Showing papers in "International Journal on Advanced Science, Engineering and Information Technology in 2018"

Smarter City: Smart Energy Grid based on Blockchain Technology

Abstract: The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger.

A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis

Abstract: Now a day the threat of malware is increasing rapidly. A software that sneaks to your computer system without your knowledge with a harmful intent to disrupt your computer operations. Due to the vast number of malware, it is impossible to handle malware by human engineers. Therefore, security researchers are taking great efforts to develop accurate and effective techniques to detect malware. This paper presents a semantic and detailed survey of methods used for malware detection like signature-based and heuristic-based. The Signature-based technique is largely used today by anti-virus software to detect malware, is fast and capable to detect known malware. However, it is not effective in detecting zero-day malware and it is easily defeated by malware that use obfuscation techniques. Likewise, a considerable false positive rate and high amount of scanning time are the main limitations of heuristic-based techniques. Alternatively, memory analysis is a promising technique that gives a comprehensive view of malware and it is expected to become more popular in malware analysis. The main contributions of this paper are: (1) providing an overview of malware types and malware detection approaches, (2) discussing the current malware analysis techniques, their findings and limitations, (3) studying the malware obfuscation, attacking and anti-analysis techniques, and (4) exploring the structure of memory-based analysis in malware detection. The detection approaches have been compared with each other according to their techniques, selected features, accuracy rates, and their advantages and disadvantages. This paper aims to help the researchers to have a general view of malware detection field and to discuss the importance of memory-based analysis in malware detection.

Blockchain technology the identity management and authentication service disruptor: A survey

Abstract: The Internet today lacks an identity protocol for identifying people and organizations. As a result, service providers needed to build and maintain their own databases of user information. This solution is costly to the service providers, inefficient as much of the information is duplicated across different providers, difficult to secure as evidenced by recent large-scale personal data breaches around the world, and cumbersome to the users who need to remember different sets of credentials for different services. Furthermore, personal information could be collected for data mining, profiling and exploitation without users' knowledge or consent. The ideal solution would be self-sovereign identity, a new form of identity management that is owned and controlled entirely by each individual user. This solution would include the individual's consolidated digital identity as well as their set of verified attributes that have been cryptographically signed by various trusted issuers. The individual provides proof of identity and membership by sharing relevant parts of their identity with the service providers. Consent for access may also be revoked hence giving the individual full control over its own data. This survey critically investigates different blockchain based identity management and authentication frameworks. A summary of the state-of-the-art blockchain based identity management and authentication solutions from year 2014 to 2018 is presented. The paper concludes with the open issues, main challenges and directions highlighted for future work in this area. In a nutshell, the discovery of this new mechanism disrupted the existing identity management and authentication solutions and by providing a more promising secure platform.

The revolution Lean Six Sigma 4.0

Abstract: Industry 4.0 makes a factory smart by applying advanced information systems and future-oriented technologies. Today, thanks to the application of the most innovative digital technologies offered by the new Industry 4.0 paradigm, in this Fourth Industrial Revolution, there is a significant “evolution” of many methodologies of Continuous Improvement, such as, e.g., Lean Six Sigma (LSS). Most of the tools of Lean Six Sigma relies on data to know in depth problems: data is necessary to drive any process improvement. The key issue is based on data integrity and on real time data. The aim of this paper consists of proving the efficiency of the so called “Lean Six Sigma 4.0”. This paper deals with engineering approaches, here applied in HealthCare environment, in order to optimise the services supply process and to reduce the waste of resources (human and/or material), while improving the Quality of Experience (QoE) of the patients. Indeed, it has been proved that the huge growth in the HealthCare costs is due to inefficient use of available resources and not-optimised service processes. Applying Lean Six Sigma 4.0 it is possible to reduce HealthCare costs, improving at the same time the QoE perceived by the patient.

Comparative Study of K-means and Fuzzy C-means Algorithms on The Breast Cancer Data

