Search results for: conventionally manufacturing techniques

Authors: Meng Su

Abstract:

High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

Procedia PDF Downloads 86

Authors: Soheila Sadeghi

Abstract:

In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

Procedia PDF Downloads 18

Authors: S. Tebibel, C. Mechati, S. Messaoudi

Abstract:

Algeria is currently experiencing the same rate of cancer progression as that registered these last years in the western countries. Colorectal cancer, constituting increasingly a major public health problem, is the most common form of cancer after breast and Neck-womb cancer at the woman and prostate cancer at the man. Our work is based on a retrospective study to determine the cases of colorectal cancer through eastern Algeria. Our goal is to carry out an epidemiological, histological and immune- histochemical study to investigate different techniques for the diagnosis of colorectal cancer and their interests and specific in detecting the disease. The study includes 110 patients (aged between 20 to 87 years) with colorectal cancer where the inclusions and exclusions criteria were established. In our study, colorectal cancer, expresses a male predominance, with a sex ratio of 1, 99 and the most affected age group is between 50 and 59 years. We noted that the colon cancer rate is higher than rectal cancer rate, whose frequencies are respectively 60,91 % and 39,09 %. In the series of colon cancer, the ADK lieberkunien is histological the most represented type, or 85,07 % of all cases. In contrast, the proportion of ADK mucinous (colloid mucous) is only 1,49% only. Well-differentiated ADKS, are very significant in our series, they represent 83,58 % of cases. Adenocarcinoma moderately and poorly differentiated, whose proportions are respectively 2,99 % and 0.05 %. For histological varieties of rectal ADK, we see in our workforce that ADK lieberkunien represent the most common histological form, or 76,74%, while the mucosal colloid is 13,95 %. Research of the mutation on the gene encoding K-ras, a major step in the targeted therapy of colorectal cancers, is underway in our study. Colorectal cancer is the subject of much promising research concern: the evaluation of new therapies (antiangiogenic monoclonal antibodies), the search for predictors of sensitivity to chemotherapy and new prognostic markers using techniques of molecular biology and proteomics.

Keywords: adenocarcinoma, age, colorectal cancer, epidemiology, histological section, sex

Procedia PDF Downloads 329

Authors: Torsten Hermanns, Thoufik Al Khawli, Wolfgang Schulz

Abstract:

In laser cutting as well as in long pulsed laser drilling of metals, it can be demonstrated that the ablation shape (the shape of cut faces respectively the hole shape) that is formed approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from the ultrashort pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in laser cutting and long pulse drilling of metals is identified, its underlying mechanism numerically implemented, tested and clearly confirmed by comparison with experimental data. In detail, there now is a model that allows the simulation of the temporal (pulse-resolved) evolution of the hole shape in laser drilling as well as the final (asymptotic) shape of the cut faces in laser cutting. This simulation especially requires much less in the way of resources, such that it can even run on common desktop PCs or laptops. Individual parameters can be adjusted using sliders – the simulation result appears in an adjacent window and changes in real time. This is made possible by an application-specific reduction of the underlying ablation model. Because this reduction dramatically decreases the complexity of calculation, it produces a result much more quickly. This means that the simulation can be carried out directly at the laser machine. Time-intensive experiments can be reduced and set-up processes can be completed much faster. The high speed of simulation also opens up a range of entirely different options, such as metamodeling. Suitable for complex applications with many parameters, metamodeling involves generating high-dimensional data sets with the parameters and several evaluation criteria for process and product quality. These sets can then be used to create individual process maps that show the dependency of individual parameter pairs. This advanced simulation makes it possible to find global and local extreme values through mathematical manipulation. Such simultaneous optimization of multiple parameters is scarcely possible by experimental means. This means that new methods in manufacturing such as self-optimization can be executed much faster. However, the software’s potential does not stop there; time-intensive calculations exist in many areas of industry. In laser welding or laser additive manufacturing, for example, the simulation of thermal induced residual stresses still uses up considerable computing capacity or is even not possible. Transferring the principle of reduced models promises substantial savings there, too.

