Search results for: RMS error
738 Deep Learning Based on Image Decomposition for Restoration of Intrinsic Representation
Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong
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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation
Procedia PDF Downloads 186737 Growth Curves Genetic Analysis of Native South Caspian Sea Poultry Using Bayesian Statistics
Authors: Jamal Fayazi, Farhad Anoosheh, Mohammad R. Ghorbani, Ali R. Paydar
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In this study, to determine the best non-linear regression model describing the growth curve of native poultry, 9657 chicks of generations 18, 19, and 20 raised in Mazandaran breeding center were used. Fowls and roosters of this center distributed in south of Caspian Sea region. To estimate the genetic variability of none linear regression parameter of growth traits, a Gibbs sampling of Bayesian analysis was used. The average body weight traits in the first day (BW1), eighth week (BW8) and twelfth week (BW12) were respectively estimated as 36.05, 763.03, and 1194.98 grams. Based on the coefficient of determination, mean squares of error and Akaike information criteria, Gompertz model was selected as the best growth descriptive function. In Gompertz model, the estimated values for the parameters of maturity weight (A), integration constant (B) and maturity rate (K) were estimated to be 1734.4, 3.986, and 0.282, respectively. The direct heritability of BW1, BW8 and BW12 were respectively reported to be as 0.378, 0.3709, 0.316, 0.389, 0.43, 0.09 and 0.07. With regard to estimated parameters, the results of this study indicated that there is a possibility to improve some property of growth curve using appropriate selection programs.Keywords: direct heritability, Gompertz, growth traits, maturity weight, native poultry
Procedia PDF Downloads 259736 Vegetation Index-Deduced Crop Coefficient of Wheat (Triticum aestivum) Using Remote Sensing: Case Study on Four Basins of Golestan Province, Iran
Authors: Hoda Zolfagharnejad, Behnam Kamkar, Omid Abdi
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Crop coefficient (Kc) is an important factor contributing to estimation of evapotranspiration, and is also used to determine the irrigation schedule. This study investigated and determined the monthly Kc of winter wheat (Triticum aestivum L.) using five vegetation indices (VIs): Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Soil Adjusted Vegetation Index (SAVI), Infrared Percentage Vegetation Index (IPVI), and Ratio Vegetation Index (RVI) of four basins in Golestan province, Iran. 14 Landsat-8 images according to crop growth stage were used to estimate monthly Kc of wheat. VIs were calculated based on infrared and near infrared bands of Landsat 8 images using Geographical Information System (GIS) software. The best VIs were chosen after establishing a regression relationship among these VIs with FAO Kc and Kc that was modified for the study area by the previous research based on R² and Root Mean Square Error (RMSE). The result showed that local modified SAVI with R²= 0.767 and RMSE= 0.174 was the best index to produce monthly wheat Kc maps.Keywords: crop coefficient, remote sensing, vegetation indices, wheat
Procedia PDF Downloads 406735 Single Imputation for Audiograms
Authors: Sarah Beaver, Renee Bryce
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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.Keywords: machine learning, audiograms, data imputations, single imputations
Procedia PDF Downloads 75734 Flow Prediction of Boundary Shear Stress with Enlarging Flood Plains
Authors: Spandan Sahu, Amiya Kumar Pati, Kishanjit Kumar Khatua
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River is our main source of water which is a form of open channel flow and the flow in open channel provides with many complex phenomenon of sciences that needs to be tackled such as the critical flow conditions, boundary shear stress and depth averaged velocity. During floods, part of a river is carried by the simple main channel and rest is carried by flood plains. For such compound asymmetric channels, the flow structure becomes complicated due to momentum exchange between main channel and adjoining flood plains. Distribution of boundary shear in subsections provides us with the concept of momentum transfer between the interface of main channel and the flood plains. Experimentally, to get better data with accurate results are very complex because of the complexity of the problem. Hence, CES software has been used to tackle the complex processes to determine the shear stresses at different sections of an open channel having asymmetric flood plains on both sides of the main channel and the results is compared with the symmetric flood plains for various geometrical shapes and flow conditions. Error analysis is also performed to know the degree of accuracy of the model implemented.Keywords: depth average velocity, non prismatic compound channel, relative flow depth, velocity distribution
Procedia PDF Downloads 142733 Cone Contrast Sensitivity of Normal Trichromats and Those with Red-Green Dichromats
