Search results for: angle classification

Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

Procedia PDF Downloads 465

Authors: Qasim Idrees, Gao Liangtian, Liu Bo, Miao Yiran

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In order to improve the efficiency and accuracy of the pressure hull structure, optimization of underwater vehicle based on response surface methodology, a method for optimizing the design of pressure hull structure was studied. To determine the pressure shell of five dimensions as a design variable, the application of thin shell theory and the Chinese Classification Society (CCS) specification was carried on the preliminary design. In order to optimize variables of the feasible region, different methods were studied and implemented such as Opt LHD method (to determine the design test sample points in the feasible domain space), parametric ABAQUS solution for each sample point response, and the two-order polynomial response for the surface model of the limit load of structures. Based on the ultimate load of the structure and the quality of the shell, the two-generation genetic algorithm was used to solve the response surface, and the Pareto optimal solution set was obtained. The final optimization result was 41.68% higher than that of the initial design, and the shell quality was reduced by about 27.26%. The parametric method can ensure the accuracy of the test and improve the efficiency of optimization.

Keywords: parameterization, response surface, structure optimization, pressure hull

Procedia PDF Downloads 229

Authors: Abbas Jafarizad, Duygu Ekinci

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In this work targeted drug delivery is proposed to decrease adverse effect of drugs with concomitant reduces in consumption and treatment outgoings. Nanoparticles (NPs) can be prepared from a variety of materials such as lipid, biodegradable polymer that prevent the drugs cytotoxicity in healthy cells, etc. One of the most important drugs used in chemotherapy is mitoxantrone (MTX) which prevents cell proliferation by inhibition of topoisomerase II and DNA repair; however, it is not selective and has some serious side effects. In this study, mentioned aim is achieved by using several nanocarriers like magnetite (Fe3O4) and their composites with magnetic graphene oxide (Fe3O4@GO). Also, cytotoxic potential of Fe3O4, Fe3O4-MTX, and Fe3O4@GO-MTX nanocomposite were evaluated on mesenchymal stem cells (MSCs). In this study, we reported the synthesis of monodisperse Fe3O4 NPs and Fe3O4@GO nanocomposite and their structures were investigated by using field emission scanning electron microscope (FESEM), Fourier transform infrared (FTIR) spectra, atomic force microscopy (AFM), Brauneur Emmet Teller (BET) isotherm and contact angle studies. Moreover, we used 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay to evaluate cytotoxic effects of MTX, Fe3O4 NPs, Fe3O4-MTX and Fe3O4@GO-MTX nanocomposite on MSCs. The in-vitro MTT results indicated that all concentrations of MTX and Fe3O4@GO-MTX nanocomposites showed cytotoxic effects while all concentrations of Fe3O4 NPs and Fe3O4-MTX NPs did not show any cytotoxic effect on stem cells. The results from this study indicated that using Fe3O4 NPs as anticancer drug delivery systems could be a trustworthy method for cancer treatment. But for reaching excellent and accurate results, further investigation is necessary.

Keywords: mitoxantrone, magnetite, magnetic graphene oxide, MTT assay, mesenchymal stem cells

Procedia PDF Downloads 269

Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

Procedia PDF Downloads 137

Authors: Onur Yaşar, Okan Bilge, Ortaç Onmuş

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The aim of this study is to investigate and compare the locomotion structures, especially the bone structures, of two different dolphin species, the Common bottlenose dolphin Tursiops truncatus and the Harbor porpoise Phocoena phocoena, and to provide a more detailed and descriptive comparison. To compare the structures of bones of two study species; first, the Spinous Process (SP), Inferior Articular Process (IAP), Laminae Vertebrae (LA), Foramen Vertebrae (FV), Corpus Vertebrae (CV), Transverse Process (TP) were determined and then the length of the Spinous Process (LSP), length of the Foramen Vertebrae (LFV), area of the Corpus Vertebrae (ACV), and length of the Transverse Process (LTP) were measured from the caudal view. The spine consists of a total of 61 vertebrae (7 cervical, 13 thoracic, 14 lumbar, and 27 caudal vertebrae) in the Common bottlenose dolphin, while the Harbor Porpoise has 63 vertebrae (7 cervical, 12 thoracic, 14 lumbar, 30 caudal. In the Common bottlenose dolphin, epiphyseal ossification was between the 21st caudal vertebra and the 27th caudal vertebra, while in the Harbor porpoise, it was observed in all vertebrae. Ankylosing spondylitis was observed in the C1 and C2 vertebrae in the Common bottlenose dolphin and in all cervical vertebrae between C1 and C6 in the Harbor porpoise. We argue that this difference in fused cervical vertebrae between the two species may be due to the fact that the neck movements of the Harbor porpoise in the vertical and horizontal axes are more limited than those of the Common bottlenose dolphin. We also think that as the number of fused cervical vertebrae increases, underwater maneuvers are performed at a wider angle, but to test this idea, we think that different species of dolphins should be compared and the different age groups should be investigated.

