bluuebunny/crossref_metadata_2025 huggingface dataset:
A mho type phase comparator relay guideline using phase comparison technique for a power system
Authors: González-Cueto José Antonio , Sánchez Zaid García , Sánchez Gustavo Crespo , Hernandez Hernan , Silva Ortega Jorge Iván , Martínez Díaz Vicente Leonel
Published: February 2021

This paper presents a mho distance relay simulation based on the phase comparison technique using a typical electrical power systems analysis software for two cases: when the operation state is close to the static voltage limit and during a dynamic perturbation in the system. The paper evaluates the impedance variations caused by complex voltage values, the mho polarization, and the comparator operating region into the complex plane. In addition, the paper found the information for the dynamic perturbations from the outputs considering a mid-term stability program. The simulation of the mho-phase comparator in the static voltage proximity limit detects unit distance elements with impedance measured close to reach the threshold in the steady-state. Dynamic mho simulations in the complex plane are successfully tested by plotting time phase difference curves on the comparator input signals. Relay programmers can use these curves to analyze other phase comparators applications and the corresponding models in the complex plane.
Coyote multi-objective optimization algorithm for optimal location and sizing of renewable distributed generators
Authors: Abdallah E. M. , El Sayed M. I. , Elgazzar M. M. , Hassan Amal A.
Published: February 2021

Research on the integration of renewable distributed generators (RDGs) in radial distribution systems (RDS) is increased to satisfy the growing load demand, reducing power losses, enhancing voltage profile, and voltage stability index (VSI) of distribution network. This paper presents the application of a new algorithm called ‘coyote optimization algorithm (COA)’ to obtain the optimal location and size of RDGs in RDS at different power factors. The objectives are minimization of power losses, enhancement of voltage stability index, and reduction total operation cost. A detailed performance analysis is implemented on IEEE 33 bus and IEEE 69 bus to demonstrate the effectiveness of the proposed algorithm. The results are found to be in a very good agreement.
Towards the prospection of an optimal thermal response of ZnO surge arrester in HV power system
Authors: Bendakir Abdelhakim , Bayadi Abdelhafid , DIB Djalel
Published: February 2021

In order to understand the thermal and electrical properties of surge arrester under standard climatic conditions, it would be useful to evaluate the heating progression in its different elements. These are constituted of heterogeneous materials, which have physical and electrical nonlinear properties along the surge arrester. Temperature predicting solutions for zinc oxide ceramic blocks provide fundamental elements of the electrothermal material characteristics in view of the lifetime estimation. Electrothermal phenomenon analysis of surge arresters is based on empirically-determined parameter models, using finite element method (FEM) simulated on COMSOL Multiphysics software which is a more precise approach compared to the existing models. In this paper, the behavioural model has been developed to study the surge arresters in both cases: the steady state and overvoltage state (lightning). Previous published works validate the first simulation results. This work is therefore extended to consider other operating states, which illustrate the dependence of the type of surge on the amplitudes and durations of electrothermal phenomena.
Offshore wind power integration to support weak grid voltage for industrial loads using VSC-HVDC transmission system
Authors: Daoud Ahmed A. , Abouzeid Ahmed F. , Dessouky Sobhy S.
Published: February 2021

This paper investigates the integration of the offshore wind power plant into the grid using voltage source converter high-voltage direct current (VSC-HVDC). The paper proposes both offshore and onshore converter stations control to support voltage variation in grid. Heavy industrial loads result in a weak grid. In this paper, the effect on industrial loads by the grid strength is shown. Then the paper proposes a solution for the grid voltage support for industrial loads connected to weak grids. The results showed that the increase of grid voltage from 0.7 pu to 1 pu at full load condition that provides a continuous operation without any interruption. The system was modelled using MATLAB/Simulink package.
Design of type-1 servo controller for grid voltage modulated direct-power control of single-phase grid-connected PV inverter
Authors: Radwan Eyad , Nour Mutasim , Baniyounes Ali , Al Olimat Khalid S.
Published: February 2021

