Search results for: robust ranking technique

Authors: Neha Ahirwar

Abstract:

In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

Procedia PDF Downloads 43

Authors: Ines Vieira, Luisa Faria

Abstract:

Globally, half of all mental disorders begin by age 14 and the current gap of poorly addressed adolescent mental health has future consequences in adulthood. Schoolwork pressure to achieve good performance in secondary education might lead to lower levels of life satisfaction in youth and individual emotional competencies are crucial in this life stage. The present study aimed to determine how mindfulness relates to school achievements and well-being in adolescence and whether such a relationship might be mediated by emotional intelligence. We also studied the moderation interaction effects of gender and the involvement in non-curricular activities. A sample of 597 Portuguese adolescents aged 15 to 17 years old (N=597; 292 girls; 298 boys), enrolled in secondary education completed self-report measures of mindfulness (CAMM), emotional intelligence (TEIQue-ASF) and well-being (SWLS) in their Portuguese versions. Using SPSS and AMOS, the results were obtained through path analyses and multiple linear regression. A Confirmatory Factor Analysis was also conducted. The correlation coefficients reported a positive and statistically significant relationship between mindfulness, emotional intelligence and well-being. Regression analysis indicated that mindfulness reduced its influence on well-being and on school results when emotional intelligence was added to the model. Overall, our results provided further evidence supporting the development of robust hypotheses by perceiving the relevance of mindfulness and individual emotional competencies to school achievements and well-being in a way of improving adolescents’ health, wellness, and school success.

Keywords: mindfulness, emotional intelligence, well-being, adolescence, school

Procedia PDF Downloads 53

Authors: Surya Prakash Gajagouni, Akram Alfantazi, Imad Barsoum

Abstract:

Austenitic stainless steels are widely recognized for their exceptional corrosion resistance and mechanical properties, rendering them indispensable materials across various industries from construction to biomedical applications. However, in chloride and high temperature atmosphere it to further enhance their surface properties, anodization has emerged as a promising surface treatment technique. Anodization modifies the surface of stainless steels by creating a protective oxide layer, improving corrosion resistance and imparting additional functional characteristics. This paper explores the structural and corrosion characteristics of anodized austenitic stainless steels (AISI 304) using a two-step anodic technique. We utilized a perchloric acid-based electrolyte followed by an ammonium fluoride-based electrolyte. This sequential approach aimed to cultivate deeper and intricately self-ordered nanopore oxide arrays on a substrate made of 304 stainless steel. Electron Microscopic (SEM and TEM) images revealed nanoporous layered structures with increased length and crack development correlating with higher voltage and anodization time. Surface composition and chemical oxidation state of surface-treated SS were determined using X-ray photoelectron spectroscopy (XPS) techniques, revealing a surface layer rich in Ni and suppressed Cr, resulting in a thin film composed of Ni and Fe oxide compared to untreated SS. Electrochemical studies demonstrated enhanced corrosion resistance in a strong alkaline medium compared to untreated SS. Understanding the intricate relationship between the structural features of anodized stainless steels and their corrosion resistance is crucial for optimizing the performance of these materials in diverse applications. This study aims to contribute to the advancement of surface engineering strategies for enhancing the durability and functionality of austenitic stainless steels in aggressive environments.

Keywords: austenitic stainless steel, anodization, nanoporous oxides, marine corrosion

Procedia PDF Downloads 23

Authors: Thamer Alshuwaili, Yonglin Ren, Bob Du, Manjree Agarwal

Abstract:

The warehouse beetle, Trogoderma variabile Ballion (Coleoptera: Dermestidae), is a major pest of packaged and processed stored products. Warehouse beetle is the common name which was given by Okumura (1972). This pest has been reported to infest 119 different commodities, and it is distributed throughout the tropical and subtropical parts of the world. Also, it is difficult to control because of the insect's ability to stay without food for long times, and it can survive for years under dry conditions and low-moisture food, and it has also developed resistance to many insecticides. The young larvae of these insects can cause damage to seeds, but older larvae prefer to feed on whole grains. The percentage of damage caused by these insects range between 30-70% in the storage. T. variabile is the species most responsible for causing significant damage in grain stores worldwide. Trogoderma spp. is a huge problem for cereal grains, and there are many countries, such as the USA, Australia, China, Kenya, Uganda and Tanzania who have specific quarantine regulations against possible importation. Also, grain stocks can be almost completely destroyed because of the massive populations the insect may develop. However, the purpose of the current research was to optimize conditions to collect volatile organic compound from Trogoderma variabile at different life stages by using headspace solid phase microextraction (SPME) coupled with gas chromatography-mass spectrometry (GC-MS) and flame ionization detection (FID). Using SPME technique to extract volatile from insects is an efficient, straightforward and nondestructive method. Result of the study shows that 15 insects were optimal number for larvae and adults. Selection of the number of insects depend on the height of the peak area and the number of peaks. Sixteen hours were optimized as the best extraction time for larvae and 8 hours was the optimal number of adults.

