Search results for: discriminate accuracy

Authors: K. Abuzayan, H. Alabed, J. Ezarrugh, M. Agila

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Static and dynamic balance are essential in daily and sports life. Many factors have been identified as influencing static balance control. Therefore, the aim of this study was to apply the (XCoM) method and other relevant variables (CoP, CoM, Fh, KE, P, Q, and, AI) to investigate sport related activities such as hopping and jumping. Many studies have represented the CoP data without mentioning its accuracy, so several experiments were done to establish the agreement between the CoP and the projected CoM in a static condition. Five male healthy (Mean ± SD:- age 24.6 years ±4.5, height 177 cm ± 6.3, body mass 72.8 kg ± 6.6) participated in this study. Results found that The implementation of the XCoM method was found to be practical for evaluating both static and dynamic balance. The general findings were that the CoP, the CoM, the XCoM, Fh, and Q were more informative than the other variables (e.g. KE, P, and AI) during static and dynamic balance. The XCoM method was found to be applicable to dynamic balance as well as static balance.

Keywords: centre of mass, static balance, dynamic balance, extrapolated centre of mass

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Authors: Ebrahim Alrashed

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Wireless sensor networks are vulnerable to attacks from adversaries attempting to disrupt their operations. Sink-hole attacks are a type of attack where an adversary node drops data forwarded through it and hence affecting the reliability and accuracy of the network. Since sensor nodes have limited battery power, it is essential that any solution to the sinkhole attack problem be very energy-aware. In this paper, we present a reliable and energy efficient scheme to forward data from source nodes to the base station while under sink-hole attack. The scheme also detects sink-hole attack nodes and avoid paths that includes them.

Keywords: energy-aware routing, reliability, sink-hole attack, WSN

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Authors: Angela U. Makolo

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Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

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Authors: Qurat Ul Ain Saleem, Kanwal Ameen

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The library and information science discipline is universally known as a feminist profession. It is considered a suitable field for females in Pakistan like a few other fields such as teaching and healthcare. It is also reflected through the uneven enrollment at graduate levels in library schools across the country as there are more females as compared to males. However, that uneven ratio does not really translate in the profession after passing out. There are more males in the professional as compared to females, as well as males can be seen on managerial and administrative posts majorly. A few females who joined the perception remain underrated and are hardly seen at managerial or administrative positions in the academic libraries. Therefore, this study was designed to highlight the perceptions of those females who have joined the profession to identify the issues related to equality faced by them as a professional. A qualitative research design based on a semi-structured interview was selected as an appropriate method to achieve the objectives of this study. Female librarians working in the higher education commission’s recognized public and private sector universities of Punjab, Pakistan, were selected as the population for this study. Female librarians shared that inequalities and discrimination based on face value, experience, communication, and relationship with the manager are common at their workplaces. They added that managers prefer male professionals to deal with delegation or presentations though we both can do that. Female professionals from the private sector believed that library managers make final hiring and selection decisions based on job duties and gender. However, the one with strong references will be preferred for the job. Also, private-sector employees suffered more prejudice due to the non-availability of proper patterns of promotions and increments. The government personnel said there is always a proper board/procedure for hiring and promotions; therefore, it is difficult for them to identify any inequality. Participants were dissatisfied with their managers for not allowing them to attend training and conferences. The majority of participants from the private sector said they wouldn't speak up to prejudice because they are afraid of losing their jobs and their voice is lost in a male-dominated society where males hold numerous authoritative positions and females are considered less competent. Nonetheless, the discrimination and inequalities affected the work motivation and enthusiasm of employees. Therefore, organizations should not discriminate against the staff in terms of facilities and benefits. The sample may not represent the true picture of gender equality in university libraries of Pakistan due to less number of participants and limited geographical boundaries. It is also assumed that some females may refrain from disclosing factual information or some may exaggerate the facts as a large number of participants requested to become part of the study. Equal opportunities should be offered to female library professionals to uplift and involve them to mitigate the perception of gender dominance. The organizations or immediate authorities should allow their staff to participate in training opportunities to learn modern practices to better serve the community.

Keywords: equality-workplace, libraries as workplace, female professionals, librarians-Pakistan

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Authors: Andrew Lock, Alastair Allen

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Fragile watermarking has been proposed as a means of adding additional security or functionality to biometric systems, particularly for authentication and tamper detection. In this paper we describe an experimental study on the effect of watermarking iris images with a particular class of fragile algorithm, reversible algorithms, and the ability to correctly perform iris recognition. We investigate two scenarios, matching watermarked images to unmodified images, and matching watermarked images to watermarked images. We show that different watermarking schemes give very different results for a given capacity, highlighting the importance of investigation. At high embedding rates most algorithms cause significant reduction in recognition performance. However, in many cases, for low embedding rates, recognition accuracy is improved by the watermarking process.

