Search results for: competitive and cooperative learning.

Authors: Yesuselvi Manickam, Tan Soon Chin

Abstract:

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

Abstract:

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

Abstract:

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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Authors: I. Pelizzoni, C. Cavallini, I. Salvaderi, F. Cavallini

Abstract:

We present the improvement in writing skills obtained by 94 participants (aged between six and 10 years) with special educational needs through a writing enhancement program based on fluency principles. The study was planned and conducted with a single-subject experimental plan for each of the participants, in order to confirm the results in the literature. These results were obtained using precision teaching (PT) methodology to increase the number of written graphemes per minute in the pre- and post-test, by curriculum based measurement (CBM). Results indicated an increase in the number of written graphemes for all participants. The average overall duration of the intervention is 144 minutes in five months of treatment. These considerations have been analyzed taking account of the complexity of the implementation of measurement systems in real operational contexts (an Italian learning center) and important aspects of replicability and cost-effectiveness of such interventions.

Keywords: Precision teaching, writing skills, CBM, Italian Learning Center.

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Authors: H. Anıl, G. Kar

Abstract:

In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations, since the geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning and time series methods, the total generation of the power plants belonging to Zorlu Doğal Electricity Generation, which has a high installed capacity in terms of geothermal, was predicted for the first one-week and first two-weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: Machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting.

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Authors: Heba Mohammed Nagy, Walid Mohamed Aly, Osama Fathy Hegazy

Abstract:

Educational  data mining  is  a  specific  data   mining field applied to data originating from educational environments, it relies on different  approaches to discover hidden knowledge  from  the  available   data. Among these approaches are   machine   learning techniques which are used to build a system that acquires learning from previous data. Machine learning can be applied to solve different regression, classification, clustering and optimization problems.

In  our  research, we propose  a “Student  Advisory  Framework” that  utilizes  classification  and  clustering  to  build  an  intelligent system. This system can be used to provide pieces of consultations to a first year  university  student to  pursue a  certain   education   track   where  he/she  will  likely  succeed  in, aiming  to  decrease   the  high  rate   of  academic  failure   among these  students.  A real case study  in Cairo  Higher  Institute  for Engineering, Computer  Science  and  Management  is  presented using  real  dataset   collected  from  2000−2012.The dataset has two main components: pre-higher education dataset and first year courses results dataset. Results have proved the efficiency of the suggested framework.

Keywords: Classification, Clustering, Educational Data Mining (EDM), Machine Learning.

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Authors: M. Rodionov, Z. Dedovets

Abstract:

This article discusses ways to implement a differentiated approach to developing academic motivation for mathematical studies which relies on defining the primary structural characteristics of motivation. The following characteristics are considered: features of realization of cognitive activity, meaningmaking characteristics, level of generalization and consistency of knowledge acquired by personal experience. The assessment of the present level of individual student understanding of each component of academic motivation is the basis for defining the relevant educational strategy for its further development.

Keywords: Learning activity, mathematics, motivation, student.

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Authors: Zuraini Hanim Zaini, Wan Fatimah Wan Ahmad

Abstract:

Multimedia courseware has been accepted as a tool that can support teaching and learning process. 'Li2D' courseware was developed to assist student-s visualization on the topic of Loci in Two Dimension. This paper describes an evaluation on the effectiveness and usability of a 'Li2D' courseware. The quasi experiment was used for the effectiveness evaluation. Usability evaluation was accomplished based on four constructs of usability, namely: efficiency, learnability, screen design and satisfaction. An evaluation on the multimedia elements was also conducted. A total of 63 students of Form Two are involved in the study. The students are divided into two groups: control and experimental. The experimental group had to interact with 'Li2D' courseware as part of the learning activities while the control group used the conventional learning methods. The results indicate that the experimental group performed better than the control group in understanding the Loci in Two Dimensions topic. In terms of usability, the results showed that the students agreed on the usability in multimedia elements in the 'Li2D' courseware.

