Deep reinforcement learning for conservation decisions by Marcus Lapeyrolerie, Melissa S. Chapman, Kari E. A. Norman, Carl Boettiger

“…Ecologists must establish a better understanding of how these algorithms work and fail if we are to realize this potential and avoid the pitfalls such a transition would bring. …”
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Classification of the Condition of Cancer Patients Receiving Home Health Care with Machine Learning Methods by Mürsel Kahveci

“…The ANN classifier achieved 88.6% accuracy for all cancer types.The use of machine learning algorithms may provide a more sensitive and objective way to evaluate patients' response to treatment. …”
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A review on amino acid based protein classification using supervised artificial intelligence (AI) models by Rida Zulfiqar, Talha Ahmed Khan, Abdullah Ayub Khan, Rubaika Akhtar, Sajid Ullah

“…In light of this, the primary objective of the current work was to assess how well various ML and DL algorithms performed on the Protein family database (PFAM), which serves as a reference dataset for the protein classification problem. …”
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Multi-objective optimization of HESS control for optimal frequency regulation in a power system with RE penetration by Ousama M.T. Ajami, Rodney H.G. Tan, Mithulan Nadarajah, Farah Adilah Jamaludin, Adlan Bagus Pradana

“…To optimize the control parameters with the best objectives, all possible sets of objectives with four different optimization algorithms are studied. The three control models considered for the HESS incorporate Virtual Synchronous Generator (VSG) or Virtual Inertia (VI) control with independent or simultaneous optimization of control parameters. …”
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Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision by Quan Wang, Mengnan Wang, Jiadong Sun, Deji Chen, Pei Shi

“…Machine learning methods are divided into point-distance-based, hyperplane-based, tree-based, and neural network-based classification algorithms. Deep learning models are classified into two types: accuracy-oriented and efficiency-oriented. …”
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TECTONOPHYSICAL ZONING OF ACTIVE FAULTS OF THE BAIKAL RIFT SYSTEM by Yu. L. Rebetsky, A. A. Dobrynina, V. A. Sankov

“…The second important element of fault zoning was electronic maps of active faults of Eurasia hosted on the GIN RAS sever. Within the algorithmic framework for Rebetsky’s method of cataclastic analysis, both of these datasets allowed calculating the Coulomb stresses for the segments of the BRS faults. …”
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Classification and Time-Frequency Localization of Arbitrary LPWAN Signals With Radial Deformable DETR by Chun Ho Kong, Haibo Hu

“…Therefore, it is important not only to classify but also to localize the frequency, bandwidth, and time of these LPWAN signals on the air for management, security, or band planning purposes. SOTA algorithms usually look through the whole received signal on the time domain or frequency domain only to perform classification tasks, without finding out the corresponding time-frequency location of the signal. …”
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Exploring the roles of AI-powered e-tutors in enhancing self-directed learning in open distance e-learning courses by Mncedisi Christian Maphalala, Rachel Gugu Mkhasibe, Dumsani Wilfred Mncube

“…The paper also addresses the practical challenges and limitations of implementing AI-powered e-tutoring, such as technology access, data privacy, and the continuous improvement of AI algorithms. The study contributes to the expanding knowledge of AI in education, providing practical insights for academics, policymakers, and technology developers, equipping them with the necessary information to leverage AI to strengthen self-directed learning in ODeL environments. …”
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Empirical Evaluation on GPU, Overclocking, and LoRA for Deep Learning on Embedded Systems by Evandro Raphaloski, Mariana Caravanti De Souza, Edson Takashi Matsubara

“…The findings of this study will contribute to other researchers in discerning and choosing algorithms to be used in the deployment and retraining of deep learning models on embedded and mobile platforms that have not yet been well explored in the literature.…”
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Adverse event profile differences between maribavir and valganciclovir: findings from the FDA adverse event reporting system by Haolin Teng, Shengnan Zhang, Jinyu Yu, Faping Li

“…We applied various disproportionation analysis techniques, including the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN) and multi-item gamma Poisson shrinker (MGPS) algorithms, to identify and quantify potential signals of AEs associated with maribavir and valganciclovir.ResultsThere were 999 and 3,454 reports for maribavir and valganciclovir, respectively. …”
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Transfer Learning based Image Classification of Diseased Tomato Leaves with Optimal Fine-Tuning combined with Heat Map Visualization by Sivakumar Palanıswamy, Vijayakumar Vaıthyam Rengarajan, Sandhya Devi Ramıah Subburaj

“…It is presumed that the implementation of deep learning algorithms demands a large amount of data to learn complex features automatically and this can pose a challenge for applications with lesser data to achieve generalization. …”
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Fine-Tuned Machine Learning Classifiers for Diagnosing Parkinson’s Disease Using Vocal Characteristics: A Comparative Analysis by Mehmet Meral, Ferdi Ozbilgin, Fatih Durmus

“…This study seeks to assess the effectiveness of machine learning algorithms optimized to classify PD based on vocal characteristics to serve as a non-invasive and easily accessible diagnostic tool. …”
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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…In addition, we established predictive models for lung doses utilizing the Least Absolute Shrinkage and Selection Operator, ridge, and linear regression algorithms while also evaluating dosimetric parameters for both the PTV and the organs at risk (OARs). …”
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3D Point Cloud Fusion Method Based on EMD Auto-Evolution and Local Parametric Network by Wen Chen, Hao Chen, Shuting Yang

“…Experimental results demonstrate that our fusion method outperforms other classical comparison algorithms for targets such as buildings, planes, and ships, and achieves a fused RMSE of approximately 2 m and an EMD accuracy better than 0.5.…”
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Cross-Language Transfer-Learning Approach via a Pretrained Preact ResNet-18 Architecture for Improving Kanji Recognition Accuracy and Enhancing a Number of Recognizable Kanji by Vasyl Rusyn, Andrii Boichuk, Lesia Mochurad

“…During the implementation of our training algorithms, we trained a model with the CASIA-HWDB dataset with handwritten Chinese characters and used its weights to initialize models that were fine-tuned with a Kuzushiji-Kanji dataset that consists of Japanese handwritten kanji. …”
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Macro-Modelling of Rib-Knitted Tubular Parts by Tetiana Ielina, Liudmyla Halavska, Nataliia Ausheva

“…This model allows its implementation into algorithms, describing the peculiarities of the stretching process. …”
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Development of a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease: A secondary analysis of cross-sectional national survey by Sudarshan Pant, Hyung Jeong Yang, Sehyun Cho, EuiJeong Ryu, Ja Yun Choi

“…A residual neural network (ResNN) model was developed and compared with five machine learning algorithms (random forest, decision tree, Gaussian Naive Bayes, K-nearest neighbor, and AdaBoost) and two deep learning models (multilayer perceptron and TabNet). …”
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Application of deep learning in malware detection: a review by Yafei Song, Dandan Zhang, Jian Wang, Yanan Wang, Yang Wang, Peng Ding

“…This work compares and reports a classification of malware detection work based on deep learning algorithms. The 2011–2025 articles were considered, and the latest work focused on the literature for the 2018–2025 years; after screening, 72 articles were selected for the initial study. …”
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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…After data preprocessing and feature selection, six supervised ML algorithms—Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost—were applied using Python 3.9. …”
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Hysteresis in Neuron Models with Adapting Feedback Synapses by Sebastian Thomas Lynch, Stephen Lynch

“…This work has the potential to improve learning algorithms, increase understanding of neural memory formation, and inform neuromorphic engineering research.…”
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