Abstract: Breast cancer is one of the most common forms of cancer having a worldwide prevalence. Continuous research is going on for detecting breast cancer in its early stage as the possibility of cure is very high in the early stage. The two main objectives of this work were: firstly, to compare the performance of k-means and fuzzy c-means (FCM) clustering algorithms; and secondly, to make an attempt to carefully consider and examine, from multiple points of view, the combination of different computational measures for k-means and FCM algorithms for a potential to achieve better clustering accuracy. K-means and FCM algorithms have been considered to understand the impact of clustering on the breast cancer data. The execution of k-means algorithm is based on centroid, distance, split method, threshold, epoch, BCW attribute, and number of iterations; while FCM is executed on the basis of fuzziness value and termination condition. The breast cancer Wisconsin (BCW) dataset was used for the experimentation. The combination of variance and same centroid offers better outcome in terms of k-means algorithm. The highest and lowest classification accuracies are (94.7%, 77.1 %) and (94.4%, 88.5%) for foggy and random centroid, respectively. The overall average positive prediction accuracy obtained by this approach is approximately 92%. In case of FCM, the highest and lowest classification accuracies are (97.2%, 91.1 %), (97.2%, 90.9%), (97.8%, 90.4%), and (97.1%, 90.2%) for different combination of fuzziness and termination criteria. The average highest and lowest classification accuracies are (95.7%, 94.7 %), (95.9%, 93.6%), (95.3%, 94.2%), and (95.6%, 93.7%) for the same combination in the case of FCM. K-means algorithm was more prominent and consistent in terms of computation time as FCM required more time to carry out several fuzzy calculations and iterations. The findings of this work provide an incisive and extensive understanding of the computational parameters used with k-means and c-means algorithms. The computational results indicate that FCM algorithm was found to be prominent and consistent than k-means algorithm when executed with different iterations, fuzziness values, and termination criteria. It is more potentially capable in classifying BCW dataset as the classification accuracy is more important than time.

Mobile Forensic Tools Evaluation for Digital Crime Investigation

Abstract: Instant Messaging is a popular smartphone’s application. One example of Instant Messaging application is WhatsApp. WhatsApp is widely used judging from its users that reach more than 1 Billion users in January 2017. WhatsApp’s security recently has been updated with latest encryption type and technology by implementing end-to-end encryption. The number of users or possible crime target and security features in WhatsApp can lead to crime by people that have criminal intentions. Investigators need to use mobile forensic methodologies and tools for investigating smartphone and finding out the crime evidence. However, investigators are often facing challenges during the investigation because of incompatibility between forensic tools and mobile technology. This research will experiment using available forensic tools with NIST forensic method for extracting latest WhatsApp’s artifacts. Forensics tools capabilities will be evaluated and compared to find its strengths and weaknesses.

A review of multimodal interaction technique in augmented reality environment

Abstract: Augmented Reality (AR) has proposed several types of interaction techniques such as 3D interactions, natural interactions, tangible interactions, spatial awareness interactions and multimodal interactions. Usually, interaction technique in AR involve unimodal interaction technique that only allows user to interact with AR content by using one modality such as gesture, speech, click, etc. Meanwhile, the combination of more than one modality is called multimodal. Multimodal can contribute to human and computer interaction more efficient and will enhance better user experience. This is because, there are a lot of issues have been found when user use unimodal interaction technique in AR environment such as fat fingers. Recent research has shown that multimodal interface (MMI) has been explored in AR environment and has been applied in various domain. This paper presents an empirical study of some of the key aspects and issues in multimodal interaction augmented reality, touching on the interaction technique and system framework. We reviewed the question of what are the interaction techniques that have been used to perform a multimodal interaction in AR environment and what are the integrated components applied in multimodal interaction AR frameworks. These two questions were used to be analysed in order to find the trends in multimodal field as a main contribution of this paper. We found that gesture, speech and touch are frequently used to manipulate virtual object. Most of the integrated component in MMI AR framework discussed only on the concept of the framework components or the information centred design between the components. Finally, we conclude this paper by providing ideas for future work involving this field.

Influences of heating temperatures on physical properties, spray characteristics of bio-oils and fuel supply system of a conventional diesel engine

Abstract: Alternative fuels need to satisfy the strict requirements of the use for diesel engines aiming at enhancing the performance and reducing pollutant emissions. The use of straight bio-oils for diesel engines entails improving their disadvantages such as high density, high surface tension and kinematic viscosity (tri-physical parameters). There have been some as-used methods for reduction of the above-mentioned negative effects related to straight bio-oil disadvantage, however, the adequately-heating method may be considered as a simple one helping the physical parameters of straight bio-oils to reach stable and highly-confident values which are close to those of traditional diesel fuel. As a consequence, the spray and atomization, combustion, performance, and emissions of diesel engines fueled with preheated bio-oils are improved. In this work, a study of the dependence of the density, surface tension and kinematic viscosity of coconut oil (a type of bio-oils) on temperatures (from 40-110 o C) within a wide variety are conducted. In the first stage, the influence study of temperature on tri-physical parameters is carried out on the basis of experimental correlation and as-described mathematical equation. In the second stage, the influence study of tri-physical parameters on spray and atomization parameters including penetration length (L b ) and Sauter mean diameter (SMD), and the influence of tri-physical parameters on fuel supply system are investigated. The optimal range of temperature for the as-used bio-oils is found after analyzing and evaluating the obtained results regarding the physical properties and spray characteristics, as well as compared with those of diesel fuel. The confident level over 95% from the regression correlation equation between the above-mentioned tri-physical parameters and temperature is presented. Additionally, the measured spray parameters, the calculated values of frictional head loss and fuel flow rate are thoroughly reported.

