Search results for: prediction of neoadjuvant chemotherapy effect

Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: aerothermoelasticity, elastic deformation, structural temperature, multi-field coupling

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Authors: Mahkameh Asadi, Habibollah Dadgar

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The precise definition margin of high and low-grade glioma is crucial for choosing best treatment approach after surgery and radio-chemotherapy. The aim of the current study was to assess the O-(2-18F-fluoroethyl)-L-tyrosine (18F-FET) positron emission tomography (PET)/computed tomography (CT) in patients with low (LGG) and high grade glioma (HGG). We retrospectively analyzed 18F-FET PET/CT of 10 patients (age: 33 ± 12 years) with suspicious for recurrent LGG and HGG. The final decision of recurrence was made by magnetic resonance imaging (MRI) and registered clinical data. While response to radio-chemotherapy by MRI is often complex and sophisticated due to the edema, necrosis, and inflammation, emerging amino acid PET leading to better interpretations with more specifically differentiate true tumor boundaries from equivocal lesions. Therefore, integrating amino acid PET in the management of glioma to complement MRI will significantly improve early therapy response assessment, treatment planning, and clinical trial design.

Keywords: positron emission tomography, amino acid positron emission tomography, magnetic resonance imaging, low and high grade glioma

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Authors: Trupti Diwan

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Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute.

Keywords: classification, educational data mining, student failure, grammar-based genetic programming

Procedia PDF Downloads 401

Authors: Siddhant Yadav, Ann Vincent, Sanjeev Nanda, Karen M. Fischer, Jessica A. Wright

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Statement of the Problem: Despite the growing popularity of cannabis in the general population, there are several unknowns regarding its use, specific reasons for use, patient’s choice of products, health benefits, and adverse effects. The aim of our study was to evaluate patient-reported information related to cannabis use that was recorded in the electronic medical records. Methodology & Theoretical Orientation: We manually reviewed the electronic medical records of cannabis users who were part of a large pharmacogenomic study. Data abstracted included demographics, level of education, concurrent alcohol and tobacco use, type of cannabis utilized, formulation, indication, symptomatic improvement, or adverse effects reported. Following this, we did a descriptive statistical analysis. Findings: Our sample of 164 cannabis users were predominantly female (73.2%); 66% of users reported using cannabis for medical indications. Of the 109 patients who recorded information pertaining to alcohol/tobacco use, two-thirds of cannabis users reported concurrent use of alcohol, and about half of them were former or current tobacco users. The mean age of cannabis use was 66 years. Regarding the type of cannabis, 34.1% reported using marijuana, 32.3% reported CBD use, 1.8% reported using THC, and 1.2% reported using Marinol. Oral formulations (capsules, oils, suspensions, brownies, cakes, and tea) were the most common route (44 %). Indications for use included chronic pain (n=76), anxiety (n=9), counteracting side effects of chemotherapy (n=4), and palliative reasons (n=2). Fifty-eight of the 76 users endorsed improvement in chronic pain (80%), 5 users reported improvement in anxiety, and 2 reported improvement in side effects of chemotherapy. Conclusion & Significance: The majority of our cannabis users were Caucasian females, and there was a high likelihood of coinciding use of alcohol/tobacco in patients using cannabis. Most of our patients used the oral formulation for chronic pain. Importantly, a considerable number of patients reported improvements in chronic pain, anxiety, and side effects of chemotherapy.

Keywords: cannabis use, adverse effects, medical practice, indications

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Authors: Smail Tigani, Mohamed Ouzzif

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This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one.

Keywords: discrete state, Markov Chains, linear regression, auto-adaptive systems, decision making, Monte Carlo Simulation

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Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

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

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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

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: Vishal Bhatnagar

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The objective of this study is to compare the efficacy of epidural analgesia of two amide local anesthetics, ropivacaine and levobupivacaine, with fentanyl for postoperative analgesia in major abdominal surgery CRS+HIPEC. Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS+HIPEC) are done for primary peritoneal malignancies or peritoneal spread of malignant neoplasm. CRS and HIPEC are considered one of the most painful surgery among all major abdominal surgeries. Poorly managed postoperative pain elevates stress, increases anxiety, causes prolonged Hospital stay, increases opioid requirement and side effects, increases the cost of treatment and psychological effects on patient and family. It affects the quality of life of patients. The epidural technique provides better postoperative analgesia, earlier recovery of bowel function, fewer side effects, higher patient satisfaction, and an improvement in life quality in the postoperative days after abdominal surgery than other analgesic techniques.

