Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Filter Icon

Filters article

Years

FAQ Arrow
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,939 Article Results

Hybrid scheduling algorithms in cloud computing: a review

10.11591/ijece.v12i1.pp880-895
Neeraj Arora , Rohitash Kumar Banyal
Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms.
Volume: 12
Issue: 1
Page: 880-895
Publish at: 2022-02-01

Robust least square approach for optimal development of quadratic fuel quantity function for steam power stations

10.11591/ijeecs.v25.i2.pp732-740
Ikenna Onyegbadue , Cosmas Ogbuka , Theophilus Madueme
Ordinary Least Square (OLS) and Robust Least Square (RLS) consisting of Least Absolute Residual and Bi-square approaches were deployed to obtain the fuel consumption characteristic curve and the coefficients of the quadratic fuel consumption function for thermal stations in Nigeria. Results were compared based on convergence property, Root Mean Square Error, R-Square value, Adjusted R-Square value, and Width Interval of coefficients. Valve Point loading effects of Egbin and Sapele power stations were used to develop the quadratic fuel consumption characteristic curve and function. The average difference in width interval for the coefficients a, b, c, d, and e of the two stations, after comparing Bi-square and OLS technique, were 0.02084, 8.5055, 1856.565, 520.8855, and 0.0082, respectively. The R-square values obtained from the Bi-Square technique were superior to the OLS technique with arithmetic differences of 0.6196 and 0.5254 for Egbin and Sapele generating stations, respectively. Bi-Square technique also offered better adjusted R-Square value for Egbin and Sapele stations with arithmetic differences of 0.622 and 0.5287, respectively. Bi-Square technique offered smaller Root Mean Square Error than the Ordinary Least Square technique for both stations. The coefficients obtained from Bi-Square technique were used to develop the fuel quantity function for the studied stations.
Volume: 25
Issue: 2
Page: 732-740
Publish at: 2022-02-01

Asymmetric image encryption scheme based on Massey Omura scheme

10.11591/ijece.v12i1.pp1040-1047
Najlae Falah Hameed Al Saffar , Inaam R. Al-Saiq , Rewayda Razaq Mohsin Abo Alsabeh
Asymmetric image encryption schemes have shown high resistance against modern cryptanalysis. Massey Omura scheme is one of the popular asymmetric key cryptosystems based on the hard mathematical problem which is discrete logarithm problem. This system is more secure and efficient since there is no exchange of keys during the protocols of encryption and decryption. Thus, this work tried to use this fact to propose a secure asymmetric image encryption scheme. In this scheme the sender and receiver agree on public parameters, then the scheme begin deal with image using Massey Omura scheme to encrypt it by the sender and then decrypted it by the receiver. The proposed scheme tested using peak signal to noise ratio, and unified average changing intensity to prove that it is fast and has high security.
Volume: 12
Issue: 1
Page: 1040-1047
Publish at: 2022-02-01

Classification of three pathological voices based on specific features groups using support vector machine

10.11591/ijece.v12i1.pp946-956
Muneera Altayeb , Amani Al-Ghraibah
Determining and classifying pathological human sounds are still an interesting area of research in the field of speech processing. This paper explores different methods of voice features extraction, namely: Mel frequency cepstral coefficients (MFCCs), zero-crossing rate (ZCR) and discrete wavelet transform (DWT). A comparison is made between these methods in order to identify their ability in classifying any input sound as a normal or pathological voices using support vector machine (SVM). Firstly, the voice signal is processed and filtered, then vocal features are extracted using the proposed methods and finally six groups of features are used to classify the voice data as healthy, hyperkinetic dysphonia, hypokinetic dysphonia, or reflux laryngitis using separate classification processes. The classification results reach 100% accuracy using the MFCC and kurtosis feature group. While the other classification accuracies range between~60% to~97%. The Wavelet features provide very good classification results in comparison with other common voice features like MFCC and ZCR features. This paper aims to improve the diagnosis of voice disorders without the need for surgical interventions and endoscopic procedures which consumes time and burden the patients. Also, the comparison between the proposed feature extraction methods offers a good reference for further researches in the voice classification area.
Volume: 12
Issue: 1
Page: 946-956
Publish at: 2022-02-01

Measurement of an electroretinogram signal and display waves on graphical user interface by laboratory virtual instrument engineering workbench

10.11591/ijeecs.v25.i2.pp980-988
Mustafa F. Mahmood , Huda Farooq Jameel , Mayss Alreem Nizar Hammed
The electroretinogram (ERG) is an electrophysiological recording method that measures the retinal electrical potential. The electrical reaction is quantified by electrical interaction of the indicator electrode with the cornea or at various levels inside the retina. However, such ERG systems suffer from certain limitations and challenges, such as high cost, low a/b-wave amplitude, and the outcomes do not provide any information about patients. In this work, we designed and implemented a real-time prototype for an ERG system for measuring eye waves via diode-transistor logic (DTL)- electrode and AD624AD-model. In addition, a graphical user interface (GUI) via virtual instrument engineering workbench (LabVIEW) was used. The developed system achieved high amplitude for ERG a/b-waves of about 100 and 700 mV. In terms of a/b-waves in the system, the findings show that this study has good results for optimizing the measurement of ERG signals. The method showed satisfactory accuracy of about 92.5% for 10 participants aged 20-60 years and comprising both genders
Volume: 25
Issue: 2
Page: 980-988
Publish at: 2022-02-01