Keywords: asymptotic ablation shape, interactive process simulation, laser drilling, laser cutting, metamodeling, reduced modeling

Procedia PDF Downloads 201

Authors: Jalil ur Rehman, Ramesh C. Tailor, Muhammad Isa Khan, Jahnzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott

Abstract:

Purpose: The purpose of this analysis was to investigate dose distribution of different techniques (3D-CRT, IMRT and VMAT) of head and neck cancer using 3-dimensional dosimeter called PRESAGETM Dosimeter. Materials and Methods: Computer tomography (CT) scans of radiological physics center (RPC) head and neck anthropomorphic phantom with both RPC standard insert and PRESAGETM insert were acquired separated with Philipp’s CT scanner and both CT scans were exported via DICOM to the Pinnacle version 9.4 treatment planning system (TPS). Each plan was delivered twice to the RPC phantom first containing the RPC standard insert having TLD and film dosimeters and then again containing the Presage insert having 3-D dosimeter (PRESAGETM) by using a Varian True Beam linear accelerator. After irradiation, the standard insert including point dose measurements (TLD) and planar Gafchromic® EBT film measurement were read using RPC standard procedure. The 3D dose distribution from PRESAGETM was read out with the Duke Midsized optical scanner dedicated to RPC (DMOS-RPC). Dose volume histogram (DVH), mean and maximal doses for organs at risk were calculated and compared among each head and neck technique. The prescription dose was same for all head and neck radiotherapy techniques which was 6.60 Gy/friction. Beam profile comparison and gamma analysis were used to quantify agreements among film measurement, PRESAGETM measurement and calculated dose distribution. Quality assurances of all plans were performed by using ArcCHECK method. Results: VMAT delivered the lowest mean and maximum doses to organ at risk (spinal cord, parotid) than IMRT and 3DCRT. Such dose distribution was verified by absolute dose distribution using thermoluminescent dosimeter (TLD) system. The central axial, sagittal and coronal planes were evaluated using 2D gamma map criteria(± 5%/3 mm) and results were 99.82% (axial), 99.78% (sagital), 98.38% (coronal) for VMAT plan and found the agreement between PRESAGE and pinnacle was better than IMRT and 3D-CRT plan excludes a 7 mm rim at the edge of the dosimeter. Profile showed good agreement for all plans between film, PRESAGE and pinnacle and 3D gamma was performed for PTV and OARs, VMAT and 3DCRT endow with better agreement than IMRT. Conclusion: VMAT delivered lowered mean and maximal doses to organs at risk and better PTV coverage during head and neck radiotherapy. TLD, EBT film and PRESAGETM dosimeters suggest that VMAT was better for the treatment of head and neck cancer than IMRT and 3D-CRT.

Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD, PRESAGETM

Procedia PDF Downloads 373

Authors: Ghadeer A.Al-Jabaree, Husam Aldeen Al-Khadash, M. Nassar

Abstract:

This study aimed at investigating the effect of well-designed corporate governance system on the financial stability of insurance companies listed in ASE. Further, this study provides a comprehensive model for evaluating and analyzing insurance companies' financial position and prospective for comparing the degree of corporate governance application provisions among Jordanian insurance companies. In order to achieve the goals of the study, a whole population that consist of (27) listed insurance companies was introduced through the variables of (board of director, audit committee, internal and external auditor, board and management ownership and block holder's identities). Statistical methods were used with alternative techniques by (SPSS); where descriptive statistical techniques such as means, standard deviations were used to describe the variables, while (F) test and ANOVA analysis of variance were used to test the hypotheses of the study. The study revealed the existence of significant effect of corporate governance variables except local companies that are not listed in ASE on financial stability within control variables especially debt ratio (leverage),where it's also showed that concentration in motor third party doesn't have significant effect on insurance companies' financial stability during study period. Moreover, the study concludes that Global financial crisis affect the investment side of insurance companies with insignificant effect on the technical side. Finally, some recommendations were presented such as enhancing the laws and regulation that help the appropriate application of corporate governance, and work on activating the transparency in the disclosures of the financial statements and focusing on supporting the technical provisions for the companies, rather than focusing only on profit side.