Keywords: HIPEC, postoperative analgesia, cytoreductive surgery, VAS score, rescue analgesia

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

Procedia PDF Downloads 191

Authors: Yong Zhao, Jian He, Cheng Zhang

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Cardiovascular diseases caused by hypertension are extremely threatening to human health, and early diagnosis of hypertension can save a large number of lives. Traditional hypertension detection methods require special equipment and are difficult to detect continuous blood pressure changes. In this regard, this paper first analyzes the principle of heart rate variability (HRV) and introduces sliding window and power spectral density (PSD) to analyze the time domain features and frequency domain features of HRV, and secondly, designs an HRV-based hypertension prediction network by combining Resnet, attention mechanism, and multilayer perceptron, which extracts the frequency domain through the improved ResNet18 features through a modified ResNet18, its fusion with time-domain features through an attention mechanism, and the auxiliary prediction of hypertension through a multilayer perceptron. Finally, the network was trained and tested using the publicly available SHAREE dataset on PhysioNet, and the test results showed that this network achieved 92.06% prediction accuracy for hypertension and outperformed K Near Neighbor(KNN), Bayes, Logistic, and traditional Convolutional Neural Network(CNN) models in prediction performance.

Keywords: feature extraction, heart rate variability, hypertension, residual networks

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Authors: Leonardo Juan Ramírez López, Javier Oswaldo Rodriguez Velasquez

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We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.

Keywords: attractor , cardiac, entropy, holter, mathematical , prediction

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Authors: Mukurdipi Ray, Seema Singh

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Extrauterine endometrial stromal sarcoma (ESS) is a rare entity and typified by delayed recurrence of primary ESS. Here, we present an unusual case of uterine ESS in a woman with a history of hysterectomy. A 19-year-old girl, underwent a hysterectomy and bilateral salpingo-oophorectomy for uterine ESS 12 months ago and now after remaining disease free for nine months ago she presented with ascites along with pelvic and peritoneal mass. Intraoperatively, the large omental mass was found, and optimal cytoreduction with total omentomy (supracolic and infracolic ) total peritonectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) was offered to the patient. Final histopathology report showed the involvement of only omentum by ESS cells. Immunohistochemistry (IHC) and receptor study were done and it was positive for CD-10 and desmin and negative for CK- 7. This case highlights the rarity of extrauterine ESS in the omentum with a known history of primary uterine ESS which was treated successfully with the above-mentioned procedure. Though active and long-term surveillance is recommended to monitor for late recurrences.

Keywords: endrometrial stromal sarcoma, complete cytoreduction, hyperthermic intra peritoneal chemotherapy, total omentectomy

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Authors: Christoph Ostermair

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We deal with the effect of correlation between prospects on human decision making under uncertainty as proposed by the comparatively new and promising model of “salience theory of choice under risk”. In this regard, we show that the theory entails the prediction that the inconsistency of choices, known as the Allais paradox, should not be an issue in the context of “real-life decision making”, which typically corresponds to situations of subjective uncertainty. The Allais paradox, probably the best-known anomaly regarding expected utility theory, would then essentially have no practical relevance. If, however, empiricism contradicts this prediction, salience theory might suffer a serious setback. Explanations of the model for variable human choice behavior are mostly the result of a particular mechanism that does not come to play under perfect correlation. Hence, if it turns out that correlation between prospects – as typically found in real-world applications – does not influence human decision making in the expected way, this might to a large extent cost the theory its explanatory power. The empirical literature regarding the Allais paradox under subjective uncertainty is so far rather moderate. Beyond that, the results are hard to maintain as an argument, as the presentation formats commonly employed, supposably have generated so-called event-splitting effects, thereby distorting subjects’ choice behavior. In our own incentivized experimental study, we control for such effects by means of two different choice settings. We find significant event-splitting effects in both settings, thereby supporting the suspicion that the so far existing empirical results related to Allais paradoxes under subjective uncertainty may not be able to answer the question at hand. Nevertheless, we find that the basic tendency behind the Allais paradox, which is a particular switch of the preference relation due to a modified common consequence, shared by two prospects, is still existent both under an event-splitting and a coalesced presentation format. Yet, the modal choice pattern is in line with the prediction of salience theory. As a consequence, the effect of correlation, as proposed by the model, might - if anything - only weaken the systematic choice pattern behind the Allais paradox.

