Search results for: Business User Training

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

Abstract:

Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%-40% compared to a traditional RL model.

Keywords: Control system, hydroponics, machine learning, reinforcement learning.

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Authors: Martin Leroux, Sylvain Brisebois

Abstract:

Eating a meal is among the Activities of Daily Living, but it takes a lot of time and effort for people with physical or functional limitations. Dedicated technologies are cumbersome and not portable, while general-purpose assistive robots such as wheelchair-based manipulators are too hard to control for elaborate continuous motion like eating. Eating with such devices has not previously been automated, since there existed no description of a feeding motion for uncontrolled environments. In this paper, we introduce a feeding mode for assistive manipulators, including a mathematical description of trajectories for motions that are difficult to perform manually such as gathering and scooping food at a defined/desired pace. We implement these trajectories in a sequence of movements for a semi-automated feeding mode which can be controlled with a very simple 3-button interface, allowing the user to have control over the feeding pace. Finally, we demonstrate the feeding mode with a JACO robotic arm and compare the eating speed, measured in bites per minute of three eating methods: a healthy person eating unaided, a person with upper limb limitations or disability using JACO with manual control, and a person with limitations using JACO with the feeding mode. We found that the feeding mode allows eating about 5 bites per minute, which should be sufficient to eat a meal under 30min.

Keywords: Assistive robotics, Automated feeding, Elderly care, Trajectory design, Human-Robot Interaction.

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Authors: Akram Hatami, Naser Firoozi, Vesa Puhakka

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Ethics and responsibility are rapidly becoming a distinguishing feature of organizations. In this paper, we analyze ethics and responsibility in shadow factories in China. We engage ourselves with Bauman’s moral impulse perspective because his idea can contextualize ethics and responsibility. Moral impulse is a feeling of a selfless, infinite and unconditional responsibility towards, and care for, Others. We analyze a case study from a secondary data source because, for such a critical phenomenon as business ethics in shadow factories, collecting primary data is difficult, since they are unregistered factories. We argue that there has not been enough attention given to the ethics and responsibility in shadow factories in China. Our main goal is to demonstrate that, considering the Other, more importantly the employees, in ethical decision-making is a simple instruction beyond the narrow version of ethics by ethical codes and rules.

Keywords: Moral impulse, responsibility, shadow factories, the other.

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Authors: Goh Yen Nee

Abstract:

This research aimed to find out the determining factors for ISO 14001 EMS implementation among SMEs in Malaysia from the Resource based view. A cross-sectional approach using survey was conducted. A research model been proposed which comprises of ISO 14001 EMS implementation as the criterion variable while physical capital resources (i.e. environmental performance tracking and organizational infrastructures), human capital resources (i.e. top management commitment and support, training and education, employee empowerment and teamwork) and organizational capital resources (i.e. recognition and reward, organizational culture and organizational communication) as the explanatory variables. The research findings show that only environmental performance tracking, top management commitment and support and organizational culture are found to be positively and significantly associated with ISO 14001 EMS implementation. It is expected that this research will shed new knowledge and provide a base for future studies about the role played by firm-s internal resources.

Keywords: ISO 14001 Environmental Management System, Malaysia, Resource based view, SMEs

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Authors: Muhammad Nizam, Azah Mohamed, Majid Al-Dabbagh, Aini Hussain

Abstract:

This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN.

Keywords: Dynamic voltage collapse, prediction, artificial neural network, support vector machines

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Authors: S. Qaedi, S. Seyedtabaii

Abstract:

Non-Destructive evaluation of in-service power transformer condition is necessary for avoiding catastrophic failures. Dissolved Gas Analysis (DGA) is one of the important methods. Traditional, statistical and intelligent DGA approaches have been adopted for accurate classification of incipient fault sources. Unfortunately, there are not often enough faulty patterns required for sufficient training of intelligent systems. By bootstrapping the shortcoming is expected to be alleviated and algorithms with better classification success rates to be obtained. In this paper the performance of an artificial neural network, K-Nearest Neighbour and support vector machine methods using bootstrapped data are detailed and shown that while the success rate of the ANN algorithms improves remarkably, the outcome of the others do not benefit so much from the provided enlarged data space. For assessment, two databases are employed: IEC TC10 and a dataset collected from reported data in papers. High average test success rate well exhibits the remarkable outcome.