Keywords: Effectiveness, usability and multimedia elements, Loci in Two Dimensions.

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Authors: Iruka C. Anugwo, Winston M. Shakantu

Abstract:

The objective of this study is to uncover the basic business-forces that necessitated the survival and sustainable performance of the medium scale contractors in the South African construction market. This study is essential as it set to contribute towards long-term strategic solutions for combating the incessant failure of start-ups construction organizations within South African. The study used a qualitative research methodology; as the most appropriate approach to elicit and understand, and uncover the phenomena that are basic business-forces for the active contractors in the market. The study also adopted a phenomenological study approach; and in-depth interviews were conducted with 20 medium scale contractors in Port Elizabeth, South Africa, between months of August to October 2015. This allowed for an in-depth understanding of the critical and basic business-forces that influenced their survival and performance beyond the first five years of business operation. Findings of the study showed that for potential contractors (startups), to survival in the competitive business environment such as construction industry, they must possess the basic business-forces. These forces are educational knowledge in construction and business management related disciplines, adequate industrial experiences, competencies and capabilities to delivery excellent services and products as well as embracing the spirit of entrepreneurship. Convincingly, it can be concluded that the strategic approach to minimize the endless failure of startups construction businesses; the potential construction contractors must endeavoring to access and acquire the basic educationally knowledge, training and qualification; need to acquire industrial experiences in collaboration with required competencies, capabilities and entrepreneurship acumen. Without these basic business-forces as been discovered in this study, the majority of the contractors gaining entrance in the market will find it difficult to develop and grow a competitive and sustainable construction organization in South Africa.

Keywords: Basic business-forces, medium scale contractors, South Africa, sustainable organisations.

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Authors: M. Rodionov, Z. Dedovets

Abstract:

The article analyses the composition and structure of the motivationally oriented methodological system of teaching mathematics (purpose, content, methods, forms, and means of teaching), viewed through the prism of the student as the subject of the learning process. Particular attention is paid to the problem of methods of teaching mathematics, which are represented in the form of an ordered triad of attributes corresponding to the selected characteristics. A systematic analysis of possible options and their methodological interpretation enriched existing ideas about known methods and technologies of training, and significantly expanded their nomenclature by including previously unstudied combinations of characteristics. In addition, examples outlined in this article illustrate the possibilities of enhancing the motivational capacity of a particular method or technology in the real learning practice of teaching mathematics through more free goal-setting and varying the conditions of the problem situations. The authors recommend the implementation of different strategies according to their characteristics in teaching and learning mathematics in secondary schools.

Keywords: Education, methodological system, teaching of mathematics, teachers, lesson, students motivation, secondary school.

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

Abstract:

Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: Cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing.

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Authors: Bhekisipho Twala, Mantepu Masetshaba, Ramapulana Nkoana

Abstract:

Multiple classifier systems combine several individual classifiers to deliver a final classification decision. However, an increasingly controversial question is whether such systems can outperform the single best classifier, and if so, what form of multiple classifiers system yields the most significant benefit. Also, multi-target tracking detection using multiple sensors is an important research field in mobile techniques and military applications. In this paper, several multiple classifiers systems are evaluated in terms of their ability to predict a system’s failure or success for multi-sensor target tracking tasks. The Bristol Eden project dataset is utilised for this task. Experimental and simulation results show that the human activity identification system can fulfil requirements of target tracking due to improved sensors classification performances with multiple classifier systems constructed using boosting achieving higher accuracy rates.

Keywords: Single classifier, machine learning, ensemble learning, multi-sensor target tracking.