Keywords: Allais paradox, common consequence effect, models of decision making under risk and uncertainty, salience theory

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

Procedia PDF Downloads 407

Authors: Afroze Ibrahim Baqapuri, Ilya Trofimov

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Sponsored search is a multi-billion dollar industry and makes up a major source of revenue for search engines (SE). Click-through-rate (CTR) estimation plays a crucial role for ads selection, and greatly affects the SE revenue, advertiser traffic and user experience. We propose a novel architecture of solving CTR prediction problem by combining artificial neural networks (ANN) with decision trees. First, we compare ANN with respect to other popular machine learning models being used for this task. Then we go on to combine ANN with MatrixNet (proprietary implementation of boosted trees) and evaluate the performance of the system as a whole. The results show that our approach provides a significant improvement over existing models.

Keywords: neural networks, sponsored search, web advertisement, click prediction, click-through rate

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Authors: Akram Khaleghei, Ghosheh Balagh, Viliam Makis

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In this paper, we investigate the residual life prediction problem for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model with unknown parameters. The parameter estimation procedure based on an EM algorithm is developed and the formulas for the conditional reliability function and the mean residual life are derived, illustrated by a numerical example.

Keywords: partially observable system, hidden Markov model, competing risks, residual life prediction

Procedia PDF Downloads 387

Authors: Anne Prip, Kirsten Alling Møller, Dorte Lisbet Nielsen, Mary Jarden, Marie-Helene Olsen, Anne Kjaergaard Danielsen

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Background: The development in cancer care has shifted towards shorter hospital stays and more outpatient treatment. Today, cancer care and treatment predominantly takes place in outpatient settings where encounters between patients and health care professionals are often brief. This development will probably continue internationally as the global cancer burden seems to be growing significantly. Furthermore, the number of patients who require ambulatory treatments such as chemotherapy is increasing. Focusing on the encounters between health care professionals and patients during oncology treatment has thus become increasingly important due to a growing trend in outpatient cancer management. Objective: The aim of the systematic review was to summarize the literature from the perspective of the patient, on experiences of and the need for communication and relationships with the health care professional during chemotherapy treatment in an outpatient setting. Method: The review was designed and carried out according to the PRISMA guidelines and PICO framework. The systematic search was conducted in Medline, CINAHL, The Cochrane Library and Joanna Briggs Institute Evidence Based Practice Database. Results: In all, 1174 studies were identified by literature search. After duplicates were removed, the remaining studies (n = 1053) were screened for inclusion. Nine studies were included; qualitative (n = 5) and quantitative (n = 4) as they met the inclusions criteria. The review identified that communication and relationships between health care professionals and patients were important for the patients’ ability to cope with cancer and also had an impact on patients’ satisfaction with care in the outpatient clinic. Furthermore, the review showed that hope and positivity was a need and strategy for patients with cancer and was facilitated by health care professionals. Finally, it revealed that outpatient clinic visits framed and influenced communication and relationships. Conclusions: This review identified the significance of communication and the relationships between patients and health care professionals in the outpatient setting as it supports patients’ ability to cope with cancer. The review showed the need for health care professionals to pay attention to the relational aspects of communication in an outpatient clinic as encounters are often brief. Furthermore, the review helps to specify which elements of the communication are central in the patient-health care professional interaction from the patients' perspective. Finally, it shows a need for more research to investigate which type of interaction and intervention would be the most effective in supporting patients’ coping during chemotherapy in an outpatient clinic.

