bluuebunny/crossref_metadata_2025_split huggingface dataset:
Does Artificial Intelligence Prevail in Poverty Measurement?
Authors: Isnin@Hamdan Rusnita , Bakar Azuraliza Abu , Sani Nur Samsiah 
Published: June 2020

Abstract
Artificial Intelligence (AI) has been used widely by many domains in academic research to explore and learn much ambiguity information from small to large dataset. It is also tremendously implemented in daily lives especially in late 20 centuries in diverse formation to enhance business scalability and improving business operation for better services and performances. This trend is also seen to evolve in the field of socioeconomic studies, with an individual or household economic and social status relative to the rest of society. Is this technology present in the field of socioeconomic especially in poverty measurement? What is the form of problem solved? Therefore, the authors try to answers these question through systematic review method from the existence of poverty measurement research until the beginning of 2019. A systematic literature search was performed in the Web of Science and Scopus to identify all potential relevant studies using Kitchenham, 2007 guideline. Of the 53 article documents, 15 papers were selected after subsequent title/abstract and full text screening related to poverty measurement. The findings show that Linear Regression is a popular method chosen and closely followed by Random Forest and Deep Learning. Most studies diversify the use of data sources to predict poverty more accurately. The tendency to use satellites data can be seen more significantly than other types of data. Overall from 2007 to early 2019, the potential for using AI in the socioeconomic remains open.
Performance Analysis of Robust Locations Estimators
Authors: Okwonu F. Z. , Zahayu Md. Y. 
Published: June 2020

Abstract
This paper describes novel computational procedures that reduces the influence of outliers by transformational process. It transforms the influential observations to moderate data points. The procedures utilize all the information contained in the data set. These techniques are compared with the existing robust procedures, such as the weighted mean based on Mahalanobis distance, minimum covariance determinant (MCD), trimmed and winsorized mean. The performance analysis showed that the proposed techniques performed comparably with the well-established robust procedures based on the real data set.
Dynamic behaviour of EDM system through mathematical model
Authors: Dehghani D , Yahya A , Khamis N H 
Published: June 2020

Abstract
Electrical discharge machining (EDM) is a stochastic machining process which widely used to generate dies and molds. However, information about the EDM process is still at the earlier stage which lead to experience many challenges for further developments. Experimental analysis is time consuming as well as a costly procedure, due to the highly stochastic and complex nature of the process. Therefore, process modeling is an alternative to reduce the experimental costs related to the technology. This research proposes method to design a mathematical model of electrical discharge machining (EDM) system. The model will be used to understand the effects of machining parameters into the dynamic behaviour of EDM system based on the sparking phases and pulse power generator.
Numerical Analysis of Bolon as Traditional Batak Toba House During An Earthquake: A Study in Village of Siallagan And Silalahi North Sumatera
Authors: Tarigan Johannes , Nursyamsi Nursyamsi , Hani Sheila 
Published: June 2020

Abstract
There were many two-story building collapsed in The Province of Sumatera during an earthquake. One structure that did not collapse is the Traditional Batak House, which mostly made of wood. Traditional Batak House still stands strong until now eventhough it was built years before the presence of building code. The layout of the structure, the frame concept and also the earthquake force is calculated using Guideline SNI 7973:2013 for wood construction, and SNI 03-1726-2012 for earthquake structure resistant structure design. The research observed traditional Batak Toba house from two villages, the village of Siallagan and Silalahi which is neighboring village. The houses have different model of timber roof construction and also different height.
The structure then analyses with the help of structure software application in seismic condition. One of the outcomes using software computation was natural frequency. The ω for both houses in Village of Siallagan and Silalahi are 12,697 rad/sec and 13,846 rad/sec. while the period of the structure are 0,494 second and 0,454 second. The maximum value of the dynamic response expressed in the shear force of the traditional house for both village is 28,87 kN. The maximum floor displacement for Village of Siallagan is 4.01 cm, and for the Village of Silalahi is 4.14 cm.
Physicochemical and Antibacterial Activity of Soap Enriched with Harumanis Pruned Leaves Extract
Authors: Bahari Rohazita , Kasim Khairul Farihan , Shahrir Nurul Nadiah 
Published: June 2020

