bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modeling and Simulation for Integrating Four Reactors in the Continuous Catalytic Regeneration Reforming Process Using MATLAB
Authors: Babaqi Badiea S. , Takriff Mohd S. , Othman Nur Tantiyani A. , Abu Hasan Hassimi , Mahmoudi Ebrahim 
Published: July 2021

AbstractModeling and simulation were developed of the continuous catalytic regeneration reforming process for integrating four reactors and studying the effecting operating parameters to improve the process performance. The process model included four reactors as stack and used to investigate the profiles of reformate composition products and research octane number. The main reactions that occur in reforming reactors included five reactions namely; dehydrogenation, dehydrocyclization, isomerization, hydrocracking and hydrodealkylation. The kinetic and thermodynamic parameters were estimated by the particle swarm optimization approach to monitor the process behavior. The expected results have been validated of the model simulation with comparison to data of actual plant, and average absolute deviation (AAD%) of two parameters; reformate composition and octane number reached 0.04% and 1.3%, respectively. This evaluation was a fair agreement and within the acceptable limit.
Planning, Design and Simulation of a Network Access Based on FTTH-EPON for Hadhramout University
Authors: Bakarman H A , Alsaqaf A , Ba’afiah M , Baqhoom F , Baraja M 
Published: July 2021

Abstract
In our country copper infrastructure from Central Office (CO) to the end user is responsible for delivering poor Broadband services with loss reach 86% of the overall capacity speed. Not to mention the high prices and the impact of this inferior service to the education and economy of the country. Therefore, This paper presents a procedural proposal solution for planning, design and simulation of Ethernet Passive Optical Network (EPON) performance through Fiber-to-the-Home (FTTH) technology applied on College Of Engineering and Petroleum campus at Hadhramout University as it the best way to overcome all the problems related with copper. Since the fiber terminates at the campus, we termed the network, Fiber To The Campus (FTTCampus). FTTCampus planning the location network was developed through Google Earth and AUTOCAD software which was implemented to map out the college location areas. The proposed optical network system design has been simulated based on EPON IEEE 802.3ah standards through Optisystem software program. Simulation network components such as optical transmitters, Optical network terminals (ONTs), and optical splitters among others were allocated at different coverage areas and various network links were connected among these coverage areas. A downstream network of EPON has been simulated using Continuous Wave Laser (CW Laser) and Directed-modulated Laser (DM Laser) as different optical. Validation was performed with consistent output performance indicators like quality factor, optical source power (dBm) and bit error rate (BER). This paper has a main objective in designing an affordable and feasible network and it is accomplished by utilizing EPON that merges the low-cost Ethernet technology and low-cost PON structure with the DM Laser as an optical source. This unique work revealed that, FTTCampus could be a reliable technology in an educational academic institution, if implemented.
Hand Gesture to Control a Low Cost 3-D Printed Arm using Android Application
Authors: Mohd Noor Nurul Muthmainnah binti , Musrijan Mohd Muzammel bin 
Published: July 2021

Abstract
In this era, the technology on computer usage is highly increasing and demanding especially in engineering, business and communication sector. Many applications are built based on a smartphone technology. The idea of this paper is to develop a hand gesture system based on Android application in order to control the 3-D printed arm. A MIT application invertor was used in developing an algorithm of the hand gesture controller. The app inventor can be used and accessed with accelerometer sensor that already built-in the smartphone via a Bluetooth connection. The Android applications was used as a communication tool between the Arduino Uno and the 3-D printed arm. The accelerometer sensor was acted as an input signal to control the hand gesture of the 3-D printed arm by the change or tilt of smartphones position. The output of this system is a servo motor that was mounted on the arm and used to drive the fingers. In this project, there system was classified by two; hardware part that consists of 3D-printed arm, Arduino, electrical component, and smartphone, meanwhile, the software part consists of MIT application inverter. The result shows the 3-D printed arm was able to be implemented with the wireless and Android application.
A New Filtering Algorithm for Generating Path Loss Model to Improve Accuracy of Trilateration-Based Indoor Positioning System
Authors: Susanto L , Wibisono W , Ahmad T 
Published: July 2021