Authors: Tatsuya Iizuka, Takushi Kawamorita, Tomoya Handa, Hitoshi Ishikawa
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We report normative cone contrast sensitivity values and sensitivity and specificity values for a computer-based color vision test, the cone contrast test-HD (CCT-HD). The participants included 50 phakic eyes with normal color vision (NCV) and 20 dichromatic eyes (ten with protanopia and ten with deuteranopia). The CCT-HD was used to measure L, M, and S-CCT-HD scores (color vision deficiency, L-, M-cone logCS≦1.65, S-cone logCS≦0.425) to investigate the sensitivity and specificity of CCT-HD based on anomalous-type diagnosis with animalscope. The mean ± standard error L-, M-, S-cone logCS for protanopia were 0.90±0.04, 1.65±0.03, and 0.63±0.02, respectively; for deuteranopia 1.74±0.03, 1.31±0.03, and 0.61±0.06, respectively; and for age-matched NCV were 1.89±0.04, 1.84±0.04, and 0.60±0.03, respectively, with significant differences for each group except for S-CCT-HD (Bonferroni corrected α = 0.0167, p < 0.0167). The sensitivity and specificity of CCT-HD were 100% for protan and deutan in diagnosing abnormal types from 20 to 64 years of age, but the specificity decreased to 65% for protan and 55% for deutan in older persons > 65. CCT-HD is comparable to the diagnostic performance of the anomalous type in the anomaloscope for the 20-64-year-old age group. However, the results should be interpreted cautiously in those ≥ 65 years. They are more susceptible to acquired color vision deficiencies due to the yellowing of the crystalline lens and other factors.Keywords: cone contrast test HD, color vision test, congenital color vision deficiency, red-green dichromacy, cone contrast sensitivity
Procedia PDF Downloads 93732 Methaheuristic Bat Algorithm in Training of Feed-Forward Neural Network for Stock Price Prediction
Authors: Marjan Golmaryami, Marzieh Behzadi
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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange
Procedia PDF Downloads 539731 Understanding Health-Related Properties of Grapes by Pharmacokinetic Modelling of Intestinal Absorption
Authors: Sophie N. Selby-Pham, Yudie Wang, Louise Bennett
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Consumption of grapes promotes health and reduces the risk of chronic diseases due to the action of grape phytochemicals in regulation of Oxidative Stress and Inflammation (OSI). The bioefficacy of phytochemicals depends on their absorption in the human body. The time required for phytochemicals to achieve maximal plasma concentration (Tₘₐₓ) after oral intake reflects the time window of maximal bioefficacy of phytochemicals, with Tₘₐₓ dependent on physicochemical properties of phytochemicals. This research collated physicochemical properties of grape phytochemicals from white and red grapes to predict their Tₘₐₓ using pharmacokinetic modelling. The predicted values of Tₘₐₓ were then compared to the measured Tₘₐₓ collected from clinical studies to determine the accuracy of prediction. In both liquid and solid intake forms, white grapes exhibit a shorter Tₘₐₓ range (0.5-2.5 h) versus red grapes (1.5-5h). The prediction accuracy of Tₘₐₓ for grape phytochemicals was 33.3% total error of prediction compared to the mean, indicating high prediction accuracy. Pharmacokinetic modelling allows prediction of Tₘₐₓ without costly clinical trials, informing dosing frequency for sustained presence of phytochemicals in the body to optimize the health benefits of phytochemicals.Keywords: absorption kinetics, phytochemical, phytochemical absorption prediction model, Vitis vinifera
Procedia PDF Downloads 141730 Signal Strength Based Multipath Routing for Mobile Ad Hoc Networks
Authors: Chothmal
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In this paper, we present a route discovery process which uses the signal strength on a link as a parameter of its inclusion in the route discovery method. The proposed signal-to-interference and noise ratio (SINR) based multipath reactive routing protocol is named as SINR-MP protocol. The proposed SINR-MP routing protocols has two following two features: a) SINR-MP protocol selects routes based on the SINR of the links during the route discovery process therefore it select the routes which has long lifetime and low frame error rate for data transmission, and b) SINR-MP protocols route discovery process is multipath which discovers more than one SINR based route between a given source destination pair. The multiple routes selected by our SINR-MP protocol are node-disjoint in nature which increases their robustness against link failures, as failure of one route will not affect the other route. The secondary route is very useful in situations where the primary route is broken because we can now use the secondary route without causing a new route discovery process. Due to this, the network overhead caused by a route discovery process is avoided. This increases the network performance greatly. The proposed SINR-MP routing protocol is implemented in the trail version of network simulator called Qualnet.Keywords: ad hoc networks, quality of service, video streaming, H.264/SVC, multiple routes, video traces
