Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destination Earth by Mikko Strahlendorff, Anni Kröger, Golda Prakasam, Miriam Kosmale, Mikko Moisander, Heikki Ovaskainen, Asko Poikela

“…The Copernicus Global Land Monitoring Service’s Soil Water Index (SWI) satellite-based observations from 2015 to 2023 at 10,000 locations in Europe were used as the predictand (target parameter) to train an artificial intelligence (AI) model to predict soil wetness with XGBoost (eXtreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine) implementations of gradient boosting algorithms. …”
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Editorial by Bulent Cavas

“…(Indonesia and Australia), evaluates a training program that equips teachers with Arduino-based science project skills. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…Leveraging cross-technique ground truth annotations during training improved the accuracy of DL models in detecting and segmenting BMs. …”
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Klasifikasi Katarak Berdasarkan Optic Disc Citra Fundus Smartphone: Perbandingan Ekstraksi Ciri Tekstur Dan Metode Neural Network by Muchtar Ali Setyo Yudono, Fabrobi Fazlur Ridha, Dani Mardiyana, Faturrohman Al-Ghozi, Aldi Maulana

“…This research aims to develop an automatic cataract classification system using smartphone-based fundus cameras, offering a more cost-effective and portable solution compared to conventional devices. The study evaluates the performance of three neural network algorithms: Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), and Radial Basis Function Neural Network (RBFNN) for cataract classification. …”
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On First-Principle Robot Building in Undergraduate Robotics Education in the Robotic System Levels Model by Bryan Van Scoy, Peter Jamieson, Veena Chidurala

“…However, robotics is inherently multi-disciplinary and requires knowledge of controls and automation, embedded systems, sensors, signal processing, algorithms, and artificial intelligence. This makes training the future robotics workforce a challenge. …”
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MM-VSM: Multi-Modal Vehicle Semantic Mesh and Trajectory Reconstruction for Image-Based Cooperative Perception by Márton Cserni, András Rövid, Zsolt Szalay

“…The effectiveness of the algorithm is proven through a real-world smart valet parking application, which is evaluated in our university parking area with real vehicles. …”
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Comparison of Deep Learning Techniques in Detection of Sickle Cell Disease. by Mabirizi, Vicent, Kawuma, Simon, Kyarisiima, Addah, Bamutura, David, Atwiine, Barnabas, Nanjebe, Deborah, Oyesigye, Adolf Mukama

“…This is evidenced by some models and algorithms with ≥90% prediction accuracy. From the literature, most of the proposed methods are trained and tested on pre-trained deep learning models like VGG16, VGG19, ResNet, Inception_V3, and ReNet. …”
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Fake News Detection Using Machine Learning Ensemble Methods by Iftikhar Ahmad, Muhammad Yousaf, Suhail Yousaf, Muhammad Ovais Ahmad

“…By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. …”
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REU-Net: A Remote Sensing Image Building Segmentation Network Based on Residual Structure and the Edge Enhancement Attention Module by Tianen Yuan, Bo Hu

“…This method provides a reliable foundation for the further optimization of building segmentation algorithms for remote sensing images.…”
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Optimization of Oil Yield from the Macro Algae Spirogyra by Solvent Extraction Process Using RSM and ANN by S. Aravind, Debabrata Barik, Nagaraj Ashok

“…The coefficient of determination, R2 of 98.92%, and the mean absolute percentage deviation (MAPD) of 0.492% for ANN revealed that the current model created with a network topology of 3 : 11 : 1 with tansig (hyperbolic tangent sigmoid) transfer function in the input layer and purelin (pure linear) transfer function in the output layer trained with trainlm (Levenberg–Marquardt) algorithm found to provide the optimal solution with better accuracy in prediction of the output. …”
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Parallel Simulation Multi-Sample Task Scheduling Approach Based on Deep Reinforcement Learning in Cloud Computing Environment by Yuhao Xiao, Yping Yao, Feng Zhu

