Search results for: scale invariant feature

Authors: Karina C. Azzolin, Luis E. Kosteski, Alisson S. Milani, Raquel C. Zydeck

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This work aims to represent Numerically tests experimentally developed in reduced scale walls with horizontal and inclined cuts by using the Lattice Discrete Element Method (LDEM) implemented On de Abaqus/explicit environment. The cuts were performed with depths of 20%, 30%, and 50% On the walls subjected to centered and eccentric loading. The parameters used to evaluate the numerical model are its strength, the failure mode, and the in-plane and out-of-plane displacements.

Keywords: structural masonry, wall chases, small scale, numerical model, lattice discrete element method

Procedia PDF Downloads 177

Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

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Authors: Supriya Manikrao Ovhal

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In India, the summer monsoon rainfall occurs owing to large scale convection with reference to continental ITCZ. It was found that convection over tropical ocean increases with SST from 26 to 28 degree C, and when SST is above 29 degree C, it sharply decreases for warm pool areas of Indian and for monsoon areas of West Pacific Ocean. The reduction in convection can be influenced by large scale subsidence forced by nearby or remotely generated deep convection, thus it was observed that under the influence of strong large scale rising motion, convection does not decreases but increases monotonically with SST even if SST value is higher than 29.5 degree C. Since convection is related to SST gradient, that helps to generate low level moisture convergence and upward vertical motion in the atmosphere. Strong wind fields like cross equatorial low level jet stream on equator ward side of the warm pool are produced due to convection initiated by SST gradient. Areas having maximum SST have low SST gradient, and that result in feeble convection. Hence it is imperative to mention that the oceanic role (other than SST) could be prominent in influencing large Scale Atmospheric convection. Since warm oceanic surface somewhere or the other contributes to penetrate the heat radiation to the subsurface of the ocean, and as there is no studies seen related to oceanic subsurface role in large Scale Atmospheric convection, in the present study, we are concentrating on the oceanic subsurface contribution in large Scale Atmospheric convection by considering the SST gradient, mixed layer depth (MLD), thermocline, barrier layer. The present study examines the probable role of subsurface ocean parameters in influencing convection.

Keywords: sst, d20, olr, wind

Procedia PDF Downloads 93

Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

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Authors: Nitin Shravan, Sudarsun Santhiappan, B. Sivaselvan

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Subsection identification, in the context of Electronic Health Records (EHRs), is identifying the important sections for down-stream tasks like auto-coding. In this work, we classify the text present in EHRs according to their information, using machine learning and deep learning techniques. We initially describe briefly about the problem and formulate it as a text classification problem. Then, we discuss upon the methods from the literature. We try two approaches - traditional feature extraction based machine learning methods and deep learning methods. Through experiments on a private dataset, we establish that the deep learning methods perform better than the feature extraction based Machine Learning Models.

Keywords: deep learning, machine learning, semantic clinical classification, subsection identification, text classification

Procedia PDF Downloads 217

Authors: Agnieszka S. Dzielendziak, Lindsay-Marie Armstrong, Matthew E. Potter, Robert Raja, Pier J. A. Sazio

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Reducing carbon dioxide, CO₂, is one of the greatest global challenges. Conversion of CO₂ for utilisation across synthetic fuel, pharmaceutical, and agrochemical industries offers a promising option, yet requires significant research to understanding the complex multiscale processes involved. To experimentally understand and optimize such processes at that catalytic sites and exploring the impact of the process at reactor scale, is too expensive. Computational methods offer significant insight and flexibility but require a more detailed multi-scale approach which is a significant challenge in itself. This work introduces a computational approach which incorporates detailed catalytic models, taken from experimental investigations, into a larger-scale computational flow dynamics framework. The reactor-scale species transport approach is modified near the catalytic walls to determine the influence of catalytic clustering regions. This coupling approach enables more accurate modelling of velocity, pressures, temperatures, species concentrations and near-wall surface characteristics which will ultimately enable the impact of overall reactor design on chemical conversion performance.

Keywords: catalysis, CCU, CO₂, multi-scale model

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Authors: Syeda Mainaaz Unnisa, Deepa Suresh

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Due to the increase in usage of public domain channels, such as the internet, and communication technology, there is a concern about the protection of intellectual property and security threats. This interest has led to growth in researching and implementing techniques for information hiding. Steganography is the art and science of hiding information in a private manner such that its existence cannot be recognized. Communication using steganographic techniques makes not only the secret message but also the presence of hidden communication, invisible. Steganalysis is the art of detecting the presence of this hidden communication. Parallel to steganography, steganalysis is also gaining prominence, since the detection of hidden messages can prevent catastrophic security incidents from occurring. Steganalysis can also be incredibly helpful in identifying and revealing holes with the current steganographic techniques, which makes them vulnerable to attacks. Through the formulation of new effective steganalysis methods, further research to improve the resistance of tested steganography techniques can be developed. Feature-based steganalysis method for JPEG images calculates the features of an image using the L1 norm of the difference between a stego image and the calibrated version of the image. This calibration can help retrieve some of the parameters of the cover image, revealing the variations between the cover and stego image and enabling a more accurate detection. Applying this method to various steganographic schemes, experimental results were compared and evaluated to derive conclusions and principles for more protected JPEG steganography.

