Machine Learning Ensemble Classifiers for Feature Selection in Rice Cultivars by Chandrakumar Thangavel, D Sakthipriya

“…Machine Learning is crucial for seed prediction, germination, crop production, soil moisture, and land suitability evaluation. …”
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Improving Mental Health Diagnosis with Hybrid Ensemble Models: A Data-Driven Approach by Malave Sachin, Khemani Bharti, Kelkar Rucha, Balekundri Urvi, Bogar Shravani, Kolekar Areen

“…This study examines how emotional and behavioural indicators might be used to predict mental health issues using machine learning (ML) algorithms. …”
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Enhanced Light-Gradient Boosting Machine (GBM)-Based Artificial Intelligence-Blockchain-Based Telesurgery in Sixth Generation Communication Using Optimization Concept by Punitha S., Preetha K. S.

“…In the future, recent deep learning algorithms can be considered for drone-assisted telesurgery framework together with the consideration of hybrid optimization algorithms. …”
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Deep reinforcement learning for inverse inorganic materials design by Christopher Karpovich, Elton Pan, Elsa A. Olivetti

“…We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning (ML) algorithms to highlight the plausibility of the identified target compositions. …”
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Multi-tier cooperative caching in fog radio access network by Yanxiang JIANG, Chengyu XIA

“…Aiming at the problem of reducing the load of the backward link in the edge buffer and fog wireless access network technology,a multi-tier cooperative caching scheme in F-RAN was proposed to further reduce the backhaul traffic load.In particular,by considering the network topology,content popularity prediction and link capacity,the optimization problem was decomposed into knapsack subproblems in multi-tiers,and effective greedy algorithms were proposed to solve the corresponding subproblems.Simulation results show that the proposed multi-tier cooperative caching scheme can effectively reduce the backhaul traffic and achieve relatively high cache hit rate.…”
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A Device for the Rapid Detection of Benzodiazepines and Synthetic Cannabinoids via Fluorescence Spectroscopy and Machine Learning by A. Power, M. Gardner, C. Pudney

“…Results: Development of the first prototype of the device is nearing completion, and lab data has been collected for training the device's drug-detecting predictive model. Current experiments with established supervised-learning algorithms show favourable results in distinguishing Synthetic Cannabinoids. …”
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Enhancing Problem-Solving Reliability with Expert Systems and Krulik-Rudnick Indicators by Lita Sari, Jufriadif Ma'am, Addini Yusmar, Khairiyah Khadijah, Sri Wahyuni, Naufal Ibnu Salam

“…This research aims to build an expert system to determine the level of problem-solving using Krulik and Rudnick's problem-solving indicators processed using the forward chaining and certainty factor algorithms. The study had five stages: data analysis, rule generation, certainty measurement, prediction, and testing. …”
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Fuzzy logic in real-time decision making for autonomous drones by Abdelwahed Motwakel, Adnan Shaout, Arif Muntasa, Manar Ahmed Hamza, Anwer Mustafa Hilal, Sitelbanat Abdel-gaddir Alhadi, Elmouez Samir Abd Elhamee

“… The rapid advancement of drone technology has expanded their applications across various sectors, necessitating robust real-time decision-making systems. Traditional algorithms often falter in dynamic and unpredictable environments. …”
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ML-based Plant Stress Detection from IoT-sensed Reduced Electromes by Marcos De Oliveira Jr, Gregory Sedrez, Gerson Geraldo H. Cavalheiro

“…The results validated the proposed approach, with the best performance obtained with the PAA+SAX techniques combined with the SVM algorithm, achieving good data reduction and improving stress detection, without compromising data quality. …”
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Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

“…Venant’s system of equations is built to generate the feature dataset, which is then combined with the random forest algorithm to create a nonlinear prediction model. An example analysis demonstrates that the optimal feedforward time of the open channel gate group is negatively connected with the flow condition and that the method can manage the water distribution error within 13.97% and the water level error within 13%. …”
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MPN-RRT*: A New Method in 3D Urban Path Planning for UAV Integrating Deep Learning and Sampling Optimization by Yue Zheng, Ang Li, Zihan Chen, Yapeng Wang, Xu Yang, Sio-Kei Im

“…Compared to conventional RRT*, the MPN-RRT* achieves a 47.8% reduction in planning time (from 89.58 s to 46.77 s) and a 19.8% shorter path length (from 476.23 m to 381.76 m) in simpler environments, alongside smoother trajectories quantified by a 91.2% reduction in average acceleration (from 14.67 m/s² to 1.29 m/s²). …”
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The Role of Artificial Intelligence (AI) in the Future of Forestry Sector Logistics by Leonel J. R. Nunes