Abstract
Global Positioning System (GPS) is used to determine someone’s or something’s position. It is very accurate. But, GPS cannot be used in an indoor environment as its signal is blocked by an object (i.e. roof). To overcome this problem, Indoor Positioning System (IPS) is used in the indoor environment. There are two methods that IPS can use to determine the position. The first method is pattern matching. This method is highly accurate. But, the computation cost is expensive. The second method is the path loss model. Its computation cost is cheap. However, it is fairly accurate. In this paper, a new filter algorithm is proposed to improve path loss model accuracy. The algorithm works by filtering the received signal strength index (RSSI) in the training phase. The algorithm works by replacing the RSSI value at a certain distance by considering the current and the previous RSSI value. The experiment result shows that the proposed method has 3% better accuracy than the existing method.
Malware Detection Using Machine Learning Algorithms Based on Hardware Performance Counters: Analysis and Simulation
Authors: Bawazeer Omar , Helmy Tarek , Al-hadhrami Suheer 
Published: July 2021

Abstract
In the last decade, Hardware Performance Counters (HPCs) events are increasingly used by Machine Learning (ML) algorithms for malware detection. Modern processors provide a variety of HPCs to measure and monitor processes’ events such as memory accesses, instructions, etc. during their execution. In this paper, an analysis study to categorize the machine learning algorithms based on HPCs that have been used for malware detection is introduced. Besides, the most efficient and effective features of HPCs that have been exploited to recognize the abnormal activities on various systems are identified. Furthermore, the Neural Network (NN) algorithms including Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Full Order Radial Basis Function (RBF) algorithms are used to simulate several experiments from the literature. The simulation results show that the accuracy of MLP, CNN, and Full Order RBF are 96.95%, 98.22%, and 98.68%, respectively.
Design of Reconfigurable Antenna for RFID System
Authors: Sivakumar Renukka , Sabapathy Thennarasan , Jusoh Muzammil , Soh Ping Jack , Osman Mohamed Nasrun , Salem Al-Bawri Samir , Jayaprakasam Suhanya , Mohd Yasin Mohd Najib , Saluja Nitin 
Published: July 2021

Abstract
This paper proposes a reconfigurable antenna for RFID system which can operate between 860MHz to 960MHz frequency that belongs to ultra-high frequency (UHF) band used in Malaysia with the center frequency of 910MHz. One rectangular slot and two triangle-shaped slots are used in designing this antenna. A good circular polarization obtained from the slotted structure along the diagonal axis in the design. RF pin diodes are used as the switching mechanism of the antenna. However, in this work to proof the concept of switching mechanism, copper pins are used as artificial switches. Parasitic elements are deployed on the right and left side of the driven element to assist the radiation pattern reconfiguration. Overall, the proposed antenna able to steer the beam at approximately at -30°, -16°, and 10° with peak gain of 3.2dB and average gain of 2.5dB. With this result, overall coverage of UHF RFID reader antenna could be improved.
Effect of Sodium Hydroxide on Mechanical Characteristics of Kenaf Fibers Reinforced Concrete
Authors: Baarimah A O , Syed Mohsin S M , Alaloul W S , Ba-naimoon M S 
Published: July 2021

Abstract
This article shows the experimental analysis of sodium hydroxide (NaOH) effects on the mechanical properties upon the addition of kenaf fibres to the concrete mixture. The compression and flexural tests were carried out on seven different concrete mixtures for 28 days to evaluate the mechanical characteristics. Hence, the experimental trials comprise of seven different mixtures of concrete; the control (without the addition of kenaf fibre) was represented as the first mixture. The following six concrete mixtures contain different kenaf fibre volume fractions (1% and 2%); they were treated with 1%, 3% and 6% of sodium hydroxide concentrations in order to investigate their performances. The experimental results show that the compressive and flexural strengths of kenaf fibres reinforced concrete increase as sodium hydroxide concentration increases. Besides, it is worth noting that the addition of kenaf fibres has an insignificant effect on the compressive strength of the concrete owing to the reduction in concrete density. Therefore, the results of this study demonstrated that the concrete flexural strength increased to up to 12%) upon the addition of 1% kenaf fibre associated with 6% NaOH. Moreover, the failure state increasingly changed to ductile from brittle.
Threat Assessment Model in Electrical Power Grid Environment
Authors: Qassim Qais Saif , Jamil Norziana , Mahdi Mohammed Najah , Rahim Fiza Abd , Cob Zaihisma Che , Sidek Lariyah Mohd 
Published: July 2021

