Search results for: data augmentation

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

Procedia PDF Downloads 327

Authors: Dahna Alkahtani, Faryal Suraya, Fadah Alanazi

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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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Authors: Monika Bansal

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Background: Placement of dental implant in narrow alveolar ridge is challenging to be treated. GBR procedure is currently most widely used to augment the deficient alveolar ridges and to treat the fenestration and dehiscence around dental implants. Thus, the objectives of the present study were to evaluate as well as compare the clinical performance of collagen membrane and titanium mesh for horizontal bone augmentation at dehisced implant sites. Methods and material: Total 12 single edentulous implant sites with buccal bone deficiency in 8 subjects were equally divided and treated simultaneously with either of the two membranes and DBBM(Bio-Oss) bone graft. Primary outcome measurements in terms of defect height and defect width were made using a calibrated plastic periodontal probe. Re-entry surgery was performed to remeasure the augmented site and to remove Ti-mesh at 6th month. Independent paired t-tests for the inter-group comparison and student-paired t-tests for the intra-group comparison were performed. The differences were considered to be significant at p ≤ 0.05. Results: Mean defect fill with respect to height and width was 3.50 ± 0.54 mm (87%) and 2.33 ± 0.51 mm (82%) for collagen membrane and 3.83 ± 0.75 mm (92%) and 2.50 ± 0.54 mm (88%) for Ti-mesh group respectively. Conclusions: Within the limitation of the study, it was concluded that mean defect height and width after 6 months were statistically significant within the group without significant difference between them, although defect resolution was better in Ti-mesh.

Keywords: collagen membrane, dehiscence, dental implant, horizontal bone, augmentation, ti-mesh

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Authors: Nicholas Milburn, Yu Liang, Dalei Wu

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Athlete performance scoring within the climbing do-main presents interesting challenges as the sport does not have an objective way to assign skill. Assessing skill levels within any sport is valuable as it can be used to mark progress while training, and it can help an athlete choose appropriate climbs to attempt. Machine learning-based methods are popular for complex problems like this. The dataset available was composed of dynamic force data recorded during climbing; however, this dataset came with challenges such as data scarcity, imbalance, and it was temporally heterogeneous. Investigated solutions to these challenges include data augmentation, temporal normalization, conversion of time series to the spectral domain, and cross validation strategies. The investigated solutions to the classification problem included light weight machine classifiers KNN and SVM as well as the deep learning with CNN. The best performing model had an 80% accuracy. In conclusion, there seems to be enough information within climbing force data to accurately categorize climbers by skill.

Keywords: classification, climbing, data imbalance, data scarcity, machine learning, time sequence

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

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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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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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Authors: Kruttika Bhuse

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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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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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Authors: Yogesh C. Sharma, A. Seetharaman

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The decision-making process in humans is a complicated system influenced by a variety of intrinsic and extrinsic factors. Human decisions have a ripple effect on subsequent decisions. In this study, the scope of human decision making is limited to employees. In an organisation, a person makes a variety of decisions from the time they are hired to the time they retire. The goal of this research is to identify various elements that influence decision-making. In addition, the environment in which a decision is made is a significant aspect of the decision-making process. Employees in today's workplace use artificial intelligence (AI) systems for automation and decision augmentation. The impact of AI systems on the decision-making process is examined in this study. This research is designed based on a systematic literature review. Based on gaps in the literature, limitations and the scope of future research have been identified. Based on these findings, a research framework has been designed to identify various factors affecting employee decision making. Employee decision making is influenced by technological advancement, data-driven culture, human trust, decision automation-augmentation, and workplace motivation. Hybrid human-AI systems require the development of new skill sets and organisational design. Employee psychological safety and supportive leadership influences overall job satisfaction.

Keywords: employee decision making, artificial intelligence (AI) environment, human trust, technology innovation, psychological safety

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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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Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

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Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

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Authors: Sharique Ahmed, Khushtar Anwar Salman

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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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Authors: Pankhuri Bansal, Sanjeev Kumar

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We examined the exotic role of electrical poling of layered BCZT-NZFO bulk composite for sustainable advancement of magnetoelectric (ME) technology. Practically, it seems quite difficult to access the full potential of ME composites due to their weak ME coupling performances. Using a standard poling protocol, we successfully deployed the coupling performance of laminated ME composite, comprised of a ferroelectric (FE) layer of BCZT and a ferrite layer of NZFO. However, the ME coupling constant of laminated composite is optimized by lowering the volume fraction of the FE component to strengthen the mechanical strain in the piezoelectric layer while fixing the thickness of the magnetostrictive ferrite layer. Here, we employed systematic zero field cooled (ZFC) and field cooled (FC) electrical poling protocol on morphotropic phase boundary (MPB) based BCZT composition, well-appreciated for it’s remarkable electromechanical activity. We report a record augmentation in magnetoelectric coupling as a consequence of a prudent field-cooled poling mechanism. On the basis of our findings, we emphasize that the degree of magnetoelectricity may be significantly improved for the miniaturization of efficient devices via proper execution of the poling technique.