Keywords: biometrics, iris recognition, reversible watermarking, vision engineering

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Authors: Supunmali Ahangama, Danny Chiang Choon Poo

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Health analytics (HA) is used in healthcare systems for effective decision-making, management, and planning of healthcare and related activities. However, user resistance, the unique position of medical data content, and structure (including heterogeneous and unstructured data) and impromptu HA projects have held up the progress in HA applications. Notably, the accuracy of outcomes depends on the skills and the domain knowledge of the data analyst working on the healthcare data. The success of HA depends on having a sound process model, effective project management and availability of supporting tools. Thus, to overcome these challenges through an effective process model, we propose an HA process model with features from the rational unified process (RUP) model and agile methodology.

Keywords: agile methodology, health analytics, unified process model, UML

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Ali Farzan

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Classification of data objects or patterns is a major part in most of Decision making systems. One of the popular and commonly used classification methods is Decision Tree (DT). It is a hierarchical decision making system by which a binary tree is constructed and starting from root, at each node some of the classes is rejected until reaching the leaf nods. Each leaf node is a representative of one specific class. Finding the splitting criteria in each node for constructing or training the tree is a major problem. Particle Swarm Optimization (PSO) has been adopted as a metaheuristic searching method for finding the best splitting criteria. Result of evaluating the proposed method over benchmark datasets indicates the higher accuracy of the new PSO based decision tree.

Keywords: decision tree, particle swarm optimization, splitting criteria, metaheuristic

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Authors: Sheikh Samsuzzhan Alam

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The present study investigates the selection of variogram model in analyzing spatial variations of environmental variables with the trend. Sometimes, the autofitted theoretical variogram does not really capture the true nature of the empirical semivariogram. So proper exploration and analysis are needed to select the best variogram model. For this study, an open source data collected from California Soil Resource Lab1 is used to explain the problems when fitting a theoretical variogram. Five most commonly used variogram models: Linear, Gaussian, Exponential, Matern, and Spherical were fitted to the experimental semivariogram. Ordinary kriging methods were considered to evaluate the accuracy of the selected variograms through cross-validation. This study is beneficial for selecting an appropriate theoretical variogram model for environmental variables.

Keywords: anisotropy, cross-validation, environmental variables, kriging, variogram models

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Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner

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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.

Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.

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Authors: Sen Yung Lee, Chih Cheng Huang, Te Wen Tu

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A solution methodology without using integral transformation is proposed to develop analytical solutions for transient heat conduction in nonuniform hollow cylinders with time-dependent boundary condition at the outer surface. It is shown that if the thermal conductivity and the specific heat of the medium are in arbitrary polynomial function forms, the closed solutions of the system can be developed. The influence of physical properties on the temperature distribution of the system is studied. A numerical example is given to illustrate the efficiency and the accuracy of the solution methodology.

Keywords: analytical solution, nonuniform hollow cylinder, time-dependent boundary condition, transient heat conduction

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of World-View 2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows effectively and automatically.

Keywords: spectral index, shadow detection, remote sensing images, World-View 2

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Authors: Guler S., Guzel E., Gulen M.

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Fiber Reinforced Polymer (FRP) materials are commonly used in construction sector to enhance the strength and ductility capacities of structural elements. The equations on confined compressive strength of FRP wrapped concrete cylinders is described in the 7th chapter of the Turkish Earthquake Code (TEC-07) that enter into force in 2007. This study aims to evaluate the applicability of TEC-07 on confined compressive strengths of circular FRP wrapped concrete cylinders. To this end, a large number of data on circular FRP wrapped concrete cylinders are collected from the literature. It is clearly seen that the predictions of TEC-07 on circular FRP wrapped the FRP wrapped columns is not same accuracy for different ranges of concrete strengths.

Keywords: Fiber Reinforced Polymer (FRP), concrete cylinders, Turkish Earthquake Code, earthquake

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Authors: Jeries J. Abou-Hanna, Bradley Storm

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This paper looks to reduce the complexity of determining stress intensity factors while maintaining high levels of accuracy by the use of a linearized layering approach. Many techniques for stress intensity factor determination exist, but they can be limited by conservative results, requiring too many user parameters, or by being too computationally intensive. Multiple notch geometries with various crack lengths were investigated in this study to better understand the effectiveness of the proposed method. By linearizing the average stresses in radial layers around the crack tip, stress intensity factors were found to have error ranging from -10.03% to 8.94% when compared to analytically exact solutions. This approach proved to be a robust and efficient method of accurately determining stress intensity factors.