AbstractAt the heart of every critical national infrastructure, there is a SCADA system. The wide range of supported applications has made this life-saving management system the key element of every industrial control infrastructure in various sectors. The complexity and sophistication of SCADA systems have been increased dramatically over the last few years. Various cyberattacks have targeted these systems by exploiting zero-day vulnerabilities or targeting a nearby Internet-enabled appliance or computer system. To this end, many efforts have been proposed previously to model the threats on SCADA systems to eliminate potential danger and diminish the catastrophic consequences in the physical domain. The main contribution of this work is to present a multi-facets threat assessment model to aid security teams to systematically perform various cybersecurity analysis practices such as penetration testing, vulnerability assessment and risk analysis.
Selective Harmonic Elimination of Five Level Cascaded H-Bridge Inverter Using the Newton-Raphson Technique
Authors: Saifizi M. , Kasdi N. S. , Rahim H. A. , Mashagba H. A. , Mustafa Wan Azani , Aihsan M. Z. , Sulong Syahmi M. M. 
Published: July 2021

Abstract
Multilevel inverter becomes an alternative for high power and medium voltage applications recently and is more popular in renewable energy. The cascaded H-bridge multilevel inverter is more suitable for the Photovoltaic application because it acts as a separate DC source for each bridge. This paper presents a selective harmonic elimination (SHE) modulation for single-phase cascaded H-bridge (CHB) five level inverter. The optimum angle of the transcendental equations is solved the fundamental and harmonic elements using the Newton-Raphson (NR) process. The proposed selective harmonic elimination (SHE) scheme is tested using MATLAB/Simulink simulation. These simulation results are then verified through experiment using microcontroller. The proposed SHE is efficient in eliminating the 5th order harmonics and producing a higher quality output waveform with a better harmonic profile.
Plant Diseases Classification using Machine Learning
Authors: Xian Tan Soo , Ngadiran Ruzelita 
Published: July 2021

Abstract
Plant diseases are one of source of obstruction in the quality and productivity of plants which can lead to the shortage of food supply. Therefore, plant disease classification is essential to the agriculture industry. The objective of this research is to classify the plant diseases by assessing the images of the leaves with the application of Extreme Learning Machine (ELM), a Machine Learning classification algorithm with a single layer feed-forward neural network. This work proposed image features as input where the image is pre-processed via HSV colour space and features extraction via Haralick textures. The features are then fitted in the ELM classifier to perform the model training and testing. The accuracy of ELM is then calculated after the testing has been done. The dataset used comprises of tomato plant leaves which is a subset of the Plant-Village dataset. The results produced from the ELM shows a better accuracy that is 84.94% when compared to other models such as the Support Vector Machine and Decision Tree.
Water Pipeline Leakage Monitoring System based on Internet of Things
Authors: Che N N , Omar K N F , Azir Ku , Kamarudzaman M F 
Published: July 2021

Abstract
Water leakage detection of pipeline in the resident household is important to ensure that water supply networks can operate effectively. This paper presents an experimental investigation and prototype of a water pipeline leakage monitoring system based on a low-cost microcontroller, Arduino, with three types of sensors. The system will monitor the turbidity of the water before pump the water into water tank of household. Then, later if there any leakages, a notification will be given to the user either through SMS or Telegram notification. These leakages are being detected using water flow and water sensors. A simple mobile application has been designed to monitor the condition of water pipeline remotely and allowed user to remotely call the repairing services if the leakage happened when there are no one at home. Simulation analysis and experimental results indicate that the proposed leakage identification method can effectively identify water pipeline leakage and has lower consumption than other methods.
An Artificial Intelligent Approach for Black Oil PVT Properties
Authors: Abedelrigeeb Al-Gathe , El-Banbi Ahmed , Abdel Fattah Kh. A. , El-Metwally K. A. 
Published: July 2021

