As the potential for autonomous vehicles to be integrated on a large scale into modern traffic systems continues to grow, ensuring safe navigation in dynamic environments is crucial for smooth integration. To guarante...
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Alzheimer’s Disease (AD) is a prevalent neurodegenerative disease that has a substantial influence on society. Precise estimation of Alzheimer’s disease development is essential for prompt diagnosis and customized t...
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ISBN:
(数字)9798350357530
ISBN:
(纸本)9798350357547
Alzheimer’s Disease (AD) is a prevalent neurodegenerative disease that has a substantial influence on society. Precise estimation of Alzheimer’s disease development is essential for prompt diagnosis and customized treatment. This paper proposes a hybrid model that combines Particle Swarm Optimization (PSO), Long Short-Term Memory networks (LSTM), and Convolutional Neural Networks (CNN) to improve prediction performance. The model examines the evolution of AD using a data-focused method, making use of an extensive MRI dataset with important neuroimaging variables. AD causes mental impairment and loss of memory, which complicates medical care provided to them. Data already in the public domain are used to predict future events or outcomes for forecasting that is essential for treating the disease. Conventional approaches, like CNN and LSTM, are useful for predicting the course of an illness because they can handle temporal patterns and spatial interdependence in datasets, respectively. Using the advantages of both methodologies, a new hybrid approach is proposed in this article. In order to optimize the hybrid model variables, this work employs Particle Swarm Optimization (PSO), a highly efficient optimization approach to increase the accuracy of model forecasts. The hybrid CNN-LSTM model, with and without PSO, demonstrated accurate predictions of AD progression. Performance metrics such as F1-score, precision, accuracy, recall and ROC AUC were used to evaluate model efficacy. This research contributes to predictive analytics in healthcare, offering innovative solutions for enhancing patient outcomes in AD management.
Our work focuses, on designing and proposing a force estimation sensor system for Geological phenomena data collection and analysis, based on Biomimetic. In this sense, biomimetic term, is used, as proposed by Schmitt...
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Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at dif...
Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution.
Simple, low-cost one-step fabrication donor-Acceptor hybrid thin films are highly desired for the practical realization of low-cost organic solar cells. In this work, oriented and hybrid thin films of P3HT/CdS were su...
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Elliptic curve is a major area of research due to its application in elliptic curve cryptography. Due to their small key sizes, they offer the twofold advantage of reduced storage and transmission requirements. This a...
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In this study, we describe an evaluation of the positional accuracy of stereo Visual SLAM with noisy feature points removed by blurring. We constructed an experimental environment that intentionally degrades the accur...
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In a predefined geographical field, a group of sensor nodes communicating wirelessly form a Wireless Sensor Network (WSN). The sensors' goal is to upload the sensed data to the control station to determine if any ...
In a predefined geographical field, a group of sensor nodes communicating wirelessly form a Wireless Sensor Network (WSN). The sensors' goal is to upload the sensed data to the control station to determine if any immediate action should be taken or to analyze and monitor the situation. In clustering algorithms, the sensors are grouped as clusters, where each cluster has one sensor selected as the cluster head (CH) responsible for all inter-cluster communication. In a crisis scenario, the CH might be non-functional, resulting in a disconnected cluster. An enhanced WSN weighted cluster routing scheme is proposed in this paper. A cluster index based on distance, rewarding index, and energy is used to select the CH and cluster members (CM). The proposed scheme aims at ensuring that the data is uploaded even though the CH is non-functional by selecting a redundant CH for every node. Therefore, no matter how many sensors are inactive, a CH is still selected to ensure inter-cluster communication. The delays generated in the proposed routing scheme are studied using MATLAB simulation. In addition, the effect of different weights is studied on the delay.
In this study, we implemented our entropy-based swarm model to an autonomous waypoint navigation application for a group of multi-rotor Unmanned Aerial Vehicles (UAVs) through a set course in free space. Multi-UAVs of...
In this study, we implemented our entropy-based swarm model to an autonomous waypoint navigation application for a group of multi-rotor Unmanned Aerial Vehicles (UAVs) through a set course in free space. Multi-UAVs of multiple group sizes were run with variations in parameters, and the path lengths traveled were measured to determine the most efficient configurations, and we investigated the impact of varying parameters on the swarm behavior performance. The simulation of the UAV kinematics and environment was performed in AirSim. The results show that the swarm model with different parameter setup operates successfully and the effects of the parameter selection on our multi-UAV swarm model are discussed.
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