Keywords: Trogoderma variabile, warehouse beetle , GC-MS, Solid phase microextraction

Procedia PDF Downloads 110

Authors: Araceli Martínez Ortiz

Abstract:

An educational research effort is analyzed using systems theory to document the power of collective impact when addressing multiple factors contributing towards the retention of students majoring in science, technology, engineering and mathematics (STEM) academic programs. This research promotes understanding on how networked communities may work effectively toward a shared vision and mutually aligned activities that result in sustained, large scale change. The actions of a team of researchers in their third year of collaboration are presented to describe a model that positively aligns work efforts resulting in greater total gains. The goals of the multiple programs managed by the funded program team are to: 1) expand the number of students who choose to study a STEM field of study; 2) promote student collaborative learning; 3) support faculty understanding of the funds of knowledge of diverse students and 4) establish innovative and robust STEM education research that will lead to the development of nationally replicable, scalable models for broadening participation in STEM. The impacts of this research effort are measured through quantitative statistical analysis of the changes in second-year STEM undergraduate student retention rates and representation rates of women, Hispanics and African American STEM majors.

Keywords: collaborative impact, diversity, student retention, systems theory, STEM education

Procedia PDF Downloads 243

Authors: V. S. Bushkova

Abstract:

It is well known that in recent years magnetic materials have received increased attention due to their properties. For this reason a significant number of patents that were published during the last decade are oriented towards synthesis and study of such materials. The aim of this work is to create and study ferrite nanocrystalline materials with spinel structure, using sol-gel technology with participation of auto-combustion. This method is perspective in that it is a cheap and low-temperature technique that allows for the fine control on the product’s chemical composition.

Keywords: magnetic materials, ferrites, sol-gel technology, nanocrystalline powders

Procedia PDF Downloads 391

Authors: Gayathri Nagarajan, L. D. Dhinesh Babu

Abstract:

Health care is one of the prominent industries that generate voluminous data thereby finding the need of machine learning techniques with big data solutions for efficient processing and prediction. Missing data, incomplete data, real time streaming data, sensitive data, privacy, heterogeneity are few of the common challenges to be addressed for efficient processing and mining of health care data. In comparison with other applications, accuracy and fast processing are of higher importance for health care applications as they are related to the human life directly. Though there are many machine learning techniques and big data solutions used for efficient processing and prediction in health care data, different techniques and different frameworks are proved to be effective for different applications largely depending on the characteristics of the datasets. In this paper, we present a framework that uses ensemble machine learning technique gradient boosted trees for data classification in health care big data. The framework is built on Spark platform which is fast in comparison with other traditional frameworks. Unlike other works that focus on a single technique, our work presents a comparison of six different machine learning techniques along with gradient boosted trees on datasets of different characteristics. Five benchmark health care datasets are considered for experimentation, and the results of different machine learning techniques are discussed in comparison with gradient boosted trees. The metric chosen for comparison is misclassification error rate and the run time of the algorithms. The goal of this paper is to i) Compare the performance of gradient boosted trees with other machine learning techniques in Spark platform specifically for health care big data and ii) Discuss the results from the experiments conducted on datasets of different characteristics thereby drawing inference and conclusion. The experimental results show that the accuracy is largely dependent on the characteristics of the datasets for other machine learning techniques whereas gradient boosting trees yields reasonably stable results in terms of accuracy without largely depending on the dataset characteristics.

