Search results for: accuracy of payment time

Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

Procedia PDF Downloads 61

Authors: Vesile Evrim, Aliyu Awwal

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By the evolvement in technology, the way of expressing opinions switched the direction to the digital world. The domain of politics as one of the hottest topics of opinion mining research merged together with the behavior analysis for affiliation determination in text which constitutes the subject of this paper. This study aims to classify the text in news/blogs either as Republican or Democrat with the minimum number of features. As an initial set, 68 features which 64 are constituted by Linguistic Inquiry and Word Count (LIWC) features are tested against 14 benchmark classification algorithms. In the later experiments, the dimensions of the feature vector reduced based on the 7 feature selection algorithms. The results show that Decision Tree, Rule Induction and M5 Rule classifiers when used with SVM and IGR feature selection algorithms performed the best up to 82.5% accuracy on a given dataset. Further tests on a single feature and the linguistic based feature sets showed the similar results. The feature “function” as an aggregate feature of the linguistic category, is obtained as the most differentiating feature among the 68 features with 81% accuracy by itself in classifying articles either as Republican or Democrat.

Keywords: feature selection, LIWC, machine learning, politics

Procedia PDF Downloads 368

Authors: Manlika Rajchakit

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This paper is concerned with robust mean square stability of uncertain stochastic switched discrete time-delay systems. The system to be considered is subject to interval time-varying delays, which allows the delay to be a fast time-varying function and the lower bound is not restricted to zero. Based on the discrete Lyapunov functional, a switching rule for the robust mean square stability for the uncertain stochastic discrete time-delay system is designed via linear matrix inequalities. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.

Keywords: robust mean square stability, discrete-time stochastic systems, hybrid systems, interval time-varying delays, lyapunov functional, linear matrix inequalities

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Authors: Sachin Kumar

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Fuzzy fractional diffusion equation is widely useful to depict different physical processes arising in physics, biology, and hydrology. The motive of this article is to deal with the fuzzy fractional diffusion equation. We study a mathematical model of fuzzy space-time fractional diffusion equation in which unknown function, coefficients, and initial-boundary conditions are fuzzy numbers. First, we find out a fuzzy operational matrix of Legendre polynomial of Caputo type fuzzy fractional derivative having a non-singular Mittag-Leffler kernel. The main advantages of this method are that it reduces the fuzzy fractional partial differential equation (FFPDE) to a system of fuzzy algebraic equations from which we can find the solution of the problem. The feasibility of our approach is shown by some numerical examples. Hence, our method is suitable to deal with FFPDE and has good accuracy.

Keywords: fractional PDE, fuzzy valued function, diffusion equation, Legendre polynomial, spectral method

Procedia PDF Downloads 180

Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

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The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

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Authors: Shreya Thusoo, Karan Modi, Ankit Kumar Jha, Rajesh Kumar

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Since the evolution of computational tools and simulation software, there has been considerable increase in research on Soil Structure Interaction (SSI) to decrease the computational time and increase accuracy in the results. To aid the designer with a proper understanding of the response of structure in different soil types, the presented paper compares the deformation, shear stress, acceleration and other parameters of multi-storey building for a specific input ground motion using Response-spectrum Analysis (RSA) method. The response of all the models of different heights have been compared in different soil types. Finite Element Simulation software, ANSYS, has been used for all the computational purposes. Overall, higher response is observed with SSI, while it increases with decreasing stiffness of soil.

Keywords: soil-structure interaction, response spectrum, analysis, finite element method, multi-storey buildings

Procedia PDF Downloads 459

Authors: A. V. Patil

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The microwave dielectric relaxation study of diethanolamine with triethanolamine binary mixture have been determined over the frequency range of 10 MHz to 20 GHz, at various temperatures using time domain reflectometry (TDR) method for 11 concentrations of the system. The present work reveals molecular interaction between same multi-functional groups [−OH and –NH2] of the alkanolamines (diethanolamine and triethanolamine) using different models such as Debye model, Excess model, and Kirkwood model. The dielectric parameters viz. static dielectric constant (ε0) and relaxation time (τ) have been obtained with Debye equation characterized by a single relaxation time without relaxation time distribution by the least squares fit method.

