The accuracy of Automatic Speech Recognition (ASR) systems is influenced by the quality and context of speech signals, particularly in telephonic environments prone to errors like channel drops and noise, leading to h...
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This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cu...
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Online shopping is growing in popularity as internet services are used more frequently. Many consumers consider online shopping due to the comfort, ease of access, and variety of products offered. However, this wide v...
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In today’s digital age, phishing is a significant threat. It is a clever approach in which propagation of a website is done, and customers are enticed to a false website where customers are asked to enter critical pe...
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Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information w...
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This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML)...
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Recommendation systems play a pivotal role in assisting users in fnding items, services, or products that align with their preferences. These systems typically rely on personal parameters, user reviews, or a combinati...
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Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds...
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Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executin...
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Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare
This paper investigates a security problem of simultaneously addressing two types of attacks:Eavesdropping and *** authors model the target system as a discrete-event system(DES)with subsets of concealable events and ...
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This paper investigates a security problem of simultaneously addressing two types of attacks:Eavesdropping and *** authors model the target system as a discrete-event system(DES)with subsets of concealable events and protectable events,in order to make the proposed methodology applicable to various practical systems and employ two existing works of DES security:Degree of opacity and state ***,the authors consider that all protectable events are observable,and some observable events are *** addition,protectable events cannot be protected once they are *** such a constraint,the goal is to figure out which events to conceal and which transitions to protect so that the prescribed requirements of degree of opacity and state protection are *** this work the authors decide which events to conceal as all transitions of a given event label are concealed or not *** proposed problem formulation also requires a solution to only involve absolutely necessary protectable events in order for the system to avoid superfluous protection *** authors first examine a general version of our security problem with an intuitive algorithm to compute acceptable solutions,and then present a special version which results in a reduced computation time compared to the general version.
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