Search results for: cancer tissue classification.

Authors: Javid Iqbal, Zhu Shifan

Abstract:

The development in the construction of unconventional ships and the implementation of lightweight materials have shown a large impulse towards finite element (FE) method, making it a general tool for ship design. This paper briefly presents the modeling and analysis techniques of ship structures using FE method for complex boundary conditions which are difficult to analyze by existing Ship Classification Societies rules. During operation, all ships experience complex loading conditions. These loads are general categories into thermal loads, linear static, dynamic and non-linear loads. General strength of the ship structure is analyzed using static FE analysis. FE method is also suitable to consider the local loads generated by ballast tanks and cargo in addition to hydrostatic and hydrodynamic loads. Vibration analysis of a ship structure and its components can be performed using FE method which helps in obtaining the dynamic stability of the ship. FE method has developed better techniques for calculation of natural frequencies and different mode shapes of ship structure to avoid resonance both globally and locally. There is a lot of development towards the ideal design in ship industry over the past few years for solving complex engineering problems by employing the data stored in the FE model. This paper provides an overview of ship modeling methodology for FE analysis and its general application. Historical background, the basic concept of FE, advantages, and disadvantages of FE analysis are also reported along with examples related to hull strength and structural components.

Keywords: Dynamic analysis, finite element methods, ship structure, vibration analysis.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2378

Authors: S. M. Seyedhosseini, A. Makui, M. Ghadami

Abstract:

Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.

Keywords: Case based reasoning, Genetic algorithm, Groupdecision making, Product management.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2137

Authors: Zaineb Ben Messaoud, Dorra Gargouri, Saida Zribi, Ahmed Ben Hamida

Abstract:

This paper presents a formant-tracking linear prediction (FTLP) model for speech processing in noise. The main focus of this work is the detection of formant trajectory based on Hidden Markov Models (HMM), for improved formant estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of a time- sequence of peaks which satisfies continuity constraints on parameter; the within peaks are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning multi-band spectral subtraction stage to reduce the effect of residue noise on formants (2) estimation stage where an initial estimate of the LP model of speech for each frame is obtained (3) a formant classification using probability models of formants and Viterbi-decoders. The evaluation results for the estimation of the formant tracking LP model tested in Gaussian white noise background, demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and LPC variable order analysis, results in a significant reduction in errors and distortions. The performance was evaluated with noisy natual vowels extracted from international french and English vocabulary speech signals at SNR value of 10dB. In each case, the estimated formants are compared to reference formants.

Keywords: Formants Estimation, HMM, Multi Band Spectral Subtraction, Variable order LPC coding, White Gauusien Noise.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1931

Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

Abstract:

Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: Communication signal, feature extraction, holder coefficient, improved cloud model.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 648

Authors: N. H. Harun, A. S. Abdul Nasir, M. Y. Mashor, R. Hassan

Abstract:

Leukaemia is a blood cancer disease that contributes to the increment of mortality rate in Malaysia each year. There are two main categories for leukaemia, which are acute and chronic leukaemia. The production and development of acute leukaemia cells occurs rapidly and uncontrollable. Therefore, if the identification of acute leukaemia cells could be done fast and effectively, proper treatment and medicine could be delivered. Due to the requirement of prompt and accurate diagnosis of leukaemia, the current study has proposed unsupervised pixel segmentation based on clustering algorithm in order to obtain a fully segmented abnormal white blood cell (blast) in acute leukaemia image. In order to obtain the segmented blast, the current study proposed three clustering algorithms which are k-means, fuzzy c-means and moving k-means algorithms have been applied on the saturation component image. Then, median filter and seeded region growing area extraction algorithms have been applied, to smooth the region of segmented blast and to remove the large unwanted regions from the image, respectively. Comparisons among the three clustering algorithms are made in order to measure the performance of each clustering algorithm on segmenting the blast area. Based on the good sensitivity value that has been obtained, the results indicate that moving kmeans clustering algorithm has successfully produced the fully segmented blast region in acute leukaemia image. Hence, indicating that the resultant images could be helpful to haematologists for further analysis of acute leukaemia.

