Search results for: Competitive learning

Authors: Ifeyinwa E. Achumba, Djamel Azzi, Rinat Khusainov

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There are three approaches to complete Bayesian Network (BN) model construction: total expert-centred, total datacentred, and semi data-centred. These three approaches constitute the basis of the empirical investigation undertaken and reported in this paper. The objective is to determine, amongst these three approaches, which is the optimal approach for the construction of a BN-based model for the performance assessment of students- laboratory work in a virtual electronic laboratory environment. BN models were constructed using all three approaches, with respect to the focus domain, and compared using a set of optimality criteria. In addition, the impact of the size and source of the training, on the performance of total data-centred and semi data-centred models was investigated. The results of the investigation provide additional insight for BN model constructors and contribute to literature providing supportive evidence for the conceptual feasibility and efficiency of structure and parameter learning from data. In addition, the results highlight other interesting themes.

Keywords: Bayesian networks, model construction, parameterlearning, structure learning, performance index, model comparison.

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Authors: Sihem Mostefai, Abdelaziz Bouras, Mohamed Batouche

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To achieve competitive advantage nowadays, most of the industrial companies are considering that success is sustained to great product development. That is to manage the product throughout its entire lifetime ranging from design, manufacture, operation and destruction. Achieving this goal requires a tight collaboration between partners from a wide variety of domains, resulting in various product data types and formats, as well as different software tools. So far, the lack of a meaningful unified representation for product data semantics has slowed down efficient product development. This paper proposes an ontology based approach to enable such semantic interoperability. Generic and extendible product ontology is described, gathering main concepts pertaining to the mechanical field and the relations that hold among them. The ontology is not exhaustive; nevertheless, it shows that such a unified representation is possible and easily exploitable. This is illustrated thru a case study with an example product and some semantic requests to which the ontology responds quite easily. The study proves the efficiency of ontologies as a support to product data exchange and information sharing, especially in product development environments where collaboration is not just a choice but a mandatory prerequisite.

Keywords: Information exchange, product lifecyclemanagement, product ontology, semantic interoperability.

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Authors: Muniyasamy Kalanchiam, Moorthy Chinnasamy

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In the competitive environment of aircraft industries it becomes absolutely necessary to improve the efficiency, performance of the aircrafts to reduce the development and operating costs considerably, in order to capitalize the market. An important contribution to improve the efficiency and performance can be achieved by decreasing the aircraft weight through considerable usage of composite materials in primary aircraft structures. In this study, a type of composite material called Carbon Fiber Reinforced Plastic (CFRP) is explored for the usage is aircraft skin panels. Even though there were plenty of studies and research has been already carried out, here a practical example of an aircraft skin panel is taken and substantiated the benefits of composites material usage over the metallic skin panel. A crown skin panel of a commercial aircraft is designed using both metal and composite materials. Stress analysis has been carried out for both and margin of safety is estimated for the critical load cases. The skin panels are compared for manufacturing, tooling, assembly and cost parameters. Detail step by step comparison between metal and composite constructions are studied and results are tabulated for better understanding.

Keywords: Composites, CFRP, Aircraft Structure, Skin panel.

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Authors: Rohan Putatunda, Aryya Gangopadhyay

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Acoustic sensors, if embedded in smart street lights, can help in capturing the activities (car honking, sirens, events, traffic, etc.) in cities. Needless to say, the acoustic data from such scenarios are complex due to multiple audio streams originating from different events, and when decomposed to independent signals, the amount of retrieved data volume is small in quantity which is inadequate to train deep neural networks. So, in this paper, we address the two challenges: a) separating the mixed signals, and b) developing an efficient acoustic classifier under data paucity. So, to address these challenges, we propose an architecture with supervised deep learning, where the initial captured mixed acoustics data are analyzed with Fast Fourier Transformation (FFT), followed by filtering the noise from the signal, and then decomposed to independent signals by fast independent component analysis (Fast ICA). To address the challenge of data paucity, we propose a multi feature-based deep neural network with high performance that is reflected in our experiments when compared to the conventional convolutional neural network (CNN) and multi-layer perceptron (MLP).

