Analysis of taskable mobile IoT sensing systems for coverage and throughput

Agarkar, Aarti Amod; Hussain, Md. Zair; Raja, J. Thimmia; Haldorai, Anandakumar; Selvakanmani, S.; Thangamani, M.

doi:10.1007/s13198-023-01872-w

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…In study [14] examines the strategic behavior of a corporation that engages in the sale of two items with vertical differentiation in terms of quality. The firm operates within a two-period framework and interacts with customers that possess forward-looking decision-making capabilities.…”

Section: Behavior Operationsmentioning

confidence: 99%

Future Scope of Logistics and Supply Chain - An Detailed Analysis

Cai

2024

JEBI

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Logistics and Supply chain play a crucial role in the functioning of commercial operations. A supply chain encompasses the process of acquiring and distributing commodities, while logistics pertains to the transportation, warehousing, and overall handling of resources and products. Both commercial and emergency supply chains encounter various obstacles, however they may derive valuable insights from one other's tactics and business models. Logistics comprises several components, including transportation, storage, inventory management, packaging, and information processing. The primary objective of logistics management is to effectively coordinate the availability of appropriate resources, ensuring their timely delivery to the designated destination. Supply chain management is a strategic approach that combines essential business activities in order to enhance value for both customers and stakeholders. Supply chain management encompasses the harmonization of several entities, including suppliers, manufacturers, warehouses, transporters, retailers, and consumers, with the objective of fulfilling client demands while simultaneously reducing expenses. There is a prevailing agreement among scholars that SCM and logistics constitute a crucial area of study. However, it is noteworthy that there is a scarcity of literature evaluations pertaining to this subject matter. This study aims to identify and discuss prominent topics in contemporary research by conducting a comprehensive literature assessment from the standpoint of operations management. Furthermore, we have derived some insights and identified potential avenues for further study in this particular domain.

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Section: Behavior Operationsmentioning

confidence: 99%

Future Scope of Logistics and Supply Chain - An Detailed Analysis

Cai

2024

JEBI

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“…The variance and mean are calculated for the two flights. Statistical tests, such as the t-test, are used for the analysis of this particular characteristic [29]. Following the completion of the test, if the two means fall within the same interval of importance, they are deemed to be equivalent.…”

Section: Feature Selectionmentioning

confidence: 99%

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

Helgo

2023

JRS

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This paper examines the use of machine learning and deep learning algorithms in the aviation industry, with a specific emphasis on aircraft diagnosis/prognosis, predictive maintenance, feature selection, and flight data monitoring (FDM). This study highlights the potential use of these algorithms in enhancing the efficacy and effectiveness of various aircraft operations. In the field of aviation prognosis and diagnosis, many designs have been acknowledged as efficient for defect identification, calculation of remaining usable life, and prediction of excessive vibration in aero-engines. The architectural models discussed in this paper include deep autoencoders, deep belief networks, long short-term memory networks, and convolutional neural networks. The use of feature selection and scalar feature selection methodologies has been seen to augment the efficacy of FDM (Feature Detection and Matching) algorithms by means of identifying noteworthy features and detecting highly linked features. The application of machine learning algorithms in the domain of predictive maintenance enables real-time assessment of equipment health, hence reducing possible hazards and improving overall equipment performance. The research results emphasize the importance of flight data monitoring in improving safety and operational efficiency in the field of civil aviation. The application of machine learning approaches, namely classification algorithms, facilitates the analysis of flight data for the aim of identifying unsafe behaviors or violations from established operational standards.

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A Review on Smart Navigation Techniques for Automated Vehicle

Haldorai,

Murugan

et al. 2024

EAI/Springer Innovations in Communication and Computing

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Analysis of taskable mobile IoT sensing systems for coverage and throughput

Cited by 5 publications

References 15 publications

Future Scope of Logistics and Supply Chain - An Detailed Analysis

Future Scope of Logistics and Supply Chain - An Detailed Analysis

Deep Learning and Machine Learning Algorithms for Enhanced Aircraft Maintenance and Flight Data Analysis

A Review on Smart Navigation Techniques for Automated Vehicle

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