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Showing papers in "Indonesian Journal of Electrical Engineering and Computer Science in 2020"


Journal ArticleDOI
TL;DR: This study has clarified the best candidate routing algorithms integrated with minimum processing times and low blocking probabilities for modern parallel computing systems.
Abstract: This study has clarified the best candidate routing algorithms integrated with minimum processing times and low blocking probabilities for modern parallel computing systems. Different methods were employed, such as the fast window method (FWM), fast bitwise window method (FBWM), and fast improved window method (FIWM), to upgrade the processing time and reduce the network delay time. In addition, different algorithms were studied such as the fast window ascending, the fast window descending, the fast window sequential algorithm, and the fast window sequential down algorithms; these were studied to show the numerical results of the networks’ blocking probabilities, processing times, and delay times.

82 citations


Journal ArticleDOI
TL;DR: This study has emphasized the important role of pulse position modulation transmission coders, which exhibit superior performance in max.
Abstract: The present study has outlined laser-measured rate equations with various transmission coders for optimum data transmission error rates. Various modulation transmission coders are employed, such as a pulse position modulation coder, a differential pulse intensity modulation coder, and a four band/five band modulation transmission coder, in order to create optimized data rates of up to 40 GB/s for a fiber extension length of up to 100 km. This study has emphasized the important role of pulse position modulation transmission coders, which exhibit superior performance in max. Q parameter and min. data error rates, even for high data rate transmission.

77 citations


Journal ArticleDOI
TL;DR: In this article, the authors presented the interaction between tangential/sagittal cylindrical thin lens in the titled plane parallel crystal and optimized the focal length for both thin lenses in resonator crystal to upgrade the resonator system operation efficiency.
Abstract: This work has presented the interaction between tangential/sagittal cylindrical thin lens in the titled plane parallel crystal. Stability criterion parameters are measured under the control of curvature radius of a spherical mirror, the thickness of the tilted plane crystal, the refractive index of tilted plane crystal, the thickness for a plate of matter and phase angle of the sagittal cylindrical thin lens. Beam radius waist is plotted against the focal length of the tangential cylindrical thin lens. Focal length for both thin lens in resonator crystal is optimized to upgrade the resonator system operation efficiency.

68 citations


Journal ArticleDOI
TL;DR: The field of optical fiber was used in the detection of some diseases associated with oral and dental medicine to reduce the use of radiation in the diagnosis of tooth caries and saves time because it gives the diagnosis at the same time while the patient is in the treatment unit of the dental clinic.
Abstract: There is no doubt that radiation has many side effects in our lives, so our goal in this study is to reduce our use of radiation in the diagnosis of tooth caries, and in order to achieve this goal we used the field of optical fiber in the detection of some diseases associated with oral and dental medicine. This diagnosis was accomplished by shedding light on the teeth, to be diagnosed and creating an image that allows the doctor to examine them and determine whether there are caries or any root problems. The principle is also used to detect oral cancer, fractures, and cracks in bones. This study also allows us to detect early caries, and this method saves time because it gives the diagnosis at the same time while the patient is in the treatment unit of the dental clinic. Thus the main advantages of this method are, enable the dentist to view images on the display unit attached to the clinic in real-time, compare current photos with old photos of the same patient. Also, the dentist can create a photo archive of each tooth individually and can retrieve and compare them whenever he wants.

67 citations


Journal ArticleDOI
TL;DR: In this paper, the Z resonator with Brewster crystal in the presence of a flat mirror for measuring the standing wave ratio has been outlined and the beam radius variations against phase angle and curvature radius of spherical mirror in T and S planes.
Abstract: This study has outlined the Z resonator shaped with Brewster crystal in the presence of flat mirror for measuring the standing wave ratio. Stability parameter and beam radius are simulated versus thickness, refractive index of the crystal and first and second folding ranges. Beam radius variations are studied against phase angle and curvature radius of spherical mirror in T and S planes. Intermode beat frequency of the system is 216.276 MHz and total cavity length is 693.078 mm. It is important the standing wave ratio is dependent on stability parameter and beam radius variations.