Procedia PDF Downloads 240729 Herbal Medicines Used for the Cure of Jaundice among the Some Tribal Populations of Madhya Pradesh, India
Authors: Awdhesh Narayan Sharma
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The use of herbal medicines for the cure of various ailments among the tribal population is as old as human origin itself. Most of the tribal populations of Madhya Pradesh inhabit in remote and inaccessible ecological setup. From long back, tribals and forests are interrelated to each other. They use an enormous range of wild plants for their basic needs and medicines. The tribal developed a unique understanding with wild plants, herbs, etc., and earned specialized knowledge of disease pattern and curative therapy-through hard experiences, common sense, trial, and error methods. They have passed this knowledge through traditions, taboos, totems, folklore by words of mouth from generation to generation. Here, an attempt has been made to study the possible aspects of herbal medicine for the cure of Jaundice among the tribal populations of Madhya Pradesh, India, through primary data as well as available secondary data. The data have been collected from the 305 Bharias of Patalkot, Madhya Pradesh, India, and included available secondary source of data by various investigators. It may be concluded that a sizable herbal medicinal plants' wealth exists in Madhya Pradesh, India, which still awaits for scientific exploration. The existing herbal medicines used for the cure of jaundice need an extensive investigation from the pharmaceutical point of view.Keywords: Bharias, herbal medicine, tribal, Madhya Pradesh
Procedia PDF Downloads 168728 Analytical Modelling of Surface Roughness during Compacted Graphite Iron Milling Using Ceramic Inserts
Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz
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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis
Procedia PDF Downloads 443727 Performance Analysis of a Combined Ordered Successive and Interference Cancellation Using Zero-Forcing Detection over Rayleigh Fading Channels in Mimo Systems
Authors: Jamal R. Elbergali
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Multiple Input Multiple Output (MIMO) systems are wireless systems with multiple antenna elements at both ends of the link. Wireless communication systems demand high data rate and spectral efficiency with increased reliability. MIMO systems have been popular techniques to achieve these goals because increased data rate is possible through spatial multiplexing scheme and diversity. Spatial Multiplexing (SM) is used to achieve higher possible throughput than diversity. In this paper, we propose a Zero-Forcing (ZF) detection using a combination of Ordered Successive Interference Cancellation (OSIC) and Zero Forcing using Interference Cancellation (ZF-IC). The proposed method used an OSIC based on Signal to Noise Ratio (SNR) ordering to get the estimation of last symbol (x ̃_(N_T )), then the estimated last symbol is considered to be an input to the ZF-IC. We analyze the Bit Error Rate (BER) performance of the proposed MIMO system over Rayleigh Fading Channel, using Binary Phase Shift Keying (BPSK) modulation scheme. The results show better performance than the previous methods.Keywords: SNR, BER, BPSK, MIMO, modulation, zero forcing (ZF), OSIC, ZF-IC, spatial multiplexing (SM)
Procedia PDF Downloads 419726 Spectrum Allocation Using Cognitive Radio in Wireless Mesh Networks
Authors: Ayoub Alsarhan, Ahmed Otoom, Yousef Kilani, Abdel-Rahman al-GHuwairi
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Wireless mesh networks (WMNs) have emerged recently to improve internet access and other networking services. WMNs provide network access to the clients and other networking functions such as routing, and packet forwarding. Spectrum scarcity is the main challenge that limits the performance of WMNs. Cognitive radio is proposed to solve spectrum scarcity problem. In this paper, we consider a cognitive wireless mesh network where unlicensed users (secondary users, SUs) can access free spectrum that is allocated to spectrum owners (primary users, PUs). Although considerable research has been conducted on spectrum allocation, spectrum assignment is still considered an important challenging problem. This problem can be solved using cognitive radio technology that allows SUs to intelligently locate free bands and access them without interfering with PUs. Our scheme considers several heuristics for spectrum allocation. These heuristics include: channel error rate, PUs activities, channel capacity and channel switching time. Performance evaluation of the proposed scheme shows that the scheme is able to allocate the unused spectrum for SUs efficiently.Keywords: cognitive radio, dynamic spectrum access, spectrum management, spectrum sharing, wireless mesh networks
Procedia PDF Downloads 521725 Improve Closed Loop Performance and Control Signal Using Evolutionary Algorithms Based PID Controller
