Electrical and Electronic Engineering - Research Publications
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ItemNo Preview AvailableRobust Ensemble Machine Learning Model for Filtering Phishing URLs: Expandable Random Gradient Stacked Voting Classifier (ERG-SVC)(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021)
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ItemNo Preview AvailableBlockchain and Cryptocurrencies(IGI Global, 2021)In financial trading, cryptocurrencies like bitcoin use decentralization, traceability, and anonymity features to perform transactional activities. These digital currencies, using the emerging blockchain technologies, are forming the basis of the largest unregulated markets in the world. This creates various regulatory challenges, including the illicit purchase of drugs and weapons, money laundering, and funding terrorist activities. This chapter analyzes various legal and ethical implications, their effects, and various solutions to overcome the inherent issues that are currently faced by the policymakers and regulators. The authors present the result of an analysis of 30 recently published peer-reviewed scientific publications and suggest various mechanisms that can help in the detection and prevention of illegal activities that currently account for a substantial proportion of cryptocurrency trading. They suggest methods and applications that can also be used to identify the dark marketplaces in the future.
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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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ItemAn ab-initio Computational Method to Determine Dielectric Properties of Biological Materials(NATURE PUBLISHING GROUP, 2013-05-08)Frequency dependent dielectric properties are important for understanding the structure and dynamics of biological materials. These properties can be used to study underlying biological processes such as changes in the concentration of biological materials, and the formation of chemical species. Computer simulations can be used to determine dielectric properties and atomic details inaccessible via experimental methods. In this paper, a unified theory utilizing molecular dynamics and density functional theory is presented that is able to determine the frequency dependent dielectric properties of biological materials in an aqueous solution from their molecular structure alone. The proposed method, which uses reaction field approximations, does not require a prior knowledge of the static dielectric constant of the material. The dielectric properties obtained from our method agree well with experimental values presented in the literature.
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ItemIntelligent Sensing in Dynamic Environments Using Markov Decision Process(MDPI, 2011-01)In a network of low-powered wireless sensors, it is essential to capture as many environmental events as possible while still preserving the battery life of the sensor node. This paper focuses on a real-time learning algorithm to extend the lifetime of a sensor node to sense and transmit environmental events. A common method that is generally adopted in ad-hoc sensor networks is to periodically put the sensor nodes to sleep. The purpose of the learning algorithm is to couple the sensor's sleeping behavior to the natural statistics of the environment hence that it can be in optimal harmony with changes in the environment, the sensors can sleep when steady environment and stay awake when turbulent environment. This paper presents theoretical and experimental validation of a reward based learning algorithm that can be implemented on an embedded sensor. The key contribution of the proposed approach is the design and implementation of a reward function that satisfies a trade-off between the above two mutually contradicting objectives, and a linear critic function to approximate the discounted sum of future rewards in order to perform policy learning.
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ItemCharacterization of Extremely Low Frequency Magnetic Fields from Diesel, Gasoline and Hybrid Cars under Controlled Conditions(MDPI, 2015-02)This study characterizes extremely low frequency (ELF) magnetic field (MF) levels in 10 car models. Extensive measurements were conducted in three diesel, four gasoline, and three hybrid cars, under similar controlled conditions and negligible background fields. Averaged over all four seats under various driving scenarios the fields were lowest in diesel cars (0.02 μT), higher for gasoline (0.04-0.05 μT) and highest in hybrids (0.06-0.09 μT), but all were in-line with daily exposures from other sources. Hybrid cars had the highest mean and 95th percentile MF levels, and an especially large percentage of measurements above 0.2 μT. These parameters were also higher for moving conditions compared to standing while idling or revving at 2500 RPM and higher still at 80 km/h compared to 40 km/h. Fields in non-hybrid cars were higher at the front seats, while in hybrid cars they were higher at the back seats, particularly the back right seat where 16%-69% of measurements were greater than 0.2 μT. As our results do not include low frequency fields (below 30 Hz) that might be generated by tire rotation, we suggest that net currents flowing through the cars' metallic chassis may be a possible source of MF. Larger surveys in standardized and well-described settings should be conducted with different types of vehicles and with spectral analysis of fields including lower frequencies due to magnetization of tires.