“…Results: The results demonstrated that optimization algorithms reduced transportation costs and carbon emissions. …”
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Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation by Radhey Lal, Rajiv Kumar Singh, Dinesh Kumar Nishad, Saifullah Khalid

“…The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Monte Carlo simulations. …”
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Iterative Assessment of Edge Criticality: Efficiency Enhancement or Hidden Insufficiency Detection by Vasily Lubashevskiy, Hamza Ejjbiri, Ihor Lubashevsky

“…Second, the analysis reveals hidden inner insufficiency in edge ranking for some algorithms, as evidenced by the fact that algorithm iterations can reduce decomposition efficiency. …”
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Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning by Nanding Li, Naoshi Kondo, Yuichi Ogawa, Keiichiro Shiraga, Mizuki Shibasaki, Daniele Pinna, Moriyuki Fukushima, Shinichi Nagaoka, Tateshi Fujiura, Xuehong De, Tetsuhito Suzuki

“…This study developed a handheld camera system capable of capturing cattle fundus images and predicting vitamin A levels in real time using deep learning. 4000 fundus images from 50 Japanese Black cattle were used to train and test the prediction algorithms, and the model achieved an average 87%, 83%, and 80% accuracy for three levels of vitamin A deficiency classification (particularly 87% for severe level), demonstrating the effectiveness of camera system in vitamin A deficiency prediction, especially for screening and early warning. …”
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An Adaptive Weight Physics-Informed Neural Network for Vortex-Induced Vibration Problems by Ping Zhu, Zhonglin Liu, Ziqing Xu, Junxue Lv

“…In this study, a VIV dataset of a cylindrical body with different degrees of freedom is used to compare the performance of the PINN and three PINN optimization algorithms. The findings suggest that, compared to a standard PINN, the AW-PINN lowers the mean squared error (MSE) on the test set by 50%, significantly improving the prediction accuracy. …”
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Overcoming the Barriers That Obscure the Interlinking and Analysis of Clinical Data Through Harmonization and Incremental Learning by Vasileios C. Pezoulas, Konstantina D. Kourou, Fanis Kalatzis, Themis P. Exarchos, Evi Zampeli, Saviana Gandolfo, Andreas Goules, Chiara Baldini, Fotini Skopouli, Salvatore De Vita, Athanasios G. Tzioufas, Dimitrios I. Fotiadis

“…Lexical and semantic matching methods are used to align the structure of the heterogeneous, curated cohort data along with incremental learning algorithms including class imbalance handling and hyperparameter optimization to enable the development of disease prediction models. …”
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Ultra-Low Dose Computed Tomography Imaging in Quantifying Bone Trauma and Disorders: A Cross-Sectional Study by Fariba Zarei, Seyedeh Marzieh Ahmadi, Saber Dehbani-Zadeh, Anahita Jafari, Vyas Akondi, Sabyasachi Chatterjee, Seyed Alireza Mirhosseini, Rezvan Ravanfar Haghighi

“…The sensitivity and specificity of ULD-CT images for bone trauma and disorders were 67%–95% and 100%, respectively, with about a 98% dose reduction.Conclusion: The ULD-CT protocol for bone imaging achieved a remarkable dose reduction, while the image quality was reported as acceptable. …”
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Transcriptome analysis provides new insights into the berry size in ‘Summer Black’ grape under a two-crop-a-year cultivation system by Peiyi Ni, Shengdi Yang, Yunzhang Yuan, Chunyang Zhang, Hengliang Zhu, Jing Ma, Shuangjiang Li, Guoshun Yang, Miao Bai

“…Then the time-ordered gene co-expression network (TO-GCN) analysis with the breadth-first search algorithm showed that DEGs in the GCN were divided into 8 levels. …”
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The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1) by M. Buehner, J.-F. Caron, E. Lapalme, A. Caya, P. Du, Y. Rochon, S. Skachko, M. Bani Shahabadi, S. Heilliette, M. Deshaies-Jacques, W. Chang, M. Sitwell

“…In addition to describing the current MIDAS applications, a sample of the results from these systems is presented to demonstrate their performance in comparison with either systems from before the switch to using MIDAS software or similar systems at other numerical weather prediction (NWP) centres. The modular software design also allows the code that implements high-level components (e.g. observation operators, error covariance matrices, state vectors) to easily be used in many different ways depending on the application, such as for both variational and ensemble DA algorithms, for estimating the observation impact on short-term forecasts, and for performing various observation pre-processing procedures. …”
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