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Comparative study of off-grid and grid-connected hybrid power system: issues, future prospects and policy framework

10.11591/ijeecs.v22.i2.pp752-759
Bankole Adebanji , Oluwaseun Atoki , Taiwo Fasina , Oluwumi Adetan , Adewale Abe
A sustainable energy system is of utmost importance for any significant development in any nation.This work identified some obstacles inhibiting rapid renewable energy growth in Nigeria and recommended some policy measures in overcoming them. Moreover, a comparative study of off-grid (OG) and grid-connected (GC) small hydro-solar photovoltaic-diesel hybrid system was carried out using Oyan river, Abeokuta, Nigeria as a case study. The hybrid components were modeled with and without the grid. The hydro solar resources data of the area were collected and analyzed using hybrid optimization model for electric renewable (HOMER) software. The simulation results proved that the GC hybrid power system is better than the OG hybrid power system in technical and economic terms depending on the location. This paper, therefore, proposed the use of OG hybrid power system for electrification of distant villages especially where extending the grid seems infeasible and the use of GC hybrid power system in the urban areas. The work will assist power sector stakeholders in making informed decisions towards the growth of hybrid power system technology in Nigeria.
Volume: 22
Issue: 2
Page: 752-759
Publish at: 2021-05-01

Breast tumor segmentation in mammography image via Chan-Vese technique

10.11591/ijeecs.v22.i2.pp809-817
Mohammed Y. Kamil , Eman A. Radhi
The accurate segmentation of tumours is a crucial stage of diagnosis and treatment, reducing the damage that breast cancer causes, which is the most common type of cancer among women, especially after the age of forty. The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. As algorithms often lose an automatic path toward the boundaries of the tumour at try to determine the site of this type of tumour. The study aims to create a clear contrast between the tumour and the healthy breast area. For this purpose, we used a Gaussian filter as a pre-processing as it works to intensify the low-frequency components while reducing the high-frequency components as the breast structure is enhanced and noise suppression. Then, CLAHE was used to improve the contrast of the image, which increases the contrast between the tumour and the surrounding tissue and sharpens the edges of the tumour. Next, the tumour was segmented by using the Chan-Vese method with appropriate parameters defined. The proposed method was applied to all abnormal mammogram images taken from a publicly available mini-MIAS database. The proposed model was tested in two ways, the first is statistical that got results (90.1, 94.8, 95.5, 92.1, 99.5) for Jaccard, Dice, PF-Score, precision, and sensitivity respectively. And the other is based on the segmented region's characteristics that results showed the algorithm could identify the tumour with high efficiency.
Volume: 22
Issue: 2
Page: 809-817
Publish at: 2021-05-01

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

10.11591/ijeecs.v22.i2.pp1124-1134
Shaik Mazhar Hussain , Kamaludin Mohamad Yusof , Rolito Asuncion , Shaik Ashfaq Hussain
Internet of vehicles (IoV) is an emerging area that gives support for vehicles via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation of more than one radio access network. The mobility of vehicles demands to make handover in such heterogeneous network. The vehicles at short range uses dedicated short range communication (DSRC), while it has to use better technology for long range and any type of traffic. Usually, the previous work will directly select the network for handover or it connects with available radio access. Due to this, the occurrence of handover takes place frequently.  In this paper, the integration of DSRC, LTE and mmWave 5G on IoV is incorporated with novel handover decision making, network selection and routing. The handover decision is to ensure whether there is a need for vertical handover by using Dynamic Q-learning algorithm that uses entropy function for threshold prediction as per the current characteristics of the environment. Then the network selection is based on fuzzy-convolution neural network (F-CNN) that creates fuzzy rules from signal strength, distance, vehicle density, data type and line of sight. V2V chain routing is proposed to select V2V pairs using jellyfish optimization algorithm (JOA) that takes in account of channel, vehicle and transmission metrics. This system is developed in OMNeT++ simulator and the performances are evaluated in terms of success probability, handover failure, unnecessary handover, mean throughput, delay and packet loss.
Volume: 22
Issue: 2
Page: 1124-1134
Publish at: 2021-05-01

A new 2-D multi-stable chaotic attractor and its MultiSim electronic circuit design