“…In a real cloud environment, the proposed method demonstrates runtime reductions of 4–11% and execution cost savings of 11–22% compared to the Round-Robin algorithm, Best Fit algorithm, and genetic algorithm.…”
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A Comparative Study of Loan Approval Prediction Using Machine Learning Methods by Vahid Sinap

“…In addition, determining the effect of K-Best and Recursive Feature Elimination feature selection methods on model performances is another important objective of the research. Furthermore, the evaluation of the effectiveness of techniques such as cross-validation (K-Fold) and Train, Test and Validation in measuring the performance of models is also among the objectives of the research. …”
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Surrogate-Assisted Multi-Objective Optimization for Simultaneous Three-Dimensional Packing and Motion Planning Problems Using the Sequence-Triple Representation by Ziang Liu, Tomoya Kawabe, Tatsushi Nishi, Shun Ito, Tomofumi Fujiwara

“…Training data is generated by solving the motion planning problem for a set of packing solutions using the rapidly exploring random tree algorithm. …”
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Bio-Inspired Object Detection and Tracking in Aerial Images: Harnessing Northern Goshawk Optimization by Agnivesh Pandey, Rohit Raja, Sumit Srivastava, Krishna Kumar, Manoj Gupta, Chanyanan Somthawinpongsai, Aziz Nanthaamornphong

“…This study presents a novel approach for object detection and tracking in aerial images using a multi-scale Northern Goshawk Pyramid Generative Adversarial Network (NGPGAN). The research evaluates different algorithms and features to identify people, trees, cars, and buildings in real-world drone videos, addressing challenges in pinpointing specific objects among multiple entities. …”
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Bayesian curriculum generation in sparse reward reinforcement learning environments by Onur Akgün, N. Kemal Üre

“…This adaptive feature significantly enhances the efficiency of the learning process. Empirical evaluations conducted in various environments, characterized by maze-like structures, discrete or continuous settings, and the presence of adversarial entities hindering the agent’s mission, demonstrate the algorithm’s efficacy and its superiority over conventional methods. …”
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Towards a Framework for Acquisition and Analysis of Speeches to Identify Suspicious Contents through Machine Learning by Md. Rashadur Rahman, Mohammad Shamsul Arefin, Md. Billal Hossain, Mohammad Ashfak Habib, A. S. M. Kayes

“…We have considered speeches of Bangla language and developed our own dataset that contains about 5000 suspicious and nonsuspicious samples for training and validating our model. A comparative analysis of accuracy among other machine learning algorithms such as logistic regression, SVM, KNN, Naive Bayes, and decision tree is performed in order to evaluate the effectiveness of the system. …”
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Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography by Ruiting Jia, Baozhi Liu, Mohsin Ali

“…Performance measures, including accreditation, sensitivity, specificity, and the AUROC, were used to evaluate the model’s performance during training and validation. …”
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A reliable score-based routing protocol using a fog-assisted intrusion detection system in vehicular ad-hoc networks by Samira Tahajomi Banafshehvaragh, Mani Zarei, Amir Masoud Rahmani

“…First, RSR-IDS pre-processes data using minimum–maximum normalization and Pearson’s correlation coefficient. The IDS is trained using three machine learning-based algorithms and a voting technique to reduce false detection. …”
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Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets by Abbas Saffari, Seyed Hamid Zahiri, Navid Khozein Ghanad

“…Accuracy detection of targets for different SNRs (−20, −15, −10, −5, 0, 5, 10, 15, 20) and different viewing angles (10, 20, 30, 40, 50, 60, 70, 80) is evaluated. For a more fair comparison, multilayer perceptron neural network with two back-propagation (MLP-BP) training methods and gray wolf optimization (MLP-GWO) algorithm were used. …”
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An Ensemble Learning Approach for Drought Analysis and Forecasting in Central Bangladesh by Md. Alomgir Hossain, Momotaz Begum, Md. Nasim Akhtar, Md. Alamin Talukder, Nomanur Rahman, Mahfuzur Rahman

“…The focus of the study is to evaluate the accuracy of machine learning (ML) algorithms in predicting meteorological droughts in central region of Bangladesh from 1990 to 2022, using data from seven meteorological stations. …”
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