Search results for: hybrid learning

Authors: M. R. Aghaebrahimi, S. H. Zahiri, M. Amiri

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The necessity of solving multi dimensional complicated scientific problems beside the necessity of several objective functions optimization are the most motive reason of born of artificial intelligence and heuristic methods. In this paper, we introduce a new method for multiobjective optimization based on learning automata. In the proposed method, search space divides into separate hyper-cubes and each cube is considered as an action. After gathering of all objective functions with separate weights, the cumulative function is considered as the fitness function. By the application of all the cubes to the cumulative function, we calculate the amount of amplification of each action and the algorithm continues its way to find the best solutions. In this Method, a lateral memory is used to gather the significant points of each iteration of the algorithm. Finally, by considering the domination factor, pareto front is estimated. Results of several experiments show the effectiveness of this method in comparison with genetic algorithm based method.

Keywords: Function optimization, Multiobjective optimization, Learning automata.

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Authors: Syed Sultan Ahmed

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The current education system in India is adept in equipping and assessing the scholastic development of children. However, there is an immediate need to strengthen co-scholastic areas like life-skills, values and attitudes to equip students to face real life challenges. Audio-visual technology and their respective media can make a significant contribution to a value based learning curriculum. Thus, co-scholastic skills need to be effectively nurtured by a medium that is entertaining and impactful. Films in general have a tremendous impact in our society. Films with a positive message make a formidable learning experience that can influence and inspire generations of learners. Leveraging on this powerful medium, EduMedia India Pvt. Ltd. has introduced School Cinema a well researched film-based learning module supported by a fun and exciting workbook, designed to introduce and reaffirm life-skills and values to children, thereby having a positive influence on their attitudes.

Keywords: Co-Scholastics, Entertaining, Educative, Holistic- Development

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Authors: Adel M. Sharaf, Foad H. Gandoman

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Nowadays, Photovoltaic-PV Farms/ Parks and large PV-Smart Grid Interface Schemes are emerging and commonly utilized in Renewable Energy distributed generation. However, PVhybrid- Dc-Ac Schemes using interface power electronic converters usually has negative impact on power quality and stabilization of modern electrical network under load excursions and network fault conditions in smart grid. Consequently, robust FACTS based interface schemes are required to ensure efficient energy utilization and stabilization of bus voltages as well as limiting switching/fault onrush current condition. FACTS devices are also used in smart grid- Battery Interface and Storage Schemes with PV-Battery Storage hybrid systems as an elegant alternative to renewable energy utilization with backup battery storage for electric utility energy and demand side management to provide needed energy and power capacity under heavy load conditions. The paper presents a robust interface PV-Li-Ion Battery Storage Interface Scheme for Distribution/Utilization Low Voltage Interface using FACTS stabilization enhancement and dynamic maximum PV power tracking controllers. Digital simulation and validation of the proposed scheme is done using MATLAB/Simulink software environment for Low Voltage- Distribution/Utilization system feeding a hybrid Linear-Motorized inrush and nonlinear type loads from a DC-AC Interface VSC-6- pulse Inverter Fed from the PV Park/Farm with a back-up Li-Ion Storage Battery.

Keywords: AC FACTS, Smart grid, Stabilization, PV-Battery Storage, Switched Filter-Compensation (SFC).

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Authors: Chalermpol Tapsai

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The objectives of this research were to develop and find the efficiency of integrated online lessons by investigating the usage of online lessons, the relationship between learners’ background knowledge, and the achievement after learning with online lessons. The sample group in this study consisted of 97 students randomly selected from 121 students registering in 1/2012 at Trimitwittayaram Learning Center. The sample technique employed stratified sample technique of 4 groups according to their proficiency, i.e. high, moderate, low, and non-knowledge. The research instrument included online lessons in integration model on the topic of Java Programming, test after each lesson, the achievement test at the end of the course, and the questionnaires to find learners’ satisfaction. The results showed that the efficiency of online lessons was 90.20/89.18 with the achievement of after learning with the lessons higher than that before the lessons at the statistically significant level of 0.05. Moreover, the background knowledge of the learners on the programming showed the positive relationship with the achievement learning at the statistically significant level at 0.05. Learners with high background knowledge employed less exercises and samples than those with lower background knowledge. While learners with different background in the group of moderate and low did not show the significant difference in employing samples and exercises.