Keywords: FFT, ICA, vehicle classification, multi-feature DNN, CNN, MLP.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

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This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: Designing, education management, information systems, matrix technology, process affinity.

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Authors: P. Phenpun, S. Wareewan

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This qualitative study aimed to describe the opinions in relation to humanized care emerging from the volunteer activities of nursing students at Boromarajonani College of Nursing, Chonburi, Thailand. One hundred and twenty-seven second-year nursing students participated in this study. The volunteer activity model was composed of preparation, implementation, and evaluation through a learning log, in which students were encouraged to write their daily activities after completing practical training at the healthcare center. The preparation content included three main categories: service minded, analytical thinking, and client participation. The preparation process took over three days that accumulates up to 20 hours only. The implementation process was held over 10 days, but with a total of 70 hours only, with participants taking part in volunteer work activities at a healthcare center. A learning log was used for evaluation and data were analyzed using content analysis. The findings were as follows. With service minded, there were two subcategories that emerged from volunteer activities, which were service minded towards patients and within themselves. There were three categories under service minded towards patients, which were rapport, compassion, and empathy service behaviors, and there were four categories under service minded within themselves, which were self-esteem, self-value, management potential, and preparedness in providing good healthcare services. In line with analytical thinking, there were two components of analytical thinking, which were analytical skill for their works and analytical thinking for themselves. There were four subcategories under analytical thinking for their works, which were evidence based thinking, real situational thinking, cause analysis thinking, and systematic thinking, respectively. There were four subcategories under analytical thinking for themselves, which were comparative between themselves, towards their clients that leads to the changing of their service behaviors, open-minded thinking, modernized thinking, and verifying both verbal and non-verbal cues. Lastly, there were three categories under participation, which were mutual rapport relationship; reconsidering client’s needs services and providing useful health care information.

Keywords: Humanized care service, volunteer activity, nursing student, and learning log.

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Authors: Karolína Vozková, Matěj Kuc

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Net fee and commission income is one of the key elements of a bank’s core income. In the current low-interest rate environment, this type of income is gaining importance relative to net interest income. This paper analyses the effects of bank and country specific determinants of net fee and commission income on a set of cooperative banks from European countries in the 2007-2014 period. In order to do that, dynamic panel data methods (system Generalized Methods of Moments) were employed. Subsequently, alternative panel data methods were run as robustness checks of the analysis. Strong positive impact of bank concentration on the share of net fee and commission income was found, which proves that cooperative banks tend to display a higher share of fee income in less competitive markets. This is probably connected with the fact that they stick with their traditional deposit-taking and loan-providing model and fees on these services are driven down by the competitors. Moreover, compared to commercial banks, cooperatives do not expand heavily into non-traditional fee bearing services under competition and their overall fee income share is therefore decreasing with the increased competitiveness of the sector.

Keywords: Cooperative banking, dynamic panel data models, net fee, commission income, system GMM.

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Authors: Leandro Marcos Alves Vaz

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The teaching of physics in Brazilian public schools emphasizes strongly the theoretical aspects of this science, showing its philosophical and mathematical basis, but neglecting its experimental character. Perhaps the lack of science laboratories explains this practice. In this work, we present a method of teaching physics using the computer. As alternatives to real experiments, we have the trials through simulators, many of which are free software available on the internet. In order to develop a study on the use of simulators in teaching, knowing the impossibility of simulations on all topics in a given subject, we combined these programs with phenomenological and/or experimental texts in order to mitigate this limitation. This study proposes the use of simulators and the debate using phenomenological/experimental texts on electrostatic theme in groups of the 3rd year of EJA (Adult and Youth Education) in order to verify the advantages of this methodology. Some benefits of the hybridization of the traditional method with the tools used were: Greater motivation of the students in learning, development of experimental notions, proactive socialization to learning, greater easiness to understand some concepts and the creation of collaborative activities that can reduce timidity of part of the students.

Keywords: Physics teaching, simulators, youth and adult education, experimentation, electrostatic.