Keywords: Dissolved gas analysis, Transformer incipient fault, Artificial Neural Network, Support Vector Machine (SVM), KNearest Neighbor (KNN)

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Authors: Mecheri Zeid Belmecheri, Maamar Ahfir, Izzet Kale

Abstract:

Automatic cardiac auscultation is still a subject of research in order to establish an objective diagnosis. Recorded heart sounds as Phonocardiogram (PCG) signals can be used for automatic segmentation into components that have clinical meanings. These are the first sound, S1, the second sound, S2, and the systolic and diastolic components, respectively. In this paper, an automatic method is proposed for the robust segmentation of heart sounds. This method is based on calculating an intermediate sawtooth-shaped signal from the length variation of the recorded PCG signal in the time domain and, using its positive derivative function that is a binary signal in training a Recurrent Neural Network (RNN). Results obtained in the context of a large database of recorded PCGs with their simultaneously recorded Electrocardiograms (ECGs) from different patients in clinical settings, including normal and abnormal subjects, show on average a segmentation testing performance average of 76% sensitivity and 94% specificity.

Keywords: Heart sounds, PCG segmentation, event detection, Recurrent Neural Networks, PCG curve length.

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Authors: Romisaa Ali

Abstract:

The Policy Gradient approach is a subset of the Deep Reinforcement Learning (DRL) combines Deep Neural Networks (DNN) with Reinforcement Learning (RL). This approach finds the optimal policy of robot movement, based on the experience it gains from interaction with its environment. Unlike previous policy gradient algorithms, which were unable to handle the two types of error variance and bias introduced by the DNN model due to over- or underestimation, this algorithm is capable of handling both types of error variance and bias. This article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: Deep neural networks, deep reinforcement learning, Proximal Policy Optimization, state-of-the-art, trust region policy optimization.

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Authors: Marina Laškarin, Vlado Galičić

Abstract:

Quick adoption of e-business and emerging influence of “Electronic Word of Mouth e-WOM” communication on guests made leading hotel brands successful examples of electronic guest relationship management. Main reasons behind such success are well established procedures in collection, analysis and usage of highly valuable data available on the Internet, generated through some form of e-GRM programme. E-GRM is more than just a technology solution. It’s a system which balance respective guest demands, hotel technological capabilities and organizational culture of employees, discharging the universal approach in guest relations “same for all”. The purpose of this research derives from the necessity of determining the importance of monitoring and applying e-WOM communication as one of the methods used in managing guest relations. This paper analyses and compares different hotelier’s opinions on e-WOM communication.

Keywords: Brand loyalty, e-WOM communication, GRM programmes.

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Authors: Ali Hussian Ali AlTimemy, Fawzi M. Al Naima

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This paper presents the prediction of kidney dysfunction using different neural network (NN) approaches. Self organization Maps (SOM), Probabilistic Neural Network (PNN) and Multi Layer Perceptron Neural Network (MLPNN) trained with Back Propagation Algorithm (BPA) are used in this study. Six hundred and sixty three sets of analytical laboratory tests have been collected from one of the private clinical laboratories in Baghdad. For each subject, Serum urea and Serum creatinin levels have been analyzed and tested by using clinical laboratory measurements. The collected urea and cretinine levels are then used as inputs to the three NN models in which the training process is done by different neural approaches. SOM which is a class of unsupervised network whereas PNN and BPNN are considered as class of supervised networks. These networks are used as a classifier to predict whether kidney is normal or it will have a dysfunction. The accuracy of prediction, sensitivity and specificity were found for each type of the proposed networks .We conclude that PNN gives faster and more accurate prediction of kidney dysfunction and it works as promising tool for predicting of routine kidney dysfunction from the clinical laboratory data.

Keywords: Kidney Dysfunction, Prediction, SOM, PNN, BPNN, Urea and Creatinine levels.