Keywords: big data analytics, ensemble machine learning, gradient boosted trees, Spark platform

Procedia PDF Downloads 228

Authors: Anna Ivanovskaya, Alexandra E. L. Overland, Swetha Chandrasekaran, Buddhinie S. Jayathilake

Abstract:

Iron-based redox flow batteries (IRFB) are promising for grid-scale storage because of their low-cost and environmental safety. Earth-abundant iron can enable affordable grid-storage to meet DOE’s target material cost <$20/kWh and levelized cost for storage $0.05/kWh. In conventional redox flow batteries, energy is stored in external electrolyte tanks and electrolytes are circulated through the cell units to achieve electrochemical energy conversions. However, IRFBs are hybrid battery systems where metallic iron deposition at the negative side of the battery controls the storage capacity. This adds complexity to the design of a porous structure of 3D-electrodes to achieve a desired high storage capacity. In addition, there is a need to control parasitic hydrogen evolution reaction which accompanies the metal deposition process, increases the pH, lowers the energy efficiency, and limits the durability. To achieve sustainable operation of IRFBs, electrolyte pH, which affects the solubility of reactants and the rate of parasitic reactions, needs to be dynamically readjusted. In the present study we explore the impact of complexing agents on maintaining solubility of the reactants and find the optimal electrolyte conditions and battery operating regime, which are specific for IRFBs with additives, and demonstrate the robust operation.

Keywords: flow battery, iron-based redox flow battery, IRFB, energy storage, electrochemistry

Procedia PDF Downloads 64

Authors: Kkochorong Park, Keun Cheon Kim, Hyoban Lee, Eun Ju Lee, Bongsoo Kim

Abstract:

Introduction of biomaterials such as DNA, RNA, proteins is important for many research areas. There are many methods to introduce biomaterials into living organisms like tissue and cells. To introduce biomaterials, several indirect methods including virus‐mediated delivery, chemical reagent (i.e., lipofectamine), electrophoresis have been used. Such methods are passive delivery using an endocytosis process of cell, reducing an efficiency of delivery. Unlike the indirect delivery method, it has been reported that a direct delivery of exogenous biomolecules into nucleus have been more efficient to expression or integration of biomolecules. Nano-sized material is beneficial for detect signal from cell or deliver stimuli/materials into the cell at cellular and molecular levels, due to its similar physical scale. Especially, because 1 dimensional (1D) nanomaterials such as nanotube, nanorod and nanowire with high‐aspect ratio have nanoscale geometry and excellent mechanical, electrical, and chemical properties, they could play an important role in molecular and cellular biology. In this study, by using single crystalline 1D noble metal nanowire, we fabricated nano-sized 1D injector which can successfully interface with living cells and directly deliver biomolecules into several types of cell line (i.e., stem cell, mammalian embryo) without inducing detrimental damages on living cell. This nano-bio technology could be a promising and robust tool for introducing exogenous biomaterials into living organism.

Keywords: DNA, gene delivery, nanoinjector, nanowire

Procedia PDF Downloads 259

Authors: Hamed Seddighi Khavidak

Abstract:

Voluntary work has a lot of economic and social benefits for a country, but the economic value is ignored because it is voluntary. The aim of this study is reviewing Monetary Value of Voluntary Work methods and comparing opportunity cost method and replacement cost method both in theory and in practice. Beside monetary value, in this study, we discuss cost-benefit analysis of health and safety plan in the New Year that conducted by young volunteers of Red Crescent society of Iran. Method: We discussed eight methods for monetary value of voluntary work including: Alternative-Employment Wage Approach, Leisure-Adjusted OCA, Volunteer Judgment OCA, Replacement Wage Approach, Volunteer Judgment RWA, Supervisor Judgment RWA, Cost of Counterpart Goods and Services and Beneficiary Judgment. Also, for cost benefit analysis we drew on 'value audit and volunteer investment' (VIVA) technique that is used widely in voluntary organizations like international federation of Red Cross and Red Crescent societies. Findings: In this study, using replacement cost approach, voluntary work by 1034 youth volunteers was valued 938000000 Riyals and using Replacement Wage Approach it was valued 2268713232 Riyals. Moreover, Yazd Red Crescent Society spent 212800000 Riyals on food and other costs for these volunteers. Discussion and conclusion: In this study, using cost benefit analysis method that is Volunteer Investment and Value Audit (VIVA), VIVA rate showed that for every Riyal that the Red Crescent Society invested in the health and safety of New Year's travelers in its volunteer project, four Riyals returned, and using the wage replacement approach, 11 Riyals returned. Therefore, New Year's travelers health and safety project were successful and economically, it was worthwhile for the Red Crescent Society because the output was much bigger than the input costs.

