The importance of rare earth metals for clean energy technology and the threat to their supply has prompted several researchers to consider alternative techniques to mining. One strategy that is gaining traction in th...
详细信息
A modular radio frequency plasma cathode has been designed to assess the sensitivity of performance to several geometric parameters, operating conditions, and propellants. The plasma cathode is designed to operate wit...
详细信息
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these in...
详细信息
The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and
Percutaneous Nephrolithotomy (PCNL) is a procedure to remove kidney stones by needle insertion into the kidney followed by dilation of the needle path for surgical tools to enter and stones to be removed. Current tech...
详细信息
Depression is a debilitating, yet underdiagnosed mental illness due to the subjectivity of current screening and time and resource restrictions. Large language models (LLMs) can potentially address these difficulties....
详细信息
ISBN:
(数字)9798331531003
ISBN:
(纸本)9798331531010
Depression is a debilitating, yet underdiagnosed mental illness due to the subjectivity of current screening and time and resource restrictions. Large language models (LLMs) can potentially address these difficulties. Using the Extended Distress Analysis Interview Corpus dataset, containing 105 interview transcripts, we propose MInDS, an automated, modular LLM inferencing pipeline, to optimize depression screening. Our results indicate that LLMs can effectively screen for depression with a 0.8 balanced accuracy. LLM inferencing with a shortened transcript can perform similarly to inferencing with the entire transcript. Our findings may aid the future development of LLMs for depression screening.
The development of mobile health (mHealth) assessment machine learning models requires data gathering studies in which smartphone sensor data is gathered continuously from users' phones as they live their lives &q...
详细信息
ISBN:
(纸本)9798350302639
The development of mobile health (mHealth) assessment machine learning models requires data gathering studies in which smartphone sensor data is gathered continuously from users' phones as they live their lives "In-the-wild". Periodically, participants annotate their sensor data with health, wellness and context labels, which serve as ground truth for machine learning models that can predict a user's health from their smartphone data. However, as the scale of such studies increases, it becomes difficult to analyze such data and build machine learning models that can work across increasingly diverse, heterogeneous participants. Additionally, non-visual analytics approaches have limited interpretability. This paper innovatively takes a visual analytics approach instead. We propose Visualizing COMmunity Phenotypes (VICOMP), an interactive visual analytics framework for exploring complex population-level smartphone-sensed data. Our approach is based on the concept of Community Phenotypes, which effectively visualizes the groups (or phenotypes) that study participant profiles belong to based on how similar they are (communities). Visual representations of community phenotypes within a large population facilitates sensemaking of group patterns. Using VICOMP, analysts can construct multiple community phenotypes using configurable clustering algorithms of sensed and reported information, and explore and reason about them. VICOMP enables analysts to discover homogenous phenotypical sub-groups within a larger heterogeneous population, for whom group-specific machine learning models are more accurate than one-size-fits-all population-level models. VICOMP depicts community phenotypes using accessible visual metaphors such as superimposed bars to visualize community wellness reports, dimension reduction, projections and heatmaps to represent the distribution of smartphone-sensed features. Connected views facilitate contextualization of health and wellness measures across communi
The use of autonomous underwater vehicles (AUVs) for the real-time estimation of a plume generated by an underwater stationary or moving source finds important applications. A mathematical estimation framework is pres...
详细信息
The Federal Highway Administration’s Mobile Asphalt Technology Center (MATC), a traveling asphalt laboratory and field testing program, debuted its new trailer at the 2023 Transportation Research Board (TRB) Annual M...
详细信息
The Federal Highway Administration’s Mobile Asphalt Technology Center (MATC), a traveling asphalt laboratory and field testing program, debuted its new trailer at the 2023 Transportation Research Board (TRB) Annual Meeting in Washington, DC. MATC supports State agencies and the asphalt pavement community in exploring innovative laboratory testing procedures and construction testing methods by conducting demonstrations onsite.
We introduce the problem of Table Reclamation. Given a Source Table and a large table repository, reclamation finds a set of tables that, when integrated, reproduce the source table as closely as possible. Unlike quer...
详细信息
Generative Adversarial Networks (GANs) are a generative framework with a notorious reputation for instability. Despite significant work in attempting to improve stability, training remains extremely difficult in pract...
详细信息
暂无评论