Keywords: Integration model, Online lessons.

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Authors: Porntipa Taksin, Jutamas Wongchan, Amornrat Karamee

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The researchers’ experience of student affairs in 2011-2013, we found that few undergraduate nursing students become student association members who participated in co-curricular activities, they have limited skill of self-directed-learning and leadership. We developed “A Leadership Promoting Program” using Self-Directed Learning concept. The program included six activities: 1) Breaking the ice, Decoding time, Creative SMO, Know me-Understand you, Positive thinking, and Creative dialogue, which include four aspects of these activities: decision-making, implementation, benefits, and evaluation. The one-group, pretest-posttest quasi-experimental research was designed to examine the effects of the program on participation in co-curricular activities. Thirty five students participated in the program. All were members of the board of undergraduate nursing student association of Boromarajonani College of Nursing, Chonburi. All subjects completed the questionnaire about participation in the activities at beginning and at the end of the program. Data were analyzed using descriptive statistics and dependent t-test. The results showed that the posttest scores of all four aspects mean were significantly higher than the pretest scores (t=3.30, p<.01). Three aspects had high mean scores, Benefits (Mean = 3.24, S.D. = 0.83), Decision-making (Mean = 3.21, S.D. = 0.59), and Implementation (Mean=3.06, S.D.=0.52). However, scores on evaluation falls in moderate scale (Mean = 2.68, S.D. = 1.13). Therefore, the Leadership Promoting Program based on Self-Directed Learning Approach could be a method to improve students’ participation in co-curricular activities and leadership.

Keywords: Participation in co-curricular activities, undergraduate nursing students, leadership promoting program, self-directed learning.

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Authors: Mohd Fairuz Iskandar Othman, Nazrulazhar Bahaman, Zulkiflee Muslim, Faizal Abdollah

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This paper discusses a curriculum approach that will give emphasis on practical portions of teaching network security subjects in information and communication technology courses. As we are well aware, the need to use a practice and application oriented approach in education is paramount. Research on active learning and cooperative groups have shown that students grasps more and have more tendency towards obtaining and realizing soft skills like leadership, communication and team work as opposed to the more traditional theory and exam based teaching and learning. While this teaching and learning paradigm is relatively new in Malaysia, it has been practiced widely in the West. This paper examines a certain approach whereby students learning wireless security are divided into and work in small and manageable groups where there will be 2 teams which consist of black hat and white hat teams. The former will try to find and expose vulnerabilities in a wireless network while the latter will try their best to prevent such attacks on their wireless networks using hardware, software, design and enforcement of security policy and etc. This paper will try to show that the approach taken plus the use of relevant and up to date software and hardware and with suitable environment setting will hopefully expose students to a more fruitful outcome in terms of understanding of concepts, theories and their motivation to learn.

Keywords: Curriculum approach, wireless networks, wirelesssecurity.

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Authors: Engin Yesil, Leon Urbas

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Modeling of complex dynamic systems, which are very complicated to establish mathematical models, requires new and modern methodologies that will exploit the existing expert knowledge, human experience and historical data. Fuzzy cognitive maps are very suitable, simple, and powerful tools for simulation and analysis of these kinds of dynamic systems. However, human experts are subjective and can handle only relatively simple fuzzy cognitive maps; therefore, there is a need of developing new approaches for an automated generation of fuzzy cognitive maps using historical data. In this study, a new learning algorithm, which is called Big Bang-Big Crunch, is proposed for the first time in literature for an automated generation of fuzzy cognitive maps from data. Two real-world examples; namely a process control system and radiation therapy process, and one synthetic model are used to emphasize the effectiveness and usefulness of the proposed methodology.

Keywords: Big Bang-Big Crunch optimization, Dynamic Systems, Fuzzy Cognitive Maps, Learning.

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Authors: Htay Htay Yi, Zin May Aye

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Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers.

