Search results for: learning flow.

Authors: A. T. Zaki

Abstract:

The flipped classroom approach as a mode of blended learning was formally introduced to students of the English language modules at the British University in Egypt (BUE) at the start of the academic year 2015/2016. This paper aims to study the impact of the flipped classroom approach after three semesters of implementation. It will restrict itself to the examination of students’ achievement rates, student satisfaction, and how different student cohorts have benefited differently from the flipped practice. The paper concludes with recommendations of how the experience can be further developed.

Keywords: Achievement rates, developmental experience, Egypt, flipped classroom, higher education, student cohorts, student satisfaction.

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Authors: Vijay Kumar, Jagdev Singh, Yaduvir Singh, Sanjay Sood

Abstract:

In a practical power system, the power plants are not located at the same distance from the center of loads and their fuel costs are different. Also, under normal operating conditions, the generation capacity is more than the total load demand and losses. Thus, there are many options for scheduling generation. In an interconnected power system, the objective is to find the real and reactive power scheduling of each power plant in such a way as to minimize the operating cost. This means that the generator’s real and reactive powers are allowed to vary within certain limits so as to meet a particular load demand with minimum fuel cost. This is called optimal power flow problem. In this paper, Economic Load Dispatch (ELD) of real power generation is considered. Economic Load Dispatch (ELD) is the scheduling of generators to minimize total operating cost of generator units subjected to equality constraint of power balance within the minimum and maximum operating limits of the generating units. In this paper, genetic algorithms are considered. ELD solutions are found by solving the conventional load flow equations while at the same time minimizing the fuel costs.

Keywords: ELD, Equality constraints, Genetic algorithms, Strings.

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Authors: Virendra J. Majarikar, Harikrishnan N. Unni

Abstract:

This paper sets to demonstrate a modeling of electrokinetic mixing employing electroosmotic stationary and time-dependent microchannel using alternate zeta patches on the lower surface of the micromixer in a lab on chip microfluidic device. Electroosmotic flow is amplified using different 2D and 3D model designs with alternate and geometric zeta potential values such as 25, 50, and 100 mV, respectively, to achieve high concentration mixing in the electrokinetically-driven microfluidic system. The enhancement of electrokinetic mixing is studied using Finite Element Modeling, and simulation workflow is accomplished with defined integral steps. It can be observed that the presence of alternate zeta patches can help inducing microvortex flows inside the channel, which in turn can improve mixing efficiency. Fluid flow and concentration fields are simulated by solving Navier-Stokes equation (implying Helmholtz-Smoluchowski slip velocity boundary condition) and Convection-Diffusion equation. The effect of the magnitude of zeta potential, the number of alternate zeta patches, etc. are analysed thoroughly. 2D simulation reveals that there is a cumulative increase in concentration mixing, whereas 3D simulation differs slightly with low zeta potential as that of the 2D model within the T-shaped micromixer for concentration 1 mol/m³ and 0 mol/m³, respectively. Moreover, 2D model results were compared with those of 3D to indicate the importance of the 3D model in a microfluidic design process.

Keywords: COMSOL, electrokinetic, electroosmotic, microfluidics, zeta potential.

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Authors: Srimuruganandam. B, S.M. Shiva Nagendra

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In this paper the influence of heterogeneous traffic on the temporal variation of ambient PM10, PM2.5 and PM1 concentrations at a busy arterial route (Sardar Patel Road) in the Chennai city has been analyzed. The hourly PM concentration, traffic counts and average speed of the vehicles have been monitored at the study site for one week (19th-25th January 2009). Results indicated that the concentrations of coarse (PM10) and fine PM (PM2.5 and PM1) concentrations at SP road are having similar trend during peak and non-peak hours, irrespective of the days. The PM concentrations showed daily two peaks corresponding to morning (8 to 10 am) and evening (7 to 9 pm) peak hour traffic flow. The PM10 concentration is dominated by fine particles (53% of PM2.5 and 45% of PM1). The high PM2.5/PM10 ratio indicates that the majority of PM10 particles originate from re-suspension of road dust. The analysis of traffic flow at the study site showed that 2W, 3W and 4W are having similar diurnal trend as PM concentrations. This confirms that the 2W, 3W and 4W are the main emission source contributing to ambient PM concentration at SP road. The speed measurement at SP road showed that the average speed of 2W, 3W, 4W, LCV and HCV are 38, 40, 38, 40 and 38 km/hr and 43, 41, 42, 40 and 41 km/hr respectively for the weekdays and weekdays.