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Authors: Ghulam Rasool, Nadim Asif

Abstract:

The advent of modern technology shadows its impetus repercussions on successful Legacy systems making them obsolete with time. These systems have evolved the large organizations in major problems in terms of new business requirements, response time, financial depreciation and maintenance. Major difficulty is due to constant system evolution and incomplete, inconsistent and obsolete documents which a legacy system tends to have. The myriad dimensions of these systems can only be explored by incorporating reverse engineering, in this context, is the best method to extract useful artifacts and by exploring these artifacts for reengineering existing legacy systems to meet new requirements of organizations. A case study is conducted on six different type of software systems having source code in different programming languages using the architectural recovery framework.

Keywords: Reverse Engineering, Architecture recovery, Architecture artifacts, Reengineering.

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Authors: Kalsom Abd Wahab, Khalid Hassan Abdesamed

Abstract:

The purpose of this paper is to examine the financing practices of SMEs in Libya in two different phases of business life cycle: start-up and matured stages. Moreover, SMEs- accessing bank loan issues is also identified. The study was conducted by taking into account the aspect of demand. The findings are based on a sample of 76 SMEs in Libya through the adoption of questionnaires. The results have pinpointed several things- evidently, SMEs use informal financing sources which prefer personal savings; SME owners are willing to apply for bank loan, that the most pressing problem has been identified, not to apply bank loan is loan with interest (religion factor).

Keywords: SMEs, Formal Finance (loan from bank), Informal Finance, Loan with interest (religion factor), Libya.

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Authors: Andrzej Przemyslaw Kusnierczak

Abstract:

Advances in technology (e.g. the internet, telecommunication) and political changes (fewer trade barriers and an enlarged European Union, ASEAN, NAFTA and other organizations) have led to develop international competition and expand into new markets. Companies in Thailand, Asia and around the globe are increasingly being pressured on price and for faster time to enter the market. At the same time, new markets are appearing and many companies are looking for changes and shifts in their domestic markets. These factors have enabled the rapid growth for companies and globalizing many different business activities during the product development process from research and development (R&D) to production. This research will show and clarify methods how to develop global product. Also, it will show how important is a global product impact into Thai Economy development.

Keywords: Development, global, management, product.

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Authors: F.B. Georgise, K. D. Thoben, M. Seifert

Abstract:

This paper presents a research agenda on the SCOR model adaptation. SCOR model is designated to measure supply chain performance and logistics impact across the boundaries of individual organizations. It is at its growing stage of its life cycle and is enjoying the leverage of becoming the industry standard. The SCOR model has been developed and used widely in developed countries context. This research focuses on the SCOR model adaptation for the manufacturing industry in developing countries. With a necessary understanding of the characteristics, difficulties and problems of the manufacturing industry in developing countries- supply chain; consequently, we will try to designs an adapted model with its building blocks: business process model, performance measures and best practices.

Keywords: developing countries, manufacturing industry, SCOR model adaptation

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Authors: M.V Rajesh, Archana R, A Unnikrishnan, R Gopikakumari, Jeevamma Jacob

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The System Identification problem looks for a suitably parameterized model, representing a given process. The parameters of the model are adjusted to optimize a performance function based on error between the given process output and identified process output. The linear system identification field is well established with many classical approaches whereas most of those methods cannot be applied for nonlinear systems. The problem becomes tougher if the system is completely unknown with only the output time series is available. It has been reported that the capability of Artificial Neural Network to approximate all linear and nonlinear input-output maps makes it predominantly suitable for the identification of nonlinear systems, where only the output time series is available. [1][2][4][5]. The work reported here is an attempt to implement few of the well known algorithms in the context of modeling of nonlinear systems, and to make a performance comparison to establish the relative merits and demerits.

Keywords: Multilayer neural networks, Radial Basis Functions, Clustering algorithm, Back Propagation training, Extended Kalmanfiltering, Mean Square Error, Nonlinear Modeling, Cramer RaoLower Bound.