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Authors: Olugbade Damola, Adekomi Adebimbo, Sofowora Olaniyi Alaba

Abstract:

One of the major problems facing education in Nigeria is the provision of quality Science and Technology education. Inadequate teaching facilities, non-usage of innovative teaching strategies, ineffective classroom management, lack of students’ motivation and poor integration of ICT has resulted in the increase in percentage of students who failed Basic Science and Technology in Junior Secondary Certification Examination for National Examination Council in Nigeria. To address these challenges, the Federal Government came up with a road map on education. This was with a view of enhancing quality education through integration of modern technology into teaching and learning, enhancing quality assurance through proper monitoring and introduction of innovative methods of teaching. This led the researcher to investigate how MOODLE LMS could be used to enhance students’ learning outcomes in BST. A sample of 120 students was purposively selected from four secondary schools in Ogbomoso. The experimental group was taught using MOODLE LMS, while the control group was taught using the conventional method. Data obtained were analyzed using mean, standard deviation and t-test. The result showed that MOODLE LMS was an effective learning platform in teaching BST in junior secondary schools (t=4.953, P<0.05). Students’ attitudes towards BST was also enhanced through MOODLE LMS (t=15.632, P<0.05). The use of MOODLE LMS significantly enhanced students’ retention (t=6.640, P<0.05). In conclusion, the Federal Government efforts at enhancing quality assurance through integration of modern technology and e-learning in Secondary schools proved to have yielded good result has students found MOODLE LMS to be motivating and interactive. Attendance was improved.

Keywords: MOODLE, learning management system, quality assurance, basic science and technology.

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Authors: Salah Algabli

Abstract:

As online learning takes center stage, Arabic language instructors face the imperative to adapt their practices for the digital realm. This study investigates the experiences of online Arabic instructors to unveil the pedagogical opportunities and challenges this format presents. Utilizing a transcendental phenomenological approach with 15 diverse participants, the research shines a light on the unique realities of online language teaching at the university level, specifically in the United States. The study proposes theoretical and practical solutions to maximize the benefits of online language learning while mitigating its challenges. Recommendations cater to instructors, researchers, and program coordinators, paving the way for enhancing the quality of online Arabic language education. The findings highlight the need for pedagogical approaches tailored to the online environment, ultimately shaping a future where both instructors and learners thrive in this digital landscape.

Keywords: Online Arabic language learning, pedagogical opportunities and challenges, online Arabic teachers, online language instruction, digital pedagogy.

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Authors: Lucie Valečková

Abstract:

In contemporary global and dynamically developing environment there is a need of the strategic planning fundamental. It is complicated, but at the same time important process from the point of view of continual keeping of competitive advantage. The aim of the paper is formulation of strategic goals for the needs of the small enterprises. There will be used Balanced Scorecard as a balanced system of the indicators for the clearing and transferring vision into particular goals. In particular perspectives the theme will be focused on strategic goals. Consequently will be mention the concept of the competitiveness IDINMOSU. This connect to Balanced Scorecard.

Keywords: Corporate strategic management, strategic goals, Balanced Scorecard, strategic operations

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Authors: Yananda Siraphatthada

Abstract:

The objectives of this research were to study the level of influencing factors, leadership, supply chain management, innovation, competitive advantages, business success, and affecting factors to the business success of the mobile phone system service providers in Bangkok Metropolitan. This research was done by the quantitative approach and the qualitative approach. The quantitative approach was used for questionnaires to collect data from the 331 mobile service shop managers franchised by AIS, Dtac and TrueMove. The mobile phone system service providers/shop managers were randomly stratified and proportionally allocated into subgroups exclusive to the number of the providers in each network. In terms of qualitative method, there were in-depth interviews of 6 mobile service providers/managers of Telewiz and Dtac and TrueMove shop to find the agreement or disagreement with the content analysis method. Descriptive Statistics, including Frequency, Percentage, Means and Standard Deviation were employed; also, the Structural Equation Model (SEM) was used as a tool for data analysis. The content analysis method was applied to identify key patterns emerging from the interview responses. The two data sets were brought together for comparing and contrasting to make the findings, providing triangulation to enrich result interpretation. It revealed that the level of the influencing factors – leadership, innovation management, supply chain management, and business competitiveness had an impact at a great level, but that the level of factors, innovation and the business, financial success and nonbusiness financial success of the mobile phone system service providers in Bangkok Metropolitan, is at the highest level. Moreover, the business influencing factors, competitive advantages in the business of mobile system service providers which were leadership, supply chain management, innovation management, business advantages, and business success, had statistical significance at .01 which corresponded to the data from the interviews.