Keywords: particulate matter, heterogeneous traffic, fineparticles, coarse particles, vehicle speed, weekend and weekday.

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Authors: Amogh Katti, J. Sai Asritha, D. Nivedh, M. Kalyan Srinivas, B. Somnath Chakravarthi

Abstract:

Today's modern education system uses Learning Management System (LMS) portals for the scoring and grading of student performances, to maintain student records, and teachers are instructed to accept assignments through online submissions of .pdf, .doc, .ppt, etc. There is a risk of data tampering in the traditional portals; we will apply the Blockchain model instead of this traditional model to avoid data tampering and also provide a decentralized mechanism for overall fairness. Blockchain technology is a better and also recommended model because of the following features: consensus mechanism, decentralized system, cryptographic encryption, smart contracts, Ethereum blockchain. The proposed system ensures data integrity and tamper-proof assignment submission and grading, which will be helpful for both students and also educators.

Keywords: Education technology, learning management system, decentralized applications, blockchain.

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Authors: Yaojun Wang, Yaoqing Wang

Abstract:

Stock selection is an important decision-making problem. Many machine learning and data mining technologies are employed to build automatic stock-selection system. A profitable stock-selection system should consider the stock’s investment value and the market timing. In this paper, we present a hybrid system including both engage for stock selection. This system uses a case-based reasoning (CBR) model to execute the stock classification, uses a decision-tree model to help with market timing and stock selection. The experiments show that the performance of this hybrid system is better than that of other techniques regarding to the classification accuracy, the average return and the Sharpe ratio.

Keywords: Case-based reasoning, decision tree, stock selection, machine learning.

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Authors: Rustam Asnawi, Wan Fatimah Wan Ahmad

Abstract:

Currently electronic slide (e-slide) is one of the most common styles in educational presentation. Unfortunately, the utilization of e-slide for the visually impaired is uncommon since they are unable to see the content of such e-slides which are usually composed of text, images and animation. This paper proposes a model for presenting e-slide in multimodal presentation i.e. using conventional slide concurrent with voicing, in both languages Malay and English. At the design level, live multimedia presentation concept is used, while at the implementation level several components are used. The text content of each slide is extracted using COM component, Microsoft Speech API for voicing the text in English language and the text in Malay language is voiced using dictionary approach. To support the accessibility, an auditory user interface is provided as an additional feature. A prototype of such model named as VSlide has been developed and introduced.

Keywords: presentation, self-learning, slide, visually impaired

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Authors: Tatjana Eitrich, Bruno Lang

Abstract:

This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.

Keywords: Support Vector Machines, Shared Memory Parallel Computing, Large Data

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Authors: Omnia F. Elkommos

Abstract:

