A deep learning based prediction model for effective elastic properties of porous materials by Chang Liu, Ran Guo, Yangming Su

“…Abstract With the development of the economy and society, porous materials have been widely used in various fields due to their unique structure and function. Therefore, it is of great significance to analyze the mechanical properties of porous materials. …”
Get full text

Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock by Connor Reynolds, Joshua Colmer, Hannah Rees, Ehsan Khajouei, Rachel Rusholme-Pilcher, Hiroshi Kudoh, Antony N. Dodd, Anthony Hall

Get full text

Learning Control of Fixed-Wing Unmanned Aerial Vehicles Using Fuzzy Neural Networks by Erdal Kayacan, Mojtaba Ahmadieh Khanesar, Jaime Rubio-Hervas, Mahmut Reyhanoglu

“…The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. …”
Get full text

The Impact of Mnemonic Strategies and Inquiry-Based Learning on Memory Retention and Creativity in Biology Education by Nurul Azizah, Aditya Marianti, Wiwi Isnaeni, Endah Peniati

“…In the latest curriculum, students are expected to understand the function of enzymes and be familiar with the metabolic processes that occur in the body. …”
Get full text

APFed: Adaptive personalized federated learning for intrusion detection in maritime meteorological sensor networks by Xin Su, Guifu Zhang

“…With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks (MMSNs). …”
Get full text

Combining Physics and Mathematics Learning: A Taylor Series Analysis of an Oscillating Magnetic Field by S. Ortuño-Molina, A. Garmendía-Martínez, P. Fernández de Córdoba, J. C. Castro-Palacio, J. A. Monsoriu, F. M. Muñoz-Pérez

Get full text

Establishing predictive machine learning models for drug responses in patient derived cell culture by Abbi Abdel-Rehim, Oghenejokpeme Orhobor, Gareth Griffiths, Larisa Soldatova, Ross D. King

Get full text

Deep reinforcement learning-based routing framework for bidirectional communication in UAV-UGV networks by Prabhakar Saxena, Gayatri M. Phade

“…Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) performs crucial function in many applications like military operations, disaster management, hazardous operations and surveillance. …”
Get full text

RDW-YOLO: A Deep Learning Framework for Scalable Agricultural Pest Monitoring and Control by Jiaxin Song, Ke Cheng, Fei Chen, Xuecheng Hua

Get full text

A review on physics-informed machine learning for monitoring metal additive manufacturing process by Shoulan Yang, Shitong Peng, Jianan Guo, Fengtao Wang

Get full text

Bibliometric Analysis of The Use of Science Learning Media In Elementary Schools During A Decade by Anggun Taruna Puspitasari, Wahono Widodo, Dian Permatasari Kusuma Dayu

“…Additionally, this research found that technology-based learning resources, such as interactive and digital media, function well, dominating research trends. …”
Get full text

Identification of Relevant ECG Features for Epileptic Seizure Prediction Using Interpretable Machine Learning by Azra Abtahi, Philippe Ryvlin, Amir Aminifar

“…We also investigate the significance and impact of features in a seizure prediction Machine Learning (ML) model utilizing ECG signals, aiming to shed light on the intricate relationship between cardiac function and epileptic seizures. …”
Get full text

Apple varieties, diseases, and distinguishing between fresh and rotten through deep learning approaches. by Tao Zhang, Mustafa Mhamed, Qu Zhang, Liling Yang, Z H A O Xiaohui, Gu Haiyan, Zhao Zhang

Get full text

The Learning Inquiry with Mnemonic Strategy for the Formation of Long-Term Memory and Creative Thinking Ability by Nurul Azizah, Aditya Marianti, Wiwi Isnaeni, Endah Peniati

“…In the latest curriculum, students are expected to understand the function of enzymes and be familiar with the metabolic processes that occur in the body. …”
Get full text

Evaluation of a Deep Learning Model for Automatic Detection of Schizophrenia Using EEG Signals by Swetha Padmavathi Polisetty, Radhamani Ellapparaj, Karthikeyan M P

“…After data preprocessing to reduce noise and artifacts from EEGs, an 11-layer deep learning model consisting of convolution and LSTM layers with LeakyReLU activation function and different kernel sizes was implemented to automatically extract and classify features. …”
Get full text

Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning by Jean de Dieu Nibigira, Richard Marchand

“…This paper presents a novel way of inferring ionospheric electron density profiles and electron temperature profiles using machine learning. The analysis is based on the Nearest Neighbour (NNB) and Radial Basis Function (RBF) regression models. …”
Get full text

A survey of reinforcement and deep reinforcement learning for coordination in intelligent traffic light control by Aicha Saadi, Noureddine Abghour, Zouhair Chiba, Khalid Moussaid, Saadi Ali

“…Reinforcement learning (RL) enables a single agent to learn and perform optimal actions independently, whereas multi-agent reinforcement learning (MARL) enables traffic light controllers to learn, exchange and optimize their actions. …”
Get full text

DeepDISC-photoz: Deep Learning-Based Photometric Redshift Estimation for Rubin LSST by Grant Merz, Xin Liu, Samuel Schmidt, Alex I. Malz, Tianqing Zhang, Doug Branton, Colin J. Burke, Melissa Delucchi, Yaswant Sai Ejjagiri, Jeremy Kubica, Yichen Liu, Olivia Lynn, Drew Oldag, The LSST Dark Energy Science Collaboration

Get full text

A machine learning neural network architecture for the accelerating universe based modified gravity by Zulqurnain Sabir, Basma Souayeh, Zahraa Zaiour, Alyn Nazal, Mir Waqas Alam, Huda Alfannakh

Get full text

Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification. by Xuemei Bai, Yuqing Zhang, Chenjie Zhang, Zhijun Wang

“…Subsequently, an innovative loss function is proposed to effectively reduce the interference of label noise on the training process by refining the generation process of pseudo-labels, which solves the negative impact of inaccurate pseudo-labels on model training. …”
Get full text