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Authors: Teerayout Wattanasupachoke

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This research project is developed in order to study managerial styles of modern Thai executives. The thorough understanding will lead to continuous improvement and efficient performance of Thai business organizations. Regarding managerial skills, Thai executives focus heavily upon human skills. Also, the negotiator roles are most emphasis in their management. In addition, Thai executives pay most attention to the fundamental management principles including Harmony and Unity of Direction of the organizations. Moreover, the management techniques, consisting of Team work and Career Planning are of their main concern. Finally, Thai executives wish to enhance their firms- image and employees- morale through conducting the ethical and socially responsible activities. The major tactic deployed to stimulate employees- ethical behaviors and mindset is Code of Ethics development.

Keywords: Management, Managerial Styles, Asian Executives, Thailand.

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Authors: Samson Davoyan, Tatevik Sahakyan

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The purpose of this report is to suggest a new methodology for the assessment of the comparative efficiency of the reforms made in different countries by an integral index. We have highlighted the reforms made in post-crisis period in 21 former socialist countries. The integral index describes the social-economic development level. The integral index contains of six indexes: The Global Competitiveness Index, Doing Business, The Corruption Perception, The Index of Economic Freedom, The Human Development, and The Democracy Index, which are reported by different international organizations. With the help of our methodology we first summarized the above-mentioned 6 indexes and attained 1 general index, besides, our new method enables us to assess the comparative efficiency of the reforms made in different countries by analyzing them. The purpose is to reveal the opportunities and threats of socialeconomic reforms in different directions.

Keywords: Assessment, comparative, effectiveness, reforms

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Authors: D. B. L. Bong, K.C. Lai, A. Joseph

Abstract:

The uses of road map in daily activities are numerous but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the difficulties in updating road map. The research started with using Satellite Image (SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space Segmentation & Edge Detection (Hybrid SCSS-EDGE) algorithm was developed to extract roads automatically from satellite-taken images. In order to extract the road network accurately, the satellite image must be analyzed prior to the extraction process. The characteristics of these elements are analyzed and consequently the relationships among them are determined. In this study, the road regions are extracted based on colour space elements and edge details of roads. Besides, edge detection method is applied to further filter out the non-road regions. The extracted road regions are validated by using a segmentation method. These results are valuable for building road map and detecting the changes of the existing road database. The proposed Hybrid Simple Colour Space Segmentation and Edge Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks fully automatic, where the user only needs to input a high-resolution satellite image and wait for the result. Moreover, this system can work on complex road network and generate the extraction result in seconds.

Keywords: Road Network Recognition, Colour Space, Edge Detection, Urban Planning.

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Authors: Zuhaib Ashfaq Khan, Imran Khan, Nandana Rajatheva

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In this paper, hybrid FDMA-TDMA access technique in a cooperative distributive fashion introducing and implementing a modified protocol introduced in [1] is analyzed termed as Power and Cooperation Diversity Gain Protocol (PCDGP). A wireless network consists of two users terminal , two relays and a destination terminal equipped with two antennas. The relays are operating in amplify-and-forward (AF) mode with a fixed gain. Two operating modes: cooperation-gain mode and powergain mode are exploited from source terminals to relays, as it is working in a best channel selection scheme. Vertical BLAST (Bell Laboratories Layered Space Time) or V-BLAST with minimum mean square error (MMSE) nulling is used at the relays to perfectly detect the joint signals from multiple source terminals. The performance is analyzed using binary phase shift keying (BPSK) modulation scheme and investigated over independent and identical (i.i.d) Rayleigh, Ricean-K and Nakagami-m fading environments. Subsequently, simulation results show that the proposed scheme can provide better signal quality of uplink users in a cooperative communication system using hybrid FDMATDMA technique.

Keywords: Cooperation Diversity, Best Channel Selectionscheme, MIMO relay networks, V-BLAST, QRdecomposition, and MMSE.

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Authors: Ibrahim Shaker, Amr El Hossany, Moustafa Osman, Mohamed El Raey

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This paper will explore integration model between GIS–SCADA system and enclosure quantification model to approach the impact of failure-safe event. There are real demands to identify spatial objects and improve control system performance. Nevertheless, the employed methodology is predicting electro-mechanic operations and corresponding time to environmental incident variations. Open processing, as object systems technology, is presented for integration enclosure database with minimal memory size and computation time via connectivity drivers such as ODBC:JDBC during main stages of GIS–SCADA connection. The function of Geographic Information System is manipulating power distribution in contrast to developing issues. In other ward, GIS-SCADA systems integration will require numerical objects of process to enable system model calibration and estimation demands, determine of past events for analysis and prediction of emergency situations for response training.

