Electrical and Electronic Engineering - Research Publications
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ItemThe use and analysis of anti-plagiarism software: Turnitin tool for formative assessment and feedback(WILEY, 2017-11)Abstract This analysis investigates the efficiency of the Turnitin software as a formative writing tool. The inquiry is especially looking into undergraduate and postgraduate students’ experiences while using Turnitin. The perceptions and experiences of students will be prioritized in the study with the purpose of determining ways to improve Turnitin from students’ point of view. Turnitin obtains text matches or similarity index values of 3,173 assignments submitted on subjects uploaded between 2012 and 2014 by university students. We statistically analyzed the similarity index values or levels of plagiarism percentage between the first and the last assignments, using the two‐sample Kolmogorov–Smirnov test, and we found that there was a significant improvement (p = 0.002). Hence, our results demonstrated that using Turnitin as a formative writing tool, allows students to prepare an assignment in an academically acceptable way, during the second half of the semester, with less plagiarism. The results found in this study suggests an insignificant difference between the draft version and final version of the same assignment (p = 0.192). Similarity index values are also different for different courses, such as writing based project subject and mathematics based engineering subject have different values (p < 0.0001). We also observed that students seem to be able to fool Turnitin tool by uploading images of the assignments instead of the text. Nevertheless, the nature of the subject, individual talent, learning approach, time contribution, and the exclusion of consecutive word count may affect the plagiarism percentage. Our results also indicate that there is a substantial benefit in using Turnitin as an educational writing tool rather than a punitive tool, as the use of Turnitin, promotes student learning outcomes with significantly improved academic skills. Thus, this paper provides an insight into avoiding high levels of plagiarism by using Turnitin as a preemptive tool.
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ItemReview: Data Security Models Developed by Blockchain Technology for Different Business Domains(IEEE Press, 2019-10-24)This study investigates a range of data security models developed to achieve data security using blockchain technology for different industrial domains. Current industries utilize data-driven mechanisms for decision making with concern focussed on ensuring data security. Blockchain has the potential to secure data by integrating different information systems since data is decentralized, encrypted and validated by the whole network. This study includes an analysis of blockchain security models using data extracted from 30 peer-reviewed scientific publications over two years (2017-2019). This study analyzed three components of the publications, including the process involved in securing data, the stage of development for securing data and in which industry a model is best applied. Results of the research show that the majority of articles (51.11%) cited Blockchain as a key feature of data security for improvising the data sharing process in industries. This study also finds that the stage of implementation most commonly featured is the proposal stage with potential architectures yet to be implemented (30%). Finally, this study shows that models are applied in industrial domains such as enterprises using data analytics, finance, Internet of Things (IoT), healthcare, education, and cloud service providers. This study finds that security models are most often applied to industries and supply chain management models (28.13%). It is recommended that industry professionals conduct further research to customize the data security models in their own domain. This study gives clear guidelines to researchers of suitable frameworks, processes and consensus mechanism to utilize Blockchain in Industries for data security.
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ItemForecasting Trading-Time based Profit-Making Strategies in Forex Industry: Using Australian Forex Data(IEEE Press, 2019-10-24)Due to the constant fluctuation on global currency rates, it is challenging to make predictions on trading in foreign exchange (Forex) currency market without an intensive analysis; hence, traders struggle to make a profit. This study aims to analyze the relationship between the trade open time and profit in the Forex currency market to help traders to increase the chance of winning trades and make a profit. We developed a technique to observe the most suitable time duration to trade and the profit. This technique assists traders to enhance the chance of winning trades and make a profit by identifying whether it is more likely to make a profit when they keep the trade opened for a longer time or a shorter time. A Forex dataset (N=1,000,000 trades) from a third-party broker database based in Australia has been used. The collected data were filtered according to the popularity of currency pairs. Five currency pairs (as EUR vs USD, GBP vs JPY, USD vs JPY, GBP vs USD and EUR vs JPY) were further analyzed using Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel and K-Means clustering algorithms. It showed that EUR vs USD and USD vs JPY have sensitive movements of profit with the trading time. The highest profit was observed trading time in between 5 to 15 minutes. Our analysis illustrates that shorter time traders are making more profits than the longer time traders. Hence, this study demonstrates that Forex traders make a profit when the market has a unique volatile situation. This study should be useful as a reference for researches in Forex market analyses and Forex Industry to utilize profit-making strategies.
