Department of Computer Engineering

This article presents a measure of semantic similarity in an is-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e ectiveness.

Energy generation and consumption are the main aspects of social life due to the fact that modern people's necessity for energy is a crucial ingredient for existence. Therefore, energy efficiency is regarded as the best economical approach to provide safer and affordable energy for both utilities and consumers, through the enhancement of energy security and reduction of energy emissions. One of the problems of cloud computing service providers is the high rise in the cost of energy, efficiency together with carbon emission with regards to the running of their internet data centres (IDCs). In order to mitigate these issues, smart micro-grid was found to be suitable in increasing the energy efficiency, sustainability together with the reliability of electrical services for the IDCs. Therefore, this paper presents idea on how smart micro-grids can bring down the disturbing cost of energy, carbon emission by the IDCs with some level of energy efficiency all in an effort to attain green cloud computing services from the service providers. In specific term, we aim at achieving green information and communication technology (ICT) in the field of cloud computing in relations to energy efficiency, cost-effectiveness and carbon emission reduction from cloud data center's perspective.

Energy generation and consumption are the main aspects of social life due to the fact that modern people's necessity for energy is a crucial ingredient for existence. Therefore, energy efficiency is regarded as the best economical approach to provide safer and affordable energy for both utilities and consumers, through the enhancement of energy security and reduction of energy emissions. One of the problems of cloud computing service providers is the high rise in the cost of energy, efficiency together with carbon emission with regards to the running of their internet data centres (IDCs). In order to mitigate these issues, smart micro-grid was found to be suitable in increasing the energy efficiency, sustainability together with the reliability of electrical services for the IDCs. Therefore, this paper presents idea on how smart micro-grids can bring down the disturbing cost of energy, carbon emission by the IDCs with some level of energy efficiency all in an effort to attain green cloud computing services from the service providers. In specific term, we aim at achieving green information and communication technology (ICT) in the field of cloud computing in relations to energy efficiency, cost-effectiveness and carbon emission reduction from cloud data center's perspective.

Smart devices such as smart phones and tablets have become part and parcel of our lives in recent years. The effects of these new smart devices can been seen everywhere. Students, especially, use these devices in their daily learning process and the application of these tools will be more and more important in the following years. Therefore, it's very important to keep students' attention continuous with the assist of these smart devices. Smart devices help students to study anytime in anywhere, so students learn whenever they want without any limitations. These roles also help students to keep in touch with their teachers. It will also help them to be integrated in social life more easily. This study is about developing a complete software tool to be used in the education of students who need special education. The software is designed for smart devices such as smart phones and tablets. Students who are mentally handicapped may get benefit of using this software for a complete learning session or as a supporting tool. The software is developed on Android platform. In the current version, the concepts of "less" and "more" are considered. The target students are Turkish students who need special education. INTRODUCTION Mobile applications become more popular with the rapid development of mobile technology. The educational software development also benefit from this rapid development of mobile technology. It is possible to support the face-to-face education with the computer aided tools. Moreover, mobile devices such as smart phones and tablets are available today for educational purposes. Thus, there is a need for effective software and tools to make these devices useful for education.

Smart devices such as smartphones and tablets have become part and parcel of our lives in recent years. The
effects of these new smart devices can be seen everywhere. Students, especially, use these devices in their daily learning process and the application of these tools will be more and more important in the following years. Therefore, it’s very important to keep students’ attention continuous with the assist of these smart devices. Smart devices help students to study anytime anywhere, so students learn whenever they want without any limitations. These roles also help students to keep in touch with their teachers. It will also help them to be integrated into social life more easily. This study is about developing a complete software tool to be used in the education of students who need special education. The software is designed for smart devices such as smartphones and tablets. Students who are mentally handicapped may get the benefit of using this software for a complete learning session or as a supporting tool. The software is developed on the Android platform. In the current version, the concepts of "less" and "more" are considered. The target students are Turkish students who need special education.