Keywords: corporate governance, financial stability and solvency margin, insurance companies, Jordan

Procedia PDF Downloads 479

Authors: Emad A. Mohammed

Abstract:

The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

Procedia PDF Downloads 120

Authors: Terentes Printzios Dimitrios, Loanna Gourgouli, Vlachopoulos Charalambos

Abstract:

Aims: Atrial fibrillation (AF) remains one of the major causes of stroke, heart failure, sudden death and cardiovascular morbidity. Ablation techniques are becoming more appealing after the latest results of randomized trials showing the overall clinical benefit. On the other hand, imaging techniques and the frontier application of 3D printing are emerging as a valuable ally for cardiac procedures. However, no randomized trial has directly assessed the impact of preprocedural imaging and especially 3D printing guidance for AF ablation. The present study is designed to investigate for the first time the effect of 3D printing of the heart on the safety and effectiveness of the ablation procedure. Methods and design: The 3D-GALA trial is a randomized, open-label, controlled, multicentre clinical trial of 2 parallel groups designed to enroll a total of 100 patients undergoing ablation using cryo-balloon for paroxysmal and persistent AF. Patients will be randomized with a patient allocation ratio of 1: 1 to preprocedural MRI scan of the heart and 3D printing of left atrium and pulmonary veins and cryoablation versus standard cryoablation without imaging. Patients will be followed up to 6 months after the index procedure. The primary outcome measure is the reduction of radiation dose and contrast amount during pulmonary veins isolation. Secondary endpoints will include the percentage of atrial fibrillation relapse at 24h-Holter electrocardiogram monitoring at 6 months after initial treatment. Discussion: To our knowledge, the 3D-GALA trial will be the first study to provide evidence about the clinical impact of preprocedural imaging and 3D printing before cryoablation.

Keywords: atrial fibrillation, cardiac MRI, cryoablation, 3-d printing

Procedia PDF Downloads 160

Authors: Mokhlisur Rahman

Abstract:

Consumers' understanding of the products depends on what levels of information the advertisement contains. Consumers' attitudes vary widely depending on factors such as their level of environmental awareness, their perception of the company's motives, and the perceived effectiveness of the advertising campaign. Considering the growing eco-consciousness among consumers and their concern for the environment, strategies for green advertising have become equally significant for companies to attract new consumers. It is important to understand consumers' habits of purchasing, knowledge, and attitudes regarding eco-friendly products depending on promotion because of the limitless options of the products in the market. Additionally, encouraging consumers to buy sustainable products requires a platform that can message the world that being a stakeholder in sustainability is possible if consumers show eco-friendly behavior on a larger scale. Social media platforms provide an excellent atmosphere to promote companies' sustainable efforts to be connected engagingly with their potential consumers. The unique strategies of green advertising use techniques to carry information and rewards for the consumers. This study aims to understand the consumer behavior and effectiveness of green advertising by experimenting in collaboration with three companies in promoting their eco-friendly products using green designs on the products. The experiment uses three sustainable personalized offerings, Nike shoes, H&M t-shirts, and Patagonia school bags. The experiment uses a pretest and posttest design. 300 randomly selected participants take part in this experiment and survey through Facebook, Twitter, and Instagram. Nike, H&M, and Patagonia share the post of the experiment on their social media homepages with a video advertisement for the three products. The consumers participate in a pre-experiment online survey before making a purchase decision to assess their attitudes and behavior toward eco-friendly products. The audio-only feature explains the product's information, like their use of recycled materials, their manufacturing methods, sustainable packaging, and their impact on the environment during the purchase while the consumer watches the product video. After making a purchase, consumers take a post-experiment survey to know their perception and behavior toward eco-friendly products. For the data analysis, descriptive statistical tools mean, standard deviation, and frequencies measure the pre- and post-experiment survey data. The inferential statistical tool paired sample t-test measures the difference in consumers' behavior and attitudes between pre-purchase and post-experiment survey results. This experiment provides consumers ample time to consider many aspects rather than impulses. This research provides valuable insights into how companies can adopt sustainable and eco-friendly products. The result set a target for the companies to achieve a sustainable production goal that ultimately supports companies' profit-making and promotes consumers' well-being. This empowers consumers to make informed choices about the products they purchase and support their companies of interest.

Keywords: green-advertising, sustainability, consumer-behavior, social media

Procedia PDF Downloads 68

Authors: Rihab Hamdi, Amel Hadri Hamida, Fatiha Khelili, Sakina Zerouali, Ouafae Bennis

Abstract:

This paper features a comparative study performance of sliding mode controller (SMC) for closed-loop voltage control of direct current to direct current (DC-DC) three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM) with SMC based on hysteresis modulation (HM) where an adaptive feedforward technique is adopted. On one hand, for the PWM-based SM, the approach is to incorporate a fixed-frequency PWM scheme which is effectively a variant of SM control. On the other hand, for the HM-based SM, oncoming an adaptive feedforward control that makes the hysteresis band variable in the hysteresis modulator of the SM controller in the aim to restrict the switching frequency variation in the case of any change of the line input voltage or output load variation are introduced. The results obtained under load change, input change and reference change clearly demonstrates a similar dynamic response of both proposed techniques, their effectiveness is fast and smooth tracking of the desired output voltage. The PWM-based SM technique has greatly improved the dynamic behavior with a bit advantageous compared to the HM-based SM technique, as well as provide stability in any operating conditions. Simulation studies in MATLAB/Simulink environment have been performed to verify the concept.