In the course of teaching stylistics to undergraduate students of the Department of English Language and Literature, Faculty of Arts and Humanities, the linguistic tool kit of theories comes in handy and useful for the better understanding of the different literary genres: Poetry, drama, and short stories. In the present paper, a model of teaching of stylistics is compiled and suggested. It is a collaborative group project technique for use in the undergraduate diverse specialisms (Literature, Linguistics and Translation tracks) class. Students initially are introduced to the different linguistic tools and theories suitable for each literary genre. The second step is to apply these linguistic tools to texts. Students are required to watch videos performing the poems or play, for example, and search the net for interpretations of the texts by other authorities. They should be using a template (prepared by the researcher) that has guided questions leading students along in their analysis. Finally, a practical analysis would be written up using the practical analysis essay template (also prepared by the researcher). As per collaborative learning, all the steps include activities that are student-centered addressing differentiation and considering their three different specialisms. In the process of selecting the proper tools, the actual application and analysis discussion, students are given tasks that request their collaboration. They also work in small groups and the groups collaborate in seminars and group discussions. At the end of the course/module, students present their work also collaboratively and reflect and comment on their learning experience. The module/course uses a drama play that lends itself to the task: ‘The Bond’ by Amy Lowell and Robert Frost. The project results in an interpretation of its theme, characterization and plot. The linguistic tools are drawn from pragmatics, and discourse analysis among others.

Keywords: Applied linguistic theories, collaborative learning, cooperative principle, discourse analysis, drama analysis, group project, online acting performance, pragmatics, speech act theory, stylistics, technology enhanced learning.

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Authors: Alexandros Leontitsis, Archana P. Sangole

Abstract:

This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map.

Keywords: Parameter estimation, self-organizing feature maps, spherical topology.

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Authors: Watcharawalee Lertlum, Borworn Papasratorn

Abstract:

Conventional WBL is effective for meaningful student, because rote student learn by repeating without thinking or trying to understand. It is impossible to have full benefit from conventional WBL. Understanding of rote student-s intention and what influences it becomes important. Poorly designed user interface will discourage rote student-s cultivation and intention to use WBL. Thus, user interface design is an important factor especially when WBL is used as comprehensive replacement of conventional teaching. This research proposes the influencing factors that can enhance student-s intention to use the system. The enhanced TAM is used for evaluating the proposed factors. The research result points out that factors influencing rote student-s intention are Perceived Usefulness of Homepage Content Structure, Perceived User Friendly Interface, Perceived Hedonic Component, and Perceived (homepage) Visual Attractiveness.

Keywords: E-learning, Web-Based learning, Intention to use, Rote student, Influencing.

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Authors: Rahib Abiyev, Vasif H. Abiyev, C. Ardil

Abstract:

In this paper the development of neural network based fuzzy inference system for electricity consumption prediction is considered. The electricity consumption depends on number of factors, such as number of customers, seasons, type-s of customers, number of plants, etc. It is nonlinear process and can be described by chaotic time-series. The structure and algorithms of neuro-fuzzy system for predicting future values of electricity consumption is described. To determine the unknown coefficients of the system, the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The developed system is applied for predicting future values of electricity consumption of Northern Cyprus. The simulation of neuro-fuzzy system has been performed.

Keywords: Fuzzy logic, neural network, neuro-fuzzy system, neuro-fuzzy prediction.

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Authors: Rachel Baruch

Abstract:

This article describes the implementation of an experimental model for teaching ICT tools and digital environments in teachers training college. In most educational systems in the Western world, new programs were developed in order to bridge the digital gap between teachers and students. In spite of their achievements, these programs are limited due to several factors: The teachers in the schools implement new methods incorporating technological tools into the curriculum, but meanwhile the technology changes and advances. The interface of tools changes frequently, some tools disappear and new ones are invented. These conditions require an experimental model of training the pre-service teachers. The appropriate method for instruction within the domain of ICT tools should be based on exposing the learners to innovations, helping them to gain experience, teaching them how to deal with challenges and difficulties on their own, and training them. This study suggests some principles for this approach and describes step by step the implementation of this model.

Keywords: ICT tools, e-learning, pre-service teachers.

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Authors: Bruno Trstenjak, Dzenana Donko

Abstract:

Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: Case based reasoning, classification, expert's knowledge, hybrid model.