66 citations


Journal ArticleDOI
TL;DR: This review article aims to present clearly the issues that HR researchers face and for which computer scientists seek solutions by summarizing the IT solutions already made in human resources for the period between 2008 and 2018.
Abstract: In the last few years, all companies have been interested in the analysis of data related to Human Resources and have focused on human capital, which is considered as the major factor influencing the company’s development and all its activities at all levels of human resource policies. Data analysis (HR analytics) will significantly improve business profitability over the next years.We started with an extensive survey of different human resources problems and risks reported by HR specialists, then a comprehensive review of recent research efforts on computer science techniques proposed to solve these problems and finally focusing on suggested artificial intelligence methods. This review article will be an archive and a reference for computer scientists working on HR by summarizing the IT solutions already made in human resources for the period between 2008 and 2018. It aims to present clearly the issues that HR researchers face and for which computer scientists seek solutions. It summarizes at the same time the recent and different methods, IT approaches and tools already used by highlighting those using artificial intelligence.

44 citations


Journal ArticleDOI
TL;DR: The goal of this paper is to find feasible solutions to enhance the healthcare living facilities using remote health monitoring (RHM) and IoMT and the enhancement of the prevention, prognosis, diagnosis and treatment abilities using Io MT and RHM.
Abstract: The latest advances and trends in information technology and communication have a vital role in healthcare industries. Theses advancements led to the internet of medical things (IoMT) which provides a continuous, remote and real-time monitoring of patients. The IoMT architectures still face many challenges related to the bandwidth, communication protocols, big data and data volume, flexibility, reliability, data management, data acquisition, data processing and analytics availability, cost effectiveness, data security and privacy, and energy efficiency. The goal of this paper is to find feasible solutions to enhance the healthcare living facilities using remote health monitoring (RHM) and IoMT. In addition, the enhancement of the prevention, prognosis, diagnosis and treatment abilities using IoMT and RHM is also discussed. A case study of monitoring the vital signs of diabetic patients using real-time data processing and IoMT is also presented .

39 citations


Journal ArticleDOI
TL;DR: This paper addresses a comparison study on scientific unstructured text document classification (e-books) based on the full text where applying the most popular topic modeling approach (LDA, LSA) to cluster the words into a set of topics as important keywords for classification.
Abstract: With the rapid growth of information technology, the amount of unstructured text data in digital libraries is rapidly increased and has become a big challenge in analyzing, organizing and how to classify text automatically in E-research repository to get the benefit from them is the cornerstone. The manual categorization of text documents requires a lot of financial, human resources for management. In order to get so, topic modeling are used to classify documents. This paper addresses a comparison study on scientific unstructured text document classification (e-books) based on the full text where applying the most popular topic modeling approach (LDA, LSA) to cluster the words into a set of topics as important keywords for classification. Our dataset consists of (300) books contain about 23 million words based on full text. In the used topic models (LSA, LDA) each word in the corpus of vocabulary is connected with one or more topics with a probability, as estimated by the model. Many (LDA, LSA) models were built with different values of coherence and pick the one that produces the highest coherence value. The result of this paper showed that LDA has better results than LSA and the best results obtained from the LDA method was ( 0.592179 ) of coherence value when the number of topics was 20 while the LSA coherence value was (0.5773026) when the number of topics was 10.

30 citations


Journal ArticleDOI
TL;DR: The obtained result has shown that real time temperature monitoring data can be transferred to authentic observer by utilizing internet of things (IoT) applications.
Abstract: The recent advances in electronics and microelectronics devices allow the development of newly low-cost monitoring tools used by peoples for health preventive purposes. Sensors used in medical equipments convert various forms of human body vital signs into electrical signals. Therefore, the healthcare monitoring systems considering non-invasive and wearable sensors with integrated communication mediums allow an efficient solution to live a comfortable home life. This paper presents the remote monitoring of human body temperature (HBT) wirelessly by means of Arduino controller with different sensors and open source internet connection. The proposed monitoring system uses an internet network via wireless fieldity (wifi) connection to be linked with online portal on smart phone or computer. The proposed system is comprised of an Arduino controller, LM-35 (S1), MLX-90614 (S2) temperature sensors and ESP-wifi shield module. The obtained result has shown that real time temperature monitoring data can be transferred to authentic observer by utilizing internet of things (IoT) applications. The findings from this research indicates that the difference of average temperature in between Sensor S1 and S2 is about 15 0 C