Keywords: fracture mechanics, finite element method, stress intensity factor, stress linearization

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Hamed Zolfaghari, Mojtaba Kord

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After feasibility study the design phase is started and the rest of other phases are highly dependent on this phase. forecasting the duration of design phase could do a miracle and would save a lot of time. This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick duration estimation of project design phase, hence improving operational efficiency and competitiveness of a design construction company. 3 data sets of three years composed of daily time spent for different design projects are used to train and validate the ML models to perform multiple projects. Our study concluded that Artificial Neural Network (ANN) performed an accuracy of 0.94.

Keywords: time estimation, machine learning, Artificial neural network, project design phase

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Authors: Tian Xia, Yuan Yan Tang

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In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two changeling image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea-sky line of the infrared image. So it is fit for IR small infrared target detection.

Keywords: small target detection, local contrast, human vision system, Laplacian of Gaussian

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Authors: Eva L. Sanjuán, Jacinto Martín, M. Isabel Parra, Mario M. Pizarro

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In Extreme Value Theory, inference estimation for the parameters of the distribution is made employing a small part of the observation values. When block maxima values are taken, many data are discarded. We developed a new Bayesian inference model to seize all the information provided by the data, introducing informative priors and using the relations between baseline and limit parameters. Firstly, we studied the accuracy of the new model for three baseline distributions that lead to a Gumbel extreme distribution: Exponential, Normal and Gumbel. Secondly, we considered mixtures of Normal variables, to simulate practical situations when data do not adjust to pure distributions, because of perturbations (noise).

Keywords: bayesian inference, extreme value theory, Gumbel distribution, highly informative prior

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Authors: S. Houcine Habib, B. Elkhalil Hachi, Mohamed Guesmi, Mohamed Haboussi

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In this paper, dynamic fracture behaviors of cracked orthotropic structure are modeled using extended finite element method (X-FEM). In this approach, the finite element method model is first created and then enriched by special orthotropic crack tip enrichments and Heaviside functions in the framework of partition of unity. The mixed mode stress intensity factor (SIF) is computed using the interaction integral technique based on J-integral in order to predict cracking behavior of the structure. The developments of these procedures are programmed and introduced in a self-software platform code. To assess the accuracy of the developed code, results obtained by the proposed method are compared with those of literature.

Keywords: X-FEM, composites, stress intensity factor, crack, dynamic orthotropic behavior

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Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

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In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

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Authors: Marina Gold, Yves Jackson, David Parrat

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The high predominance of Latin American migrants in Geneva from countries where Chagas disease is endemic (Bolivia, Brazil, Argentina, Colombia) is increasing the incidence of chronic Chagas-related problems, especially cardiovascular complications. The precarious migratory status of what are mostly undocumented migrants complicates access to health and affects patients’ and doctors’ health perceptions regarding screening, treatment and monitoring of Chagas-related health concerns. This project results from a 3 year collaboration between the Geneva University Hospital and the NGO Mundo Sano to understand the following questions: 1) how do Latin American migrants perceive their health? 2) What do they understand from Chagas disease? 3) Are patients’ and doctors’ health perceptions similar or do they have competing agendas? This paper aims to present the results of a long-term study that interrogates health perceptions among Latin American migrants in Geneva. The first phase consisted in completing surveys at three community screening events (2016, 2017. 2018), and the results of these surveys reveal the subordination of the importance of health to that of having met economic family obligation. That is, health is important only when it becomes an impediment to economic gain. The contradictory result emerged that people are aware of the importance of health prevention in order to ensure long-term health, but they do not always have agency over their life-style habits (healthy food, regular exercise, emotional stability). The second phase of the research collected open-ended interviews with selected participants, in order to explore in more detail how Latin American migrants deal with Chagas in a different socio-political and economic context to that of endemic countries. These interviews (5 in total) reveal mixed methods of managing health: social networks, access to health care transnationally (in Geneva, Spain and back in their home country), and different valuations of health problems in each situation. The third phase consisted in observations of doctor-patient consultations and further extended interviews with patients to determine doctor/patient health perceptions around Chagas disease. This phase is ongoing, but it has yielded preliminarily observations regarding the expectations that patients’ have of doctors, and the understanding of doctors’ to patients’ complex situations. Positive and complementary health perceptions include patients’ feeling that doctors in Geneva are more understanding, more knowledgeable and less racist than those in their home country, who do not provide detailed information about Chagas or its treatment and discriminate against them for being indigenous or from poor rural areas, enabling a better communication between doctors and patients. Possible conflicting health perceptions include patients addressing their health concerns more holistically and encountering the specialist’s limitations to only treating one health concern, given time limitations and lack of competition with their colleagues (the general practitioner that referred the patient, for example). The implications of this study extend the case of Chagas disease in Geneva and is relevant for all chronic concerns and migratory contexts of precarity.