Keywords: anatomy, morphometry, vertebrae, common bottlenose dolphin, Tursiops truncatus, harbour porpoise, Phocoena phocoena

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Authors: Iftikhar A. Tayubi, Aabdulrahman Alsubhi, Abdullah Althrwi

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The deoxyribonucleic acid text provides a single source of high-quality Cryptography about Deoxyribonucleic acid sequence for structural biologists. We will provide an intuitive, well-organized and user-friendly web interface that allows users to encrypt and decrypt Deoxy Ribonucleic Acid sequence text. It includes complex, securing by using Algorithm to encrypt and decrypt Deoxy Ribonucleic Acid sequence. The utility of this Deoxy Ribonucleic Acid Sequence Text is that, it can provide a user-friendly interface for users to Encrypt and Decrypt store the information about Deoxy Ribonucleic Acid sequence. These interfaces created in this project will satisfy the demands of the scientific community by providing fully encrypt of Deoxy Ribonucleic Acid sequence during this website. We have adopted a methodology by using C# and Active Server Page.NET for programming which is smart and secure. Deoxy Ribonucleic Acid sequence text is a wonderful piece of equipment for encrypting large quantities of data, efficiently. The users can thus navigate from one encoding and store orange text, depending on the field for user’s interest. Algorithm classification allows a user to Protect the deoxy ribonucleic acid sequence from change, whether an alteration or error occurred during the Deoxy Ribonucleic Acid sequence data transfer. It will check the integrity of the Deoxy Ribonucleic Acid sequence data during the access.

Keywords: algorithm, ASP.NET, DNA, encrypt, decrypt

Procedia PDF Downloads 225

Authors: S. P. Pradhan, Amulya Ratna Roul

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A number of landslides are occurring during the rainy season along Rayagada-Koraput Railway track for past three years. The track was constructed about 20 years ago. However, the protection measures are not able to control the recurring slope failures now. It leads to a loss to Indian Railway and its passengers ultimately leading to wastage of time and money. The slopes along Rayagada-Koraput track include both rock and soil slopes. The rock types include mainly Khondalite and Charnockite whereas soil slopes are mainly composed of laterite ranging from less weathered to highly weathered laterite. The field studies were carried out in one of the critical slope. Field study was followed by the kinematic analysis to assess the type of failure. Slake Durability test, Uniaxial Compression test, specific gravity test and triaxial test were done on rock samples to calculate and assess properties such as weathering index, unconfined compressive strength, density, cohesion, and friction angle. Following all the laboratory tests, rock mass rating was calculated. Further, from Kinematic analysis and Rock Mass Ratingbasic, Slope Mass Rating was proposed for each slope. The properties obtained were used to do the slope stability simulations using finite element method based modelling. After all the results, suitable protection measures, to prevent the loss due to slope failure, were suggested using the relation between Slope Mass Rating and protection measures.

Keywords: landslides, slope stability, rock mass rating, slope mass rating, numerical simulation

Procedia PDF Downloads 176

Authors: Azhar Ulhaq, Zubair Yameen, Almas Anjum

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Frequency behaviour of self-excited induction generator (SEIG) wind turbines during control mode transition from grid connected to islanded mode is studied in detail. A robust control scheme for frequency regulation based on combined action of STATCOM, energy storage system (ESS) and pitch angle control for wind powered microgrid (MG) is proposed. Suggested STATCOM controller comprises a 3-phase voltage source converter (VSC) that contains insulated gate bipolar transistors (IGBTs) based pulse width modulation (PWM) inverters along with a capacitor bank. Energy storage system control consists of current controlled voltage source converter and battery bank. Both of them acting simultaneously after detection of island compensates for reactive and active power demands, thus regulating frequency at point of common coupling (PCC) and also improves load stability. STATCOM integrates at point of common coupling and ESS is connected to microgrids main bus. Results reveal that proposed control not only stabilizes frequency during transition duration but also minimizes sudden frequency imbalance caused by load variation or wind intermittencies in islanded operation. System is investigated with and without suggested control scheme. The efficacy of proposed strategy has been verified by simulation in MATLAB/Simulink.

