In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generati...
详细信息
Commonsense understanding poses a significant challenge, especially in complex languages like Arabic. However, recent advancements in deep learning have facilitated improvements in various language tasks, including th...
详细信息
Text-to-video retrieval (TVR) has made significant progress with advances in vision and language representation learning. Most existing methods use real-valued and hash-based embeddings to represent the video and text...
详细信息
The cybersecurity of National Critical Health Infrastructure is of utmost importance. If its security is not prioritized, it can directly impact the safety and well-being of patients, healthcare professionals, the gen...
详细信息
ISBN:
(纸本)9798350353266
The cybersecurity of National Critical Health Infrastructure is of utmost importance. If its security is not prioritized, it can directly impact the safety and well-being of patients, healthcare professionals, the general public, and the economy of the affected state. National Critical Health Care Infrastructure is a complex system involving multiple components essential for providing quality healthcare services to individuals in a country. To enable seamless access to healthcare services, EMRS have been implemented across the globe and thus cyber physical systems have become the medium of service delivery. With the criticality of these systems in mind, should a cyber-attack occur, all parties dependent on these infrastructures will be negatively affected. There are myriad cyber-attacks, such as advanced persistent threat attacks targeted at critical national infrastructure, which adversely compromise the security of the technologies in such infrastructures. The critical healthcare infrastructure is no different from other cyber physical critical infrastructures and thus has been a target of Advanced Persistent Threat (APT) groups for an extended period now. Machine learning solutions have been used to effectively protect, defend and respond to cyberattacks in software defined networks, SCADA for instance. As a result, this paper intends to ascertain the machine learning-inspired security controls that can be used to protect National Health Care Infrastructure against APT attacks. This review follows a mixed-method systematic literature review to answer the research question. The study's results reveal that machine learning has been majorly employed in detecting APT in critical infrastructure, not health-critical infrastructure and that the resilience of machine learning-inspired security controls has not been thoroughly researched for cyber health care systems. Even though machine learning has been tremendously applied in identifying APT, numerous challenges still
Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimizat...
详细信息
At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct...
详细信息
At present,the entity and relation joint extraction task has attracted more and more scholars'attention in the field of natural language processing(NLP).However,most of their methods rely on NLP tools to construct dependency trees to obtain sentence structure *** adjacency matrix constructed by the dependency tree can convey syntactic *** trees obtained through NLP tools are too dependent on the tools and may not be very accurate in contextual semantic *** the same time,a large amount of irrelevant information will cause *** paper presents a novel end-to-end entity and relation joint extraction based on the multihead attention graph convolutional network model(MAGCN),which does not rely on external *** generates an adjacency matrix through a multi-head attention mechanism to form an attention graph convolutional network model,uses head selection to identify multiple relations,and effectively improve the prediction result of overlapping *** authors extensively experiment and prove the method's effectiveness on three public datasets:NYT,WebNLG,and *** results show that the authors’method outperforms the state-of-the-art research results for the task of entities and relation extraction.
With the development of artificial intelligence in recent years, target tracking algorithms based on deep learning have been more and more widely used in the fields of unmanned aerial vehicles (UAVs), autonomous drivi...
详细信息
One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’de...
详细信息
One of the most widely used smartphone operating systems,Android,is vulnerable to cutting-edge malware that employs sophisticated *** malware attacks could lead to the execution of unauthorized acts on the victims’devices,stealing personal information and causing hardware *** previous studies,machine learning(ML)has shown its efficacy in detecting malware events and classifying their ***,attackers are continuously developing more sophisticated methods to bypass ***,up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile ***,this study employed ML algorithms to classify Android applications into malware or goodware using permission and application programming interface(API)-based features from a recent *** overcome the dataset imbalance issue,RandomOverSampler,synthetic minority oversampling with tomek links(SMOTETomek),and RandomUnderSampler were applied to the Dataset in different *** results indicated that the extra tree(ET)classifier achieved the highest accuracy of 99.53%within an elapsed time of 0.0198 s in the experiment that utilized the RandomOverSampler ***,the explainable Artificial Intelligence(EAI)technique has been applied to add transparency to the high-performance ET *** global explanation using the Shapely values indicated that the top three features contributing to the goodware class are:Ljava/net/URL;->openConnection,Landroid/location/LocationManager;->getLastKgoodwarewnLocation,and *** the other hand,the top three features contributing to themalware class are Receive_Boot_Completed,Get_Tasks,and Kill_Background_*** is believed that the proposedmodel can contribute to proactively detectingmalware events in Android devices to reduce the number of victims and increase users’trust.
This paper aims to provide a comprehensive analysis of the benefits of employing GraphLIME (Local Interpretable Model Explanations for Graph Neural Networks) for reliable diabetes mellitus prediction. Our focus is on ...
详细信息
The development of computertechnology has brought new breakthroughs to the medical field. Taking heart disease as an example, as one of the main causes of death worldwide, its high incidence rate and mortality have b...
详细信息
暂无评论