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Authors: P. Priakanth, P. Thangaraj

Abstract:

Due to the complex network architecture, the mobile adhoc network-s multihop feature gives additional problems to the users. When the traffic load at each node gets increased, the additional contention due its traffic pattern might cause the nodes which are close to destination to starve the nodes more away from the destination and also the capacity of network is unable to satisfy the total user-s demand which results in an unfairness problem. In this paper, we propose to create an algorithm to compute the optimal MAC-layer bandwidth assigned to each flow in the network. The bottleneck links contention area determines the fair time share which is necessary to calculate the maximum allowed transmission rate used by each flow. To completely utilize the network resources, we compute two optimal rates namely, the maximum fair share and minimum fair share. We use the maximum fair share achieved in order to limit the input rate of those flows which crosses the bottleneck links contention area when the flows that are not allocated to the optimal transmission rate and calculate the following highest fair share. Through simulation results, we show that the proposed protocol achieves improved fair share and throughput with reduced delay.

Keywords: MAC-layer, MANETs, Multihop, optimal rate, Transmission.

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Authors: Refaat M Mohamed, Ayman El-Baz, Aly A. Farag

Abstract:

This paper introduces a novel approach to estimate the clique potentials of Gibbs Markov random field (GMRF) models using the Support Vector Machines (SVM) algorithm and the Mean Field (MF) theory. The proposed approach is based on modeling the potential function associated with each clique shape of the GMRF model as a Gaussian-shaped kernel. In turn, the energy function of the GMRF will be in the form of a weighted sum of Gaussian kernels. This formulation of the GMRF model urges the use of the SVM with the Mean Field theory applied for its learning for estimating the energy function. The approach has been tested on synthetic texture images and is shown to provide satisfactory results in retrieving the synthesizing parameters.

Keywords: Image Modeling, MRF, Parameters Estimation, SVM Learning.

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Authors: Abdul Rahman Mohd Kasim, Mohd Ariff Admon, Sharidan Shafie

Abstract:

The present paper considers the steady free convection boundary layer flow of a viscoelastics fluid with constant temperature in the presence of heat generation. The boundary layer equations are an order higher than those for the Newtonian (viscous) fluid and the adherence boundary conditions are insufficient to determine the solution of these equations completely. The governing boundary layer equations are first transformed into non-dimensional form by using special dimensionless group. Computations are performed numerically by using Keller-box method by augmenting an extra boundary condition at infinity and the results are displayed graphically to illustrate the influence of viscoelastic K, heat generation γ , and Prandtl Number, Pr parameters on the velocity and temperature profiles. The results of the surface shear stress in terms of the local skin friction and the surface rate of heat transfer in terms of the local Nusselt number for a selection of the heat generation parameterγ (=0.0, 0.2, 0.5, 0.8, 1.0) are obtained and presented in both tabular and graphical formats. Without effect of the internal heat generation inside the fluid domain for which we take γ = 0.0, the present numerical results show an excellent agreement with previous publication.

Keywords: Free Convection, Boundary Layer, CircularCylinder, Viscoelastic Fluid, Heat Generation

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Authors: Heide Lukosch, Roy Van Bussel, Sebastiaan Meijer

Abstract:

Serious games have proven to be a useful instrument to engage learners and increase motivation. Nevertheless, a broadly accepted, practical instructional design approach to serious games does not exist. In this paper, we introduce the use of an instructional design model that has not been applied to serious games yet, and has some advantages compared to other design approaches. We present the case of mechanics mechatronics education to illustrate the close match with timing and role of knowledge and information that the instructional design model prescribes and how this has been translated to a rigidly structured game design. The structured approach answers the learning needs of applicable knowledge within the target group. It combines advantages of simulations with strengths of entertainment games to foster learner-s motivation in the best possible way. A prototype of the game will be evaluated along a well-respected evaluation method within an advanced test setting including test and control group.

Keywords: Serious Gaming, Simulation, Complex Learning.