Keywords: voluntary work, monetary value, youth, red crescent society

Procedia PDF Downloads 201

Authors: R. Daira

Abstract:

The electronic speckle interferometry technique used to measure the deformations of scatterers process is based on the subtraction of interference patterns. A speckle image is first recorded before deformation of the object in the RAM of a computer, after a second deflection. The square of the difference between two images showing correlation fringes observable in real time directly on monitor. The interpretation these fringes to determine the deformation. In this paper, we present experimental results of deformation out of the plane of two samples in aluminum, electronic boards and stainless steel.

Keywords: optical method, holography, interferometry, deformation

Procedia PDF Downloads 390

Authors: Syed Ahsan, Saleh Alshomrani, Ishtiaq Rasool, Ali Hassan

Abstract:

In this oral presentation, we will provide an overview of the technical and semantic architecture of a desert border security and critical infrastructure protection security system. Modern border security systems are designed to reduce the dependability and intrusion of human operators. To achieve this, different types of sensors are use along with video surveillance technologies. Application of these technologies in a harsh desert environment of Saudi Arabia poses unique challenges. Environmental and geographical factors including high temperatures, desert storms, temperature variations and remoteness adversely affect the reliability of surveillance systems. To successfully implement a reliable, effective system in a harsh desert environment, the following must be achieved: i) Selection of technology including sensors, video cameras, and communication infrastructure that suit desert environments. ii) Reduced power consumption and efficient usage of equipment to increase the battery life of the equipment. iii) A reliable and robust communication network with efficient usage of bandwidth. Also, to reduce the expert bottleneck, an ontology-based intelligent information systems needs to be developed. Domain knowledge unique and peculiar to Saudi Arabia needs to be formalized to develop an expert system that can detect abnormal activities and any intrusion.

Keywords: border security, sensors, abnormal activity detection, ontologies

Procedia PDF Downloads 469

Authors: Majdi Anwar Quttainah, William Paczkowski, Ali Muhammad

Abstract:

Determining the value of a privately held firms confound those in academia as well as practitioners in the fields of appraisal, forensic accounting, and law. Divergent parties to the transfer look to apply the valuation technique to serve their own best interests. This paper seeks to explore how agency theory induces owners to choose the form of their businesses at inception and how this choice will affect the appraisers’ valuation of the firm at the transfer of ownership.

Keywords: organizational form, agency theory, value

Procedia PDF Downloads 418

Authors: Nicolas Haber

Abstract:

The study addresses the design process of integrated product-service offerings as a measure of answering environmental sustainability concerns by replacing stand-alone physical artefacts with comprehensive solutions relying on functional results rather than conventional product sales. However, views regarding this transformation are dissimilar and differentiated: The study discusses the importance and requirements of product-service systems before analysing the theoretical studies accomplished in the extent of their design and development processes. Based on this, a framework, built on a design science approach, is proposed, where the distinct approaches from the literature are merged towards a unified structure serving as a generic methodology to designing product-service systems. Each stage of this model is then developed to present a holistic design proposal called the Functional Engineered Product-Service System (FEPSS) model. Product-service systems are portrayed as customisable solutions tailored to specific settings and defined circumstances. Moreover, the approaches adopted to guide the design process are diversified. A thorough analysis of the design strategies and development processes however, allowed the extraction of a design backbone, valid to varied situations and contexts whether they are product-oriented, use-oriented or result-oriented. The goal is to guide manufacturers towards an eased adoption of these integrated offerings, given their inherited environmental benefits, by proposing a robust all-purpose design process.

Keywords: functional product, integrated product-service offerings, product-service systems, sustainable design

Procedia PDF Downloads 278

Authors: P. Jayashree, S. Rajkumar

Abstract:

With the trend of virtual and augmented reality environments booming to provide a life like experience, gaming is a major tool in supporting such learning environments. In this work, a variant of Voronoi heuristics, employing supervised learning for the TRON game is proposed. The paper discusses the features that would be really useful when a machine learning bot is to be used as an opponent against a human player. Various game scenarios, nature of the bot and the experimental results are provided for the proposed variant to prove that the approach is better than those that are currently followed.