Keywords: energy storage system, island, wind, STATCOM, self-excited induction generator, SEIG, transient

Procedia PDF Downloads 149

Authors: Nabaouia.Maktouf, Ali Ben Moussa, Saïd Turki

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This paper discusses the effect of an attached flexible membrane on the control of fluid around a circular cylinder. A parametric study has been investigated for different positions, sizes, modes as well as frequencies of oscillation of the flexible membrane. The numerical investigation was conducted for a Reynolds number equal to 150 using the commercial code Fluent 16.0 and parallel calculation into 4 processors. The motion of the flexible membrane was managed by the dynamic mesh and compiled into Fluent as a user-defined function. The first part of this paper discusses the effect of changing the position of a flexible membrane sized 8° as an angle of aperture on the aerodynamic coefficients. Results show that the flexible membrane placed at 110° from the stagnation point presents more non-linearity on the behavior of the drag coefficient compared to the drag behavior when placed at 180°, relative to the stagnation point. The effect of the size of the flexible surface was studied for the corresponding angles of aperture: 32° and 42°, respectively. The effect of modes (modes 1, 2, and 3) of vibrations has been investigated at a constant frequency of vibration f=2Hz for angles 32° and 42°. All the calculations have been done with a constant amplitude A =0.001m. A non-linearity of the drag coefficient was clearly observed for all the sizes, modes as well as frequencies of excitation. The Fast Fourier transformation shows the appearance of the natural shedding frequency and the multiples of the frequency of excitation. An increase in the modes of oscillation leads to a more linear behavior of the drag coefficient.

Keywords: fluid flow control, numerical simulation, dynamic mesh, aerodynamic forces, flexible membrane

Procedia PDF Downloads 69

Authors: Salah Brahim Belakhdar, Tari Mohammed Amin, Rafai Abderrahmen, Amalsi Bilal

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Shear strength of sandy soils has been considered as the important parameter to study the stability of different civil engineering structures when subjected to monotonic, cyclic, and earthquake loading conditions. The proposed research investigated the effect of grading characteristics on the shear strength and mechanical behaviour of granular classes of sands mixed with salt in loose and dense states (Dr=15% and 90%). The laboratory investigation aimed at understanding the extent or degree at which shear strength of sand-silt mixture soil is affected by its gradation under static loading conditions. For the purpose of clarifying and evaluating the shear strength characteristics of sandy soils, a series of Casagrande shear box tests were carried out on different reconstituted samples of sand-silt mixtures with various gradations. The soil samples were tested under different normal stresses (100, 200, and 300 kPa). The results from this laboratory investigation were used to develop insight into the shear strength response of sand and sand-silt mixtures under monotonic loading conditions. The analysis of the obtained data revealed that the grading characteristics (D10, D50, Cu, ESR, and MGSR) have a significant influence on the shear strength response. It was found that shear strength can be correlated to the grading characteristics for the sand-silt mixture. The effective size ratio (ESR) and mean grain size ratio (MGSR) appear as pertinent parameters to predict the shear strength response of the sand-silt mixtures for soil gradation under study.

Keywords: mechanical behavior, silty sand, friction angle, cohesion, fines content

Procedia PDF Downloads 369

Authors: Zhipeng Feng, Huanhuan Qi, Pingchuan Shen, Fenggang Zang, Yixiong Zhang

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Numerical simulations of vortex-induced vibration of a three-dimensional flexible tube under uniform turbulent flow are calculated when Reynolds number is 1.35×10⁴. In order to achieve the vortex-induced vibration, the three-dimensional unsteady, viscous, incompressible Navier-Stokes equation and LES turbulence model are solved with the finite volume approach, the tube is discretized according to the finite element theory, and its dynamic equilibrium equations are solved by the Newmark method. The fluid-tube interaction is realized by utilizing the diffusion-based smooth dynamic mesh method. Considering the vortex-induced vibration system, the variety trends of lift coefficient, drag coefficient, displacement, vertex shedding frequency, phase difference angle of tube are analyzed under different frequency ratios. The nonlinear phenomena of locked-in, phase-switch are captured successfully. Meanwhile, the limit cycle and bifurcation of lift coefficient and displacement are analyzed by using trajectory, phase portrait, and Poincaré sections. The results reveal that: when drag coefficient reaches its minimum value, the transverse amplitude reaches its maximum, and the “lock-in” begins simultaneously. In the range of lock-in, amplitude decreases gradually with increasing of frequency ratio. When lift coefficient reaches its minimum value, the phase difference undergoes a suddenly change from the “out-of-phase” to the “in-phase” mode.

