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SDN architectures are frequently used by organizations for the management of their networks and the detection of anomalous traffic in a single domain. However, in the real world, anomalous traffic might result in attacks like distributed denial of service (DDoS) that affect numerous domains. During intrusion detection, each SDN domain has to send real traffic data of a large volume to the multi-domain controller, exposing its sensitive information. This paper proposes a smart multistage framework for detecting attacks and ensuring privacy at no additional cost. This work utilized the recent unbalanced InSDN dataset for experimentation. It also uses an oversampling technique that reduces the imbalance rate for each attack type and selects the smallest possible training size and feature set size for an increase in detection accuracy and a reduction in computational complexity. Then, a multi-class classifier method for intrusion detection that does not require regularization or hyperparameter tuning, called ensemble-learning-based shallow decision tree (ELSDT) is proposed. Furthermore, the performance of the proposed classifier on the InSDN dataset is assessed on an SDN testbed. Experimental results show the ability of the proposed smart multistage privacy-preserving framework to make a significant reduction in the training sample size and feature set size to 87% and 76%, respectively. It also shows its outperformance in recent literature works by 5.67% improved accuracy.

Cognitive workload evaluation is vital in any critical working environment for assessing the user's mental status. Despite the fact that there are many methods for evaluating cognitive strain, the model that uses electroencephalography (EEG) data remains the most promising one. Brain related activities can be used to assess various mental states and also help us to determine mental effort. This study calculates the cognitive workload of people while performing multitasking mental tasks. Here the 'STEW' dataset is used to measure mental effort. 'No task' and 'simultaneous capacity (SIMKAP)-based multitasking activity' are the two tasks in the dataset. For the study we have chosen only the SIMKAP task dataset. The cognitive workload assessment from optimised EEG features using XGBoost classifier (CWAOEX) framework is proposed, in which numerous features from EEG brain signals are retrieved and the grey wolf optimiser (GWO) is utilised to select the best ones. The data is then categorised according to the best feature set. The XGBoost algorithm is employed in the classification step. The recommended method has the classification accuracy of 94.25 in categorising the workload as low, moderate and high which is better than the current methods.

ABSTRACTOver the past few decades, there has been tremendous development in machine learning paradigms used in automatic speech recognition (ASR) for home automation to space exploration. Though commercial speech recognizers are available for certain well-defined applications like dictation and transcription, many issues in ASR like recognition in noisy environments, multilingual recognition, and multi-modal recognition are yet to be addressed effectively. A comprehensive review of common machine learning techniques like artificial neural networks, support vector machines, and Gaussian mixture models along with hidden Markov models employed in ASR is provided. A thorough review on the recent developments in deep learning which has provided significant improvements in ASR performance, along with its relevance in the future of ASR, is also presented.