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Authors: Andreas Heindl, Alexander K. Seewald, Martin Schepelmann, Radu Rogojanu, Giovanna Bises, Theresia Thalhammer, Isabella Ellinger

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Bone remodeling occurs by the balanced action of bone resorbing osteoclasts (OC) and bone-building osteoblasts. Increased bone resorption by excessive OC activity contributes to malignant and non-malignant diseases including osteoporosis. To study OC differentiation and function, OC formed in in vitro cultures are currently counted manually, a tedious procedure which is prone to inter-observer differences. Aiming for an automated OC-quantification system, classification of OC and precursor cells was done on fluorescence microscope images based on the distinct appearance of fluorescent nuclei. Following ellipse fitting to nuclei, a combination of eight features enabled clustering of OC and precursor cell nuclei. After evaluating different machine-learning techniques, LOGREG achieved 74% correctly classified OC and precursor cell nuclei, outperforming human experts (best expert: 55%). In combination with the automated detection of total cell areas, this system allows to measure various cell parameters and most importantly to quantify proteins involved in osteoclastogenesis.

Keywords: osteoclasts, machine learning, ellipse fitting.

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Authors: Agni Stylianou-Georgiou, Eliza Pitri

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This study is a design narrative that discusses researchers’ new learning based on changes made in pedagogies and learning opportunities in the context of a Cognitive Psychology and an Art History undergraduate course. The purpose of this study was to investigate how to encourage creative problem-based play in tertiary education engaging instructors and student-teachers in designing educational games. Course instructors modified content to encourage flexible thinking during game design problem-solving. Qualitative analyses of data sources indicated that Thinking Birds’ questions could encourage flexible thinking as instructors engaged in creative problem-based play. However, student-teachers demonstrated weakness in adopting flexible thinking during game design problem solving. Further studies of student-teachers’ shifting perspectives during different instructional design tasks would provide insights for developing the Thinking Birds’ questions as tools for creative problem solving.

Keywords: Creative problem-based play, educational games, flexible thinking, tertiary education.

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Authors: Sacide Güzin Mazman, Yasemin Koçak Usluel

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Possible advantages of technology in educational context required the defining boundaries of formal and informal learning. Increasing opportunity to ubiquitous learning by technological support has revealed a question of how to discover the potential of individuals in the spontaneous environments such as social networks. This seems to be related with the question of what purposes in social networks have been being used? Social networks provide various advantages in educational context as collaboration, knowledge sharing, common interests, active participation and reflective thinking. As a consequence of these, the purpose of this study is composed of proposing a new model that could determine factors which effect adoption of social network applications for usage in educational context. While developing a model proposal, the existing adoption and diffusion models have been reviewed and they are thought to be suitable on handling an original perspective instead of using completely other diffusion or acceptance models because of different natures of education from other organizations. In the proposed model; social factors, perceived ease of use, perceived usefulness and innovativeness are determined four direct constructs that effect adoption process. Facilitating conditions, image, subjective norms and community identity are incorporated to model as antecedents of these direct four constructs.

Keywords: Adoption of innovation, educational context, social networks.

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Authors: J. Hossen, A. Rahman, K. Samsudin, F. Rokhani, S. Sayeed, R. Hasan

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The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.

Keywords: Apriori algorithm, Fuzzy C-means, MAFIE, TSK

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Authors: Rishabh Garg, Anuja Vyas, Aamna Khan, Muhammad Azwan Tariq

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The present study focuses on integrating Machine Learning and Genomics, hereafter termed ‘GenoLearning’, to develop Artificial Life (AL). This is achieved by leveraging gene editing to imbue genes with sequences capable of performing desired functions. To accomplish this, a specialized sub-network of Siamese Neural Network (SNN), named Transformer Architecture specialized in Sequence Analysis of Genes (TASAG), compares two sequences: the desired and target sequences. Differences between these sequences are analyzed, and necessary edits are made on-screen to incorporate the desired sequence into the target sequence. The edited sequence can then be synthesized chemically using a Computerized DNA Synthesizer (CDS). The CDS fabricates DNA strands according to the sequence displayed on a computer screen, aided by microprocessors. These synthesized DNA strands can be inserted into an ovum to initiate further development, eventually leading to the creation of an Embot, and ultimately, an H-Bot. While this study aims to explore the potential benefits of Artificial Intelligence (AI) technology, it also acknowledges and addresses the ethical considerations associated with its implementation.

