The recognition of abnormal signal of wearable sensor is of great significance to the application value of the device. In order to improve the accuracy of abnormal signal recognition of wearable sensors and indirectly...
详细信息
machinelearning (ML) has been widely applied to medical science for decades. As common knowledge, the progress of many diseases is often chronic and dynamic. Longitudinal data, or time-series data, has better descrip...
详细信息
Finding fugitive offenders after they have committed a crime or an illegal act takes time and effort. It is challenging for law enforcement authorities to complete this work on their own given the rising population de...
详细信息
Diabetes is a disorder that develops in the human body when blood glucose or sugar levels are extremely high. machinelearning (ML) is subfield of Artificial Intelligence (AI) that is built on the idea that systems an...
详细信息
Cardiovascular disease is one of the primary reasons for death in the world today. It has evolved into one of the most challenging illnesses to identify. By a recent WHO research, heart disorders are on the rise. As a...
详细信息
In the age of big data, the retail industry generates a vast amount of sales data daily. Analyzing and mining these data can facilitate management decision-making and provide great benefits to enterprises. With compet...
详细信息
New techniques of detection and diagnosis are needed for Alzheimer's disease (AD) is a major concern for society's health. machinelearning (ML) has been identified as a viable approach in this context due to ...
详细信息
ISBN:
(纸本)9798350343274
New techniques of detection and diagnosis are needed for Alzheimer's disease (AD) is a major concern for society's health. machinelearning (ML) has been identified as a viable approach in this context due to its capacity to allow the development of predictive models that can help in the identification of AD across its various phases. This paper provides an overview of current developments in developing ML models for application in Alzheimer's disease screening and detection. The paper opens with a discussion of the importance of early identification and recognition of Alzheimer's disease by detailing the disease's pathophysiology and describing its clinical signs. data from neuroimaging (e.g., MRI, PET scans), clinical data (e.g., cognitive evaluations, genetic information), and new biomarkers (e.g., markers in cerebrospinal fluid) are all discussed next. The report then delves into the development of ML methods in AD studies, showing the shift from classical statistical approaches to cutting-edge algorithms like deep learning and ensemble approaches. This article delves into the significance of feature selection and extraction in improving model performance and readability. Moreover, the paper discusses difficulties connected to the accessibility of data, the presence of bias, and the requirement of standardized datasets and evaluation criteria. The review's meat and potatoes are an in-depth look at the latest research and top-tier ML models for AD diagnosis and detection. There is a large variety of techniques that may be used to these models, including as Transfer learning from different domains, semi-supervised learning, supervised learning, and unsupervised learning. The article also discusses how multimodal data fusion techniques could help improve diagnostic precision. The evaluation places equal emphasis on the interpretability and therapeutic usefulness of models as they do on their development. The article talks about how to incorporate model predictions
In this paper, stock selection strategy design based on machinelearning and multi-factor analysis is a research hotspot in quantitative investment field. Four machinelearning algorithms including support vector mach...
详细信息
In recent years, the technology of wireless communication has developed rapidly. For instance, 5G and 6G are a next-generation communication technology. Because of the potential of 5G and 6 G, large quantities of data...
详细信息
machinelearning has a crucial role in people's lives. machinelearning can be divided into four parts: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. It can help ...
详细信息
暂无评论