Keywords: Business success, mobile service providers.

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Authors: Faisal Kalota

Abstract:

Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: Analytics, Big Data in Education, Hadoop, Learning Analytics.

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Authors: Duangkamol Thitivesa

Abstract:

This paper explores the use of project work in a content-based instruction in a Rajabhat University, Thailand. The use of project is to promote kinds of learning expected of student teachers as stated by Thailand Quality Framework: TQF. The kinds of learning are grouped into five domains: Ethical and moral development, knowledge, cognitive skill, interpersonal skills and responsibility, and analytical and communication skills. The content taught in class is used to lead the student teachers to relate their previously-acquired linguistic knowledge to meaningful realizations of the language system in passages of immediate relevance to their professional interests, teaching methods in particular. Two research questions are formulate to guide this study: 1) To what degree are the five domains of learning expected of student teachers after the use of project in a content class?, and 2) What is the academic achievement of the students’ writing skills, as part of the learning domains stated by TQF, against the 70% attainment target after the use of project to enhance the skill? The sample of the study comprised of 38 fourth-year English major students. The data was collected by means of a summative achievement test, student writing works, an observation checklist, and project diary. The scores in the summative achievement test were analyzed by mean score, standard deviation, and t-test. Project diary serves as students’ record of the language acquired during the project. List of structures and vocabulary noted in the diary has shown students’ ability to attend to, recognize, and focus on meaningful patterns of language forms.

Keywords: Thailand Quality Framework, Project Work, Writing skill.

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Authors: T. Damak, O. Kriaa, A. Baccar, M. A. Ben Ayed, N. Masmoudi

Abstract:

In the last few years, Automatic Number Plate Recognition (ANPR) systems have become widely used in the safety, the security, and the commercial aspects. Forethought, several methods and techniques are computing to achieve the better levels in terms of accuracy and real time execution. This paper proposed a computer vision algorithm of Number Plate Localization (NPL) and Characters Segmentation (CS). In addition, it proposed an improved method in Optical Character Recognition (OCR) based on Deep Learning (DL) techniques. In order to identify the number of detected plate after NPL and CS steps, the Convolutional Neural Network (CNN) algorithm is proposed. A DL model is developed using four convolution layers, two layers of Maxpooling, and six layers of fully connected. The model was trained by number image database on the Jetson TX2 NVIDIA target. The accuracy result has achieved 95.84%.

Keywords: Automatic number plate recognition, character segmentation, convolutional neural network, CNN, deep learning, number plate localization.

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Authors: Maria Jokela, Matti Kutila, Jukka Laitinen, Florian Ahlers, Nicolas Hautière, TobiasSchendzielorz

Abstract:

It has been shown that in most accidents the driver is responsible due to being distracted or misjudging the situation. In order to solve such problems research has been dedicated to developing driver assistance systems that are able to monitor the traffic situation around the vehicle. This paper presents methods for recognizing several circumstances on a road. The methods use both the in-vehicle warning systems and the roadside infrastructure. Preliminary evaluation results for fog and ice-on-road detection are presented. The ice detection results are based on data recorded in a test track dedicated to tyre friction testing. The achieved results anticipate that ice detection could work at a performance of 70% detection with the right setup, which is a good foundation for implementation. However, the full benefit of the presented cooperative system is achieved by fusing the outputs of multiple data sources, which is the key point of discussion behind this publication.

Keywords: Smart roads, traffic monitoring, traffic scenedetection.

