Kurdish Dialects and Neighbor Languages Automatic Recognition

Dialect recognition is one of the most hot topics in the speech analysis area. In this study a system for dialect and language recognition is developed using phonetic and a style based features. The study suggests a new set of feature using one-dimensional LBP feature.  The results show that the proposed LBP set of feature is useful to improve dialect and language recognition accuracy. The acquired data involved in this study are three Kurdish dialects (Sorani, Badini and Hawrami) with three neighbor languages (Arabic, Persian and Turkish). The study proposed a new method to interpret the closeness of the Kurdish dialects and their neighbor languages using confusion matrix and a non-metric multi-dimensional visualization technique. The result shows that the Kurdish dialects can be clustered and linearly separated from the neighbor languages

Real-Time Wireless Network of Mobile Sensor Nodes Based on ZigBee Protocol: Design and Implementation

Wireless sensor networks are an evolving technology for a wide range of environments due to its low-cost and importance that has been implemented by the recent delivery of the IEEE 802.15.4 standard (ZigBee standard) for application layers. The benefit of this module is to develop the designing skills on the wireless networks using ZigBee protocol that is providing a standardized base set of solutions for control systems and sensor .In this paper, a wireless network consisting of four mobile sensor nodes is designed and implemented. Thermal sensor is used in each node to measure the temperature. After the temperature was measured correctly, the system is modified in terms of having a better reliability by implementing the CRC technique. Furthermore, a TDMA and CSMA algorithms are applied to the nodes in a way that the four nodes of the network must be able to transmit and receive the data without any collision. The system is applied on a modern embedded board is PICDEM-Z BOARD. This board has excellent features such as high performance core PLCI8F4620, memory and Rf transceiver work with ZigBee protocol. The results show high flexibility and reliability in the measuring and exchanging the data between all the nodes within one second in real time.

An Investigation on Disparity Responds of Machine Learning Algorithms to Data Normalization Method

Data normalization can be useful in eliminating the effect of inconsistent ranges in some machine learning (ML) techniques and in speeding up the optimization process in others. Many studies apply different methods of data normalization with an aim to reduce or eliminate the impact of data variance on the accuracy rate of ML-based models. However, the significance of this impact aligning with the mathematical concept of the ML algorithms still needs more investigation and tests. To identify that, this work proposes an investigation methodology involving three different ML algorithms, which are support vector machine (SVM), artificial neural network (ANN), and Euclidean-based K-nearest neighbor (E-KNN). Throughout this work, five different datasets have been utilized, and each has been taken from different application fields with different statistical properties. Although there are many data normalization methods available, this work focuses on the min-max method, because it actively eliminates the effect of inconsistent ranges of the datasets. Moreover, other factors that are challenging the process of min-max normalization, such as including or excluding outliers or the least significant feature, have also been considered in this work. The finding of this work shows that each ML technique responds differently to the min-max normalization. The performance of SVM models has been improved, while no significant improvement happened to the performance of ANN models. It is been concluded that the performance of E-KNN models may improve or degrade with the min-max normalization, and it depends on the statistical properties of the dataset

Real-Time Wireless Network of Mobile Sensor Nodes Based on ZigBee Protocol: Design and Implementation

Wireless sensor networks are an evolving technology for a wide range of environments due to its low-cost and importance that has been implemented by the recent delivery of the IEEE 802.15.4 standard (ZigBee standard) for application layers. The benefit of this module is to develop the designing skills on the wireless networks using ZigBee protocol that is providing a standardized base set of solutions for control systems and sensor .In this paper, a wireless network consisting of four mobile sensor nodes is designed and implemented. Thermal sensor is used in each node to measure the temperature. After the temperature was measured correctly, the system is modified in terms of having a better reliability by implementing the CRC technique. Furthermore, a TDMA and CSMA algorithms are applied to the nodes in a way that the four nodes of the network must be able to transmit and receive the data without any collision. The system is applied on a modern embedded board is PICDEM-Z BOARD. This board has excellent features such as high performance core PLCI8F4620, memory and Rf transceiver work with ZigBee protocol. The results show high flexibility and reliability in the measuring and exchanging the data between all the nodes within one second in real time.

