Efficient Algorithm Based on Adaptive Window–Model Predictive Control for Automatic Stacking in Warehouse Center

Chang, Qing; Sebghati, Ashkan

doi:10.1109/access.2021.3094417

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Among the machine learning techniques suggested in [11] for identifying fake websites are (Lagrangian Support Vector Machine) LSVM, (Logistic regression) LR, Random Forest (RF), Naive Bayes (NB), and statistical techniques for finding Concept Drifts in websites. Only a few studies have produced practical results, according to the author in [12], intending to foster research on intelligent security techniques based on a cyclic process that begins with the discovery of new threats and ends with the analysis and development of prevention measures.…”

Section: Related Workmentioning

confidence: 99%

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Alsuwat¹,

Solaiman²,

Alsuwat³

2023

Computers, Materials &Amp; Continua

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Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers' (and/or benign equivalents') dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network. The goal of this architecture combination is to accurately represent data and create an effective cyber security prediction agent. The intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol (SAW_WDA). The network has been analyzed by authentication protocol to avoid malware attacks. The data of network users will be collected and classified using multilayer perceptron gradient decision tree (MLPGDT) classifiers. Based on the classification output, the decision for the detection of attackers and authorized users will be identified. The experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput, end-end delay, network security, network concept drift, and results based on classification with regard to accuracy, memory, and precision and F-1 score.

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Section: Related Workmentioning

confidence: 99%

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Alsuwat¹,

Solaiman²,

Alsuwat³

2023

Computers, Materials &Amp; Continua

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Comprehensive Review of Robotized Freight Packing

Pantoja-Benavides,

Giraldo,

Montes

et al. 2024

Logistics

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Background: This review addresses the emerging field of automated packing cells, which lies at the intersection of robotics and packing problems. Integrating these two fields is critical for optimizing logistics and e-commerce operations. The current literature focuses on packing problems or specific robotic applications without addressing their integration. Methods: To bridge this gap, we conducted a comprehensive review of 46 relevant studies, analyzing various dimensions, including the components of robotic packing cells, the types of packing problems, the solution approaches, and performance comparisons. Results: Our review reveals a significant trend towards addressing online packing problems, which reflects the dynamic nature of logistics operations where item information is often incomplete. We also identify several research gaps, such as the need for standardized terminologies, comprehensive methodologies, and the consideration of real-world constraints in robotic algorithms. Conclusions: This review uniquely integrates insights from robotics and packing problems, providing a structured framework for future research. It highlights the importance of considering practical robotic constraints. It proposes a research structure that enhances the reproducibility and comparability of results in real-world scenarios. By doing so, we aim to guide future research efforts and facilitate the development of more robust and practical automated packing systems.

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Efficient Algorithm Based on Adaptive Window–Model Predictive Control for Automatic Stacking in Warehouse Center

Cited by 2 publications

References 12 publications

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

Comprehensive Review of Robotized Freight Packing

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