Search results for: conventional learning

Authors: Y. Bai, M. Bodlak, V. Frolov, S. Huber, V. Jary, I. Konorov, D. Levit, J. Novy, D. Steffen, O. Subrt, M. Virius

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In general, state-of-the-art Data Acquisition Systems (DAQ) in high energy physics experiments must satisfy high requirements in terms of reliability, efficiency and data rate capability. This paper presents the development and deployment of a debugging tool named DAQ Debugger for the intelligent, FPGA-based Data Acquisition System (iFDAQ) of the COMPASS experiment at CERN. Utilizing a hardware event builder, the iFDAQ is designed to be able to readout data at the average maximum rate of 1.5 GB/s of the experiment. In complex softwares, such as the iFDAQ, having thousands of lines of code, the debugging process is absolutely essential to reveal all software issues. Unfortunately, conventional debugging of the iFDAQ is not possible during the real data taking. The DAQ Debugger is a tool for identifying a problem, isolating the source of the problem, and then either correcting the problem or determining a way to work around it. It provides the layer for an easy integration to any process and has no impact on the process performance. Based on handling of system signals, the DAQ Debugger represents an alternative to conventional debuggers provided by most integrated development environments. Whenever problem occurs, it generates reports containing all necessary information important for a deeper investigation and analysis. The DAQ Debugger was fully incorporated to all processes in the iFDAQ during the run 2016. It helped to reveal remaining software issues and improved significantly the stability of the system in comparison with the previous run. In the paper, we present the DAQ Debugger from several insights and discuss it in a detailed way.

Keywords: DAQ debugger, data acquisition system, FPGA, system signals, Qt framework.

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Authors: R. Ibrahim, M. Azmirruddin, M. Jabir, N. Johari, M. Muhamad, A. R. A. Talib

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Inconel718 has been widely used as a super alloy in aerospace application due to the high strength at elevated temperatures, satisfactory oxidation resistance and heat corrosion resistance. In this study, the Inconel718 has been fabricated using high technology of Metal Injection Molding (MIM) process due to the cost effective technique for producing small, complex and precision parts in high volume compared with conventional method through machining. Through MIM, the binder system is one of the most important criteria in order to successfully fabricate the Inconel718. Even though, the binder system is a temporary, but failure in the selection and removal of the binder system will affect on the final properties of the sintered parts. Therefore, the binder system based on palm oil derivative which is palm stearin has been formulated and developed to replace the conventional binder system. The rheological studies of the mixture between the powder and binders system have been determined properly in order to be successful during injection into injection molding machine. After molding, the binder holds the particles in place. The binder system has to be removed completely through debinding step. During debinding step, solvent debinding and thermal pyrolysis has been used to remove completely of the binder system. The debound part is then sintered to give the required physical and mechanical properties. The results show that the properties of the final sintered parts fulfill the Standard Metal Powder Industries Federation (MPIF) 35 for MIM parts.

Keywords: Binder system, rheological study, metal injection molding, debinding and sintered parts.

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Authors: Revoti Prasad Bora, Nikita Katyal

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Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, cannibalization, promotion, baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression.

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Authors: Mohamed EL Boujnouni, Mohamed Jedra, Noureddine Zahid

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Support Vector Domain Description (SVDD) is one of the best-known one-class support vector learning methods, in which one tries the strategy of using balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. As all kernel-based learning algorithms its performance depends heavily on the proper choice of the kernel parameter. This paper proposes a new approach to select kernel's parameter based on maximizing the distance between both gravity centers of normal and abnormal classes, and at the same time minimizing the variance within each class. The performance of the proposed algorithm is evaluated on several benchmarks. The experimental results demonstrate the feasibility and the effectiveness of the presented method.

Keywords: Gravity centers, Kernel’s parameter, Support Vector Domain Description, Variance.

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Authors: Suzana Basaruddin, Haryani Haron, Siti Arpah Noodin

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Managing knowledge of research is one way to ensure just in time information and knowledge to support research strategist and activities. Unfortunately researcher found the vital research knowledge in IHL (Institutions of Higher Learning) are scattered, unstructured and unorganized. Aiming on lay aside conceptual foundations for understanding and developing OMS (Organizational Memory System) to facilitate research in IHL, this research revealed ten factors contributed to the needs of research in the IHL and seven internal challenges of IHL in promoting research to their academic members. This study then suggested a comprehensive support of managing research knowledge using Organizational Memory System (OMS). Eight OMS characteristics to support research were identified. Finally the initial work in designing OMS was projected using knowledge taxonomy. All analysis is derived from pertinent research paper related to research in IHL and OMS. Further study can be conducted to validate and verify results presented.