Keywords: artificial Intelligence, automation, machine learning, TRON game, Voronoi heuristics

Procedia PDF Downloads 446

Authors: Gayle Nunley

Abstract:

Between the mid-nineteenth and early twentieth century, a number of Western European women broke with gender norms of their time and undertook to write and publish accounts of their own international journeys. In addition to contributing to their contemporaries’ progressive reimagining of the space and place of female experience within the public sphere, these often orientalism-tinged texts have come to provide key source material for the analysis of gendered voice in the narration of Empire, particularly with regard to works associated with Europe’s then-ascendant imperial powers, Britain and France. Incorporation of contemporaneous writings from the once-dominant Empires of Iberian Europe introduces an important additional lens onto this process. By bringing to bear geographic notions of placedness together with discourse analysis, the examination of works by Iberian Europe’s female travelers in conjunction with those of their more celebrated Northern European peers reveals a pervasive pattern of conjoined belonging and displacement traceable throughout the broader corpus, while also underscoring the insufficiency of binary paradigms of gendered voice. The re-situating of women travelers’ participation in the European imperial project to include voices from the Iberian south creates a more robust understanding of these writers’ complex, and often unexpectedly modern, engagement with notions of gender, mobility, ‘otherness’ and contact-zone encounter acted out both within and against the imperial paradigm.

Keywords: colonialism, orientalism, Spain, travel writing, women travelers

Procedia PDF Downloads 96

Authors: Kishore Kumar R., Kaustav Sengupta, Shalini Sood Sehgal, Poornima Santhanam

Abstract:

Fashion is a human expression that is constantly changing. One of the prime factors that consistently influences fashion is the change in colour preferences. The role of colour in our everyday lives is very significant. It subconsciously explains a lot about one’s mindset and mood. Analyzing the colours by extracting them from the outfit images is a critical study to examine the individual’s/consumer behaviour. Several research works have been carried out on extracting colours from images, but to the best of our knowledge, there were no studies that extract colours to specific apparel and identify colour patterns geographically. This paper proposes a framework for accurately extracting colours from T-shirt images and predicting dominant colours geographically. The proposed method consists of two stages: first, a U-Net deep learning model is adopted to segment the T-shirts from the images. Second, the colours are extracted only from the T-shirt segments. The proposed method employs the iMaterialist (Fashion) 2019 dataset for the semantic segmentation task. The proposed framework also includes a mechanism for gathering data and analyzing India’s general colour preferences. From this research, it was observed that black and grey are the dominant colour in different regions of India. The proposed method can be adapted to study fashion’s evolving colour preferences.

Keywords: colour analysis in t-shirts, convolutional neural network, encoder-decoder, k-means clustering, semantic segmentation, U-Net model

Procedia PDF Downloads 93

Authors: Kumudini Apsara Munasinghe, Devin Gregory Lissau, Ryan Thomas Wade

Abstract:

Fresh fruits and vegetables are rich in vitamins, minerals, and fibers and help maintain a healthy weight over high-calorie food. Eating fruits and vegetables protects us from free radicals produced by metabolic reactions and safeguards us from cardiovascular disease and cancer. However, there has been an increased concern about foodborne diseases tied to contaminated farmers’ market produce. In addition, very little information is available about the contribution of eating raw fruits and vegetables to human exposure to antibiotic-resistant bacteria. This research aims to identify bacteria isolated from farmers’ market fruits and vegetables and understand their antibiotic resistance. Vegetables and fruits were collected from farmers’ markets around Frostburg and Cumberland areas in Maryland and transported to the microbiology lab at Frostburg State University for the isolation of bacteria. Bacteria were extracted from tomatoes, cucumber, strawberry, and lettuce using Tryptic soy broth overnight at 37°C, and Tryptic Soy agar was used for the streak plate technique to isolate bacteria. Pure cultures were used to identify bacteria using biochemical reactions after conducting Gram staining technique. The research used many biochemical reactions, including Mannitol Salt agar, MacConkey agar, and Eosin Methylene blue agar, for identification. Antibiotic sensitivity was tested for many different types of antibiotics, including amoxicillin, penicillin, tetracycline, ampicillin, and erythromycin. Most prevalent bacteria in the isolates were Staphylococcus, Bacillus, Micrococcus, Enterococcus, Enterobacter, Citrobacter, and other bacteria from the family Enterobacteriaceae. The data obtained from this research will be useful to educate and train farmers and individuals involved in post-harvest processes such as transportation and selling in farmers’ markets. Further results for bacterial antibiotic resistance will be obtained, and unculturable bacteria will be identified by next-generation DNA sequencing.