Keywords: DC-DC converter, hysteresis modulation, parallel multi-cells converter, pulse-width modulation, robustness, sliding mode control

Procedia PDF Downloads 156

Authors: Pomila Sharma

Abstract:

Rajasthan is among regions with greatest climate sensitivity and lowest adaptive capabilities. In many parts of the Rajasthan surface water which can be highly turbid and contaminated with fecal coliform bacteria is used for drinking purposes. The majority rely almost exclusively upon traditional sources of highly turbid and untreated pathogenic surface water for their domestic water needs. In many parts of rural areas of Rajasthan, it is still difficult to obtain clean water, especially remote habitations with no groundwater due to quality issues or depletion and limited feasibility to connect with surface water schemes due to low density of population in these areas to justify large infrastructure investment. The most viable sources are rain water harvesting, community managed open wells, private wells, ponds and small-scale irrigation reservoirs have often been the main traditional sources of rural drinking water. Turbidity is conventionally removed by treating the water with expensive chemicals. This study has to investigate the use of crushed seeds from the tree Moringa oleifera (drumstick) as a natural alternative to conventional coagulant chemicals. The use of Moringa oleifera seed powder can produce potable water of higher quality than the original source. Moringa oleifera a native species of northern India, the tree is now grown extensively throughout the tropics and found in many countries of Africa, Asia & South America. The seeds of tree contains significant quantities of low molecular weight, water soluble proteins which carries the positive charge when the crushed seeds are added to water. This protein binds in raw water with negatively charged turbid water with bacteria, clay, algae, etc. Under proper mixing, these particles make flocks, which may be left to settle by gravity or be removed by filtration. Using Moringa oleifera as a replacement coagulation in such surface sources of arid and semi-arid areas can meet the need for water purification in remote places of Rajasthan state of India. The present study accesses to find out laboratory based investigation of the effect of seeds of Moringa tree on its coagulation effectiveness (purification) using turbid water samples of surface source of the Rajasthan state. In this study, moringa seed powder showed that filtering with seed powder may diminish water pollution and bacterial counts. Results showed Moringa oleifera seeds coagulate 90-95% of turbidity and color efficiently leading to an aesthetically clear supernatant & reduced about 85-90% of bacterial load reduction in samples.

Keywords: bacterial load, coagulant, turbidity, water purification

Procedia PDF Downloads 126

Authors: Micheale Yifter Weldemichael, Hailay Mehari Gebremedhn, Teklehaimanot Hailesslasie Teklu

Abstract:

Recent developments in targeted genome editing accelerated genetic research and opened new potentials to improve crops for better yields and quality. Given the significance of cereal crops as a primary source of food for the global population, the utilization of contemporary genome editing techniques like CRISPR/Cas9 is timely and crucial. CRISPR/Cas technology has enabled targeted genomic modifications, revolutionizing genetic research and exploration. Application of gene editing through CRISPR/Cas9 in enhancing sorghum is particularly vital given the current ecological, environmental, and agricultural challenges exacerbated by climate change. As sorghum is one of the main staple foods of our region and is known to be a resilient crop with a high potential to overcome the above challenges, the application of genome editing technology will enhance the investigation of gene functionality. CRISPR/Cas9 enables the improvement of desirable sorghum traits, including nutritional value, yield, resistance to pests and diseases, and tolerance to various abiotic stresses. Furthermore, CRISPR/Cas9 has the potential to perform intricate editing and reshape the existing elite sorghum varieties, and introduce new genetic variations. However, current research primarily focuses on improving the efficacy of the CRISPR/Cas9 system in successfully editing endogenous sorghum genes, making it a feasible and successful undertaking in sorghum improvement. Recent advancements and developments in CRISPR/Cas9 techniques have further empowered researchers to modify additional genes in sorghum with greater efficiency. Successful application and advancement of CRISPR techniques in sorghum will aid not only in gene discovery and the creation of novel traits that regulate gene expression and functional genomics but also in facilitating site-specific integration events. The purpose of this review is, therefore, to elucidate the current advances in sorghum genome editing and highlight its potential in addressing food security issues. It also assesses the efficiency of CRISPR-mediated improvement and its long-term effects on crop improvement and host resistance against parasites, including tissue-specific activity and the ability to induce resistance. This review ends by emphasizing the challenges and opportunities of CRISPR technology in combating parasitic plants and proposing directions for future research to safeguard global agricultural productivity.