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Authors: Takahiro Nakamura, Kenji Takahashi, Hiroyuki Nishida, Shunjiro Shinohara, Takeshi Matsuoka, Ikkoh Funaki, Takao Tanikawa, Tohru Hada

Abstract:

In order to realize long-lived electric propulsion systems, we have been investigating an electrodeless plasma thruster. In our concept, a helicon plasma is accelerated by the magnetic nozzle for the thrusts production. In addition, the electromagnetic thrust can be enhanced by the additional radio-frequency rotating electric field (REF) power in the magnetic nozzle. In this study, a direct measurement of the electromagnetic thrust and a probe measurement have been conducted using a laboratory model of the thruster under the condition without the REF power input. Fromthrust measurement, it is shown that the thruster produces a sub-milli-newton order electromagnetic thrust force without the additional REF power. The thrust force and the density jump are observed due to the discharge mode transition from the inductive coupled plasma to the helicon wave excited plasma. The thermal thrust is theoretically estimated, and the total thrust force, which is a sum of the electromagnetic and the thermal thrust force and specific impulse are calculated to be up to 650 μN (plasma production power of 400 W, Ar gas mass flow rate of 1.0 mg/s) and 210 s (plasma production power of 400 W, Ar gas mass flow rate of 0.2 mg/s), respectively.

Keywords: Electric propulsion, Helicon plasma, Lissajous acceleration, Thrust stand.

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Authors: Paola Gattinoni, Laura Scesi, Elena Cerino Adbin, Daniele Cremonesi

Abstract:

The interaction of tunneling or mining with groundwater has become a very relevant problem not only due to the need to guarantee the safety of workers and to assure the efficiency of the tunnel drainage systems, but also to safeguard water resources from impoverishment and pollution risk. Therefore it is very important to forecast the drainage processes (i.e., the evaluation of drained discharge and drawdown caused by the excavation). The aim of this study was to know better the system and to quantify the flow drained from the Fontane mines, located in Val Germanasca (Turin, Italy). This allowed to understand the hydrogeological local changes in time. The work has therefore been structured as follows: the reconstruction of the conceptual model with the geological, hydrogeological and geological-structural study; the calculation of the tunnel inflows (through the use of structural methods) and the comparison with the measured flow rates; the water balance at the basin scale. In this way it was possible to understand what are the relationships between rainfall, groundwater level variations and the effect of the presence of tunnels as a means of draining water. Subsequently, it the effects produced by the excavation of the mining tunnels was quantified, through numerical modeling. In particular, the modeling made it possible to observe the drawdown variation as a function of number, excavation depth and different mines linings.

Keywords: Groundwater, Italy, numerical model, tunneling.

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Authors: Hekmatpanah, M.

Abstract:

Since the beginning of human history, human activities have caused many changes in the environment. Today, a particular attention should be paid to gaining knowledge about water quality of wetlands which are pristine natural environments rich in genetic reserves. If qualitative conditions of industrial areas (in terms of both physicochemical and biological conditions) are not addressed properly, they could cause disruption in natural ecosystems, especially in rivers. With regards to the quality of water resources, determination of pollutant sources plays a pivotal role in engineering projects as well as designing water quality control systems. Thus, using different methods such as flow duration curves, dischargepollution load model and frequency analysis by HYFA software package, risk of various industrial pollutants in international and ecologically important Gavkhoni wetland is analyzed. In this study, a station located at Varzaneh City is used as the last station on Zayanderud River, from where the river water is discharged into the wetland. Results showed that elements- concentrations often exceeded the allowed level and river water can endanger regional ecosystem. In addition, if the river discharge is managed on Q25 basis, this basis can lower concentrations of elements, keeping them within the normal level.

Keywords: Pollutants Risk, Industry, Flow Discharge, Management, Gavkhoni Wetland

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Authors: Ghada A. Alfattni

Abstract:

Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: Imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour.

