Classification of EEG signals for facial expression and motor execution with deep learning

Recently, algorithms of machine learning are widely used with the field of electroencephalography (EEG) brain-computer interfaces (BCI). The preprocessing stage for the EEG signals is performed by applying the principle component analysis (PCA) algorithm to extract the important features and reducing the data redundancy. A model for classifying EEG, time series, signals for facial expression and some motor execution processes had been designed. A neural network of three hidden layers with deep learning classifier had been used in this work. Data of four different subjects were collected by using a 14 channels Emotiv EPOC+ device. EEG dataset samples including ten action classes for the facial expression and some motor execution movements are recorded. A classification results with accuracy range (91.25-95.75%) for the collected samples were obtained with respect to: number of samples for each class, total number of EEG dataset samples and type of activation function within the hidden and the output layer neurons. A time series EEG signal was taken as signal values not as image or histogram, analysed and classified with deep learning to obtain the satisfied results of accuracy

Software engineering based fault tolerance model for information system in plants shopping center

The rapid development of mobile phone technologies in recent years promoted them for being used in various areas of life, such as commercial, health, transportation and tourism and other uses. In this paper, a software engineering based fault tolerance model is proposed to manage the expected faults in the adopted servers. The underlying QR based information system in plants shopping center employs different local serves allocated at local shops that are connected to the main server. In a fault case detection at any local server, the main server can cover the management of the system until the maintenance is completed. This is performed in efficient way as the main server keeps a copy of the information for all local branches. It is important to note that the self-checking process is adopted for fault detection. After completing the maintenance, a copy of the updated information is sent back to the investigated local server including all sales, etc. Moreover, the main and local servers contain information about all offer’s plants in different languages and in text and image form, customer's information and admin's information. The proposed system is tested in several cases to prove the efficiency and effectivity in retrieving and managing information and data as well as the fault tolerance administration

Design and implementation a network mobile application for plants shopping center using QR code

During the revolution of developing mobile phone applications, they can be used in different fields like business, health, transportation, communications and tourism, and other uses. This paper presents QR based information management system for plants shopping centers. This system includes two main sides: mobile and server. The proposed application is used as a substitute for the human guide that each visitor needs in the plants shopping center. The complete information can be provided on any seedling displayed in the shop depending on the QR code technology. Each branch of the same enterprise includes sub-server that is linked to the main server using a private computer network. The server side contains information of all plants and all branches for such enterprise in the form of text and image in different languages. The proposed application facilitates the movement of the customer inside the place, the ease in preparing bills electronically that helps the visitor to save the time of queue to preparing the bill and paying. The proposed system is tested over different case studies to prove the efficiency in terms of information and selling management

ROLE OF DATA MINING IN E-GOVERNMENT FRAMEWORK

In e-government, the mining techniques are considered as a procedure for extracting data from the related webapplication to be converted into useful knowledge. In addition, there are different methods of mining that can be applied to differentgovernment data. The significant ideas behind this paper are to produce a comprehensive study amongst the previous research workin improving the speed of queries to access the database and obtaining specific predictions. The provided study compares datamining methods, database management, and types of data. Moreover, a proposed model is introduced to put these different methodstogether for improving the online applications. These applications produce the ability to retrieve the information, matching keywords,indexing database, and performing the prediction from a vast amount of data

Viterbi optimization for crime detection and identification

In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way

Performance evaluation of ad-hoc based aerial monitoring system

Recently there is a huge interest in designing and implementing systems that can be used in surveillance and emergency situations. These systems are designed and implemented using two main technologies that are: Mobile Ad-hoc Networks (MANETs) and the Unmanned Aerial Vehicles (UAVs). MANETs with its unique characteristics of rapid deployment, self-organization and cost effectivenes had made it a popular topic for designers and developers to design and implement such systems. In this paper, a prototype system was designed and implemented using MANETs and UAVs; this system can be developed to be used as an aerial monitoring system in surveillance and security issues, the system was used to record and send a real-time video from source to destination node over a multihop path. This system was first implemented and tested using testbed method, then it was simulated using network simulator (NS-3) with two case studies to evaluate the performance of the system using two routing protocols (Ad-hoc On-Demand Destance Vector AODV[1] and Optimised Link State Routing OLSR [2]). The evaluating metrics used here are; delay, average jitter, packet loss ratio (PLR) and packet delivery factor (PDF) against variable number of nodes. The optained results of the test bed method showed the configuration parameters and self-organization characteristics of MANET, the results obtained from the simulation platform illustrated that the OLSR had outperformed the AODV protocol in dense networks and the optimum number of nodes needed to cover the simulation area were 90 nodes

Evaluation of efficient vehicular ad hoc networks based on a maximum distance routing algorithm

Traffic management at road intersections is a complex requirement that has been an important topic of research and discussion. Solutions have been primarily focused on using vehicular ad hoc networks (VANETs). Key issues in VANETs are high mobility, restriction of road setup, frequent topology variations, failed network links, and timely communication of data, which make the routing of packets to a particular destination problematic. To address these issues, a new dependable routing algorithm is proposed, which utilizes a wireless communication system between vehicles in urban vehicular networks. This routing is position-based, known as the maximum distance on-demand routing algorithm (MDORA). It aims to find an optimal route on a hop-by-hop basis based on the maximum distance toward the destination from the sender and sufficient communication lifetime, which guarantee the completion of the data transmission process. Moreover, communication overhead is minimized by finding the next hop and forwarding the packet directly to it without the need to discover the whole route first. A comparison is performed between MDORA and ad hoc on-demand distance vector (AODV) protocol in terms of throughput, packet delivery ratio, delay, and communication overhead. The outcome of the proposed algorithm is better than that of AODV

Improvement of Criminal Identification by Smart Optimization Method

Data-mining methods, which can be optimized via different methods, are applied in crime detection. This work, the decision tree algorithm is used for classifying and optimizing its structure with the smart method. This method is applied to two datasets: Iraq and India criminals. The goal of the proposed method is to identify criminals using a mining method based on smart search. This contribution helps in the acquisition of better results than those provided by traditional mining methods via controlling the size of the tree through decreasing leaf size

Evaluation of efficient vehicular ad hoc networks based on a maximum distance routing algorithm

Traffic management at road intersections is a complex requirement that has been an important topic of research and discussion. Solutions have been primarily focused on using vehicular ad hoc networks (VANETs). Key issues in VANETs are high mobility, restriction of road setup, frequent topology variations, failed network links, and timely communication of data, which make the routing of packets to a particular destination problematic. To address these issues, a new dependable routing algorithm is proposed, which utilizes a wireless communication system between vehicles in urban vehicular networks. This routing is position-based, known as the maximum distance on-demand routing algorithm (MDORA). It aims to find an optimal route on a hop-by-hop basis based on the maximum distance toward the destination from the sender and sufficient communication lifetime, which guarantee the completion of the data transmission process. Moreover, communication overhead is minimized by finding the next hop and forwarding the packet directly to it without the need to discover the whole route first. A comparison is performed between MDORA and ad hoc on-demand distance vector (AODV) protocol in terms of throughput, packet delivery ratio, delay, and communication overhead. The outcome of the proposed algorithm is better than that of AODV

Improvement of Criminal Identification by Smart Optimization Method

Data-mining methods, which can be optimized via different methods, are applied in crime detection. This work, the decision tree algorithm is used for classifying and optimizing its structure with the smart method. This method is applied to two datasets: Iraq and India criminals. The goal of the proposed method is to identify criminals using a mining method based on smart search. This contribution helps in the acquisition of better results than those provided by traditional mining methods via controlling the size of the tree through decreasing leaf size