Keywords: Machine Learning, Genomics, Genetronics, DNA, Transformer, Siamese Neural Network, Gene Editing, Artificial Life, H-Bot, Zoobot.

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Authors: Yassamina Berkane, Leïla Kloul, Yoann Demoli

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Quality of life, environmental impact, congestion of mobility means, and infrastructures remain significant challenges for urban mobility. Solutions like car sharing, spatial redesign, eCommerce, and autonomous vehicles will likely increase the unit veh-km and the density of cars in urban traffic, thus reducing congestion. However, the impact of such solutions is not clear for researchers. Congestion arises from growing populations that must travel greater distances to arrive at similar locations (e.g., workplaces, schools) during the same time frame (e.g., rush hours). This paper first reviews the research and application cases of urban congestion methods through recent years. Rethinking the question of time, it then investigates people’s willingness and flexibility to adapt their arrival and departure times from workplaces. We use neural networks and methods of supervised learning to apply a methodology for predicting peoples’ intentions from their responses in a questionnaire. We created and distributed a questionnaire to more than 50 companies in the Paris suburb. Obtained results illustrate that our methodology can predict peoples’ intentions to reschedule their activities (work, study, commerce, etc.).

Keywords: Urban mobility, decongestion, machine learning, neural network.

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Authors: Sunee Pongpinigpinyo, Wanchai Rivepiboon

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Word sense disambiguation is one of the most important open problems in natural language processing applications such as information retrieval and machine translation. Many approach strategies can be employed to resolve word ambiguity with a reasonable degree of accuracy. These strategies are: knowledgebased, corpus-based, and hybrid-based. This paper pays attention to the corpus-based strategy that employs an unsupervised learning method for disambiguation. We report our investigation of Latent Semantic Indexing (LSI), an information retrieval technique and unsupervised learning, to the task of Thai noun and verbal word sense disambiguation. The Latent Semantic Indexing has been shown to be efficient and effective for Information Retrieval. For the purposes of this research, we report experiments on two Thai polysemous words, namely  /hua4/ and /kep1/ that are used as a representative of Thai nouns and verbs respectively. The results of these experiments demonstrate the effectiveness and indicate the potential of applying vector-based distributional information measures to semantic disambiguation.

Keywords: Distributional semantics, Latent Semantic Indexing, natural language processing, Polysemous words, unsupervisedlearning, Word Sense Disambiguation.

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Authors: Abu Bakar Sedek Abdul Jamak, Rohani Salleh, Subarna Sivapalan, Azrai Abdullah

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This research focuses on micro-enterprise of Malaysian Malays that are involved in very small-scaled business activities. Among them include food stall and burger stall operators, night market hawkers, grocery store operators as well as construction and small service activities works. The study seeks to explore why some micro-entrepreneurs still lag in entrepreneurship and what needs to be rectified. This quantitative study is conducted on 173 Malay micro-enterprise owners (MEOs) and 58 Malay failed microenterprise owners (FMEOs) involved in all range of businesses throughout the state of Perak, Malaysia. The main aims are to identify the gaps between the failed micro-enterprise owners (FMEOs) and existing micro-enterprise owners (MEOs) and the problems faced among FMEOs. The results reveal that the MEOs had strong motivations and better marketing approaches as compared to FMEOs. Furthermore, the FMEOs failed in the business ventures mainly due to lack of management, sales and marketing skills and poor competitive abilities to keep up with rivals.