10.11591/ijeecs.v22.i2.pp699-707
Sundarapandian Vaidyanathan , Aceng Sambas , Mohamad Afendee Mohamed , Mustafa Mamat , W. S. Mada Sanjaya , Sudarno Sudarno
A new multi-stable system with a double-scroll chaotic attractor is developed in this paper. Signal plots are simulated using MATLAB and multi-stability is established by showing two different coexisting double-scroll chaotic attractors for different states and same set of parameters. Using integral sliding control, synchronized chaotic attractors are achieved between drive-response chaotic attractors. A MultiSim circuit is designed for the new chaotic attractor, which is useful for practical engineering realizations.
Volume: 22
Issue: 2
Page: 699-707
Publish at: 2021-05-01

Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive

10.11591/ijeecs.v22.i2.pp660-669
Salah Eddine Rezgui , Hocine Benalla , Houda Bouhebel
This paper presents a hybrid algorithm that combines the particle swarm optimization method with the bacteria foraging technique, named: BF-PSO. The aim is to achieve more efficient and precise parameters determination of the regulators that leads to performance improvement in the speed-loop control of an induction motor (IM) implemented in a direct torque control (DTC). The approach consists of tuning the proportional-integral (PI) parameters that meet high dynamics and tracking behavior using the hybrid BF-PSO algorithm. Investigations have been completed with Matlab/Simulink and several performance tests are conducted. The comparison results are exposed with the most used indices in the controllers' tuning with optimization techniques. It will be shown that the presented technique presents better quality results compared to the conventional method of calculated PI.
Volume: 22
Issue: 2
Page: 660-669
Publish at: 2021-05-01

Data mining technique to analyse and predict crime using crime categories and arrest records

10.11591/ijeecs.v22.i2.pp1052-1060
Most. Rokeya Khatun , Safial Islam Ayon , Md. Rahat Hossain , Md. Jaber Alam
Generally, crimes influence organisations as it starts occurring frequently in society. Because of having many dimensions of crime data, it is difficult to mine the available information using off the shelf or statistical data analysis tools. Improving this process will aid the police as well as crime protection agencies to solve the crime rate in a faster period. Also, criminals can often be identified based on crime data. Data mining includes strategies at the convergence of machine learning and database frameworks. Using this concept, we can extract previously unknown useful information and their patterns of occurrence from unstructured data. The sole purpose of this paper is to give an idea of how data mining can be utilised by crime investigation agencies to discover relevant precautionary measures from prediction rates. Data sets are analysed by some supervised classification algorithms, namely decision tree, K-nearest neighbours (KNN) and random forest algorithms. Crime forecasting is done for frequently occurring crimes like robbery, assault, theft, etc. Specifically, the results indicate the superiority of the random forest algorithm in test accuracy.
Volume: 22
Issue: 2
Page: 1052-1060
Publish at: 2021-05-01

An android-based mobile educational game for disaster preparedness: an input to risk reduction management

10.11591/ijeecs.v22.i2.pp936-943
Gene Marck Bañares Catedrilla , Jefferson Llobit Lerios , Sherwin Banaag Sapin , Manuel C Lanuang , Chester Alexis C Buama
The Philippines is one of the countries in the world who suffers in different disasters, particularly natural disasters. Every year, there are more than twenty incidents recorded in the country related to different disasters which involve numerous lives of its citizens. It is found that most Filipinos have lack of knowledge in terms of disaster preparation specially, teenagers. This paper intended to develop a mobile-based game that aims to spread awareness on what to do during disasters. Upon development, forty-five (45) respondents were chosen to test the reliability of the application which composed of elementary students, household owners, police officers, fire fighters and IT experts. Further, ISO 25010 was adapted and modified in assessing the project. The results showed that the application is strongly acceptable and gives appropriate output in terms of disaster preparation garnering a total mean of 3.83
Volume: 22
Issue: 2
Page: 936-943
Publish at: 2021-05-01

Deepenz: prediction of enzyme classification by deep learning

10.11591/ijeecs.v22.i2.pp1108-1115
Hamza Chehili , Salah Eddine Aliouane , Abdelhafedh Bendahmane , Mohamed Abdelhafid Hamidechi
Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests  show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification.
Volume: 22
Issue: 2
Page: 1108-1115
Publish at: 2021-05-01