Keywords: False negative rate, intrusion detection system, machine learning methods, performance.

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Authors: Prompt Udomdech, Eleni Papadonikolaki, Andrew Davies

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Building Information Modelling (BIM) is the major technological trend among built environment organisations. Digitalising businesses and operations, BIM brings forth a digital transformation in any built environment industry. The adoption of BIM presents challenges for organisations, especially Small- and Medium-sized Enterprises (SMEs). The main problem for built environment SMEs is the lack of project actors with adequate BIM competences. The research highlights learning in projects as the key and explores into the learning of BIM in projects of designers and engineers within Thai design and engineering SMEs. The study uncovers three impeding attributes which are: a) lack of English proficiency; b) unfamiliarity with digital technologies; and c) absence of public standards. This research expands on the literature of BIM competences and adoption.

Keywords: BIM competences and adoption, digital transformation, learning in projects, SMEs, and developing built environment industry.

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: Disaster management, real-time demand, reinforcement learning, relief demand.

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Authors: Ayman El Sabagh, Celaleddin Barutçular, Hirofumi Saneoka

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Drought is one of the most serious problems posing a grave threat to cereals production including maize. Maize improvement in drought-stress tolerance poses a great challenge as the global need for food and bio-energy increases. Thus, the current study was planned to explore the variations and determine the performance of target traits of maize hybrids at grain growth stage under drought conditions during 2014 under Adana, Mediterranean climate conditions, Turkey. Maize hybrids (Sancia, Indaco, 71May69, Aaccel, Calgary, 70May82, 72May80) were evaluated under (irrigated and water stress). Results revealed that, grain yield and yield traits had a negative effects because of water stress conditions compared with the normal irrigation. As well as, based on the result under normal irrigation, the maximum biological yield and harvest index were recorded. According to the differences among hybrids were found that, significant differences were observed among hybrids with respect to yield and yield traits under current research. Based on the results, grain weight had more effect on grain yield than grain number during grain filling growth stage under water stress conditions. In this concern, according to low drought susceptibility index (less grain yield losses), the hybrid (Indaco) was more stable in grain number and grain weight. Consequently, it may be concluded that this hybrid would be recommended for use in the future breeding programs for production of drought tolerant hybrids.

Keywords: Drought susceptibility index, grain filling, grain yield, maize, water stress.

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Authors: Ghazy M.R. Assassa, Mona F. M. Mursi, Hatim A. Aboalsamh

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Traditional principal components analysis (PCA) techniques for face recognition are based on batch-mode training using a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where within the framework of continuous learning, new training images are continuously added to the original set; this would trigger a costly continuous re-computation of the eigen space representation via repeating an entire batch-based training that includes the old and new images. Incremental PCA methods allow adding new images and updating the PCA representation. In this paper, two incremental PCA approaches, CCIPCA and IPCA, are examined and compared. Besides, different learning and testing strategies are proposed and applied to the two algorithms. The results suggest that batch PCA is inferior to both incremental approaches, and that all CCIPCAs are practically equivalent.

Keywords: Candid covariance-free incremental principal components analysis (CCIPCA), face recognition, incremental principal components analysis (IPCA).

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Authors: Sandra Valdez-Hernández

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Motivation is one of the most important factors when teaching language. Most institutions, at least in Mexico, pay low attention to the types of motivation students have when they are studying English; however, considering the motivation they have may lead to better understanding about their needs and purposes for learning English and the professors may understand and focus on their interests for making them persist in action through the course. This topic has been widely investigated in different countries, but more research needs to be done in Mexico to shed light on this area of potential impact. This quantitative study examines how students (n = 180) at a Rural University in Quintana Roo perceive their different types of motivation, intrinsic and extrinsic, instrumental, and integrative and the attitudes for the language. The findings reveal a high degree of intrinsic and instrumental motivation and provide insights into the perceived attitudes for learning English. Finding ways to persist in action may lead to better comprehending the reasons for learning English.

Keywords: Attitudes for motivation, types of motivation, Extrinsic and Intrinsic motivation, instrumental and integrative motivation.