Keywords: antibiotic resistance, farmers markets, fruits, bacteria, vegetables

Procedia PDF Downloads 53

Authors: Devendra Gupta, S. P. Ambesh, P. K Singh

Abstract:

Introduction: Minimally invasive coronary artery bypass grafting (MICABG) is a less invasive method of performing surgical revascularization. Minimally invasive direct coronary artery bypass (MIDCAB) provides many anesthetic challenges including one lung ventilation (OLV), managing myocardial ischemia, and pain. We present an early and midterm result of the use of this technique with OLV. Method: We enrolled 62 patients for analysis operated between 2008 and 2012. Patients were anesthetized and left endobronchial tube was placed. During the procedure left lung was isolated and one lung ventilation was maintained through right lung. Operation was performed utilizing off pump technique of coronary artery bypass grafting through a minimal invasive incision. Left internal mammary artery graft was done for single vessel disease and radial artery was utilized for other grafts if required. Postoperative ventilation was done with single lumen endotracheal tube. Median follow-up is 2.5 years (6 months to 4 years). Results: Median age was 58.5 years (41-77) and all were male. Single vessel disease was present in 36, double vessel in 24 and triple vessel disease in 2 patients. All the patients had normal left ventricular size and function. In 2 cases difficulty were encounter in placement of endobronchial tube. In 1 case cuff of endobronchial tube was ruptured during intubation. High airway pressure was developed on OLV in 1 case and surgery was accomplished with two lung anesthesia with low tidal volume. Mean postoperative ventilation time was 14.4 hour (11-22). There was no perioperative and 30 day mortality. Conversion to median sternotomy to complete the operation was done in 3.23% (2 out of 62 patients). One patient had acute myocardial infarction postoperatively and there were no deaths during follow-up. Conclusion: MICABG is a safe and effective method of revascularization with OLV in low risk candidates for coronary artery bypass grafting.

Keywords: MIDCABG, one lung ventilation, coronary artery bypass grafting, endobronchial tube

Procedia PDF Downloads 411

Authors: Benyamina, Ouldabess, Bensalah

Abstract:

The aim of this study is to use membrane technique for filtration of a coloring solution. the preparation of the micro-filtration membranes is based on a natural clay powder with a low cost, deposited on macro-porous ceramic supports. The micro-filtration membrane provided a very large permeation flow. Indeed, the filtration effectiveness of membrane was proved by the total discoloration of bromothymol blue solution with initial concentration of 10-3 mg/L after the first minutes.

Keywords: the inorganic membrane, micro-filtration, coloring solution, natural clay powder

Procedia PDF Downloads 498

Authors: In-Sung Lee, Un-Hwan Park, Jun-Hyeok Heo, Tae-Hyeon Oh, Dae-Gyu Park

Abstract:

Statistical Energy Analysis is to be the most effective CAE Method for air-born noise analysis in the Automotive area. This study deals with a method to predict the noise level inside of the car under the steady-state condition using the SEA model of car for air-born noise analysis. We can identify weakened part due to the acoustic material properties using it. Therefore, it is useful for the material structural design.