Keywords: diethanolamine, excess properties, kirkwood properties, time domain reflectometry, triethanolamine

Procedia PDF Downloads 281

Authors: Shaoying Guo, Yanyun Xu, Meng Zhang, Weiqing Huang

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The wireless communication network is developing rapidly, thus the wireless security becomes more and more important. Specific emitter identification (SEI) is an vital part of wireless communication security as a technique to identify the unique transmitters. In this paper, a SEI method based on multiscale dispersion entropy (MDE) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The algorithms of MDE and RCMDE are used to extract features for identification of five wireless devices and cross-validation support vector machine (CV-SVM) is used as the classifier. The experimental results show that the total identification accuracy is 99.3%, even at low signal-to-noise ratio(SNR) of 5dB, which proves that MDE and RCMDE can describe the communication signal series well. In addition, compared with other methods, the proposed method is effective and provides better accuracy and stability for SEI.

Keywords: cross-validation support vector machine, refined com- posite multiscale dispersion entropy, specific emitter identification, transient signal, wireless communication device

Procedia PDF Downloads 117

Authors: Rabih Nehme, Abdullah Al Mutawa

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The general goal behind this research is to gain a better understanding of factors leading to dysfunctional behavior of auditors. Recent accounting scandals -Enron, Waste Management Inc., WorldCom, Xerox Corporation, etc. -provided an ample proof of how the role of auditors has become the basis of controversial debates in many circles and instances in our modern time. The majority of lawsuits and accounting scandals seem to have a central topic in focus, namely the question ''Where were the auditors? The survey we offer up for research is made up of 34 questions that are designed to analyse the perception of auditors and the cause of dysfunctional behavior. The object of this research is comprised of auditors positioned and employed at the Big Four audit firms in Kuwait. Dysfunctional behavior (DB) is measured against two signal proxies of dysfunctional behavior; premature sign-off and under reporting of chargeable time. DB is analysed against time budget pressure and time deadline pressure. The research results' suggest that the general belief among auditors is that the profession of accountancy predetermines their tendency to commit certain patterns of dysfunctional behavior. Having our investigation conducted at the Big Four audit firms, we have come to the conclusion that there is a general difference in behavior patterns among perceptions of dysfunctional behavior and normal skeptic professional behavior.

Keywords: big four, dysfunctional behavior, time budget, time deadline

Procedia PDF Downloads 445

Authors: Yuhanis Yusof, Zuriani Mustaffa, Siti Sakira Kamaruddin

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Data mining is a fundamental technique in identifying patterns from large data sets. The extracted facts and patterns contribute in various domains such as marketing, forecasting, and medical. Prior to that, data are consolidated so that the resulting mining process may be more efficient. This study investigates the effect of different data normalization techniques, which are Min-max, Z-score, and decimal scaling, on Swarm-based forecasting models. Recent swarm intelligence algorithms employed includes the Grey Wolf Optimizer (GWO) and Artificial Bee Colony (ABC). Forecasting models are later developed to predict the daily spot price of crude oil and gasoline. Results showed that GWO works better with Z-score normalization technique while ABC produces better accuracy with the Min-Max. Nevertheless, the GWO is more superior that ABC as its model generates the highest accuracy for both crude oil and gasoline price. Such a result indicates that GWO is a promising competitor in the family of swarm intelligence algorithms.

Keywords: artificial bee colony, data normalization, forecasting, Grey Wolf optimizer

Procedia PDF Downloads 455

Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

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Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

Procedia PDF Downloads 363

Authors: Alaa Alrakaf, Sk. Md. Mizanur Rahman

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Since the last two decades’ Arabic natural language processing (ANLP) has become increasingly much more important. One of the key issues related to ANLP is ambiguity. In Arabic language different pronunciation of one word may have a different meaning. Furthermore, ambiguity also has an impact on the effectiveness and efficiency of Machine Translation (MT). The issue of ambiguity has limited the usefulness and accuracy of the translation from Arabic to English. The lack of Arabic resources makes ambiguity problem more complicated. Additionally, the orthographic level of representation cannot specify the exact meaning of the word. This paper looked at the diacritics of Arabic language and used them to disambiguate a word. The proposed approach of word sense disambiguation used Diacritizer application to Diacritize Arabic text then found the most accurate sense of an ambiguous word using Naïve Bayes Classifier. Our Experimental study proves that using Arabic Diacritics with Naïve Bayes Classifier enhances the accuracy of choosing the appropriate sense by 23% and also decreases the ambiguity in machine translation.