This paper presents direct control of active and reactive power using grid voltage modulation for single-phase grid-connected photovoltaic inverter. A design of type-1 servo system based on pole-placement method is proposed to control the power flow using a simplified multiple-input multiple-output (MIMO) model of the system. Phase-locked loop (PLL) with a quarter cycle time delay is used to estimate the grid phase angle for the purpose of performing the stationary and synchronous reference frame transformation. Unipolar pulse width modulation (PWM) technique is used to control a single-phase inverter with 2.7 KVA capacity connected to the photovoltaic system. The proposed controller can simply be tuned using minimum number of controller gains to achieve the transient and steady-state performance requirements. The proposed system, was capable of operating for a wide range of solar irradiance levels with a power factor in the range of 0.95 (leading/lagging), for the reactive power compensation purposes.
Substrate treatment for the increment of electric power potential from plants microbial fuel cells
Authors: Acosta-Coll Melisa , Ospino-Castro Adalberto , Carbonell-Navarro Stalin , Escobar-Duque Jaider , Peña-Gallardo Rafael , Zamora-Musa Ronald
Published: February 2021

Plants microbial fuel cells (PMFC) is novel sytem that generates renewable, clean, and sustainable electricity with minimal environmental impact. However, PMFC has limitations in power generation and current density, since its production values is lower than other renewable technologies. Different studies show that the highest limitation for energy generation through MFC is the high resistivity of the cathode, and the solution is to replace the metallic electrodes with non-metallic materials to obtain a better performance, however, the application of these materials requires complex interdisciplinary work. This study conducted three experimental tests using metallic electrodes for the extraction of electrons and combined a black earth substrate with different natural materials, types of plants, and water to determine their influence in the increment of the electric power output.
A new Li-ion battery charger with charge mode selection based on 0.18 um CMOS for phone applications
Authors: Farah Fouad , El Alaoui Mustapha , Elboutahiri Abdellali , Ouremchi Mounir , El Khadiri Karim , Tahiri Ahmed , Qjidaa Hassan
Published: February 2021

A new architecture of Li-Ion battery charger with charge mode selection is presented in this work. To ensure high efficiency, good accuracy and complete protection mode, we propose an architecture based on variable current source, temperature detector and power control. To avoid the risk of damage, the Li- Ion batteries charging process must change between three modes of current (trickle current (TC), constant current (CC), and constant voltage (CV)) in order to charge the battery with degrading current. However, the interest of this study is to develop a fast battery charger with high accuracy that is able to switch between charging modes without reducing its power efficiency, and to guarantee a complete protection mode. The proposed charger circuit is designed to control the charging process in three modes using the charging mode selection. The obtained results show that the Li-ion batteries can be successfully charged in a short time without reducing their efficiency. The proposed charger is implemented in 180 nm CMOS technology with a maximum charging current equal to 1 A and a maximum battery voltage equal to 4.22 V, (with input range 2.7-4.5 V). The chip area is 1.5 mm2 and the power efficiency is 90.09 %.
Hybrid memristor-CMOS implementation of logic gates design using LTSpice
Authors: Wan Mohamad Sharif Wan Mohd Hashimi , Md Idros Mohd Faizul , Mutalib Al-Junid Syed Abdul , Osman Fairul Nazmi , Abdul Razak Abdul Hadi , Halim Abdul Karimi , Adib Harun Muhammad
Published: February 2021

In this paper, a hybrid memristor-CMOS implementation of logic gates simulated using LTSpice. Memristors' implementation in computer architecture designs explored in various design structures proposed by researchers from all around the world. However, all prior designs have some drawbacks in terms of applicability, scalability, and performance. In this research, logic gates design based on the hybrid memristor-CMOS structure presented. 2-inputs AND, OR, NAND, NOR, XOR, and XNOR are demonstrated with minimum components requirements. In addition, a 1-bit full adder circuit with high performance and low area consumption is also proposed. The proposed full adder only consists of 4 memristors and 7 CMOS transistors. Half design of the adder base on the memristor component created. Through analysis and simulations, the memristor implementation on designing logic gates using memristor-CMOS structure demonstrated using the generalized metastable switch memristor (MSS) model and LTSpice. In conclusion, the proposed approach improves speed and require less area.
Design and implementation of an I-V curvetracer dedicated to characterize PV panels
Authors: Zegrar Mansour , Zerhouni M’hamed Houari , Benmessaoud Mohamed Tarik , Zerhouni Fatima Zohra
Published: February 2021

In recent years, solar photovoltaic energy is becoming very important in the generation of green electricity. Solar photovoltaic effect directly converts solar radiation into electricity. The output of the photovoltaic module MPV depends on several factors as solar irradiation and cell temperature. A curve tracer is a system used to acquire the PV current-voltage characteristics, in real time, in an eﬃcient manner. The shape of the I-V curve gives useful information about the possible anomalies of a PV device. This paper describes an experimental system developed to measure the current–voltage curve of a MPV under real conditions. The measurement is performed in an automated way. This present paper presents the design, and the construction of I-V simple curve tracer for photovoltaic modules. This device is important for photovoltaic (PV) performance assessment for the measurement, extraction, elaboration and diagnose of entire current-voltage I-V curves for several photovoltaic modules. This system permits to sweep the entire I-V curve, in short time, with different climatic and loads conditions. An experimental test bench is described. This tracer is simple and the experimental results present good performance. Simulation and experimental tests have been carried out. Experimental results presented good performance.
Model reduction of unstable systems based on balanced truncation algorithm
Authors: Vu Ngoc Kien , Nguyen Hong Quang
Published: February 2021