Keywords: Air dispersion model, integration power system, SCADA systems, GIS system, environmental management.

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Authors: Jeff Clarine, Chang-Shyh Peng, Daisy Sang

Abstract:

Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.

Keywords: Bioassay, machine learning, preprocessing, virtual screen.

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Authors: Tessy Badriyah, Jim S. Briggs, Dave R. Prytherch

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It is well known that Logistic Regression is the gold standard method for predicting clinical outcome, especially predicting risk of mortality. In this paper, the Decision Tree method has been proposed to solve specific problems that commonly use Logistic Regression as a solution. The Biochemistry and Haematology Outcome Model (BHOM) dataset obtained from Portsmouth NHS Hospital from 1 January to 31 December 2001 was divided into four subsets. One subset of training data was used to generate a model, and the model obtained was then applied to three testing datasets. The performance of each model from both methods was then compared using calibration (the χ2 test or chi-test) and discrimination (area under ROC curve or c-index). The experiment presented that both methods have reasonable results in the case of the c-index. However, in some cases the calibration value (χ2) obtained quite a high result. After conducting experiments and investigating the advantages and disadvantages of each method, we can conclude that Decision Trees can be seen as a worthy alternative to Logistic Regression in the area of Data Mining.

Keywords: Decision Trees, Logistic Regression, clinical outcome, risk of mortality.

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Authors: Sri Sumartiningsih

Abstract:

In elevating performance in competetive sports, an athlete must continously train in achieving maximum performance,but needs to pay attention to recovery therapy, that is to recover from fatigue as well as injury.The correct recovery therapy will assist in process of recovery and helps in the training in achieving better performace. Binahong (Anredera cordifolia) was proven empirically by the locals in assisting speedy recovery from an injury.Clinical research with lab animals receiving blunt trauma injury, microscopically shown signs of: 1) redness, 2) heatiness, 3) swelling and, 4) lack of activity. There is also microscopic indication of: 1) infiltration of inflame cells (migration of cells to the trauma area), 2) Cells necrosis, 3) Congestion (as a result of dead red blood cells), 4) uedema. On administration of Binahong for 3 days, there is a significant drop of 5% in cell inflammation, 2% increase of fibroblast (cell membrance) count.Conclutin: Binahong do assist in reducing cell inflammation and increase counts of cells fibroblast. Suggestion: In helping athlete's to recover from force injury, we need study about Binahong's roots to inflammation cell and healing of injuried cell.

Keywords: Binahong, sport injury, hematoma

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Authors: Hamed Rahmani, Wim Groot

Abstract:

The term "youth not in employment, education or training (NEET)" refers to a combination of youth unemployment and school dropout. This study investigates the variables that increase the risk of becoming NEET in Iran. A selection bias-adjusted Probit model was employed using machine learning to identify these risk factors. We used cross-sectional data obtained from the Statistical Center of Iran and the Ministry of Cooperatives Labor and Social Welfare that are taken from the labor force survey conducted in the spring of 2021. We look at years of education, work experience, housework, the number of children under the age of 6 years in the home, family education, birthplace, and the amount of land owned by households. Results show that hours spent performing domestic chores enhance the likelihood of youth becoming NEET, and years of education, years of potential work experience decrease the chance of being NEET. The findings also show that female youth born in cities were less likely than those born in rural regions to become NEET.

Keywords: NEET youth, probit, CART, machine learning, unemployment.

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

Abstract:

Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: Heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation.