Keywords: Arabic natural language processing, machine learning, machine translation, Naive bayes classifier, word sense disambiguation

Procedia PDF Downloads 339

Authors: Yiwei Huang, Chunyu Zhao, Jingjing Ding

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Electrical Resistivity Tomography has been widely used in the medicine and the geology, such as the imaging of the lung impedance and the analysis of the soil impedance, etc. Linear Back Projection is the core algorithm of Electrical Resistivity Tomography, but the traditional Linear Back Projection can not make full use of the information of the electric field. In this paper, an imaging method of Parallel Electrode Linear Back Projection for Electrical Resistivity Tomography is proposed, which generates the electric field distribution that is not linearly related to the traditional Linear Back Projection, captures the new information and improves the imaging accuracy without increasing the number of electrodes by changing the connection mode of the electrodes. The simulation results show that the accuracy of the image obtained by the inverse operation obtained by the Parallel Electrode Linear Back Projection can be improved by about 20%.

Keywords: electrical resistivity tomography, finite element simulation, image optimization, parallel electrode linear back projection

Procedia PDF Downloads 134

Authors: Fatemeh Parhizkar, Mojtaba Yamani, Abdolla Behboodi, Masoomeh Hashemi

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The mangrove forests are natural and valuable gifts that exist in some parts of the world, including Iran. Regarding the threats faced by these forests and the declining area of them all over the world, as well as in Iran, it is very necessary to manage and monitor them. The current study aimed to investigate the changes in mangrove forests and the relationship between these changes and the marine hydrodynamics and coastal morphology in the area between qeshm island and the west coast of the Hormozgan province (i.e. the coastline between Mehran river and Bandar-e Pol port) in the 49-year period. After preprocessing and classifying satellite images using the SVM, MLC, and ANN classifiers and evaluating the accuracy of the maps, the SVM approach with the highest accuracy (the Kappa coefficient of 0.97 and overall accuracy of 98) was selected for preparing the classification map of all images. The results indicate that from 1972 to 1987, the area of these forests have had experienced a declining trend, and in the next years, their expansion was initiated. These forests include the mangrove forests of Khurkhuran wetland, Muriz Deraz Estuary, Haft Baram Estuary, the mangrove forest in the south of the Laft Port, and the mangrove forests between the Tabl Pier, Maleki Village, and Gevarzin Village. The marine hydrodynamic and geomorphological characteristics of the region, such as average intertidal zone, sediment data, the freshwater inlet of Mehran river, wave stability and calmness, topography and slope, as well as mangrove conservation projects make the further expansion of mangrove forests in this area possible. By providing significant and up-to-date information on the development and decline of mangrove forests in different parts of the coast, this study can significantly contribute to taking measures for the conservation and restoration of mangrove forests.

Keywords: mangrove forests, marine hydrodynamics, coastal morphology, west of strait of Hormuz, Iran

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Authors: Khaled Salah

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Model order reduction has been one of the most challenging topics in the past years. In this paper, a hybrid solution of genetic algorithm (GA) and simulated annealing algorithm (SA) are used to approximate high-order transfer functions (TFs) to lower-order TFs. In this approach, hybrid algorithm is applied to model order reduction putting in consideration improving accuracy and preserving the properties of the original model which are two important issues for improving the performance of simulation and computation and maintaining the behavior of the original complex models being reduced. Compared to conventional mathematical methods that have been used to obtain a reduced order model of high order complex models, our proposed method provides better results in terms of reducing run-time. Thus, the proposed technique could be used in electronic design automation (EDA) tools.