Abstract
The aim of this study was to formulate antibacterial soap from Harumanis pruned leaves extract. The antibacterial properties of Harumanis leaves extract were tested using disc diffusion test against five bacteria i.e. Bacillus subtilis, Escherichia coli, Pseudomonas putida, Bacillus megaterium and Bacillus licheniformis. Methanol, ethanol and distilled water were used to extract the Harumanis pruned leaves. Ethanol is the best solvent to extract antimicrobial properties in Harumanis pruned leaves as it shows the largest inhibition zone, whereas, water is the weakest solvent to extract antimicrobial properties in Harumanis pruned as it shows minimum inhibition zone for all the microbes used. The best extract of Harumanis pruned leaves (i.e. ethanol extract) was used in the formulation of the antibacterial soap. The result shows that formulated soap with Harumanis pruned extract have the ability to inhibit all the five microbes tested and therefore, it has the potential to be commercialized as antibacterial soap in the near future.
Drowsiness Detection Based on Facial Landmark and Uniform Local Binary Pattern
Authors: Navastara Dini Adni , Putra Widhera Yoza Mahana , Fatichah Chastine 
Published: June 2020

Abstract
When driving a vehicle, it is often challenging for someone to force his condition to keep driving even though in sleepy condition, thus causing a traffic accident. One of the characteristics of drowsy drivers is the eyes are closed for a certain period. This research proposes a system to detect drowsiness, thus can alert the drowsy driver. The first step is to detect the face using a Funnel-structured cascade algorithm. And then extract the facial landmark features on the face to get the eyes location. The features of eyes are extracted by using a Uniform Local Binary Pattern (ULBP) and the Eyes Aspect Ratio (EAR). EAR is the distance between points at eye landmarks. After the features have been extracted, the system classifies the eyes, whether closed or open by using Support Vector Machine (SVM) method. The system calculates the percentage of eye closure (PERCLOS) to detect drowsiness. Based on the experimental results, the proposed method yields the best accuracy of 95.5% and the optimal value of PERCLOS in drowsiness detection is greater than or equal to 60% with a period of 20 frames.
Fault Tree Analysis to Measure WSN Connectivity Reliability
Authors: Shahabuddin Mohd Zaki B. , Mat Bistaman Izwan Nurli B. , Subri Nor Izham B. 
Published: June 2020

Abstract
Wireless Sensor Network (WSN) is developed with limited capabilities to operate at low cost, using low power, and has non-exist physical topology infrastructure. Somehow, WSN must be connectively reliable to transmit the events data between communicating nodes. In this paper, we propose a method to measure WSN reliability by using the Fault Tree Analysis to identify the specific desired event that contributes to WSN connectivity failure. The computation of Boolean algebra to derive the minimal cut set of network graph discovers the components that are prone to failure. The components need to be resolved to reduce the probability of failure in WSN connectivity. The proposed Fault Tree Analysis and its related Boolean algebra computation verifies the reliability of WSN connectivity is improved by the means of network redundancy design.
Lighting Control System for Energy Management System and Energy Efficiency Analysis
Authors: Abdul Wahab M S , Ramli N A 
Published: June 2020

Abstract
Most of the students are not aware of electrical energy consumption and it will lead to the waste of energy. Additionally, energy expenditure for a university on electrical energy consumption is extremely high and its burden the institution to pay the cost. One of the proposals to solve this problem is by implementing an energy management system. The main purpose of this research is to develop an energy management system for the lighting system and to analyze energy efficiency based on energy consumption. In this research, an energy audit and lighting audit has been performed to analyze the performance of the lighting system. Then, energy efficiency measures for the lighting system has been implemented. This implementation involving the replacement of the LED lamp, installation of a motion sensor and the development of smartphone applications. The energy usage has been measured and recorded in the IoT platform for analysis purposes. Based on MS ISO 8995:2005, the lighting system in the campus is in a good performance and followed the standard. By implementing the energy management system, the cost of energy usage can be saved up to 22.82% and the time taken for the payback is 40.589 months.
Performance comparison of HTTP/2 for Common E-Commerce Web Frameworks with Traditional HTTP
Authors: Siduzzaman Md , Hossan Md Monir , Alom Rakibul , Sarwar Talha Bin , Miah Md Saef Ullah 
Published: June 2020

Abstract
HTTP/2 is a cutting-edge Web convention predicated on Google’s SPDY convention which tries to tackle the deficiencies and rigidity of HTTP/1. As e-commerce websites have become a significant medium for Online shopping, this paper demonstrates that whether HTTP/2 can authentically avail the performance of an e-commerce web browsing over HTTP or not. This paper states that we have studied about the HTTP/2 Implementation & performance analysis for prevalent web frameworks, where we have culled two different e-commerce web frameworks Laravel, WordPress (WooCommerce). At first, we have implemented two e-commerce sites in HTTP, then we have implemented those into HTTP/2. We additionally deployed them on the live server. By utilizing the Webserver Stress Tool & Selenium Web Driver, we have evaluated the performance under the sundry network environments. Selenium has given better results among all. But the webserver stress tool has exhibited some errors only for the http/2. This is our only constraint for this work. But still, we are endeavoring to resolve the problem. Simulation results have shown how HTTP/2 has influenced the page load time in our e-commerce websites. After analyzing all the simulation results, we have decided that Laravel is a more superior e-commerce web framework for both protocols, especially for HTTP/2.
Self-regulation Strategic Framework for minimizing distraction in digital society
Authors: Anthonysamy Lilian , Choo Koo Ah , Hin Hew Soon 
Published: June 2020