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Authors: A.K. Baliarsingh, S. Panda, A.K. Mohanty, C. Ardil

Abstract:

This paper presents a systematic approach for designing Unified Power Flow Controller (UPFC) based supplementary damping controllers for damping low frequency oscillations in a single-machine infinite-bus power system. Detailed investigations have been carried out considering the four alternatives UPFC based damping controller namely modulating index of series inverter (mB), modulating index of shunt inverter (mE), phase angle of series inverter (δB ) and phase angle of the shunt inverter (δE ). The design problem of the proposed controllers is formulated as an optimization problem and Real- Coded Genetic Algorithm (RCGA) is employed to optimize damping controller parameters. Simulation results are presented and compared with a conventional method of tuning the damping controller parameters to show the effectiveness and robustness of the proposed design approach.

Keywords: Power System Oscillations, Real-Coded Genetic Algorithm (RCGA), Flexible AC Transmission Systems (FACTS), Unified Power Flow Controller (UPFC), Damping Controller.

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Authors: Partha P. Gopmandal, S. Bhattacharyya

Abstract:

We have studied the migration of a charged permeable aggregate in electrolyte under the influence of an axial electric field and pressure gradient. The migration of the positively charged aggregate leads to a deformation of the anionic cloud around it. The hydrodynamics of the aggregate is governed by the interaction of electroosmotic flow in and around the particle, hydrodynamic friction and electric force experienced by the aggregate. We have computed the non-linear Nernest-Planck equations coupled with the Dracy- Brinkman extended Navier-Stokes equations and Poisson equation for electric field through a finite volume method. The permeability of the aggregate enable the counterion penetration. The penetration of counterions depends on the volume charge density of the aggregate and ionic concentration of electrolytes at a fixed field strength. The retardation effect due to the double layer polarization increases the drag force compared to an uncharged aggregate. Increase in migration sped from the electrophretic velocity of the aggregate produces further asymmetry in charge cloud and reduces the electric body force exerted on the particle. The permeability of the particle have relatively little influence on the electric body force when Double layer is relatively thin. The impact of the key parameters of electrokinetics on the hydrodynamics of the aggregate is analyzed.

Keywords: Electrophoresis, Advective flow, Polarization effect, Numerical solution.

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Authors: Ion Smeureanu, Marian Dardala, Adriana Reveiu

Abstract:

The new programming technologies allow for the creation of components which can be automatically or manually assembled to reach a new experience in knowledge understanding and mastering or in getting skills for a specific knowledge area. The project proposes an interactive framework that permits the creation, combination and utilization of components that are specific to mathematical training in high schools. The main framework-s objectives are: • authoring lessons by the teacher or the students; all they need are simple operating skills for Equation Editor (or something similar, or Latex); the rest are just drag & drop operations, inserting data into a grid, or navigating through menus • allowing sonorous presentations of mathematical texts and solving hints (easier understood by the students) • offering graphical representations of a mathematical function edited in Equation • storing of learning objects in a database • storing of predefined lessons (efficient for expressions and commands, the rest being calculations; allows a high compression) • viewing and/or modifying predefined lessons, according to the curricula The whole thing is focused on a mathematical expressions minicompiler, storing the code that will be later used for different purposes (tables, graphics, and optimisations). Programming technologies used. A Visual C# .NET implementation is proposed. New and innovative digital learning objects for mathematics will be developed; they are capable to interpret, contextualize and react depending on the architecture where they are assembled.

Keywords: Adaptor, automatic assembly learning component and user control.

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

Abstract:

Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision making has not been far-fetched. Proper classification of these textual information in a given context has also been very difficult. As a result, a systematic review was conducted from previous literature on sentiment classification and AI-based techniques. The study was done in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that could correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy using the knowledge gain from the evaluation of different artificial intelligence techniques reviewed. The study evaluated over 250 articles from digital sources like ACM digital library, Google Scholar, and IEEE Xplore; and whittled down the number of research to 52 articles. Findings revealed that deep learning approaches such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Bidirectional Encoder Representations from Transformer (BERT), and Long Short-Term Memory (LSTM) outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also required to develop a robust sentiment classifier. Results also revealed that data can be obtained from places like Twitter, movie reviews, Kaggle, Stanford Sentiment Treebank (SST), and SemEval Task4 based on the required domain. The hybrid deep learning techniques like CNN+LSTM, CNN+ Gated Recurrent Unit (GRU), CNN+BERT outperformed single deep learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of development simplicity and AI-based library functionalities. Finally, the study recommended the findings obtained for building robust sentiment classifier in the future.