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Authors: T. Hopkin, S. Lu, P. Rogers, M. Sexton

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Housing Associations (HAs) contribute circa 20% of the UK’s housing supply. HAs are however under increasing pressure as a result of funding cuts and rent reductions. Due to the increased pressure, a number of processes are currently being reviewed by HAs, especially how they manage and learn from defects. Learning from defects is considered a useful approach to achieving defect reduction within the UK housebuilding industry. This paper contributes to our understanding of how HAs learn from defects by undertaking an initial round table discussion with key HA stakeholders as part of an ongoing collaborative research project with the National House Building Council (NHBC) to better understand how house builders and HAs learn from defects to reduce their prevalence. The initial discussion shows that defect information runs through a number of groups, both internal and external of a HA during both the defects management process and organizational learning (OL) process. Furthermore, HAs are reliant on capturing and recording defect data as the foundation for the OL process. During the OL process defect data analysis is the primary enabler to recognizing a need for a change to organizational routines. When a need for change has been recognized, new options are typically pursued to design out defects via updates to a HAs Employer’s Requirements. Proposed solutions are selected by a review board and committed to organizational routine. After implementing a change, both structured and unstructured feedback is sought to establish the change’s success. The findings from the HA discussion demonstrates that OL can achieve defect reduction within the house building sector in the UK. The paper concludes by outlining a potential ‘learning from defects model’ for the housebuilding industry as well as describing future work.

Keywords: Defects, new homes, housing associations, organizational learning.

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Authors: Sitalakshmi Venkatraman, Fiona Wahr, Anthony de Souza-Daw, Samuel Kaspi

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There is a growing emphasis on generic skills in higher education to match the changing skill-set requirements of the labour market. However, researchers and policy makers have not arrived at a consensus on the generic skills that actually contribute towards workplace employability and performance that complement and/or underpin discipline-specific graduate attributes. In order to strengthen the qualifications framework, a range of ‘generic’ learning outcomes have been considered for students undergoing higher education programs and among them it is necessary to have the fundamental generic skills such as literacy and numeracy at a level appropriate to the qualification type. This warrants for curriculum design approaches to contextualise the form and scope of these fundamental generic skills for supporting both students’ learning engagement in the course, as well as the graduate attributes required for employability and to progress within their chosen profession. Little research is reported in integrating such generic skills into discipline-specific learning outcomes. This paper explores the literature of the generic skills required for graduates from the discipline of Information Technology (IT) in relation to an Australian higher education institution. The paper presents the rationale of a proposed Bachelor of IT curriculum designed to contextualize the learning of these generic skills within the students’ discipline studies.

Keywords: Curriculum, employability, generic skills, graduate attributes, higher education, information technology.

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Authors: B. Ferdousi, J. Bari

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Advanced technology can provide vital tools to promote authentic, meaningful, and efficient assessments that measure students' achievement of learning objectives in higher education. However, it also brings several challenges in the learning process. This literature review-based paper describes the challenges in ensuring academic integrity and cybersecurity when students' knowledge and performance are assessed in a digital environment. The paper also reviews the strategies that can be implemented to address these challenges. Using students' authentication and authorship verification of their classwork, designing and developing e-assessments, technology accessibility and instructor training are probable solutions to address these challenges. Given the increasing adoption of digital technology in assessing students' effective learning achievement, this paper will help enhance knowledge and in-depth understanding of measures needed in using technology in academic assessment.

Keywords: Fairness, cybersecurity, e-authentication, academic integrity, e-assessment.

