Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers by Devotha G. Nyambo, Edith T. Luhanga, Zaipuna O. Yonah, Fidalis D. N. Mujibi

“…Based on the divergent performance of the various algorithms evaluated, it is evident that, despite similar information being available for the study populations, the uniqueness of the data from each country provided an over-riding influence on cluster robustness and prediction accuracy. …”
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Un enfoque de machine learning para apoyar la identificación de la deuda técnica en arquitectura by Boris Rainiero Pérez-Gutiérrez, Darío Ernesto Correal, Fredy Humberto Vera-Rivera

“…Methods: This proposal relies on information collected from artifacts produced during the architectural design process to build a dataset that allows the evaluation of different supervised learning algorithms and thus establish the one with the best accuracy. …”
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Rolling Cargo Management Using a Deep Reinforcement Learning Approach by Rachid Oucheikh, Tuwe Löfström, Ernst Ahlberg, Lars Carlsson

“…The artificial intelligence agent, representing the tug master, is trained and evaluated in a challenging environment based on the Unity3D learning framework, called the ML-Agents, and using proximal policy optimization. …”
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Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar by Pengxiang Li, Mingzhou Bai, Xin Li, Chenyang Liu

“…An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. …”
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Path Planning Based on Combination of Sampling and Learning-From-Demonstration for Static Obstacle Avoidance of Autonomous Vehicles by Youngmin Yoon, Ara Jo

“…This paper presents a sampling-based path planning algorithm for autonomous vehicles with the existence of static obstacles. …”
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Text mining for improved exposure assessment. by Kristin Larsson, Simon Baker, Ilona Silins, Yufan Guo, Ulla Stenius, Anna Korhonen, Marika Berglund

“…Using these features, we trained a supervised machine learning algorithm to automatically classify PubMed abstracts according to the exposure taxonomy. …”
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Detecting Anomalies in Sequences of Short Text Using Iterative Language Models by Cynthia Freeman, Ian Beaver, Abdullah Mueen

“…We introduce a technique for detecting anomalies in sequences of short textual data by adaptively and iteratively learning low perplexity language models. Our algorithm defines a short textual item as anomalous when its cross-entropy exceeds the upper confidence interval of a trained additive regression model. …”
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Assessment of the application of each multibeam echosounder data product for monitoring of Laminaria digitata in the UK by Jacob Berry, Cassandra Nanlal

“…An image-based processing methodology using a random forests algorithm was used to generate classification models, which were trained and tested using ground truth samples obtained through video imagery. …”
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Image Super-Resolution via Adaptive Regularization Term of Compressed Sensing by Yintao Liu, Chao Ren, Hongjuan Shao, Qirui Liu, Yan Zhang

“…In this paper, the sparse transform domain is trained by the K-SVD algorithm and the accuracy evaluation indexes of the experiments use peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). …”
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An Improved Artificial Neural Network Model for Effective Diabetes Prediction by Muhammad Mazhar Bukhari, Bader Fahad Alkhamees, Saddam Hussain, Abdu Gumaei, Adel Assiri, Syed Sajid Ullah

“…This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP-SCGNN) algorithm to predict diabetes effectively. …”
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Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks. by Daniel Walke, Daniel Steinbach, Sebastian Gibb, Thorsten Kaiser, Gunter Saake, Paul C Ahrens, David Broneske, Robert Heyer

“…Finally, we discovered that feature slope and importance highly differ between trained algorithms (e.g., XGBoost and GNN) on the same data basis.…”
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The feasibility of endoscopic stapes surgery: A three‐dimensional planning method by Maaike Jellema, Mijs Buter, Esther E. Blijleven, Koen Willemsen, H. Chien Nguyen, Robert J. Stokroos, Inge Wegner, Henricus G. X. M. Thomeer

“…Finally, the outcomes of the pre‐operative planning and the actual procedure were directly compared to evaluate the algorithm's accuracy and usability. Results The preoperative planning method achieved an accuracy rate of 70% in predicting the need for scutum removal. …”
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Use of deep learning in early software bug detection by Syed Mibran Hassan Zaidi, Maria Khan, Mustafa Latif, Ali Akhtar, Sallar Khan

“…This was achieved by using an ensemble parallel technique for training the Convolutional Neural Network and Random Forest algorithm in a parallel way. …”
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Review of iris segmentation and recognition using deep learning to improve biometric application by Rasheed Hind Hameed, Shamini Sara Swathy, Mahmoud Moamin A., Alomari Mohammad Ahmed

“…The main problem is concerned with selecting the best deep learning (DL) algorithm to classify and estimate biometric iris biometric iris. …”
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Automated Semantic Segmentation of Arctic Surface Water Features with Very-High Resolution Satellite X-Band Radar Imagery and U-Net Deep Learning: Segmentation sémantique automatis... by Michael Allan Merchant, Masoud Mahdianpari, Laura Bourgeau-Chavez, Ben DeVries, Aaron Berg

“…Our study proposes a modified U-Net encoder-decoder model for this task, optimized using the Nadam algorithm. Otsu thresholding was leveraged to rapidly generate 512 × 512-pixel patches for the U-Net, resulting in an efficient and automated training pipeline. …”
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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

“…They confirmed once again that the trained convolutional neural network can be successfully used to evaluate the soiling state of photovoltaic surfaces.…”
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RACH Traffic Prediction in Massive Machine Type Communications by Hossein Mehri, Hani Mehrpouyan, Hao Chen

“…Furthermore, we develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. …”
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Modeling Flood Susceptibility Utilizing Advanced Ensemble Machine Learning Techniques in the Marand Plain by Ali Asghar Rostami, Mohammad Taghi Sattari, Halit Apaydin, Adam Milewski

“…The frequency ratio method was used to evaluate the significance of each factor. Model performance was evaluated using statistical measures, including the Receiver Operating Characteristic (ROC) curve. …”
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Exploring nanoparticle dynamics in binary chemical reactions within magnetized porous media: a computational analysis by Saleem Nasir, Abdallah Berrouk, Asim Aamir

“…The present work investigates the applicability of back-propagation artificial neural networks in conjunction with the Levenberg-Marquardt algorithm for evaluating heat transmission in hybrid nanofluids. …”
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Detection of Flexible Pavement Surface Cracks in Coastal Regions Using Deep Learning and 2D/3D Images by Carlos Sanchez, Feng Wang, Yongsheng Bai, Haitao Gong

“…To efficiently collect and evaluate thousands of lane-miles, automated processes utilizing image-capturing techniques and detection algorithms are applied to perform these tasks. …”
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