Authors: Mehdi Shahbazian, Alireza Aarabi, Mohsen Hadiyan
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Proportional-Integral-Derivative (PID) controllers are the most widely used controllers in industry because of its simplicity and robustness. Different values of PID parameters make different step response, so an increasing amount of literature is devoted to proper tuning of PID controllers. The problem merits further investigation as traditional tuning methods make large control signal that can damages the system but using evolutionary algorithms based tuning methods improve the control signal and closed loop performance. In this paper three tuning methods for PID controllers have been studied namely Ziegler and Nichols, which is traditional tuning method and evolutionary algorithms based tuning methods, that are, Genetic algorithm and particle swarm optimization. To examine the validity of PSO and GA tuning methods a comparative analysis of DC motor plant is studied. Simulation results reveal that evolutionary algorithms based tuning method have improved control signal amplitude and quality factors of the closed loop system such as rise time, integral absolute error (IAE) and maximum overshoot.Keywords: evolutionary algorithm, genetic algorithm, particle swarm optimization, PID controller
Procedia PDF Downloads 474724 Sporting Events among the Disabled between Excellence and Ideal in Motor Performance: Analytical Descriptive Study in Some Paralympic Sports
Authors: Guebli Abdelkader, Reguieg Madani, Belkadi Adel, Sbaa Bouabdellah
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The identification of mechanical variables in the motor performance trajectory has a prominent role in improving skill performance, error-exceeding, it contributes seriously to solving some problems of learning and training. The study aims to highlight the indicators of motor performance for Paralympic athletes during the practicing sports between modelling and between excellence in motor performance, this by taking into account the distinction of athlete practicing with special behavioral skills for the Paralympic athletes. In the study, we relied on the analysis of some previous research of biomechanical performance indicators during some of the events sports (shooting activities in the Paralympic athletics, shooting skill in the wheelchair basketball). The results of the study highlight the distinction of disabled practitioners of sporting events identified in motor performance during practice, by overcoming some physics indicators in human movement, as a lower center of body weight, increase in offset distance, such resistance which requires them to redouble their efforts. However, the results of the study highlighted the strength of the correlation between biomechanical variables of motor performance and the digital level achievement similar to the other practitioners normal.Keywords: sports, the disabled, motor performance, Paralympic
Procedia PDF Downloads 270723 System of Quality Automation for Documents (SQAD)
Authors: R. Babi Saraswathi, K. Divya, A. Habeebur Rahman, D. B. Hari Prakash, S. Jayanth, T. Kumar, N. Vijayarangan
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Document automation is the design of systems and workflows, assembling repetitive documents to meet the specific business needs. In any organization or institution, documenting employee’s information is very important for both employees as well as management. It shows an individual’s progress to the management. Many documents of the employee are in the form of papers, so it is very difficult to arrange and for future reference we need to spend more time in getting the exact document. Also, it is very tedious to generate reports according to our needs. The process gets even more difficult on getting approvals and hence lacks its security aspects. This project overcomes the above-stated issues. By storing the details in the database and maintaining the e-documents, the automation system reduces the manual work to a large extent. Then the approval process of some important documents can be done in a much-secured manner by using Digital Signature and encryption techniques. Details are maintained in the database and e-documents are stored in specific folders and generation of various kinds of reports is possible. Moreover, an efficient search method is implemented is used in the database. Automation supporting document maintenance in many aspects is useful for minimize data entry, reduce the time spent on proof-reading, avoids duplication, and reduce the risks associated with the manual error, etc.Keywords: e-documents, automation, digital signature, encryption
Procedia PDF Downloads 384722 Random Subspace Neural Classifier for Meteor Recognition in the Night Sky
Authors: Carlos Vera, Tetyana Baydyk, Ernst Kussul, Graciela Velasco, Miguel Aparicio
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This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed.Keywords: contour orientation histogram, meteors, night sky, RSC neural classifier, stars
Procedia PDF Downloads 132721 A Model for Diagnosis and Prediction of Coronavirus Using Neural Network
