AIMC Topic: Neural Networks, Computer

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Design Space Exploration of a Sparse MobileNetV2 Using High-Level Synthesis and Sparse Matrix Techniques on FPGAs.

Sensors (Basel, Switzerland)
Convolution Neural Networks (CNNs) are gaining ground in deep learning and Artificial Intelligence (AI) domains, and they can benefit from rapid prototyping in order to produce efficient and low-power hardware designs. The inference process of a Deep...

Part-Based Obstacle Detection Using a Multiple Output Neural Network.

Sensors (Basel, Switzerland)
Detecting the objects surrounding a moving vehicle is essential for autonomous driving and for any kind of advanced driving assistance system; such a system can also be used for analyzing the surrounding traffic as the vehicle moves. The most popular...

Ensem-HAR: An Ensemble Deep Learning Model for Smartphone Sensor-Based Human Activity Recognition for Measurement of Elderly Health Monitoring.

Biosensors
Biomedical images contain a huge number of sensor measurements that can provide disease characteristics. Computer-assisted analysis of such parameters aids in the early detection of disease, and as a result aids medical professionals in quickly selec...

Language models can learn complex molecular distributions.

Nature communications
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility of generative models for the inverse design of novel functional compo...

Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.

Computational intelligence and neuroscience
Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It...

Hybrid Approach Named HUAPO Technique to Guide the Lander Based on the Landing Trajectory Generation for Unmanned Lunar Mission.

Computational intelligence and neuroscience
This manuscript proposes a hybrid method for landing trajectory generation of unmanned lunar mission. The proposed hybrid control scheme is the joint execution of the human urbanization algorithm (HUA) and political optimizer (PO) with radial basis f...

The Use of Deep Learning Model for Effect Analysis of Conventional Friction Power Confinement.

Computational and mathematical methods in medicine
Nonlinear friction could affect the high-precision motion system, resulting in poor tracking accuracy in the end. This is due to the fact that the Lugre friction model's parameter identification process comprises both static and dynamic parameter ide...

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs.

BioMed research international
Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings publi...

Empirical Method for Thyroid Disease Classification Using a Machine Learning Approach.

BioMed research international
There are many thyroid diseases affecting people all over the world. Many diseases affect the thyroid gland, like hypothyroidism, hyperthyroidism, and thyroid cancer. Thyroid inefficiency can cause severe symptoms in patients. Effective classificatio...

Improving work detection by segmentation heuristics pre-training on factory operations video.

PloS one
The measurement of work time for individual tasks by using video has made a significant contribution to a framework for productivity improvement such as value stream mapping (VSM). In the past, the work time has been often measured manually, but this...