Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 18365

Search results for: data augmentation

18365 Medical Image Augmentation Using Spatial Transformations for Convolutional Neural Network

Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

Abstract:

The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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18364 Deep Feature Augmentation with Generative Adversarial Networks for Class Imbalance Learning in Medical Images

Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

Abstract:

This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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18363 Data Augmentation for Automatic Graphical User Interface Generation Based on Generative Adversarial Network

Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

Abstract:

As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

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18362 Gluteal Augmentation: A Historical Perspective on Society's Fascination with Buttock Size

Authors: Shane R. Jackson

Abstract:

Gluteal augmentation with fat grafting, commonly referred to as the Brazilian Butt Lift, is the fastest-growing cosmetic surgical procedure, despite the risks and controversy that surrounds it. While many commentators attribute this rise in popularity with current societal trends towards public sharing of private life, the fascination with buttock size is in fact a much older human trait. By searching beyond medical literature and delving into historical sources, from ancient civilisations, through the Renaissance and Victorian eras to the ‘Instagram generation’ of the present day, this paper examines the differences – and similarities – in society’s ideal buttock shape and size. Furthermore, the ways in which these various cultures have altered their appearance to achieve this ideal are also examined, looking at the influence of the broader historical context. A deeper understanding of the historical, cultural and psychosocial factors that influence a patient’s desire for buttock augmentation allows the clinician to formulate a well-rounded surgical plan.

Keywords: augmentation, Brazilian butt lift, buttock, fat graft, gluteal

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18361 The Implication of Augmentation Cystoplasty with Mitrofanoff Channel on Reproduction Age Group and Outcome of Pregnancy

Authors: Amal A. Qedrah, Sofia A. Malik, Madiha Akbar

Abstract:

The aim of this article is to share a rare clinical case of pregnancy and surgical delivery in a patient who has undergone augmentation cystoplasty with mitrofanoff channel in the past. Methods: This case report is about a woman who conceived naturally at the age of 27, previously underwent augmentation cystoplasty at the age of 10 years with mitrofanoff procedure using self-clean intermittent catheterization. Furthermore, this pregnancy was complicated by the presence of preeclampsia diagnosed at term and PROM. Following the failure of induction for intrapartum preeclampsia, the patient delivered a healthy baby via low transverse cesarean section at 38 weeks done at Latifa Hospital, Dubai. Conclusion: The procedure is done at a pediatric or young age, after which most patients reach reproductive age. There is no contraindication to pregnancy vaginally or surgically; however, this case was complicated by preeclampsia, due to which this patient was taken for a cesarean section. It is advisable to consult a urologist frequently along with taking regular bacteriological urine samples and blood samples with renal ultrasonography for the evaluation of the kidney. Antibacterial treatment or prophylaxis should be used during pregnancy if necessary and intermittent self-catherization is mostly performed routinely. It is also important to have a urologist on standby during the surgery in order to avoid and/or fix any complications that might come forth.

Keywords: augmentation cystoplasty, cesarean section, delivery, mitrofanoff channel

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18360 Content-Aware Image Augmentation for Medical Imaging Applications

Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

Abstract:

Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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18359 Infrastructure Project Management and Implementation: A Case Study Of the Mokolo-Crocodile Water Augmentation Project in South Africa

Authors: Elkington Sibusiso Mnguni

Abstract:

The Mokolo-Crocodile Water Augmentation Project (MCWAP) is located in the Limpopo Province in the northern-western part of South Africa. Its purpose is to increase water supply by 30 million cubic meters per year to meet current and future demand for users, including power stations, mining houses, and the local municipality in the Lephalale area. This paper documents the planning and implementation aspects of the MCWAP infrastructure project. The study will add to the body of knowledge with respect to bulk water infrastructure development in water-scarce regions. The method used to gather and collate relevant data and information was the desktop study. The key finding was that the project was successfully completed in 2015 using conventional project management and construction methods. The project is currently being operated and maintained by the National Department of Water and Sanitation.