Model reduction of a system is an approximation of a higher-order system to a lower-order system while the dynamic behavior of the system is almost unchanged. In this paper, we will discuss model order reduction (MOR) strategies for unstable systems, in which the method based on the balanced truncation algorithm will be focused on. Since each MOR algorithm has its strengths and weakness, practical applications should be suitable for each specific requirement. Simulation results will demonstrate the correctness of the algorithms.
myBas driving cycle for Kuala Terengganu city
Authors: Norbakyah J. S. , Nordiyana M. I. , Anida I. N. , Ayob A. F. , Salisa A. R.
Published: February 2021

Driving cycles are series of data points that represent vehicle speed versus time sequenced profile developed for specific road, route, city or certain location. It is widely utilized in the application of vehicle manufacturers, environmentalists and traffic engineers. Since the vehicles are one of the higher air pollution sources, driving cycle is needed to evaluate the fuel consumption and exhaust emissions. The main objectives in this study are to develop and characterize the driving cycle for myBAS in Kuala Terengganu city using established k-means clustering method and to analyse the fuel consumption and emissions using advanced vehicle simulator (ADVISOR). Operation of myBAS offers 7 trunk routes and one feeder route. The research covered on two operation routes of myBAS which is Kuala Terengganu city-feeder and from Kuala Terengganu to Jeti Merang where the speed-time data is collected using on-board measurement method. In general, driving cycle is made up of a few micro-trips, defined as the trip made between two idling periods. These micro-trips cluster by using the k-means clustering method and matrix laboratory software (MATLAB) is used in developing myBAS driving cycle. Typically, developing the driving cycle based on the real-world in resulting improved the fuel economy and emissions of myBAS.
A new 4-D hyperchaotic hidden attractor system: Its dynamics, coexisting attractors, synchronization and microcontroller implementation
Authors: Jasim Basil H. , Hassan Kadhim H. , Omran Khulood Moosa
Published: February 2021

In this paper, a simple 4-dimensional hyperchaotic system is introduced. The proposed system has no equilibria points, so it admits hidden attractor which is an interesting feature of chaotic systems. Another interesting feature of the proposed system is the coexisting of attractors where it shows periodic and chaotic coexisting attractors. After introducing the system, the system is analyzed dynamically using numerical and theoretical techniques. In this analysis, Lyapunov exponents and bifurcation diagrams have been used to investigate chaotic and hyperchaotic nature, the ranges of system parameters for different behaviors and the route for chaos and coexisting attractors regions. In the next part of our work, a synchronization control system for two identical systems is designed. The design procedure uses a combination of simple synergetic control with adaptive updating laws to identify the unknown parameters derived basing on Lyapunov theorem. Microcontroller (MCU) based hardware implementation system is proposed and tested by using MATLAB as a display side. As an application, the designed synchronization system is used as a secure analog communication system. The designed MCU system with MATLAB Simulation is used to validate the designed synchronization and secure communication systems and excellent results have been obtained.
Chirplet signal design by FPGA
Authors: Al_Dujaili Mohammed Jawad , Al_Awadi Aws Majeed
Published: February 2021

The ever-expanding growth of the electronics and communications industries present new challenges for researchers. One of these challenges is the generation of the required bandwidth signal over a specific time frame that is used in a variety of contexts, particularly radar systems. To improve the range resolution in the radar along with better SNR, it is necessary to reduce the signal bandwidth and increase the peak power. There are some restrictions for narrowband signals like power limitation, pulse shaping, and the production of unwanted harmonics. So as a solution pulse compression techniques are suggested. Pulse compression is a process that modulating the transmitted pulse to achieve a wideband signal and then at the receiver, the received signal correlates with the transmitted pulse to achieve narrowband representations of data. Chirp is the most common signal used in pulse compression. The chirp signal is produced using linear frequency modulation. In this study, we attempted to add an amplitude modulation to the chirp signal and evaluate its performance by implementation on FPGA. The outcome signal is called chirplet and simulation will show that it enhance target detection and image quality in imaging radars like SAR.
New adaptation method based on cross layer and TCP over protocols to improve QoS in mobile ad hoc network
Authors: Hanin Moulay Hicham , Amnai Mohamed , Fakhri Youssef
Published: February 2021

