MeDML: Med-Dynamic Meta Learning - A multi-layered representation to identify provider fraud in healthcare by Nitish Kumar, Deepak Chaurasiya, Alok Singh, Siddhartha Asthana, Kushagra Agarwal, Ankur Arora

“…We test the dynamically generated meta embedding using various downstream models and show that it outperforms all baseline algorithms for provider fraud prediction task.…”
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Smart Farming: AI and IoT-Based Solutions for Real-Time Agriculture Monitoring by Kadao Anjali Krushna, Shivaji Ghorpade Bipin

“…The machine learning models are used to predict possible points of problems like disease outbreaks or nutrient deficiencies so that appropriate steps can be taken preemptively. …”
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Machine Learning-Based Classification for Crop-Type Mapping Using the Fusion of High-Resolution Satellite Imagery in a Semiarid Area by Aicha Moumni, Abderrahman Lahrouni

“…Three machine learning classifier algorithms, artificial neural network (ANN), support vector machine (SVM), and maximum likelihood (ML), were applied to identify and map crop types in irrigated perimeter. …”
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Evaluation method of distribution network operation status based on local fuzzy measure in boundary region by Bing Yu, Peng Xie, Zhonglin Ding, Letian Li, Changan Chen, Chunfeng Jing

“…The research results found that the five evaluation indicators of the proposed algorithm were 112, 0, 2, 26, and 5, respectively, all of which were superior to the comparison algorithms. …”
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Data-driven decoding of quantum error correcting codes using graph neural networks by Moritz Lange, Pontus Havström, Basudha Srivastava, Isak Bengtsson, Valdemar Bergentall, Karl Hammar, Olivia Heuts, Evert van Nieuwenburg, Mats Granath

“…Accurate, maximum likelihood, decoders are computationally very expensive whereas decoders based on more efficient algorithms give sub-optimal performance. In addition, the accuracy will depend on the quality of models and estimates of error rates for idling qubits, gates, measurements, and resets, and will typically assume symmetric error channels. …”
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Features of mammalian microRNA promoters emerge from polymerase II chromatin immunoprecipitation data. by David L Corcoran, Kusum V Pandit, Ben Gordon, Arindam Bhattacharjee, Naftali Kaminski, Panayiotis V Benos

“…<h4>Background</h4>MicroRNAs (miRNAs) are short, non-coding RNA regulators of protein coding genes. miRNAs play a very important role in diverse biological processes and various diseases. Many algorithms are able to predict miRNA genes and their targets, but their transcription regulation is still under investigation. …”
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IMPROVEMENT OF THE RUSSIAN CITIES’ TRANSPORT INFRASTRUCTURE by E. A. Safronov, K. E. Safronov

“…Therefore, the developed algorithms and recommendations for reducing the volume of transport infrastructure helps to improve its performance and availability. …”
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Learning-based parallel acceleration for HaplotypeCaller by Xiangxing Lai, Minguang Xiao, Lingling Weng, Zhiguang Chen

“…This paper introduces a learning-based framework LPA (learning-based parallel acceleration), leveraging model to accurately predict the computational complexity of data. By employing adaptive data segmentation algorithms and Multi-Knapsack Problem (MKP) based task scheduling, LPA significantly alleviates computational skew. …”
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PENERAPAN PARTICLE SWARM OPTIMIZATION PADA ALGORITMA C 4.5 UNTUK SELEKSI PENERIMAAN KARYAWAN by Agus Wiyatno

“…In this study created a C 4.5 Algorithm model and C 4.5 Algorithm model based on particle swarm optimization to get the rule in employees selection and provide a more accurate value of accuracy. …”
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Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease by Xia Wang, Lin Zhang

“…Current therapies face challenges in targeting specific immune pathways and predicting treatment responses. Methods Multi-cohort transcriptomic data were integrated to identify inflammation-related genes (IRGs). …”
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Role of Bioinformatics in Agriculture by M. N. V. Prasad Gajula, Anuj Kumar, E. A. Siddiq, A. K. Polumetla

“…Bioinformatics is the use of computer technology, mathematical algorithms, and statistics with concepts in the life sciences to solve biological problems. …”
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Graph-based two-level indicator system construction method for smart city information security risk assessment by Li Yang, Kai Zou, Yuxuan Zou

“…For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

“…Because of the privacy concerns about the transaction data, it is essential not to leak it when training prediction models for credit card fraud analysis. Challenges for credit card fraud monitoring include highly imbalanced datasets and the need for advanced models to detect fraud patterns. …”
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Detecting Obfuscated Malware Infections on Windows Using Ensemble Learning Techniques by Yadigar Imamverdiyev, Elshan Baghirov, John Chukwu Ikechukwu

“…The findings contribute to cybersecurity efforts by comprehensively assessing machine learning algorithms for obfuscated malware detection through memory analysis. …”
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Applying Internet information technology combined with deep learning to tourism collaborative recommendation system. by Meng Wang

“…Compared with traditional methods, the proposed algorithm can provide users with personalized travel products more accurately in personalized travel recommendations. …”
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History of Location Objectives (Hilal Tracker): Development of Astronomical Observations from 2015-Present by Marataon Ritonga, Alya Fathi Muhammad Hasibuan, Rizqan Arya Perdana Nazara

“…This article explains the history of the development of hilal Tracker technology and methods, including integrating astronomical data, using satellite imagery, algorithms for predicting hilal visibility, and cooperation with various local and international institutions. …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…Additionally, while intelligent algorithms can assist in tuning, they often require large volumes of simulated data, leading to high computational costs. …”
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Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT by Hafiyyan Putra Pratama, Dewi Indriati Hadi Putri, Hafiziani Eka Putri, Elysa Nensy Irawan, Makna A’raaf Kautsar

“…The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. …”
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Towards solving NLP tasks with optimal transport loss by Rishabh Bhardwaj, Tushar Vaidya, Soujanya Poria

“…Loss functions are essential to computing the divergence of a model’s predicted distribution from the ground truth. Such functions play a vital role in machine learning algorithms as they steer the learning process. …”
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Enhanced Brain Tumor Segmentation Using CBAM-Integrated Deep Learning and Area Quantification by Rafiqul Islam, Sazzad Hossain

“…The qualitative metrics typically analyze the overlap between predicted tumor masks and ground truth annotations, providing information on the segmentation algorithms’ accuracy and dependability. …”
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