Reconfigurable of current-mode differentiator and integrator based-on current conveyor transconductance amplifiers

10.11591/ijece.v12i1.pp208-218
Soontorn Srisoontorn , Angkana Charoenmee , Suphaphorn Panikhom , Thitiporn Janda , Suttipong Fungdetch , Khunpan Patimaprakorn , Adirek Jantakun
The reconfigurable of the differentiator and integrator based on current conveyor transconductance amplifiers (CCTAs) have been presented in this paper. The proposed configurations are provided with two CCTAs and grounded elements. The configurations can be operated in the differentiator and integrator by selecting external passive elements. The input and output currents have low and high impedances, respectively; therefore, the configurations can be cascaded without additional current buffer. The proposed configurations can be electronically tuned by external direct current (DC) bias currents, and it also has slight fluctuation with temperature. An application of universal filter is demonstrated to confirm the ability of the proposed configurations. The results of simulation with Pspice program are accordance with the theoretical analysis.
Volume: 12
Issue: 1
Page: 208-218
Publish at: 2022-02-01

Hybrid dynamic chunk ensemble model for multi-class data streams

10.11591/ijeecs.v25.i2.pp1115-1122
Varsha Sachin Khandekar , Pravin Shrinath
In the analysis more specifically in the classification of continuous data stream using machine learning algorithms joint occurrence of concept drift and imbalanced issue becomes more provocative. Also, imbalance issue is again more challenging when the data stream is multi-class with minority class and that is too with data-difficulty factors. Incremental learning with ensemble models found more promising in handling theses issues. But most of the approaches are for two-class data streams which can’t be utilized for multiclass data streams. In this paper we have designed hybrid dynamic chunk ensemble model (HDCEM) for the classification of multi-class insect-data stream for handling imbalance and concept drift issue. To deal with imbalance issue we have proposed effective split bagging algorithm which has achieved better performance on minority class recall and F-measure on arriving dynamic chunks of data from multi-class data stream. HDCEM model can adapt to abrupt and gradual drift because it has combined features of both online and chunk-based learning together. It has achieved average 78% minority class recall in abrupt insect data stream and 71% in gradual drift insect stream.
Volume: 25
Issue: 2
Page: 1115-1122
Publish at: 2022-02-01

Comparison of feed forward and cascade forward neural networks for human action recognition

10.11591/ijeecs.v25.i2.pp892-899
Aditi Jahagirdar , Rashmi Phalnikar
Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network.
Volume: 25
Issue: 2
Page: 892-899
Publish at: 2022-02-01

Free space optical communication system for indoor applications based on printed circuit board design

10.11591/ijeecs.v25.i2.pp1030-1037
Alsharef Mohammad , Mohammed S. Alzaidi , Mahmoud M. A. Eid , Vishal Sorathiya , Sunil Lavadiya , Shobhit K. Patel , Ahmed Nabih Zaki Rashed
This study clarified an overview of wired and wireless optical communication system block diagram with practical applications. Freespace optical (FSO) communication is a trending field that is rising so fast to replace electromagnetic waves in a communication, so we have presented a theoretical circuit as an example and modified it to fit and work in communication purposes, simulation is used and then practical work is done and printed circuit board (PCB) is designed. Light emitting diode (LED) have been used as transmitter and Photo Transistor as a receiver and variable resistance to change voltage sent to the LED that indicates the change in the transmitted signal.
Volume: 25
Issue: 2
Page: 1030-1037
Publish at: 2022-02-01

Power quality improvement of distribution systems asymmetry caused by power disturbances based on particle swarm optimization-artificial neural network

10.11591/ijeecs.v25.i2.pp666-679
Ismael Kareem Saeed , Kamal Sheikhyounis
With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.
Volume: 25
Issue: 2
Page: 666-679
Publish at: 2022-02-01

Efficient wireless power transmission to remote the sensor in restenosis coronary artery

10.11591/ijeecs.v25.i2.pp771-779
Mokhalad Alghrairi , Nasri Sulaiman , Wan Zuha Wan Hasan , Haslina Jaafar , Saad Mutashar
In this study, the researchers have proposed an alternative technique for designing an asymmetric 4 coil-resonance coupling module based on the series-to-parallel topology at 27 MHz industrial scientific medical (ISM) band to avoid the tissue damage, for the constant monitoring of the in-stent restenosis coronary artery. This design consisted of 2 components, i.e., the external part that included 3 planar coils that were placed outside the body and an internal helical coil (stent) that was implanted into the coronary artery in the human tissue. This technique considered the output power and the transfer efficiency of the overall system, coil geometry like the number of coils per turn, and coil size. The results indicated that this design showed an 82% efficiency in the air if the transmission distance was maintained as 20 mm, which allowed the wireless power supply system to monitor the pressure within the coronary artery when the implanted load resistance was 400 Ω.
Volume: 25
Issue: 2
Page: 771-779
Publish at: 2022-02-01