29 citations


Journal ArticleDOI
TL;DR: This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks and shows the results of deep models by research experiment.
Abstract: Text classification is a fundamental task in several areas of natural language processing (NLP), including words semantic classification, sentiment analysis, question answering, or dialog management. This paper investigates three basic architectures of deep learning models for the tasks of text classification: Deep Belief Neural (DBN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), these three main types of deep learning architectures, are largely explored to handled various classification tasks. DBN have excellent learning capabilities to extracts highly distinguishable features and good for general purpose. CNN have supposed to be better at extracting the position of various related features while RNN is modeling in sequential of long-term dependencies. This paper work shows the systematic comparison of DBN, CNN, and RNN on text classification tasks. Finally, we show the results of deep models by research experiment. The aim of this paper to provides basic guidance about the deep learning models that which models are best for the task of text classification.

29 citations


Journal ArticleDOI
TL;DR: A Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification.
Abstract: In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.

Journal ArticleDOI
TL;DR: The results of the simulation and security analysis showed that the new DNA coding is suitable for text encryption/decryption and that the super-chaotic map is suited for hiding/extract the encrypted data, which indicates that the proposed encryption algorithm has a good encryption and hiding effect.
Abstract: In this paper, a secure data encryption using DNA sequence operation in a new and innovative direction different from the traditional direction, DNA coding uses the eight rules of DNA code based on a sequence of letter in text that provides the possibility of encrypting the same letter or word in more than one form in one text-based on sequence of this letter or word in the text. Then hiding technique implemented based on a hyperchaotic system. To increase the security of encryption text, we use the hyperchaotic system for obtaining the color image position that used to hide on it. The proposed steganography method hides a letter of the encrypted text in each pixel of the cover image, thus giving the possibility of hiding large text data. Some metrics have been applied to the proposed algorithm such as NPCR analyses, MSE, Correlation, and BER, The results of the simulation and security analysis showed that the new DNA coding is suitable for text encryption/decryption and that the super-chaotic map is suitable for hiding/extract the encrypted data, which indicates that the proposed encryption algorithm has a good encryption and hiding effect. Can resist brute statistical attack, force attack, differential.

Journal ArticleDOI
TL;DR: This study has proposed a new procedure for RFM analysis using the k-Means method and eight indexes of validity to determine the optimal number of clusters namely Elbow Method, Silhouette Index, Calinski-Harabasz Index, Davies-Bouldin Index, Ratkowski Index, Hubert Index, Ball-Hall Index, and Krzanowski-Lai Index which can improve the objectivity and similarity of data in product segmentation.
Abstract: RFM stands for Recency, Frequency, and Monetary. RFM is a simple but effective method that can be applied to market segmentation. RFM analysis is used to analyze customer’s behavior which consists of how recently the customers have purchased (recency), how often customer’s purchases (frequency), and how much money customers spend (monetary). In this study, RFM analysis has been used for product segmentation is to be arrayed in terms of recent sales (R), frequent sales (F), and the total money spent (M) using the data mining method. This study has proposed a new procedure for RFM analysis (in product segmentation) using the k-Means method and eight indexes of validity to determine the optimal number of clusters namely Elbow Method, Silhouette Index, Calinski-Harabasz Index, Davies-Bouldin Index, Ratkowski Index, Hubert Index, Ball-Hall Index, and Krzanowski-Lai Index, which can improve the objectivity and similarity of data in product segmentation so that it can improve the accuracy of the stock management process. The evaluation results showed that the optimal number of clusters for the k-Means method applied in the RFM analysis consists of three clusters (segmentation) with a variance value of 0.19113.

Journal ArticleDOI
TL;DR: This paper presents a novel design 2:1 QCA-Multiplexer in two forms that is very simple, highly efficient and can be used to produce many logical functions.
Abstract: Quantum-dot Cellular Automata (QCA) is one of the most important computing technologies for the future and will be the alternative candidate for current CMOS technology. QCA is attracting a lot of researchers due to many features such as high speed, small size, and low power consumption. QCA has two main building blocks (majority gate and inverter) used for design any Boolean function. QCA also has an inherent capability that used to design many important gates such as XOR and Multiplexer in optimal form without following any Boolean function. This paper presents a novel design 2:1 QCA-Multiplexer in two forms. The proposed design is very simple, highly efficient and can be used to produce many logical functions. The proposed design output comes from the inherent capabilities of quantum technology. New 4:1 QCA-Multiplexer has been built using the proposed structure. The output waveforms showed the wonderful performance of the proposed design in terms of the number of cells, area, and latency.

