochemistry test. …”
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Simulation-based inference of surface accumulation and basal melt rates of an Antarctic ice shelf from isochronal layers by Guy Moss, Vjeran Višnjević, Olaf Eisen, Falk M. Oraschewski, Cornelius Schröder, Jakob H. Macke, Reinhard Drews

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Multi-objective optimization of an origami inspired super-expandable scaffold for distraction osteogenesis by MA Bagheri, CE Aubin, ML Nault, I Villemure

“…The optimized OISES configurations maintain porosity near 80 % and high surface area-to-volume ratios—parameters associated with osteogenesis—while exhibiting large recoverable deformations. …”
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An Improved Decision Support System for Identification of Abnormal EEG Signals Using a 1D Convolutional Neural Network and Savitzky-Golay Filtering by Unmesh Shukla, Geetika Jain Saxena, Manish Kumar, Anil Singh Bafila, Amit Pundir, Sanjeev Singh

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Justification of the Architecture a Promising Automated System for Monitoring Radiation, Chemical and Biological Environment Using Artificial Intelligence by S. A. Sharov, D. S. Batinov, M. A. Osipov, M. V. Domnin, S. A. Morozov, M. A. Golyshev, Yu. I. Khripkov, A. V. Nadein, I. V. Chebykin, V. D. Vasin, M. M. Bets

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Non-invasive methods to assess seed quality based on ultra-weak photon emission and delayed luminescence by Adriano Griffo, Stefanie Sehmisch, Frédéric Laager, Andrea Pagano, Alma Balestrazzi, Anca Macovei, Andreas Börner

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Improved deep learning method and high-resolution reanalysis model-based intelligent marine navigation by Zeguo Zhang, Zeguo Zhang, Zeguo Zhang, Liang Cao, Liang Cao, Liang Cao, Jianchuan Yin, Jianchuan Yin, Jianchuan Yin

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Finite-size effects in molecular simulations: a physico-mathematical view by Benedikt M. Reible, Carsten Hartmann, Luigi Delle Site

“…Furthermore, the statistical nature of machine learning implies questions about the number of parameters and the size of the training set. …”
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Comprehensive Review of Privacy, Utility, and Fairness Offered by Synthetic Data by A. Kiran, P. Rubini, S. Saravana Kumar

“…Artificial Intelligence (AI) and Machine Learning (ML) are the key components of automation. …”
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Optimized breast cancer diagnosis using self-adaptive quantum metaheuristic feature selection by Alok Kumar Shukla, Shubhra Dwivedi, Deepak Singh, Sunil Kumar Singh, Diwakar Tripathi, Ram Kishan Dewangan

“…Most importantly, a self-adaptive genetic algorithm (GA) is also incorporated into TLBO to tradeoff between exploration and exploitation to handle slow convergence and exploitation competence, and simultaneously optimizing parameters of support vector machines (SVM) and the best features subset is our primary objective. …”
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Longwall top coal caving technological process in the notation of the system-functional approach by Cherkasov P.V.

“…Based on the conducted system-functional analysis, a stand structure has been developed for measuring the volume of rock mass produced using machine vision and projection of a grid of laser lines, which makes it possible to measure the volume of rock mass in the boundary conditions of illumination, humidity and dustiness, and It is also necessary to determine the influence of environmental factors on the quality of recognition of a laser grid by a machine vision system…”
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Hydrogen reaction rate modeling based on convolutional neural network for large eddy simulation by Quentin Malé, Corentin J. Lapeyre, Nicolas Noiray

“…It is also tested on two filter and downsampling parameters and two global equivalence ratios between those used during training. …”
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