Son on yılda mobil cihazlar ve çeşitli uygulamaların hızı gelişimi bütün alanları etkilemiştir. Bilim ve eğitim de bu etkilenmeden nasibini almıştır. Buna ek olarak, akıllı cihazların eğitim amaçlı kullanımının artmasıyla eğitim uygulamalarına duyulan ihtiyaç daha da önem kazanmaktadır. Bu bölümde, okul öncesi öğrencilerine veya özel eğitime ihtiyaç duyan öğrencilere temel kavramları öğreten ve öğretmenlere yardımcı olmak üzere tasarlanmış bir mobil uygulama anlatılmaktadır. Uygulama “miktar”, “uzunluk”, “genişlik” ve “boyut” gibi temel kavramların öğretilmesi için öğretim modülleri içerir. Buna ek olarak, her bir kavram için, öğrencilerin öğrenilen kavramlara göre nesnelerin ettiklerini görebildikleri bir bölümde içermektedir. Temel kavramları öğretmek için yinelemeli bir algoritma kullanılmıştır. Algoritma bir ön testle başlar ve öğrencinin kavramlarla ilgili yeterliliklerini ortaya çıkarmayı hedeflemekledir. Ön test sonuçlarına göre her bir kavram için öğretim prosedürleriyle devam eder. Uygulama tamamen Android platformunda uygulanmakta ve iki farklı okulda test edilmektedir. Test sonuçları uygulamanın mevcut versiyonunda istisnai öğrencilerin öğrenme süreçlerini hızlandırdığını göstermektedir. Öğretme faaliyeti sırasında öğretmenleri destekler. Ayrıca, dâhil edilecek diğer kavramların uyarlanması için bir temel oluşturmuştur.

IoT has continued to grow bigger since from its inception. Many mobile devices are now available, the internet and its application have only grown bigger and better. As IoT is continually growing, so also is the complexity, as a result issues pertaining routing have also increased. Many researches have been made in attempt to proffer solutions that will either minimize or eliminate these routing issues. Different routing protocols have been designed with different specifications for different applications of the IoT. Also, attempts have been made to implement routing protocols of other types of networks in the IoT. In this thesis, three Wireless Sensor Networks-Adhoc On-Demand Distance Vector, Dynamic Source routing protocol and Optimized Link State routing protocol have been simulated and compared in typical IoT scenarios. Their performance was evaluated using three performance metrics and then they were compared; the performance metrics are Routing Overhead, Average End to End Delay and Throughput. Different numbers of nodes with different percentages of mobile nodes were analyzed. Specifically, number of nodes analyzed were 20, 40, 60 and 70 with the number of mobile nodes 10, 15 and 20 using OPNET while with NS 3 20, 60 and 100 nodes were analyzed. For each of the number of nodes, all the number of mobile nodes were evaluated. The routing protocols were analyzed using the OPNET Simulation Software and NS-3and the environment size for the simulation was 1000m by 1000m.

With the growing trend of the need for Internet of Things (IoT) and its applications in different aspects of life, this paper will make familiar the use of IoT more domestically in homes, offices, organizations, etc. to ensure that these places are kept clean at all times. Internet of Robotic Things (IoRT) which is a subset of IoT will be made with robotic cleaners which will be instructed to clean different rooms/places from their stations directly from a server room. Another thing that will be exposed in this paper is the use of these robots externally as service providers for people. This involves the concept of Service Robots which is expected to dominate the IoRT industry in the coming years. Finally, the methods of powering the robot are discussed.

The outbreak of the novel corona virus disease (COVID-19) in December 2019 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) on 11th of March 2020. Currently, the outbreak has affected more than 200 countries with more than 37 million confirmed cases and more than 1 million death tolls as of 10 October 2020. Reverse-transcription polymerase chain reaction (RT-PCR) is the standard method for detection of COVID-19 disease, but it has many challenges such as false positives, low sensitivity, expensive, and requires experts to conduct the test. As the number of cases continue to grow, there is a high need for developing a rapid screening method that is accurate, fast, and cheap. Chest X-ray (CXR) scan images can be considered as an alternative or a confirmatory approach as they are fast to obtain and easily accessible. Though the literature reports a number of approaches to classify CXR images and detect the COVID-19 infectio...

IoT has continued to grow bigger since from its inception. Many mobile devices are now available, the internet and its application have only grown bigger and better. As IoT is continually growing, so also is the complexity, as a result issues pertaining routing have also increased. Many researches have been made in attempt to proffer solutions that will either minimize or eliminate these routing issues. Different routing protocols have been designed with different specifications for different applications of the IoT. Also, attempts have been made to implement routing protocols of other types of networks in the IoT. In this thesis, three Wireless Sensor Networks – Adhoc On-Demand Distance Vector, Dynamic Source routing protocol and Optimized Link State routing protocol have been simulated and compared in typical IoT scenarios. Their performance was evaluated using three performance metrics and then they were compared; the performance metrics are Routing Overhead, Average End to End D...