Keywords: vortex induced vibration, limit cycle, LES, CFD, FEM

Procedia PDF Downloads 276

Authors: Almas Rajguru

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The Oligocene succession of the Tapti- Daman area is one of the established petroleum plays in Tapti-Daman block of the Mumbai Offshore Basin. Despite good control and production history, the sand geometry and continuity of reservoir character of these sediments are less understood as most reservoirs are thin and fall below seismic resolution. The present work focuses on a detailed analysis of the Early Oligocene Mahuva Formation at the reservoir scale through laboratory studies (sedimentology and biostratigraphy) of core and sidewall cores in integration with electro logs for firming up facies’ distribution, micro-depositional environment and sequence stratigraphy, diagenesis and reservoir characterization from seventeen wells from North Tapti-C-37 area in Tapti Daman Block, WOB. The thick shale/claystone with thin interbeds of sandstone and siltstones of deeper marine in the lower part of Mahuva Fm represents deposition in a transgressive regime. The overlying interbedded sandstone, glauconitic-siltstone/fine-grained sandstone, and thin beds of packstone/grainstone within highly fissile shale were deposited in a prograding tide-dominated delta during late-rise normal regression. Nine litho facies (F1-F9) representing deposition in various microenvironments of the tide-dominated delta are identified based on their characteristic sediment texture, structure and microfacies. Massive, gritty sandstone (F1) with poorly sorted sands lithic fragments with calcareous and Fe-rich matrix represents channel fill sediments. High-angle cross-stratified sandstone (F2) deposited in rapidly shifting/migrating bars under strong tidal currents. F3 records the laterally accreted tidal-channel point bars. F3 (low-angle cross-stratified to parallel bedded sandstone) and F4 (Clean sandstone) are often associated with F2 in a tidal bar complex. F5 (interbedded thin sand and mud) and F6 (bioturbated sandstone) represent tidal flat deposits. High energy open marine carbonate shoals (F8) and fossiliferous sandstone in offshore bars (F7) represent deepening up facies. Shallow marine standstill conditions facilitated the deposition of thick shale (F9) beds. The reservoir facies (F1-F6) are commonly poorly to moderately sorted; bimodal, immature sandstone represented by quartz-wacke. The framework grains are sub-angular to sub-rounded, medium to coarse-grained (occasionally gritty) embedded within argillaceous (kaolinite/chlorite/chamosite) to highly Fe-rich matrix (sideritic). The facies F7 and F8, representing the sandy packstone and grainstone facies, respectively, exhibit poor reservoir characteristics due to sanitization, diagenetic compaction and matrix-filled intergranular spaces. The various diagenetic features such as the presence of authigenic clays (kaolinite/dickite/smectite); ferruginous minerals like siderite, pyrite, hematite and other iron oxides; bioturbations; glauconite; calcite and quartz cementation, precipitation of gypsum, pressure solution and other compaction effects are identified. These diagenetic features, wherever present, have reduced porosity and permeability thereby adversely affecting reservoir quality. Tidal bar sandstones possess good reservoir characteristics such as moderate to good sorting, fair to good porosity and geometry that facilitates efficient lateral extension and vertical thickness of reservoir. The sand bodies of F2, F3 and F4 facies of Well L, M and Q deposited in a tidal bar complex exhibit good reservoir quality represented by relatively cleaner, poorly burrowed, loose, friable sandstone with good porosity. Sandstone facies around these wells could prove a potential hydrocarbon reservoir and could be considered for further exploration.