Keywords: genetic algorithm, simulated annealing, model reduction, transfer function

Procedia PDF Downloads 129

Authors: Keunhong Chae, Seokho Yoon

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Recently, the global navigation satellite system (GNSS) such as Galileo and GPS is employing more satellites to provide a higher degree of accuracy for the location service, thus calling for a more efficient signaling scheme among the satellites used in the overall GNSS network. In that the network throughput is improved, the spatial diversity can be one of the efficient signaling schemes; however, it requires multiple antenna that could cause a significant increase in the complexity of the GNSS. Thus, a diversity scheme called the cooperative signaling was proposed, where the virtual multiple-input multiple-output (MIMO) signaling is realized with using only a single antenna in the transmit satellite of interest and with modeling the neighboring satellites as relay nodes. The main drawback of the cooperative signaling is that the relay nodes receive the transmitted signal at different time instants, i.e., they operate in an asynchronous way, and thus, the overall performance of the GNSS network could degrade severely. To tackle the problem, several modified cooperative signaling schemes were proposed; however, all of them are difficult to implement due to a signal decoding at the relay nodes. Although the implementation at the relay nodes could be simpler to some degree by employing the time-reversal and conjugation operations instead of the signal decoding, it would be more efficient if we could implement the operations of the relay nodes at the source node having more resources than the relay nodes. So, in this paper, we propose a novel cooperative signaling scheme, where the data signals are combined in a unique way at the source node, thus obviating the need of the complex operations such as signal decoding, time-reversal and conjugation at the relay nodes. The numerical results confirm that the proposed scheme provides the same performance in the cooperative diversity and the bit error rate (BER) as the conventional scheme, while reducing the complexity at the relay nodes significantly. Acknowledgment: This work was supported by the National GNSS Research Center program of Defense Acquisition Program Administration and Agency for Defense Development.

Keywords: global navigation satellite network, cooperative signaling, data combining, nodes

Procedia PDF Downloads 269

Authors: Chung-Chih Lin, Wen-Teng Wang, Chin-Chiuan Kuo, Chieh-Liang Wu

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The state of melt viscosity in injection process is significantly influenced by the setting parameters due to that the shear rate of injection process is higher than other processes. How to determine plastic melt viscosity during injection process is important to understand the influence of setting parameters on the melt viscosity. An apparatus named as pressure sensor bushing (PSB) module that is used to evaluate the melt viscosity during injection process is developed in this work. The formulations to coupling melt viscosity with fill time and injection pressure are derived and then the melt viscosity is determined. A test mold is prepared to evaluate the accuracy on viscosity calculations between the PSB module and the conventional approaches. The influence of melt viscosity on the tensile strength of molded part is proposed to study the consistency of injection quality.

Keywords: injection molding, melt viscosity, tensile test, pressure sensor bushing (PSB)

Procedia PDF Downloads 456

Authors: Maria C. Mariani, Md Al Masum Bhuiyan, Osei K. Tweneboah, Hector G. Huizar

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This work is devoted to the study of modeling geophysical time series. A stochastic technique with time-varying parameters is used to forecast the volatility of data arising in geophysics. In this study, the volatility is defined as a logarithmic first-order autoregressive process. We observe that the inclusion of log-volatility into the time-varying parameter estimation significantly improves forecasting which is facilitated via maximum likelihood estimation. This allows us to conclude that the estimation algorithm for the corresponding one-step-ahead suggested volatility (with ±2 standard prediction errors) is very feasible since it possesses good convergence properties.

Keywords: Augmented Dickey Fuller Test, geophysical time series, maximum likelihood estimation, stochastic volatility model

