Relay Selection and Subcarrier-Pair Based Energy-Efficient Resource Allocation for Multirelay Cooperative OFDMA Networks by Wanming Hao, Shouyi Yang, Wanliang Hao

“…Numerical studies are conducted to evaluate the EE of the proposed algorithms.…”
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Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

“…Additionally, the model demonstrated strong reliability in maintaining standardized prediction errors below 0.2 for most instruments, with probabilities of achieving this threshold being 96.43% for WHOQoL HIV Bref and 88.44% for ESTAR, while lower probabilities were observed for TSQM (44%) and WRFQ (51%).Conclusion: The results from our machine learning algorithms are promising for predicting PROMs and PREMs in AIDS settings. …”
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Improving machine learning predictions to estimate fishing effort using vessel's tracking data by J. Samarão, A. Moreno, M.B. Gaspar, M.M. Rufino

“…This information can identify areas with high fishing density, warranting protection of their main fishing grounds against other users (i.e. ocean grabbing), while also signalling potential stock depletion requiring management interventions and preserving the ecosystems from which these fisheries depend on.In this study, we propose a series of steps to enhance the performance of Machine Learning algorithms in estimating fishing effort. We assessed seven supervised ML algorithms, including Logistic Regression, Ridge Classifier, Random Forest Classifier, K-Neighbours, Gradient Boosting Classifier, LinearSVC, Recurrent Neural Networks and XGBoost, using four case studies, from bivalve dredge and octopus pots and traps fisheries.First, in a preliminary statistical analysis between common error measures derived from the confusion matrix was decided to use accuracy, precision, and sensitivity as evaluation criteria. …”
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Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach by Hongyang Wang, Junpeng Ding, Shuochen Wang, Long Li, Jinqiu Song, Dongsheng Bai

“…Feature engineering was initially performed, followed by a comparative analysis of five machine learning algorithms (Logistic Regression, SVM, Random Forest, XGBoost, and LightGBM) and the deep learning TabNet model. …”
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Framework for smartphone-based grape detection and vineyard management using UAV-trained AI by Sergio Vélez, Mar Ariza-Sentís, Mario Triviño, Antonio Carlos Cob-Parro, Miquel Mila, João Valente

“…On one hand, drone, or Unmanned Aerial Vehicles (UAV) imagery combined with deep learning algorithms has revolutionised agriculture by automating plant health classification, disease identification, and fruit detection. …”
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Determining the Factors and Evolutionary Trends of Customer Engagement in Businesses: A Bibliometric Analysis by Reza Abbasi Mobarakabadi, Amir khanlari, Nader Seyyedamiri

“…All analyses were conducted using R programming languages. The algorithms employed included thematic analysis, correspondence analysis, clustering algorithms, and co-occurrence analysis of words.     …”
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Machine learning for predicting severe dengue in Puerto Rico by Zachary J. Madewell, Dania M. Rodriguez, Maile B. Thayer, Vanessa Rivera-Amill, Gabriela Paz-Bailey, Laura E. Adams, Joshua M. Wong

“…Conclusions ML models, especially gradient boosting algorithms, outperformed traditional warning signs in predicting severe dengue. …”
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Estimating Trends in Cardiovascular Disease Risk for the EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa a... by Shaun Scholes, Jennifer S Mindell, Mari Toomse-Smith, Annibale Cois, Kafui Adjaye-Gbewonyo

“…Predicted CVD risk using the Globorisk algorithms was lower on average in absolute terms, but the pattern of change was very similar. …”
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Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision by Lucas Waltz, Sushma Katari, Chaeun Hong, Adit Anup, Julian Colbert, Anirudh Potlapally, Taylor Dill, Canaan Porter, John Engle, John Engle, Christopher Stewart, Hari Subramoni, Scott Shearer, Raghu Machiraju, Osler Ortez, Laura Lindsey, Arnab Nandi, Sami Khanal

“…IntroductionAdvancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. …”
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Discussion on Artificial Intelligence Safety and Ethical Issues by Chen Xinyu, Hui Tianfang, Li Yanlin, Yang Haoyuan

“…Regarding ethical considerations, this paper identifies the origins of algorithmic bias and argues for mitigating it through rigorous testing, validation, and regulatory frameworks. …”
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An Exploration of the Triplet Periodicity in Nucleotide Sequences with a Mature Self-Adaptive Spectral Rotation Approach by Bo Chen, Ping Ji

“…The applications of the algorithms on simulated datasets show their capabilities in detecting such a potential sequence pattern.…”
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Research for SARIMA and PatchTSMixer Models on the IEA Monthly Statistics Dataset by Hu Yuwei

“…The rapid evolution of intelligent algorithms has led to their extensive application in time-series forecasting, particularly in predicting electricity consumption. …”
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Modelling potential impacts of climate change on the oak spatial distribution (Case study: Ilam and Lorestan provinces) by Mohammad Safaei, Hani Rezayan, Parviz Zeaiean Firouzabadi, Ali asghar Torahi

“…The results of measuring the accuracy of models by AUC indicated the good performance of all algorithms and Random Forest achieved the highest accuracy (AUC = 0.95) among other methods. …”
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Measuring. Monitoring. Management. Control by B.V. Chuvykin, I.A. Dolgova, G.V. Milovzorov

“…Examples of recovery algorithms for three classes of measuring pulse signals are given. …”

The Impact of Artificial Intelligence Technologies on International Relations by A. D. Korobkov

“…The automation of human activity has been growing every year for the past ten years thanks to AI algorithms, increasing the amount of big data and computing power. …”
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Dufour Effect on Transient MHD Double Convection Flow of Fractionalized Second-Grade Fluid with Caputo–Fabrizio Derivative by Imran Siddique, Sehrish Ayaz, Fahd Jarad

“…The Laplace transform and Laplace inversion numerical algorithms are used to derive the analytical and semianalytical solutions for the dimensionless concentration, temperature, and velocity fields. …”
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Analysis of damage control of thin plate with piezoelectric actuators using finite element and machine learning approach by Asraar Anjum, Abdul Aabid, Meftah Hrairi

“…To optimize performance, we utilized machine learning algorithms to examine how these characteristics affect the repair process. …”
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In-Network Filtering Schemes for Type-Threshold Function Computation in Wireless Sensor Networks by Guillermo G. Riva, Jorge M. Finochietto

“…Both of them are based on the integration of metaheuristics and learning algorithms inspired by nature. In particular, we consider the computation of the maximum function as case study for these schemes. …”
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A security detection approach based on autonomy-oriented user sensor in social recommendation network by Shanshan Wan, Ying Liu

“…Then, AOUSD is first simulated on NetLogo and it is compared with other algorithms based on the Amazon data. The results prove the advantages of AOUSD in the efficiency and accuracy on shilling attack detection.…”
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Bulk Handling Facility Modeling and Simulation for Safeguards Analysis by Benjamin B. Cipiti, Nathan Shoman

“…Machine learning techniques are being applied, but these techniques need large amounts of data for training and testing the algorithms. The SSPM can provide that training data. …”
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