Keywords: CRISPR/Cas9, genome editing, quality, sorghum, stress, yield

Procedia PDF Downloads 4

Authors: Bhupinder Singh, Joy Prakash Misra

Abstract:

This article provides the information regarding machining of hastelloy-X on wire electro spark machining (WEDM). Experimental investigation has been carried out by varying pulse-on time (TON), pulse-off time (TOFF), peak current (IP) and spark gap voltage (SV). Effect of these parameters is studied on material removal rate (MRR). Experiments are designed as per box-behnken design (BBD) technique of response surface methodology (RSM). Analysis of variance (ANOVA) results indicates that TON, TOFF, IP, SV, TON x IP are significant parameters that influenced the MRR, and it is depicted that value of MRR is more at high discharge energy (HDE) and less at low discharge energy (LDE). Furthermore, miniature impeller and miniature gear (OD≤10MM) is fabricated by WEDM at optimized condition.

Keywords: advanced manufacturing, WEDM, super alloy, gear

Procedia PDF Downloads 212

Authors: Bitewulign Mekonnen

Abstract:

Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

Procedia PDF Downloads 74

Authors: Midhun Paul, T. Radha Ramanan

Abstract:

The objective of this work is to present a state-of-the-art literature review highlighting the challenges in the research of the scheduling of assembly job shop problem and providing an insight on how the future directions of the research would be. The number of work has been substantial that it requires a review to enable one to understand the origin of the research and how it is getting evolved. This review paper presents a comprehensive review of the literature dealing with various studies carried on assembly job shop scheduling. The review details the evolution of the AJS from the perspective of other scheduling problems and also presents a classification scheme. The work also identifies the potential directions for future research, which we believe to be worthwhile considering.

Keywords: assembly job shop, future directions, manufacturing, scheduling

Procedia PDF Downloads 399

Authors: Saikiran Subbagari, Avinash Malladhi

Abstract:

Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

Procedia PDF Downloads 125

Authors: Javad Karimi, Prashanth Konda Gokuldoss

Abstract:

Microstructural inhomogeneity in additively manufactured materials affects the material properties. The present study aims in minimizing such microstructural inhomogeneity in Ti6Al4V alloy fabricated using selective laser melting (SLM) from the gas atomized powder. A detailed and systematic study of the effect of remelting on the microstructure and mechanical properties of Ti6Al4V manufactured by SLM was compared with electron beam melting and spark plasma sintering.

Keywords: additive manufacturing, selective laser melting, Ti6Al4V, microstructure

Procedia PDF Downloads 152

Authors: Raana Babadi Fathipour

Abstract:

Tea, a beverage widely cherished across the globe, has captured the interest of scholars with its recent acknowledgement for possessing noteworthy health advantages. Of particular significance is its proven ability to ward off ailments such as cancer and cardiovascular afflictions. Moreover, within the realm of culinary creations, lipid oxidation poses a significant challenge for food product development. In light of these aforementioned concerns, this present discourse turns its attention towards exploring diverse methodologies employed in extracting polyphenols from various types of tea leaves and examining their utility within the vast landscape of the ever-evolving food industry. Based on the discoveries unearthed in this comprehensive investigation, it has been determined that the fundamental constituents of tea are polyphenols possessed of intrinsic health-enhancing properties. This includes an assortment of catechins, namely epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate. Moreover, gallic acid, flavonoids, flavonols and theaphlavins have also been detected within this aromatic beverage. Of these myriad components examined vigorously in this study's analysis, catechin emerges as particularly beneficial. Multiple techniques have emerged over time to successfully extract key compounds from tea plants, including solvent-based extraction methodologies, microwave-assisted water extraction approaches and ultrasound-assisted extraction techniques. In particular, consideration is given to microwave-assisted water extraction method as a viable scheme which effectively procures valuable polyphenols from tea extracts. This methodology appears adaptable for implementation within sectors such as dairy production along with meat and oil industries alike.