Procedia PDF Downloads 294

Authors: Rahul Patel, S. P. Dave, M. V Shah

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Ahmedabad is a rapidly growing city in western India that is experiencing significant urbanization and industrialization. With projections indicating that it will become a metropolitan city in the near future, various construction activities are taking place, making soil testing a crucial requirement before construction can commence. To achieve this, construction companies and contractors need to periodically conduct soil testing. This study focuses on the process of creating a spatial database that is digitally formatted and integrated with geotechnical data and a Geographic Information System (GIS). Building a comprehensive geotechnical Geo-database involves three essential steps. Firstly, borehole data is collected from reputable sources. Secondly, the accuracy and redundancy of the data are verified. Finally, the geotechnical information is standardized and organized for integration into the database. Once the Geo-database is complete, it is integrated with GIS. This integration allows users to visualize, analyze, and interpret geotechnical information spatially. Using a Topographic to Raster interpolation process in GIS, estimated values are assigned to all locations based on sampled geotechnical data values. The study area was contoured for SPT N-Values, Soil Classification, Φ-Values, and Bearing Capacity (T/m2). Various interpolation techniques were cross-validated to ensure information accuracy. The GIS map generated by this study enables the calculation of SPT N-Values, Φ-Values, and bearing capacities for different footing widths and various depths. This approach highlights the potential of GIS in providing an efficient solution to complex phenomena that would otherwise be tedious to achieve through other means. Not only does GIS offer greater accuracy, but it also generates valuable information that can be used as input for correlation analysis. Furthermore, this system serves as a decision support tool for geotechnical engineers. The information generated by this study can be utilized by engineers to make informed decisions during construction activities. For instance, they can use the data to optimize foundation designs and improve site selection. In conclusion, the rapid growth experienced by Ahmedabad requires extensive construction activities, necessitating soil testing. This study focused on the process of creating a comprehensive geotechnical database integrated with GIS. The database was developed by collecting borehole data from reputable sources, verifying its accuracy and redundancy, and organizing the information for integration. The GIS map generated by this study is an efficient solution that offers greater accuracy and generates valuable information that can be used as input for correlation analysis. It also serves as a decision support tool for geotechnical engineers, allowing them to make informed decisions during construction activities.

Keywords: arcGIS, borehole data, geographic information system (GIS), geo-database, interpolation, SPT N-value, soil classification, φ-value, bearing capacity

Procedia PDF Downloads 50

Authors: Tatyana Aleksandrovna Barbasova, Lev Sergeevich Kazarinov, Olga Valerevna Kolesnikova, Aleksandra Aleksandrovna Filimonova

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We regard forecasting of energy consumption by private production areas of a large industrial facility as well as by the facility itself. As for production areas the forecast is made based on empirical dependencies of the specific energy consumption and the production output. As for the facility itself implementation of the task to minimize the energy consumption forecasting error is based on adjustment of the facility’s actual energy consumption values evaluated with the metering device and the total design energy consumption of separate production areas of the facility. The suggested procedure of optimal energy consumption was tested based on the actual data of core product output and energy consumption by a group of workshops and power plants of the large iron and steel facility. Test results show that implementation of this procedure gives the mean accuracy of energy consumption forecasting for winter 2014 of 0.11% for the group of workshops and 0.137% for the power plants.

Keywords: energy consumption, energy consumption forecasting error, energy efficiency, forecasting accuracy, forecasting

Procedia PDF Downloads 426

Authors: Ainouna Bouziane

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The ability of Electron Tomography to recover the 3D structure of catalysts, with spatial resolution in the subnanometer scale, has been widely explored and reviewed in the last decades. A variety of experimental techniques, based either on Transmission Electron Microscopy (TEM) or Scanning Transmission Electron Microscopy (STEM) have been used to reveal different features of nanostructured catalysts in 3D, but High Angle Annular Dark Field imaging in STEM mode (HAADF-STEM) stands out as the most frequently used, given its chemical sensitivity and avoidance of imaging artifacts related to diffraction phenomena when dealing with crystalline materials. In this regard, our group has developed a methodology that combines image denoising by undecimated wavelet transforms (UWT) with automated, advanced segmentation procedures and parameter selection methods using CS-TVM (Compressed Sensing-total variation minimization) algorithms to reveal more reliable quantitative information out of the 3D characterization studies. However, evaluating the accuracy of the magnitudes estimated from the segmented volumes is also an important issue that has not been properly addressed yet, because a perfectly known reference is needed. The problem particularly complicates in the case of multicomponent material systems. To tackle this key question, we have developed a methodology that incorporates volume reconstruction/segmentation methods. In particular, we have established an approach to evaluate, in quantitative terms, the accuracy of TVM reconstructions, which considers the influence of relevant experimental parameters like the range of tilt angles, image noise level or object orientation. The approach is based on the analysis of material-realistic, 3D phantoms, which include the most relevant features of the system under analysis.