Keywords: Acute Leukaemia Images, Clustering Algorithms, Image Segmentation, Moving k-Means.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2738

Authors: Sujata Upgupta, Prasoon Kumar Singh

Abstract:

The expansion of large scale coal mining into forest areas is a potential hazard for the local biodiversity and wildlife. The objective of this study is to provide a picture of the threat that coal mining poses to the forests of the East Bokaro landscape. The vulnerable forest areas at risk have been assessed and the priority areas for conservation have been presented. The forested areas at risk in the current scenario have been assessed and compared with the past conditions using classification and buffer based overlay approach. Forest vulnerability has been assessed using an analytical framework based on systematic indicators and composite vulnerability index values. The results indicate that more than 4 km² of forests have been lost from 1973 to 2016. Large patches of forests have been diverted for coal mining projects. Forests in the northern part of the coal field within 1-3 km radius around the coal mines are at immediate risk. The original contiguous forests have been converted into fragmented and degraded forest patches. Most of the collieries are located within or very close to the forests thus threatening the biodiversity and hydrology of the surrounding regions. Based on the vulnerability values estimated, it was concluded that more than 90% of the forested grids in East Bokaro are highly vulnerable to mining. The forests in the sub-districts of Bermo and Chandrapura have been identified as the most vulnerable to coal mining activities. This case study would add to the capacity of the forest managers and mine managers to address the risk and vulnerability of forests at a small landscape level in order to achieve sustainable development.

Keywords: Coal mining, forest, indicators, vulnerability.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1124

Authors: N. Nelasov, D. Safonov, M. Babaev, E. Mirzojan, O. Eroshenko, M. Morgunov, A. Erofeeva

Abstract:

We have shown previously that reflected high intensity motion signals (RIMS) can be used for detection of left ventricular (LV) diastolic dysfunction (DD). It is also well known, that left atrial (LA) dimension can be used as a marker of DD. In this study we decided to analyze the diagnostic role of new echocardiographic morphofunctional diastolic index (MFDI) in differentiation of normal filling of LV from pseudonormal and restrictive. MFDI includes LA dimension and velocity of early diastolic component ea of RIMS (MFDI = LA/ea).  

343 healthy subjects and patients with various cardiac pathology underwent dopplerechocardiographic exam. According to the criteria of "Don" classification scheme 155 subjects had signs of normal LV filling (N) and 55 - of pseudonormal and restrictive filling (PN + R). LA dimension was performed in standard manner. RIMS were registered by conventional pulsed wave Doppler from apical 4-chamber view, when the sample volume was positioned between the tips of mitral leaflets. The velocity of early diastolic component of RIMS was measured. After calculation of MFDI mean values of this index in two groups (N and PN + R) were compared. The cutoff value of MFDI for differentiation of patients with N and PN + R was determined.

Mean value of MFDI in subjects with normal filling was 1.38+0.33 and in patients with pseudonormal and restrictive filling 2.43+0.43; p<0.0001. The cutoff value of MFDI > 2.0 separated subjects with normal LV filling from subjects with pseudonormal and restrictive filling with sensitivity 89.1% and specificity 97.4%.

Keywords: Dopplerechocardiography, diastolic dysfunction, left atrium, reflected high intensity motion signals.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1554

Authors: Meng Fanchao, Zhan Dechen, Xu Xiaofei

Abstract:

Software reuse can be considered as the most realistic and promising way to improve software engineering productivity and quality. Automated assistance for software reuse involves the representation, classification, retrieval and adaptation of components. The representation and retrieval of components are important to software reuse in Component-Based on Software Development (CBSD). However, current industrial component models mainly focus on the implement techniques and ignore the semantic information about component, so it is difficult to retrieve the components that satisfy user-s requirements. This paper presents a method of business component retrieval based on specification matching to solve the software reuse of enterprise information system. First, a business component model oriented reuse is proposed. In our model, the business data type is represented as sign data type based on XML, which can express the variable business data type that can describe the variety of business operations. Based on this model, we propose specification match relationships in two levels: business operation level and business component level. In business operation level, we use input business data types, output business data types and the taxonomy of business operations evaluate the similarity between business operations. In the business component level, we propose five specification matches between business components. To retrieval reusable business components, we propose the measure of similarity degrees to calculate the similarities between business components. Finally, a business component retrieval command like SQL is proposed to help user to retrieve approximate business components from component repository.