Keywords: camellia sinensis, extraction, food application, shelf life, tea

Procedia PDF Downloads 55

Authors: Onur Onder, Celal Tanuca, Mahir Ozil, Halil Sen, Alkım Ozkan, Engin Ceylan, Ali Istek, Ozgur Saglam

Abstract:

White balance adjustment of LCD TVs is an important procedure which has a direct influence on quality perception. Existing methods adjust RGB gain and offset values in different white levels during production. This paper suggests an iterative method in which the gamma is pre-adjusted during the design stage, and only 80% white is adjusted during production by modifying only RGB gain values (offset values are not modified). This method reduces the white balance adjustment time, contributing to the total efficiency of the production. Experiment shows that the adjustment results are well within requirements.

Keywords: color temperature, LCD panel deviation, LCD TV manufacturing, white balance

Procedia PDF Downloads 202

Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

Abstract:

This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

Procedia PDF Downloads 48

Authors: Chi-Chung Marven Chick, Chu-Po Ho, Sau-Chuen Joe Au, Wing-Fai Sidney Wong, Chi-Wai Kan

Abstract:

Recently, 3D printing becomes popular process for manufacturing, especially has special attention in textile applications. However, there are various types of 3D printing materials, including plastic, resin, rubber, ceramics, gold, platinum, silver, iron, titanium but not all these materials are suitable for textile application. Generally speaking, 3D printing of textile mainly uses thermoplastic polymers such as acrylonitrile butadiene styrene (ABS), polylactide (PLA), polycaprolactone (PCL), thermoplastic polyurethane (TPU), polyethylene terephthalate glycol-modified (PETG), polystyrene (PS), polypropylene (PP). Due to the characteristics of the polymers, 3D printed textiles usually have low air permeability and poor comfortable. Therefore, in this paper, we will review the possible materials suitable for textile application with desired physical and mechanical properties.

Keywords: 3D printing, 3D printing materials, textile, properties

Procedia PDF Downloads 43

Authors: Mandlenkosi George Robert Mahlobo, Tumelo Seadira, Major Melusi Mabuza, Peter Apata Olubambi

Abstract:

Cathodic protection (CP) has been widely considered a suitable technique for mitigating corrosion of buried metal structures. Plenty of efforts have been made in developing techniques, in particular non-destructive techniques, for monitoring and quantifying the effectiveness of CP to ensure the sustainability and performance of buried steel structures. The aim of this study was to investigate the evolution of the electrochemical processes at the steel/soil interface during the application of CP on steel in simulated soil. Carbon steel was subjected to electrochemical tests with NS4 solution used as simulated soil conditions for 4 days before applying CP for a further 11 days. A previously modified non-destructive voltammetry technique was applied before and after the application of CP to measure the corrosion rate. Electrochemical impedance spectroscopy (EIS), in combination with mathematical modeling through equivalent electric circuits, was applied to determine the electrochemical behavior at the steel/soil interface. The measured corrosion rate was found to have decreased from 410 µm/yr to 8 µm/yr between days 5 and 14 because of the applied CP. Equivalent electrical circuits were successfully constructed and used to adequately model the EIS results. The modeling of the obtained EIS results revealed the formation of corrosion products via a mixed activation-diffusion mechanism during the first 4 days, while the activation mechanism prevailed in the presence of CP, resulting in a protective film. The x-ray diffraction analysis confirmed the presence of corrosion products and the predominant protective film corresponding to the calcareous deposit.

Keywords: carbon steel, cathodic protection, NS4 solution, voltammetry, EIS

Procedia PDF Downloads 44

Authors: Bashayer Al-Enezi, Budoor Al-Sabti, Eman Al-Durai, Fatmah Kalban, Meshael Ahmed

Abstract:

Production planning and control principles are concerned with planning, controlling and balancing all aspects of manufacturing including raw materials, finished goods, production schedules, and equipment requirements. Hence, an effective production planning and control system is very critical to the success of any factory. This project will focus on the application of production planning and control principles on “The National Canned Food Production and Trading Company (NCFP)” factory to find problems or areas for improvement.

