TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction by I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner

“…Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. …”
Get full text

Improving earthquake prediction accuracy in Los Angeles with machine learning by Cemil Emre Yavas, Lei Chen, Christopher Kadlec, Yiming Ji

“…Abstract This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. …”
Get full text

The RMaP challenge of predicting RNA modifications by nanopore sequencing by Jannes Spangenberg, Stefan Mündnich, Anne Busch, Stefan Pastore, Anna Wierczeiko, Winfried Goettsch, Vincent Dietrich, Leszek P. Pryszcz, Sonia Cruciani, Eva Maria Novoa, Kandarp Joshi, Ranjan Perera, Salvatore Di Giorgio, Paola Arrubarrena, Irem Tellioglu, Chi-Lam Poon, Yuk Kei Wan, Jonathan Göke, Andreas Hildebrandt, Christoph Dieterich, Mark Helm, Manja Marz, Susanne Gerber, Nicolo Alagna

“…Results demonstrate that a low prediction error and a high prediction accuracy can be achieved on these modifications across different approaches and algorithms. …”
Get full text

Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset by Sahrul Fahrezi Fahrezi, Adhitya Nugraha, Ardytha Luthfiarta, Nauval Dwi Primadya

“…One approach to attacking IoT devices is to perform analysis based on network traffic using machine learning algorithms such as AdaBoost. An IoT device attack prediction model was created for the purpose of predicting IoT device attacks based on network traffic. …”
Get full text

IMPLEMENTATION OF THE PARALLEL ALGORITHM OF NONISOTHERMAL HEAT AND MOISTURE MIGRATION TASK SIMULATION IN NATURAL DISPERSE ENVIRONMENTS by P. K. Shalkevich, S. P. Kundas, I. A. Gishkeluk

“…Comparative studies of the computing model performance of contaminants transport by the proposed algorithm and use of Matlab at the standard instruction level showed a significant reduction in compu-tation time.…”
Get full text

Generative and predictive neural networks for the design of functional RNA molecules by Aidan T. Riley, James M. Robson, Aiganysh Ulanova, Alexander A. Green

“…SANDSTORM and GARDN thus represent powerful new predictive and generative tools for the development of RNA molecules with improved function.…”
Get full text

Toward Intelligent Fading Channel Prediction: A Comprehensive Survey by Ramoni Adeogun

“…Through this survey, we aim to provide a foundation for future research in intelligent channel prediction, highlighting the need for more sophisticated and adaptive algorithms to cope with the increasing complexity of wireless communication systems.…”
Get full text

Research on Customer Churn Prediction Using Machine Learning Models by Jia Xiaolei

“…With the increasing availability of customer data and advancements in machine learning techniques, accurate churn prediction has become more feasible and impactful. This research compares and analyzes the advantages and disadvantages of three different machine learning algorithms applied to customer churn prediction: random forest, decision tree, and neural network. …”
Get full text

Robust Predictive Maintenance for Robotics via Unsupervised Transfer Learning by Arash Golibagh Mahyari, Thomas locher

“…In this paper, we propose a novel solution based on transfer learning which addresses a well-known challenge in predictive maintenance algorithms by passing the knowledge of the trained model from one task to another in order to prevent the need for retraining and to eliminate such false alarms. …”
Get full text

Portable X-ray fluorescence sensor for ecofriendly, low-cost, and fast assessment of eucalypt charcoal attributes by Renata Andrade, Lucas Benedet, Marcelo Mancini, Sérgio Henrique Godinho Silva, Camila da Silva Freitas, Marco Aurélio Carbone Carneiro, Nilton Curi

“…The objective of this study was to evaluate the use of pXRF data in machine-learning models trained to predict attributes of eucalypt charcoal. pXRF data (elemental contents) from 276 charcoal samples were used to train predictive models using six machine-learning algorithms. …”
Get full text

Vehicle Collision Warning Based on Combination of the YOLO Algorithm and the Kalman Filter in the Driving Assistance System by Guihua Miao, Weihe Wang, Jinjun Tang, Fang Li, Yunyi Liang

“…This study applies the YOLO algorithm to detect the vehicle and uses the Kalman filter to track the vehicle. …”
Get full text

Algorithm for Automating Input and Processing of Initial Data for Computer Forecasting of Spatial Migration of Radionuclides in Soils by P. K. Shalkevich, A. O. Dalmatava

Get full text

Efficient Sleep Scheduling Algorithm for Target Tracking in Double-Storage Energy Harvesting Sensor Networks by Hongbin Chen, Qian Zeng, Feng Zhao

“…At first, we modify the probability-based prediction and sleep scheduling (PPSS) algorithm to track the target and further use another sleep scheduling algorithm we proposed to wake tracking nodes when the target is likely to be missed (i.e., it is unsuccessful to wake next-moment tracking nodes). …”
Get full text

Application of quantum-inspired evolutionary algorithm in the analysis of near infrared diffuse transmission spectroscopy of apples by LI Jun-liang, WANG Cong-qing

“…Second, the variables were selected with QEA, and QEA-PLS model was built. Meanwhile, genetic algorithm (GA) -PLS model was developed to contrast with QEA-PLS model. …”
Get full text

Novel fruit fly algorithm for global optimisation and its application to short-term wind forecasting by Yang Chen, DeChang Pi

“…Simulation results show that SEDMFOA has better prediction effect than traditional algorithms.…”
Get full text

Use of Machine Learning to Predict California Bearing Ratio of Soils by Semachew Molla Kassa, Betelhem Zewdu Wubineh

Get full text

Predicting Financial Market Volatility with Modern Model and Traditional Model by R. G. Aldeki

“…The major topic investigates how classical methods (ARCH and GARCH) and well-known machine learning algorithms, support vector regression, and hybrid methods. …”
Get full text

Unsupervised Learning for Heart Disease Prediction: Clustering-Based Approach by Jetty Janani., Sk Sajida Sultana., Polepalle Ranga Bhavitha., Parusu Vishwitha.

“…This paper on the prediction of heart disease addresses the application of unsupervised machine learning algorithms, digs up the latent pattern of risk in the data of patients for early diagnosis, and intervenes. …”
Get full text

Machine learning modeling for predicting adherence to physical activity guideline by Ju-Pil Choe, Seungbak Lee, Minsoo Kang

“…Variables were categorized into demographic, anthropometric, and lifestyle categories. 18 prediction models were created by 6 ML algorithms and evaluated via accuracy, F1 score, and area under the curve (AUC). …”
Get full text

INFORMATIONAL AND ALGORITHMIC SUPPORT FOR DISTRIBUTION AND MARkETING LOGISTICS CONTROL SySTEMS OF PHARMACEUTICAL ENTERPRISES by A. V. Kostrov, V. F. Kornyushko, S. Kaybullaeva, O. M. Nikolaeva

“…Drug procurement management algorithms implemented with the help of ERP and based on the ABC/XYZ analysis methodology, algorithms and informational models for pharmaceutical companies’ logistic system of inventory management in warehouses are proposed. …”
Get full text