Abstract: Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.
Abstract: In order to evaluate the growth and yield properties of two Sorghum-Sudangrass hybrids under different irrigation levels, an investigation was done in the experiment site of Collage of Agriculture, University of Duhok, Kurdistan region of Iraq (36°5´38⸗ N, 42°52´02⸗ E) in the years 2015-16. The experiment was conducted under Randomized Complete Block Design (RCBD) with three replications, which main factor was irrigation treatments (I100, I75 and I50) according to evaporation pan class A and type of Sorghum-Sudangrass hybrids (KH12SU9001, G1) and (KH12SU9002, G2) were factors of subplots. The parameters studied were: plant height (cm), number of green leaves per plant; leaf area (m2/m2), stem thickness (mm), percent of protein, fresh and dry biomass (ton.ha-1) and also crop water productivity. The results of variance analysis showed that KH12SU9001 variety had more amount of leaf area, percent of protein, fresh and dry biomass yield in comparison to KH12SU9002 variety. By comparing effects of irrigation levels on vegetative growth and yield properties, results showed that amount of plant height, fresh and dry biomass weight was decreased by decreasing irrigation level from full irrigation regime to 5 o% of irrigation level. Also, results of crop water productivity (CWP) indicated that improvement in quantity of irrigation would impact fresh and dry biomass yield significantly. Full irrigation regime was recorded the highest level of CWP (1.28-1.29 kg.m-3).
Abstract: Obesity is associated with increased fat mass as well as fat percentage. Minerals are the elements, which are of vital importance. In this study, the relationships between body as well as bone mineral profile and the percentage as well as mass values of fat, fat-free portion, protein, skeletal muscle were evaluated in adult men with normal body mass index (N-BMI), and those classified according to different stages of obesity. A total of 103 adult men classified into five groups participated in this study. Ages were within 19-79 years range. Groups were N-BMI (Group 1), overweight (OW) (Group 2), first level of obesity (FLO) (Group 3), second level of obesity (SLO) (Group 4) and third level of obesity (TLO) (Group 5). Anthropometric measurements were performed. BMI values were calculated. Obesity degree, total body fat mass, fat percentage, basal metabolic rate (BMR), visceral adiposity, body mineral mass, body mineral percentage, bone mineral mass, bone mineral percentage, fat-free mass, fat-free percentage, protein mass, protein percentage, skeletal muscle mass and skeletal muscle percentage were determined by TANITA body composition monitor using bioelectrical impedance analysis technology. Statistical package (SPSS) for Windows Version 16.0 was used for statistical evaluations. The values below 0.05 were accepted as statistically significant. All the groups were matched based upon age (p > 0.05). BMI values were calculated as 22.6 ± 1.7 kg/m2, 27.1 ± 1.4 kg/m2, 32.0 ± 1.2 kg/m2, 37.2 ± 1.8 kg/m2, and 47.1 ± 6.1 kg/m2 for groups 1, 2, 3, 4, and 5, respectively. Visceral adiposity and BMR values were also within an increasing trend. Percentage values of mineral, protein, fat-free portion and skeletal muscle masses were decreasing going from normal to TLO. Upon evaluation of the percentages of protein, fat-free portion and skeletal muscle, statistically significant differences were noted between NW and OW as well as OW and FLO (p < 0.05). However, such differences were not observed for body and bone mineral percentages. Correlation existed between visceral adiposity and BMI was stronger than that detected between visceral adiposity and obesity degree. Correlation between visceral adiposity and BMR was significant at the 0.05 level. Visceral adiposity was not correlated with body mineral mass but correlated with bone mineral mass whereas significant negative correlations were observed with percentages of these parameters (p < 0.001). BMR was not correlated with body mineral percentage whereas a negative correlation was found between BMR and bone mineral percentage (p < 0.01). It is interesting to note that mineral percentages of both body as well as bone are highly affected by the visceral adiposity. Bone mineral percentage was also associated with BMR. From these findings, it is plausible to state that minerals are highly associated with the critical stages of obesity as prominent parameters.