Due to rapid growth of multimedia traffic used over the mobile ad-hoc networks (MANETs), to keep up with the progress of this constraints MANETs protocols becoming increasingly concerned with the quality of service. In view of the random mobility nodes in MANET, TCP becomes more unreliability in case of higher energy consumption and packet loss. In this paper we proposed a new optimization approach to enhance decision making of TCP based on some changes of IEEE 802.11 MAC uses cross layer approach. The aim is to minimize the impact of retransmissions of packet lost and energy consumption in order to analysed and chose the appropriate routing protocol for TCP that can be enhance QoS MANET. Our simulation results based QoS study using NS3 show that, our proposed achieves better performance of TCP in MANETs significantly, and also improved the throughput, energy consumption and facilitates the traffic transmission over routing protocol.
The quality of service of the deployed LTE technology by mobile network operators in Abuja-Nigeria
Authors: Kelechi Anabi Hilary , Samson Uyi Aiyudubie , Simeon Matthew , Obinna Okoyeigbo , Alex Aligbe , Aderemi Atayero Aaron
Published: February 2021

In this study, the real-world performance analysis of four Nigerian mobile network operators (MNOs), namely MTN, GLO, Airtel, and 9Mobile long-term evolution (LTE) cellular network, were analyzed and compared. The Nigerian MNOs utilize 5 MHz, 10 MH, and 20 MHz channel bandwidths based on third-generation partnership project’s (3 GPPs) recommendation. The presented analysis shows the uplink (UL), and downlink (DL) throughputs gaps in mobility condition as well as other LTE’s system quality of service (QoS) key performance indicators (KPI’s) of: Connection drop rate, connection failure rate, peak physical downlink throughput, minimum radio link control (RLC) downlink throughput threshold and latency are not strictly followed. The reason may be due to a lack of regulatory oversight enforcement. The comparative studies showed that MTN provides the best QoS. The introduction of novel LTE QoS metrics herein referred to as national independent wireless broadband quality reporting (NIWBQR) is the significant contribution of this study. The goal of this study is to show the quality of the network as it affects the user's experience. Important observation showed that all the MNOs are not adhering to the 3 GPPs specified user plane latency of 30 ms and control plane latency of 100 ms, respectively, which makes video streaming and low latency communication a near-impossible task.
Fog computing scheduling algorithm for smart city
Authors: Alsmadi Ahmad Mohammad , Ali Aloglah Roba Mahmoud , Abu-darwish Nisrein Jamal sanad , Smadi Ahmad Al , Alshabanah Muneerah , Alrajhi Daniah , Alkhaldi Hanouf , Alsmadi Mutasem K.
Published: February 2021

With the advent of the number of smart devices across the globe, increasing the number of users using the Internet. The main aim of the fog computing (FC) paradigm is to connect huge number of smart objects (billions of object) that can make a bright future for smart cities. Due to the large deployments of smart devices, devices are expected to generate huge amounts of data and forward the data through the Internet. FC also refers to an edge computing framework that mitigates the issue by applying the process of knowledge discovery using a data analysis approach to the edges. Thus, the FC approaches can work together with the internet of things (IoT) world, which can build a sustainable infrastructure for smart cities. In this paper, we propose a scheduling algorithm namely the weighted round-robin (WRR) scheduling algorithm to execute the task from one fog node (FN) to another fog node to the cloud. Firstly, a fog simulator is used with the emergent concept of FC to design IoT infrastructure for smart cities. Then, spanning-tree routing (STP) protocol is used for data collection and routing. Further, 5G networks are proposed to establish fast transmission and communication between users. Finally, the performance of our proposed system is evaluated in terms of response time, latency, and amount of data used.
Broadband microstrip patch antenna at 28 GHz for 5G wireless applications
Authors: Fante Kinde Anlay , Gemeda Mulugeta Tegegn
Published: February 2021