Keywords: air-born noise, material structural design, acoustic material properties, absorbing

Procedia PDF Downloads 411

Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

Abstract:

One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

Procedia PDF Downloads 86

Authors: Kumari Sweta, Ajanta Goswami, Abhilasha Dixit

Abstract:

Landslide is a geomorphic process that plays an essential role in the evolution of the hill-slope and long-term landscape evolution. But its abrupt nature and the associated catastrophic forces of the process can have undesirable socio-economic impacts, like substantial economic losses, fatalities, ecosystem, geomorphologic and infrastructure disturbances. The estimated fatality rate is approximately 1person /100 sq. Km and the average economic loss is more than 550 crores/year in the Himalayan belt due to landslides. This study presents a comparative performance of a statistical bivariate method and a machine learning technique for landslide susceptibility mapping in and around Dharamshala, Himachal Pradesh. The final produced landslide susceptibility maps (LSMs) with better accuracy could be used for land-use planning to prevent future losses. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. A total of 209 landslide locations were identified in using high-resolution linear imaging self-scanning (LISS IV) data. The thematic maps of parameters influencing landslide occurrence were generated using remote sensing and other ancillary data in the GIS environment. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. LSMs were prepared using information value (Info Val), and Fuzzy Expert System (FES) models. Info Val is a statistical bivariate method, in which information values were calculated as the ratio of the landslide pixels per factor class (Si/Ni) to the total landslide pixel per parameter (S/N). Using this information values all parameters were reclassified and then summed in GIS to obtain the landslide susceptibility index (LSI) map. The FES method is a machine learning technique based on ‘mean and neighbour’ strategy for the construction of fuzzifier (input) and defuzzifier (output) membership function (MF) structure, and the FR method is used for formulating if-then rules. Two types of membership structures were utilized for membership function Bell-Gaussian (BG) and Trapezoidal-Triangular (TT). LSI for BG and TT were obtained applying membership function and if-then rules in MATLAB. The final LSMs were spatially and statistically validated. The validation results showed that in terms of accuracy, Info Val (83.4%) is better than BG (83.0%) and TT (82.6%), whereas, in terms of spatial distribution, BG is best. Hence, considering both statistical and spatial accuracy, BG is the most accurate one.

Keywords: bivariate statistical techniques, BG and TT membership structure, fuzzy expert system, information value method, machine learning technique

Procedia PDF Downloads 113

Authors: Elias K. Maragos, Petros E. Maravelakis, Apostolos I. Linardis

Abstract:

Health2020 is a promising framework of policies provided by the World Health Organization (WHO) and aiming to diminish the health and well-being inequalities among the citizens of the European Union (EU) countries. The major demographic, social and environmental changes, in addition to the resent economic crisis prevent the unobstructed and successful implementation of this framework. The unemployment rates and the percentage of people at risk of poverty have increased among the citizens of EU countries. At the same time, the adopted fiscal, economic policies do not help governments to serve their social role and mitigate social and health inequalities. In those circumstances, there is a strong pressure to organize all health system resources efficiently and wisely. In order to provide a unified and value-based framework of valuation, we propose a valuation framework using data envelopment analysis (DEA) and dynamic DEA. We believe that the adopted methodology could provide a robust tool which can capture the degree of success with which policies have been implemented and is capable to determine which of the countries developed the requested policies efficiently and which of the countries have been lagged. Using the proposed methodology, we evaluated the performance of 28 EU member-countries in relation to the Health2020 peripheral targets. We adopted several versions of evaluation, measuring the effectiveness and the efficiency of EU countries from 2011 to 2016. Our results showed stability in technological changes and revealed a group of countries which were benchmarks in most of the years for the inefficient countries.

Keywords: DEA, Health2020, health inequalities, malmquist index, policies evaluation, well-being

Procedia PDF Downloads 132

Authors: Venuka Kuruwita Arachchige Don, Mohamed Shaheen, Chris Goodier

Abstract:

Aerated autoclaved concrete (AAC) is known for its lightweight, easy handling, high thermal insulation, and extremely porous structure. Investigation of pore behavior in AAC is crucial for characterizing the material, standardizing design and production techniques, enhancing the mechanical, durability, and thermal performance, studying the effectiveness of protective measures, and analyzing the effects of weather conditions. The significant details of pores are complicated to observe with acknowledged accuracy. The High-resolution Field Emission Scanning Electron Microscope (FESEM) image analysis is a promising technique for investigating the pore behavior and density of AAC, which is adopted in this study. Mercury intrusion porosimeter and gas pycnometer were employed to characterize porosity distribution and density parameters. The analysis considered three different densities of AAC blocks and three layers in the altitude direction within each block. A set of understandings was presented to extract and analyze the details of pore shape, pore size, pore connectivity, and pore percentages from FESEM images of AAC. Average pore behavior outcomes per unit area were presented. Comparison of porosity distribution and density parameters revealed significant variations. FESEM imaging offered unparalleled insights into porosity behavior, surpassing the capabilities of other techniques. The analysis conducted from a multi-staged approach provides porosity percentage occupied by various pore categories, total porosity, variation of pore distribution compared to AAC densities and layers, number of two-dimensional and three-dimensional pores, variation of apparent and matrix densities concerning pore behaviors, variation of pore behavior with respect to aluminum content, and relationship among shape, diameter, connectivity, and percentage in each pore classification.