Keywords: Business component, business operation, business data type, specification matching.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1369

Authors: Farhad H. Mustafa, Mohamad S. Jaafar , Asaad H. Ismail, Ahamad F. Omar, Zahra A. Timimi, Hend A. A. Houssein

Abstract:

The process of laser absorption in the skin during laser irradiation was a critical point in medical application treatments. Delivery the correct amount of laser light is a critical element in photodynamic therapy (PDT). More amounts of laser light able to affect tissues in the skin and small amount not able to enhance PDT procedure in skin. The knowledge of the skin tone laser dependent distribution of 635 nm radiation and its penetration depth in skin is a very important precondition for the investigation of advantage laser induced effect in (PDT) in epidermis diseases (psoriasis). The aim of this work was to estimate an optimum effect of diode laser (635 nm) on the treatment of epidermis diseases in different color skin. Furthermore, it is to improve safety of laser in PDT in epidermis diseases treatment. Advanced system analytical program (ASAP) which is a new approach in investigating the PDT, dependent on optical properties of different skin color was used in present work. A two layered Realistic Skin Model (RSM); stratum corneum and epidermal with red laser (635 nm, 10 mW) were used for irradiative transfer to study fluence and absorbance in different penetration for various human skin colors. Several skin tones very fair, fair, light, medium and dark are used to irradiative transfer. This investigation involved the principles of laser tissue interaction when the skin optically injected by a red laser diode. The results demonstrated that the power characteristic of a laser diode (635 nm) can affect the treatment of epidermal disease in various color skins. Power absorption of the various human skins were recorded and analyzed in order to find the influence of the melanin in PDT treatment in epidermal disease. A two layered RSM show that the change in penetration depth in epidermal layer of the color skin has a larger effect on the distribution of absorbed laser in the skin; this is due to the variation of the melanin concentration for each color.

Keywords: Photodynamic therapy, Realistic skin model, Laser, Light penetration, simulation, Optical properties of skin, Melanin.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2338

Authors: D. Jayalakshmi, S. Bhosale

Abstract:

This paper, in its first part, presents the state-of-the-art literature of design approaches for geosynthetic reinforced unpaved roads. The literature starting since 1970 and the critical appraisal of flexible pavement design by Giroud and Han (2004) and Jonathan Fannin (2006) is presented. The design example is illustrated for Indian conditions. The example emphasizes the results computed by Giroud and Han's (2004) design method with the Indian road congress guidelines by IRC SP 72 -2015. The input data considered are related to the subgrade soil condition of Maharashtra State in India. The unified soil classification of the subgrade soil is inorganic clay with high plasticity (CH), which is expansive with a California bearing ratio (CBR) of 2% to 3%. The example exhibits the unreinforced case and geotextile as reinforcement by varying the rut depth from 25 mm to 100 mm. The present result reveals the base thickness for the unreinforced case from the IRC design catalogs is in good agreement with Giroud and Han (2004) approach for a range of 75 mm to 100 mm rut depth. Since Giroud and Han (2004) method is applicable for both reinforced and unreinforced cases, for the same data with appropriate Nc factor, for the same rut depth, the base thickness for the reinforced case has arrived for the Indian condition. From this trial, for the CBR of 2%, the base thickness reduction due to geotextile inclusion is 35%. For the CBR range of 2% to 5% with different stiffness in geosynthetics, the reduction in base course thickness will be evaluated, and the validation will be executed by the full-scale accelerated pavement testing set up at the College of Engineering Pune (COE), India.

Keywords: Base thickness, design approach, equation, full scale accelerated pavement set up, Indian condition.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 585

Authors: Adel Dalilottojari, Bahman Delalat, Frances J. Harding, Michaelia P. Cockshell, Claudine S. Bonder, Nicolas H. Voelcker

Abstract:

Endothelial Progenitor Cell (EPC) based therapies continue to be of interest to treat ischemic events based on their proven role to promote blood vessel formation and thus tissue re-vascularisation. Current strategies for the production of clinical-grade EPCs requires the in vitro isolation of EPCs from peripheral blood followed by cell expansion to provide sufficient quantities EPCs for cell therapy. This study aims to examine the use of different biomolecules to significantly improve the current strategy of EPC capture and expansion on collagen type I (Col I). In this study, four different biomolecules were immobilised on a surface and then investigated for their capacity to support EPC capture and proliferation. First, a cell microarray platform was fabricated by coating a glass surface with epoxy functional allyl glycidyl ether plasma polymer (AGEpp) to mediate biomolecule binding. The four candidate biomolecules tested were Col I, collagen type II (Col II), collagen type IV (Col IV) and vascular endothelial growth factor A (VEGF-A), which were arrayed on the epoxy-functionalised surface using a non-contact printer. The surrounding area between the printed biomolecules was passivated with polyethylene glycol-bisamine (A-PEG) to prevent non-specific cell attachment. EPCs were seeded onto the microarray platform and cell numbers quantified after 1 h (to determine capture) and 72 h (to determine proliferation). All of the extracellular matrix (ECM) biomolecules printed demonstrated an ability to capture EPCs within 1 h of cell seeding with Col II exhibiting the highest level of attachment when compared to the other biomolecules. Interestingly, Col IV exhibited the highest increase in EPC expansion after 72 h when compared to Col I, Col II and VEGF-A. These results provide information for significant improvement in the capture and expansion of human EPC for further application.