Authors: Sajjad Baghernezhad
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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm
Procedia PDF Downloads 63720 Investigation of Dry Ice Mixed Novel Hybrid Lubri-Coolant in Sustainable Machining of Ti-6AL-4V Alloy: A Comparison of Experimental and Modelling
Authors: Muhammad Jamil, Ning He, Aqib Mashood Khan, Munish Kumar Gupta
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Ti-6Al-4V has numerous applications in the medical, automobile, and aerospace industries due to corrosion resistivity, structural stability, and chemical inertness to most fluids at room temperature. These peculiar characteristics are beneficial for their application and present formidable challenges during machining. Machining of Ti-6Al-4V produces an elevated cutting temperature above 1000oC at dry conditions. This accelerates tool wear and reduces product quality. Therefore, there is always a need to employ sustainable/effective coolant/lubricant when machining such alloy. In this study, Finite Element Modeling (FEM) and experimental analysis when cutting Ti-6Al-4V under a distinctly developed dry ice mixed hybrid lubri-coolant are presented. This study aims to model the milling process of Ti-6Al-4V under a proposed novel hybrid lubri-coolant using different cutting speeds and feed per tooth DEFORM® software package was used to conduct the FEM and the numerical model was experimentally validated. A comparison of experimental and simulation results showed a maximum error of no more than 6% for all experimental conditions. In a nutshell, it can be said that the proposed model is effective in predicting the machining temperature precisely.Keywords: friction coefficient, heat transfer, finite element modeling (FEM), milling Ti-6Al-4V
Procedia PDF Downloads 47719 Transient Enhanced LDO Voltage Regulator with Improved Feed Forward Path Compensation
Authors: A. Suresh, Sreehari Rao Patri, K. S. R. Krishnaprasad
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An ultra low power capacitor less low-dropout voltage regulator with improved transient response using gain enhanced feed forward path compensation is presented in this paper. It is based on a cascade of a voltage amplifier and a transconductor stage in the feed forward path with regular error amplifier to form a composite gain-enhanced feed forward stage. It broadens the gain bandwidth and thus improves the transient response without substantial increase in power consumption. The proposed LDO, designed for a maximum output current of 100 mA in UMC 180 nm, requires a quiescent current of 69 µA. An undershoot of 153.79mV for a load current changes from 0mA to 100mA and an overshoot of 196.24mV for current change of 100mA to 0mA. The settling time is approximately 1.1 µs for the output voltage undershoot case. The load regulation is of 2.77 µV/mA at load current of 100mA. Reference voltage is generated by using an accurate band gap reference circuit of 0.8V.The costly features of SOC such as total chip area and power consumption is drastically reduced by the use of only a total compensation capacitance of 6pF while consuming power consumption of 0.096 mW.Keywords: capacitor-less LDO, frequency compensation, transient response, latch, self-biased differential amplifier
Procedia PDF Downloads 445718 Efficiency of Google Translate and Bing Translator in Translating Persian-to-English Texts
Authors: Samad Sajjadi
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Machine translation is a new subject increasingly being used by academic writers, especially students and researchers whose native language is not English. There are numerous studies conducted on machine translation, but few investigations have assessed the accuracy of machine translation from Persian to English at lexical, semantic, and syntactic levels. Using Groves and Mundt’s (2015) Model of error taxonomy, the current study evaluated Persian-to-English translations produced by two famous online translators, Google Translate and Bing Translator. A total of 240 texts were randomly selected from different academic fields (law, literature, medicine, and mass media), and 60 texts were considered for each domain. All texts were rendered by the two translation systems and then by four human translators. All statistical analyses were applied using SPSS. The results indicated that Google translations were more accurate than the translations produced by the Bing Translator, especially in the domains of medicine (lexis: 186 vs. 225; semantic: 44 vs. 48; syntactic: 148 vs. 264 errors) and mass media (lexis: 118 vs. 149; semantic: 25 vs. 32; syntactic: 110 vs. 220 errors), respectively. Nonetheless, both machines are reasonably accurate in Persian-to-English translation of lexicons and syntactic structures, particularly from mass media and medical texts.Keywords: machine translations, accuracy, human translation, efficiency
Procedia PDF Downloads 70717 Enhancing Academic Writing Through Artificial Intelligence: Opportunities and Challenges
Authors: Abubakar Abdulkareem, Nasir Haruna Soba
