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).
Worldwide Interoperability for Microwave Access (WiMAX), which is also known as IEEE 802.16 standard, supports last-mile broadband access wireless networks. WiMAX has many advantages including wide coverage area and high bandwidth. These advantages enable WiMAX to support long transmission range and high data rate compared to cellular and WiFi network. WiMAX technology uses a number of scheduling techniques in the Medium Access Control layer, which is responsible for the utilization of available resources in the networks and distribute them among users in order to ensure the desired quality of service. In this study, we propose a Modified Weighted Round Robin (MWRR) scheduler in order to decrease the average end-to-end delay and improve the average throughput. The proposed scheduling technique has been designed and simulated using the QualNet 5.0.2 network simulator. In order to evaluate the performance of our proposed approach, we compared our results to the results of well-known scheduling techniques (Weighted Round Robin, Strict Priority, and Weighted Fair Queuing). The average percentage of improvement was around 4%.
Internet of Things (IoT) has grown increasingly in the past decade. This growth brings up several challenging issues for a successful continuous operation of IoT applications. Some of these challenges that need to be taken care of are resource constraints, central server overload, and the risk of illegal use of private data. On the other side, Blockchain technology is increasingly popular and has gained huge success in cryptocurrencies. It offers numerous vital qualities such as a technique for consensus, peer communications, confidence-building without a trustworthy third party, and a transaction controlled by conditions and functions using the intelligent contract technique. Blockchain is an excellent candidate to establish a decentralized, autonomous IoT system addressing the above issues. This study proposes an IoT-Blockchain integration architecture using an Ethereum Blockchain infrastructure within a rich-thin client IoT approach to address the challenges created by the limited IoT resources while implementing the Blockchain mining technique in IoT systems. The architecture depends on load distribution between the resources. Limited resource devices are the thin-clients, while the higher resource devices are assigned as rich-clients. Both clients can access the blockchain and collect the data, but rich-client only can execute the mining process. In addition, we implement a healthcare system based on our architecture in which surgical process management is carried out. We also prove our solution's efficiency by testing and comparing the architecture with typical IoT-based blockchain architectures. We conclude that our blockchain-IoT architecture is suited for many IoT applications while avoiding difficulties created by IoT device limitations.
The IEEE 802.15.4 is a standard for Wireless Personal Area Network (PAN) that supports low data rate, low cost, low complexity and low power consumption applications. The CSMA/CA algorithm of the IEEE 802.15.4 MAC layer employs the Binary Exponential Backoff (BEB) function to compute the backoff delay for each node. Using BEB function, it is possible that two or more nodes may collide if they choose the same backoff exponent value. Consequently this will increase collision and network contention level which will degrade the network overall performance. To overcome this problem, this paper proposes a Fibonacci Backoff (FIB) function to compute the backoff interval. In FIB, each node shall wait for an incremental backoff periods as they need to access the channel. The performance of FIB algorithm is compared against the BEB function.
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