Journal ArticleDOI
TL;DR: This paper will focus on the study of the main component in ITS systems and present a review of the major V2V benefits related to driver safety by focusing primarily on the recent developments of these systems.
Abstract: The field of automated vehicle technology is developing rapidly developing. While it is likely to be many years before self-driving cars are commercially viable and used in a wide range of conditions by the general public, technological advances are speeding along the automated technology continuum towards this destination. Automated vehicle technologies troth with significant social benefits such as reduced injuries and deaths, increased road efficiency, mobility. Automated vehicles can improve traffic safety, balance traffic flows, maximize road usage by offering driver warnings and/or assuming vehicle control in dangerous situations, as well as provide motorists with the best end-to-end transportation experience and reduce emissions, which are the most important goals of modern smart traffic control infrastructures. Exchanging data and integration of such systems with Vehicle-to-Vehicle (V2V) may be a keystone to successful readying of vehicular ad-hoc networks (VANETs) and will simply be the following step of this evolution, with dynamic period of time data exchange between all the players of the traffic dominant system and fostering cooperative urban quality. One of the applications of this concept is to provide vehicles and roads with the ability to make road time more enjoyable and also to make roads safer. These applications are typical examples of what an Intelligent Transportation System (ITS) is called, whose objective is to improve security by using new information and communication technologies (NTIC). In this paper, we will focus on the study of the main component in ITS systems and present a review of the major V2V benefits related to driver safety by focusing primarily on the recent developments of these systems.

Journal ArticleDOI
TL;DR: Electroencephalography acquisition tool was utilized to collect brain signals from 40 subjects and objectively reflected stress features induced by virtual reality (VR) technology and indicated that the power ratios can discriminate the data characteristics of brainwaves for stress assessment.
Abstract: This paper presents an analysis of stress feature using the power ratio of frequency bands including Alpha to Beta and Theta to Beta. In this study, electroencephalography (EEG) acquisition tool was utilized to collect brain signals from 40 subjects and objectively reflected stress features induced by virtual reality (VR) technology. The EEG signals were analyzed using Welch’s fast Fourier transform (FFT) to extract power spectral density (PSD) features which represented the power of a signal distributed over a range of frequencies. Slow wave versus fast wave (SW/FW) of EEG has been studied to discriminate stress from resting baseline. The results showed the Alpha/Beta ratio and Theta/Beta ratio are negatively correlated with stress and indicated that the power ratios can discriminate the data characteristics of brainwaves for stress assessment.

Journal ArticleDOI
TL;DR: A new method for improving accuracy of classification of breast cancer datasets is proposed with the use of Hoeffding trees for normal classification and naive Bayes for reducing data dimensionality.
Abstract: The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naive Bayes Classifications is good at predicting outcomes and often outperforms other classifications techniques. Ones of the reasons behind this strong performance of NBC is the assumptions of conditional Independences among the initial parameters and the predictors. However, this assumption is not always true and can cause loss of accuracy. Hoeffding trees assume the suitability of using a small sample to select the optimal splitting attribute. This study proposes a new method for improving accuracy of classification of breast cancer datasets. The method proposes the use of Hoeffding trees for normal classification and naive Bayes for reducing data dimensionality.

Journal ArticleDOI
TL;DR: The proposed approach is the initial steps to make a prototype for the automatic detection of rice false smut using faster R-CNN, which is able to detect the RFS using rectangular labelling from on-field images.
Abstract: Rice false smut is one of the most dangerous diseases in rice at the ripening phase caused by Ustilaginoidea Virens. It is one of the most important grain diseases in rice production worldwide. Its epidemics not only lead to yield loss but also reduce grain quality because of multiple mycotoxins generated by the causative pathogen. The pathogen infects developing spikelets and specifically converts individual grain into rice false smut ball. Rice false smut balls seem to be randomly formed in some grains on a panicle of a plant in the paddy field. In this study, we suggest a novel approach for the detection of rice false smut based on faster R-CNN. The process of faster R-CNN comprises regional proposal generation and object detection. The both tasks are done in same convolutional network. Because of such design it is faster for object detection. The faster R-CNN is able to detect the RFS using rectangular labelling from on-field images. The proposed approach is the initial steps to make a prototype for the automatic detection of RFS.