Keywords: Cardiovascular disease, cell microarray platform, cell therapy, endothelial progenitor cells, high throughput screening.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1387

Authors: J. Pazourek, K. Šmejkal, P. Kollár, J. Rajchard, J. Šinko, Z. Balounová, E. Vlková, H. Salmonová

Abstract:

Pectinatella magnifica (Leidy, 1851) is an invasive freshwater animal that lives in colonies. A colony of Pectinatella magnifica (a gelatinous blob) can be up to several feet in diameter large and under favorable conditions it exhibits an extreme growth rate. Recently European countries around rivers of Elbe, Oder, Danube, Rhine and Vltava have confirmed invasion of Pectinatella magnifica, including freshwater reservoirs in South Bohemia (Czech Republic). Our project (Czech Science Foundation, GAČR P503/12/0337) is focused onto biology and chemistry of Pectinatella magnifica. We monitor the organism occurrence in selected South Bohemia ponds and sandpits during the last years, collecting information about physical properties of surrounding water, and sampling the colonies for various analyses (classification, maps of secondary metabolites, toxicity tests). Because the gelatinous matrix is during the colony lifetime also a host for algae, bacteria and cyanobacteria (co-habitants), in this contribution, we also applied a high performance liquid chromatography (HPLC) method for determination of potentially present cyanobacterial toxins (microcystin-LR, microcystin-RR, nodularin). Results from the last 3-year monitoring show that these toxins are under limit of detection (LOD), so that they do not represent a danger yet. The final goal of our study is to assess toxicity risks related to fresh water resources invaded by Pectinatella magnifica, and to understand the process of invasion, which can enable to control it.

Keywords: Cyanobacteria, freshwater resources, Pectinatella magnifica invasion, toxicity monitoring.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1830

Authors: Ahmed Rekik, Mourad Zribi, Ahmed Ben Hamida, Mohamed Benjelloun

Abstract:

This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.

Keywords: Unsupervised classification, Pearson system, Satellite image, Segmentation.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1989

Authors: Aditya Arya, Hairin Taha, Ataul Karim Khan, Nayiar Shahid, Hapipah Mohd Ali, Mustafa Ali Mohd

Abstract:

This study has investigated the antidiabetic and antioxidant potential of Pseudovaria macrophylla bark extract on streptozotocin–nicotinamide induced type 2 diabetic rats. LCMSQTOF and NMR experiments were done to determine the chemical composition in the methanolic bark extract. For in vivo experiments, the STZ (60 mg/kg/b.w, 15 min after 120 mg/kg/1 nicotinamide, i.p.) induced diabetic rats were treated with methanolic extract of Pseuduvaria macrophylla (200 and 400 mg/kg·bw) and glibenclamide (2.5 mg/kg) as positive control respectively. Biochemical parameters were assayed in the blood samples of all groups of rats. The pro-inflammatory cytokines, antioxidant status and plasma transforming growth factor βeta-1 (TGF-β1) were evaluated. The histological study of the pancreas was examined and its expression level of insulin was observed by immunohistochemistry. In addition, the expression of glucose transporters (GLUT 1, 2 and 4) were assessed in pancreas tissue by western blot analysis. The outcomes of the study displayed that the bark methanol extract of Pseuduvaria macrophylla has potentially normalized the elevated blood glucose levels and improved serum insulin and C-peptide levels with significant increase in the antioxidant enzyme, reduced glutathione (GSH) and decrease in the level of lipid peroxidation (LPO). Additionally, the extract has markedly decreased the levels of serum pro-inflammatory cytokines and transforming growth factor beta-1 (TGF-β1). Histopathology analysis demonstrated that Pseuduvaria macrophylla has the potential to protect the pancreas of diabetic rats against peroxidation damage by downregulating oxidative stress and elevated hyperglycaemia. Furthermore, the expression of insulin protein, GLUT-1, GLUT-2 and GLUT-4 in pancreatic cells was enhanced. The findings of this study support the anti-diabetic claims of Pseudovaria macrophylla bark.