Keywords: autoclaved aerated concrete, density, imaging technique, microstructure, porosity behavior

Procedia PDF Downloads 40

Authors: Won-Ho Kim

Abstract:

In this paper, a design of H.263 based wireless video transceiver is presented for wireless camera system. It uses standard WIFI transceiver and the covering area is up to 100m. Furthermore the standard H.263 video encoding technique is used for video compression since wireless video transmitter is unable to transmit high capacity raw data in real time and the implemented system is capable of streaming at speed of less than 1Mbps using NTSC 720x480 video.

Keywords: wireless video transceiver, video surveillance camera, H.263 video encoding digital signal processing

Procedia PDF Downloads 352

Authors: Ranadhir Roy

Abstract:

Inverse problems arise in medical (molecular) imaging. These problems are characterized by large in three dimensions, and by the diffusion equation which models the physical phenomena within the media. The inverse problems are posed as a nonlinear optimization where the unknown parameters are found by minimizing the difference between the predicted data and the measured data. To obtain a unique and stable solution to an ill-posed inverse problem, a priori information must be used. Mathematical conditions to obtain stable solutions are established in Tikhonov’s regularization method, where the a priori information is introduced via a stabilizing functional, which may be designed to incorporate some relevant information of an inverse problem. Effective determination of the Tikhonov regularization parameter requires knowledge of the true solution, or in the case of optical imaging, the true image. Yet, in, clinically-based imaging, true image is not known. To alleviate these difficulties we have applied the penalty/modified barrier function (PMBF) method instead of Tikhonov regularization technique to make the inverse problems well-posed. Unlike the Tikhonov regularization method, the constrained optimization technique, which is based on simple bounds of the optical parameter properties of the tissue, can easily be implemented in the PMBF method. Imposing the constraints on the optical properties of the tissue explicitly restricts solution sets and can restore uniqueness. Like the Tikhonov regularization method, the PMBF method limits the size of the condition number of the Hessian matrix of the given objective function. The accuracy and the rapid convergence of the PMBF method require a good initial guess of the Lagrange multipliers. To obtain the initial guess of the multipliers, we use a least square unconstrained minimization problem. Three-dimensional images of fluorescence absorption coefficients and lifetimes were reconstructed from contact and noncontact experimentally measured data.

Keywords: constrained minimization, ill-conditioned inverse problems, Tikhonov regularization method, penalty modified barrier function method

Procedia PDF Downloads 260

Authors: Hadia Rafi

Abstract:

To make any decision in any work/task/project it involves many factors that needed to be looked. The analytic Hierarchy process (AHP) is based on the judgments of experts to derive the required results this technique measures the intangibles and then by the help of judgment and software analysis the comparisons are made which shows how much a certain element/unit leads another. AHP includes how an inconsistent judgment should be made consistent and how the judgment should be improved when possible. The Priority scales are obtained by multiplying them with the priority of their parent node and after that they are added.

Keywords: AHP, priority scales, parent node, software analysis

Procedia PDF Downloads 392

Authors: Jigg Pelayo, Ricardo Villar

Abstract:

Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

Procedia PDF Downloads 230

Authors: Brahim Boukabcha, Amar Rebbouh

Abstract:

The bootstrap method based on the idea of exploiting all the information provided by the initial sample, allows us to study the properties of estimators. In this article we will present a theoretical study on the different methods of bootstrapping and using the technique of re-sampling in statistics inference to calculate the standard error of means of an estimator and determining a confidence interval for an estimated parameter. We apply these methods tested in the regression models and Pareto model, giving the best approximations.

Keywords: bootstrap, error standard, bias, jackknife, mean, median, variance, confidence interval, regression models

Procedia PDF Downloads 370