Properties of DMF-fossil gasoline RON95 blends in the consideration as the alternative fuel

Abstract: The use of endless biomass sources form agricultural by-products for the renewable fuel synthesis has been being considered as the extremely useful works meeting the strict strategies of environment protection. In this work, 2,5-dimethylfuran (DMF) synthesized from available rice straw in Vietnam was mixing with fossil gasoline RON95 to determine and measure the key properties of DMF-gasoline RON95 blends based on corresponding ASTM standards in the consideration as a new alternative fuel for modern gasoline engines. Each 5% volume fraction of DMF was used for mixing purposes to create 21 samples with the change of DMF volume fractions from 0% to 100%. As a result, the linearization of density, octane number, and laten heat of vaporization was conducted; meanwhile, the stoichiometric air/fuel ratio, heating value, and self-ignition temperature of DMF-gasoline RON95 blends were also reported. This work provided the full properties of blends of DMF-gasoline RON95 blends based on experimental results, and of course, achieved results could be used for the next steps to investigate the applicability of DMF-gasoline RON95 blends to practical experiments or simulation studies.

Adding Digital Forensic Readiness as a Security Component to the IoT Domain

Abstract: The unique identities of remote sensing, monitoring, self-actuating, self–adapting and self-configuring “things” in Internet of Things (IoT) has come out as fundamental building blocks for the development of “smart environments”. This experience has begun to be felt across different IoT-based domains like healthcare, surveillance, energy systems, home appliances, industrial machines, smart grids and smart cities. These developments have, however, brought about a more complex and heterogeneous environment which is slowly becoming a home to cyber attackers. Digital Forensic Readiness (DFR) though can be employed as a mechanism for maximizing the potential use of digital evidence while minimizing the cost of conducting a digital forensic investigation process in IoT environments in case of an incidence. The problem addressed in this paper, therefore, is that at the time of writing this paper, there still exist no IoT architectures that have a DFR capability that is able to attain incident preparedness across IoT environments as a mechanism of preparing for post-event response process. It is on this premise, that the authors are proposing an architecture for incorporating DFR to IoT domain for proper planning and preparing in the case of security incidents. It is paramount to note that the DFR mechanism in IoT discussed in this paper complies with ISO/IEC 27043: 2015, 27030:2012 and 27017: 2015 international standards. It is the authors’ opinion that the architecture is holistic and very significant in IoT forensics.

A Hybrid Algorithm for Improving the Quality of Service in MANET

Abstract: A mobile ad-hoc network (MANET) exhibits a dynamic topology with flexible infrastructure. The MANET nodes may serve as both host and router functionalities. The routing feature of the MANET is a stand-alone multi-hop mobile network that can be utilized in many real-time applications. Therefore, identifying paths that ensure high Quality of Service (QoS), such as their topology and applications is a vital issue in MANET. A QoS-aware protocol in MANETs aims to find more efficient paths between the source and destination nodes of the network and, hence, the requirements of the QoS. This paper proposes a different hybrid algorithm that combines Cellular Automata (CA) with the African Buffalo Optimization (ABO), CAABO, to improve the QoS of MANETs. The CAABO optimizes the path selection in the ad-hoc on-demand distance vector (AODV) routing protocol. The test results show that with the aid of the CAABO, the AODV manifests energy and delay-aware routing protocol.