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ItemAn Analysis on Use of Deep Learning and Lexical-Semantic Based Sentiment Analysis Method on Twitter Data to Understand the Demographic Trend of Telemedicine(IEEE Press, 2019-10-24)Technology has turned into a fundamental piece of everybody's life. Social media technology is already used widely by the public to speak out once mind openly. This data can be leveraged to have a better understanding of the current state of decision making. However, Twitter data is highly unstructured. Sentiment analysis can be applied to such health-related data to extract useful information regarding public opinion. The aim of the research is to understand (i) the correlation between Deep Learning versus lexical and semantic-based sentiment prediction methods, (ii) the sentiment prediction accuracy of these methods on manually annotated sentiment dataset (iii) domain-specific knowledge on accuracy of the sentiment prediction methods, and (iv) to utilize Twitter-based sentiment to understand the influence of telemedicine in regards to heart attack and epilepsy. Four sentiment prediction methods are utilized for the research; Lexical and Semantic-based (Valence Aware Dictionary and Sentiment Reasoner (VADER) and TextBlob) and Deep Learning based (Long Short Term Memory (LSTM) and sentiment model from Stanford CoreNLP). The dataset that we retrieved consists of 1.84 million old health-related tweets. Our finding suggests that lexical and semantic-based methods for sentiment prediction offer better accuracy than Deep Learning methods; when a large enough and evenly distributed training dataset is not available. We observed that domain-specific knowledge affects the prediction accuracy of sentiment, mainly when the target text contains more domain-specific words. Sentiment prediction on Twitter data can be utilized to understand the demographic distribution of sentiment. In our case, we observed that telemedicine has a high number of positive sentiment. It is still in its infancy and has not spread to a broader demographic.
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ItemEffective text data preprocessing technique for sentiment analysis in social media data(IEEE Press, 2019-10-24)In the big data era, data is made in real-time or closer to real-time. Thus, businesses can utilize this ever-growing volume of data for the data-driven or information-driven decision-making process to improve their businesses. Social media, like Twitter, generates an enormous amount of such data. However, social media data are often unstructured and difficult to manage. Hence, this study proposes an effective text data preprocessing technique and develop an algorithm to train the Support Vector Machine (SVM), Deep Learning (DL) and Naïve Bayes (NB) classifiers to process Twitter data. We develop an algorithm that weights the sentiment score in terms of weight of hashtag and cleaned text. In this study, we (i) compare different preprocessing techniques on the data collected from Twitter using various techniques such as (stemming, lemmatization and spelling correction) to obtain the efficient method (ii) develop an algorithm to weight the scores of the hashtag and cleaned text to obtain the sentiment. We retrieved N=1,314,000 Twitter data, and we compared the popularity of two products, Google Now and Amazon Alexa. Using our data preprocessing algorithm and sentiment weight score algorithm, we train SVM, DL, NB models. The results show that stemming technique performed best in terms of computational speed. Additionally, the accuracy of the algorithm was tested against manually sorted sentiments and sentiments produced before text data preprocessing. The result demonstrated that the impact produced by the algorithm was close to the manually annotated sentiments. In terms of model performance, the SVM performed better with the accuracy of 90.3%, perhaps, due to the unstructured nature of Twitter data. Previous studies used conventional techniques; hence, no precise methods were utilized on cleaning the text. Therefore, our approach confirms that proper text data preprocessing technique plays a significant role in the prediction accuracy and computational time of the classifier when using the unstructured Twitter data.
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ItemEnergy Efficient Bitcoin Mining to Maximize the Mining Profit: Using Data from 119 Bitcoin Mining Hardware Setups(THEIIER, 2019-11-06)In recent years, cryptocurrency such as Bitcoin has attracted much global attention. Bitcoins are produced by mining as a fee for authenticating any transaction. This process requires a significant amount of computing power. In the early stage, mining started from personal computers and currently, specialized hardware developed to increase the mining speed by reducing power consumption. Therefore, this research aims to explore the impact of hardware efficiency from the early stages of Bitcoin mining hardware to current customized mining hardware for better Bitcoin mining process to maximize mining profit. In this study, we analyzed miner performance from basic to advance hardware and discuss how to identify a suitable miner to increase the profit by comparing their performance and price. We extracted data from 30 peer-reviewed scientific publications (2013-2018) describing 119 Bitcoin mining hardware setups to record the power consumption and hash rates for cryptocurrency mining. Hardware efficiencies were calculated using power consumption and hash rate, then compared with different miners for identification purposes. Our findings suggest that Nvidia GPUs are inefficient and more expensive than the ATI GPUs. Additionally, ASICs and FPGA miners are more efficient compared to CPU and GPU miners. The BitmainAntminer S9 is the most efficient miner for cryptocurrency mining. This study could be utilized to identify common hardware efficiencies to maximize Bitcoin mining profits. Moving to cloud/hosting solutions rather than spending money on upgrading mining hardware could be an exciting future research avenue.