Keywords: ambulatory chemotherapy, communication, health care professional-patient relation, nurse-patient relation, outpatient care, systematic review

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Authors: Herlin Vallejo, Jhon Osorio

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Quality is a concept that has gained importance in different scenarios over time, especially in the area of health. The nursing staff is one of the actors that contributes most to the care process and the satisfaction of the users in the evaluation of quality. However, until now, there are few tools to measure the quality of care in specialized performance scenarios. Patients receiving ambulatory cancer treatments can face various problems, which can increase their level of distress, so improving the quality of outpatient care for cancer patients should be a priority for oncology nursing. The experience of the patient in relation to the care in these services has been little investigated. The purpose of this study was to understand the perception that patients have about quality care in outpatient chemotherapy services. A qualitative, exploratory, descriptive study was carried out in 9 patients older than 18 years, diagnosed with cancer, who were treated at the Institute of Cancerology, in outpatient chemotherapy rooms, with a minimum of three months of treatment with curative intention and which had given your informed consent. The total of participants was determined by the theoretical saturation, and the selection of these was for convenience. Unstructured interviews were conducted, recorded and transcribed. The analysis of the information was done under the technique of content analysis. Three categories emerged that reflect the perception that patients have regarding quality care: patient-centered care, care with love and effects of care. Patients highlighted situations that show that care is centered on them, incorporating elements of patient-centered care from the institutional, infrastructure, qualities of care and what for them, in contrast, means inappropriate care. Care with love as a perception of quality care means for patients that the nursing staff must have certain qualities, perceive caring with love as a family affair, limits on care with love and the nurse-patient relationship. Quality care has effects on both the patient and the nursing staff. One of the most relevant effects was the confidence that the patient develops towards the nurse, besides to transform the unreal images about cancer treatment with chemotherapy. On the other hand, care with quality generates a commitment to self-care and is a facilitator in the transit of oncological disease and chemotherapeutic treatment, but from the perception of a healing transit. It is concluded that care with quality from the perception of patients, is a construction that goes beyond the structural issues and is related to an institutional culture of quality that is reflected in the attitude of the nursing staff and in the acts of Care that have positive effects on the experience of chemotherapy and disease. With the results, it contributes to better understand how quality care is built from the perception of patients and to open a range of possibilities for the future development of an individualized instrument that allows evaluating the quality of care from the perception of patients with cancer.

Keywords: nursing care, oncology service hospital, quality management, qualitative studies

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Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Sally Shepherd, Richard De Boer, Craig Murphy

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Background: Invasive lobular carcinoma (ILC) can metastasize to atypical sites within the peritoneal cavity, gastrointestinal, or genitourinary tract. Management varies depending on the symptom presentation, extent of disease burden, particularly if the primary disease is occult, and patient wishes. Case Series: 6 patients presented with general surgical presentations of ILC, including incomplete large bowel obstruction, cholecystitis, persistent lower abdominal pain, and faecal incontinence. 3 were diagnosed with their primary and metastatic disease in the same presentation, whilst 3 patients developed metastasis from 5 to 8 years post primary diagnosis of ILC. Management included resection of the metastasis (laparoscopic cholecystectomy), excision of the primary (mastectomy and axillary clearance), followed by a combination of aromatase inhibitors, biologic therapy, and chemotherapy. Survival post diagnosis of metastasis ranged from 3 weeks to 7 years. Conclusion: Metastatic ILC must be considered with any gastrointestinal or genitourinary symptoms in patients with a current or past history of ILC. Management may not be straightforward to chemotherapy if the acute pathology is resulting in a surgically resectable disease.