Keywords: electron tomography, supported catalysts, nanometrology, error assessment

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Authors: Recep Gundogan, Turgay Dindaroglu, Hikmet Gunal, Mustafa Ulukavak, Ron Bingner

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A majority of total soil losses in agricultural areas could be attributed to ephemeral gullies caused by heavy rains in conventionally tilled fields; however, ephemeral gully erosion is often ignored in conventional soil erosion assessments. Ephemeral gullies are often easily filled from normal soil tillage operations, which makes capturing the existing ephemeral gullies in croplands difficult. This study was carried out to determine topographic features, including slope and aspect composite topographic index (CTI) and initiation points of gully channels, using images obtained from unmanned aerial vehicle (UAV) images. The study area was located in Topcu stream watershed in the eastern Mediterranean Region, where intense rainfall events occur over very short time periods. The slope varied between 0.7 and 99.5%, and the average slope was 24.7%. The UAV (multi-propeller hexacopter) was used as the carrier platform, and images were obtained with the RGB camera mounted on the UAV. The digital terrain models (DTM) of Topçu stream micro catchment produced using UAV images and manual field Global Positioning System (GPS) measurements were compared to assess the accuracy of UAV based measurements. Eighty-one gully channels were detected in the study area. The mean slope and CTI values in the micro-catchment obtained from DTMs generated using UAV images were 19.2% and 3.64, respectively, and both slope and CTI values were lower than those obtained using GPS measurements. The total length and volume of the gully channels were 868.2 m and 5.52 m³, respectively. Topographic characteristics and information on ephemeral gully channels (location of initial point, volume, and length) were estimated with high accuracy using the UAV images. The results reveal that UAV-based measuring techniques can be used in lieu of existing GPS and total station techniques by using images obtained with high-resolution UAVs.

Keywords: aspect, compound topographic index, digital terrain model, initial gully point, slope, unmanned aerial vehicle

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Authors: Waqas Ahmed Khan Afridi, Subhas Chandra Mukhopadhyay, Bandita Mainali

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The research paper presents a unique approach to evapotranspiration (ET) prediction using a Support Vector Machine (SVM) learning algorithm. The study leverages real-time sensor node data to develop an accurate and adaptable prediction model, addressing the inherent challenges of traditional ET estimation methods. The integration of the SVM algorithm with real-time sensor node data offers great potential to improve spatial and temporal resolution in ET predictions. In the model development, key input features are measured and computed using mathematical equations such as Penman-Monteith (FAO56) and soil water balance (SWB), which include soil-environmental parameters such as; solar radiation (Rs), air temperature (T), atmospheric pressure (P), relative humidity (RH), wind speed (u2), rain (R), deep percolation (DP), soil temperature (ST), and change in soil moisture (∆SM). The one-year field data are split into combinations of three proportions i.e. train, test, and validation sets. While kernel functions with tuning hyperparameters have been used to train and improve the accuracy of the prediction model with multiple iterations. This paper also outlines the existing methods and the machine learning techniques to determine Evapotranspiration, data collection and preprocessing, model construction, and evaluation metrics, highlighting the significance of SVM in advancing the field of ET prediction. The results demonstrate the robustness and high predictability of the developed model on the basis of performance evaluation metrics (R2, RMSE, MAE). The effectiveness of the proposed model in capturing complex relationships within soil and environmental parameters provide insights into its potential applications for water resource management and hydrological ecosystem.

Keywords: evapotranspiration, FAO56, KNIME, machine learning, RStudio, SVM, sensors

Procedia PDF Downloads 45

Authors: Jay P. Sipani, Riki H. Patel, Trushit Upadhyaya

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Today there is a demand to monitor environmental factors almost in all research institutes and industries and even for domestic uses. The analog data measurement requires manual effort to note readings, and there may be a possibility of human error. Such type of systems fails to provide and store precise values of parameters with high accuracy. Analog systems are having drawback of storage/memory. Therefore, there is a requirement of a smart system which is fully automated, accurate and capable enough to monitor all the environmental parameters with utmost possible accuracy. Besides, it should be cost-effective as well as portable too. This paper represents the Wireless Sensor (WS) data communication using DHT11, Arduino, SIM900A GSM module, a mobile device and Liquid Crystal Display (LCD). Experimental setup includes the heating arrangement of DHT11 and transmission of its data using Arduino and SIM900A GSM shield. The mobile device receives the data using Arduino, GSM shield and displays it on LCD too. Heating arrangement is used to heat and cool the temperature sensor to study its characteristics.