Keywords: reservoir sedimentology, facies analysis, HST, tide dominated delta, tidal bars

Procedia PDF Downloads 82

Authors: Luis Fonseca, Filipe Cabral Pinto, Susana Sargento

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The increase of the world population, especially in large urban centers, has resulted in new challenges particularly with the control and optimization of public safety. Thus, in the present work, a solution is proposed for the prediction of criminal occurrences in a city based on historical data of incidents and demographic information. The entire research and implementation will be presented start with the data collection from its original source, the treatment and transformations applied to them, choice and the evaluation and implementation of the Machine Learning model up to the application layer. Classification models will be implemented to predict criminal risk for a given time interval and location. Machine Learning algorithms such as Random Forest, Neural Networks, K-Nearest Neighbors and Logistic Regression will be used to predict occurrences, and their performance will be compared according to the data processing and transformation used. The results show that the use of Machine Learning techniques helps to anticipate criminal occurrences, which contributed to the reinforcement of public security. Finally, the models were implemented on a platform that will provide an API to enable other entities to make requests for predictions in real-time. An application will also be presented where it is possible to show criminal predictions visually.

Keywords: crime prediction, machine learning, public safety, smart city

Procedia PDF Downloads 104

Authors: Khaled Nagaty, Heba Nagaty, Gerard McKee

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A signature is a behavioral biometric that is used for authenticating users in most financial and legal transactions. Signatures can be easily forged by skilled forgers. Therefore, it is essential to verify whether a signature is genuine or forged. The aim of any signature verification algorithm is to accommodate the differences between signatures of the same person and increase the ability to discriminate between signatures of different persons. This work presented in this paper proposes an automatic signature verification system to indicate whether a signature is genuine or not. The system comprises four phases: (1) The pre-processing phase in which image scaling, binarization, image rotation, dilation, thinning, and connecting ridge breaks are applied. (2) The feature extraction phase in which global and local features are extracted. The local features are minutiae points, curvature orientation, and curve plateau. The global features are signature area, signature aspect ratio, and Hu moments. (3) The post-processing phase, in which false minutiae are removed. (4) The classification phase in which features are enhanced before feeding it into the classifier. k-nearest neighbors and support vector machines are used. The classifier was trained on a benchmark dataset to compare the performance of the proposed offline signature verification system against the state-of-the-art. The accuracy of the proposed system is 92.3%.

Keywords: signature, ridge breaks, minutiae, orientation

Procedia PDF Downloads 142

Authors: Roman Medvedev, Andrey Yakshin, Konstantin Nikolaev, Sergey Yakunin, Fred Bijkerk

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Multilayer structures are used as spectroscopic elements in fluorescence analysis. These serve the purpose of analyzing soft x-ray emission spectra of materials upon excitation by x-rays or electrons. The analysis then allows quantitative determination of the x-ray emitting elements in the materials. Shorter wavelength range for this application, below 2.5nm, can be covered by using short period multilayers, with a period of 2.5 nm and lower. Thus the detrimental effect on the reflectivity of morphological roughness between materials of the multilayers becomes increasingly pronounced. Ion beam polishing was previously shown to be effective in reducing roughness in some multilayer systems with Si. In this work, we explored W/Si multilayers with the period of 2.5 nm. Si layers were polishing by Ar ions, employing low energy ions, 100 and 80 eV, with the etched Si thickness being in the range 0.1 to 0.5 nm. CuK X-ray diffuse scattering measurements revealed a significant reduction in the diffused scattering in the polished multilayers. However, Grazing Incidence CuK X-ray showed only a marginal reduction of the overall roughness of the systems. Still, measurements of the structures with Grazing Incidence Small Angle X-ray scattering indicated that the vertical correlation length of roughness was strongly reduced in the polished multilayers. These results together suggest that polishing results in the reduction of the vertical propagation of roughness from layer to layer, while only slightly affecting the overall roughness. This phenomenon can be explained by ion-induced surface roughening inherently present in the ion polishing methods. Alternatively, ion-induced densification of thin Si films should also be considered. Finally, the reflectivity of 40% at 0.84 nm at grazing incidence of 9 degrees has been obtained in this work for W/Si multilayers. Analysis of the obtained results is expected to lead to further progress in reflectance.

Keywords: interface roughness, ion polishing, multilayer structures, W/Si

Procedia PDF Downloads 127

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

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

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

Procedia PDF Downloads 423

Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

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Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

Procedia PDF Downloads 390

Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė

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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.

Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter

Procedia PDF Downloads 47

Authors: Nikola Tomasevic, Marko Batic, Sanja Vranes

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Emerging Semantic Web technologies can be seen as the next step in evolution of the intelligent facility management systems. Particularly, this considers increased usage of open source and/or standardized concepts for data classification and semantic interpretation. To deliver such facility management systems, providing the comprehensive integration and interoperability platform in from of the facility data model is a prerequisite. In this paper, one of the possible modelling approaches to provide such integrative facility data model which was based on the ontology modelling concept was presented. Complete ontology development process, starting from the input data acquisition, ontology concepts definition and finally ontology concepts population, was described. At the beginning, the core facility ontology was developed representing the generic facility infrastructure comprised of the common facility concepts relevant from the facility management perspective. To develop the data model of a specific facility infrastructure, first extension and then population of the core facility ontology was performed. For the development of the full-blown facility data models, Malpensa and Fiumicino airports in Italy, two major European air-traffic hubs, were chosen as a test-bed platform. Furthermore, the way how these ontology models supported the integration and interoperability of the overall airport energy management system was analyzed as well.

Keywords: airport ontology, energy management, facility data model, ontology modeling

Procedia PDF Downloads 444

Authors: Fatiha Mohammed Arab, Benoit Augier, Francois Deniset, Pascal Casari, Jacques Andre Astolfi

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For the top high performance foiling yachts, cavitation is often a limiting factor for take-off and top speed. This work investigates solutions to delay the onset of cavitation thanks to structural morphing. The structural morphing is based on compliant leading and trailing edge, with effect similar to flaps. It is shown here that the commonly accepted effect of flaps regarding the control of lift and drag forces can also be used to postpone the inception of cavitation. A numerical and experimental study is conducted in order to assess the effect of the geometric parameters of hydrofoil on their hydrodynamic performances and in cavitation inception. The effect of a 70% trailing edge and a 30% leading edge of NACA 0012 is investigated using Xfoil software at a constant Reynolds number 106. The simulations carried out for a range flaps deflections and various angles of attack. So, the result showed that the lift coefficient increase with the increase of flap deflection, but also with the increase of angle of attack and enlarged the bucket cavitation. To evaluate the efficiency of the Xfoil software, a 2D analysis flow over a NACA 0012 with leading and trailing edge flap was studied using Fluent software. The results of the two methods are in a good agreement. To validate the numerical approach, a passive adaptive composite model is built and tested in the hydrodynamic tunnel at the Research Institute of French Naval Academy. The model shows the ability to simulate the effect of flap by a LE and TE structural morphing due to hydrodynamic loading.

Keywords: cavitation, flaps, hydrofoil, panel method, xfoil

Procedia PDF Downloads 170

Authors: Win Thandar Soe, Innocent Mpawenimana, Mathieu Di Fazio, Cécile Belleudy, Aung Ze Ya

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Intelligent electronic equipment and automation network is the brain of high-tech energy management systems in critical role of smart homes dominance. Smart home is a technology integration for greater comfort, autonomy, reduced cost, and energy saving as well. These services can be provided to home owners for managing their home appliances locally or remotely and consequently allow them to automate intelligently and responsibly their consumption by individual or collective control systems. In this study, three smart plugs are described and one of them tested on typical household appliances. This article proposes to collect the data from the wireless technology and to extract some smart data for energy management system. This smart data is to quantify for three kinds of load: intermittent load, phantom load and continuous load. Phantom load is a waste power that is one of unnoticed power of each appliance while connected or disconnected to the main. Intermittent load and continuous load take in to consideration the power and using time of home appliances. By analysing the classification of loads, this smart data will be provided to reduce the communication of wireless sensor network for energy management system.

