Heterogeneous crowd operations involve complex procedural subtasks performed by dynamic teams with diverse agent behaviors,tailored to specific task *** of such operations include carrier aircraft support,airport grou...
Heterogeneous crowd operations involve complex procedural subtasks performed by dynamic teams with diverse agent behaviors,tailored to specific task *** of such operations include carrier aircraft support,airport ground handling,and logistics *** a hybrid virtual-physical digital twin testbed for scenario generation and plan verification in heterogeneous crowd operations addresses the issues of low credibility in virtual simulations and the high costs associated with real-world *** is becoming increasingly important in practical applications.
The paper presents a reversible data hiding method with image compression consisting of Huffman coding and block encrypted. In the proposed approach, the image is used to compressed for generating the specified space ...
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The COVID-19 epidemic has been a critical global challenge due to its high mortality rate and rapid spread. Initial diagnostic methods, such as chest X-rays and reverse transcriptase polymerase chain reaction (RT-PCR)...
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This survey offers the review of Healthcare Monitoring Systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. ...
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Non-Orthogonal Multiple Access (NOMA) systems are becoming relevant in the fast-expanding terrain of large-scale networks because of their efficiency in concurrently managing many users. This is true since NOMA system...
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ISBN:
(纸本)9798331527549
Non-Orthogonal Multiple Access (NOMA) systems are becoming relevant in the fast-expanding terrain of large-scale networks because of their efficiency in concurrently managing many users. This is true since NOMA systems let numerous users concurrently be managed. On the other hand, the intricacy of these networks leaves them vulnerable to a wide spectrum of attacks, including the more advanced and erratic NOMA attacks on the network. These strikes could produce major disturbances that would compromise the quality of service and cast questions regarding the general network security. It has been demonstrated that the effective projection of these hazards is limited by standard linear and probabilistic techniques. This is true as contemporary methods fail to adequately capture the basic non-linear dynamics of these large-scale networks. This article offers a novel method for NOMA attack prediction by means of a non-linear chaotic belief process. The results are shown here. To recreate the uncertainty and intricate interactions inside the network, the proposed method which is the logistic map which in turn generates the sequences for ensuring the accurate iterative updates which in turn provides better scalability and precision. This integrates belief networks with chaos theory. More exactly, we capture the random and nonlinear aspect of network dynamics by building belief values indicating the likelihood of an attack by use of a chaotic map. After that, the belief values proliferate across the network in search of defects and project the probability of NOMA attacks. Effectiveness of the proposed method is demonstrated by test results on a simulated large-scale network simulation. With a prediction accuracy of 92.7%, the chaotic belief mechanism obtained much above the average accuracy of 78.4% of traditional linear prediction systems. Moreover, the proposed approach lowered the false positive rate to 5.3%, substantially below the rate of 12.8% applied in the standard ap
Transformer-based language models have achieved outstanding results on a wide range of tasks, but often at the expense of high computational demands. In response, we introduce Seek2Skim, a novel token pruning met...
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Text data generated by humans in real-world scenarios often contains a substantial amount of noise, including misspellings, typographical errors, and abbreviations. Pre-trained language models (PLMs) often struggle to...
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作者：
Wanjari, KetanVerma, Prateek
Department of Computer Science and Engineering Faculty of Engineering and Technology Maharashtra Wardha442001 India
Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India
Modern image recognition has experienced dramatic improvements because of Machine Learning and Deep Learning algorithms together. This study investigates CNNs and SVMs for recognition enhancement while reviewing image...
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Lung cancer remains a leading global cause of mortality, necessitating efficient early detection. Lung cancer image analysis plays a pivotal role, yet current manual segmentation by oncologists is laborious. Our innov...
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Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues. A straightforward solution is to carefully curate training data, but given the eno...
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