Algorithm for extracting product feature from e-commerce comment

10.11591/ijeecs.v22.i2.pp1199-1207
Chanida Kaewphet , Nawaporn Wisitpongpun
Reviews of e-commerce play an important role in online purchasing decisions. Consumers are likely to read reviews and comments on products from other consumers. In addition to those opinions that reflect consumers' trust in products, it also provides each product's distinctive properties. Today, there are many online reviews, resulting in enormous comments and suggestions. However, as fully reading reviews is quite difficult, this article presents 3 algorithms for automatic extraction of product features hidden in e-commerce reviews: a traditional frequency-based product feature extraction (F-PFE), syntax analyzer system (SAS), and the hybrid approach called the frequency and syntax-based product feature extraction (FaS-PFE). The proposed algorithms were tested against 4 different types of products: shampoo, skincare, mobile phone, and tablet, using reviews from amazon.com. Based on the product review used in this study, it was found that the SAS can help improve the performance in terms of precision by 15% when compared with the traditional F-PEE approach. When considering both the word frequency and syntax, FaS-PFE clearly outperforms the other two approaches with 94.00% precision and 95.13% recall.
Volume: 22
Issue: 2
Page: 1199-1207
Publish at: 2021-05-01

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

10.11591/ijeecs.v22.i2.pp787-794
Hannah Sofian , Joel Chia Ming Than , Suraya Mohamad , Norliza Mohd Noor
Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of other structures as pre-processing steps or using the fact that the calcification often appears as a bright region. In this paper, an automated system proposed using a deep learning approach to detect the calcification absence and calcification presence in coronary artery IVUS image. A useful advantage of deep learning, compared to other methods is,  it uses representations and features directly from the raw data, bypassing the need to manually extract features, a common that required in the traditional machine learning framework. The type of deep learning architecture used is 27 layers of convolutional neural networks (CNNs) using Direct Acyclic Graph. The proposed system used 2175 images and achieved an accuracy of 98.16% for Cartesian coordinate images and 99.08% for Polar Reconstructed Coordinate images.
Volume: 22
Issue: 2
Page: 787-794
Publish at: 2021-05-01

Stress classification based on human electromagnetic radiation analysis

10.11591/ijeecs.v22.i2.pp826-834
Tengku ‘Afiah Mardhiah Tengku Zainul Akmal , Abd Hafiz Qayyum Abd Talib , Siti Zura A. Jalil , Siti Armiza Mohd Aris
Stress is a feeling of emotional or physical tension due to any events that makes one feel frustrated, angry or nervous. It a situation that trigger particular biological response when encounter a threat or challenge. This paper discussed stress classification based on human electromagnetic radiation (EMR). At first, the collected radiation frequency data is analyzed using multivariate analysis of variance (MANOVA) to identify the significance points for the classification. Then, the data is classified using locally weighted learning (LWL) algorithm. The results show stress classification using EMR based on third eye and throat chakra points obtained accuracy of more than 60%.
Volume: 22
Issue: 2
Page: 826-834
Publish at: 2021-05-01

The trends of supervisory control and data acquisition security challenges in heterogeneous networks

10.11591/ijeecs.v22.i2.pp874-883
M. Agus Syamsul Arifin , Susanto Susanto , Deris Stiawan , Mohd Yazid Idris , Rahmat Budiarto
Supervisory control and data acquisition (SCADA) has an important role in communication between devices in strategic industries such as power plant grid/network. Besides, the SCADA system is now open to any external heterogeneous networks to facilitate monitoring of industrial equipment, but this causes a new vulnerability in the SCADA network system. Any disruption on the SCADA system will give rise to a dangerous impact on industrial devices. Therefore, deep research and development of reliable intrusion detection system (IDS) for SCADA system/network is required. Via a thorough literature review, this paper firstly discusses current security issues of SCADA system and look closely benchmark dataset and SCADA security holes, followed by SCADA traffic anomaly recognition using artificial intelligence techniques and visual traffic monitoring system. Then, touches on the encryption technique suitable for the SCADA network. In the end, this paper gives the trend of SCADA IDS in the future and provides a proposed model to generate a reliable IDS, this model is proposed based on the investigation of previous researches. This paper focuses on SCADA systems that use IEC 60870-5-104 (IEC 104) protocol and distributed network protocol version 3 (DNP3) protocol as many SCADA systems use these two protocols.
Volume: 22
Issue: 2
Page: 874-883
Publish at: 2021-05-01

