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Inadequate dataset learning for major depressive disorder MRI semantic classification
(IET Image Processing, 2022)
Predicting patients with major depression (MDD) is currently a difficult task. Magnetic resonance imaging (MRI) data analysis may provide insight into individual patient responses, allowing for more customized treatment ...
Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm
(Springer, 2022-02)
This paper focuses on developing a computationally economic lightweight artificial intelligence (AI) technology for smartphones. Until date, no commercial system is available on this technology. Thus the developed breakthrough ...
Brain fMRI segmentation under emotion stimuli incorporating attention-based deep convolutional neural networks
(Applied Soft Computing, 2022)
Functional magnetic resonance imaging (fMRI) is widely used for clinical examinations, diagnosis, and treatment. By segmenting fMRI images, large-scale medical image data can be processed more efficiently. Most deep learning ...
Effect of optimization framework on rigid and non-rigid multimodal image registration
(Scienceasias, 2022)
The process of transforming or aligning two images is known as image registration. In the present era, image registration is one of the most popular transformation tools in case of, for example, satellite as well as medical ...
Underwater IoT Network by Blind MIMO OFDM Transceiver Based on Probabilistic Stone's Blind Source Separation
(Support UsContactAdmin ACM Transactions on Sensor Networks, 2022)
Telecommunications systems with Multi-Input Multi-Output (MIMO) structure using Orthogonal Frequency Division Modulation (OFDM) have great potential for efficient application to a network of Internet of Things (IoT) at a ...
The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic
(Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they ...
Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models
(Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, ...
The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak
(Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in ...
Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System
(Epidemic Analytics for Decision Supports in COVID19 Crisis, 2022)
There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of ...
Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles
(Journal of intelligent manufacturing, 2022)
Today’s Agriculture vehicles (AgV)s are expected to encompass mainly the three requirements of customers; economy, the use of High technology and reliability. In this manuscript, we investigate the technology solution for ...