Keywords: corporate memory, Institutions of Higher Learning, organizational memory system, research

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Authors: Ahcene Habbi, Yassine Boudouaoui

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This paper deals with the problem of automatic rule generation for fuzzy systems design. The proposed approach is based on hybrid artificial bee colony (ABC) optimization and weighted least squares (LS) method and aims to find the structure and parameters of fuzzy systems simultaneously. More precisely, two ABC based fuzzy modeling strategies are presented and compared. The first strategy uses global optimization to learn fuzzy models, the second one hybridizes ABC and weighted least squares estimate method. The performances of the proposed ABC and ABC-LS fuzzy modeling strategies are evaluated on complex modeling problems and compared to other advanced modeling methods.

Keywords: Automatic design, learning, fuzzy rules, hybrid, swarm optimization.

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Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: Daily activity recognition, healthcare, IoT sensors, transfer learning.

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Authors: Salam M. H. Kareem

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Instructional or teaching procedures and their proper sequence are essential for high-quality learning outcomes. These actions are the path that the teacher takes during the learning process after setting the learning objectives. Teachers and specialists in the education field should include teaching procedures with putting in place an effective mechanism for the procedure’s implementation to achieve a logical sequence with the desired output of overall education process. Determining the sequence of these actions may be a strategic process outlined by a strategic educational plan or drawn by teachers with a high level of experience, enabling them to determine those logical procedures. While specific actions may be necessary for a specific form, many Physical Education (PE) teachers can work out on various sports disciplines. This study was conducted to investigate the impact of using the teaching sequence of the teaching pyramid in raising the level of enjoyment in swimming classes. Four months later of teaching swimming skills to the control and experimental groups of the study, we figured that using the tools shown in the teaching pyramid with the experimental group led to statistically significant differences in the positive tendencies of students to participate in the swimming classes by using the traditional procedures of teaching and using of successive procedures in the teaching pyramid, and in favor of the teaching pyramid, The students are influenced by enhancing their tendency to participate in swimming classes when the teaching procedures followed are sensitive to individual differences and are based on the element of pleasure in learning, and less positive levels of the tendency of students when using traditional teaching procedures, by getting the level of skills' requirements higher and more difficult to perform. The level of positive tendencies of students when using successive procedures in the teaching pyramid was increased, by getting the level of skills' requirements higher and more difficult to perform, because of the high level of motivation and the desire to challenge the self-provided by the teaching pyramid.

Keywords: Physical education, swimming classes, teaching process, teaching pyramid.

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Authors: Yolanda Bolea, Antoni Grau, Alberto Sanfeliu

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When industrial robotics subject is taught in a degree in robotics, social and humanoid robotics concepts are rarely mentioned because this field of robotics is not used in industry. In this paper, an educational project related with industrial robotics is presented which includes social and humanoid robotics. The main motivations to realize this research are: i) humanoid robotics will be appearing soon in industry, the experience, based on research projects, indicates their deployment sooner than expected; ii) its educational interest, technology is shared with industrial robotics; iii) it is very attractive, students are interested in this part of the subject and thus they are interested in the whole subject. As a pedagogical methodology, the use of the problem-based learning is considered. Those concepts are introduced in a seminar during the last part of the subject and developed as a set of practices in the laboratory.

Keywords: Higher education in robotics, humanoid robotics, problem-based learning, social robotics.

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Authors: Anamika Jain, A. S. Thoke, R. N. Patel

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This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.

Keywords: Double circuit transmission line, Fault detection and classification, High impedance fault and Artificial Neural Network.

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Authors: Shigeaki Sakurai, Ryohei Orihara

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This paper proposes an improvement method of classification efficiency in a classification model. The model is used in a risk search system and extracts specific labels from articles posted at bulletin board sites. The system can analyze the important discussions composed of the articles. The improvement method introduces ensemble learning methods that use multiple classification models. Also, it introduces expressions related to the specific labels into generation of word vectors. The paper applies the improvement method to articles collected from three bulletin board sites selected by users and verifies the effectiveness of the improvement method.

Keywords: Text mining, Risk search system, Corporate reputation, Bulletin board site, Ensemble learning

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Authors: A. T. Zaki

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

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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

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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: Tatjana Eitrich, Bruno Lang

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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: N. Nim-on, A. Oonsivilai

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In this paper, the application of GRNN in modeling of SOFC fuel cells were studied. The parameters are of interested as voltage and power value and the current changes are investigated. In addition, the comparison between GRNN neural network application and conventional method was made. The error value showed the superlative results.