In this paper, a 28 GHz broadband microstrip patch antenna (MSPA) for 5G wireless applications is presented. The Rogers RT/Duroid5880 substrate material, with a dielectric constant of 2.2, the thickness of 0.3451 mm, and loss tangent of 0.0009, is used for the studied antenna to operate at 28 GHz center frequency. The proposed design of antenna is simulated by using CST studio suite. The simulation results highlight that the studied antenna has a return loss of -54.49 dB, a bandwidth of 1.062 GHz, a gain of 7.554 dBi. Besides, radiation efficiency and the sidelobe level of the proposed MSPA are 98% and 18.4 dB, respectively. As compared to previous MSPA designs reported in the recent scientific literature, the proposed rectangular MSPA has achieved significantly improved performance in terms of the bandwidth, beam-gain, return loss, sidelobe level, and radiation efficiency. Hence, it is a potential contender antenna type for emerging 5G wireless communication applications.
Performance analysis of sentiments in Twitter dataset using SVM models
Authors: Ramasamy Lakshmana Kumar , Kadry Seifedine , Nam Yunyoung , Meqdad Maytham N.
Published: February 2021

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.
Myanmar news summarization using different word representations
Authors: Lwin Soe Soe , Nwet Khin Thandar
Published: February 2021

There is enormous amount information available in different forms of sources and genres. In order to extract useful information from a massive amount of data, automatic mechanism is required. The text summarization systems assist with content reduction keeping the important information and filtering the non-important parts of the text. Good document representation is really important in text summarization to get relevant information. Bag-of-words cannot give word similarity on syntactic and semantic relationship. Word embedding can give good document representation to capture and encode the semantic relation between words. Therefore, centroid based on word embedding representation is employed in this paper. Myanmar news summarization based on different word embedding is proposed. In this paper, Myanmar local and international news are summarized using centroid-based word embedding summarizer using the effectiveness of word representation approach, word embedding. Experiments were done on Myanmar local and international news dataset using different word embedding models and the results are compared with performance of bag-of-words summarization. Centroid summarization using word embedding performs comprehensively better than centroid summarization using bag-of-words.
A systematic review on sequence-to-sequence learning with neural network and its models
Authors: Yousuf Hana , Lahzi Michael , Salloum Said A. , Shaalan Khaled
Published: February 2021

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.
Atar: Attention-based LSTM for Arabizi transliteration
Authors: Talafha Bashar , Abuammar Analle , Al-Ayyoub Mahmoud
Published: February 2021

A non-standard romanization of Arabic script, known as Arbizi, is widely used in Arabic online and SMS/chat communities. However, since state-of-the-art tools and applications for Arabic NLP expects Arabic to be written in Arabic script, handling contents written in Arabizi requires a special attention either by building customized tools or by transliterating them into Arabic script. The latter approach is the more common one and this work presents two significant contributions in this direction. The first one is to collect and publicly release the first large-scale “Arabizi to Arabic script” parallel corpus focusing on the Jordanian dialect and consisting of more than 25 k pairs carefully created and inspected by native speakers to ensure highest quality. Second, we present Atar, an attention-based encoder-decoder model for Arabizi transliteration. Training and testing this model on our dataset yields impressive accuracy (79%) and BLEU score (88.49).
Enhancing three variants of harmony search algorithm for continuous optimization problems
Authors: Alomoush Alaa A. , Alsewari Abdul Rahman A. , Zamli Kamal Z. , Alrosan Ayat , Alomoush Waleed , Alissa Khalid
Published: February 2021

Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
A hybrid objective function with empirical stability aware to improve RPL for IoT applications
Authors: Hassani Abdelhadi Eloudrhiri , Sahel Aicha , Badri Abdelmajid , Ilham El Mourabit
Published: February 2021

The diverse applications of the internet of things (IoT) require adaptable routing protocol able to cope with several constraints. Thus, RPL protocol was designed to meet the needs for IoT networks categorized as low power and lossy networks (LLN). RPL uses an objective function based on specific metrics for preferred parents selection through these packets are sent to root. The single routing metric issue generally doesn’t satisfy all routing performance requirements, whereas some are improved others are degraded. In that purpose, we propose a hybrid objective function with empirical stability aware (HOFESA), implemented in the network layer of the embedded operating system CONTIKI, which combines linearly three weighty metrics namely hop count, RSSI and node energy consumption. Also, To remedy to frequent preferred parents changes problems caused by taking into account more than one metric, our proposal relies on static and empirical thresholds. The designed HOFESA, evaluated under COOJA emulator against Standard-RPL and EC-OF, showed a packet delivery ratio improvement, a decrease in the power consumption, the convergence time and DIO control messages as well as it gives network stability through an adequate churn.
Hybrid feature selection method based on particle swarm optimization and adaptive local search method
Authors: Alzaqebah Malek , Jawarneh Sana , Mohammad Rami Mustafa A. , Alsmadi Mutasem K. , Al-marashdeh Ibrahim , Ahmed Eman A. E. , Alrefai Nashat , Alghamdi Fahad A.
Published: February 2021