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Authors: Jukka Heinonen

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Higher density reduces distances, private car dependency and thus reduces greenhouse gas emissions (GHGs). As a result, increased density has been given a central role among urban development targets. However, it is not just travel behavior that changes along with density. Rather, the consumption patterns, or overall lifestyles, change along with changing urban structure, particularly with changing housing types and consumption opportunities. Furthermore, elevated consumption of services, more frequent flying and less intra-household sharing have been shown to potentially outweigh the gains from reduced driving in more dense urban settlements. In this study, the geography of carbon footprints (CFs) in California is analyzed paying close attention to the household size differences and the resulting economies-of-scale advantages and disadvantages. A hybrid life cycle assessment (LCA) framework is employed together with consumer expenditure data to assess the CFs. According to the study, small urban households have the highest CFs in California. Their transport related emissions are significantly lower than those of the residents of less urbanized areas, but higher emissions from other consumption categories, together with the low degree of sharing of goods, overweigh the gains. Two functional units, per capita and per household, are used to analyze the CFs and to demonstrate the importance of household size. The lifestyle impacts visible through the consumption data are also discussed. The study suggests that there are still significant gaps in our understanding of the premises of low-carbon human settlements.

Keywords: Carbon footprint, life cycle assessment, consumption, lifestyle, household size, economies-of-scale.

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Authors: Gintarė Sauliutė, Gintaras Svecevičius

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Landfill leachates contain a number of persistent pollutants, including heavy metals. They have the ability to spread in ecosystems and accumulate in fish which most of them are classified as top-consumers of trophic chains. Fish are freely swimming organisms; but perhaps, due to their species-specific ecological and behavioral properties, they often prefer the most suitable biotopes and therefore, did not avoid harmful substances or environments. That is why it is necessary to evaluate the persistent pollutant dispersion in hydroecosystem using fish tissue metal concentration. In hydroecosystems of hybrid type (e.g. river-pond-river) the distance from the pollution source could be a perfect indicator of such a kind of metal distribution. The studies were carried out in the Kairiai landfill neighboring hybrid-type ecosystem which is located 5 km east of the Šiauliai City. Fish tissue (gills, liver, and muscle) metal concentration measurements were performed on two types of ecologically-different fishes according to their feeding characteristics: benthophagous (Gibel carp, roach) and predatory (Northern pike, perch). A number of mathematical models (linear, non-linear, using log and other transformations) have been applied in order to identify the most satisfactorily description of the interdependence between fish tissue metal concentration and the distance from the pollution source. However, the only one log-multiple regression model revealed the pattern that the distance from the pollution source is closely and positively correlated with metal concentration in all predatory fish tissues studied (gills, liver, and muscle).

Keywords: Bioaccumulation in fish, heavy metals, hydroecosystem, landfill leachate, mathematical model.

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Authors: A. Lawanna, J. Daengdej

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Case-Based Reasoning (CBR) is one of machine learning algorithms for problem solving and learning that caught a lot of attention over the last few years. In general, CBR is composed of four main phases: retrieve the most similar case or cases, reuse the case to solve the problem, revise or adapt the proposed solution, and retain the learned cases before returning them to the case base for learning purpose. Unfortunately, in many cases, this retain process causes the uncontrolled case base growth. The problem affects competence and performance of CBR systems. This paper proposes competence-based maintenance method based on deletion policy strategy for CBR. There are three main steps in this method. Step 1, formulate problems. Step 2, determine coverage and reachability set based on coverage value. Step 3, reduce case base size. The results obtained show that this proposed method performs better than the existing methods currently discussed in literature.

Keywords: Case-Based Reasoning, Case Base Maintenance, Coverage, Reachability.

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Authors: Inderpreet Kaur, Neena Gupta

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In this paper, the gain spectrum of EDFA has been broadened by implementing HTE configuration for S and C band. On using this configuration an amplification bandwidth of 76nm ranging from 1479nm to 1555nm with a peak gain of 26dB has been obtained.

Keywords: C band, DWDM system, EDFA, Gain, HTE, Hybrid Fiber Amplifier, S band.

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Authors: Yidan Shao

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With the advancement of the economy, technology promotes online teaching, and learning has become one of the common features in the educational field. Meanwhile, the dramatic expansion of the online environment provides opportunities for more learners, including second language learners. A greater command of vocabulary improves students’ learning capacity, and word acquisition and development play a critical role in learning. Furthermore, the Word Wall is an effective tool to improve students’ knowledge of words, which works for a wide range of age groups. Therefore, this study is going to use the Word Wall as an intervention to examine whether it can bring some memorization changes in an online English language class for a second language learner based on the word morphology method. The participant will take ten courses in the experiment as it plans. The findings show that the Word Wall activity plays a slight role in improving word memorizing, but it does affect instant memorization. If longer periods and more comprehensive designs of research can be applied, it is expected to have more value.