Keywords: Diabetes mellitus, Pseuduvaria macrophylla, alkaloids, caffeic acid.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2730

Authors: Syed Dawood Md. Taimur, Md. Hasanur Rahman, Syeda Fahmida Afrin, Farzana Islam

Abstract:

The concept of myocardial injury, although first recognized from animal studies, is now recognized as a clinical phenomenon that may result in microvascular damage, no-reflow phenomenon, myocardial stunning, myocardial hibernation and ischemic preconditioning. The final consequence of this event is left ventricular (LV) systolic dysfunction leading to increased morbidity and mortality. The typical clinical case of reperfusion injury occurs in acute myocardial infarction (MI) with ST segment elevation in which an occlusion of a major epicardial coronary artery is followed by recanalization of the artery. This may occur spontaneously or by means of thrombolysis and/or by primary percutaneous coronary intervention (PCI) with efficient platelet inhibition by aspirin (acetylsalicylic acid), clopidogrel and glycoprotein IIb/IIIa inhibitors. In recent years, percutaneous coronary intervention (PCI) has become a well-established technique for the treatment of coronary artery disease. PCI improves symptoms in patients with coronary artery disease and it has been increasing safety of procedures. However, peri- and post-procedural myocardial injury, including angiographical slow coronary flow, microvascular embolization, and elevated levels of cardiac enzyme, such as creatine kinase and troponin-T and -I, has also been reported even in elective cases. Furthermore, myocardial reperfusion injury at the beginning of myocardial reperfusion, which causes tissue damage and cardiac dysfunction, may occur in cases of acute coronary syndrome. Because patients with myocardial injury is related to larger myocardial infarction and have a worse long-term prognosis than those without myocardial injury, it is important to prevent myocardial injury during and/or after PCI in patients with coronary artery disease. To date, many studies have demonstrated that adjunctive pharmacological treatment suppresses myocardial injury and increases coronary blood flow during PCI procedures. In this review, we highlight the usefulness of pharmacological treatment in combination with PCI in attenuating myocardial injury in patients with coronary artery disease.

Keywords: Coronary artery disease, Percutaneous coronary intervention, Myocardial injury, Pharmacology.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2274

Authors: Andressa S. T. Gomes, Luiza A. Souza, Luciana H. Yamane, Renato R. Siman

Abstract:

The segregation of waste of electrical and electronic equipment (WEEE) in the generating source, its characterization (quali-quantitative) and identification of origin, besides being integral parts of classification reports, are crucial steps to the success of its integrated management. The aim of this paper was to count WEEE generation at the Federal University of Espírito Santo (UFES), Brazil, as well as to define sources, temporary storage sites, main transportations routes and destinations, the most generated WEEE and its recycling potential. Quantification of WEEE generated at the University in the years between 2010 and 2015 was performed using data analysis provided by UFES’s sector of assets management. EEE and WEEE flow in the campuses information were obtained through questionnaires applied to the University workers. It was recorded 6028 WEEEs units of data processing equipment disposed by the university between 2010 and 2015. Among these waste, the most generated were CRT screens, desktops, keyboards and printers. Furthermore, it was observed that these WEEEs are temporarily stored in inappropriate places at the University campuses. In general, these WEEE units are donated to NGOs of the city, or sold through auctions (2010 and 2013). As for recycling potential, from the primary processing and further sale of printed circuit boards (PCB) from the computers, the amount collected could reach U$ 27,839.23. The results highlight the importance of a WEEE management policy at the University.

Keywords: Solid waste, waste of electric and electronic equipment, waste management, institutional generation of solid waste.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1503

Authors: G. A. Khalid, M. D. Jones, R. Prabhu, A. Mason-Jones, W. Whittington, H. Bakhtiarydavijani, P. S. Theobald

Abstract:

Head injury in childhood is a common cause of death or permanent disability from injury. However, despite its frequency and significance, there is little understanding of how a child’s head responds during injurious loading. Whilst Infant Post Mortem Human Subject (PMHS) experimentation is a logical approach to understand injury biomechanics, it is the authors’ opinion that a lack of subject availability is hindering potential progress. Computer modelling adds great value when considering adult populations; however, its potential remains largely untapped for infant surrogates. The complexities of child growth and development, which result in age dependent changes in anatomy, geometry and physical response characteristics, present new challenges for computational simulation. Further geometric challenges are presented by the intricate infant cranial bones, which are separated by sutures and fontanelles and demonstrate a visible fibre orientation. This study presents an FE model of a newborn infant’s head, developed from high-resolution computer tomography scans, informed by published tissue material properties. To mimic the fibre orientation of immature cranial bone, anisotropic properties were applied to the FE cranial bone model, with elastic moduli representing the bone response both parallel and perpendicular to the fibre orientation. Biofiedility of the computational model was confirmed by global validation against published PMHS data, by replicating experimental impact tests with a series of computational simulations, in terms of head kinematic responses. Numerical results confirm that the FE head model’s mechanical response is in favourable agreement with the PMHS drop test results.