Machine learning has been expansively examined with data classification as the most popularly researched subject. The accurateness of prediction is impacted by the data provided to the classification algorithm. Meanwhile, utilizing a large amount of data may incur costs especially in data collection and preprocessing. Studies on feature selection were mainly to establish techniques that can decrease the number of utilized features (attributes) in classification, also using data that generate accurate prediction is important. Hence, a particle swarm optimization (PSO) algorithm is suggested in the current article for selecting the ideal set of features. PSO algorithm showed to be superior in different domains in exploring the search space and local search algorithms are good in exploiting the search regions. Thus, we propose the hybridized PSO algorithm with an adaptive local search technique which works based on the current PSO search state and used for accepting the candidate solution. Having this combination balances the local intensification as well as the global diversification of the searching process. Hence, the suggested algorithm surpasses the original PSO algorithm and other comparable approaches, in terms of performance.
The evolution of energy requirements of smartphones based on user behaviour and implications of the COVID-19 era
Authors: Almasri Abdullah Mahmoud , Gouveia Luis Borges
Published: February 2021

Smartphones have evolved to become frequent companions to humans. The common problem shared by Android users of smartphones was, and continues to be, about saving their batteries and preventing the need to use any recharging tools. A significant number of studies have been performed in the general field of "saving energy in smartphones". During a state of global lockdown, the use of smartphone devices has skyrocketed, and many governments have implemented location-tracking applications for their citizens as means of ensuring that the imposed governmental restrictions are being adhered to. Since smartphones are battery-powered, the opportunity to conserve electricity and ensure that the handset does not have to be charged so much or that it does not die and impede location-tracking during this period of crisis is of vital significance, impacting not only the reliability of tracking, but also the usability of the mobile itself. While there are methods to reduce the battery’s drain from mobile app use, they are not fully utilized by users. Simultaneously, the following the manuscript demonstrates the growing prevalence of mobile applications in daily lives, as well as the disproportionally increasing phone functionality, which results in the creation of a dependency towards smartphone use and the need of energy to recharge and operate theses smartphones.
Spider monkey optimization routing protocol for wireless sensor networks
Authors: Jabbar Ali H. , Alshawi Imad S.
Published: February 2021

Uneven energy consumption (UEC) is latent trouble in wireless sensor networks (WSNs) that feature a multiple motion pattern and a multi-hop routing. UEC often splits the network, reduces network life, and leads to performance degradation. Sometimes, improving energy consumption is more complicated because it does not reduce energy consumption only, but it also extends network life. This makes energy consumption balancing critical to WSN design calling for energy-efficient routing protocols that increase network life. Some energy-saving protocols have been applied to make the energy consumption among all nodes inside the network equilibrate in the expectancy and end power in almost all nodes simultaneously. This work has suggested a protocol of energy-saving routing named spider monkey optimization routing protocol (SMORP), which aims to probe the issue of network life in WSNs. The proposed protocol reduces excessive routing messages that may lead to the wastage of significant energy by recycling frequent information from the source node into the sink. This routing protocol can choose the optimal routing path. That is the preferable node can be chosen from nodes of the candidate in the sending ways by preferring the energy of maximum residual, the minimum traffic load, and the least distance to the sink. Simulation results have proved the effectiveness of the proposed protocol in terms of decreasing end-to-end delay, reducing energy consumption compared to well-known routing protocols.
Hybrid load balance based on genetic algorithm in cloud environment
Authors: Saber Walaa , Moussa Walid , Ghuniem Atef M. , Rizk Rawya
Published: February 2021

Load balancing is an efficient mechanism to distribute loads over cloud resources in a way that maximizes resource utilization and minimizes response time. Metaheuristic techniques are powerful techniques for solving the load balancing problems. However, these techniques suffer from efficiency degradation in large scale problems. This paper proposes three main contributions to solve this load balancing problem. First, it proposes a heterogeneous initialized load balancing (HILB) algorithm to perform a good task scheduling process that improves the makespan in the case of homogeneous or heterogeneous resources and provides a direction to reach optimal load deviation. Second, it proposes a hybrid load balance based on genetic algorithm (HLBGA) as a combination of HILB and genetic algorithm (GA). Third, a newly formulated fitness function that minimizes the load deviation is used for GA. The simulation of the proposed algorithm is implemented in the cases of homogeneous and heterogeneous resources in cloud resources. The simulation results show that the proposed hybrid algorithm outperforms other competitor algorithms in terms of makespan, resource utilization, and load deviation.
Fruit tree disease classification system using generative adversarial networks
Authors: Kim Changsu , Lee Hyesoo , Jung Hoekyung
Published: February 2021