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ItemSecurity and Privacy of Internet of Medical Things (IoMT) Based Healthcare Applications: A Review(THE IIER, 2019-11-06)Recent technological advancements have significantly transformed an individual's perception of the traditional way of carrying out day to day operations. Internet of Things has to be turned out to be a growing trend in various segments in the present world, including the healthcare context. However, this rapid revolution towards IoT has also created several uncertainties and questions over the security of data which is stored in various connected things. With the number of things such as sensors and devices is growing, preserving robust security and privacy of sensitive data becomes more challenging. These security and privacy issues are resulted from deteriorating the effectiveness of Internet of Things (IoT) based healthcare services and adversely impact on individual's sensitive health information. Since data in the healthcare field is critical and sensitive; security and privacy safeguarding of the IoT healthcare paradigm makes matters even more problematic. In order to gain a widespread idea about the risks and threats related to IoT healthcare application, this study reviews a variety of relevant previous publications. The primary goal of this article is to provide insights into multiple sensors devices used in IoT healthcare context and the potential security and privacy issues in different IoT layers. Data are collected from 30 peer-reviewed publications on IoT based healthcare applications published between 2016 and 2018. We have considered numerous threats, attacks, and risks that can affect different layers in IoT based healthcare applications such as (Perception Layer, Network Layer, Middleware, Application Layer, and Business Layer). We have also considered different types of sensor devices which are used in IoT based healthcare applications. For the analysis, we categorize the sensor devices as wearable, implantable, ambient and stationery. Further, we analyze the proposed solutions stated in previous articles to obtain out the most recommended solutions that can mitigate threats and risks in IoT based healthcare application context. Our results show that the network layer is the most vulnerable layer to numerous security and privacy threats and attacks. And the applications layer is the second most vulnerable layer, and wearable sensors were utilized in the majority of IoT based healthcare applications. In addition, China and the USA have the most significant focus on security and privacy of IoMT based healthcare applications. This study intends to enhance awareness among application designers, developers and users such as healthcare professionals and patients by allowing them to identify and quantify potential IoT healthcare application-related threats and risks.
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ItemNew Era in the Supply Chain Management with Blockchain: A Survey(IGI Global, 2019)The results show that transparency and auditability, security and indelibility, and distribution and sustainability are the key attributes of blockchain-based solutions in 56% of the articles reviewed. These three aspects represent the foundation of blockchain technologies which may contribute positively to improve supply management processes. Moreover, immutability, tracking and tracing, and smart contracts are also included in nearly a third of the cases. Moreover, efficiencies and costs through this technology would reduce the costs in payment of intermediaries, reduce paperwork, and help in the shipment of physical documents. Supply chain plays a critical role in the global trade and urgently needs to reassess its models in searching for greater efficiencies. Moreover, better results in visibility across the chain will increase trust for the customers and all interested parties. Secure transactions, strong security mechanisms that prevent fraud and illegal practices, could be achieved through the blockchain.
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ItemAdoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review(The Science and Information (SAI) Organization, 2019-04-30)It is essential to increase the productivity of agricultural and farming processes to improve yields and cost-effectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest sub-vertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.
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ItemPredicting the mean first passage time (MFPT) to reach any state for a passive dynamic walker with steady state variability(PUBLIC LIBRARY SCIENCE, 2018-11-29)Idealized passive dynamic walkers (PDW) exhibit limit cycle stability at steady state. Yet in reality, uncertainty in ground interaction forces result in variability in limit cycles even for a simple walker known as the Rimless Wheel (RW) on seemingly even slopes. This class of walkers is called metastable walkers in that they usually walk in a stable limit cycle, though guaranteed to eventually fail. Thus, control action is only needed if a failure state (i.e. RW stopping down the ramp) is imminent. Therefore, efficiency of estimating the time to reach a failure state is key to develop a minimal intervention controller to inject just enough energy to overcome a failure state when required. Current methods use what is known as a Mean First Passage Time (MFPT) from current state (rotary speed of RW at the most recent leg collision) to an arbitrary state deemed to be a failure in the future. The frequently used Markov chain based MFPT prediction requires an absorbing state, which in this case is a collision where the RW comes to a stop without an escape. Here, we propose a novel method to estimate an MFPT from current state to an arbitrary state which is not necessarily an absorbing state. This provides freedom to a controller to adaptively take action when deemed necessary. We demonstrate the proposed MFPT predictions in a minimal intervention controller for a RW. Our results show that the proposed method is useful in controllers for walkers showing up to 44.1% increase of time-to-fail compared to a PID based closed-loop controller.