Keywords: Micro-enterprises, Malays, entrepreneurship, Malaysia

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Authors: Hernán G. Cortés-Mora, José I. Péna-Reyes, Alfonso Herrera-Jiménez

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This article describes the implementation experience of Project-Based Learning (PBL) in an engineering course of the Universidad Nacional de Colombia, with the aim of strengthening student skills necessary for the exercise of their profession under a sustainability framework. Firstly, we present a literature review on the education for sustainability field, emphasizing the skills and knowledge areas required for its development, as well as the commitment of the Faculty of Engineering of the Universidad Nacional de Colombia, and other engineering faculties of the country, regarding education for sustainability. This article covers the general aspects of the course, describes how students team were formed, and how their experience was during the first semester of 2017. During this period two groups of students decided to develop their course project aiming to solve a problem regarding a Non-Governmental Organization (NGO) that works with head-of-household mothers in a low-income neighborhood in Bogota (Colombia). Subsequently, we show how sustainability is involved in the course, how tools are provided to students, and how activities are developed as to strengthen their abilities, which allows them to incorporate sustainability in their projects while also working on the methodology used to develop said projects. Finally, we introduce the results obtained by the students who sent the prototypes of their projects to the community they were working on and the conclusions reached by them regarding the course experience.

Keywords: Sustainability, project based learning, engineering education, higher education for sustainability.

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Authors: Georgia H. Mega

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Street art, a category of artwork displayed in public spaces, has been recognized as a potential tool for promoting reflective thinking in both adult and primary education. Educational techniques that encourage critical and creative thinking, as well as deeper reflection, have been developed and applied in educational curricula. This paper aims to explore the potential of art street in cultivating learners' reflective awareness towards multiculturalism. More specifically, two artworks displayed in public spaces have been selected: the artwork of Kleomenis Kostopoulos and the artwork of Rustam Obic. The reason of this selection is because of their strong symbolism towards multiculturalism. The street arts have been elaborated by adult (+18) and minor students (K-12) in educational settings, under the same educator’s guidance, following appropriate for each age learning techniques. Adults cultivate their reflection using Freire’s learning method, whereas minors cultivate critical thinking using visible thinking techniques from Project Zero. Through qualitative methodology (context analysis) the depth of reflection/critical thinking has been emphasized for both age groups. The case study shows that street art can play a significant role to the promotion/cultivation of deep thinking towards challenging contemporary phenomena like multiculturalism.

Keywords: Street art, observation of art works, reflective awareness, educational techniques, multiculturalism.

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Authors: Mengist Hailemariam, Silma Yoseph

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A well designed and executed Production Planning and Control (PPC) system is one of the key levers for superior performance in the current manufacturing set-up. Hence, measuring the PPC system performance has become a necessity for long term success. The present study examined PPC related issues which impact the production capacity and productivity of leather companies with special focus on Kombolcha Tannery Share Company (KTSC), Ethiopia. Physical observation, interview, and questionnaire were used to generate necessary information from the respondents and reach valid conclusions. Company annual reports were referred and analyzed to triangulate primary data. Consequently, the study revealed that KTSC runs below its capacity due to its inefficient PPC system being in use for which the root causes were identified. The study thereby conceptualizes a PPC system improvement framework comprising three pillars viz., management culture, internal capability and performance measurement together with key considerations in each case. The study findings enable the company to recognize the importance of efficient PPC system as a source of competitive advantage. It also aid managers in evaluating various PPC execution schemes to enhance productivity.

Keywords: Ethiopia, Leather manufacturing, Production planning and control, PPC improvement framework.

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Authors: Md. Zahangir Alom, Mei-Lan Piao, Md. Ashraful Alam, Nam Kim, Jae-Hyeung Park

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A new approach for facial expressions recognition based on face context and adaptively weighted sub-pattern PCA (Aw-SpPCA) has been presented in this paper. The facial region and others part of the body have been segmented from the complex environment based on skin color model. An algorithm has been proposed to accurate detection of face region from the segmented image based on constant ratio of height and width of face (δ= 1.618). The paper also discusses on new concept to detect the eye and mouth position. The desired part of the face has been cropped to analysis the expression of a person. Unlike PCA based on a whole image pattern, Aw-SpPCA operates directly on its sub patterns partitioned from an original whole pattern and separately extracts features from them. Aw-SpPCA can adaptively compute the contributions of each part and a classification task in order to enhance the robustness to both expression and illumination variations. Experiments on single standard face with five types of facial expression database shows that the proposed method is competitive.

Keywords: Aw-SpPC, Expressoin Recognition, Face context, Face Detection, PCA

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Authors: M. A. S. Fahim, J. Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realization often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: Air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter.