Smart farm refers to a farm that can remotely and automatically maintain proper growth and management of crops and livestock by integrating technology with agriculture. Currently, smart farms are concentrated in the field of smart horticulture, and although spreading research is being conducted in limited spaces. In addition, it is difficult to obtain a sufficient amount of data to be used for learning, and there is a problem that data imbalance occurs because it is difficult to obtain a similar amount for each class. In this paper, we propose a method to amplify a small amount of data and to solve the problems of imbalance data by using a feature that can learn to mimic the data of a generative adversarial network. The proposed method can create dataset of various crops and also show high hit rate. Dataset generated from crops would be used to solve problems of data imbalance by learning.
An internet of things based smart waste system
Authors: Jasim Ali M. , Qasim H. H. , Jasem Ethar Habeeb , Saihood Raed Hasan
Published: February 2021

The importance of preserving the environment from waste and its pollution lies in many matters such as preserving people health, enhancing the aesthetic character of cites, attracting tourists, and protecting society from environmental disasters. The environmental wastes are the main dilemmas in our daily life and in the world at large. With the existence of modern technology, development and the field of the internet, many solutions have been undertaken to get rid these dilemmas. In this paper, a smart waste system based on internet of things (IoT) technique has been proposed using ESP-32 Wi-Fi microcontroller. This system can be adopted to avoid the accumulation of waste in the streets that distort the face of civilization, also to reduce the burden of workers and limit the workforce. The system is based on a multiple sensors in the garbage baskets, as they measure the waste level by using ultrasonic sensor, the moisture percent and temperature degree using DHT-22 sensor. The sensors data are processed by ESP32 microcontroller and displayed to both LCD screen using I2C protocol and mobile application using IoT cloud. System baskets automatically open their covers when the person approaches with a distance less or equal to 30 cm to throw garbage. Any approval waste basket is automatically discharged through an underground dump system using conveyor belt if the basket is full by 80% garbage and/or the basket moisture reaches to 40%.
Copy-move forgery detection using convolutional neural network and K-mean clustering
Authors: Pourkashani Ava , Shahbahrami Asadollah , Akoushideh Alireza
Published: February 2021

Copying and pasting a patch of an image to hide or exaggerate something in a digital image is known as a copy-move forgery. Copy-move forgery detection (CMFD) is hard to detect because the copied part image from a scene has similar properties with the other parts of the image in terms of texture, light illumination, and objective. The CMFD is still a challenging issue in some attacks such as rotation, scaling, blurring, and noise. In this paper, an approach using the convolutional neural network (CNN) and k-mean clustering is for CMFD. To identify cloned parts candidates, a patch of an image is extracted using corner detection. Next, similar patches are detected using a pre-trained network inspired by the Siamese network. If two similar patches are not evidence of the CMFD, the post-process is performed using k-means clustering. Experimental analyses are done on MICC-F2000, MICC-F600, and MICC-F8 databases. The results showed that using the proposed algorithm we can receive a 94.13% and 96.98% precision and F1 score, respectively, which are the highest among all state-of-the-art algorithms.
High clarity speech separation using synchro extracting transform
Authors: Abdulla Shahin M. , Jayakumari J.
Published: February 2021

Degenerate unmixing estimation technique (DUET) is the most ideal blind source separation (BSS) method for underdetermined conditions with number of sources exceeds number of mixtures. Estimation of mixing parameters which is the most critical step in the DUET algorithm, is developed based on the characteristic feature of sparseness of speech signals in time frequency (TF) domain. Hence, DUET relies on the clarity of time frequency representation (TFR) and even the slightest interference in the TF plane will be detrimental to the unmixing performance. In conventional DUET algorithm, short time Fourier transform (STFT) is utilized for extracting the TFR of speech signals. However, STFT can provide on limited sharpness to the TFR due to its inherent conceptual limitations, which worsens under noise contamination. This paper presents the application of post-processing techniques like synchro squeezed transform (SST) and synchro extracting transform (SET) to the DUET algorithm, to improve the TF resolution. The performance enhancement is evaluated both qualitatively and quantitatively by visual inspection, Renyi entropy of TFR and objective measures of speech signals. The results show enhancement in TF resolution and high clarity signal reconstruction. The method also provides adequate robustness to noise contamination.
Expert cancer model using supervised algorithms with a LASSO selection approach
Authors: Ghosh Pronab , Karim Asif , Atik Syeda Tanjila , Afrin Saima , Saifuzzaman Mohd.
Published: February 2021

