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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 , Health Performance Load Testing , International Conference On Ubiquitous Technology In Communication , Multidisciplinary Digital Publishing Institute , Senior Performance Engineer , Mean Time , Machine Learning , Azure Cloud , Deep Learning , Lead Performance Engineer , Chickasaw Nations , Dynatrace Davis , Critical Parameters , Implementing Sustainable , Multi Criteria Decision Making , Cost Sensitive Learning , Rare Event Prediction , Ubiquitous Technology , Artificial Intelligence , Software Developement , Hit Industry ,

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.
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Manideep Yenugula , Health Performance Load Testing , International Conference On Ubiquitous Technology In Communication , Multidisciplinary Digital Publishing Institute , Senior Performance Engineer , Mean Time , Machine Learning , Azure Cloud , Deep Learning , Lead Performance Engineer , Chickasaw Nations , Dynatrace Davis , Critical Parameters , Implementing Sustainable , Multi Criteria Decision Making , Cost Sensitive Learning , Rare Event Prediction , Ubiquitous Technology , Artificial Intelligence ,

"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 combination of evolutionary algorithms and deep learning on Big data hinders the scalability of IIoT intrusion detection systems. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-lev ....

Industrial Internet , Class Imbalance , Computational Modeling , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Edge Computing , Evolutionary Algorithms , Fog Computing , Industrial Internet Of Things , Industrial Internet Of Things Iiot , Intrusion Detection ,

"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 deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each typ ....

Class Imbalance , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Ncrypted Traffic Classification , Generative Adversarial Networks , Intrusion Detection , Task Analysis ,