Context-aware applications, whose behavior reactively depends on the time-varying status of the surrounding environment – such as network connection, battery level, and sensors – are getting more and more pervasive ...
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In inference of untimed regular languages, given an unknown language to be inferred, an automaton is constructed to accept the unknown language from answers to a set of membership queries each of which asks whether a ...
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Autism is a brain developmental condition that may impact the everyday life of affected individuals. Toddlers suffering from ASD are labeled by difficulties in social interaction, connecting, lack of eye contact, igno...
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ISBN:
(纸本)9798331527822
Autism is a brain developmental condition that may impact the everyday life of affected individuals. Toddlers suffering from ASD are labeled by difficulties in social interaction, connecting, lack of eye contact, ignoring danger, preferring to play alone, sensitivity to loud noises, and repetitive behavior compared to normally developed (ND) children. This systematic review examines the application of artificial intelligence (AI) and machine learning (ML) models for the early detection of Autism Spectrum Disorder (ASD), emphasizing their potential for enabling timely and effective interventions. The significance of this research lies in addressing the critical need for early ASD diagnosis, which can substantially improve outcomes for affected individuals. The novelty of this study is its comprehensive analysis of cutting-edge AI and ML techniques specifically applied to ASD detection, providing insights into their efficacy and limitations. The methodology involved a systematic search of peer-reviewed literature published between 2010 and 2022 across major databases, including PubMed, IEEE Xplore, and Scopus. Studies were selected based on predefined inclusion criteria, focusing on AI and ML models used for early ASD detection. Key findings reveal that AI and ML models, particularly deep learning algorithms and ensemble methods, demonstrate promising results in early ASD detection, with some models achieving accuracy rates exceeding 95%. The review identified that behavioral data, neuroimaging, and genetic information are the most commonly used input features for these models. However, challenges persist in terms of model interpretability, dataset bias, and generalizability across diverse populations. This systematic review highlights the potential of AI and ML models as valuable tools for early ASD detection, potentially revolutionizing diagnostic processes and enabling earlier interventions. Future research should focus on developing more robust, interpretable mode
Real estate is one of the essential and challenging fields in the market which reflects the economy, and it needs constant improvement. Business intelligence nowadays plays a significant role in enhancing the process ...
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Group communication services （GCSs） are becoming increasingly important as a wide field of promising applications has emerged to serve millions of users distributed across the ***,it is challenging to make the servi...
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Group communication services （GCSs） are becoming increasingly important as a wide field of promising applications has emerged to serve millions of users distributed across the ***,it is challenging to make the service fault tolerance and scalable to fulfill the voluminous demand of users in a distributed network （DN）.While many reliable group communication protocols have been dedicated to addressing such a challenge so as to accommodate the changes in the network,they are often costly or require complicated strategies to handle the service interruptions caused by node departures or link failures,which hinders the service *** this paper,we present two schemes to address the *** first one is a location-aware replication scheme called NS,which makes replicas in a dispersed fashion that enables the services on nodes to gain immunity of failures with different patterns （e.g.,network partition and single point failure） while keeping replication overhead *** second one is a novel failure recovery scheme that exploits the independence between service recovery and structure recovery in time domain to achieve quick failure *** simulation results indicate that the two proposed schemes outperform the existing schemes and simple alternative schemes in service success rate,recovery latency,and communication cost.
A change in the dynamics of a classroom environment, created from internationalisation of student cohorts, has caused educators to reconsider their learning methods that they use. This paper presents a structured fram...
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To detect network congestion, TCP typically relies on detecting packet loss. While this is an effective approach for maintaining high throughput for bulk data transfers, a better approach for interactive, time-sensiti...
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ISBN:
(纸本)9781450330039
To detect network congestion, TCP typically relies on detecting packet loss. While this is an effective approach for maintaining high throughput for bulk data transfers, a better approach for interactive, time-sensitive, or loss-sensitive traffic would be to detect congestion prior to packet loss. Explicit Congestion Notification (ECN) is a congestion avoidance strategy that makes use of Active Queue Management to allow TCP endpoints to detect congestion without a corresponding packet drop. This congestion detection strategy is particularly useful for delay-sensitive traffic where packet retransmissions can lead to noticeable delays for the user. In this paper, we present an implementation of ECN as an addition to the TCP protocols in ns-3. We have modified all TCP variants currently in ns-3 to work with this new addition. To validate our work we tested all TCP variants and compared our implementation's behavior to previous work. Copyright ACM.
Most multiplayer games available today are of a client-server nature. This paper looks at a different approach to multiplayer gaming. A multiplayer game example was also developed to test the approach discussed in thi...
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ISBN:
(纸本)0946881324
Most multiplayer games available today are of a client-server nature. This paper looks at a different approach to multiplayer gaming. A multiplayer game example was also developed to test the approach discussed in this paper.
In the past few years the most researched and discussed topic is Machine Learning. Machine learning is used to uncover correlations from medical datasets and provide outstanding predictive capability for diseases. Whe...
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Authenticating the identity of a remote process in the face of malicious active intruders has been an active area of computer communication security. The complexity of the authentication increases when a number of pro...
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