Keywords: wireless communication, Arduino, DHT11, LCD, SIM900A GSM module, mobile phone SMS

Procedia PDF Downloads 261

Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

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Authors: Monica Gonzalez Machorro

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Dementia is hard to diagnose because of the lack of early physical symptoms. Early dementia recognition is key to improving the living condition of patients. Speech technology is considered a valuable biomarker for this challenge. Recent works have utilized conventional acoustic features and machine learning methods to detect dementia in speech. BERT-like classifiers have reported the most promising performance. One constraint, nonetheless, is that these studies are either based on human transcripts or on transcripts produced by automatic speech recognition (ASR) systems. This research contribution is to explore a method that does not require transcriptions to detect early Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This is achieved by fine-tuning a pre-trained ASR model for the downstream early AD and MCI tasks. To do so, a subset of the thoroughly studied Pitt Corpus is customized. The subset is balanced for class, age, and gender. Data processing also involves cropping the samples into 10-second segments. For comparison purposes, a baseline model is defined by training and testing a Random Forest with 20 extracted acoustic features using the librosa library implemented in Python. These are: zero-crossing rate, MFCCs, spectral bandwidth, spectral centroid, root mean square, and short-time Fourier transform. The baseline model achieved a 58% accuracy. To fine-tune HuBERT as a classifier, an average pooling strategy is employed to merge the 3D representations from audio into 2D representations, and a linear layer is added. The pre-trained model used is ‘hubert-large-ls960-ft’. Empirically, the number of epochs selected is 5, and the batch size defined is 1. Experiments show that our proposed method reaches a 69% balanced accuracy. This suggests that the linguistic and speech information encoded in the self-supervised ASR-based model is able to learn acoustic cues of AD and MCI.

Keywords: automatic speech recognition, early Alzheimer’s recognition, mild cognitive impairment, speech impairment

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Authors: Mohammad Lemar Zalmai, Osman Hurol Turkakin, Cemil Akcay, Ekrem Manisali

Abstract:

In public construction projects, determining the contract period in the award phase is one of the most important factors. The contract period establishes the baseline for creating the cash flow curve and progress payment planning in the post-award phase. If overestimated, project duration causes losses for both the owner and the contractor. Therefore, it is essential to base construction project duration on reliable forecasting. In Turkey, schedules are usually built using the bar chart (Gantt) schedule, especially for governmental construction agencies. The usage of these schedules is limited for bidding purposes. Although the bar-chart schedule is useful in some cases, it lacks logical connections between activities; it would be harder to obtain the activities that have more effects than others on the project's total duration, especially in large complex projects. In this study, a construction schedule is prepared with Critical Path Method (CPM) that addresses the above-mentioned discrepancies. CPM is a simple and effective method that displays project time and critical paths, showing results of forward and backward calculations with considering the logic relationships between activities; it is a powerful tool for planning and managing all kinds of construction projects and is a very convenient method for the construction industry. CPM provides a much more useful and precise approach than traditional bar-chart diagrams that form the basis of construction planning and control. CPM has two main application utilities in the construction field; the first one is obtaining project duration, which is called an as-planned schedule that includes as-planned activity durations with relationships between subsequent activities. Another utility is during the project execution; each activity is tracked, and their durations are recorded in order to obtain as-built schedule, which is named as a black box of the project. The latter is more useful for delay analysis, and conflict resolutions. These features of CPM have been popular around the world. However, it has not been yet extensively used in Turkey. In this study, a real construction project is investigated as a case study; CPM-based scheduling is used for establishing both of as-built and as-planned schedules. Problems that emerged during the construction phase are identified and categorized. Subsequently, solutions are suggested. Two scenarios were considered. In the first scenario, project progress was monitored based as CPM was used to track and manage progress; this was carried out based on real-time data. In the second scenario, project progress was supposedly tracked based on the assumption that the Gantt chart was used. The S-curves of the two scenarios are plotted and interpreted. Comparing the results, possible faults of the latter scenario are highlighted, and solutions are suggested. The importance of CPM implementation has been emphasized and it has been proposed to make it mandatory for preparation of construction schedule based on CPM for public construction projects contracts.