Keywords: construction, contract management, infrastructure project, project management

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18358 Contextual Toxicity Detection with Data Augmentation

Authors: Julia Ive, Lucia Specia

Abstract:

Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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18357 Aerodynamic Bicycle Torque Augmentation with a Wells Turbine in Wheels

Authors: Tsuyoshi Yamazaki, Etsuo Morishita

Abstract:

Cyclists often run through a crosswind and sometimes we experience the adverse pressure. We came to an idea that Wells turbine can be used as power augmentation device in the crosswind something like sails of a yacht. Wells turbine always rotates in the same direction irrespective of the incoming flow direction, and we use it in the small-scale power generation in the ocean where waves create an oscillating flow. We incorporate the turbine to the wheel of a bike. A commercial device integrates strain gauges in the crank of a bike and transmitted force and torque applied to the pedal of the bike as an e-mail to the driver’s mobile phone. We can analyze the unsteady data in a spreadsheet sent from the crank sensor. We run the bike with the crank sensor on the rollers at the exit of a low-speed wind tunnel and analyze the effect of the crosswind to the wheel with a Wells turbine. We also test the aerodynamic characteristics of the turbine separately. Although power gain depends on the flow direction, several Watts increase might be possible by the Wells turbine incorporated to a bike wheel.

Keywords: aerodynamics, Wells turbine, bicycle, wind engineering

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18356 An Approach for Estimation in Hierarchical Clustered Data Applicable to Rare Diseases

Authors: Daniel C. Bonzo

Abstract:

Practical considerations lead to the use of unit of analysis within subjects, e.g., bleeding episodes or treatment-related adverse events, in rare disease settings. This is coupled with data augmentation techniques such as extrapolation to enlarge the subject base. In general, one can think about extrapolation of data as extending information and conclusions from one estimand to another estimand. This approach induces hierarchichal clustered data with varying cluster sizes. Extrapolation of clinical trial data is being accepted increasingly by regulatory agencies as a means of generating data in diverse situations during drug development process. Under certain circumstances, data can be extrapolated to a different population, a different but related indication, and different but similar product. We consider here the problem of estimation (point and interval) using a mixed-models approach under an extrapolation. It is proposed that estimators (point and interval) be constructed using weighting schemes for the clusters, e.g., equally weighted and with weights proportional to cluster size. Simulated data generated under varying scenarios are then used to evaluate the performance of this approach. In conclusion, the evaluation result showed that the approach is a useful means for improving statistical inference in rare disease settings and thus aids not only signal detection but risk-benefit evaluation as well.

Keywords: clustered data, estimand, extrapolation, mixed model

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18355 Camera Model Identification for Mi Pad 4, Oppo A37f, Samsung M20, and Oppo f9

Authors: Ulrich Wake, Eniman Syamsuddin

Abstract:

The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights

Keywords: ​ One Cycle Policy, ResNet34, ResNet50, Test-Time Agumentation

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18354 A Review of Methods for Handling Missing Data in the Formof Dropouts in Longitudinal Clinical Trials

Authors: A. Satty, H. Mwambi

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Much clinical trials data-based research are characterized by the unavoidable problem of dropout as a result of missing or erroneous values. This paper aims to review some of the various techniques to address the dropout problems in longitudinal clinical trials. The fundamental concepts of the patterns and mechanisms of dropout are discussed. This study presents five general techniques for handling dropout: (1) Deletion methods; (2) Imputation-based methods; (3) Data augmentation methods; (4) Likelihood-based methods; and (5) MNAR-based methods. Under each technique, several methods that are commonly used to deal with dropout are presented, including a review of the existing literature in which we examine the effectiveness of these methods in the analysis of incomplete data. Two application examples are presented to study the potential strengths or weaknesses of some of the methods under certain dropout mechanisms as well as to assess the sensitivity of the modelling assumptions.