Keywords: production planning, operations improvement, inventory management, National Canned Food Production and Trading Company (NCFP)

Procedia PDF Downloads 488

Authors: Nishi Bhuvandas, P. V. Timbadiya, P. L. Patel, P. D. Porey

Abstract:

Recent perceived climate variability raises concerns with unprecedented hydrological phenomena and extremes. Distribution and circulation of the waters of the Earth become increasingly difficult to determine because of additional uncertainty related to anthropogenic emissions. According to the sixth Intergovernmental Panel on Climate Change (IPCC) Technical Paper on Climate Change and water, changes in the large-scale hydrological cycle have been related to an increase in the observed temperature over several decades. Although many previous research carried on effect of change in climate on hydrology provides a general picture of possible hydrological global change, new tools and frameworks for modelling hydrological series with nonstationary characteristics at finer scales, are required for assessing climate change impacts. Of the downscaling techniques, dynamic downscaling is usually based on the use of Regional Climate Models (RCMs), which generate finer resolution output based on atmospheric physics over a region using General Circulation Model (GCM) fields as boundary conditions. However, RCMs are not expected to capture the observed spatial precipitation extremes at a fine cell scale or at a basin scale. Statistical downscaling derives a statistical or empirical relationship between the variables simulated by the GCMs, called predictors, and station-scale hydrologic variables, called predictands. The main focus of the paper is on the need for using statistical downscaling techniques for projection of local hydrometeorological variables under climate change scenarios. The projections can be then served as a means of input source to various hydrologic models to obtain streamflow, evapotranspiration, soil moisture and other hydrological variables of interest.

Keywords: climate change, downscaling, GCM, RCM

Procedia PDF Downloads 391

Authors: Hira Jabbar

Abstract:

Education is vital for inclusive growth of an economy and a critical contributor for investment in human capital. Like other developing countries, Pakistan is facing enormous challenges regarding the provision of public facilities, improper infrastructure planning, accelerating rate of population and poor accessibility. The influence of the rapid advancement and innovations in GIS and RS techniques have proved to be a useful tool for better planning and decision making to encounter these challenges. Therefore present study incorporates GIS and RS techniques to investigate the spatial distribution of school facilities, identifies settlements with served and unserved population, finds potential areas for new schools based on population and develops an accessibility index to evaluate the higher accessibility for schools. For this purpose high-resolution worldview imagery was used to develop road network, settlements and school facilities and to generate school accessibility for each level. Landsat 8 imagery was utilized to extract built-up area by applying pre and post-processing models and Landscan 2015 was used to analyze population statistics. Service area analysis was performed using network analyst extension in ArcGIS 10.3v and results were evaluated for served and underserved areas and population. An accessibility tool was used to evaluate a set of potential destinations to determine which is the most accessible with the given population distribution. Findings of the study may contribute to facilitating the town planners and education authorities for understanding the existing patterns of school facilities. It is concluded that GIS and remote sensing can be effectively used in urban transport and facility planning.

Keywords: accessibility, geographic information system, landscan, worldview

Procedia PDF Downloads 311

Authors: Salman Amur Alhajri

Abstract:

Purpose: This paper explores the art of contemporary Arabic calligraphy in Oman. It explains the aesthetic features of Arabic calligraphy as a unique icon of Islamic art. This paper also explores the profile of one Omani artist, Salman Alhajri, as an example of Omani artists who have developed unique styles in this art stream. Methodology and approach: The paper is based on a theoretical study using a descriptive and case-study approach. Omani artists are fascinated by the art forms of Arabic calligraphy, which combine both spiritual meaning and aesthetic beauty. Artist Salman Alhajri is an example of a contemporary Arabic artist who uses Arabic calligraphy as the main theme in his art. Dr. Alhajri is trying to introduce the beauty of Arabic letters from a new aesthetic point of view. He also aims to create unusual visual effects that viewers can easily interact with. Even though words and phrases appear in Alhajri’s artwork, they are not conveying direct meanings: viewers can create their own meaning or expressions from them by appreciating the compositions of the artwork. Results: Arabic writing is directly related to the identity of Omani artists and their cultural background. This paper shows how the beauty of Arabic letters comes from its indefinite possibilities in designing calligraphic expressions, even within a single word, because letters can be stretched and transformed in various ways to create different compositions. Omani artists are interested in employing new media applications in this kind of practice to find new techniques for creating artwork based on Arabic writing. It is really important for all Omani artists to practice this art style because Arabic calligraphy and its flexibility introduce infinite possibilities that involve further exploration and investigation.