Journal ArticleDOI
TL;DR: The implementation of the proposed design is presented by using Spartan-3E (XC3S500E) family FPGAs and is one of the fastest hardware implementations with much greater security.
Abstract: Nowadays there is a lot of importance given to data security on the internet. The DES is one of the most preferred block cipher encryption/decryption procedures used at present. This paper presents a high throughput reconfigurable hardware implementation of DES Encryption algorithm. This achieved by using a new proposed implementation of the DES algorithm using pipelined concept. The implementation of the proposed design is presented by using Spartan-3E (XC3S500E) family FPGAs and is one of the fastest hardware implementations with much greater security. At a clock frequency of 167.448MHz for encryption and 167.870MHz for decryption, it can encrypt or decrypt data blocks at a rate of 10688Mbps.

Journal ArticleDOI
TL;DR: The effort is made to explore the effectiveness of using the deep learning algorithm more precisely CNN to predict students’ achievements which could help in predicting if student will be able to finish their degree or not and how the proposed model outperformed the existing approaches in terms of prediction accuracy.
Abstract: Educational Data Mining (EDM) research has taking an important place as it helps in exposing useful knowledge from educational data sets to be employed and serve several purposes such as predicting students’ achievements. Predicting student’s achievements might be useful for building and adopting several changes in the educational environments as a re-action in the current educational systems. Most of the existing research have used machine learning to predict students’ achievements by using diverse attributes such as family income, students gender, students absence and level etc. In this paper, the effort is made to explore the effectiveness of using the deep learning algorithm more precisely CNN to predict students’ achievements which could hlp in predicting if student will be able to finish their degree or not. The experimental results reveal how the proposed model outperformed the existing approaches in terms of prediction accuracy.

Journal ArticleDOI
TL;DR: The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods.
Abstract: The main problem of existing wind plant nowadays is that the optimum controller of single turbine degrades the total energy production of wind farm when it is located in a large wind plant. This is owing to its greedy control policy that can not cope with turbulence effect between turbines. This paper proposes a Modified Grey Wolf Optimizer (M-GWO) to improvise the controller parameter of an array of turbines such that the total energy production of wind plant is increased. The modification employed to the original GWO is in terms of the updated mechanism. This modification is expected to improve the variation of exploration and exploitation rates while avoiding the premature convergence condition. The effectiveness of the M-GWO is applied to maximize energy production of a row of ten turbines. The model of the wind plant is derived based on the real Horns Rev wind plant in Denmark. The statistical performance analysis shows that the M-GWO provides the highest total energy production as compared to the standard GWO, Particle Swarm Optimization (PSO) and Safe Experimentation Dynamics (SED) methods.

Journal ArticleDOI
TL;DR: The results show that the IIS 10.0 cluster-based web servers are more responsiveness, efficiency and stable with and without SYN flood DDoS attack, and the performance of IIS10.0 web server is better than of the Apache 2 in term of the average CPU usage and throughput.
Abstract: In recent, the high available internet service is main demand of the most people. However, online services occasionally become inaccessible due to various threats and attacks. Synchronization (SYN) flood Distributed Denial of Service (DDoS) is the most used and has a serious effect on the public network services. Hence, the outcome of this attack on the commonly utilized cluster-based web servers is systematically illustrated in this paper. Moreover, performance of Internet Information Service 10.0 (IIS 10.0) on Windows server 2016 and Apache 2 on Linux Ubuntu 16.04 server is evaluated efficiently. The performance measuring process is done on both Network Load Balancing (NLB) and High Available Proxy (HAProxy) in Windows and Linux environments respectively as methods for web server load balancing. Furthermore, stability, efficiency and responsiveness of the web servers are depended as the study evaluation metrics. Additionally, average CPU usage and throughput of the both mechanisms are measured in the proposed system. The results show that the IIS 10.0 cluster-based web servers are more responsiveness, efficiency and stable with and without SYN flood DDoS attack. Also, the performance of IIS 10.0 web server is better than of the Apache 2 in term of the average CPU usage and throughput.

