EpilNet: A Novel Approach to IoT based Epileptic Seizure Prediction and Diagnosis System using Artificial Intelligence

Epilepsy is one of the most occurring neurological diseases. The main characteristic of this disease is a frequent seizure, which is an electrical imbalance in the brain. It is generally accompanied by shaking of body parts and even leads (fainting). In the past few years, many treatments have come up. These mainly involve the use of anti-seizure drugs for controlling seizures. But in 70% of cases, these drugs are not effective, and surgery is the only solution when the condition worsens. So patients need to take care of themselves while having a seizure and be safe. Wearable electroencephalogram (EEG) devices have come up with the development in medical science and technology. These devices help in the analysis of brain electrical activities. EEG helps in locating the affected cortical region. The most important is that it can predict any seizure in advance on-site. This has resulted in a sudden increase in demand for effective and efficient seizure prediction and diagnosis systems. A novel approach to epileptic seizure prediction and diagnosis system "EpilNet" is proposed in the present paper. It is a one-dimensional (1D) convolution neural network. EpilNet gives the testing accuracy of 79.13% for five classes, leading to a significant increase of about 6-7% compared to related works. The developed Web API helps in bringing EpilNet into practical use. Thus, it is an integrated system for both patients and doctors. The system will help patients prevent injury or accidents and increase the efficiency of the treatment process by doctors in the hospitals

Swarm Intelligence-based Partitioned Recovery in Wireless Sensor Networks, Journal of Telecommunications and Information Technology, 2018, nr 3

The failure rate of sensor nodes in Heterogeneous Wireless Sensor Networks is high due to the use of low battery-powered sensor nodes in a hostile environment. Networks of this kind become non-operational and turn into disjoint segmented networks due to large-scale failures of sensor nodes. This may require the placement of additional highpower relay nodes. In this paper, we propose a network partition recovery solution called Grey Wolf, which is an optimizer algorithm for repairing segmented heterogeneous wireless sensor networks. The proposed solution provides not only strong bi-connectivity in the damaged area, but also distributes traffic load among the multiple deployed nodes to enhance the repaired network’s lifetime. The experiment results show that the Grey Wolf algorithm offers a considerable performance advantage over other state-of-the-art approaches

AMERICAN SIGN LANGUAGE FINGERSPELLING USING HYBRID DISCRETE WAVELET TRANSFORM-GABOR FILTER AND CONVOLUTIONAL NEURAL NETWORK

American Sign Language (ASL) is widely used for communication by deaf and mute people. In fingerspelling, the letters of the writing system are represented using only hands. Generally, hearing people do not understand sign language and this creates a communication gap between the signer and speaker community. A real-time ASL fingerspelling recognizer can be developed to solve this problem. Sign language recognizer can also be trained for other applications such as human-computer interaction. In this paper, a hybrid Discrete Wavelet TransformGabor filter is used on the colour images to extract features. Classifiers are evaluated on signer dependent and independent datasets. For evaluation, it is very important to consider signer dependency. Random Forest, Support Vector Machine and K-Nearest Neighbors classifiers are evaluated on the extracted set of features to classify the 24 classes of ASL alphabets with 95.8%, 94.3% and 96.7% accuracy respectively on signer dependent dataset and 49.16%, 48.75% and 50.83% accuracy respectively on signer independent dataset. Lastly, Convolutional Neural Network was also trained and evaluated on both, which produced 97.01% accuracy on signer dependent and 76.25% accuracy on signer independent dataset

Restoration of lost connectivity of partitioned wireless sensor networks

The lost connectivity due to failure of large-scale nodes plays major role to degrade the system performance by generating unnecessary overhead or sometimes totally collapse the active network. There are many issues and challenges to restore the lost connectivity in an unattended scenario, i.e. how many recovery nodes will be sufficient and on which locations these recovery nodes have to be placed. A very few centralized and distributed approaches have been proposed till now. The centralized approaches are good for a scenario where information about the disjoint network, i.e. number of disjoint segments and their locations are well known in advance. However, for a scenario where such information is unknown due to the unattended harsh environment, a distributed approach is a better solution to restore the partitioned network. In this paper, we have proposed and implemented a semi-distributed approach called Relay node Placement using Fermat Point. The proposed approach is capable of restoring lost connectivity with small number of recovery relay nodes and it works for any number of disjoint segments. The simulation experiment results show effectiveness of our approach as compared to existing benchmark approaches