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Manideep Yenugula: Pioneer in Artificial Intelligence Performance Engineering - Vimarsana News

Manideep Yenugula: Pioneer in Artificial Intelligence Performance Engineering

Manideep's remarkable journey from coding efficiency to the esteemed position of Senior Performance Engineer exemplifies his adaptability, expertise, and unwavering commitment to pushing technological boundaries.

Manideep Yenugula: A Pioneer in AI Performance Engineering - Vimarsana News

Manideep Yenugula: A Pioneer in AI Performance Engineering

Manideep's foray into performance engineering unfolded when he recognized a critical need for expertise in optimizing system performance.

"Industrial IoT intrusion detection via evolutionary cost-sensitive lea" by Akbar Telikani, Jun Shen et al. - Vimarsana News

"Industrial IoT intrusion detection via evolutionary cost-sensitive lea" by Akbar Telikani, Jun Shen et al.

Cyber-attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems. To address this issue, we introduce EvolCostDeep, a hybrid model of stacked auto-encoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The ...

Source: uow.edu.au
"A cost-sensitive deep learning based approach for network traffic clas" by Akbar Telikani, Amir H. Gandomi et al. - Vimarsana News

"A cost-sensitive deep learning based approach for network traffic clas" by Akbar Telikani, Amir H. Gandomi et al.

Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive dee...

Source: uow.edu.au