Keywords: Finite element analysis, impact simulation, infant head trauma, material properties, post mortem human subjects.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1241

Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

Abstract:

The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluates the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: lexical semantics, feature representation, semantic decision, convolutional neural network, electronic medical record

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 527

Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

Abstract:

In this work, we use machine learning and data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. Our work applies modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 385

Authors: Satyanadh Gundimada, Vijayan K Asari

Abstract:

A novel feature selection strategy to improve the recognition accuracy on the faces that are affected due to nonuniform illumination, partial occlusions and varying expressions is proposed in this paper. This technique is applicable especially in scenarios where the possibility of obtaining a reliable intra-class probability distribution is minimal due to fewer numbers of training samples. Phase congruency features in an image are defined as the points where the Fourier components of that image are maximally inphase. These features are invariant to brightness and contrast of the image under consideration. This property allows to achieve the goal of lighting invariant face recognition. Phase congruency maps of the training samples are generated and a novel modular feature selection strategy is implemented. Smaller sub regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are arranged in the order of increasing distance between the sub regions involved in merging. The assumption behind the proposed implementation of the region merging and arrangement strategy is that, local dependencies among the pixels are more important than global dependencies. The obtained feature sets are then arranged in the decreasing order of discriminating capability using a criterion function, which is the ratio of the between class variance to the within class variance of the sample set, in the PCA domain. The results indicate high improvement in the classification performance compared to baseline algorithms.

Keywords: Discriminant analysis, intra-class probability distribution, principal component analysis, phase congruency.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1816

Authors: Saba Gachpaz, Hamid Reza Heidari

Abstract:

The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. This necessitates increased resource consumption and underscores the importance of addressing sustainable agriculture development along with other environmental considerations. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for 10 different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: Land suitability, machine learning, random forest, sustainable agriculture.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 189

Authors: Roberto Sabatini, Alessandro Gardi, Mark A. Richardson

Abstract:

The availability of powerful eye-safe laser sources and the recent advancements in electro-optical and mechanical beam-steering components have allowed laser-based Light Detection and Ranging (LIDAR) to become a promising technology for obstacle warning and avoidance in a variety of manned and unmanned aircraft applications. LIDAR outstanding angular resolution and accuracy characteristics are coupled to its good detection performance in a wide range of incidence angles and weather conditions, providing an ideal obstacle avoidance solution, which is especially attractive in low-level flying platforms such as helicopters and small-to-medium size Unmanned Aircraft (UA). The Laser Obstacle Avoidance Marconi (LOAM) system is one of such systems, which was jointly developed and tested by SELEX-ES and the Italian Air Force Research and Flight Test Centre. The system was originally conceived for military rotorcraft platforms and, in this paper, we briefly review the previous work and discuss in more details some of the key development activities required for integration of LOAM on UA platforms. The main hardware and software design features of this LOAM variant are presented, including a brief description of the system interfaces and sensor characteristics, together with the system performance models and data processing algorithms for obstacle detection, classification and avoidance. In particular, the paper focuses on the algorithm proposed for optimal avoidance trajectory generation in UA applications.

Keywords: LIDAR, Low-Level Flight, Nap-of-the-Earth Flight, Near Infra-Red, Obstacle Avoidance, Obstacle Detection, Obstacle Warning System, Sense and Avoid, Trajectory Optimisation, Unmanned Aircraft.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6993

Authors: Moses, G, Osinubi, K. J.

Abstract:

A laboratory study on the influence of compactive effort on expansive black cotton specimens treated with up to 8% ordinary Portland cement (OPC) admixed with up to 8% bagasse ash (BA) by dry weight of soil and compacted using the energies of the standard Proctor (SP), West African Standard (WAS) or “intermediate” and modified Proctor (MP) were undertaken. The expansive black cotton soil was classified as A-7-6 (16) or CL using the American Association of Highway and Transportation Officials (AASHTO) and Unified Soil Classification System (USCS), respectively. The 7day unconfined compressive strength (UCS) values of the natural soil for SP, WAS and MP compactive efforts are 286, 401 and 515kN/m2 respectively, while peak values of 1019, 1328 and 1420kN/m2 recorded at 8% OPC/ 6% BA, 8% OPC/ 2% BA and 6% OPC/ 4% BA treatments, respectively were less than the UCS value of 1710kN/m2 conventionally used as criterion for adequate cement stabilization. The soaked California bearing ratio (CBR) values of the OPC/BA stabilized soil increased with higher energy level from 2, 4 and 10% for the natural soil to Peak values of 55, 18 and 8% were recorded at 8% OPC/4% BA 8% OPC/2% BA and 8% OPC/4% BA, treatments when SP, WAS and MP compactive effort were used, respectively. The durability of specimens was determined by immersion in water. Soils treatment at 8% OPC/ 4% BA blend gave a value of 50% resistance to loss in strength value which is acceptable because of the harsh test condition of 7 days soaking period specimens were subjected instead of the 4 days soaking period that specified a minimum resistance to loss in strength of 80%. Finally An optimal blend of is 8% OPC/ 4% BA is recommended for treatment of expansive black cotton soil for use as a sub-base material.

