EmoPercept: EEG-based emotion classification through perceiver

Emotions play an important role in human cognition and are commonly associated with perception, logical decision making, human interaction, and intelligence. Emotion and stress detection is an emerging topic of interest and importance in the research community. With the availability of portable, cheap, and reliable sensor devices, researchers are opting to use physiological signals for emotion classification as they are more prone to human deception, as compared to audiovisual signals. In recent years, deep neural networks have gained popularity and have inspired new ideas for emotion recognition based on electroencephalogram (EEG) signals. Recently, widespread use of transformer-based architectures has been observed, providing state-of-the-art results in several domains, from natural language processing to computer vision, and object detection. In this work, we investigate the effectiveness and accuracy of a novel transformer-based architecture, called perceiver, which claims to be able to handle inputs from any modality, be it an image, audio, or video. We utilize the perceiver architecture on raw EEG signals taken from one of the most widely used publicly available EEG-based emotion recognition datasets, i.e., DEAP, and compare its results with some of the best performing models in the domain

Design and Analysis of Lightweight Authentication Protocol for Securing IoD

The Internet-of-drones (IoD) environment is a layered network control architecture designed to maintain, coordinate, access, and control drones (or Unmanned Aerial vehicles UAVs) and facilitate drones' navigation services. The main entities in IoD are drones, ground station, and external user. Before operationalizing a drone in IoD, a control infrastructure is mandatory for securing its open network channel (Flying Ad Hoc Networks FANETs). An attacker can easily capture data from the available network channel and use it for their own purpose. Its protection is challenging, as it guarantees message integrity, non-repudiation, authenticity, and authorization amongst all the participants. Incredibly, without a robust authentication protocol, the task is sensitive and challenging one to solve. This research focus on the security of the communication path between drone and ground station and solving the noted vulnerabilities like stolen-verifier, privileged-insider attacks, and outdated-data-transmission/design flaws often reported in the current authentication protocols for IoD. We proposed a hash message authentication code/secure hash algorithmic (HMACSHA1) based robust, improved and lightweight authentication protocol for securing IoD. Its security has been verified formally using Random Oracle Model (ROM), ProVerif2.02 and informally using assumptions and pragmatic illustration. The performance evaluation proved that the proposed protocol is lightweight compared to prior protocols and recommended for implementation in the real-world IoD environment.Qatar University [IRCC-2021-010]

Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals

Mental stress is an issue that creates functional limitations in the workplace. Chronic stress leads to a number of psychophysiological sicknesses. For instance, it raises the risk of depression, heart attack, and stroke. According to the most recent findings in neuroscience, the human brain is the primary focus of mental stress. Perception of biological motion in the human brain determines the risky and stressful situations. Neural signaling of the human brain is used as an objective measure for determining the stress level of a subject. The oscillations of electroencephalography (EEG) signals are utilized for classifying human stress. EEG signals have a higher temporal resolution and are rapidly distorted with unwanted noise, resulting in a variety of artifacts. This study utilizes Extended Independent Component Analysis based approach for artifacts removal. A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials. BiLSTM is used to improve classification results. In order to validate the model performance, two publically available datasets (i.e., DEAP and SEED) are utilized. Employing these datasets, the proposed model achieves state-of-the-art results (93.1, 96.84%) for EEG signal classification to identify stress

Search-Based and Goal-Oriented Refactoring Using Unfolding of Graph Transformation Systems

To improve automation and traceability of search-based refactoring, in this thesis we propose a formulation of using graph transformation, where graphs represent object-oriented software architectures at the class level and rules describe refactoring operations. This formalisation allows us to make use of partial order semantics and an associated analysis technique, the approximated unfolding of graph transformation systems. In the unfolding we can identify dependencies and conflicts between refactoring steps leading to an implicit and therefore more scalable representation of the search space by sets of transformation steps equipped with relations of causality and conflict. To implement search based refactoring we make use of the approximated unfolding of graph transformation systems. An optimisation algorithm based on the Ant Colony paradigm is used to explore the search space, aiming to find a sequence of refactoring steps that leads to the best design at a minimal cost. Alternatively, we propose a more targeted approach, aiming at the removal of design flaws. The idea is that such sequences should be relevant to the removal of the flaw identified, i.e., contain only steps which are directly or indirectly contributes to the desired goal

Search-Based Refactoring using Unfolding of Graph Transformation Systems

To improve scalability and understandability of search-based refactoring, in this paper, we propose a formulation based on graph transformation which allows us to make use of partial order semantics and an associated analysis technique, the approximated unfolding of graph transformation systems. We use graphs to represent object-oriented software architectures at the class level and graph transformations to describe their refactoring operations. In the unfolding we can identify dependencies and conflicts between refactoring steps leading to an implicit and therefore more scalable representation of the search space. An optimisation algorithm based on the Ant Colony paradigm is used to explore this search space, aiming to find a sequence of refactoring steps that leads to the best design at a minimal costs

Denoising histopathology images for the detection of breast cancer

One of the leading causes of mortality for women worldwide, both in developing and developed economies, is breast cancer. The gold standard for diagnosing cancer is still histological diagnosis, despite major advances in medical understanding. Admittedly, due to the sophistication of histopathology images and the significant increase in workload, this process takes a long time. Therefore, this field requires the development of automated and precise histopathology image analysis tools. Using deep learning, we proposed a system for denoising, detecting, and classifying breast cancer using deep learning architectures that are designed to solve certain related problems. CNN-based architectures are used to extract features from images, which are then put into a fully connected layer for the classification of malignant and benign cells, as well as their subclasses, in the suggested framework. The effectiveness of the suggested framework is evaluated through experiments leveraging accepted benchmark data sets. We achieve an accuracy of 94% and an F1 score of more than 90%