Abstract
While advanced innovations through digital technologies are rising, they present their own difficulties, for example, digital distraction. There is a need to minimise digital distraction among digital citizens in order to create a bright digital society. Thus, the need to employ proper strategies to minimise digital distraction is vital. However, there is a dearth of research on how self-regulation strategies can help individuals improve their ability to filter digital distraction. Digital distraction cannot be ignored as it can decrease productivity and performance in learning among young adults. This paper intends to formulate a strategic framework in tackling the issue of digital distraction for brighter digital society.
A Review on Image Processing Techniques for Fisheries Application
Authors: Awalludin E A , Arsad T N T , Wan Yussof W N J Hj 
Published: June 2020

Abstract
Nowadays, the image processing technique has been used widely in the fields of science and technology. The use of the image processing technique is to improve the manual approach process to achieve accurate results. Since the manual approach shows, its time processing can be laborious to analyses extensive data and require a skilful researcher to experiment. The use of image processing technique can support to reduce several drawbacks. Therefore, many researchers prefer an alternative solution such as an automated or semiautomated system-based image to enhance the manual approach task. The image processing technology provides a variety of algorithms that modified according to the desired study is the reason why the image processing technique is desirable. Low-level processing is the early stage of image processing techniques and the steps involved are image acquisition, noise removal, image sharpening, image smoothing, image blurring, etc. Meanwhile for the Midlevel processing, adopt the use of several image processing techniques such as image segmentation, object detection, object recognition, feature extraction and classification. Therefore, image processing provides numerous applications such as in agriculture, medical image processing, ocean monitoring and conservation, facial recognition, fisheries, etc. In this paper, the uses of image processing techniques are discussed in briefly on the application of fishery study.
A Study on The Application of Convoluted PTFE For Arterial Implantation: Mechanical Testing and Properties
Authors: Tariq Mohammed Gulfaraz , Youseffi Mansour , Jamil Muhammad Mahadi bin Abdul , Rahman Nur Adilah Abd , Khaghani Seyed Ali , Sefat Farshid 
Published: June 2020

Abstract
Polytetrafluoroethylene (PTFE) is a thermoplastic polymeric material with various applications including aerospace, automotive, biomedical and electrical. PTFE surface is smooth, wear resistant, non-sticky and slippery in nature. Combined with such useful properties, the material conveys various other features such as temperature resistance up to 260°C, exceptional chemical stability, corrosion resistance, good fatigue strength and sterilisable. This work involved mechanical testing of convoluted PTFE tubes with different thicknesses, diameters and lengths. This included the following tests: volumetric expansion, tensile and elongation, rolling u wear and burst tests by applying real industrial pressures. The mechanical testing showed promising results for the convoluted PTFE tubes which could undergo extreme elongation due to the elastic nature. The % volumetric expansion exceeded the expected values (on average, 3-5% higher than the expected values). The results for the rolling u wear, burst and tensile tests also showed good properties. Based on its mechanical properties, convoluted PTFE seemed to be an appropriate material for replacing damaged/diseased arteries.
Chemical Equilibrium Optimization Via Logarithmic Penalty Function Approach
Authors: Hassan Mansur , Ali Majid Khan Majahar , Baharum Adam 
Published: June 2020

Abstract
Determining the chemical composition problem which is made up of a complex mixture specifically for chemical equilibrium has become one of the great importance, and it usually arises, especially in the performance of fuels and catalyst analysis and the fusion of the complicated organic compound. We implored a new logarithmic penalty function (LPF) to optimize (minimize) the objective function in the chemical equilibrium model. The objective function represents the expression of a mathematical model for the free energy of the chemical mixture under consideration. The result obtained happens to be more acceptable in terms of the optimal objective value and computational complexity.
The efficiency of soft sensors modelling in advanced control systems in oil refinery through the application of hybrid intelligent data mining techniques
Authors: Al-Jlibawi Ali , Othman Mohammed Lutfi Bin , Al-Huseiny Muayed S. , Aris Ishak Bin , Bahari Samsul 
Published: June 2020