Keywords: Islamic art, contemporary Arabic calligraphy, new techniques, Omani artist

Procedia PDF Downloads 348

Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

Abstract:

Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

Procedia PDF Downloads 185

Authors: Sangyun Han, Minki Kim

Abstract:

R&D outsourcing is a strategy for acquiring the competitiveness of firms as an open innovation strategy. As increasing total R&D investment of firms, the ratio of amount of R&D outsourcing in it is also increased in Korea. In this paper, we investigate the determinants and effects of R&D outsourcing of firms. Through analyzing the determinants of R&D outsourcing and effect on firm’s performance, we can find some academic and politic issues. Firstly, in the point of academic view, distinguishing the determinants of R&D outsourcing is linked why the firms do open innovation. It can be answered resource based view, core competence theory, and etc. Secondly, we can get some S&T politic implication for transferring the public intellectual properties to private area. Especially, for supporting the more SMEs or ventures, government can get the basement and the reason why and how to make the policies.

Keywords: determinants, effects, R&D, outsourcing

Procedia PDF Downloads 493

Authors: Addisu Tadesse Sahile, Tennyson Mgutshini

Abstract:

Background: Periodontal disease is a common, complex, inflammatory disease characterized by the destruction of tooth-supporting soft and hard tissues of the periodontium and a major public health problem across developed and developing countries. Objectives: The study was aimed at assessing the prevalence of periodontal disease and associated factors among diabetes patients in Addis Ababa, Ethiopia, 2018. Methods: Institutional based cross-sectional study was conducted on 388 diabetes patients selected by systematic random sampling method from March to May 2018. The study was conducted at two conveniently selected public hospitals in Addis Ababa. Data were collected with pre-tested, structured and translated questionnaire then entered to SPSS version 23 software for analysis. Descriptive statistics as a summary, in line with chi-square and binary logistics regression to identify factors associated with periodontal disease, were applied. A 95% CI with a p-value less than 5% was used as a level of significance. Results: Ninety-one percent (n=353) of participants had periodontal disease while oral examination was done in six regions. While only 9% (n=35) of participants were free of periodontal disease. The number of tooth brushings per day, correct techniques of brushing, malocclusion, and fillings that are defective were associated with periodontal disease at p < 0.05. Conclusion and recommendation: A higher prevalence of periodontal disease among diabetes patient was observed. The frequency of tooth brushing, correct techniques of brushing, malocclusion and defective fillings were associated with periodontal disease. Emphasis has to be given to oral health of diabetes patients by every concerned body so as to control the current higher burden of periodontal disease in diabetes.

Keywords: periodontal disease, risk factors, diabetes mellitus, Addis Ababa

Procedia PDF Downloads 106

Authors: Famuwagun A. A., Abiona O. O., Gbadamosi S.O., Adeboye O. A., Adebooye O. C.

Abstract:

The need to provide more information on the perceived bioactive status of the peel of plantain fruit informed the design of this research. Matured Plantain fruits were harvested, and fruits were allowed to ripen spontaneously. Samples of plantain fruit were taken every fortnight, and the peels were removed. The peels were dried using two different drying techniques (Oven drying and sun drying) and milled into powdery forms. Other samples were picked and processed in a similar manner on the first, third, seventh and tenth day until the peels of the fruits were fully ripped, resulting in eight different samples. The anti-oxidative properties of the samples using different assays (DPPH, FRAP, MCA, HRSA, SRSA, ABTS, ORAC), inhibitory activities against enzymes related to diabetes (alpha-amylase and glucosidase) and inhibition against angiotensin-converting enzymes (ACE) were evaluated. The result showed that peels of plantain fruits on the 7th day of ripening and sundried exhibited greater inhibitions against free radicals, which enhanced its antioxidant activities, resulting in greater inhibitions against alpha-amylase and alpha-glucosidase enzymes. Also, oven oven-dried sample of the peel of plantain fruit on the 7th day of ripening had greater phenolic contents than the other samples, which also resulted in higher inhibition against angiotensin converting enzymes when compared with other samples. The results showed that even though the unripe peel of plantain fruit is assumed to contain excellent bioactive activities, consumption of the peel should be allowed to ripen for seven days after maturity and harvesting so as to derive maximum benefit from the peel.

Keywords: functional ingredient, diabetics, hypertension, functional foods

Procedia PDF Downloads 33