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Artificial intelligence (AI) is developing at a rapid pace, revolutionizing several industries, including education. This talk looks at how useful AI can be for academic writing, with an emphasis on how it can help researchers be more accurate, productive, and creative. The academic world now relies heavily on AI technologies like grammar checkers, plagiarism detectors, and content generators to help with the writing, editing, and formatting of scholarly papers. This study explores the particular uses of AI in academic writing and assesses how useful and helpful these applications may be for both students and scholars. By means of an extensive examination of extant literature and a sequence of empirical case studies, we scrutinize the merits and demerits of artificial intelligence tools utilized in academic writing. Important discoveries indicate that although AI greatly increases productivity and lowers human error, there are still issues that need to be resolved, including reliance, ethical concerns, and the potential loss of critical thinking abilities. The talk ends with suggestions for incorporating AI tools into academic settings so that they enhance rather than take the place of the intellectual rigor that characterizes scholarly work. This study adds to the continuing conversation about artificial intelligence (AI) in higher education by supporting a methodical strategy that uses technology to enhance human abilities in academic writing.Keywords: artificial intelligence, academic writing, ai tools, productivity, ethics, higher education
Procedia PDF Downloads 14716 Comparison of Sensitivity and Specificity of Pap Smear and Polymerase Chain Reaction Methods for Detection of Human Papillomavirus: A Review of Literature
Authors: M. Malekian, M. E. Heydari, M. Irani Estyar
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Human papillomavirus (HPV) is one of the most common sexually transmitted infection, which may lead to cervical cancer as the main cause of it. With early diagnosis and treatment in health care services, cervical cancer and its complications are considered to be preventable. This study was aimed to compare the efficiency, sensitivity, and specificity of Pap smear and polymerase chain reaction (PCR) in detecting HPV. A literature search was performed in Google Scholar, PubMed and SID databases using the keywords 'human papillomavirus', 'pap smear' and 'polymerase change reaction' to identify studies comparing Pap smear and PCR methods for the detection. No restrictions were considered.10 studies were included in this review. All samples that were positive by pop smear were also positive by PCR. However, there were positive samples detected by PCR which was negative by pop smear and in all studies, many positive samples were missed by pop smear technique. Although The Pap smear had high specificity, PCR based HPV detection was more sensitive method and had the highest sensitivity. In order to promote the quality of detection and high achievement of the maximum results, PCR diagnostic methods in addition to the Pap smear are needed and Pap smear method should be combined with PCR techniques according to the high error rate of Pap smear in detection.Keywords: human papillomavirus, cervical cancer, pap smear, polymerase chain reaction
Procedia PDF Downloads 125715 Triangular Geometric Feature for Offline Signature Verification
Authors: Zuraidasahana Zulkarnain, Mohd Shafry Mohd Rahim, Nor Anita Fairos Ismail, Mohd Azhar M. Arsad
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Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.Keywords: biometrics, euclidean classifier, features extraction, offline signature verification, voting-based classifier
Procedia PDF Downloads 373714 Bidirectional Long Short-Term Memory-Based Signal Detection for Orthogonal Frequency Division Multiplexing With All Index Modulation
Authors: Mahmut Yildirim
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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection
Procedia PDF Downloads 62713 Data-Driven Approach to Predict Inpatient's Estimated Discharge Date
Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian
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To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven
Procedia PDF Downloads 165712 An Automatic Generating Unified Modelling Language Use Case Diagram and Test Cases Based on Classification Tree Method
Authors: Wassana Naiyapo, Atichat Sangtong