Keywords: Second language acquisition, word morphology, word memorization, the Word Wall.

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Authors: Norah Alshahrani, Abdulaziz Almaleh

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Autism Spectrum Disorder (ASD) is a condition related to issues with brain development that affects how a person recognises and communicates with others which results in difficulties with interaction and communication socially and it is constantly growing. Early recognition of ASD allows children to lead safe and healthy lives and helps doctors with accurate diagnoses and management of conditions. Therefore, it is crucial to develop a method that will achieve good results and with high accuracy for the measurement of ASD in children. In this paper, ASD datasets of toddlers and children have been analyzed. We employed the following machine learning techniques to attempt to explore ASD: Random Forest (RF), Decision Tree (DT), Na¨ıve Bayes (NB) and Support Vector Machine (SVM). Then feature selection was used to provide fewer attributes from ASD datasets while preserving model performance. As a result, we found that the best result has been provided by SVM, achieving 0.98% in the toddler dataset and 0.99% in the children dataset.

Keywords: Autism Spectrum Disorder, ASD, Machine Learning, ML, Feature Selection, Support Vector Machine, SVM.

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Electrical energy demands have increased due to population growth and the variety of new electrical load technologies. This increase demand has nearly doubled during peak hours. Consequently, that necessitates the construction of new power plant infrastructures, which is a costly approach due to the expense of construction building, future preservation like maintenance, and environmental impact. As an alternative approach, most electrical utilities increase the price of electrical usage during peak hours, encouraging consumers to use less electricity during peak periods under Time-Of-Use programs, which may not be universally suitable for all consumers. Furthermore, in some areas, the excessive demand and the lack of supply cause an electrical outage, posing considerable stress and challenges to electrical utilities and consumers. However, control systems, artificial intelligence (AI), and renewable energy (RE), when effectively integrated, provide new solutions to mitigate excessive demand during peak hours. This paper presents a power model that reduces the reliance on the power grid during peak hours by utilizing a hybrid solar system connected to a residential house with a power management controller, that prioritizes the power drives between Photovoltaic (PV) production, battery backup, and the utility electrical grid. As a result, dependence on utility grid was from 3% to 18% during peak hours, improving energy stability safely and efficiently for electrical utilities, consumers, and communities, providing a viable alternative to conventional approaches such as Time-Of-Use programs.

Keywords: Artificial intelligence, AI, control system, photovoltaic, PV, renewable energy.

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Authors: G. Theonas, D. Hobbs, D. Rigas

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The scope of this research was to study the relation between the facial expressions of three lecturers in a real academic lecture theatre and the reactions of the students to those expressions. The first experiment aimed to investigate the effectiveness of a virtual lecturer-s expressions on the students- learning outcome in a virtual pedagogical environment. The second experiment studied the effectiveness of a single facial expression, i.e. the smile, on the students- performance. Both experiments involved virtual lectures, with virtual lecturers teaching real students. The results suggest that the students performed better by 86%, in the lectures where the lecturer performed facial expressions compared to the results of the lectures that did not use facial expressions. However, when simple or basic information was used, the facial expressions of the virtual lecturer had no substantial effect on the students- learning outcome. Finally, the appropriate use of smiles increased the interest of the students and consequently their performance.

Keywords: emotion, facial expression, smile, virtual educational environment, virtual learning, virtual lecturer.

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Authors: Sotiris B. Kotsiantis

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Bagging and boosting are among the most popular re-sampling ensemble methods that generate and combine a diversity of regression models using the same learning algorithm as base-learner. Boosting algorithms are considered stronger than bagging on noise-free data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using an averaging methodology of bagging and boosting ensembles with 10 sub-learners in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-learners on standard benchmark datasets and the proposed ensemble gave better accuracy.

Keywords: Regressors, statistical learning.

