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Broadcasting in Mobile Ad Hoc Networks (MANETs) is a fundamental data dissemination mechanism with a number of important applications in, e.g., route discovery, address resolution. However, broadcasting induces what is known as the ''broadcast storm problem'' which causes severe degradation in network performance due to excessive redundant retransmission, collision, and contention. Broadcasting in MANETs has traditionally been based on flooding, which simply swamps the network with large number of rebroadcast messages in order to reach all network nodes. Although probabilistic flooding has been one of the earliest suggested schemes to broadcasting, there has not been so far any attempt to analyse its performance behaviour in a MANET environment. In an effort to fill this gap, this paper investigates using extensive ns-2 simulations the effects of a number of important system parameters in a typical MANET, including node speed, pause time, traffic load, and node density on the performance of probabilistic flooding. The results reveal that most of these parameters have a critical impact on the reachability and the number of saved rebroadcast messages achieved by probabilistic flooding.

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Electroencephalogram (EEG) is one of the most powerful tools that offer valuable information related to different abnormalities in the human brain. One of these abnormalities is the epileptic seizure. A framework is proposed for detecting epileptic seizures from EEG signals recorded from normal and epileptic patients. The suggested approach is designed to classify the abnormal signal from the normal one automatically. This work aims to improve the accuracy of epileptic seizure detection and reduce computational costs. To address this, the proposed framework uses the 54-DWT mother wavelets analysis of EEG signals using the Genetic algorithm (GA) in combination with other four machine learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Naive Bayes (NB). The performance of 14 different combinations of two-class epilepsy detection is investigated using these four ML classifiers. The experimental results show that the four classifiers produce comparable results for the derived statistical features from the 54-DWT mother wavelets; however, the ANN classifier achieved the best accuracy in most datasets combinations, and it outperformed the other examined classifiers. INDEX TERMS Electroencephalogram (EEG), discrete wavelet transform (DWT), epilepsy, artificial neural network, k-nearest neighbor (k-NN), support vector machine (SVM), naïve bayes (NB).

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