Keywords: breast cancer, gastrointestinal metastasis, invasive lobular carcinoma, metastasis

Procedia PDF Downloads 118

Authors: Angel Champion

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Chemotherapy and radiation use an acidified approach to induce apoptosis, which only kills mature cancer cells while resulting in gene and cell damage with significant levels of toxicity in tumor-affected tissues and organs. The acid approach, where the cells exterminated are not differentiated, induces the disappearance of white blood cells from the blood. This increases susceptibility to infection in severe forms of cancer spread. However, chemotherapy and radiation cannot kill cancer stem cells that metastasize, being the leading cause of 98% of cancer fatalities. With over 12 million new cancer cases symptomatic each year, including common malignancies such as Hepatocellular Carcinoma (HCC), this study aims to assess the bioactive constituents and phytochemical composition of Taraxacum Officinale (Dandelion). This analysis enables pharmaceutical quality and potency to be applied to studies on cancer cell proliferation and apoptosis. A phytochemical screening is carried out to identify the antioxidant components of Dandelion root, stem, and flower extract. The constituents tested for are phlorotannins, carbohydrates, glycosides, saponins, flavonoids, alkaloids, sterols, triterpenes, and anthraquinone glycosides. To conserve the existing phenolic compounds, a portion of the constituent tests will be examined with an acid, alcohol, or aqueous solvent. As a result, the qualitative and quantitative variations within the Dandelion extract that measure uniform effective potency are vital to the conformity for producing medicinal products. These medicines will be constructed with a consistent, uniform composition that physicians can use to control and effectively eradicate malignant diseases safely. Taraxacum Officinale's phytochemical composition comprises a highly-graded potency due to present bioactive contents that will essentially drive out malignant disease within the human body. Its high potency rate is powerful enough to eliminate both mature cancer cells and cancer stem cells without the cell and gene damage induced by chemotherapy and radiation. Correspondingly, the high margins of cancer mortality on a global scale are mitigated. This remarkable contribution to modern therapeutics will essentially optimize the margins of natural products and their derivatives, which account for 50% of pharmaceuticals in modern therapeutics, while preventing the adverse effects of radiation and chemotherapy drugs.

Keywords: antioxidant, apoptosis, metastasize, phytochemical, proliferation, potency

Procedia PDF Downloads 47

Authors: A. Babou, Y. Kerboua Ziari, Y. Kerkoub

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The risks of depletion of fossil fuel reserves and environmental problems caused by their consumption cause to consider alternative energy solutions. Hydrogen appears as a serious solution because its combustion produces only water. The objective of this study is to digitally analyze the effect of operating conditions on the process of absorption of hydrogen in a tank of metal hydride alloy Lanthanum - Nickel (LaNi 5). For this modeling of heat transfer and mass in the tank was carried .The results of numerical weather prediction are in good agreement with the experimental results.

Keywords: hydrogen, storage, energy, fuel, simulation

Procedia PDF Downloads 286

Authors: Marimuthu Gurusamy

Abstract:

In vibration analysis (with accelerometers) of rotating machines with rolling element bearing, the customers are interested to know the failure of the machine well in advance to plan the spare inventory and maintenance. But in real world most of the machines fails before the prediction of vibration analyst or Expert analysis software. Presently the prediction of failure is based on ISO 10816 vibration limits only. But this is not enough to monitor the failure of machines well in advance. Because more than 50% of the machines will fail even the vibration readings are within acceptable zone as per ISO 10816.Hence it requires further detail analysis and different techniques to predict the failure well in advance. In vibration Analysis, the velocity spectrum is used to analyse the root cause of the mechanical problems like unbalance, misalignment and looseness etc. The envelope spectrum are used to analyse the bearing frequency components, hence the failure in inner race, outer race and rolling elements are identified. But so far there is no correlation made between these two concepts. The author used both velocity spectrum and Envelope spectrum to analyse the machine behaviour and bearing condition to correlated the changes in dynamic load (by unbalance, misalignment and looseness etc.) and effect of impact on the bearing. Hence we could able to predict the expected life of the machine and bearings in the rotating equipment (with rolling element bearings). Also we used process parameters like temperature, flow and pressure to correlate with flow induced vibration and load variations, when abnormal vibration occurs due to changes in process parameters. Hence by correlation of velocity spectrum, envelope spectrum and process data with 20 years of experience in vibration analysis, the author could able to predict the rotating Equipment and its component’s deterioration and expected duration for maintenance.