Machine Learning Approach for Bottom 40 Percent Households (B40) Poverty Classification

Abstract: Malaysia citizens are categorised into three different income groups which are the Top 20 Percent (T20), Middle 40 Percent (M40), and Bottom 40 Percent (B40). One of the focus areas in the Eleventh Malaysia Plan (11MP) is to elevate the B40 household group towards the middle-income society. Based on recent studies by the World Bank, Malaysia is expected to enter the high-income economy status no later than the year 2024. Thus, it is essential to clarify the B40 population through a predictive classification as a prerequisite towards developing a comprehensive action plan by the government. This paper is aimed at identifying the best machine learning models using Naive Bayes, Decision Tree and k-Nearest Neighbors algorithm for classifying the B40 population. Several data pre-processing task such as data cleaning, feature engineering, normalisation, feature selection: Correlation Attribute, Information Gain Attribute and Symmetrical Uncertainty Attribute and sampling methods using SMOTE has been conducted to the raw dataset to ensure the quality of the training data. Each classifier is then optimized using different tuning parameter with 10-Fold Cross Validation for achieving the optimal values before the performance of the three classifiers are compared to each other. For the experiments, a dataset from National Poverty Data Bank called eKasih obtained from the Society Wellbeing Department, Implementation Coordination Unit of Prime Minister's Department (ICU JPM), consisting of 99,546 households from 3 different states: Johor, Terengganu and Pahang are used to train each of the machine learning model. The experimental results using 10-Fold Cross-Validation method demonstrates that the overall performance of Decision Tree model outperformed the other models and the significance test specified the result is statistically significance.

Feature selection from colon cancer dataset for cancer classification using Artificial Neural Network

Abstract: In the fast-growing field of medicine and its dynamic demand in research, a study that proves significant improvement to healthcare seems imperative especially when it is on cancer research. This research paved way to such significant findings by the inclusion of feature selection as one of its major components. Feature selection has become a vital task to apply data mining algorithms effectively in the real-world problems for classification. Feature selection has been the focus of interest for quite some time and much completed work related to it. Although much research conducted on the field, a study that proved a nearly perfect accuracy seems limited; hence, more scientifically driven results should be produced. Using various research on feature selection as basis for the choices in this study, the method was product of careful selection and planning. Specifically, this study used feature selection for improving classification accuracy on cancerous dataset. This study proposed Artificial Neural Network (ANN) for cancer classification with feature selection on colon cancer dataset. The study used best first search method in weka tools for feature selection. Through the process, a promising result has been achieved. The result of the experiment achieved 98.4 % accuracy for cancer classification after feature selection by using proposed algorithm. The result displayed that feature selection improved the classification accuracy based on the experiment conducted on the colon cancer dataset. The result of this experiment was comparable with the other studies on colon cancer research. It showed another significant improvement and can be considered promising for more future applications.

Classification techniques for predicting graduate employability

Abstract: Unemployment is a current issue that happens globally and brings adverse impacts on worldwide. Thus, graduate employability is one of the significant elements to be highlighted in unemployment issue. There are several factors affecting graduate employability, traditionally, excellent academic performance (i.e., cumulative grade point average, CGPA) has been the most dominant element in determining an individual’s employment status. However, researches have shown that not only CGPA determines the graduate employability; in fact other factors may influence the graduate achievement in getting a job. In this work data mining techniques are used to determine what are the factors that affecting the graduates. Therefore, the objective of this study is to identify factors that influence graduates employability. Seven years of data (from 2011 to 2017) are collected through the Malaysia’s Ministry of Education tracer study. Total number of 43863 data instances involved in this employability class model development. Three classification algorithms, Decision Tree, Support Vector Machines and Artificial Neural Networks are used and being compared for the best models. The results show decision tree J48 produces higher accuracy compared to other techniques with classification accuracy of 66.0651% and it increased to 66.1824% after the parameter tuning. Besides, the algorithm is easily interpreted, and time to build the model is small which is 0.22 seconds. This paper identified seven factors affecting graduate employability, namely age, faculty, field of study, co-curriculum, marital status, industrial internship and English skill. Among these factors, attribute age, industrial internship and faculty contain the most information and affect the final class, i.e. employability status. Therefore, the results of this study will help higher education institutions in Malaysia to prepare their graduates with necessary skills before entering the job market.

Advances in Radar Remote Sensing of Agricultural Crops: A Review

Abstract: There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field.

Development of Performance Indicators Relationships on Sustainable Healthcare Supply Chain Performance Measurement Using Balanced Scorecard and DEMATEL