Journal ArticleDOI
TL;DR: Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.
Abstract: For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

Journal ArticleDOI
TL;DR: This paper attempts to provide an overview of SG systems, by outlining SG architecture and various components, and introduces communication technologies, integration and network management tools that are involved in SG systems.
Abstract: The recent advances in technology, the increased dependence on electrical energy and the emergence of the fourth industrial revolution (Industry 4.0) were all factors in the increased need for smart, efficient and reliable energy systems. This introduced the concept of the Smart Grid (SG). A SG is a potential replacement for older power grids, capable of adapting and distributing energy based on demand. SG systems are complex. They combine various components and have high requirements for real time reliable operation. This paper attempts to provide an overview of SG systems, by outlining SG architecture and various components. It also introduces communication technologies, integration and network management tools that are involved in SG systems. In addition, the paper highlights challenges and issues that need to be addressed for a successful implementation of SG. Finally, we provide suggestions for future research directions.

Journal ArticleDOI
TL;DR: The proposed software system can be used as an application in a smart building as a security system and used to control access in smart buildings as a rule and the advancement of techniques connected around there.
Abstract: In this paper, the proposed software system based on face recognition the proposed system can be implemented in the smart building or any VIP building need security interring in general, The human face will be recognized from a stream of pictures or video feed, this technology recognizes the person according to the specific algorithm, the algorithm that employed in this paper is the Viola–Jones object detection framework by using Python. The task of the proposed facial recognition system consists of two steps, the first one was detected the human face from live video using the webcamera in the computer, and the second step recognizes if this face allowed to enter the building or not by comparing it with the existing database, the two steps depending on the OpenCV python by importing cv2 method for detecting the human face, the frames can be read or written to file with the cv2.imread and cv2.imwrite functions respectively Finally, this proposed software system can be used to control access in smart buildings as a rule and the advancement of techniques connected around there, Providing a security system is one of the most important features must be achieved in the smart buildings, this proposed system can be used as an application in a smart building as a security system. Face recognition is one of the most important applications using today for practical facial recognition. The proposed software system, depending on using OpenCV (open source computer vision) is a popular computer vision library, in 1999 this library started by Intel. The platform library sets its focus on real-time image processing and includes patent-free implementations of the latest computer vision algorithms. OpenCV 2.3.1 now comes with a programming interface to C, C++, Python, and Android. OpenCV library of python, the three algorithms that will be used in this proposed system. The currently available algorithms are: Eigenfaces → createEigenFaceRecognizer(). Fisherfaces → createFisherFaceRecognizer(). Local Binary Patterns Histograms → createLBPHFaceRecognizer(). Finally the proposed system provide entering to the building just for the authorized person according to face recognition algorithem.

Journal ArticleDOI
TL;DR: In this paper, the authors have done a study on the Internet of things and what its role in the development of education through the review for a group of previous research, and they have studied the smart class and its components and the difference from the traditional class, and then they have displayed the smart laboratories and its applications.
Abstract: There is a demand to change the contents and activities, and adapt the methods for higher education institutions, especially, universities to let researchers and educational to act more efficiently in a digital context. A well-designed campus, finally combining technology, is basic for developing digital university by facilities for learning, teaching, and research, enhancing the student trials, and supplying the convenient settings. Within digital universities, technology can improve security, reduce costs, and offer devices for faculty, scholars, academics, and students. These advantages give more attention to university processes and evolutions, the experience of researchers, and students. In this research, we have done a study on the Internet of things and what its role in the development of education through the review for a group of previous research. In addition, we have studied the smart class and its components and the difference from the traditional class, and then we have displayed the smart laboratories and its applications. At the end of the research, the great importance of Internet things in universities and its importance to the teacher and the student was concluded by learning faster and developing and improving the educational process.