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ItemNo Preview AvailableOptimizing Heating Efficiency of Hyperthermia: Specific Loss Power of Magnetic Sphere Composed of Superparamagnetic Nanoparticles(EMW Publishing, 2020-03-09)Magnetic nanoparticle (MNP) based thermal therapies have shown importance in clinical applications. However, it lacks a compromise between its robustness and limitations. We developed theoretical strategies to enhance the heating efficiency, which could be utilized in thermal therapies and calculated parameter dependence for superparamagnetic MNPs (approximative ellipsoid-shaped) within a sphere-shaped ball. Then we calculated specific loss power (SLP) for magnetic particles in a magnetic ball. The dependency of features of the nanoparticles (such as mean particle size, a number of particles, frequency and amplitude of the exposed field, relaxation time, and volume gap between particles and a sphere-shaped ball) on the SLP or the heating effect in superparamagnetic MNPs was analyzed. In this study, optimal parameter values were calculated using Kneedle Algorithm as the optimization technique to represent the accurate heating efficiency. The influence of a number of particles in a sphere-shaped ball shows that SLP of magnetic particles increases with the increasing number of particles (N); however, after N = 10 particles, the SLP increment is insignificant. The most remarkable result arising from this analysis is that when particles are closer together (less volume gap of a sphere-shaped ball), high SLP is found for the same number of particles. This model also predicts that the frequency dependency on the SLP is negligible when the frequency is higher than 10 kHz depending on the size of a sphere-shaped ball and nanoparticle parameters. This analysis has shown that the SLP of MNPs, in a sphere-shaped ball, strongly depends on magnetic parameters and properties of the particles. In brief, we have demonstrated, for the first time, impact on SLP of the accumulation of ellipsoid-shaped superparamagnetic nanoparticles into a sphere-shaped ball. This finding has essential suggestions for developing links between heating properties with loose aggregate and dense aggregate scenarios in the superparamagnetic condition.
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ItemSupervised Machine Learning Algorithms for Bioelectromagnetics: Prediction Models and Feature Selection Techniques Using Data from Weak Radiofrequency Radiation Effect on Human and Animals Cells(MDPI AG, 2020-06-26)The emergence of new technologies to incorporate and analyze data with high-performance computing has expanded our capability to accurately predict any incident. Supervised Machine learning (ML) can be utilized for a fast and consistent prediction, and to obtain the underlying pattern of the data better. We develop a prediction strategy, for the first time, using supervised ML to observe the possible impact of weak radiofrequency electromagnetic field (RF-EMF) on human and animal cells without performing in-vitro laboratory experiments. We extracted laboratory experimental data from 300 peer-reviewed scientific publications (1990–2015) describing 1127 experimental case studies of human and animal cells response to RF-EMF. We used domain knowledge, Principal Component Analysis (PCA), and the Chi-squared feature selection techniques to select six optimal features for computation and cost-efficiency. We then develop grouping or clustering strategies to allocate these selected features into five different laboratory experiment scenarios. The dataset has been tested with ten different classifiers, and the outputs are estimated using the k-fold cross-validation method. The assessment of a classifier’s prediction performance is critical for assessing its suitability. Hence, a detailed comparison of the percentage of the model accuracy (PCC), Root Mean Squared Error (RMSE), precision, sensitivity (recall), 1 − specificity, Area under the ROC Curve (AUC), and precision-recall (PRC Area) for each classification method were observed. Our findings suggest that the Random Forest algorithm exceeds in all groups in terms of all performance measures and shows AUC = 0.903 where k-fold = 60. A robust correlation was observed in the specific absorption rate (SAR) with frequency and cumulative effect or exposure time with SAR×time (impact of accumulated SAR within the exposure time) of RF-EMF. In contrast, the relationship between frequency and exposure time was not significant. In future, with more experimental data, the sample size can be increased, leading to more accurate work.
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ItemReal-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning(The Science and Information (SAI) Organization, 2020-03-01)Cryptocurrency has as of late pulled in extensive consideration in the fields of economics, cryptography, and computer science due to it is an encrypted digital currency, peer- to- peer virtual forex produced using codes, and it is much the same as another medium of the trade like real cash. This study mainly focuses to combine the Deep Learning with Data parallelism and Cloud Computing Machine learning engine as “hybrid architecture” to predict new Cryptocurrency prices by using historical Cryptocurrency data. The study has exploited 266,776 of Cryptocurrency prices values from the pilot experiment, and Deep Learning algorithm used for the price prediction. The four hybrid architecture models, namely, (i) standalone PC, (ii) Cloud computing without data parallelism (GPU-1), (iii) Cloud computing with data parallelism (GPU-4), and (iv) Cloud computing with data parallelism (GPU-8) introduced and utilized for the analysis. The performance of each model is evaluated using different performance evaluation parameters. Then, the efficiency of each model was compared using different batch sizes. An experimental result reveals that Cloud computing technology exposes new era by performing parallel computing in IoT to reduce computation time up to 90% of the Deep Learning algorithm-based Cryptocurrencies price prediction model and many other IoT applications such as character recognition, biomedical field, industrial automation, and natural disaster prediction.