Abstract: Sustainable healthcare supply chain performance measurement (SHSCPM) concept is still less developed. Globalization and pressure from stakeholder demand the operation of the supply chain to give attention to the environment effect, community, economic and intangibility assets. SHSCPM is feasibly developed for measuring the performance of simultaneous sustainability aspects and intangibility assets to meet customer satisfaction. This article discusses SHSCPM based on the balanced scorecard (BSC) with attention to the sustainability aspects, intangibility assets and relationships between the performance of perspectives and indicators. The perspectives and indicators of performance were identified by literature and the confirmed and validated by the survey to 7 expert respondents. We found 5 perspectives and 39 indicators from literature which were then confirmed to expert through a survey with an in-depth interview. From a survey that validated with a weighted average (WA) and level of consensus (LC), we found 31 valid indicators. Finally, 29 indicators from DEMATEL process were selected to be used on SHSCPM. The DEMATEL process found 2 indicators aren’t important and influence for other, namely inventory cost and regulations and laws. Besides, the four results on this study: intangibility indicators incorporated on innovation and growth were most affect to other indicators which the intangibility indicators were related with human resource, indicators on customer perspective were most important compared to other indicators, indicators on economic aspect were most important compared indicators on environmental and social aspects, and indicators on social aspect were not affected by other indicators. After that, human resource and customer were main factors for SHSCPM. Finally, relationships between perspectives and indicators used to design of BSC strategy map.

Performance Evaluation of Ad-Hoc On-Demand Distance Vector and Optimized Link State Routing Protocols in Mobile Ad-Hoc Networks

Abstract: Mobile Ad-hoc Networks (MANETs) are self-sufficient networks that can work without the need for centralized controls, pre-configuration to the routes or advance infrastructures. The nodes of a MANET are autonomously controlled, which allow them to act freely in a random manner within the MANET. The nodes can leave their MANET and join other MANETs at any time. These characteristics, however, might negatively affect the performance of the routing protocols and the overall topology of the networks. Subsequently, MANETs comprise specially designed routing protocols that reactively and/or proactively perform the routing. This paper evaluates and compares the performance of two routing protocols which are Ad-Hoc On-Demand Distance Vector (AODV) and Optimized Link State Routing (OLSR) in MANET environment. The study includes implementing a simulation to examine the performance of the routing protocols based on the variables of the nodes’ number and network size. The evaluation results show that the AODV outperforms the OLSR in most of the simulated cases. The results further show that the number of nodes and network size have a great impact on the Throughput (TH), Packet Delivery Ratio (PDR), and End-to-End delay (E2E) of the network.

Continuous Innovation: A Literature Review and Future Perspective

Abstract: Continuous Innovation (CI) has become one of the hot topics in innovation management field. However, studies focusing on the comprehensive and detailed explanation of CI concept are still limited. This paper aims to elaborate on CI concept using three fundamental questions: WHAT (what is the definition of CI and what are the determining factors?), WHY (why do companies need CI?), and HOW (how can companies develop CI?). The purpose of this paper is also to contribute in giving an understanding that is more exhaustive on CI definition, the importance of CI for companies, necessary elements in determining CI capability, and various strategies for CI development. From this literature study, a new and more comprehensive definition of CI was found, which categorized the reason why the companies need the CI and identified essential elements in determining CI capability. In addition, the mapping process produced a description of the proportion of CI development strategy as follows: technology-based (11%), People based (15%), organizational & system based (32%), strategic-based (11%), knowledge-based (22%) and collaborative & connectivity based (9%). It can be observed that current CI development strategies still focus on organizational, system based approach, and most of them (81%) rely on the internal resources of the company. Future perspectives, in this digital and internet era, which provides connectivity and the shift of the concept of, own economy to sharing economy; companies will have big potentials to work on innovation collaboratively. CI concept development should consider open innovations instead of today’s “do-it-yourself” mentality (closed innovation).

A Design and Development of micro-Learning Content in e-Learning System

Abstract: In the rapidly changing digital era, changes in learning through e-learning are required. Micro-learning contents were analyzed as a method that can replace the e-learning contents in existing regular courses implemented with large amounts of learning and large contents. In the present study, a method for easy and quick production of contents was presented for effective micro-learning services. The results of the study indicated that the micro-learning content development method presented in the present study is valid in terms of the efficiency of content development. The content development method presented in the present study is considered to bring about new changes to the existing e-learning content development methods utilizing flash, HTML5, H5P, etc.

Utilization of Papaya Seeds as Natural Coagulant for Synthetic Textile Coloring Agent Wastewater Treatment