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Authors: Ya-Ting Carolyn Yang, Ping-Han Cheng, Shi-Hui Gilbert Chang, Terry Yuan-Fang Chen, Chih-Chieh Li

Abstract:

Nanotechnology is widely applied in various areas so professionals in the related fields have to know more than nano knowledge. In the study, we focus on adopting ICT-assisted PBL in college general education to foster professionals who possess multiple abilities. The research adopted a pretest and posttest quasi-experimental design. The control group received traditional instruction, and the experimental group received ICT-assisted PBL instruction. Descriptive statistics will be used to describe the means, standard deviations, and adjusted means for the tests between the two groups. Next, analysis of covariance (ANCOVA) will be used to compare the final results of the two research groups after 6 weeks of instruction. Statistics gathered in the end of the research can be used to make contrasts. Therefore, we will see how different teaching strategies can improve students’ understanding about nanotechnology and learning skills.

Keywords: Nanotechnology, science education, project-based learning, information and communication technology.

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Authors: Ahmed Abbas Helmy, M. R. M. Rizk, Mohamed El-Sayed

Abstract:

A model predictive controller based on recursive learning is proposed. In this SISO adaptive controller, a model is automatically updated using simple recursive equations. The identified models are then stored in the memory to be re-used in the future. The decision for model update is taken based on a new control performance index. The new controller allows the use of simple linear model predictive controllers in the control of nonlinear time varying processes.

Keywords: Adaptive control, model predictive control, dynamic matrix control, online model identification

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Authors: J. Georgiou, K. Dimitropoulos, A. Manitsaris

Abstract:

Simulations play a major role in education not only because they provide realistic models with which students can interact to acquire real world experiences, but also because they constitute safe environments in which students can repeat processes without any risk in order to perceive easier concepts and theories. Virtual reality is widely recognized as a significant technological advance that can facilitate learning process through the development of highly realistic 3D simulations supporting immersive and interactive features. The objective of this paper is to analyze the influence of virtual reality-s use in chemistry instruction as well as to present an integrated web-based learning environment for the simulation of chemical experiments. The proposed application constitutes a cost-effective solution for both schools and universities without appropriate infrastructure and a valuable tool for distance learning and life-long education in chemistry. Its educational objectives are the familiarization of students with the equipment of a real chemical laboratory and the execution of virtual volumetric analysis experiments with the active participation of students.

Keywords: Chemistry, simulations, experiments, virtual reality.

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Authors: Ahmad Ghayoumi, Mehdi Ghayoumi

Abstract:

Intelligent schools are those which use IT devices and technologies as media software, hardware and networks to improve learning process. On the other hand management improvement is best described as the process from which managers learn and improve their skills not only to benefit themselves but also their employing organizations Here, we present a model Management improvement System that has been applied on some schools and have made strict improvement.

Keywords: Intelligent school, Management development system, Learning station, Teaching station

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

Abstract:

Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Ahmad Ghayoumi, Mehdi Ghayoumi

Abstract:

Intelligent schools are those which use IT devices and technologies as media software, hardware and networks to improve learning process. On the other hand Strategic management is a field that deals with the major intended and emergent initiatives taken by general managers on behalf of owners, involving utilization of resources, to enhance the performance of firms in their external environments. Here, we present a model Strategic Management System that has been applied on some schools and have made strict improvement.

Keywords: Intelligent school, Strategic management system, Learning station, Teaching station

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Authors: Mostafa Ahmed Shokshok

Abstract:

Libyan industrial companies face many challenges in today's competitive market. Quality management culture approaches is one of these challenges which may furnish the road to the Libyan industrial companies to effectively empower their employees and improve their ability to respond to the international competition. The primary objective of this paper is to design a practical approach to guide Libyan industrial companies toward successful quality culture implementation.

Keywords: Libyan manufacturing industries, TQM, Quality culture, Quality framework.

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Authors: Çiğdem Bakır, Mecit Yüzkat

Abstract:

With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software used in the study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: Preprocessing of the data used, feature detection and classification. We tried to determine the success of our study with different accuracy metrics and the results were presented comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: Decision tree, water quality, water pollution, machine learning.

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