RETRACTED ARTICLE: A Secure Machine Learning-Based Optimal Routing in
            <i>Ad Hoc</i>
            Networks for Classifying and Predicting Vulnerabilities

Yeruva, Ajay Reddy; Alomari, Esraa; S, Rashmi; Shrivastava, Anurag; Kathiravan, Muthu K.; Chaturvedi, Abhay

doi:10.1080/01969722.2023.2166241

Cited by 36 publications

(11 citation statements)

References 14 publications

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“…Furthermore, node battery depletion may cause network partitioning in addition to limited connectivity.Fully intent on broadening MANET lifetime, energy-mindful directing conventions have been proposed. Three primary methodologies are distinguished in the connected writing [2]. To start with, most extreme lifetime steering that tries to adjust energy consumption among portable hubs.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: AI-enhanced security demand and routing management for MANETs with optical technologies

Jia,

Huang,

Qin

2023

Opt Quant Electron

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The proliferation of Mobile Ad hoc Networks (MANETs), where nodes connect with one another to offer the required real-time entertainment services, is where academics are focusing more attention as a result of recent breakthroughs in wireless communication. Decentralised design and wireless connection of MANETs, however, make building safe routing a difficult problem. Artificial Intelligence (AI) and optical technologies have attracted a lot of attention as a way to address these security issues and improve network performance. This study uses a machine learning model to provide a unique security management and routing management method for MANETs. Here, trust-based multi-tier honey pot analysis with stacked reinforcement learning (MHSRL) is used to monitor the security of the network. The linear gradient Distance Vector dynamic Mamdani routing system (LGDVDMR) is used to regulate network routing.For different security-based datasets, experimental analysis is done in terms of throughput, end-end latency, packet delivery ratio, and trust analysis. Generated graph executes both the performance graph and the packet drop. The results of research studies indicate that our method locates the closest node that is the safest and finds problematic nodes with a tolerable load.Proposed technique attained throughput 96%, trust analysis 98%, end-end delay of 59%, packet delivery ratio of 79%.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: AI-enhanced security demand and routing management for MANETs with optical technologies

Jia,

Huang,

Qin

2023

Opt Quant Electron

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“…The novel proposed method in [42] predicted node mobility in ad hoc networks. An RNN-based method comprising a single feed-forward layer was used for more precise and realistic mobility predictions by depicting interactions and connections between nodes.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Method for Automatic Reliability Prediction of CR-VANETs

Bahramnejad

Movahhedinia

Naseri

2023

Preprint

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Reliability is a critical issue in vehicular networks. A Deep Learning (DL) method is proposed in this study to automatically predict the reliability of Cognitive Radio Vehicular Networks (CR-VANETs) ignored in the previous research. First, a dataset is generated based on a previously proposed method for the reliability assessment of CR-VANETs. Then, a model is proposed to predict the networks' reliability using the DL method and compared with other machine learning methods. While machine learning methods have been applied in vehicular networks, they have not been used for reliability prediction. The proposed DL model is utilized in this research to predict CR-VANETs' reliability. Based on the results, the DL model outperforms other machine learning methods for reliability prediction. The correlation coefficient and root mean square error of the test data for the DL model are 0.9862 and 0.0381. These results indicate the CR-VANETs' reliability prediction accurately using the proposed method.

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“…Another challenge is the need to ensure that chatbots are accurate and reliable, providing correct information and avoiding biases or errors. Several educational institutions and companies have already implemented chatbots in their education systems (Reddy Yeruva et al, 2023). For example, Georgia State University implemented a chatbot named "Pounce," which provides personalized support to students, answering questions and providing guidance on academic and administrative matters.…”

Section: Chatbotsmentioning

confidence: 99%

Role of AI in Education

Harry

2023

Injurity

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Artificial intelligence (AI) has the potential to revolutionize the way we learn and teach, making it more personalized, engaging, and efficient. AI in education refers to the use of artificial intelligence technologies, such as machine learning and natural language processing, to enhance the learning experience. It involves the use of algorithms that analyze data, identify patterns, and make predictions, enabling educators to personalize learning for each student. The potential benefits of using AI in education are significant. Personalized learning, one of the most significant advantages of AI in education, can lead to better student outcomes, as students can learn at their own pace and in a way that suits their learning style. Intelligent tutoring systems, chatbots, and automated grading and assessment can increase efficiency, save teachers' time, and provide more accurate and consistent feedback. However, there are also challenges associated with using AI in education. Privacy and security concerns, lack of trust, cost, and potential bias are some of the challenges that need to be addressed. Ethical considerations such as ensuring accessibility, transparency, and fairness in AI-based education systems also need to be taken into account. Despite these challenges, the potential of AI in education is immense. AI can provide better data analysis, enabling educators to make data-driven decisions. In this review we described role of AI in management, promotion of education which describe the effect of AI in education sector

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RETRACTED ARTICLE: A Secure Machine Learning-Based Optimal Routing in Ad Hoc Networks for Classifying and Predicting Vulnerabilities

Cited by 36 publications

References 14 publications

RETRACTED ARTICLE: AI-enhanced security demand and routing management for MANETs with optical technologies

RETRACTED ARTICLE: AI-enhanced security demand and routing management for MANETs with optical technologies

A Deep Learning Method for Automatic Reliability Prediction of CR-VANETs

Role of AI in Education

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