Keywords: incomplete longitudinal clinical trials, missing at random (MAR), imputation, weighting methods, sensitivity analysis

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18353 Evaluation of Intraoral Complications of Buccal Mucosa Graft in Augmentation Urethroplasty

Authors: Dahna Alkahtani, Faryal Suraya, Fadah Alanazi

Abstract:

Background: Buccal mucosal graft for urethral augmentation has surpassed other grafting options, and is now considered the standard of choice for substitution Urethroplasty. The graft has gained its popularity due to its excellent short and long-term results, easy harvesting as well as its ability in withstanding wet environments. However, although Buccal mucosal grafts are an excellent option, it is not free of complications, potential intraoral complications are bleeding, pain, swelling, injury to the nerve resulting in numbness, lip deviation or retraction. Objectives: The current study aims to evaluate the intraoral complications of buccal mucosa grafts harvested from one cheek, and used in Augmentation Urethroplasty. Methodology: The study was conducted retrospectively using the medical records of patients who underwent open augmentation urethroplasty with a buccal mucosa graft at King Khalid University Hospital, Saudi Arabia. Data collection of demographics included the type of graft used, presence or absence of strictures and its etiological factors. Pre-operative and post-operative evaluations were carried out on the subjects including the medical history, physical examination, uroflowmetry, retrograde urethrography, voiding cystourethrography and urine cultures were also noted. Further, the quality of life and complications of the procedure including the presence or occurrence of bleeding within 3-days post-procedure, the severity of pain, oral swelling after grafting, length of return to normal daily diet, painful surgical site, intake of painkillers, presence or absence of speech disturbance, numbness in the cheeks and lips were documented. Results: Thirty-two male subjects with ages ranging from 15 years to 72 years were included in the current study. Following the procedure, a hundred percent of the subjects returned to their normal daily diet by the sixth postoperative day. Further, the majority of the patients reported experiencing mild pain accounting for 61.3%, and 90.3% of the subjects reported using painkillers to control the pain. Surgical wound Pain was reportedly more common at the perineal site as 48.4% of the subjects experienced it; on the other hand, 41.9% of the patients experienced pain in the oral mucosa. The presence of speech disorders, as assessed through medical history, was found to be present in 3.2% of patients. The presence of numbness in the cheeks and lips was found in 3.2% of patients. Other complications such as parotid duct injury, delayed wound healing, non-healing wound and suture granuloma were rare as 90.3% of the subjects denied experiencing any of them, there were nonetheless reports of parotid duct injury by 6.5% of the patients, and non-healing wound by the 3.2% of patients. Conclusion: Buccal Mucosa Graft in Augmentation Urethroplasty is an ideal source of allograft, although not entirely painless; it is considerably safe with minimal intra-oral complication and undetectable strain on the patients’ quality of life.

Keywords: augmentation, buccal, graft, oral

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18352 Heat Transfer Correlations for Exhaust Gas Flow

Authors: Fatih Kantas

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Exhaust systems are key contributors to ground vehicles as a heat source. Understanding heat transfer in exhaust systems is related to defining effective parameter on heat transfer in exhaust system. In this journal, over 20 Nusselt numbers are investigated. This study shows advantages and disadvantages of various Nusselt numbers in different range Re, Pr and pulsating flow amplitude and frequency. Also (CAF) Convective Augmentation Factors are defined to correct standard Nusselt number for geometry and location of exhaust system. Finally, optimum Nusselt number and Convective Augmentation Factors are recommended according to Re, Pr and pulsating flow amplitude and frequency, geometry and location effect of exhaust system.

Keywords: exhaust gas flow, heat transfer correlation, Nusselt, Prandtl, pulsating flow

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18351 WILCKO-PERIO, Periodontally Accelerated Orthodontics

Authors: Kruttika Bhuse

Abstract:

Aim: Synergism between periodontists and orthodontists (periodontal accelerated osteogenic orthodontics- PAOO) creates crucial opportunities to enhance clinical outcomes of combined therapies regarding both disciplines and has made adult orthodontics a reality. Thus, understanding the biomechanics of bone remodelling may increase the clinical applications of corticotomy facilitated orthodontics with or without alveolar augmentation. Wilckodontics can be an attractive treatment option and be a “win-win” situation for both the dental surgeon and patient by reducing the orthodontic treatment time in adults. Materials and methods: In this review, data related to the clinical aspects, steps of procedure, biomechanics of bone, indications and contraindications and final outcome of wilckodontic shall be discussed. 50 supporting articles from various international journals and 70 clinical cases were reviewed to get a better understanding to design this wilckodontic - meta analysis. Various journals like the Journal Of Clinical And Diagnostic Research, Journal Of Indian Society Of Periodontology, Journal Of Periodontology, Pubmed, Boston Orthodontic University Journal, Good Practice Orthodontics Volume 2, have been referred to attain valuable information on wilckodontics which was then compiled in this single review study. Result: As a promising adjuvant technique based on the transient nature of demineralization-remineralisation process in healthy tissues, wilckodontics consists of regional acceleratory phenomenon by alveolar corticotomy and bone grafting of labial and palatal/lingual surfaces, followed by orthodontic force. The surgical wounding of alveolar bone potentiates tissue reorganization and healing by a way of transient burst of localized hard and soft tissue remodelling.This phenomenon causes bone healing to occur 10-50 times faster than normal bone turnover. Conclusion: This meta analysis helps understanding that the biomechanics of bone remodelling may increase the clinical applications of corticotomy facilitated orthodontics with or without alveolar augmentation. The main benefits being reduced orthodontic treatment time, increased bone volume and post-orthodontic stability.

Keywords: periodontal osteogenic accelerated orthodontics, alveolar corticotomy, bone augmentation, win-win situation

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18350 Stem Cell Augmentation Therapy for Cardiovascular Risk in Ankylosing Spondylitis: STATIN-as Study

Authors: Ashit Syngle, Nidhi Garg, Pawan Krishan

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Objective: Bone marrow derived stem cells, endothelial progenitor cells (EPCs), protect against atherosclerotic vascular damage. However, EPCs are depleted in AS and contribute to the enhanced cardiovascular risk. Statins have a protective effect in CAD and diabetes by enhancing the proliferation, migration and survival of EPCs. Therapeutic potential of augmenting EPCs to treat the heightened cardiovascular risk of AS has not yet been exploited. We aimed to investigate the effect of rosuvastatin on EPCs population and inflammation in AS. Methods: 30 AS patients were randomized to receive 6 months of treatment with rosuvastatin (10 mg/day, n=15) and placebo (n=15) as an adjunct to existing stable anti-rheumatic drugs. EPCs (CD34+/CD133+) were quantified by Flow Cytometry. Inflammatory measures (BASDAI, BASFI, CRP and ESR), pro-inflammatory cytokines (TNF-α, IL-6 and IL-1) and lipids were measured at baseline and after treatment. Results: At baseline, inflammatory measures and pro-inflammatory cytokines were elevated and EPCs depleted among both groups. EPCs increased significantly (p < 0.01) after treatment with rosuvastatin. At 6 months, BASDAI, BASFI, ESR, CRP, TNF-α, and IL-6 improved significantly in rosuvastatin group. Significant negative correlation was observed between EPCs and BASDAI, CRP and IL-6 after rosuvastatin treatment. Conclusion: First study to show that rosuvastatin augments EPCs population in AS. This defines a novel mechanism of rosuvastatin treatment in AS: the augmentation of EPCs with improvement in proinflammatory cytokines and inflammatory disease activity. The augmentation of EPCs by rosuvastatin may provide a novel strategy to prevent cardiovascular events in AS.

Keywords: ankylosing spondylitis, Endothelial Progenitor Cells, inflammation, pro-inflammatory cytokines, rosuvastatin

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18349 Oncoplastic Augmentation Mastopexy: Aesthetic Revisional Surgery in Breast Conserving Therapy

Authors: Bar Y. Ainuz, Harry M. Salinas, Aleeza Ali, Eli B. Levitt, Austin J. Pourmoussa, Antoun Bouz, Miguel A. Medina

Abstract:

Introduction: Breast conservation therapy remains the mainstay surgical treatment for early breast cancer. Oncoplastic techniques, in conjunction with lumpectomy and adjuvant radiotherapy, have been demonstrated to achieve good aesthetic results without adversely affecting cancer outcomes in the treatment of patients with macromastia or significant ptosis. In our patient population, many women present for breast conservation with pre-existing cosmetic implants or with breast volumes too small for soft tissue, only oncoplastic techniques. Our study evaluated a consecutive series of patients presenting for breast conservation undergoing concomitant oncoplastic-augmentation-mastopexy (OAM) with a contralateral augmentation-mastopexy for symmetry. Methods: OAM surgical technique involves simultaneous lumpectomy with exchange or placement of implants, oncoplastic mastopexy, and concomitant contralateral augmentation mastopexy for symmetry. Patients undergoing lumpectomy for breast conservation as outpatients were identified via retrospective chart review at a high volume private academic affiliated community-based cancer center. Patients with ptosis and either pre-existing breast implants or insufficient breast volume undergoing oncoplastic implant placement (or exchange) and mastopexy were included in the study. Operative details, aesthetic outcomes, and complications were assessed. Results: Over a continuous three-year period, with a two-surgeon cohort, 30 consecutive patients (56 breasts, 4 unilateral procedures) were identified. Patients had an average age of 52.5 years and an average BMI of 27.5, with 40% smokers or former smokers. The average operative time was 2.5 hours, the average implant size removed was 352 cc, and the average implant size placed was 300 cc. All new implants were smooth silicone, with the majority (92%) placed in a retropectoral fashion. 40% of patients received chemotherapy, and 80% of patients received whole breast adjuvant photon radiotherapy with a total radiation dose of either 42.56 or 52.56 Gy. The average and median length of follow-up were both 8.2 months. Of the 24 patients that received radiotherapy, 21% had asymmetry due to capsular contracture. A total of 7 patients (29.2%) underwent revisions for either positive margins (12.5%), capsular contracture (8.3%), implant loss (4.2%), or cosmetic concerns (4.2%). One patient developed a pulmonary embolism in the acute postoperative period and was treated with anticoagulant therapy. Conclusion: Oncoplastic augmentation mastopexy is a safe technique with good aesthetic outcomes and acceptable complication rates for ptotic patients with breast cancer and a paucity of breast volume or pre-existing implants who wish to pursue breast-conserving therapy. The revision rates compare favorably with single-stage cosmetic augmentation procedures as well as other oncoplastic techniques described in the literature. The short-term capsular contracture rates seem lower than the rates in patients undergoing radiation after mastectomy and implant-based reconstruction. Long term capsular contractures and revision rates are too early to know in this cohort.

Keywords: breast conserving therapy, oncoplastic augmentation mastopexy, capsular contracture, breast reconstruction

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18348 Heat Transfer Augmentation in a Channel with Delta Winglet Type Vortex Generators at Different Blade Angles

Authors: Nirmal Kant Singh, Anshuman Pratap Singh

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In this study the augmentation of heat transfer in a channel with delta winglet type vortex generators is evaluated. Three-dimensional numerical simulations are performed in a rectangular channel with longitudinal triangular vortex generators (LVGs). The span wise averaged Nusselt number and mean temperature are compared with and without vortex generators in the channel. The effect of variation of blade angle (15°, 30°, 45°, and 60°) is studied at a Reynolds number of 10000. The numerical results indicate that the application of LVGs effectively enhances heat transfer in the channel. The Nusselt number and mean outlet temperature were found to be greater using LVGs than in the channel without LVGs. It is observed that heat transfer increases with increase in blade angle at the same Reynolds number.

Keywords: heat transfer, rectangular channel, longitudinal vortex generators, effect of blade angle

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18347 Clothes Identification Using Inception ResNet V2 and MobileNet V2

Authors: Subodh Chandra Shakya, Badal Shrestha, Suni Thapa, Ashutosh Chauhan, Saugat Adhikari

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To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system.

Keywords: inception ResNet, convolutional neural net, deep learning, confusion matrix, data augmentation, data preprocessing

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18346 Processing Big Data: An Approach Using Feature Selection

Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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18345 Application of Deep Learning Algorithms in Agriculture: Early Detection of Crop Diseases

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

Abstract:

Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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18344 Characterization of Bovine SERPIN- Alpha-1 Antitrypsin (AAT)

Authors: Sharique Ahmed, Khushtar Anwar Salman

Abstract:

Alpha-1-antitrypsin (AAT) is a major plasma serine protease inhibitor (SERPIN). Hereditary AAT deficiency is one of the common diseases in some part of the world. AAT is mainly produced in the liver and functions to protect the lung against proteolytic damage (e.g., from neutrophil elastase) acting as the major inhibitor for neutrophil elastase. α (1)-Antitrypsin (AAT) deficiency is an under recognized genetic condition that affects approximately 1 in 2,000 to 1 in 5,000 individuals and predisposes to liver disease and early-onset emphysema. Not only does α-1-antitrypsin deficiency lead to disabling syndrome of pulmonary emphysema, there are other disorders too which include ANCA (antineutrophilic cytoplasmic antibody) positive Wegener's granulomatosis, diffuse bronchiectasis, necrotizing panniculitis in α-1-antitrypsin phenotype (S), idiopathic pulmonary fibrosis and steroid dependent asthma. Augmentation therapy with alpha-1 antitrypsin (AAT) from human plasma has been available for specific treatment of emphysema due to AAT deficiency. Apart from this several observations have also suggested a role for endogenous suppressors of HIV-1, alpha-1 antitrypsin (AAT) has been identified to be one of those. In view of its varied important role in humans, serum from a mammalian source was chosen for the isolation and purification. Studies were performed on the homogeneous fraction. This study suggests that the buffalo serum α-1-antritrypsin has characteristics close to ovine, dog, horse and more importantly to human α-1-antritrypsin in terms of its hydrodynamic properties such as molecular weight, carbohydrate content, etc. The similarities in the hydrodynamic properties of buffalo serum α-1-antitrypsin with other sources of mammalian α-1-antitrypsin mean that it can be further studied and be a potential source for "augmentation therapy", as well as a source of AAT replacement therapy to raise serum levels above the protective threshold. Other parameters like the amino acid sequence, the effect of denaturants, and the thermolability or thermostability of the inhibitor will be the interesting basis of future studies on buffalo serum alpha-1 antitrypsin (AAT).

Keywords: α-1-antitrypsin, augmentation therapy , hydrodynamic properties, serine protease inhibitor

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18343 Investigation on the Changes in the Chemical Composition and Ecological State of Soils Contaminated with Heavy Metals

Authors: Metodi Mladenov

Abstract:

Heavy metals contamination of soils is a big problem mainly as a result of industrial production. From this point of view, this is of interests the processes for decontamination of soils for crop of production with low content of heavy metals and suitable for consumption from the animals and the peoples. In the current article, there are presented data for established changes in chemical composition and ecological state on soils contaminated from non-ferrous metallurgy manufacturing, for seven years time period. There was done investigation on alteration of pH, conductivity and contain of the next elements: As, Cd, Cu, Cr, Ni, Pb, Zn, Co, Mn and Al. Also, there was done visual observations under the processes of recovery of root-inhabitable soil layer and reforestation. Obtained data show friendly changes for the investigated indicators pH and conductivity and decreasing of content of some form analyzed elements. Visual observations show augmentation of plant cover areas and change in species structure with increase of number of shrubby and wood specimens.

Keywords: conductivity, contamination of soils, chemical composition, inductively coupled plasma–optical emission spectrometry, heavy metals, visual observation

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18342 Applications of Big Data in Education

Authors: Faisal Kalota

Abstract:

Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: big data, learning analytics, analytics, big data in education, Hadoop

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18341 Analysis of Big Data

Authors: Sandeep Sharma, Sarabjit Singh

Abstract:

As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data.

Keywords: big data, unstructured data, volume, variety, velocity

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18340 Combination of Lamotrigine and Duloxetine: A Potential Approach for the Treatment of Acute Bipolar Depression

Authors: Kedar S. Prabhavalkar, Nimmy Baby Poovanpallil

Abstract:

Lamotrigine is approved for maintenance treatment of bipolar I disorder. However, its role in the treatment of acute bipolar depression is not well clear. Its efficacy in the treatment of major depressive disorders including refractory unipolar depression suggested the use of lamotrigine as an augmentation drug for acute bipolar depression. The present study aims to evaluate and perform a comparative analysis of the therapeutic effects of lamotrigine, an epileptic mood stabilizer, when used alone and in combination with duloxetine in treating acute bipolar depression at different doses of lamotrigine. Male swiss albino mice were used. For evaluation of efficacy of combination, immobility period was analyzed 30 min after the treatment from forced swim and tail suspension tests. Further amount of sucrose consumed in sucrose preference test was estimated. The combination of duloxetine and lamotrigine showed potentiation of antidepressant activity in acute models. Decrease in immobility time and increase in the amount of sucrose consumption in stressed mice were higher in combined group compared to lamotrigine monotherapy group. Brain monoamine levels were also attenuated more with combination compared to monotherapy. Results of the present study suggest potential role of lamotrigine and duloxetine combination in the treatment of acute bipolar depression.