Abstract: Technology-enhanced teaching has been in the limelight since the 90s when educators started investigating and experimenting with using computers in the classroom as a means of building 21st. century skills and motivating students. The concept of technology-enhanced strategies in education is kaleidoscopic! It has meant different things to different educators. For the purpose of this paper, however, it will be used to refer to the diverse technology-based strategies used to support and enrich the flipped learning process, in the classroom and outside. The paper will investigate how technology is put in the service of teaching and learning to improve the students’ learning experience as manifested in students’ attendance and engagement, achievement rates and finally, students’ projects at the end of the semester. The results will be supported by a student survey about relevant specific aspects of their learning experience in the modules in the study.
Abstract: The purpose of this study is to determine the effectiveness of video clips to enhance students' achievement and motivation towards learning and facilitating of history. We use narrative literature studies to illustrate the current state of the two art and science in focused areas of inquiry. We used experimental method. The experimental method is a systematic scientific research method in which the researchers manipulate one or more variables to control and measure any changes in other variables. For this purpose, two experimental groups have been designed: one experimental and one groups consisting of 30 lower secondary students. The session is given to the first batch using a computer presentation program that uses video clips to be considered as experimental group, while the second group is assigned as the same class using traditional methods using dialogue and discussion techniques that are considered a control group. Both groups are subject to pre and post-trial in matters that are handled by the class. The findings show that the results of the pre-test analysis did not show statistically significant differences, which in turn proved the equality of the two groups. Meanwhile, post-test analysis results show that there was a statistically significant difference between the experimental group and the control group at an importance level of 0.05 for the benefit of the experimental group.
Abstract: This paper reviews the comparison of Resin Rich (RR) and Vacuum Pressure Impregnation (VPI) insulation system qualities for stator bar of rotating electrical machines. Voltage endurance and tangent delta are two diagnostic tests to determine the quality of insulation systems. The paper describes the trend of dissipation factor while performing voltage endurance test for different stator bar samples made with RR and VPI insulation system methods. Some samples were made with the same strands and insulation thickness but with different main wall material to prove the influence of insulation system methods on stator bar quality. Also, some of the samples were subjected to voltage at the temperature of their insulation class, and their dissipation factor changes were measured and studied.
Abstract: In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.
Abstract: Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.
Abstract: Nanotherapy is an actual newest mode of treatment numerous diseases using nanoparticles (NPs) loading with different pharmaceuticals. NPs of biodegradable polymeric micelles (PMs) are gaining increased attention for their numerous and attractive abilities to be used in a variety of applications in the various fields of medicine. The present paper deals with the synthesis of a class of biodegradable micelle-forming polymers, namely ABA triblock-copolymer in which A-blocks represent amino-poly(ethylene glycol) (H2N-PEG) and B-block is biodegradable amino acid-based poly(ester amide) constituted of α-amino acid – L-phenylalanine. The obtained copolymer formed micelles of 70±4 nm size at 10 mg/mL concentration.
Abstract: The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.
Abstract: Building resilience, sustainable buildings, urbanization, climate change, resilient cities, are just a few examples of where the focus of research has been in the last few years. It is obvious that there is a need to rethink how we are building our cities and how we are renovating our existing buildings. However, the question remaining is how can we assure that we are building sustainable yet resilient cities? There are many aspects one can touch upon when discussing resilience in cities, but after the event of Grenfell in June 2017, it has become clear that fire resilience must be a priority. We define resilience as a holistic approach including communities, society and systems, focusing not only on resisting the effects of a disaster, but also how it will cope and recover from it. Cities are an example of such a system, where components such as buildings have an important role to play. A building on fire will have an impact on the community, the economy, the environment, and so the entire system. Therefore, we believe that fire and resilience go hand in hand when we discuss building resilient cities. This article aims at discussing the current state of the concept of fire resilience and suggests actions to support the built of more fire resilient buildings. Using the case of Grenfell and the fire safety regulations in the UK, we will briefly compare the fire regulations in other European countries, more precisely France, Germany and Denmark, to underline the difference and make some suggestions to increase fire resilience via regulation. For this research, we will also include other types of resilience such as technological resilience, discussing the structure of buildings itself, as well as community resilience, considering the role of communities in building resilience. Our findings demonstrate that to increase fire resilience, amending existing regulations might be necessary, for example, how we performed reaction to fire tests and how we classify building products. However, as we are looking at national regulations, we are only able to make general suggestions for improvement. Another finding of this research is that the capacity of the community to recover and adapt after a fire is also an essential factor. Fundamentally, fire resilience, technological resilience and community resilience are closely connected. Building resilient cities is not only about sustainable buildings or energy efficiency; it is about assuring that all the aspects of resilience are included when building or renovating buildings. We must ask ourselves questions as: Who are the users of this building? Where is the building located? What are the components of the building, how was it designed and which construction products have been used? If we want to have resilient cities, we must answer these basic questions and assure that basic factors such as fire resilience are included in our assessment.