Keywords: Bagasse ash, California bearing ratio, Compaction, Durability, Ordinary Portland cement, Unconfined compressive strength.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3522

Authors: Vladyslav Povoroznyuk, Anna Musiienko, Nataliia Zaverukha, Roksolana Povoroznyuk

Abstract:

Along with the global aging of population, the number of people with somatic diseases is increasing, including such interrelated pathologies as obesity, osteoarthritis (OA) and osteoporosis (OP). The objective of the study is to examine the connection between body mass index (BMI), OA and bone mineral density (BMD) of lumbar spine, femoral neck and trabecular bone score (TBS) in postmenopausal women with OA. We have observed 359 postmenopausal women (50-89 years old) and divided them into four groups by age: 50-59 yrs, 60-69 yrs, 70-79 yrs and over 80 years old. In addition, according to the American College of Rheumatology (ACR) Clinical classification criteria for knee and hip OA, we divided them into 2 groups: group I – 117 females with symptomatic OA (including 89 patients with knee OA, 28 patients with hip OA) and group II –242 women with a normal functional activity of large joints. Analysis of data was performed taking into account their BMI, classified by World Health Organization (WHO). Diagnosis of obesity was established when BMI was above 30 kg/m². In woman with obesity, a symptomatic OA was detected in 44 postmenopausal women (41.1%), a normal functional activity of large joints - in 63 women (58.9%). However, in women with normal BMI – 73 women, who account for 29.0% of cases, a symptomatic OA was detected. According to a chi-squared (χ2) test, a significantly higher level of BMI was detected in postmenopausal women with OA (χ2 = 5.05, p = 0.02). Women with a symptomatic OA had a significantly higher BMD of lumbar spine compared with women who had a normal functional activity of large joints. No significant differences of BMD of femoral necks or TBS were detected in either the group with OA or with a normal functional activity of large joints.

Keywords: Bone mineral density, BMD, body mass index, BMI, obesity, overweight, postmenopausal women, osteoarthritis.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 610

Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

Abstract:

A crucial part of maintaining a customer-oriented business in the telecommunications industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years, which has made it more important to understand customers’ needs in this strong market. For those who are looking to turn over their service providers, understanding their needs is especially important. Predictive churn is now a mandatory requirement for retaining customers in the telecommunications industry. Machine learning can be used to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: Machine Learning, Gradient Boosting, Logistic Regression, Churn, Random Forest, Decision Tree, ROC, AUC, F1-score.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 339

Authors: R. B. Ibrahim, M. S. Argungu, I. M. Mungadi

Abstract:

It is obvious in this present time, internet has become an indispensable part of human life since its inception. The Internet has provided diverse opportunities to make life so easy for human beings, through the adoption of various channels. Among these channels are email, internet banking, video conferencing, and the like. Email is one of the easiest means of communication hugely accepted among individuals and organizations globally. But over decades the security integrity of this platform has been challenged with malicious activities like Phishing. Email phishing is designed by phishers to fool the recipient into handing over sensitive personal information such as passwords, credit card numbers, account credentials, social security numbers, etc. This activity has caused a lot of financial damage to email users globally which has resulted in bankruptcy, sudden death of victims, and other health-related sicknesses. Although many methods have been proposed to detect email phishing, in this research, the results of multiple machine-learning methods for predicting email phishing have been compared with the use of filter-wrapper feature selection. It is worth noting that all three models performed substantially but one outperformed the other. The dataset used for these models is obtained from Kaggle online data repository, while three classifiers: decision tree, Naïve Bayes, and Logistic regression are ensemble (Bagging) respectively. Results from the study show that the Decision Tree (CART) bagging ensemble recorded the highest accuracy of 98.13% using PEF (Phishing Essential Features). This result further demonstrates the dependability of the proposed model.