Abstract: Textile wastewater contains a lot of pollutants which is hazardous if directly discharged. Coagulation and flocculation using inorganic salts were widely used to treat textile wastewater. However this method pose some drawbacks, such as high coagulant cost, large volume of sludge was produced, and potential health problems if the water is consumed. In this study we explore utilization of natural coagulant as an alternative to inorganic salts to treat textile wastewater. This study describes utilization of papaya seeds powder as a natural coagulant for synthetic textile wastewater of drimarene dark red (DDR) with initial concentration of 10 mg/L. The effect of coagulant dosage and pH to the %removal of colour in wastewater was studied using central composite design. The potential active functional groups in papaya seeds powder was characterized using Fourier Transform Infrared Spectroscopy (FTIR). It was found that papaya seeds powder contained –OH, -NH, C=O functional groups that indicate good potential as natural coagulant. The cubic model obtained was in good fit with experimental data, which was shown in R-squared value of 0.995. It was found that coagulant dosage, pH, and its interaction were significant to the removal of synthetic dye in wastewater. The decrease of pH gave higher %removal due to protonation of papaya seeds powder active coagulating agent resulting on better electrostatic interaction with dyes. The increase of dosage also gave increase in %removal until its optimum condition. After optimum condition, the %removal decreased due to colloid re-stabilization. The optimum condition was obtained at dosage of 0.57g/L and pH 1.97 with 84.77% of predicted colour removal and this result was in agreement with experimental response value.

User Acceptance Analysis of Potato Expert System Application Based on TAM Approach

Abstract: This study aims to determine the level of user acceptance of the application of potato expert system in the diagnosis of plant pests and diseases based on Android that has been developed. The application of an expert system is intended to help farmers and extension workers in particular to identify types of plant pests and diseases based on symptoms that appear and control solutions easily, quickly and accurately. In this study, the effects of perceived ease of use and the perceived usefulness on behavioural intention to use based on the perspective of farmers and extension workers in the field were measured. The method used in this study is a survey with Technology Acceptance Model (TAM) with the total respondents of 204. The results showed the perceived ease of use has a positive correlation with behavioural intention to use; perceived usefulness has a positive correlation with behavioural intention to use, and both perceived ease of use and perceived usefulness have a positive correlation with behavioural intention to use.

Visualization of shear cracks in a reinforced concrete beam using the digital image correlation

Abstract: A low-cost digital image correlation system is used to visualize the formation and propagation of concrete cracking in a reinforced concrete beam. The system employed comprises an ordinary digital camera, a remote image recording controller (a smartphone) and a freely-available, open-source image correlation software package Ncorr. In this paper, the application of this system is demonstrated to obtain a comprehensive time-lapse of longitudinal strain fields developing before and after the onset of shear cracking, thus allowing one to fully appreciate the mechanisms of shear failure in the beam. It is shown that the longitudinal strain fields obtained from the DIC system are in a good agreement with hand-drawn crack maps and that obtained from nonlinear finite element analysis.

Geotechnical Engineering Aspect Related to Pidie Jaya–Aceh Earthquake Disaster and Mitigation

Abstract: Earthquake catastrophe in Pidie Jaya has caused damages to the city of Meureudu, Aceh-Sumatra Indonesia. Based on preliminary study for buildings and infrastructures, the geotechnical engineering aspects damages-related are presented. Seismic motion effect damage of earthquakes such as liquefaction of soil, lateral spreads, and ground failure were the majority effect for infrastructures and buildings. Moreover, failures of almost of multi-storey buildings and mosques along the national road lines are because the effect of peak surface accelerations and earthquake wave propagation forces which are very close with epicenter coordinates 5.308 ° N / 96.269 ° E in 4km radius. Seismic back investigates of the Aceh’s fault seismic source as well as initial probabilistic seismic hazard analysis post-Pidie Jaya earthquake for city of Mereudu is offered. Liquefaction potential analysis from the estimation of peak ground acceleration was conducted. Geotechnical aspect and substructure failure characteristics to infrastructure and housing damages due earthquake are also reported. The earthquake has caused 104 people deaths, 2.474 unit houses in total need to be rehabilitated and rebuilt, almost 10 km of roads and 50 bridges need to be reconstructed. Some descriptive countermeasures for reconstructions of geotechnical engineering aspects and mitigation are also provided.

Augmented Reality to Promote Guided Discovery Learning for STEM Learning

Abstract: Augmented reality is one of the technologies that will impact teaching and learning. When augmented reality is applied in an educational setting, it can provide a superior learning environment because educators can add specific virtual information to enrich the existing learning materials or to create new learning materials that integrate the environment. Therefore, discussions on augmented reality applications for learning can be vastly found in the literature. Each application utilizes the resources available by using approaches appropriate for the learning outcome in question. The key problem with achieving learning outcome in analysing group one elements in a periodic table, is that some students have difficulties in visualizing an atom and what more to relate them with reactivity. The objective of this paper is to design and develop a learning application that supports science, technology, engineering and mathematics (STEM) education based on guided discovery learning to assist the students through questions based on exposure to various evidences via augmented reality. This application incorporates the use of 3D model of atoms and video of real experiments. To achieve the objective, action research is used to solve existing problem in a selected school. The application was evaluated by 25 students aged 16 years in a secondary school in Malaysia and has proven to be effective in improving the students’ marks in tests conducted at the end of the application usage. The students can get many benefits from using the augmented reality based application because it supports Education 4.0 like flexibility in teaching and learning where learning is freed for the limitation of time, place and pace of study.