Keywords: chagas disease, health perceptions, Latin American Migrants, non-endemic countries

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Authors: Aymen Laadhari, Gábor Székely

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In this work, the hemodynamics in the sinuses of Valsalva after Transcatheter Aortic Valve Implantation is numerically examined. We focus on the physical results in the two-dimensional case. We use a finite element methodology based on a Lagrange multiplier technique that enables to couple the dynamics of blood flow and the leaflets’ movement. A massively parallel implementation of a monolithic and fully implicit solver allows more accuracy and significant computational savings. The elastic properties of the aortic valve are disregarded, and the numerical computations are performed under physiologically correct pressure loads. Computational results depict that blood flow may be subject to stagnation in the lower domain of the sinuses of Valsalva after Transcatheter Aortic Valve Implantation.

Keywords: hemodynamics, simulations, stagnation, valve

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Authors: Neha Bhattacharyya, Soumendra Singh, Amrita Banerjee, Ria Ghosh, Oindrila Sinha, Nairit Das, Rajkumar Gayen, Somya Subhra Pal, Sahely Ganguly, Tanmoy Dasgupta, Tanusree Dasgupta, Pulak Mondal, Aniruddha Adhikari, Sharmila Sarkar, Debasish Bhattacharyya, Asim Kumar Mallick, Om Prakash Singh, Samir Kumar Pal

Abstract:

Background: We aim to develop an integrated device comprised of single-probe EEG and CCD-based motion sensors for a more objective measure of Attention-deficit Hyperactivity Disorder (ADHD). While the integrated device (MAHD) relies on the EEG signal (spectral density of beta wave) for the assessment of attention during a given structured task (painting three segments of a circle using three different colors, namely red, green and blue), the CCD sensor depicts movement pattern of the subjects engaged in a continuous performance task (CPT). A statistical analysis of the attention and movement patterns was performed, and the accuracy of the completed tasks was analysed using indigenously developed software. The device with the embedded software, called MAHD, is intended to improve certainty with criterion E (i.e. whether symptoms are better explained by another condition). Methods: We have used the EEG signal from a single-channel dry sensor placed on the frontal lobe of the head of the subjects (3-5 years old pre-schoolers). During the painting of three segments of a circle using three distinct colors (red, green, and blue), absolute power for delta and beta EEG waves from the subjects are found to be correlated with relaxation and attention/cognitive load conditions. While the relaxation condition of the subject hints at hyperactivity, a more direct CCD-based motion sensor is used to track the physical movement of the subject engaged in a continuous performance task (CPT) i.e., separation of the various colored balls from one table to another. We have used our indigenously developed software for the statistical analysis to derive a scale for the objective assessment of ADHD. We have also compared our scale with clinical ADHD evaluation. Results: In a limited clinical trial with preliminary statistical analysis, we have found a significant correlation between the objective assessment of the ADHD subjects with that of the clinician’s conventional evaluation. Conclusion: MAHD, the integrated device, is supposed to be an auxiliary tool to improve the accuracy of ADHD diagnosis by supporting greater criterion E certainty.

Keywords: ADHD, CPT, EEG signal, motion sensor, psychometric test

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Authors: Jutta Hämmerle-Uhl, Georg Penn, Gerhard Pötzelsberger, Andreas Uhl

Abstract:

Iris codes contain bits with different entropy. This work investigates different strategies to reduce the size of iris code templates with the aim of reducing storage requirements and computational demand in the matching process. Besides simple sub-sampling schemes, also a binary multi-resolution representation as used in the JBIG hierarchical coding mode is assessed. We find that iris code template size can be reduced significantly while maintaining recognition accuracy. Besides, we propose a two stage identification approach, using small-sized iris code templates in a pre-selection satge, and full resolution templates for final identification, which shows promising recognition behaviour.