Abstract
In absence of experimental oil fluid samples, it is usually difficult to select the suitable correlation to estimate oil properties. However, the accuracy of these empirical correlations has become insufficient for the best calculation. The main objective of this work is to test the capability of Particle Swarm Optimization with Neural Network (PSONN) and Neuro-Fuzzy (NFuzzy) approaches to predict oil properties with simply and accurately. The proposed approaches are developed based on clustering the oil data into the three groups (light, medium and heavy light oil). Over five hundred of black oil samples were collected from Middle East Field to train the hybrid models whereas additional oil data samples were selected to validate. The developed models used to estimate bubble points pressure (Pb), solution gas oil ratio at and below Pb, undersaturated oil compressibility, saturated formation volume factor, saturated and undersaturated density. The recommended guidelines and optimal configuration of the PSONN and NFuzzy models are developed to estimate any oil property in future. Statistical error analyses show that the proposed models exhibit a robust predictive capability for estimating oil properties. The validation results show the PSONN model achieve the lowest average absolute percent relative error (0.04, 2.89 and 1.0) for estimating formation volume factor, gas oil ratio and oil compressibility respectively whereas the NFuzzy model obtains the best approximation in oil density and bubble point pressure with average absolute percent relative error (0.18 and 0.97) respectively.
Development of E-Healthcare Management System using PHP, Javascript and Cascading Style Sheets
Authors: Vijayasarveswari V. , Chyin Lim Jia , Wafi N. M. , Iszaidy I. 
Published: July 2021

Abstract
It is a responsibility for higher authorities in a government/private institute to provide an essential health care service to all their own community. The current health management is quite tedious and unpractical by using manual methods in order to get an appointment from doctor. Furthermore, the current situation, Covid-19 is spreading throughout Malaysia and it is very risky for direct doctor visit. This paper is mainly focused on developing an E-healthcare management system for government/private institute to make doctor appointment online. The used programming developing tools are CSS, PHP, Javascript and Cascading Style Sheets (CSS). Patients may log in to make appointment with the doctor with its specialized. All the appointment details is processed and directly sent to the centralized database system. Then doctors confirm the appointment through their web page. Finally, patients receive an alert from doctor after the confirmation. E-Healthcare Management system is successfully developed to cater all the needs in appointments and organizing the records. In addition, current patients’ data and appointment can be retrieved just in a click of second.
MOVE Mobility Model in GreedLea Routing Protocol for Internet of Vehicle (IoV) Network
Authors: Omar Normaliza , Yaakob Naimah , Elshaikh Mohamed , Husin Zulkifli 
Published: July 2021

Abstract
Internet of Vehicles (IoV) is a broad variety of mobile transmission purposes for file sharing [l]-[5]. There are still debates on the viability of purposes using end to end multi-hop communication, since the significant number of high mobility nodes involved in the networks. The main issue is the efficiency of IoV routing protocols in cities and highways can meet the ideal delay and throughput for such purposes. In particular, it is not usually a challenge to locate a node to hold a message in urban daytime situations, where vehicles are tightly packed. Since fewer number of vehicles are running in highway scenarios and cities at night, and it might not be possible to set up end-to-end roads. In general, each protocol offered a performance evaluation in contradiction of some other protocols, giving considerable importance to a detailed performance evaluation of each protocol type. After such an assessment, it was found that geocast routing would perform best in urban areas. GreedLea routing protocol is develop to overcome the current routing protocol drawback. The development of GreedLea routing protocol involved Greedy Perimeter Stateless Routing (GPSR) and reinforcement learning method in order to deliver better performance compared to current existing routing protocol. Urban environments without obstacles has been simulated using actual maps for example intersection density. In order to measure efficiency, the metrics are: average delivery rate, average delay, average length of path and overhead. From the analysis, it shows that GreedLea offers better performance compared to GPSR for both city and highway scenario. The first section in your paper
A Literature Survey on Cloud-RAN Architecture toward 5G Green Wireless Communication Systems
Authors: Saad N S , Ariffin W N S F W , Nordin J , Zain A S M , Asri L I M 
Published: July 2021

