Glaucoma detection from retinal fundus images using graph convolution based multi-task model by Satyabrata Lenka, Zefree Lazarus Mayaluri, Ganapati Panda

“…Glaucoma diagnosis requires professionals to identify minor structural changes in the structure of the optic disc and optic cup from retinal fundus images in a short period. Deep learning algorithms have been employed effectively in the segmentation of biomedical images and the identification of diseases. …”
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A Machine Vision Method for Detecting Pineapple Fruit Mechanical Damage by Jiahao Li, Baofeng Mai, Tianhu Liu, Zicheng Liu, Zhaozheng Liang, Shuyang Liu

“…To address these challenges, this paper proposes a pineapple mechanical damage detection method based on machine vision, which segments the damaged region and calculates its area using multiple image processing algorithms. First, both color and depth images of the damaged pineapple are captured using a RealSense depth camera, and their pixel information is aligned. …”
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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network by R. Shameli, Sujatha Rajkumar

“…The attack detection in 5G SDN involves Machine learning (ML) and Deep learning (DL) algorithms to analyze large volumes of network data and identify patterns indicative of attacks. …”
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Detection and Prediction of Wind and Solar Photovoltaic Power Ramp Events Based on Data-Driven Methods: A Critical Review by Jie Zhang, Xinchun Zhu, Yigong Xie, Guo Chen, Shuangquan Liu

“…Our analysis reveals that while detection algorithms for ramp events have matured and the overall predictive performance of power forecasting models has improved, existing approaches often struggle to capture localized ramp phenomena, resulting in persistent deviations. …”
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Mapping the digital silk road: evolution and strategic shifts in Chinese social media marketing (2015–2025) by Xinrui Liang, Wan Mohd Hirwani Wan Hussain, Mohammed R. M. Salem

“…Results indicate a transition from search-based behaviour to AI-facilitated impulse purchasing, enabled by algorithmic recommendations, parasocial influencer relations, and livestream commerce. …”
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Analyzing the Impact of Geoenvironmental Factors on the Spatiotemporal Dynamics of Forest Cover via Random Forest by Hendaf N. Habeeb, Yaseen T. Mustafa

“…This study investigates the spatiotemporal dynamics of forest cover in the Duhok District in the Kurdistan Region of Iraq over a decade (2013–2023), emphasizing the impact of geoenvironmental factors via Random Forest algorithms and Landsat data. This research integrates datasets including fractional vegetation cover (FVC), groundwater levels, climate data, topography, and soil moisture data, offering a comprehensive analysis of the factors influencing forest cover. …”
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New insights into measurement variability in glaucomatous visual fields from computer modelling. by Richard A Russell, David F Garway-Heath, David P Crabb

“…Future research, using VF simulations, could be employed to provide benchmarks for measuring the performance of VF progression detection algorithms and developing new strategies for measuring VF progression.…”
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Judges versus artificial intelligence in juror decision-making in criminal trials: Evidence from two pre-registered experiments. by Eiichiro Watamura, Yichen Liu, Tomohiro Ioku

“…These findings suggest that jurors do not inherently avoid algorithmic judgments and may consider AI opinions on par with those of human judges in certain contexts. …”
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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. …”
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Enhancing Road Infrastructure Quality Assessment Through Low-Cost Inertial Sensors by T. Singh, K. Garg, S. Goel

“…The metrics established in this study can serve as the foundation for developing more sophisticated algorithms that assess road roughness based on accelerometer data, ultimately contributing to enhanced transportation efficiency and road safety.…”
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Identifying the optimal number of topics in text mining: a case study on reindeer pastoralism literature by Barbara Contiero, Øystein Holand, Giulio Cozzi

“…Text mining and topic analysis algorithms which group textual contents in the most efficient way, are becoming increasingly useful to summarise the main information contained in large data corpus of complex scientific fields. …”
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Modeling the process of searching for operationally significant information from open state information resources by E. Yu. Nikulina, L. A. Obukhova, E. A. Rogozin

“…To solve this problem, it seems possible to use Kohonen neural networks or Hamming algorithms. The apparatus of neural networks will allow you to determine patterns and optimize the search for operationally significant information.…”
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Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study by Yuchen Cui, Fujia Kang, Xinpeng Li, Xinning Shi, Han Zhang, Xianchun Zhu

“…The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.MethodsA total of 949 adults who underwent oral examinations were enrolled in our study. 5 different ML algorithms were used for model development and comparison, and feature selection was performed by feature importance ranking and feature decreasing methods. …”
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Plantention: A general-purpose, lightweight and attention-based model for multi-crop leaf disease classification by Brindha Subburaj, Rohan M, Samhruth Ananthanarayanan, Daehan Won

“…Most computerized methods involve some form of Deep Learning algorithms and models to automate the detection process. …”
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Development and validation of a clinical and computerised decision support system for management of hypertension (DSS-HTN) at a primary health care (PHC) setting. by Raghupathy Anchala, Emanuele Di Angelantonio, Dorairaj Prabhakaran, Oscar H Franco

“…<h4>Methods</h4>Development of DSS software was based on an algorithmic approach for (a) evaluation of a hypertensive patient, (b) risk stratification (c) drug management and (d) lifestyle interventions, based on Indian guidelines for hypertension II (2007). …”
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A unique Ruleset for Saltmarsh and Mangrove monitoring using Sentinel-2 by I. J. Diti, S. M. M. Rasel

“…Remote Sensing observation is one of the tools to monitor this community. However algorithms are also important for the best sensor to map it accurately. …”
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Enhanced Intrusion Detection in In-Vehicle Networks Using Advanced Feature Fusion and Stacking-Enriched Learning by Ali Altalbe

“…This work implements and validates the FFS-IDS using real-time car hacking data sets and achieves better performance than individual decision tree classifiers and popular ensemble learning methods such as Random Forest, LightGBM, AdaBoost, and ExtraTree algorithms. The results demonstrate that FFS-IDS can detect Denial of Service (DoS), Gear spoofing, and RPM spoofing attacks with up to 99&#x0025; accuracy and Fuzzy attacks with up to 97.5&#x0025; accuracy using benchmark datasets. …”
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Research on Methods for the Recognition of Ship Lights and the Autonomous Determination of the Types of Approaching Vessels by Xiangyu Gao, Yuelin Zhao

“…Finally, a two-stage discriminative model is proposed: ECA-1D-CNN is utilized for the rapid assessment of the viewing angle of the vessel. Deep learning algorithms are dynamically applied for vessel type determination within the assessed viewing angles. …”
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Development of a Smart Waste Management System for Route Optimization and Adaptive Demand Management in Dubai by H. Hassan, T. Ali, A. Tamimi

“…This paper presents an overview of the development of a Smart Waste Management System (SWMS) that integrates Geographic Information System (GIS) technology with waste route optimization algorithms and adaptive demand management strategies. …”
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Multi-View Collaborative Training and Self-Supervised Learning for Group Recommendation by Feng Wei, Shuyu Chen

“…One of the key challenges facing recommendation algorithms is data sparsity, a limitation that is even more severe in group recommendation than in traditional recommendation tasks. …”
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