Keywords: magnetoelectric, lead-free, ferroelctric, ferromagnetic, energy harvesting

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Authors: Kedar S. Prabhavalkar, Nimmy Baby Poovanpallil

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

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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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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Andriana Mkrtchyan, Vahe Khlghatyan

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The choice of a profession is one of the most important decisions people make throughout their life. With the development of modern science, technologies, and all the spheres existing in the modern world, more and more professions are being arisen that complicate even more the process of choosing. Hence, there is a need for a guiding platform to help people to choose a profession and the right career path based on their interests, skills, and personality. This review aims at analyzing existing methods of comparing PDF format documents and suggests that a 3-stage approach is implemented for the comparison, that is – 1. text extraction from PDF format documents, 2. comparison of the extracted text via NLP algorithms, 3. comparison representation using special shape and color psychology methodology.

Keywords: color psychology, data acquisition/extraction, data augmentation, disambiguation, natural language processing, outlier detection, semantic similarity, text-mining, user evaluation, visual search

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Authors: Mohamed Eftal Bin Mohamed Ebrahim, Alexander Varey

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Aim: Topical negative pressure has been shown to enhance angiogenesis during wound healing, both for open and closed wounds. Since angiogenesis is a key requirement for successful fat grafting, there may be a role for topical negative pressure as a means of enhancing the take rate during autologous fat grafting to breasts. Here we present a systematic review of the literature on this topic. Methods: Ovid and Embase were utilized, with searches ranging between 1960 – 2019. Terms (“Liposculpting” OR “Fat grafting” OR “Lipofilling” OR “Lipograft” OR “Fat transfer”) AND (“Negative Pressure” OR “Brava” OR “Kiwi”) AND (“Breast”) were merged as keywords. Inclusion criteria were females, autologous fat graft to breast with topical negative pressure prior to the procedure. Studies were excluded if there was no primary endpoint or non-original article. Results: Upon reviewing 219 articles, 2 met inclusion criteria. A total of 565 and 46 breasts in each article were treated respectively using the negative pressure device BRAVA®, with each cohort having different pre-and post-operative pressure settings. Khouri et al. cohort had higher graft survival (79%) compared to Del Vecchio et al. cohort (64%); however, the latter had fewer complications compared to Khouri’s cohort, e.g., fat necrosis, pneumothorax and infection. Conclusion: There is limited evidence regarding the use of topical negative pressure for fat grafting to the breasts. However, in the two studies published, the reported rates of success are high, suggesting there may be a benefit. Consequently, a randomized controlled trial on this area is required.

Keywords: fat grafting, lipograft, negative pressure, breast, breast augmentation, brava

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Authors: Gabriel Cucato, Marilia Correia, Wagner Domingues, Aline Palmeira, Paulo Longano, Nelson Wolosker, Raphael Ritti-Dias

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Women with peripheral arterial disease (PAD) present lower walking capacity in comparison with men. However, whether cardiovascular regulation is also different between genders is unknown. Thus, the aim of this study was to compare walking capacity and cardiovascular regulation between men and women with PAD. A total of 23 women (66±7 yrs) and 31 men (64±9 yrs) were recruited. Patients performed a 6-minute test and the onset claudication distance and total walking distance were measured. Additionally, cardiovascular regulation was assessed by arterial stiffness (pulse wave velocity and augmentation index) and heart rate variability (frequency domain). Independent T test or Mann-Whitney U test were performed. In comparison with men, women present lower onset claudication distance (108±66m vs. 143±50m; P=0.032) and total walking distance (286±83m vs. 361±91 m, P=0.007). Regarding cardiovascular regulation, there were no differences in heart rate variability SDNN (72±160ms vs. 32±22ms, P=0.587); RMSSD (75±209 vs. 25±22ms, P=0.726); pNN50 (11±17ms vs. 8±14ms, P=0.836) in women and men, respectively. Moreover, there were no difference in augmentation index (39±10% vs. 34±11%, P=0.103); pulse pressure (59±17mmHg vs. 56±19mmHg, P=0.593) and pulse wave velocity (8.6±2.6m\s vs. 9.0±2.7m/s, P=0.580). In conclusion, women have impaired walking capacity compared to men. However, sex differences were not observed on cardiovascular regulation in patients with PAD.