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Authors: Yesuselvi Manickam, Tan Soon Chin

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Internship is a supervised and structured learning experience related to one’s field of study or career goal. Internship allows students to obtain work experience and the opportunity to apply skills learned during university. Internship is a valuable learning experience for students; however, literature on employer assessment is scarce on Malaysian student’s internship experience. This study focuses on employer’s perspective on student’s performances during their three months of internship. The results are based on the descriptive analysis of 45 sets of question gathered from the on-site supervisors of the interns. The survey of 45 on-site supervisor’s feedback was collected through postal mail. It was found that, interns have not met their on-site supervisor’s expectations in many areas. The significance of this study is employer’s assessment on the internship shall be used as feedback to improve on ways how to prepare students for their internship and employments in future.

Keywords: Employers perspective, internship, structured learning, student’s performances.

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Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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Recent advances in cognitive neuroscience have allowed a step forward in perceiving the processes involved in learning from the point of view of acquiring new information or the modification of existing mental content. The evoked potentials technique reveals how basic brain processes interact to achieve adequate and flexible behaviours. The objective of this work, using evoked potentials, is to study if it is possible to distinguish if a patient suffers a specific type of learning disorder to decide the possible therapies to follow. The methodology used in this work is to analyze the dynamics of different brain areas during a cognitive activity to find the relationships between the other areas analyzed to understand the functioning of neural networks better. Also, the latest advances in neuroscience have revealed the exis-tence of different brain activity in the learning process that can be highlighted through the use of non-invasive, innocuous, low-cost and easy-access techniques such as, among others, the evoked potentials that can help to detect early possible neurodevelopmental difficulties for their subsequent assessment and therapy. From the study of the amplitudes and latencies of the evoked potentials, it is possible to detect brain alterations in the learning process, specifically in dyscalculia, to achieve specific corrective measures for the application of personalized psycho-pedagogical plans that allow obtaining an optimal integral development of the affected people.

Keywords: dyscalculia, neurodevelopment, evoked potentials, learning disabilities, neural networks

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Authors: Chinlun Lai, Lunjyh Jiang

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Due to the heavy burden and pressure of caring for infants, an integrated automatic baby watching system based on IoT smart sensing and deep learning machine vision techniques is proposed in this paper. By monitoring infant body conditions such as heartbeat, breathing, body temperature, sleeping posture, as well as the surrounding conditions such as dangerous/sharp objects, light, noise, humidity and temperature, the proposed system can analyze and predict the obvious/potential dangerous conditions according to observed data and then adopt suitable actions in real time to protect the infant from harm. Thus, reducing the burden of the caregiver and improving safety efficiency of the caring work. The experimental results show that the proposed system works successfully for the infant care work and thus can be implemented in various life fields practically.

Keywords: Baby care system, internet of things, deep learning, machine vision.

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Authors: Changqiao Wu, Guoqing Ding, Xin Chen

Abstract:

In this paper, ways of modeling dynamic measurement systems are discussed. Specially, for linear system with single-input single-output, it could be modeled with shallow neural network. Then, gradient based optimization algorithms are used for searching the proper coefficients. Besides, method with normal equation and second order gradient descent are proposed to accelerate the modeling process, and ways of better gradient estimation are discussed. It shows that the mathematical essence of the learning objective is maximum likelihood with noises under Gaussian distribution. For conventional gradient descent, the mini-batch learning and gradient with momentum contribute to faster convergence and enhance model ability. Lastly, experimental results proved the effectiveness of second order gradient descent algorithm, and indicated that optimization with normal equation was the most suitable for linear dynamic models.

Keywords: Dynamic system modeling, neural network, normal equation, second order gradient descent.

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

Abstract:

A crucial part of maintaining a customer-oriented business in the telecommunications industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years, which has made it more important to understand customers’ needs in this strong market. For those who are looking to turn over their service providers, understanding their needs is especially important. Predictive churn is now a mandatory requirement for retaining customers in the telecommunications industry. Machine learning can be used to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: Machine Learning, Gradient Boosting, Logistic Regression, Churn, Random Forest, Decision Tree, ROC, AUC, F1-score.

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