Keywords: energy management, load profile, smart plug, wireless sensor network

Procedia PDF Downloads 267

Authors: Zhanbo Cheng, Xueyu Geng

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Vegetation slope protection technique is economic, aesthetic and practical. Herbs are widely used in practice because of rapid growth, strong erosion resistance, obvious slope protection and simple method, in which the root system of grass plays a very important role. In this paper, through changing the variables value of grassroots quantity, grassroots diameter, grassroots length and grassroots reinforce layers, the direct shear tests were carried out to discuss the change of shear strength indexes of grassroots reinforced soil under different reinforce situations, and analyse the effects of grassroots characteristics on reinforced soil performance. The laboratory test results show that: (1) in the certain number of grassroots diameter, grassroots length and grassroots reinforce layers, the value of shear strength, and cohesion first increase and then reduce with the increasing of grassroots quantity; (2) in the certain number of grassroots quantity, grassroots length and grassroots reinforce layers, the value of shear strength and cohesion rise with the increasing of grassroots diameter; (3) in the certain number of grassroots diameter, and grassroots reinforce layers, the value of shear strength and cohesion raise with the increasing of grassroots length in a certain range of grassroots quantity, while the value of shear strength and cohesion first rise and then decline with the increasing of grassroots length when the grassroots quantity reaches a certain value; (4) in the certain number of grassroots quantity, grassroots diameter, and grassroots length, the value of shear strength and cohesion first climb and then decline with the increasing of grassroots reinforced layers; (5) the change of internal friction angle is small in different parameters of grassroots. The research results are of importance for understanding the mechanism of vegetation protection for slopes and determining the parameters of grass planting.

Keywords: direct shear test, reinforced soil, grassroots characteristics, shear strength indexes

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Authors: Ebru Turgal, Beyza Doganay Erdogan

Abstract:

Machine learning aims to model the relationship between the response and features. Medical decision-making researchers would like to make decisions about patients’ course and treatment, by examining the repeated measurements over time. Boosting approach is now being used in machine learning area for these aims as an influential tool. The aim of this study is to show the usage of multivariate tree boosting in this field. The main reason for utilizing this approach in the field of decision-making is the ease solutions of complex relationships. To show how multivariate tree boosting method can be used to identify important features and feature-time interaction, we used the data, which was collected retrospectively from Ankara University Chest Diseases Department records. Dataset includes repeated PF ratio measurements. The follow-up time is planned for 120 hours. A set of different models is tested. In conclusion, main idea of classification with weighed combination of classifiers is a reliable method which was shown with simulations several times. Furthermore, time varying variables will be taken into consideration within this concept and it could be possible to make accurate decisions about regression and survival problems.

Keywords: boosted multivariate trees, longitudinal data, multivariate regression tree, panel data

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Authors: Dang Thuy Binh, Truong Thi Oanh, Le Phan Khanh Hung, Luong thi Tuong Vy

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One of the greatest environmental challenges facing Asia is the management and conservation of the marine biodiversity threaten by fisheries overexploitation, pollution, habitat destruction, and climate change. To date, a few molecular taxonomical studies has been conducted on marine fauna in Vietnam. The purpose of this study was to clarify the phylogeny of economic and ecological reef fish species in Vietnam Reef fish species covering Labridae, Scaridae, Nemipteridae, Serranidae, Acanthuridae, Lutjanidae, Lethrinidae, Mullidae, Balistidae, Pseudochromidae, Pinguipedidae, Fistulariidae, Holocentridae, Synodontidae, and Pomacentridae representing 28 genera were collected from South and Center, Vietnam. Combine with Genbank sequences, a phylogenetic tree was constructed based on 16S gene of mitochondrial DNA using maximum parsimony, maximum likelihood, and Bayesian inference approaches. The phylogram showed the well-resolved clades at genus and family level. Perciformes is the major order of reef fish species in Vietnam. The monophyly of Perciformes is not strongly supported as it was clustered in the same clade with Tetraodontiformes syngnathiformes and Beryciformes. Continue sampling of commercial fish species and classification based on morphology and genetics to build DNA barcoding of fish species in Vietnam is really necessary.

Keywords: reef fish, 16s rDNA, Vietnam, phylogeny

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Authors: Kaushik Sathupadi, Sandesh Achar

Abstract:

Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Musatafa Abbas Abbood Albadr, Masri Ayob, Sabrina Tiun, Fahad Taha Al-Dhief, Mohammad Kamrul Hasan

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Breast Cancer (BC) is considered one of the most frequent reasons of cancer death in women between 40 to 55 ages. The BC is diagnosed by using digital images of the FNA (Fine Needle Aspirate) for both benign and malignant tumors of the breast mass. Therefore, this work proposes the Online Sequential Extreme Learning Machine (OSELM) algorithm for diagnosing BC by using the tumor features of the breast mass. The current work has used the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, which contains 569 samples (i.e., 357 samples for benign class and 212 samples for malignant class). Further, numerous measurements of assessment were used in order to evaluate the proposed OSELM algorithm, such as specificity, precision, F-measure, accuracy, G-mean, MCC, and recall. According to the outcomes of the experiment, the highest performance of the proposed OSELM was accomplished with 97.66% accuracy, 98.39% recall, 95.31% precision, 97.25% specificity, 96.83% F-measure, 95.00% MCC, and 96.84% G-Mean. The proposed OSELM algorithm demonstrates promising results in diagnosing BC. Besides, the performance of the proposed OSELM algorithm was superior to all its comparatives with respect to the rate of classification.