Journal ArticleDOI
TL;DR: In this paper, the authors identify the factors that influence the acceptance of e-wallet towards establishing a cashless society in Malaysia by conducting an online survey with 400 respondents from students and employees of Malaysian public universities in Klang Valley.
Abstract: The evolution of financial technology into digital payment has led to a new era of cashless society. In line with the global trend, the Malaysian Government has been committed to strengthen the agenda of a cashless society by actively promoting the use of e-Wallet through the establishment of the interoperable credit transfer framework (ICTF) policy in 2018. Although e-Wallet has been implemented since 2016, several previous studies have found that the level of acceptance is still relatively low while the main factors that influence the acceptance of e-Wallet in Malaysia still remain unclear. This study aims to identify the factors that influence the acceptance of e-Wallet towards establishing cashless society in Malaysia. Online survey using closed-ended questionnaires have been conducted among 400 respondents from students and employees of Malaysian public universities in Klang Valley. Collected data have been analyzed using descriptive statistics and inferential statistics which consist of Factor Analysis, Pearson Correlation and Multiple Linear Regression in statistical package for the social sciences (SPSS). Based on the findings, four factors are found to significantly influence e-Wallet acceptance, which consist of performance expectancy (PE), social influence (SI), facilitating conditions (FC) and trust (T). facilitating conditions (FC) is the most influential significant factor behind the acceptance of e-Wallet among Malaysians.

Journal ArticleDOI
TL;DR: The main purpose of the paper is to projective synchronous chaotic oscillation in the real four-dimensional hyperchaotic model via designing many adaptive nonlinear controllers using the Lyapunov stability theory and positive definite matrix.
Abstract: The main purpose of the paper is to projective synchronous chaotic oscillation in the real four-dimensional hyperchaotic model via designing many adaptive nonlinear controllers. Firstly, in view that there are many strategies in the design process of existing controllers, a nonlinear control strategy is considered as one of the important powerful tools for controlling the dynamical systems. The prominent advantage of the nonlinear controller lies in that it deals with known and unknown parameters. Then, the projective synchronize behavior of a four-dimensional hyperchaotic system is analyzed by using the Lyapunov stability theory and positive definite matrix, and the nonlinear control strategy is adopted to synchronize the hyperchaotic system. Finally, the effectiveness and robustness of the designed adaptive nonlinear controller are verified by simulation.

Journal ArticleDOI
TL;DR: A new approach, which is based on fusing of multiple faster Regions with convolutional neutral network (faster- RCNN) architectures, is proposed, which detects the exact location of license plates in given images with accuracy of 97%.
Abstract: Automatic license plate detection and recognition (ALPD-R) is an important and challenging application for traffic surveillance, traffic safety, security, services purposes and parking management. Generally, traditional image processing routines have been used in ALPD-R. Although the general approaches perform well on ALPD-R, new and efficient approaches are needed to improve the detection accuracies. Thus, in this paper, a new approach, which is based on fusing of multiple faster Regions with convolutional neutral network (faster- RCNN) architectures, is proposed. More specially, the deep learning (DL) is used to detect license plates in given images. The proposed license plate detection method uses three faster- RCNN modules where each faster RCNN module uses a pre-trained CNN model namely AlexNet, VGG16 and VGG19. Each faster-RCNN module is trained independently and their results are fused in fusing layer. Fusing layer use average operator on the X and Y coordinates of the outputs of the Faster-RCNN modules and maximum operator is employed on the width and height outputs of the faster-RCNN modules. A publicly available dataset is used in experiments. The accuracy is used as a performance indicator of the proposed method. For 100 testing images, the proposed method detects the exact location of license plates for 97 images. The accuracy of the proposed method is 97%.

Journal ArticleDOI
TL;DR: The proposed objective is to design and create a healthcare system centered on Mobile-IoT by collecting patient information from different sensors and alerting both the guardian and the doctor by sending emails and SMS in a timely manner.
Abstract: Smart and connected health care is of specific significance in the spectrum of applications enabled the Internet of Things (IoT). Networked sensors, either embedded inside our living system or worn on the body, enable to gather rich information regarding our physical and mental health. In specific, the accessibility of information at previously unimagined scales and spatial longitudes combined with the new generation of smart processing algorithms can expedite an advancement in the medical field, from the current post-facto diagnosis and treatment of reactive framework, to an early-stage proactive paradigm for disease prognosis combined with prevention and cure as well as overall administration of well-being rather than ailment. This paper sheds some light on the current methods accessible in the Internet of Things (IoT) domain for healthcare applications. The proposed objective is to design and create a healthcare system centered on Mobile-IoT by collecting patient information from different sensors and alerting both the guardian and thedoctor by sending emails and SMS in a timely manner. It remotely monitors the physiological parameters of the patient and diagnoses the illnesses swiftly