Background: Fossil fuel usage pose a threat to the environmental condition of humans, and coupled with it`s possible depletion, the infusion of renewable energy sources into energy mix has been on the increase for over a decade. Statistics shows that solar PV represents the largest percentage of renewable energy in terms of installations in the world, majorly due to the reduction in price of solar PV in the energy market. However, due to the inconsistency of the solar resource, that is the weather and seasonal changes, there is the challenge of meeting peak demands of consumption, hence the need for energy storage systems and demand side management is necessary in order to meet those peak demands during periods of no energy production. Materials and Methods: In this study two (2) methods of optimization were taken into consideration, which are the use of energy storage and the demand side management. Simulations were carried out using sunny web design to see the benefit of using sto...

The ever-increasing widespread use of the Internet of Things and its applications has generated massive amounts of data. IoT sensor-generated datasets typically have a time-series structure and relational metadata to describe them. Time series data are typically data that have timestamps and can be obtained from sensors and IoT devices. Data prediction is required to maximize the potential of IoT-generated data, and anomaly detection and correction are needed to preserve data quality and integrity. Traditional machine learning models are incapable of analyzing the gigantic amounts of IoT-generated data. On the other hand, deep learning can properly analyze large data volumes, leading to increased use in the IoT domain. This research has examined the use of deep learning models for prediction, anomaly detection, and correction of data generated by IoT devices. The study found that deep learning is widely used in different fields today to analyze IoT-generated data. The research also outlines some challenges being faced while using deep learning models for IoT data analysis. More research is suggested in this study to expose more challenges and tackle the current challenges to achieve better IoT data analysis using deep learning. INDEX TERMS Internet of Things (IoT), deep learning, IoT data analysis, time series.

The primary goal of this paper is to describe a proposed framework for identifying human face expressions. A methodology has been proposed and developed to identify facial emotions using an axes-angular feature extracted from facial landmarks for 4D dynamic facial expression video data. The 4D facial expression recognition (FER) problem is modeled as an unbalanced problem using the full video sequence. The proposed dataset includes landmarks that are positioned to be fiducial features: around the brows, eyes, nose, cheeks, and lips. Following the initial facial landmark preprocessing, feature extraction is carried out. Input feature vectors from gamma axes and magnitudes in three-dimensional Euclidean space are constructed. This paper develops a new feature vector by concatenating the angle and magnitude features and compares them to a model created independently using angle features. In all three models, several filter-based feature selection techniques are used to estimate feature importance. The framework employs a multi-class support vector machine (SVM) on MATLAB, and the true positive rate (TPR) and average recognition rates (ARR) are used as performance metrics. In terms of average classification accuracy, the final results are compared to conventional state-of-the-art approaches. The results revealed a highly informative combined feature, demonstrating the efficiency of the proposed landmark-based strategy to classify facial expressions.

Solar energy technologies represent a viable alternative to fossil fuels for meeting increasing global energy demands. However, to increase the production of solar technologies in the global energy mix, the cost of production should be as competitive as other sources. This study focuses on the implementation of machine learning for estimating the thermophysical properties of nanofluids for nanofluid-based solar energy technologies as this would make the synthesis of nanofluids cost-effective. The prediction of thermal conductivity has gained a lot of research attention, whereas, the viscosity of nanofluids has less concentration of studies. The accurate prediction of the viscosity of hybrid nanofluids is important in estimating the heat transfer performance of nanofluids as regards their pump power requirements and convective heat transfer coefficient in several applications. The rigor of experimentations of hybrid nanofluids has necessitated the need for developing efficient and robust machine learning models for accurately estimating the viscosity of hybrid nanofluids for solar applications. Several studies were aimed at developing a predictive model for the viscosity of nanofluids; however, these models are limited to specific types of nanofluids. This study is aimed at developing a robust machine learning algorithm for predicting the viscosity of several hybrid nanofluids from reliable experimental data (700 datasets) culled from literature. This study implements a novel optimizable Gaussian process regression (O-GPR), which have not been previously used in this area, and compares the result with other commonly used machine learning algorithms like, Boosted tree regression (BTR), Artificial neural network (ANN), support vector regression (SVR), to accurately predict the viscosity of a wide range of Newtonian-based hybrid nanofluid. The input parameters used in training the machine learning models were temperature (T), volume fraction (VF), the acentric factor of the base fluid (ACF), nanoparticle size (NS), and nanoparticle density (ND). The prediction performance of the machine learning algorithms was tested using statistical metrics and was compared with theoretical models. The O-GPR model showed superior predictive performance with an R 2 of 0.999998 and an MSE of 0.0002552. The study conclusively states that the high accuracy prediction of thermophysical properties of nanofluid using robust machine learning models makes the design of nanofluidbased solar energy technologies more cost-effective.