Supervised machine learning for jamming transition in traffic flow with fluctuations in acceleration and braking

Khan, Naveed Ahmad; Laouini, Ghaylen; Alshammari, Fahad Sameer; Khalid, Majdi; Aamir, Nudrat

doi:10.1016/j.compeleceng.2023.108740

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…In addition, machine learning approaches can be optimized and computationally efficient, allowing researchers to make accurate predictions and successfully solve complex problems in a timely and reliable manner. Some possible applications of machine learning algorithms include the transition of disturbances due to changes in braking and acceleration in traffic flows 25 , heat transfer in a micropolar fluid with isothermal and isoflux boundary conditions 26 , stiffness solutions for polytropic. gas spheres and electric circuit models 27 , linearization of circular sector oscillator inelastic vibration analysis 28 and inclined longitudinal porous trapezoidal and rectangular trapezoidal fin heat transfer analysis.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

Sulaiman,

Khalaf,

Khan

et al. 2024

Sci Rep

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This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

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Section: Introductionmentioning

confidence: 99%

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

Sulaiman,

Khalaf,

Khan

et al. 2024

Sci Rep

Self Cite

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“…Therefore, it is crucial to identify them at the right moment and stop them from spreading to a wider group in order to maintain social peace and the status quo of law and order. Unapproved or shady computer or network activity is discovered and reported using intrusion detection systems (IDSs) [11,12] using machine learning techniques (ML) [13,14]. Whereas client-based IDSs, the focus of this research, are designed to monitor client system operations, network-based IDSs are used to analyze network traffic across multiple clients.…”

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confidence: 99%

A Fine-Grained System Driven of Attacks Over Several New Representation Techniques Using Machine Learning

Al Ghamdi

2023

IEEE Access

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Machine Learning (ML) techniques, especially deep learning, are crucial to many contemporary real world systems that use Computational Intelligence (CI) as their core technology, including self-deriving vehicles, assisting machines, and biometric authentication systems. We encounter a lot of attacks these days. Drive-by-download is used to covertly download websites when we view them, and emails we receive often have malicious attachments. The affected hosts and networks sustain significant harm as a result of the infection. Therefore, identifying malware is crucial. Recent attacks, however, is designed to evade detection using Intrusion Detection System (IDS). It is essential to create fresh signatures as soon as new malware is found in order to stop this issue. Using a variety of cutting-edge representation methodologies, we develop attack taxonomy and examine it. 1) N-gram-based representation: In this tactic, we look at a number of random representations that take into account a technique of sampling the properties of the graph. 2) Signature-based representation: This technique uses the idea of invariant representation of the graph, which is based on spectral graph theory. One of the main causes is that a ML system setup is rely on a number of variables, including the input dataset, ML architecture, attack creation process, and defense strategy. To find any hostile attacks in the network system, we employ IDS with Deep Neural Network (DNN). In conclusion, the efficacy and efficiency of the suggested framework with Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are assessed using the assessment indicators, including throughput, latency rate, accuracy and precision. The findings of the suggested model with a detection rate of 93%, 14%, 95.63% and 95% in terms of throughput, latency rate, accuracy and precision, which is based on adversarial assault, were better and more effective than CNN and SVM models. Additionally at the end we contrast the performance of the suggested model with that of earlier research that makes use of the same dataset, NSL-KDD, as we do in our scenario.

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Knowledge Distillation Based on Narrow-Deep Networks

Zhou,

Wang,

2024

Neural Process Lett

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Deep neural networks perform better than shallow neural networks, but the former tends to be deeper or wider, introducing large numbers of parameters and computations. We know that networks that are too wide have a high risk of overfitting and networks that are too deep require a large amount of computation. This paper proposed a narrow-deep ResNet, increasing the depth of the network while avoiding other issues caused by making the network too wide, and used the strategy of knowledge distillation, where we set up a trained teacher model to train an unmodified, wide, and narrow-deep ResNet that allows students to learn the teacher’s output. To validate the effectiveness of this method, it is tested on Cifar-100 and Pascal VOC datasets. The method proposed in this paper allows a small model to have about the same accuracy rate as a large model, while dramatically shrinking the response time and computational effort.

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Supervised machine learning for jamming transition in traffic flow with fluctuations in acceleration and braking

Cited by 6 publications

References 20 publications

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

A Fine-Grained System Driven of Attacks Over Several New Representation Techniques Using Machine Learning

Knowledge Distillation Based on Narrow-Deep Networks

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