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The processes in software development by Object Oriented methodology have many stages those take time and high cost. The inconceivable error in system analysis process will affect to the design and the implementation process. The unexpected output causes the reason why we need to revise the previous process. The more rollback of each process takes more expense and delayed time. Therefore, the good test process from the early phase, the implemented software is efficient, reliable and also meet the user’s requirement. Unified Modelling Language (UML) is the tool which uses symbols to describe the work process in Object Oriented Analysis (OOA). This paper presents the approach for automatically generated UML use case diagram and test cases. UML use case diagram is generated from the event table and test cases are generated from use case specifications and Graphic User Interfaces (GUI). Test cases are derived from the Classification Tree Method (CTM) that classify data to a node present in the hierarchy structure. Moreover, this paper refers to the program that generates use case diagram and test cases. As the result, it can reduce work time and increase efficiency work.Keywords: classification tree method, test case, UML use case diagram, use case specification
Procedia PDF Downloads 155711 Ambiguity Resolution for Ground-based Pulse Doppler Radars Using Multiple Medium Pulse Repetition Frequency
Authors: Khue Nguyen Dinh, Loi Nguyen Van, Thanh Nguyen Nhu
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In this paper, we propose an adaptive method to resolve ambiguities and a ghost target removal process to extract targets detected by a ground-based pulse-Doppler radar using medium pulse repetition frequency (PRF) waveforms. The ambiguity resolution method is an adaptive implementation of the coincidence algorithm, which is implemented on a two-dimensional (2D) range-velocity matrix to resolve range and velocity ambiguities simultaneously, with a proposed clustering filter to enhance the anti-error ability of the system. Here we consider the scenario of multiple target environments. The ghost target removal process, which is based on the power after Doppler processing, is proposed to mitigate ghosting detections to enhance the performance of ground-based radars using a short PRF schedule in multiple target environments. Simulation results on a ground-based pulsed Doppler radar model will be presented to show the effectiveness of the proposed approach.Keywords: ambiguity resolution, coincidence algorithm, medium PRF, ghosting removal
Procedia PDF Downloads 143710 Encryption and Decryption of Nucleic Acid Using Deoxyribonucleic Acid Algorithm
Authors: Iftikhar A. Tayubi, Aabdulrahman Alsubhi, Abdullah Althrwi
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The deoxyribonucleic acid text provides a single source of high-quality Cryptography about Deoxyribonucleic acid sequence for structural biologists. We will provide an intuitive, well-organized and user-friendly web interface that allows users to encrypt and decrypt Deoxy Ribonucleic Acid sequence text. It includes complex, securing by using Algorithm to encrypt and decrypt Deoxy Ribonucleic Acid sequence. The utility of this Deoxy Ribonucleic Acid Sequence Text is that, it can provide a user-friendly interface for users to Encrypt and Decrypt store the information about Deoxy Ribonucleic Acid sequence. These interfaces created in this project will satisfy the demands of the scientific community by providing fully encrypt of Deoxy Ribonucleic Acid sequence during this website. We have adopted a methodology by using C# and Active Server Page.NET for programming which is smart and secure. Deoxy Ribonucleic Acid sequence text is a wonderful piece of equipment for encrypting large quantities of data, efficiently. The users can thus navigate from one encoding and store orange text, depending on the field for user’s interest. Algorithm classification allows a user to Protect the deoxy ribonucleic acid sequence from change, whether an alteration or error occurred during the Deoxy Ribonucleic Acid sequence data transfer. It will check the integrity of the Deoxy Ribonucleic Acid sequence data during the access.Keywords: algorithm, ASP.NET, DNA, encrypt, decrypt
Procedia PDF Downloads 224709 Vertical Accuracy Evaluation of Indian National DEM (CartoDEM v3) Using Dual Frequency GNSS Derived Ground Control Points for Lower Tapi Basin, Western India
Authors: Jaypalsinh B. Parmar, Pintu Nakrani, Ashish Chaurasia
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Digital Elevation Model (DEM) is considered as an important data in GIS-based terrain analysis for many applications and assessment of processes such as environmental and climate change studies, hydrologic modelling, etc. Vertical accuracy of DEM having geographically dynamic nature depends on different parameters which affect the model simulation outcomes. Vertical accuracy assessment in Indian landscape especially in low-lying coastal urban terrain such as lower Tapi Basin is very limited. In the present study, attempt has been made to evaluate the vertical accuracy of 30m resolution open source Indian National Cartosat-1 DEM v3 for Lower Tapi Basin (LTB) from western India. The extensive field investigation is carried out using stratified random fast static DGPS survey in the entire study region, and 117 high accuracy ground control points (GCPs) have been obtained. The above open source DEM was compared with obtained GCPs, and different statistical attributes were envisaged, and vertical error histograms were also evaluated.Keywords: CartoDEM, Digital Elevation Model, GPS, lower Tapi basin
Procedia PDF Downloads 352