New record species of Puntius (Pisces: Cyprinidae) from West Sumatra based on Cytochrome Oxidase1Gene

Abstract: Biodiversity study on Puntius has been conducted in West Sumatera using a molecular technique. From the genetic analysis using the sequence of CO1 gene, the study discovered: (1) A new record species of Puntius in Diatas Lake, Batang Lembang, Batang Gumanti, Muara Pingai rivers (located in the eastern part of the Bukit Barisan mountain range) which is Barbodes binotatus banksi or B. banksi. (2). A new record of subspecies in Maninjau Lake and its tributary (located in the western part of the Bukit Barisan mountain range) which is Barbodes banksi maninjau. (3) A new record of subspecies in Batang Kuranji, Batang Katik, Batang Tarok and Lubuk Paraku rivers (located in the western part of the Bukit Barisan mountain range) which is Barbodes banksi kuranji. The results of this study add the evidence that the presence of Bukit Barisan mountain range in Sumatra Island contributed to genetic diversity, evolutionary process and speciation mechanism of freshwater fish in Sumatra. It is important to pay attention to the development of district or area in Sumatra.

Redefining selection of features and classification algorithms for room occupancy detection

Abstract: The exponential growth of todays technologies has resulted in the growth of high-throughput data with respect to both dimensionality and sample size. Therefore, efficient and effective supervision of these data becomes increasing challenging and machine learning techniques were developed with regards to knowledge discovery and recognizing patterns from these data. This paper presents machine learning tool for preprocessing tasks and a comparative study of different classification techniques in which a machine learning tasks have been employed in an experimental set up using a dataset archived from the UCI Machine Learning Repository website. The objective of this paper is to analyse the impact of refined feature selection on different classification algorithms to improve the prediction of classification accuracy for room occupancy. Subsets of the original features constructed by filter or information gain and wrapper techniques are compared in terms of the classification performance achieved with selected machine learning algorithms. Three feature selection algorithms are tested, specifically the Information Gain Attribute Evaluation (IGAE), Correlation Attribute Evaluation (CAE) and Wrapper Subset Evaluation (WSE) algorithms. Following a refined feature selection stage, three machine learning algorithms are then compared, consisting the Multi-Layer Perceptron (MLP), Logistic Model Trees (LMT) and Instance Based k (IBk). Based on the feature analysis, the WSE was found to be optimal in identifying relevant features. The application of feature selection is certainly intended to obtain a higher accuracy performance. The experimental results also demonstrate the effectiveness of Instance Based k compared to other ML classifiers in providing the highest performance rate of room occupancy prediction.

Evaluation of Community Resilience Aspects of Sri Lankan Coastal Districts

Abstract: This research is carried out to evaluate important community resilience aspects of coastal districts in Sri Lanka and to provide suitable recommendations to strengthen them. After carrying out an indepth literature survey and interviewing key personnel who are involved in the field of Disaster Management and Disaster Risk Reduction, existing status of the coastal hazards, multi-hazard assessments, early warning mechanisms, national policies, guidelines and efforts and regional cooperation were identified. During the literature survey, it was observed that Sri Lanka has developed a Hazard profile for the country and has an Early Warning Dissemination System which seems to function quite well by the book. What is more, the country is in the process of orienting the existing national policies and guidelines with the post 2015 global standards such as the Sendai framework and Sustainable Development Goals. Sri Lanka being a member of Indian Ocean Tsunami Warning and Mitigation System (IOTWMS) and Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES) depicts that the country has a good regional cooperation in terms of Early Warning. Even though Sri Lanka lacks efficient and sustainable resilience mechanisms focused on the coastal communities, national efforts are underway to build up the coastal resilience. Training and public awareness campaigns, efficient funds, properly maintained hierarchy and concern to the coastal ecosystems are some of the enablers identified in this study which are associated in building coastal resilience. Developing and updating a multi-hazard map, improving the interagency cooperation and driving towards a people-centred Multi-Hazard Early Warning System (MHEWS) are some of the recommendations given after the analysis

Development of Aerial Online Intelligent Plant Monitoring System for Oil Palm (Elaeis guineensis Jacq.) Performance to External Stimuli