One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.
Automatic segmentation of large bowl obstruction area with hough transform from erect abdominal radiograph images
Authors: Kim Kwang Baek , Song Doo Heon , Woo Young Woon
Published: February 2021

Large bowel obstruction is less frewuent but often appears acute and needs emergent treatment. Erect abdominal radiograph is usually the first imaging study performed in patients suspected of having large bowel obstruction. However, that mordality suffers from operator subjectivity thus a fully automatic computer aied tool is necessary. In this paper, we peopose an automatic large bowel feature (air-fluid region) segmentation method based on Canny edge detection and Hough transform. In experiment, the proposed method was successful in finding target region from large bowel obstruction patients’ radiographic images in all 30 cases provided. Whilie limited only applicable to the large bowel obstruction cases, the proposed method is practically feasible in application.
Predictive model of water stress in tenera oil palm by means of spectral signature methods
Authors: Galvez Valencia Angie Marcela , Garces-Gomez Yeison Alberto , Lemus Rodriguez Erwin Leandro , Arango Argoti Miguel Andres
Published: February 2021

Agriculture as a competitive business, seeks to improve productivity within crops with a more sustainable environmental management. It is important that agriculture includes new technologies that allow it to generate differential, precise and real-time information. In Colombia, the current lack of knowledge about techniques that allow early identification of water stress in African palm could generate a loss in the investment made in the fertilization of the crop, cause an increase in diseases, pests, and susceptibility to compaction or abortions in female flowers that would lead to decreases in production. In this work, a predictive model is established to quantify water stress based on spectral, physiological and soil information in African palm plants. To this end, a study was carried out in an oil palm plantation where treatments were established with 3 ranges of humidity. It was found that the indices with the highest correlation with the biophysical variable soil moisture were: NDVI_1 and NDVI_16 for treatment 1, SR_4 for treatment 2 and NDVI_16 and NDVI_20 for treatment 3. Finally, the third order polynomial regression model that obtained higher correlation coefficients of Pearson R^2=0.73 was selected as the most suitable model to estimate soil moisture content for treatments 2 and 3.
Bleeding recognition technique in wireless capsule endoscopy images using fuzzy logic and principal component analysis
Authors: Al Mamun A. , Em P. P. , Ghosh T. , Hossain M. M. , Hasan M. G. , Sadeque M. G.
Published: February 2021

Wireless capsule endoscopy is the most innovative technology to perceive the entire gastrointestinal (GI) tract in recent times. It can diagnose inner diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps. in a discretion way. It creates immense pressure and onus for clinicians to perceive a huge number of image frames, which is time-consuming and makes human oversight errors. Therefore a computer-automated system has been introduced for bleeding detection. A unique fuzzy logic technique is proposed to extract the specified bleeding and non-bleeding information from the image data. A particular quadratic support vector machine (QSVM) classifier is employed to classify the obtained statistical features from the bleeding and non-bleeding images incorporating principal component analysis (PCA). After extensive experiments on clinical data, 98% sensitivity, 98.4% accuracy, 98% specificity, 93% precision, 95.4% F1-score, and 99% negative predicted value have been achieved, which outperforms some of the states of art methods in this regard. It is optimistic that the proposed methodology would significantly contribute to bleeding detection techniques and diminish the additional onus of the physicians.
Sensitivity of MAPE using detection rate for big data forecasting crude palm oil on k-nearest neighbor
Authors: Khowarizmi Al , Syah Rahmad , M. Nasution Mahyuddin K. , Elveny Marischa
Published: February 2021

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become a study in the field of data science. Forecasting that learns through data in the light age is able to solve problems with large-scale data or big data. With the big data, the performance of the k-Nearest Neighbor (k-NN) method can be tested with several accuracy measurements. Generally, accuracy measurement uses MAPE so it is necessary to conduct sensitivity on MAPE by combining it with the detection rate which is the difference technique. In addition, the k-NN process has been developed for the sake of running sensitivity by performing normalized distance using normalized Euclidean distance so that in this paper using the crude palm oil (CPO) price dataset, it is able to forecast and become a future model and apply it to Business Intelligence and analysis. In the final stage of this paper, the accuracy value in doing big data forecasting on CPO prices with MAPE is 0.013526% and MAPE sensitivity combined with a detection rate of 0.000361% so that future processes using different methods need to involve detection rates.