Mobile communication (2G, 3G & 4G) and future interest of 5G in Pakistan: a review

10.11591/ijeecs.v22.i2.pp1061-1068
Muhammad Saqib Iqbal , Zulhasni Abdul Rahim , Syed Aamer Hussain , Norulhusna Ahmad , Hazilah Mad Kaidi , Robiah Ahmad , Rudzidatul Akmam Dziyauddin
The use of mobile communication is growing radically with every passing year. The new reality is the fifth generation (5G) of mobile communication technology. 5G requires expensive infrastructural adjustment and upgradation. Currently, Pakistan has one of the most significant numbers of biometrically verified mobile users. However, at the same time, the country lags incredibly in the field of mobile internet adoption, with just half of the mobile device owners avail broadband subscription. It is a viable market with a large segment yet to be tapped. With the advancing progression in Pakistan towards the internet of things (IoT) connectivity, i.e., solar-powered home solutions, smart city projects, and on-board diagnostics (OBD), the urgency for speed, bandwidth and reliability are on the rise. In this paper, Pakistan's prevalent mobile communication networks, i.e., second, third and fourth generation (2G, 3G and 4G), were analyzed and examined in light of the country's demographics and challenges. The future of 5G in Pakistan was also discussed. The study revealed that non-infrastructural barriers influence the low adoption rate, which is the main reason behind the spectrum utilization gap, i.e., the use of 3G, and the 4G spectrum is minimal.
Volume: 22
Issue: 2
Page: 1061-1068
Publish at: 2021-05-01

Predicting temperature of Erbil City applying deep learning and neural network

10.11591/ijeecs.v22.i2.pp944-952
Sardar M. R. K. Al- Jumur , Shahab Wahhab Kareem , Raghad Z. Yousif
One of the most significant and daunting activities in today's world is temperature prediction. The meteorologists traditionally predict temperature via some statistical models aimed to forecast the fluctuations that might have happened to atmospheric parameters such as temperature, humidity, etc. The main objective of this paper is to build an intelligent temperature prediction model of Erbil city in KRG/ Iraq based on a historical dataset from 1992 to 2016 in each year there are twelve months’ average temperature readings from (January to December). Hence to resolve this prediction problem an up-to-date deep learning neural network has been used, the network model is based on long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture which employed to estimate the future average temperature. The implementing model uses the dataset from real-time 30 weather stations deployed in the area of the city. The prediction performance of the proposed recurrent neural network model has been compared with some state of art algorithms like Adeline neural network, Autoregressive neural network (NAR), and  generalized regression neural network (GRNN). The results show that the proposed model based on deep learning gives minimum prediction error.
Volume: 22
Issue: 2
Page: 944-952
Publish at: 2021-05-01

K-means method for clustering learning classes

10.11591/ijeecs.v22.i2.pp835-841
A. D. Indriyanti , D. R. Prehanto , T. Z. Vitadiar
Learning class is a collection of several students in an educational institution. Every beginning of the school year the educational institution conducts a grouping class test. However, sometimes class grouping is not in accordance with the ability of students. For this reason, a system is needed to be able to see the ability of students according to the desired parameters. Determination of the weight of test scores is done using the K-Means method as a grouping method. Iteration or repetition process in the K-Means method is very important because the weight value is still very possible to change. Therefore, the repetition process is carried out to produce a value that does not change and is used to determine the ability level of students. The results of the class grouping test scores affect the ability of students. Application of K-Means method is used in building an information system grouping student admissions in an educational institution. Acceptance of students will be grouped into 3 groups of learning classes. The results of testing the system that applies K-Means method and based on data on the admission of prospective students from educational institutions have very high accuracy with an error rate of 0.074. 
Volume: 22
Issue: 2
Page: 835-841
Publish at: 2021-05-01
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