Keywords: lamotrigine, duloxetine, acute bipolar depression, augmentation

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18339 Research of Data Cleaning Methods Based on Dependency Rules

Authors: Yang Bao, Shi Wei Deng, WangQun Lin

Abstract:

This paper introduces the concept and principle of data cleaning, analyzes the types and causes of dirty data, and proposes several key steps of typical cleaning process, puts forward a well scalability and versatility data cleaning framework, in view of data with attribute dependency relation, designs several of violation data discovery algorithms by formal formula, which can obtain inconsistent data to all target columns with condition attribute dependent no matter data is structured (SQL) or unstructured (NoSQL), and gives 6 data cleaning methods based on these algorithms.

Keywords: data cleaning, dependency rules, violation data discovery, data repair

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18338 Wireless Sensor Network to Help Low Incomes Farmers to Face Drought Impacts

Authors: Fantazi Walid, Ezzedine Tahar, Bargaoui Zoubeida

Abstract:

This research presents the main ideas to implement an intelligent system composed by communicating wireless sensors measuring environmental data linked to drought indicators (such as air temperature, soil moisture , etc...). On the other hand, the setting up of a spatio temporal database communicating with a Web mapping application for a monitoring in real time in activity 24:00 /day, 7 days/week is proposed to allow the screening of the drought parameters time evolution and their extraction. Thus this system helps detecting surfaces touched by the phenomenon of drought. Spatio-temporal conceptual models seek to answer the users who need to manage soil water content for irrigating or fertilizing or other activities pursuing crop yield augmentation. Effectively, spatio-temporal conceptual models enable users to obtain a diagram of readable and easy data to apprehend. Based on socio-economic information, it helps identifying people impacted by the phenomena with the corresponding severity especially that this information is accessible by farmers and stakeholders themselves. The study will be applied in Siliana watershed Northern Tunisia.

Keywords: WSN, database spatio-temporal, GIS, web mapping, indicator of drought

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18337 Experimental Analysis on Heat Transfer Enhancement in Double Pipe Heat Exchanger Using Al2O3/Water Nanofluid and Baffled Twisted Tape Inserts

Authors: Ratheesh Radhakrishnan, P. C. Sreekumar, K. Krishnamoorthy

Abstract:

Heat transfer augmentation techniques ultimately results in the reduction of thermal resistance in a conventional heat exchanger by generating higher convective heat transfer coefficient. It also results in reduction of size, increase in heat duty, decrease in approach temperature difference and reduction in pumping power requirements for heat exchangers. Present study deals with compound augmentation technique, which is not widely used. The study deals with the use of Alumina (Al2O3)/water nanofluid and baffled twisted tape inserts in double pipe heat exchanger as compound augmentation technique. Experiments were conducted to evaluate the heat transfer coefficient and friction factor for the flow through the inner tube of heat exchanger in turbulent flow range (8000Keywords: enhancement, heat transfer coefficient, friction factor, twisted tape, nanofluid

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18336 Mining Big Data in Telecommunications Industry: Challenges, Techniques, and Revenue Opportunity

Authors: Hoda A. Abdel Hafez

Abstract:

Mining big data represents a big challenge nowadays. Many types of research are concerned with mining massive amounts of data and big data streams. Mining big data faces a lot of challenges including scalability, speed, heterogeneity, accuracy, provenance and privacy. In telecommunication industry, mining big data is like a mining for gold; it represents a big opportunity and maximizing the revenue streams in this industry. This paper discusses the characteristics of big data (volume, variety, velocity and veracity), data mining techniques and tools for handling very large data sets, mining big data in telecommunication and the benefits and opportunities gained from them.

Keywords: mining big data, big data, machine learning, telecommunication

Procedia PDF Downloads 268