Keywords: vibration analysis, velocity spectrum, envelope spectrum, prediction of deterioration

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Authors: Edwaldo R. B. Monteiro, Patricia D. M. Plentz, Edson R. De Pieri

Abstract:

Mobile robotics is gaining an increasingly important role in modern society. Several potentially dangerous or laborious tasks for human are assigned to mobile robots, which are increasingly capable. Many of these tasks need to be performed within a specified period, i.e., meet a deadline. Missing the deadline can result in financial and/or material losses. Mechanisms for predicting the missing of deadlines are fundamental because corrective actions can be taken to avoid or minimize the losses resulting from missing the deadline. In this work we propose a simple but reliable deadline missing prediction mechanism for mobile robots through the use of historical data and we use the Pioneer 3-DX robot for experiments and simulations, one of the most popular robots in academia.

Keywords: deadline missing, historical data, mobile robots, prediction mechanism

Procedia PDF Downloads 377

Authors: Chang Su Woo, Hyun Sung Park

Abstract:

Useful lifetime evaluations of rail-pads were very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of rail pads. In this study, we performed properties and accelerated heat aging tests of rail pads considering degradation factors and all environmental conditions including operation, and then derived a lifetime prediction equation according to changes in hardness, thickness, and static spring constants in the Arrhenius plot to establish how to estimate the aging of rail pads. With the useful lifetime prediction equation, the lifetime of e-clip pads was 2.5 years when the change in hardness was 10% at 25°C; and that of f-clip pads was 1.7 years. When the change in thickness was 10%, the lifetime of e-clip pads and f-clip pads is 2.6 years respectively. The results obtained in this study to estimate the useful lifetime of rail pads for high speed trains can be used for determining the maintenance and replacement schedule for rail pads.

Keywords: rail pads, accelerated test, Arrhenius plot, useful lifetime prediction, mechanical engineering design

Procedia PDF Downloads 296

Authors: H. A. Mansour

Abstract:

The objective of this research work is studying the water use prediction, prediction technology for water use by action agencies, and others involved in conservation, planning, and environmental assessment of the Water Erosion Prediction Project (WEPP) simulation model. Models the important physical, processes governing erosion in Egypt (climate, infiltration, runoff, ET, detachment by raindrops, detachment by flowing water, deposition, etc.). Simulation of the non-uniform slope, soils, cropping/management., and Egyptian databases for climate, soils, and crops. The study included important parameters in Egyptian conditions as follows: Water Balance & Percolation, Soil Component (Tillage impacts), Plant Growth & Residue Decomposition, Overland Flow Hydraulics. It could be concluded that we can adapt the WEPP simulation model to determining the previous important parameters under Egyptian conditions.

Keywords: WEPP, adaptation, soil properties, tillage impacts, water balance, soil percolation

Procedia PDF Downloads 266

Authors: Joseph C. Chen, Venkata Mohan Kudapa

Abstract:

Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations

Procedia PDF Downloads 118

Authors: Vuk M. Popovic, Dunja D. Popovic

Abstract:

Software maintenance phase is started once a software project has been developed and delivered. After that, any modification to it corresponds to maintenance. Software maintenance involves modifications to keep a software project usable in a changed or a changing environment, to correct discovered faults, and modifications, and to improve performance or maintainability. Software maintenance and management of software maintenance are recognized as two most important and most expensive processes in a life of a software product. This research is basing the prediction of maintenance, on risks and time evaluation, and using them as data sets for working with neural networks. The aim of this paper is to provide support to project maintenance managers. They will be able to pass the issues planned for the next software-service-patch to the experts, for risk and working time evaluation, and afterward to put all data to neural networks in order to get software maintenance prediction. This process will lead to the more accurate prediction of the working hours needed for the software-service-patch, which will eventually lead to better planning of budget for the software maintenance projects.

Keywords: laboratory information system, maintenance engineering, neural networks, software maintenance, software maintenance costs

Procedia PDF Downloads 325