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2 ). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.Qassim University, represented by the Deanship of Scientific Research, (coc-2019-2-2-I-5422

Study the Behavior of Long Spiral Tube Adsorber for Oxygen Separation from Air

Single long spiral tube column (25 mm diameter, and 4 m bed length) had been constructed to study the separation of oxygen from air using commercial 13X zeolite. The effect of adsorption pressure on the system breakthrough curves was studied. Single column with initial air pressurizing simulates the work of 2- columns, 4-steps PSA process, whereas single column with initial intermediate pure oxygen pressurizing simulates the work of 2-columns, 6-steps PSA process with pressure equalization steps of the two columns. No significant effect of pressure on the product oxygen purity is noticed when pressure increased from 2 to 5 bar in both cases. For initial air pressurizing case, the average maximum effluent oxygen purity of 88% is obtained. The range of zeolite loading capacity is q=0.25-0.35 mole N2/kg zeolite, and only 40% of the range has been utilized before breakthrough time. Whereas for initial oxygen pressurizing case, the maximum oxygen purity of 95% is obtained. The range of zeolite loading capacity is q=0.39-0.87 mole N2/kg zeolite, and 95% of the range has been utilized before breakthrough time, which agree well with the equilibrium data of multicomponent Langmuir adsorption equation

Quantum Genetic Algorithm for Highly Constrained Optimization Problems

Quantum computing appears as an alternative solution for solving computationally intractable problems. This paper presents a new constrained quantum genetic algorithm designed specifically for identifying the extreme value of a highly constrained optimization problem, where the search space size _database is massive and unsorted_ cannot be handled by the currently available classical or quantum processor, called the highly constrained quantum genetic algorithm (HCQGA). To validate the efficiency of the suggested quantum method, maximizing the energy efficiency with respect to the target user bit rate of an uplink multi-cell massive multiple-input and multiple- output (MIMO) system is considered as an application. Simulation results demonstrate that the proposed HCQGA converges rapidly to the optimum solution compared with its classical benchmark

A multimedia courseware for human heart anatomical and functional illustration

Advances in computer science have provided unique opportunities to apply Interactive Multimedia (IMM) courseware to a wide variety of medical and health care functions. Courseware can be called an easy to learn, teachable and course materials which is an important in Information Communication Technology world today. It helps the learners/students to improve their knowledge, skills and creativity. One area which holds the high ability for using computer systems is medical and health science education. This paper describes the design of an IMM courseware for learning about Human Heart. It proposes a Human Heart Anatomical and Functional Illustration (HHAFI) courseware for students, health officials and everyone interested in having a healthy heart. The HHAFI courseware is implemented by Toolbook Instructor and presented on Windows platform. The courseware includes an introduction that describes the heart and recall such as mechanisms of the heart, heart diseases, healthy tips and living a healthy lifestyle. The HHAFI courseware is tested with the student to identify the improvement in their knowledge and measure the level of interest in the topic. The HHAFI courseware provides learning and interactive training functions for interested individuals

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

open access articlePrediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption

Classification, Potential Routes and Risk of Emerging Pollutants/Contaminant

Emerging contaminants (ECs), encompass both natural and synthetic chemicals that are present or transformed to new chemical compounds in water bodies across the globe. They are presently not checked in the environment but poses a serious health threat to human and ecosystem as well as environmental damage. ECs are released into environment during the anthropogenic activities such as water treatments, fumigation, farming etc. More than 1036 ECs and their biotransformation have been identified by the NORMAN project, established in 2005 by the European Commission. They were further classified into different categorizes/classes including disinfection by-products, pesticides, pharmaceuticals and personal care products, nanomaterials, benzotriazoles, benzothiazoles among others. The potential sources, path route and their health implication on human were also discussed. The presence of ECs in our environments is global issue that requires urgent attention