Abstract
Quality variables cannot be automatically measured to all nor be measured at a high cost, infrequently, nor with high delays, such as laboratory analysis and online analyser. Therefore, data-driven soft sensors are inferential models which use online available sensors, such as temperature, pressure, and flow rate among others, to predict the quality variables. Soft sensors which are built using historical data of the processes are normally developed from the supervisory control and data acquisition (SCADA) systems connected with PLC or DCS (distribution control systems) as the daily reports on the oil refinery processes. These systems are then obtained from laboratory observation/measurements. Notably, the main issue in the development of the soft sensor is the treatment of missing data, outlier detection, selection of input variables, model training, validation, and soft sensor maintenance to adopt the heavy-duty oil refineries to improve the products of the crude oil and increase yield. In this article, the improvement in the virtual sensor based on hybrid soft computing methods (FLS and NN), which are combined into ANFIS, will be employed to construct the soft sensor model. Moreover, RST will be used to reduce the fuzzy rules and discretisation method to optimise and deal with the large continuous data. It was found from the implementation of rough set theory and discretisation methods that these two methods solved the complexity and nonlinearity of the soft sensor model. This model was employed for the refining process measurements data of the oil refinery from two different crude oil sources, in which the database of the measurements and processes was combined to improve the quality of data and discover the knowledge stored in the data pattern. It was indicated from this study result that the ANFIS model is able to manage the complex data to predict two important parameters of light naphtha (API and RVP) compared to the simple regression model. Additionally, controlling and monitoring the process are crucial actions performed to achieve the 4th industrial revolution and IoT. This study has contributed to the assistance in breaking the barriers of privacy between oil industries and the applicability of soft sensors modelling in the changes of data sources to achieve remarkable data analysis. The analyses result of RVP show the efficiency of ANFIS compare with linear regression regarding the generalization and overfitting.
Bayesian Regularization-Trained Multi-layer Perceptron Neural Network Predictive Modelling of Phenol Degradation using ZnO/Fe2O3 photocatalyst
Authors: Haiqi Omer Al , Nour Abdurahman Hamid , Ayodele Bamidele Victor , Bargaa Rushdi 
Published: June 2020

Abstract
The processing of crude oil in the onshore platform often results in the generation of produce water containing harmful organic pollutants such as phenol. If the produce water is not properly treated to get rid of the organic pollutants, human exposure when discharged could be detrimental to health. Photocatalytic degradation of the organic pollutant has been a proven, non-expensive techniques of removing these harmful organic compounds from the produce water. However, the detail experimentation is often tedious and costly. One way to investigate the non-linear relationship between the parameters for effective performance of the photodegradation is by artificial neural network modelling. This study investigates the predictive modelling of photocatalytic phenol degradation from crude oil wastewater using Bayesian regularization-trained multilayer perceptron neural network (MLPNN). The ZnO/Fe2O3 photocatalyst used for the photodegradation was prepared using sol-gel method and employed for the phenol degradation study in a batch reactor under solar irradiation. Twenty-six datasets generated by Box-Behken experimental design was used for the training of the MLPNN with input variables as irradiation time, initial phenol concentration, photocatalyst dosage and the pH of the solution while the output layer consist of phenol degradation. Several MLPNN architecture was tested to obtain an optimized 4 5 1 configuration with the least mean standard error (MSE) of 1.27. The MLPNN with the 4 5 1 architecture resulted in robust prediction of phenol degradation from the wastewater with coefficient of determination (R) of 0.999.
Investigating Synergistic Effects Of Surfactants And Nanoparticles On Emulsion Stability
Authors: Abdul-Razzaq R , Jaafar M Z , Bandyopadhyay S 
Published: June 2020

Abstract
A challenge of making oil production viable is emerging with time because most of the oil reserves have been exploited using primary and secondary recovery methods. Chemicals such as surfactants have been used to increase oil production through a method called chemical enhanced oil recovery. However, the application of this method is experiencing difficulties because of excessive use of surfactants that not only has negative effect on the economics of the project, but also poses severe environmental concerns. Another method that is being widely proposed is to apply emulsion flooding to enhance oil production. In order to maximize the impact of this method and as a result achieve higher oil recovery factor, it is vital to maintain the stability of the emulsion used. One of the claimed methods to improve the stability of emulsion is the application of synergistic effect between nanoparticles (NPs) and surfactants. This article aims on investigating the stability of emulsion using bottle test when applying the synergistic effect between NPs and surfactants with varying concentrations of the NPs, and surfactant charge. An anionic surfactant - sodium dodecyl sulphate (SDS) and a cationic surfactant - cetyltrimethylammonium bromide (CTAB) have been utilized in this study. Nano-silica was selected as the NPs used in this study. It was found that synergistic effect is more prominent between SDS and nano-silica with decrease in emulsion phase height percentage (measure of emulsion stability) going down to 22% compared with 43% for combination of CTAB and nano-silica, and 51% in case of using only surfactants. This has proved that the synergistic effect is beneficial in enhancing the emulsion stability, which can be implemented in the application of emulsion flooding where the stability of the emulsion is crucial.