Autism spectrum disorders (ASD) are neurodevelopmental disorders that are marked by enduring difficulties with speech, nonverbal communication, and restricted or repetitive behaviors. Early detection and intervention ...
Autism spectrum disorders (ASD) are neurodevelopmental disorders that are marked by enduring difficulties with speech, nonverbal communication, and restricted or repetitive behaviors. Early detection and intervention can greatly improve outcomes for people with ASD. Recently, deep learning algorithms have been applied to aid in the early detection of ASD using facial images. In this work, modifications of the commonly used VGG16 and VGG19 models for image recognition tasks are proposed to improve the performance of detecting ASD from a child’s frontal face image. The proposed model is unique, as it alters the architecture of existing models, adds an attentional mechanism, and applys transfer learning. These changes are intended to decrease the chance of overfitting and enhance the model’s capacity to capture subtle face characteristics. The performance of the updated model is assessed through accuracy, which is 82.55% for VGG19 and 80% for VGG16 model, and contrasted the outcomes of the original model. Performance of the modified model is also compared with that of the original model. The obtained results show that the modified model outperforms in detecting ASD from facial images, suggesting that the proposed modification is non-invasive for early detection of ASD and has the potential to contribute to the development of efficient tools.
Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a c...
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To protect privacy, Park et al. proposed a blockchain-enabled privacy-preserving scheme (BPPS) to achieve demand response in the smart grid environment. Park et al. claimed that their scheme could resist various attac...
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ISBN:
(数字)9798350394924
ISBN:
(纸本)9798350394931
To protect privacy, Park et al. proposed a blockchain-enabled privacy-preserving scheme (BPPS) to achieve demand response in the smart grid environment. Park et al. claimed that their scheme could resist various attacks and ensure both of privacy and data integrity. However, with thorough analysis of their scheme, we find that it suffers from three flaws.
Technological advancements are accelerating in the modern day;one such advancement is augmented reality (AR) technology. Naturally, these advancements have an effect on various sectors of life, one of which is educati...
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Customer segmentation is an essential area of business analytics today. Accurate customer segmentation is access to improves the efficiency of marketing campaigns and customer satisfaction. This study employs multiple...
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Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control ce...
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Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.
In this paper, we introduce the Enhanced Smart Exponential-Threshold-Linear (Enhanced-SETL) algorithm, a new approach that uses the multi-variable Deep Reinforcement Learning (DRL) framework to simultaneously optimize...
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In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistica...
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This study conducts a systematic literature review (SLR) that focuses on optimizing deep-learning techniques for post-earthquake building damage detection. By employing the PRISMA protocol, this review aimed to collec...
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ISBN:
(数字)9798350392036
ISBN:
(纸本)9798350392043
This study conducts a systematic literature review (SLR) that focuses on optimizing deep-learning techniques for post-earthquake building damage detection. By employing the PRISMA protocol, this review aimed to collect, analyze, and summarize the findings of various studies published between 2014 and 2024. This research identifies and evaluates optimization methods such as hyperparameter tuning, transfer learning, and data augmentation, highlighting their effectiveness and implementation. Challenges such as data quality, computational costs, and model interpretability were also discussed. The findings indicate that while deep learning techniques significantly enhance the accuracy and efficiency of damage detection, further research is required to address existing challenges and improve model robustness in diverse earthquake scenarios.
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