Abstract: Researches in bio-robotics fields have been done en-masse. Development in intelligence monitoring systems for agricultural application have unfold the possibility to observe individual plant response upon receiving external stimuli. In this study, artificial bio pores, 30 cm in diameter, were introduced to the ranges of oil palm trees in three commercial plantations. Various applications methods of bio pores, in particular depths and numbers per plant were investigated. The bio pores drilled around the root zone of the trees using an earth auger, and filled with chopped semi-decomposed fronds and midribs from the plantation maintenance (pruning). A robotic quadcopter drone with 2.7K camera, operated with pre-set flight-plan, employed to record the crown image of oil palm trees under observation. The drone flown at the altitude of 23±0.1 meters above the crown, recording each crown individually. Focus and setting of drone’s cameras was set to automatic, enabling unbiased image recording. The weather conditions (sun radiation, cloud covering, wing speed) upon images recording were measured and recorded. When recording the images, the drone’s GPS-assisted hovering system maintained its position in both axes (horizontal and vertical), producing identical image acquisition for each crown. All plants’ crown was observed at 0, 30, 60, and 120 days after bio pores introduced. Image processing software was developed to segment and extract vegetation index (Vis) information from the images. Plants’ morphological conditions (height, radial, and new leaf) were measured and analyse by statistical methods to understand various bio pores applications influences to plants development. Crown images were processed, and its features extracted and correlated with chlorophyll in leaves. Models developed to predict chlorophyll contents (A, B, and Total) in crown and Vis analyses methods were used to compare individual plant responding to this external stimulus by means of rotational-pivot charts. Results showed that intensive bio pores introduction promote plant’s radial development and the emergence of new leaves. Furthermore, chlorophylls contents in leaves of plants with substantial bio pores applications were greater compared to normal plants. Models showed that optical features extracted from crown images obtained high coefficient of correlation (R2) with leaves chlorophyll contents. This study has paved the way for wisdom agricultural application in Indonesian oil palm industry.

Applying Lean to Healthcare Delivery Processes - a Case-based Research

Abstract: The purpose of this paper is to demonstrate that Lean principles and methodology should be applied on a regular basis to the entire process flow of healthcare delivery systems. With reference to an actual case-based research, this article demonstrates how patient flows can be successfully optimized if Lean is not limited to single processes/contexts only, and it is applied to achieve holistic process improvement of an entire system. The complexity of healthcare delivery systems requires inclusive investigation from various points of view. This is why case-study-based research has been used to investigate dynamic, experiential and complex processes and areas, such as the ones featured by this article. The methodological basis for this research has been a twelve-step optimization approach outlined by the authors during a previous successful Lean programme. This same approach has been applied to optimize patient flows in the emergency departments of four different hospitals in Northern Italy. The research has involved teams composed of medical, nursing, technical and administrative staff. The results outlined in the article suggest that inclusive application of Lean tools leads to effective process optimization and a better working environment, when in connection with a systematic and holistic optimization approach. Feedback from participants was obtained through a satisfaction survey and a project assessment; it reported enthusiastic project acceptance and good teamwork climate. Among the results of the research performed in the four hospitals, several measures have been effectively implemented to reduce the lead-time for patients from registration to discharge. At the same time, patient-staff ratio and quality of care have been either maintained or even improved. However, lack of a definite conclusive evaluation can be explained by the research project still being implemented. The value of this paper lies in demonstrating how Lean contributes to achieve better process performance and high staff satisfaction, when implemented within the whole supply chain of a healthcare delivery system on a regular basis.

Study of Cotton Fabric Dyeing Process With Some Mordant Methods By Using Gambier (Uncaria gambir Roxb) Extract

Abstract: Natural dye that was re-extracted from raw gambier is used to dye cotton fabrics. Aluminum sulfate, calcium oxide, and ferrous sulfate were used as mordants. Dyeing had used four different mordant methods which namely pre, simultaneous, post, and combined (pre and post) mordant. The chemical components in dry gambier extract were tested by using X Ray-Diffraction. The dyed cotton fabrics were evaluated by their color strength (K/S), color difference values (L*, a*, and b*), fastness to washing, rubbing and light. The results have shown that the main components of gambier were anhydrous catechins, catechins, and pyrocatechol. The use of post mordant and combined mordant methods with calcium oxide mordant had produced higher color strength (K/S) than others. The fastness to washing and rubbing values were in a range from good to excellent, while the average of the fastness to light was in a range from moderate to good. The amount of mordant metal that was bound to the fabric was between 15-40%.