Keywords: Ensemble, hybrid, filter-wrapper, phishing.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 123

Authors: Marc Solé, Francesc Giné, Magda Valls, Nina Bijedic

Abstract:

What people say on social media has turned into a rich source of information to understand social behavior. Specifically, the growing use of Twitter social media for political communication has arisen high opportunities to know the opinion of large numbers of politically active individuals in real time and predict the global political tendencies of a specific country. It has led to an increasing body of research on this topic. The majority of these studies have been focused on polarized political contexts characterized by only two alternatives. Unlike them, this paper tackles the challenge of forecasting Spanish political trends, characterized by multiple political parties, by means of analyzing the Twitters Users political tendency. According to this, a new strategy, named Tweets Analysis Strategy (TAS), is proposed. This is based on analyzing the users tweets by means of discovering its sentiment (positive, negative or neutral) and classifying them according to the political party they support. From this individual political tendency, the global political prediction for each political party is calculated. In order to do this, two different strategies for analyzing the sentiment analysis are proposed: one is based on Positive and Negative words Matching (PNM) and the second one is based on a Neural Networks Strategy (NNS). The complete TAS strategy has been performed in a Big-Data environment. The experimental results presented in this paper reveal that NNS strategy performs much better than PNM strategy to analyze the tweet sentiment. In addition, this research analyzes the viability of the TAS strategy to obtain the global trend in a political context make up by multiple parties with an error lower than 23%.

Keywords: Political tendency, prediction, sentiment analysis, Twitter.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 796

Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

Abstract:

The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: Deep learning, data mining, gender predication, MOOCs.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1307

Authors: K. Nirmala Devi, V. Murali Bhaskaran

Abstract:

Traditional document representation for classification follows Bag of Words (BoW) approach to represent the term weights. The conventional method uses the Vector Space Model (VSM) to exploit the statistical information of terms in the documents and they fail to address the semantic information as well as order of the terms present in the documents. Although, the phrase based approach follows the order of the terms present in the documents rather than semantics behind the word. Therefore, a semantic concept based approach is used in this paper for enhancing the semantics by incorporating the ontology information. In this paper a novel method is proposed to forecast the intraday stock market price directional movement based on the sentiments from Twitter and money control news articles. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, political events and investor’s sentiment etc. The stock market series are generally dynamic, nonparametric, noisy and chaotic by nature. The sentiment analysis along with wisdom of crowds can automatically compute the collective intelligence of future performance in many areas like stock market, box office sales and election outcomes. The proposed method utilizes collective sentiments for stock market to predict the stock price directional movements. The collective sentiments in the above social media have powerful prediction on the stock price directional movements as up/down by using Granger Causality test.

Keywords: Bag of Words, Collective Sentiments, Ontology, Semantic relations, Sentiments, Social media, Stock Prediction, Twitter, Vector Space Model and wisdom of crowds.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2759

Authors: Gholamreza Habibi

Abstract:

In order to study seed yield and seed yield components in bean under reduced irrigation condition and assessment drought tolerance of genotypes, 15 lines of White beans were evaluated in two separate RCB design with 3 replications under stress and non stress conditions. Analysis of variance showed that there were significant differences among varieties in terms of traits under study, indicating the existence of genetic variation among varieties. The results indicate that drought stress reduced seed yield, number of seed per plant, biological yield and number of pod in White been. In non stress condition, yield was highly correlated with the biological yield, whereas in stress condition it was highly correlated with harvest index. Results of stepwise regression showed that, selection can we done based on, biological yield, harvest index, number of seed per pod, seed length, 100 seed weight. Result of path analysis showed that the highest direct effect, being positive, was related to biological yield in non stress and to harvest index in stress conditions. Factor analysis were accomplished in stress and nonstress condition a, there were 4 factors that explained more than 76 percent of total variations. We used several selection indices such as Stress Susceptibility Index ( SSI ), Geometric Mean Productivity ( GMP ), Mean Productivity ( MP ), Stress Tolerance Index ( STI ) and Tolerance Index ( TOL ) to study drought tolerance of genotypes, we found that the best Stress Index for selection tolerance genotypes were STI, GMP and MP were the greatest correlations between these Indices and seed yield under stress and non stress conditions. In classification of genotypes base on phenotypic characteristics, using cluster analysis ( UPGMA ), all allels classified in 5 separate groups in stress and non stress conditions.

Keywords: Cluster analysis, factor analysis, path analysis, selection index, White bean

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2102