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Authors: Arūnas Burinskas

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Although Europe is on the threshold of a new industrial revolution called Industry 4.0, many believe that this will increase the flexibility of production, the mass adaptation of products to consumers and the speed of their service; it will also improve product quality and dramatically increase productivity. However, as expected, all the benefits of Industry 4.0 face many of the inevitable changes and challenges they pose. One of them is the inevitable transformation of current competition and business models. This article examines the possible results of competitive conversion from the classic Bertrand and Cournot models to qualitatively new competition based on innovation. Ability to deliver a new product quickly and the possibility to produce the individual design (through flexible and quickly configurable factories) by reducing equipment failures and increasing process automation and control is highly important. This study shows that the ongoing transformation of the competition model is changing the game. This, together with the creation of complex value networks, means huge investments that make it particularly difficult for small and medium-sized enterprises. In addition, the ongoing digitalization of data raises new concerns regarding legal obligations, intellectual property, and security.

Keywords: Bertrand and Cournot Competition, competition model, Industry 4.0, industrial organization, monopolistic competition.

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Authors: Claudio D'Alonzo

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According to recent tendencies, progress on resource efficiency is possible and it will lead to economic, environmental, and social benefits. The passage to a circular economy system, in which all the materials and energy will maintain their value for as long as possible, waste is reduced and only a few resources are used, is one of the most relevant parts of the European Union's environmental policy to develop a sustainable, competitive and low-carbon economy. The concept of circular economy is to be found in Decision 1386/2013/EU of the European Parliament and of the Council on a General Union Environment Action Programme to 2020 named “Living well, within the limits of our planet”. To modernise waste management systems in the Union and to consolidate the European model as one of the most effective in the world, a revised waste legislative framework entered into force in July 2018. Regarding the Italian legislation, the laws to be modified are the Legislative Decree 3 April 2006, n. 152 and the laws ruling waste management, end-of-waste, by-products and, the regulatory principles regarding circular economy. European rules on end-of-waste are not fully harmonised and so there are legal challenges. The target to be achieved is full consistency between the laws implementing waste and chemicals policies. Only in this way, materials will be safe, fit-for-purpose and designed for durability; additionally, they will have a low environmental impact.

Keywords: Circular economy, end-of-waste, legislation, secondary raw materials.

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Authors: Jiann-Ming Wu, Hsiao-Chang Chen, Chun-Chang Wu, Pei-Hsun Hsu

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This work explores blind image deconvolution by recursive function approximation based on supervised learning of neural networks, under the assumption that a degraded image is linear convolution of an original source image through a linear shift-invariant (LSI) blurring matrix. Supervised learning of neural networks of radial basis functions (RBF) is employed to construct an embedded recursive function within a blurring image, try to extract non-deterministic component of an original source image, and use them to estimate hyper parameters of a linear image degradation model. Based on the estimated blurring matrix, reconstruction of an original source image from a blurred image is further resolved by an annealed Hopfield neural network. By numerical simulations, the proposed novel method is shown effective for faithful estimation of an unknown blurring matrix and restoration of an original source image.

Keywords: Blind image deconvolution, linear shift-invariant(LSI), linear image degradation model, radial basis functions (rbf), recursive function, annealed Hopfield neural networks.

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Authors: Shivangi Agrawal, Hsiu-I Ting

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Entrepreneurship is an essential source of economic growth and a prominent factor influencing socio-economic development. Entrepreneurship education educates and enhances entrepreneurial activity. This study aims to understand current trends in entrepreneurship education and evaluate the effectiveness of diverse entrepreneurship education programs. An increasing number of universities offer entrepreneurship education courses to create and successfully continue entrepreneurial ventures. Despite the prevalence of entrepreneurship education, research studies lack inconsistency about the effectiveness of entrepreneurship education to promote and develop entrepreneurship. Strategies to develop entrepreneurial attitudes and intentions among individuals are hindered by a lack of understanding of entrepreneurs' educational purposes, components, methodology, and resources required. Lack of adequate entrepreneurship education has been linked with low self-efficacy and lack of entrepreneurial intent. Moreover, in the age of digitisation and during the COVID-19 pandemic, digital learning platforms (e.g. online entrepreneurship education courses and programs) and other digital tools (e.g. digital game-based entrepreneurship education) have become more relevant to entrepreneurship education. This paper contributes to the continuation of academic literature in entrepreneurship education by evaluating and assessing current trends in entrepreneurship education programs, leading to better understanding to reduce gaps between entrepreneurial development requirements and higher education institutions.