Keywords: SOFC, GRNN, Fuel cells.

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Authors: Omnia F. Elkommos

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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

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

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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

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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: Guy Leshem, Ya'acov Ritov

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Traffic Management and Information Systems, which rely on a system of sensors, aim to describe in real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art focuses on data analysis, little is done in the sense of prediction. In this paper, we describe a machine learning system for traffic flow management and control for a prediction of traffic flow problem. This new algorithm is obtained by combining Random Forests algorithm into Adaboost algorithm as a weak learner. We show that our algorithm performs relatively well on real data, and enables, according to the Traffic Flow Evaluation model, to estimate and predict whether there is congestion or not at a given time on road intersections.

Keywords: Machine Learning, Boosting, Classification, TrafficCongestion, Data Collecting, Magnetic Loop Detectors, SignalizedIntersections, Traffic Signal Timing Optimization.

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Authors: Bruno Trstenjak, Dzenana Donko

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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: Jitendra Jain, Cassandra Hidajat, Hansraj Riteesh Bookun

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Graft patency underpins long-term prognosis after coronary artery bypass grafting surgery (CABG). The benefits of the combined use of only the left internal mammary artery and radial artery, referred to as total arterial revascularisation (TAR), on long-term clinical outcomes and quality of life are relatively unknown. The aim of this study was to identify whether there were differences in long term clinical outcomes between recipients of TAR compared to a cohort of mostly arterial revascularization involving the left internal mammary, at least one radial artery and at least one saphenous vein graft. A retrospective analysis was performed on all patients who underwent TAR or were re-vascularized with supplementary saphenous vein graft from February 1996 to December 2004. Telephone surveys were conducted to obtain clinical outcome parameters including major adverse cardiac and cerebrovascular events (MACCE) and Short Form (SF-36v2) Health Survey responses. A total of 176 patients were successfully contacted to obtain postop follow up results. The mean follow-up length from time of surgery in our study was TAR 12.4±1.8 years and conventional 12.6±2.1. PCS score was TAR 45.9±8.8 vs LIMA/Rad/ SVG 44.9±9.2 (p=0.468) and MCS score was TAR 52.0±8.9 vs LIMA/Rad/SVG 52.5±9.3 (p=0.723). There were no significant differences between groups for NYHA class 3+ TAR 9.4% vs. LIMA/Rad/SVG 6.6%; or CCS 3+ TAR 2.35% vs. LIMA/Rad/SVG 0%.

Keywords: CABG, MACCEs, quality of life, total arterial revascularization.

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

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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: Heide Lukosch, Roy Van Bussel, Sebastiaan Meijer

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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: Ghada A. Alfattni

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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: Ion Smeureanu, Marian Dardala, Adriana Reveiu

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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

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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

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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: Rahman Ali, Muhammad Sajjad, Farkhund Iqbal, Muhammad Sadiq Hassan Zada, Mohammed Hussain

Abstract:

Emission of Carbon Dioxide (CO₂) has adversely affected the environment. One of the major sources of CO₂ emission is transportation. In the last few decades, the increase in mobility of people using vehicles has enormously increased the emission of CO₂ in the environment. To reduce CO₂ emission, sustainable transportation system is required in which smart parking is one of the important measures that need to be established. To contribute to the issue of reducing the amount of CO₂ emission, this research proposes a smart parking system. A cloud-based solution is provided to the drivers which automatically searches and recommends the most preferred parking slots. To determine preferences of the parking areas, this methodology exploits a number of unique parking features which ultimately results in the selection of a parking that leads to minimum level of CO₂ emission from the current position of the vehicle. To realize the methodology, a scenario-based implementation is considered. During the implementation, a mobile application with GPS signals, vehicles with a number of vehicle features and a list of parking areas with parking features are used by sorting, multi-level filtering, exploratory data analysis (EDA, Analytical Hierarchy Process (AHP)) and weighted sum model (WSM) to rank the parking areas and recommend the drivers with top-k most preferred parking areas. In the EDA process, “2020testcar-2020-03-03”, a freely available dataset is used to estimate CO₂ emission of a particular vehicle. To evaluate the system, results of the proposed system are compared with the conventional approach, which reveal that the proposed methodology supersedes the conventional one in reducing the emission of CO₂ into the atmosphere.

Keywords: CO2 emission, IoT, EDA, Weighted Sum Model, WSM, regression, smart parking system.

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