Abstract
Cell network architectures confront enormous challenges due to the unusual rise in mobile data traffic usage, thus reducing spectrum accessibility and heavy power consumption. Research communities are looking for breakthroughs in the development of advanced infrastructure networks to meet increased consumer demand and, at the same time, to reduce network operators’ capital and operational costs. Cloud Radio Access Network (Cloud-RAN) architecture is a recently proposed solution for wireless networks and is considered a strong candidate for potential 5G cellular coverage worldwide. This paper presents a literature survey on Cloud-RAN architectures toward Green 5G wireless communication systems and highlights Cloud-RAN benefits and limitations. Furthermore, this paper discusses Cloud-RAN’s potential enhancement techniques, including robust beamforming, multiple-input-multiple-output (MIMO) with non-orthogonal multiple access (NOMA) cooperative scheme, massive MIMO, mmWave communication, clustering techniques, coalition formation, energy harvesting and reinforcement learning output.
Broadband Sub-6GHz Slot-based MIMO Antenna for 5G NR Bands Mobile Applications
Authors: Al-Bawri Samir Salem , Islam Mohammad Tariqul , Singh Mandeep Jit , Alyan Emad , Jusoh Muzammil , Sabapathy Thennarasan , Padmanathan Surentiran , Hossain Kabir 
Published: July 2021

Abstract
A slot-based broadband single and MIMO antennas for 5G New Radio (5G NR) mobile applications are presented in this paper. The proposed MIMO antenna is investigated by loading two antennas into a low-cost material with the partial ground for sub-6GHz. Each antenna element is consisted of a hexagonal-shaped slot and is composed of a 50Ω microstrip feed line. The WLAN 5-GHz band and 5G NR Bands n77/n78/n79 are covered with good impedance matching. Besides, the mutual coupling between the adjacent elements is less than 15-dB whereas the desired antenna elements gains are 3.19 dBi and 3.09 dBi at 3.5 GHz 4.2 GHz respectively.
Emotion Recognition Using Convolutional Neural Network (CNN)
Authors: Badrulhisham Nur Alia Syahirah , Mangshor Nur Nabilah Abu 
Published: July 2021

Abstract
Emotion is an expression that human use in expressing their feelings. It can be express through facial expression, body language and voice tone. Humans’ facial expression is a major way in conveying emotion since it is the most powerful, natural and universal signal to express humans’ emotion condition. However, humans’ facial expression has similar patterns, and it is very confusing in recognizing the expression using naked eye. For instance, afraid and surprised is very similar to one another. Thus, this will lead to confusion in determining the facial expression. Hence, this study aims to develop a mobile based application for emotion recognition that can recognize emotion based on facial expression in real-time. The Deep Learning based technique, Convolutional Neural Network (CNN) is implemented in this study. The MobileNet algorithm is deployed to train the model for recognition. There are four types of facial expressions to be recognized which are happy, sad, surprise, and disgusting. As the result, this study obtained 85% recognition accuracy. In the future, the developed application could be improved by adding more face expression categories.
Acne Type Recognition for Mobile-Based Application Using YOLO
Authors: Isa Nur Ashiqin Mat , Mangshor Nur Nabilah Abu 
Published: July 2021

Abstract
Acne is a chronic skin condition that happens to most teenagers at the age of 12 and 25. Several types of acne are found as non-inflammatory and inflammatory skin disease. As the number of people facing acne problems increasing and there is the need to have an automated application for recognizing acne, this study proposed a mobile-based application that able to recognizes acne types. This study used the Deep Learning technology method, YOLOv4 in detecting and recognizing acne. There are four types of acne covered in this study which are cyst, papule, pustule, and whitehead. The dataset used for the purpose of training and testing the model is from the DermNet NZ dataset. Based on the testing conducted, the application achieved 91.25% average of accuracy. It is believe, the integration of YOLO with any existing Deep Learning approach could improve the recognition rate in future.