Software Escalation Prediction Based on Deep Learning in the Cognitive Internet of Vehicles

Wang, Ranran; Zhang, Yin; Fortino, Giancarlo; Guan, Qingxu; Liu, Jiangchuan; Song, Jeungeun

doi:10.1109/tits.2022.3140903

Cited by 16 publications

(7 citation statements)

References 29 publications

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“…As a result of transmitting emergency alert messages, exchanging neighborhood requests, and relaying data over the Internet, buffer flow traffic safety limits are extended. 15,16…”

Section: Data Traffic Forwarding and Buffer Management For Vanetsmentioning

confidence: 99%

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Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks: A review

Ravi

Kumar²,

et al. 2023

Int J Network Mgmt

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SummaryThe rapid growth of vehicular applications has resulted in high demand for Internet technology, which demands an unprecedented network capacity and a high quality of service (QoS). In vehicular ad hoc networks (VANETs), since nodes (vehicles) are highly mobile. The dynamic nature of the network topology in the VANET system changes due to frequent changes in link connectivity. The vehicles‐to‐vehicles (V2V), vehicles‐to‐infrastructure (V2I), and QoS, as well as the heterogeneity of applications within the VANET. VANETs have been introduced to make driving comfortable by providing safety and support to drivers. Due to the flexibility and offloading schemes available in‐vehicle applications, there are some limitations. However, there are many issues in providing optimum service provisioning and scheduling in the vehicular environment. In VANETs, BSs and roadside units (RSUs) improve QoS. However, Internet services transmit packets to vehicles using stochastic models, and it predicts the traffic on a VANET. We provide open challenges to drive stochastic models in this direction.

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“…As a result of transmitting emergency alert messages, exchanging neighborhood requests, and relaying data over the Internet, buffer flow traffic safety limits are extended. 15,16…”

Section: Data Traffic Forwarding and Buffer Management For Vanetsmentioning

confidence: 99%

“…As shown in Figures 2 and 3, multihop data dissemination has significant advantages in terms of security and transmission. As a result of transmitting emergency alert messages, exchanging neighborhood requests, and relaying data over the Internet, buffer flow traffic safety limits are extended 15,16 …”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks: A review

Ravi

Kumar²,

et al. 2023

Int J Network Mgmt

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“…A full complete round indicates that a specific base station is able to collect all the data that are being sent from the sensor nodes stationed outside. This is also used to reduce delay and minimize energy usage in the network that comes with the sensor [18]. There are different types of data gathering, as it is mentioned below:…”

Section: Investigating the Utilization Of The Networked With Distribu...mentioning

confidence: 99%

AI-Assisted Chinese Language Teaching System in Distributed Sensor Networks

Shan

2023

SCPE

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The Chinese learning system gained enormous importance to support the population of China with recent technologies. The AI and ML-assisted classes are useful for learners. However, it can cut down the employment of teachers. Various planning and act was passed to reform the Chinese education system with artificial intelligence and machine language. On the other hand, this education process is interesting, customized, and unsupervised based. It was observed that the human touch study process is more satisfying and clears doubts easily. The key object of data gathering sensor-based network is accumulating all the data from the nodes and sending those sensed data to the base station. These nodes are basically working while focusing on one thought: all the nodes present in the sensor-based system are well aware of all the other nodes currently working in the same network and transmitting data in the base station. Although AI comes with a few challenges like lack of resources, training, and various security issues, if these are overcome with proper strategies, AI can be the equipment to reign in the 21${}^{st}$ century. Additionally, LLMs and NLP modeling are described in the results that helped to understand the use of different models. Moreover, the Benefits of LLMs and NLP for Chinese language training are discussed in the study.

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“…In today's interconnected world, software is being used in different domains ranging from financial institutions to power plant management to communication networks to smart cities to mobile applications, and many more. Software projects are becoming more challenging to handle due to their dynamic nature (Wang et al, 2022). The most challenging is the adaptation to the agile requirements of the world.…”

Section: Introductionmentioning

confidence: 99%

“…to their dynamic nature (Wang et al, 2022). The most challenging is the adaptation to the agile requirements of the world.…”

mentioning

confidence: 99%

A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions

Yenduri

Gadekallu

2023

Expert Systems

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The software is changing rapidly with the invention of advanced technologies and methodologies. The ability to rapidly and successfully upgrade software in response to changing business requirements is more vital than ever. For the long‐term management of software products, measuring software maintainability is crucial. The use of soft computing techniques for software maintainability prediction has shown immense promise in software maintenance process by providing accurate prediction of software maintainability. To better understand the role of soft computing techniques for software maintainability prediction, we aim to provide a systematic literature review of soft computing techniques for predicting software maintainability. Firstly, we provide a detailed overview of software maintainability. Following this, we explore the fundamentals of software maintainability and the reasons for adopting soft computing methodologies for predicting software maintainability. Later, we examine the soft computing approaches employed in the process of software maintainability prediction. Furthermore, we discuss the difficulties and potential solutions associated with the use of soft computing techniques in predicting maintainability of software. Finally, we conclude the review with some promising future directions to drive further research innovations and developments in this promising area. This systematic literature review provides a comprehensive overview of the soft computing strategies utilized for software maintainability for future researchers.

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Software Escalation Prediction Based on Deep Learning in the Cognitive Internet of Vehicles

Cited by 16 publications

References 29 publications

Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks: A review

Stochastic modeling and performance analysis in balancing load and traffic for vehicular ad hoc networks: A review

AI-Assisted Chinese Language Teaching System in Distributed Sensor Networks

A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions

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