The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known...
详细信息
The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based Computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just
Thermoelectric modules fabricated by traditional welding fall short of achieving optimal conversion efficiencies, primarily due to performance degradation of materials at high temperatures, severe elemental diffusion,...
详细信息
Thermoelectric modules fabricated by traditional welding fall short of achieving optimal conversion efficiencies, primarily due to performance degradation of materials at high temperatures, severe elemental diffusion, and residual thermal stress at the interface. Transient liquid phase bonding enables the realization of joints with high-melting-point compounds at low bonding temperatures, providing a promising solution for achieving "low-temperature bonding and high-temperature service." Owing to the low eutectic point of the solder and high melting points of compounds, we optimize the design of a germanium-telluride-based module at 533 K, which is successfully applied at the hot-side temperature of 773 K. Attributed to high-performance materials and reliable joints, the module realizes a high conversion efficiency of ∼15.1% and remains stable throughout 150 h of service. By adopting the same approach, the low- (Bi2Te3) and high-temperature (half-Heusler) modules are assembled. We provide a promising and general route for the assembly of full-temperature-range thermoelectric devices.
Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometime...
详细信息
Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare *** problem is inherently complex due to the high dimensionality of medical data,irrelevan...
详细信息
Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare *** problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical *** challenges often lead to inefficient and less *** predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational *** work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel *** features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction *** hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the *** enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional *** proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over ***,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medi
India's agriculture, the nation's largest industry, faces significant challenges due to current economic conditions, with crop loss from man-wildlife conflicts being a pressing issue. Traditional methods like ...
详细信息
Social media platforms have become a critical space for businesses to interact with their audience. LinkedIn, a professional networking platform, offers metrics like reactions, comments, and shares to gauge post engag...
详细信息
Federated matrix factorization (FedMF) has recently emerged as a privacy-friendly paradigm which runs matrix factorization (MF) in a federated learning (FL) setting and enables users to keep their individual rating da...
详细信息
Early-stage dementia detection is a significant challenge in the healthcare field. Many individuals remain unaware of their cognitive decline until the condition progresses. This research aims to develop a personalize...
详细信息
In ground-air networks, dual-function radar and communication (DFRC) enables a base station (BS) to sense and send communication signals to unmanned aerial vehicles (UAVs) simultaneously, which has enormous potential....
详细信息
While previous optimization results have suggested that deep neural networks tend to favour low-rank weight matrices, the implications of this inductive bias on generalization bounds remain underexplored. In this pape...
详细信息
暂无评论