Cascaded layered recurrent neural network for indoor localization in wireless sensor networks - Turabieh, H.; Sheta, A. (Institute of Electrical and Electronics Engineers Inc., 2019)
The growth in using various smart wireless devices in the last few decades has given rise to indoor localization service (ILS). Indoor localization is defined as the process of locating a user location in an indoor environment. Indoor device localization has been widely studied due to its popular applications in public settlement planning, health care zones, disaster management, the implementation of location-based services (LBS) and the Internet of Things (IoT). The ILS problem can be formulated as a learning problem utilizing Wi-Fi technology. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in a various indoor location. Developing a classification model with high accuracy can be achieved using a machine learning approach. Artificial Neural Network is one of the most successful trends in machine learning. In this article, we provide our initial idea of using Cascaded Layered Recurrent Neural Network (L-RNN) for the classification of user localization in an indoor environment. Several neural network models were trained, with the best performance attainment is reported. The experimental results marked that the presented L-RNN model is highly accurate for indoor localization and can be utilized for many applications. © 2019 IEEE.
Keywords: Classification models; Disaster management; Disaster prevention; Disasters; Forecasting; Indoor Environment; Indoor environment; Indoor localization; Indoor positioning systems; Internet of thing (IOT); Internet of things; Layered Recurrent Neural Network; Location; Location based services; Machine learning; Machine learning approaches; Multilayer neural networks; Network layers; Neural network model; Prediction; Recurrent neural networks; Telecommunication services; User Localization; User localization; Wireless local area networks (WLAN); Wireless sensor networks; dept.csc

Identification of the Tennessee Eastman Chemical Process Reactor Using Genetic Programming - Faris, Hossam; Sheta, A. (International Journal of Advanced Science and Technology, 2013)
Keywords: dept.csc; peer-reviewed; ⛔ No DOI found

Multiple Waypoint Mobile Robot Path Planning Using Neighborhood Search Genetic Algorithms - Maddi, D.R.; Sheta, A.; Mahdy, A.; Turabieh, H. (Association for Computing Machinery, 2019)
In this paper, we present a Neighborhood Search Genetic Algorithms (NSGAs) for mobile robot path planning. GAs have been used successfully in a variety of path planning problem because they can search the space of all possible paths and provide the optimal one. The convergence process of GAs might be lengthy compared to traditional search techniques that depend on local search methods. We propose a hybrid approach that allows GAs to combine both the advantages of GAs and local search algorithms. GAs will create a multiple waypoint path allowing a mobile robot to navigate through static obstacles and finding the optimal path in order to approach the target location without collision. The proposed NSGAs has been examined over four different path planning case studies with varying complexity. The performance of the enhanced GA has been compared with A-star algorithm (A∗) standard GA, particle swarm optimization (PSO) algorithm. The obtained results show that the proposed approach is able to get good results compared to other algorithms. © 2019 ACM.
Keywords: Air navigation; Convergence process; Gases; Genetic Algorithm; Genetic algorithms; Local search (optimization); Local search algorithm; Local search method; Mobile robots; Neighborhood Search; Neighborhood search; Particle swarm optimization (PSO); Particle swarm optimization algorithm; Path Planning; Path planning problems; Robot path-planning; Robot programming; Robotics; Search technique; dept.csc

Medical Data Classification Using Binary Brain Storm Optimization Algorithm - Ogwo, O.; Turabieh, H.; Sheta, A.; King, S. (Association for Computing Machinery, 2019)
With the growing access to technology in the medical domain, an increased volume of medical data is recorded. The size and complexity of these data make the process of analysis of meaningful discoveries of beneficial patterns more challenging. This problem has attracted numerous researchers around the world. Statistical methods have been employed to handle medical data for diagnosis purposes. Unfortunately, these methods were less capable of dealing with these massive and complex datasets. To solve this problem, we suggest a process to classify medical data which includes feature selection and classification using a number of supervised learning techniques. Binary Brain Storm Optimization (BBSO) is used for feature selection, which is a population search approach that simulates the process of electing the best idea (solution), among others. We simulated six different classifiers: Naive-Bayes, K-Nearest Neighbor, Support Vector Machine, Linear Discriminant Analysis, Decision Tree and Random Forest. Five datasets adopted from the UCI Machine Learning Repository, (Breast Cancer, Diabetes, Heart Disease, Chronic Kidney, and SPECT), are employed as a benchmark test data. The performance of BBSO is evaluated using accuracy on the datasets using the various classifiers. Experimental results show that the proposed approach improves the classification performance for better medical diagnosis. © 2019 ACM.
Keywords: Benchmarking; Classification performance; Complex datasets; Computer aided diagnosis; Decision trees; Discriminant analysis; Diseases; Feature extraction; Feature selection and classification; K-nearest neighbors; Learning systems; Linear discriminant analysis; Medical domains; Nearest neighbor search; Optimization algorithms; Robotics; Storms; Support vector machines; UCI machine learning repository; classification; dept.csc; feature selection; medical diagnosis

Application of electrical capacitance tomography for imaging conductive materials in industrial processes - Deabes, W.; Sheta, A.; Bouazza, K.E.; Abdelrahman, M. (Journal of Sensors, 2019)
This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior. © 2019 Wael Deabes et al.
Keywords: Capacitance; Capacitance measurement; Conductive materials; Electric impedance tomography; Electrical Capacitance Tomography; Electrical capacitance tomography systems; Image reconstruction; Image reconstruction algorithm; Industrial processs; Inverse problems; Its efficiencies; Lost foam castings; Metal distributions; Neural networks; Reconstructed image; dept.csc; peer-reviewed

Deep learning based plant disease detection for smart agriculture - Ale, L.; Sheta, A.; Li, L.; Wang, Y.; Zhang, N. (Institute of Electrical and Electronics Engineers Inc., 2019)
Deep learning is a promising approach for fine- grained disease severity classification for smart agriculture, as it avoids the labor-intensive feature engineering and segmentation-based threshold. In this work, we first propose a Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources. Then, we propose a lightweight Deep Neural Networks (DNN) approach that can run on Internet of Things (IoT) devices with constrained resources. To reduce the size and computation cost of the model, we further simplify the DNN model and reduce the size of input sizes. The proposed models are trained with different image sizes to find the appropriate size of the input images. Experiment results are provided to evaluate the performance of the proposed models based on real- world dataset, which demonstrate the proposed models can accurately detect plant disease using low computational resources. © 2019 IEEE.
Keywords: Agriculture; Classification; Classification (of information); Computational resources; Computing resource; Constrained resources; Convolution; Convolutional Neural Network; Convolutional networks; Convolutional neural networks; Corp Disease; Deep learning; Deep neural networks; Detection; Digital Epidemiology; Error detection; Feature engineerings; Internet of Things (IOT); Internet of things; Learning systems; Plants Disease; Smart agricultures; Transfer learning; Transfer learning methods; dept.csc