Keywords: exercise, intermittent claudication, cardiovascular load, arterial stiffness

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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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Authors: Belayneh Matebie, Michael Melese

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The study focuses on addressing the challenges in classifying and ensuring the quality of Eragrostis Teff, a small and round grain that is the smallest cereal grain. Employing a traditional classification method is challenging because of its small size and the similarity of its environmental characteristics. To overcome this, this study employs a machine learning approach to develop a source and quality classification system for Teff cereal. Data is collected from various production areas in the Amhara regions, considering two types of cereal (high and low quality) across eight classes. A total of 5,920 images are collected, with 740 images for each class. Image enhancement techniques, including scaling, data augmentation, histogram equalization, and noise removal, are applied to preprocess the data. Convolutional Neural Network (CNN) is then used to extract relevant features and reduce dimensionality. The dataset is split into 80% for training and 20% for testing. Different classifiers, including FVGG16, FINCV3, QSCTC, EMQSCTC, SVM, and RF, are employed for classification, achieving accuracy rates ranging from 86.91% to 97.72%. The ensemble of FVGG16, FINCV3, and QSCTC using the Max-Voting approach outperforms individual algorithms.

Keywords: Teff, ensemble learning, max-voting, CNN, SVM, RF

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

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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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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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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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Authors: Pranav Gunhal

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This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.

Keywords: sentiment analysis, twitter, Karnataka elections, congress, BJP, transformers, Indic languages, AI, novel architectures, IndicBERT, lok sabha elections

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Authors: Pascale Brasseur, Binu Gurung, Nicholas Halfpenny, James Eaton

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Development of minimally invasive treatments in recent years provides a potential alternative to invasive surgical interventions which are of limited value to patients with spinal metastases due to short life expectancy. A systematic review was conducted to explore the efficacy and safety of radiofrequency ablation (RFA), a minimally invasive treatment in patients with spinal metastases. EMBASE, Medline and CENTRAL were searched from database inception to March 2017 for randomised controlled trials (RCTs) and non-randomised studies. Conference proceedings for ASCO and ESMO published in 2015 and 2016 were also searched. Fourteen studies were included: three prospective interventional studies, four prospective case series and seven retrospective case series. No RCTs or studies comparing RFA with another treatment were identified. RFA was followed by cement augmentation in all patients in seven studies and some patients (40-96%) in the remaining seven studies. Efficacy was assessed as pain relief in 13/14 studies with the use of a numerical rating scale (NRS) or a visual analogue scale (VAS) at various time points. Ten of the 13 studies reported a significant decrease in pain outcome, post-RFA compared to baseline. NRS scores improved significantly at 1 week (5.9 to 3.5, p < 0.0001; 8 to 4.3, p < 0.02 and 8 to 3.9, p < 0.0001) and this improvement was maintained at 1 month post-RFA compared to baseline (5.9 to 2.6, p < 0.0001; 8 to 2.9, p < 0.0003; 8 to 2.9, p < 0.0001). Similarly, VAS scores decreased significantly at 1 week (7.5 to 2.7, p=0.00005; 7.51 to 1.73, p < 0.0001; 7.82 to 2.82, p < 0.001) and this pattern was maintained at 1 month post-RFA compared to baseline (7.51 to 2.25, p < 0.0001; 7.82 to 3.3; p < 0.001). A significant pain relief was achieved regardless of whether patients had cement augmentation in two studies assessing the impact of RFA with or without cement augmentation on VAS pain scores. In these two studies, a significant decrease in pain scores was reported for patients receiving RFA alone and RFA+cement at 1 week (4.3 to 1.7. p=0.0004 and 6.6 to 1.7, p=0.003 respectively) and 15-36 months (7.9 to 4, p=0.008 and 7.6 to 3.5, p=0.005 respectively) after therapy. Few minor complications were reported and these included neural damage, radicular pain, vertebroplasty leakage and lower limb pain/numbness. In conclusion, the efficacy and safety of RFA were consistently positive between prospective and retrospective studies with reductions in pain and few procedural complications. However, the lack of control groups in the identified studies indicates the possibility of selection bias inherent in single arm studies. Controlled trials exploring efficacy and safety of RFA in patients with spinal metastases are warranted to provide robust evidence. The identified studies provide an initial foundation for such future trials.

Keywords: pain relief, radiofrequency ablation, spinal metastases, systematic review

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Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

Abstract:

The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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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

Procedia PDF Downloads 464