Abstract: Goal: Purpose of the project was to make a plastic surgery prediction by using pre-operative images for the plastic surgeries’ patients and to show this prediction on a screen to compare between the current case and the appearance after the surgery. Methods: To this aim, we implemented a software which used data from the internet for facial skin diseases, skin burns, pre-and post-images for plastic surgeries then the post- surgical prediction is done by using K-nearest neighbor (KNN). So we designed and fabricated a smart mirror divided into two parts a screen and a reflective mirror so patient's pre- and post-appearance will be showed at the same time. Results: We worked on some skin diseases like vitiligo, skin burns and wrinkles. We classified the three degrees of burns using KNN classifier with accuracy 60%. We also succeeded in segmenting the area of vitiligo. Our future work will include working on more skin diseases, classify them and give a prediction for the look after the surgery. Also we will go deeper into facial deformities and plastic surgeries like nose reshaping and face slim down. Conclusion: Our project will give a prediction relates strongly to the real look after surgery and decrease different diagnoses among doctors. Significance: The mirror may have broad societal appeal as it will make the distance between patient's satisfaction and the medical standards smaller.
Abstract: The recognition of the objects contained in images has always presented a challenge in the field of research because of several difficulties that the researcher can envisage because of the variability of shape, position, contrast of objects, etc. In this paper, we will be interested in the recognition of objects. The classical Hough Transform (HT) presented a tool for detecting straight line segments in images. The technique of HT has been generalized (GHT) for the detection of arbitrary forms. With GHT, the forms sought are not necessarily defined analytically but rather by a particular silhouette. For more precision, we proposed to combine the results from the GHT with the results from a calculation of similarity between the histograms and the spatiograms of the images. The main purpose of our work is to use the concepts from recognition to generate sentences in Arabic that summarize the content of the image.
Abstract: Seamless learning is becoming a research hotspot in recent years, and the emerging of micro-lectures, flipped classroom has strengthened the development of seamless learning. Based on the characteristics of the seamless learning across time and space and the course structure of the flipped classroom, and the theories of language learning, we put forward the language learning model which can support ‘seamless flipped’ environment (abbreviated as ‘S-F’). Meanwhile, the characteristics of the ‘S-F’ learning environment, the corresponding framework construction and the activity design of diversified corpora were introduced. Moreover, a language learning app named ‘Daily Speaking’ was developed to facilitate the practice of the language learning model in ‘S-F’ environment. In virtue of the learning case of Shanghai language, the rationality and feasibility of this framework were examined, expecting to provide a reference for the design of ‘S-F’ learning in different situations.
Abstract: ICT tools and platforms endorse more and more
educational process. Many models and techniques for people to
be educated and trained about specific topics and skills do exist,
as classroom lectures with textbooks, computers, handheld devices
and others. The choice to what extent ICT is applied within
learning contexts is related to personal access to technologies as
well as to the infrastructure surrounding environment. Among recent
techniques, the adoption of Virtual Reality (VR) and Augmented
Reality (AR) provides significant impulse in fully engaging users
senses. In this paper, an application of AR/VR within Geographic
Information Systems (GIS) context is presented. It aims to provide
immersive environment experiences for educational and training
purposes (e.g. for civil protection personnel), useful especially for
situations where real scenarios are not easily accessible by humans.