Design and development of DrawBot using image processing

10.11591/ijece.v12i1.pp365-375
Krithika Vaidyanathan , Nandhini Murugan , Subramani Chinnamuthu , Sivashanmugam Shivasubramanian , Surya Raghavendran , Vimala Chinnaiyan
Extracting text from an image and reproducing them can often be a laborious task. We took it upon ourselves to solve the problem. Our work is aimed at designing a robot which can perceive an image shown to it and reproduce it on any given area as directed. It does so by first taking an input image and performing image processing operations on the image to improve its readability. Then the text in the image is recognized by the program. Points for each letter are taken, then inverse kinematics is done for each point with MATLAB/Simulink and the angles in which the servo motors should be moved are found out and stored in the Arduino. Using these angles, the control algorithm is generated in the Arduino and the letters are drawn.
Volume: 12
Issue: 1
Page: 365-375
Publish at: 2022-02-01

Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers

10.11591/ijeecs.v25.i2.pp1131-1139
Mohammed Boukabous , Mostafa Azizi
Sentiment analysis (SA) is widely used today in many areas such as crime detection (security intelligence) to detect potential security threats in realtime using social media platforms such as Twitter. The most promising techniques in sentiment analysis are those of deep learning (DL), particularly bidirectional encoder representations from transformers (BERT) in the field of natural language processing (NLP). However, employing the BERT algorithm to detect crimes requires a crime dataset labeled by the lexiconbased approach. In this paper, we used a hybrid approach that combines both lexicon-based and deep learning, with BERT as the DL model. We employed the lexicon-based approach to label our Twitter dataset with a set of normal and crime-related lexicons; then, we used the obtained labeled dataset to train our BERT model. The experimental results show that our hybrid technique outperforms existing approaches in several metrics, with 94.91% and 94.92% in accuracy and F1-score respectively.
Volume: 25
Issue: 2
Page: 1131-1139
Publish at: 2022-02-01

Different analytical frameworks and bigdata model for Internet of Things

10.11591/ijeecs.v25.i2.pp1159-1166
Ayushi Chahal , Preeti Gulia , Nasib Singh Gill
Sensor devices used in internet of things (IoT) enabled environment produce large amount of data. This data plays a major role in bigdata landscape. In recent years, correlation, and implementation of bigdata and IoT is being extrapolated. Nowadays, predictive analytics is gaining attention of many researchers for big IoT data analytics. This paper summarizes different sort of IoT analytical platforms which consist in-built features for further use in machine learning, MATLAB, and data security. It emphasizes on different machine learning algorithms that plays important role in big IoT data analytics. Besides different analytical frameworks, this paper highlights the proposed model for bigdata in IoT domain and elaborates different forms of data analytical methods. Proposed model comprises different phases i.e., data storing, data cleaning, data analytics, and data visualization. These phases cover the basic characteristics of bigdata V’s model and most important phase is data analytics or big IoT analytics. This model is implemented using an IoT dataset and results are presented in graphical and tabular form using different machine learning techniques. This study enhances researchers’ knowledge about various IoT analytical platforms and usability of these platforms in their respective problem domains.
Volume: 25
Issue: 2
Page: 1159-1166
Publish at: 2022-02-01

Modelling on-demand preprocessing framework towards practical approach in clinical analysis of diabetic retinopathy

10.11591/ijece.v12i1.pp585-595
Prakruthi Mandya Krishnegowda , Komarasamy Ganesan
Diabetic retinopathy (DR) refers to a complication of diabetes and a prime cause of vision loss in middle-aged people. A timely screening and diagnosis process can reduce the risk of blindness. Fundus imaging is mainly preferred in the clinical analysis of DR. However; the raw fundus images are usually subjected to artifacts, noise, low and varied contrast, which is very hard to process by human visual systems and automated systems. In the existing literature, many solutions are given to enhance the fundus image. However, such approaches are particular and limited to a specific objective that cannot address multiple fundus images. This paper has presented an on-demand preprocessing frame work that integrates different techniques to address geometrical issues, random noises, and comprehensive contrast enhancement solutions. The performance of each preprocessing process is evaluated against peak signal-to-noise ratio (PSNR), and brightness is quantified in the enhanced image. The motive of this paper is to offer a flexible approach of preprocessing mechanism that can meet image enhancement needs based on different preprocessing requirements to improve the quality of fundus imaging towards early-stage diabetic retinopathy identification.
Volume: 12
Issue: 1
Page: 585-595
Publish at: 2022-02-01
Show 861 of 1996

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration