AIMC Topic: Neural Networks, Computer

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Deep Learning-Based Non-Intrusive Commercial Load Monitoring.

Sensors (Basel, Switzerland)
Commercial load is an essential demand-side resource. Monitoring commercial loads helps not only commercial customers understand their energy usage to improve energy efficiency but also helps electric utilities develop demand-side management strategi...

Non-Deep Active Learning for Deep Neural Networks.

Sensors (Basel, Switzerland)
One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative u...

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements.

Sensors (Basel, Switzerland)
The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type commun...

Personalized Activity Recognition with Deep Triplet Embeddings.

Sensors (Basel, Switzerland)
A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data generated by individual users, resulting in very poor performance for some subjects. We present an approach to personalized...

Prediction of pH Value of Aqueous Acidic and Basic Deep Eutectic Solvent Using COSMO-RS σ Profiles' Molecular Descriptors.

Molecules (Basel, Switzerland)
The aim of this work was to develop a simple and easy-to-apply model to predict the pH values of deep eutectic solvents (DESs) over a wide range of pH values that can be used in daily work. For this purpose, the pH values of 38 different DESs were me...

A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging.

Scientific reports
Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically ex...

Comparing machine learning and deep learning regression frameworks for accurate prediction of dielectrophoretic force.

Scientific reports
An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accu...

Non-iterative learning machine for identifying CoViD19 using chest X-ray images.

Scientific reports
CoViD19 is a novel disease which has created panic worldwide by infecting millions of people around the world. The last significant variant of this virus, called as omicron, contributed to majority of cases in the third wave across globe. Though less...

Ability to Predict Melanoma Within 5 Years Using Registry Data and a Convolutional Neural Network: A Proof of Concept Study.

Acta dermato-venereologica
Research relating to machine learning algorithms, including convolutional neural networks, has increased during the past 5 years. The aim of this pilot study was to investigate how accurately a convolutional neural network, trained on Swedish registr...

Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks.

Soft matter
Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties can lead to the formation of long-range structure. The dependence of this structure on the sequence necessarily implie...