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Authors: V. Prapulla, A. Mitra, R. Bhattacharjee, S. Nandi

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We present a simplified equalization technique for a π/4 differential quadrature phase shift keying ( π/4 -DQPSK) modulated signal in a multipath fading environment. The proposed equalizer is realized as a fractionally spaced adaptive decision feedback equalizer (FS-ADFE), employing exponential step-size least mean square (LMS) algorithm as the adaptation technique. The main advantage of the scheme stems from the usage of exponential step-size LMS algorithm in the equalizer, which achieves similar convergence behavior as that of a recursive least squares (RLS) algorithm with significantly reduced computational complexity. To investigate the finite-precision performance of the proposed equalizer along with the π/4 -DQPSK modem, the entire system is evaluated on a 16-bit fixed point digital signal processor (DSP) environment. The proposed scheme is found to be attractive even for those cases where equalization is to be performed within a restricted number of training samples.

Keywords: Adaptive decision feedback equalizer, Fractionally spaced equalizer, π/4 DQPSK signal, Digital signal processor.

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Authors: Wissam B. Fahed

Abstract:

Entrepreneurship has become an important and extensively researched concept in business studies. Research on foreign direct investment (FDI) has become widespread due to the growth of FDI and its importance in globalization. Most entrepreneurship studies examined the importance and influence of entrepreneurial orientation in a micro-level context. On the other hand, studies and research concerning FDI used statistical techniques to analyze the effect, determinants, and motives of FDI on a macroeconomic level, ignoring empirical studies on other noneconomic determinants. In order to bridge the gap between the theory and empirical evidence on FDI and the theory and research on entrepreneurship, this study examines the impact of entrepreneurship on inward foreign direct investment. The relationship between entrepreneurship and foreign direct investment is investigated through regression analysis of pooled time-series and cross-sectional data. The results suggest that entrepreneurship has a significant effect on FDI.

Keywords: Entrepreneurship, foreign direct investment, globalization, economic freedom.

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Authors: Jia Xin Low, Keng Wah Choo

Abstract:

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: Convolutional neural network, discrete wavelet transform, deep learning, heart sound classification.

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Authors: Mahmoud Shaker, Ala'a Imran

Abstract:

Greenhouse is a building, which provides controlled climate conditions to the plants to keep them from external hard conditions. Greenhouse technology gives freedom to the farmer to select any crop type in any time during year. The quality and productivity of plants inside greenhouse is highly dependent on the management quality and a good management scheme is defined by the quality of the information collected from the greenhouse environment. Therefore, Continuous monitoring of environmental variables such as temperature, humidity, and soil moisture gives information to the grower to better understand, how each factor affects growth and how to manage maximal crop productiveness. In this piper, we designed and implemented climate monitoring with irrigation control system based on Wireless Sensor Network (WSN) technology. The designed system is characterized with friendly to use, easy to install by any greenhouse user, multi-sensing nodes, multi-PAN ID, low cast, water irrigation control and low operation complexity. The system consists of two node types (sensing and control) with star topology on one PAN ID. Moreover, greenhouse manager can modifying system parameters such as (sensing node addresses, irrigation upper and lower control limits) by updating corresponding data in SDRAM memory. In addition, the designed system uses 2*16 characters. LCD to display the micro climate parameters values of each plants row inside the greenhouse.

Keywords: ZigBee, WSN, Arduino platform, Greenhouse automation, micro climate monitoring, smart Irrigation control.

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Authors: Samir Abdelhamid, Noureddine Bouguechal

Abstract:

We report in this paper the model adopted by our system of continuous speech recognition in Arab language SySRA and the results obtained until now. This system uses the database Arabdic-10 which is a corpus of word for the Arab language and which was manually segmented. Phonetic decoding is represented by an expert system where the knowledge base is translated in the form of production rules. This expert system transforms a vocal signal into a phonetic lattice. The higher level of the system takes care of the recognition of the lattice thus obtained by deferring it in the form of written sentences (orthographical Form). This level contains initially the lexical analyzer which is not other than the module of recognition. We subjected this analyzer to a set of spectrograms obtained by dictating a score of sentences in Arab language. The rate of recognition of these sentences is about 70% which is, to our knowledge, the best result for the recognition of the Arab language. The test set consists of twenty sentences from four speakers not having taken part in the training.

Keywords: Continuous speech recognition, lexical analyzer, phonetic decoding, phonetic lattice, vocal signal.

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