Keywords: iris recognition, compact iris code, fast matching, best bits, pre-selection identification, two-stage identification

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Authors: Huseyin Altundag, Emel Bina, Esra Altıntıg

Abstract:

The aim of this study is determining amount of Al, Cu, Fe, Mn, Ni, Pb and Zn in the samples of honey which are gathered from Sakarya and Istanbul regions. In this study the evaluation of the trace elements in honeys samples are gathered from Sakarya and Istanbul, Turkey. The sample preparation phase is performed via wet decomposition method and microwave digestion system. The accuracy of the method was corrected by the standard reference material, Tea Leaves (INCY-TL-1) and NIST SRM 1515 Apple leaves. The comparison between gathered data and literature values has made and possible resources of the contamination to the samples of honey have handled. The obtained results will be presented in ICCIS 2015: XIII International Conference on Chemical Industry and Science.

Keywords: Wet decomposition, Microwave digestion, Trace element, Honey, ICP-OES

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Authors: Vishweshwar V. Dixit

Abstract:

Kannudi is a reference editor introducing a method of input for Kannada, called OHOK!, that is, Ottu Hāku Ottu Koḍu!. This is especially suited for pressure-sensitive input devices, though the current online implementation uses the regular mechanical keyboard. OHOK! has three possible modes, namely, sva-ottu (self-conjunct), kandante (as you see), and andante (as you say). It may be noted that kandante mode does not follow the phonetic order. However, this model may work well for those who are inclined to visualize as they type rather than vocalize the sounds. Kannudi also demonstrates how domain knowledge can be effectively used to potentially increase speed, accuracy, and user-friendliness. For example, selection of a default vowel, automatic shunyification, and arkification. Also implemented are four types of Deletes that are necessary for phono-syllabic languages like Kannada.

Keywords: kannada, conjunct, reference editor, pressure input

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Authors: Davit Mirzoyan, Ararat Khachatryan

Abstract:

A process corner monitoring circuit (PCMC) is presented in this work. The circuit generates a signal, the logical value of which depends on the process corner only. The signal can be used in both digital and analog circuits for testing and compensation of process variations (PV). The presented circuit uses only metal-oxide-semiconductor (MOS) transistors, which allow increasing its detection accuracy, decrease power consumption and area. Due to its simplicity the presented circuit can be easily modified to monitor parametrical variations of only n-type and p-type MOS (NMOS and PMOS, respectively) transistors, resistors, as well as their combinations. Post-layout simulation results prove correct functionality of the proposed circuit, i.e. ability to monitor the process corner (equivalently die-to-die variations) even in the presence of within-die variations.

Keywords: detection, monitoring, process corner, process variation

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Authors: Mohammad Bagher Anvari, Arman Shojaei

Abstract:

Incremental launching, a widely adopted bridge erection technique, offers numerous advantages for bridge designers. However, accurately simulating and modeling the dynamic behavior of the bridge during each step of the launching process proves to be tedious and time-consuming. The perpetual variation of internal forces within the deck during construction stages adds complexity, exacerbated further by considerations of other load cases, such as support settlements and temperature effects. As a result, there is an urgent need for a reliable, simple, economical, and fast algorithmic solution to model bridge construction stages effectively. This paper presents a novel Finite Element (FE) model that focuses on studying the static behavior of bridges during the launching process. Additionally, a simple method is introduced to normalize all quantities in the problem. The new FE model overcomes the limitations of previous models, enabling the simulation of all stages of launching, which conventional models fail to achieve due to underlying assumptions. By leveraging the results obtained from the new FE model, this study proposes solutions to improve the accuracy of conventional models, particularly for the initial stages of bridge construction that have been neglected in previous research. The research highlights the critical role played by the first span of the bridge during the initial stages, a factor often overlooked in existing studies. Furthermore, a new and simplified model termed the "semi-infinite beam" model, is developed to address this oversight. By utilizing this model alongside a simple optimization approach, optimal values for launching nose specifications are derived. The practical applications of this study extend to optimizing the nose-deck system of incrementally launched bridges, providing valuable insights for practical usage. In conclusion, this paper introduces a comprehensive Finite Element model for studying the static behavior of bridges during incremental launching. The proposed model addresses limitations found in previous approaches and offers practical solutions to enhance accuracy. The study emphasizes the importance of considering the initial stages and introduces the "semi-infinite beam" model. Through the developed model and optimization approach, optimal specifications for launching nose configurations are determined. This research holds significant practical implications and contributes to the optimization of incrementally launched bridges, benefiting both the construction industry and bridge designers.

Keywords: incremental launching, bridge construction, finite element model, optimization

Procedia PDF Downloads 110