Keywords: Artificial Intelligence, Natural Language Processing, Sentiment Analysis, Social Network, Text.

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Authors: Shih-Bin Wang, Ping Yuan, Syu-Fang Liu, Ming-Jun Kuo

Abstract:

By the application of an improved back-propagation neural network (BPNN), a model of current densities for a solid oxide fuel cell (SOFC) with 10 layers is established in this study. To build the learning data of BPNN, Taguchi orthogonal array is applied to arrange the conditions of operating parameters, which totally 7 factors act as the inputs of BPNN. Also, the average current densities achieved by numerical method acts as the outputs of BPNN. Comparing with the direct solution, the learning errors for all learning data are smaller than 0.117%, and the predicting errors for 27 forecasting cases are less than 0.231%. The results show that the presented model effectively builds a mathematical algorithm to predict performance of a SOFC stack immediately in real time. Also, the calculating algorithms are applied to proceed with the optimization of the average current density for a SOFC stack. The operating performance window of a SOFC stack is found to be between 41137.11 and 53907.89. Furthermore, an inverse predicting model of operating parameters of a SOFC stack is developed here by the calculating algorithms of the improved BPNN, which is proved to effectively predict operating parameters to achieve a desired performance output of a SOFC stack.

Keywords: a SOFC stack, BPNN, inverse predicting model of operating parameters, optimization of the average current density

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

Abstract:

Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.

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Authors: Hisham Md Suhadi, Faaizah Shahbodin, Jamaluddin Hashim

Abstract:

The study is the way to identify the problems that occur in organizing short course’s lifelong learning in the information and communication technology (ICT) education which are faced by the lecturer and staff at the Mara Skill Institute and Industrial Training Institute in Pahang Malaysia. The important aspects of these issues are classified to five which are selecting the courses administrative. Fifty lecturers and staff were selected as a respondent. The sample is selected by using the non-random sampling method purpose sampling. The questionnaire is used as a research instrument and divided into five main parts. All the data that gain from the questionnaire are analyzed by using the SPSS in term of mean, standard deviation and percentage. The findings showed, there are the problems occur in organizing the short course for lifelong learning in ICT education.

Keywords: Lifelong education, information and communication technology (ICT), short course, ICT education, courses administrative.

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Authors: Abdullah M. Alodhaiani, Yasir A. Alturki, Mohamed A. Elkady

Abstract:

Complex power flow distribution factors, which relate line complex power flows to the bus injected complex powers, have been widely used in various power system planning and analysis studies. In particular, AC distribution factors have been used extensively in the recent power and energy pricing studies in free electricity market field. As was demonstrated in the existing literature, many of the electricity market related costing studies rely on the use of the distribution factors. These known distribution factors, whether the injection shift factors (ISF’s) or power transfer distribution factors (PTDF’s), are linear approximations of the first order sensitivities of the active power flows with respect to various variables. This paper presents a novel model for evaluating the universal distribution factors (UDF’s), which are appropriate for an extensive range of power systems analysis and free electricity market studies. These distribution factors are used for the calculations of lines complex power flows and its independent of bus power injections, they are compact matrix-form expressions with total flexibility in determining the position on the line at which line flows are measured. The proposed approach was tested on IEEE 9-Bus system. Numerical results demonstrate that the proposed approach is very accurate compared with exact method.

Keywords: Distribution Factors, Power System, Sensitivity Factors, Electricity Market.

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