Keywords: as-built, case-study, critical path method, Turkish government sector projects

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Authors: Tridipa Biswas, Kamal Pandey

Abstract:

A Digital Elevation Model (DEM) is a simple representation of a surface in 3 dimensional space with elevation as the third dimension along with X (horizontal coordinates) and Y (vertical coordinates) in rectangular coordinates. DEM has wide applications in various fields like disaster management, hydrology and watershed management, geomorphology, urban development, map creation and resource management etc. Cartosat-1 or IRS P5 (Indian Remote Sensing Satellite) is a state-of-the-art remote sensing satellite built by ISRO (May 5, 2005) which is mainly intended for cartographic applications.Cartosat-1 is equipped with two panchromatic cameras capable of simultaneous acquiring images of 2.5 meters spatial resolution. One camera is looking at +26 degrees forward while another looks at –5 degrees backward to acquire stereoscopic imagery with base to height ratio of 0.62. The time difference between acquiring of the stereopair images is approximately 52 seconds. The high resolution stereo data have great potential to produce high-quality DEM. The high-resolution Cartosat-1 stereo image data is expected to have significant impact in topographic mapping and watershed applications. The objective of the present study is to generate high-resolution DEM, quality evaluation in different elevation strata, generation of ortho-rectified image and associated accuracy assessment from CARTOSAT-1 data based Ground Control Points (GCPs) for Aglar watershed (Tehri-Garhwal and Dehradun district, Uttarakhand, India). The present study reveals that generated DEMs (10m and 30m) derived from the CARTOSAT-1 stereo pair is much better and accurate when compared with existing DEMs (ASTER and CARTO DEM) also for different terrain parameters like slope, aspect, drainage, watershed boundaries etc., which are derived from the generated DEMs, have better accuracy and results when compared with the other two (ASTER and CARTO) DEMs derived terrain parameters.

Keywords: ASTER-DEM, CARTO-DEM, CARTOSAT-1, digital elevation model (DEM), ortho-rectified image, photogrammetry, RPC, stereo pair, terrain parameters

Procedia PDF Downloads 290

Authors: Waseem Shahzad, Ayesha Tahir Khan, Hamid Hussain Awan

Abstract:

Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced.

Keywords: ant colony optimization, parallel Ant-MinerPB, vertical partitioning, classification rule discovery

Procedia PDF Downloads 276

Authors: Ahlam Inayatullah Badrul Munir, M. Husaini A. Rahman, Mohd Sukri Hassan

Abstract:

Real-time PCR is a molecular biology technique that is currently being widely used for halal services to differentiating between porcine and bovine DNA. The useful of technique become very important for student or workers (who works in the laboratory) to learn how the technique could be run smoothly without fail. Same concept with conventional PCR, real-time PCR also needed DNA template, primer, enzyme polymerase, dNTP, and buffer. The difference is in real-time PCR, have additional component namely fluorescent dye. The most common use of fluorescent dye in real-time PCR is SYBR green. The purpose of this study was to find out how sensitive, specific and efficient real-time PCR technique was combined with SYBR green method and specific primers of CYT b. The results showed that real-time PCR technique using SYBR Green, capable of detecting porcine and bovine DNA concentrations up to 0.0001 µl/ng. The level of efficiency for both types of DNA was 91% (90-110). Not only that in specific primer CYT b bovine primer could detect only bovine DNA, and porcine primer could detect only porcine primer. So, from the study could be concluded that real-time PCR technique that was combined with specific primer CYT b and SYBR green method, was sensitive, specific and efficient to detect porcine and bovine DNA.

Keywords: sensitivity, specificity, efficiency, real-time PCR, SYBR green, Cytochrome b, porcine DNA, bovine DNA

Procedia PDF Downloads 299