Keywords: breast cancer, machine learning, online sequential extreme learning machine, artificial intelligence

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Authors: Shriya Shukla, Lachin Fernando

Abstract:

Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Panwasn Mahalawalert

Abstract:

The purposes of this research were analyzed differential item functioning and differential test functioning of SWUSAT aptitude test classification by sex variable. The data used in this research is the secondary data from Srinakharinwirot University Scholastic Aptitude Test 2013 (SWUSAT). SWUSAT test consists of four subjects. There are verbal ability test, number ability test, reasoning ability test and spatial ability test. The data analysis was analyzed in 2 steps. The first step was analyzing descriptive statistics. In the second step were analyzed differential item functioning (DIF) and differential test functioning (DTF) by using the DIFAS program. The research results were as follows: The results of DIF and DTF analysis for all 10 tests in year 2013. Gender was the characteristic that found DIF all 10 tests. The percentage of item number that found DIF is between 6.67% - 60%. There are 5 tests that most of items favors female group and 2 tests that most of items favors male group. There are 3 tests that the number of items favors female group equal favors male group. For Differential test functioning (DTF), there are 8 tests that have small level.

Keywords: aptitude test, differential item functioning, differential test functioning, educational measurement

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Authors: Sushila Jaiswal, Awadhesh Kumar Jaiswal

Abstract:

Background: Primary melanocytic tumors of the central nervous system (CNS) are uncommon lesions and arise from the melanocytes located within the leptomeninges. Aim and objective: The aim of the study was to evaluate the clinical details, histomorphology of the primary melanocytic tumor of CNS. Method: The study was performed by the retrospective review of the case records of the primary melanocytic tumors of CNS diagnosed in our department. The formalin-fixed, paraffin embedded tissue blocks and tissue sections were retrieved and reviewed. Results: Seven cases (6 males, 1 female; age range- 16-40 years; mean age- 27 years) of primary melanocytic tumors of CNS were retrieved over last seven years. The tumor was intracranial (n=5; frontal – 1 case, parietal – 1 case, cerebello-pontine angle- 1 case, occipital -1 case, foramen magnum-1 case) and intra spinal (n=2; cervical – 2 cases). All patients presented with the neurological deficits related to the location of the tumor. Four cases were malignant melanoma; two were melanocytoma of intermediate grade and remaining one was melanocytoma. On histopathology, melanocytoma and melanoma both displayed sheets of well-differentiated melanocytes having round to oval nuclei with finely dispersed chromatin, occasional single eosinophilic nucleoli and a moderate amount of cytoplasm with abundant granular melanin pigment. The absence of mitosis and macronucleoli was noticed in melanocytoma while melanoma showed frequent mitosis and macronucleoli. On immunohistochemistry, both showed diffuse strong HMB45 and S-100 immunopositivity. Conclusion: Primary melanocytic tumors of CNS are rare and predominantly seen in males. It is important to differentiate melanoma from melanocytoma as prognosis of later is good.

Keywords: melanocytoma, melanoma, brain tumor, melanin

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Authors: Ruangdech Sirikit

Abstract:

The purposes of this study were analyzed differential item functioning and differential test functioning of SWUSAT aptitude test classification by sex variable. The data used in this research is the secondary data from Srinakharinwirot University Scholastic Aptitude Test 2011 (SWUSAT 2011) SWUSAT test consists of four subjects. There are verbal ability test, number ability test, reasoning ability test and spatial ability test. The data analysis was carried out in 2 steps. The first step was analyzing descriptive statistics. In the second step were analyzed differential item functioning (DIF) and differential test functioning (DTF) by using the DIFAS program. The research results were as follows: The results of data analysis for all 10 tests in year 2011. Sex was the characteristic that found DIF all 10 tests. The percentage of item number that found DIF was between 10% - 46.67%. There are 4 tests that most of items favors female group. There are 3 tests that most of items favors male group and there are 3 tests that the number of items favors female group equal favors male group. For Differential test functioning (DTF), there are 8 tests that have small DIF effect variance.

Keywords: differential item functioning, differential test functioning, SWUSAT, aptitude test

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