Keywords: entrepreneurship education, digital technologies, academic entrepreneurship, COVID-19

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Authors: Bahar Urhan Torun, Havva Nur Tarakcı

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At the present time, online marketing has become the common target of small and full-scale organizations. Today’s humanbeing who has to spend most of their time in front of the computer because of his job, prefers to socialize by internet due to the easy access to technology. So online marketing area expands day by day. All business organizations from the smallest to the biggest are in a race in order to get a cut from the virtual market share in an extreme competitive environment. However these organizations which use the internet to reach more consumers cannot determine their target group accurately, so this is the biggest handicap of online marketing sales nowadays. The aim of this study is to determine some significant elements about need for communicating efficiently with the consumer on the internet on online marketing. The strategies that can be used in order to increase sales and the limitations of virtual environment where cannot be communicated with the consumer face to face are argued in this study’s scope. As a consequence it is thought that to study on this subject because of lacking and also being limited efficiency of researches and outputs. Within this scope suggesting some proposals about how to communicate efficiently with the consumer and also offering the consumers’ demands efficiently is the essential objective of this study.

Keywords: Communication, competition, consumer, online marketing.

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Authors: S. Shubha, B. N. Meera

Abstract:

Physics Education Research (PER) results have shown that students do not achieve the expected level of competency in understanding the concepts of different domains of Physics learning when taught by the traditional teaching methods, the concepts of Electricity and Magnetism (E&M) being one among them. Simulation being one of the valuable instructional tools renders an opportunity to visualize varied experiences with such concepts. Considering the electric force concept which requires extensive use of vector representations, we report here the outcome of the research results pertaining to the student understanding of this concept and the role of simulation in using vector representation. The simulation platform provides a positive impact on the use of vector representation. The first stage of this study involves eliciting and analyzing student responses to questions that probe their understanding of the concept of electrostatic force and this is followed by four stages of student interviews as they use the interactive simulations of electric force in one dimension. Student responses to the questions are recorded in real time using electronic pad. A validation test interview is conducted to evaluate students' understanding of the electric force concept after using interactive simulation. Results indicate lack of procedural knowledge of the vector representation. The study emphasizes the need for the choice of appropriate simulation and mode of induction for learning.

Keywords: Electric Force, Interactive, Representation, Simulation.

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Authors: Hany Alashwal, Safaai Deris, Razib M. Othman

Abstract:

Understanding proteins functions is a major goal in the post-genomic era. Proteins usually work in context of other proteins and rarely function alone. Therefore, it is highly relevant to study the interaction partners of a protein in order to understand its function. Machine learning techniques have been widely applied to predict protein-protein interactions. Kernel functions play an important role for a successful machine learning technique. Choosing the appropriate kernel function can lead to a better accuracy in a binary classifier such as the support vector machines. In this paper, we describe a Bayesian kernel for the support vector machine to predict protein-protein interactions. The use of Bayesian kernel can improve the classifier performance by incorporating the probability characteristic of the available experimental protein-protein interactions data that were compiled from different sources. In addition, the probabilistic output from the Bayesian kernel can assist biologists to conduct more research on the highly predicted interactions. The results show that the accuracy of the classifier has been improved using the Bayesian kernel compared to the standard SVM kernels. These results imply that protein-protein interaction can be predicted using Bayesian kernel with better accuracy compared to the standard SVM kernels.

Keywords: Bioinformatics, Protein-protein interactions, Bayesian Kernel, Support Vector Machines.

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Authors: Hira Lal Gope, Hidekazu Fukai

Abstract:

The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: Convolutional neural networks, coffee bean, peaberry, sorting, support vector machine.

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