First acknowledgments are promising for building an effective tool
that helps civil protection personnel training with risk reduction.
Abstract: In the 21st century, the disciplinary boundaries of past centuries that we often create through mainstream art historical classification, techniques and sources may have been eroded by visual culture, which seems to provide a more inclusive umbrella for the new ways artists go about the creative process and its resultant commodities. Over the past four decades, artists in Africa have resorted to new materials, techniques and themes which have affected our ways of research on these artists and their art. Frontline artists such as El Anatsui, Yinka Shonibare, Erasmus Onyishi are demonstrating that any material is just suitable for artistic expression. Most of times, these materials come with their own techniques/effects and visual syntax: a combination of materials compounds techniques, formal aesthetic indexes, halo effects, and iconography. This tends to challenge the categories and we lean on to view, think and talk about them. This renders our main stream art historical research methods inadequate, thus suggesting new discursive concepts, terms and theories. This paper proposed the Africanist eclectic methods derived from the dual framework of Masquerade Theory and What Comes Behind Six is More Than Seven. This paper shares thoughts/research on art historical methods, terminological re-alignments on classification/source data, presentational format and interpretation arising from the emergent trends in our subject. The outcome provides useful tools to mediate new thoughts and experiences in recent African art and visual culture.
Abstract: This study examines the effects of auditor switch, auditee’s industry, and auditee’s location on audit fees in Australia. It uses fee data of Australian Securities Exchange 500 companies, considering all industry classifications throughout the country from 2006 until 2016. Main findings show that auditor switch does not affect audit fees. However, auditee’s industry affects audit fees. This effect occurs in information technology, financials, energy, and materials sectors among the top 500 companies. Financials, energy, and materials sectors face a fee rise, whereas information technology has a fee cut. The extent of fee changes is different among various industries, wherein the financial sector has the highest increase. Further, auditee’s location affects audit fees. Top 500 companies in Hobart, Perth, and Brisbane face a fee reduction, wherein the highest cut is in Hobart. Further analysis suggests that the Australian audit market is being increasingly concentrated in the hands of the Big Four audit firms.
Abstract: Cognitive decline and frailty is apparent in older adults leading to an increased likelihood of the risk of falling. Currently health care professionals have to make professional decisions regarding such risks, and hence make difficult decisions regarding the future welfare of the ageing population. This study uses health data from The Irish Longitudinal Study on Ageing (TILDA), focusing on adults over the age of 50 years, in order to analyse health risk factors and predict the likelihood of falls. This prediction is based on the use of machine learning algorithms whereby health risk factors are used as inputs to predict the likelihood of falling. Initial results show that health risk factors such as long-term health issues contribute to the number of falls. The identification of such health risk factors has the potential to inform health and social care professionals, older people and their family members in order to mitigate daily living risks.
Abstract: Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.
Abstract: Communication signal modulation recognition
technology is one of the key technologies in the field of modern
information warfare. At present, communication signal automatic
modulation recognition methods are mainly divided into two major
categories. One is the maximum likelihood hypothesis testing method
based on decision theory, the other is a statistical pattern recognition
method based on feature extraction. Now, the most commonly used
is a statistical pattern recognition method, which includes feature
extraction and classifier design. With the increasingly complex
electromagnetic environment of communications, how to effectively
extract the features of various signals at low signal-to-noise ratio
(SNR) is a hot topic for scholars in various countries. To solve this
problem, this paper proposes a feature extraction algorithm for the
communication signal based on the improved Holder cloud feature.
And the extreme learning machine (ELM) is used which aims at
the problem of the real-time in the modern warfare to classify
the extracted features. The algorithm extracts the digital features
of the improved cloud model without deterministic information in
a low SNR environment, and uses the improved cloud model to
obtain more stable Holder cloud features and the performance of the
algorithm is improved. This algorithm addresses the problem that
a simple feature extraction algorithm based on Holder coefficient
feature is difficult to recognize at low SNR, and it also has a
better recognition accuracy. The results of simulations show that the
approach in this paper still has a good classification result at low
SNR, even when the SNR is -15dB, the recognition accuracy still
reaches 76%.