Keywords: cover image, feature-based steganalysis, information hiding, steganalysis, steganography

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Authors: Xianglu Tang, Zhenxue Jiang, Zhuo Li

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Characterization of shale multi scale heterogeneity is an important part to evaluate size and space distribution of shale gas reservoirs in sedimentary basins. The origin of shale heterogeneity has always been a hot research topic for it determines shale micro characteristics description and macro quality reservoir prediction. Shale multi scale heterogeneity was discussed based on thin section observation, FIB-SEM, QEMSCAN, TOC, XRD, mercury intrusion porosimetry (MIP), and nitrogen adsorption analysis from 30 core samples in Silurian Longmaxi formation. Results show that shale heterogeneity can be characterized by pore structure and mineral composition. The heterogeneity of shale pore is showed by different size pores at nm-μm scale. Macropores (pore diameter > 50 nm) have a large percentage of pore volume than mesopores (pore diameter between 2~ 50 nm) and micropores (pore diameter < 2nm). However, they have a low specific surface area than mesopores and micropores. Fractal dimensions of the pores from nitrogen adsorption data are higher than 2.7, what are higher than 2.8 from MIP data, showing extremely complex pore structure. This complexity in pore structure is mainly due to the organic matter and clay minerals with complex pore network structures, and diagenesis makes it more complicated. The heterogeneity of shale minerals is showed by mineral grains, lamina, and different lithology at nm-km scale under the continuous changing horizon. Through analyzing the change of mineral composition at each scale, random arrangement of mineral equal proportion, seasonal climate changes, large changes of sedimentary environment, and provenance supply are considered to be the main reasons that cause shale minerals heterogeneity from microcosmic to macroscopic. Due to scale effect, the change of shale multi scale heterogeneity is a discontinuous process, and there is a transformation boundary between homogeneous and in homogeneous. Therefore, a shale multi scale heterogeneity changing model is established by defining four types of homogeneous unit at different scales, which can be used to guide the prediction of shale gas distribution from micro scale to macro scale.

Keywords: heterogeneity, homogeneous unit, multiscale, shale

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Authors: Ayesha Sohail, Khadija Maqbool, Anila Asif, Haroon Ahmad

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The field of tissue engineering is an active area of research. Bone tissue engineering helps to resolve the clinical problems of critical size and non-healing defects by the creation of man-made bone tissue. We will design and validate an efficient numerical model, which will simulate the effective diffusivity in bone tissue engineering. Our numerical model will be based on the finite element analysis of the diffusion-reaction equations. It will have the ability to optimize the diffusivity, even at multi-scale, with the variation of time. It will also have a special feature, with which we will not only be able to predict the oxygen, glucose and cell density dynamics, more accurately, but will also sort the issues arising due to anisotropy. We will fix these problems with the help of modifying the governing equations, by selecting appropriate spatio-temporal finite element schemes, by adaptive grid refinement strategy and by transient analysis.

Keywords: scaffolds, porosity, diffusion, transient analysis

Procedia PDF Downloads 541

Authors: Nalini C. Iyer, Shraddha H., Anagha B. Varahamurthy, Dikshith C. S., Ishwar G. Kubasad, Vinayak I. Karalatti, Pavan B. Mulimani

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The current scenario of the pandemic due to COVID-19 has led to the awareness among the people to avoid unneces-sary contact in public places. There is a need to avoid contact with physical objects to stop the spreading of infection. The contactless feature has to be included in the systems in public places wherever possible. For example, attendance monitoring systems with fingerprint biometric can be replaced with a contactless feature. One more important protocol followed in the current situation is temperature monitoring and screening. The paper describes an attendance system with a contactless feature and temperature screening for the university. The system displays a QR code to scan, which redirects to the student login web page only if the location is valid (the location where the student scans the QR code should be the location of the display of the QR code). Once the student logs in, the temperature of the student is scanned by the contactless temperature sensor (mlx90614) with an error of 0.5°C. If the temperature falls in the range of the desired value (range of normal body temperature), then the attendance of the student is marked as present, stored in the database, and the door opens automatically. The attendance is marked as absent in the other case, alerted with the display of temperature, and the door remains closed. The door is automated with the help of a servomotor. To avoid the proxy, IR sensors are used to count the number of students in the classroom. The hardware system consisting of a contactless temperature sensor and IR sensor is implemented on the microcontroller, NodeMCU.

Keywords: NodeMCU, IR sensor, attendance monitoring, contactless, temperature

Procedia PDF Downloads 185

Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: Fathi Kallel, Abdulelah Alabd Uljabbar, Abdulrahman Aldukhail, Abdulaziz Alomran

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The brain is an important organ in our body since it is responsible about the majority actions such as vision, memory, etc. However, different diseases such as Alzheimer and tumors could affect the brain and conduct to a partial or full disorder. Regular diagnosis are necessary as a preventive measure and could help doctors to early detect a possible trouble and therefore taking the appropriate treatment, especially in the case of brain tumors. Different imaging modalities are proposed for diagnosis of brain tumor. The powerful and most used modality is the Magnetic Resonance Imaging (MRI). MRI images are analyzed by doctor in order to locate eventual tumor in the brain and describe the appropriate and needed treatment. Diverse image processing methods are also proposed for helping doctors in identifying and analyzing the tumor. In fact, a large Computer Aided Diagnostic (CAD) tools including developed image processing algorithms are proposed and exploited by doctors as a second opinion to analyze and identify the brain tumors. In this paper, we proposed a new advanced CAD for brain tumor identification, classification and feature extraction. Our proposed CAD includes three main parts. Firstly, we load the brain MRI. Secondly, a robust technique for brain tumor extraction is proposed. This technique is based on both Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). DWT is characterized by its multiresolution analytic property, that’s why it was applied on MRI images with different decomposition levels for feature extraction. Nevertheless, this technique suffers from a main drawback since it necessitates a huge storage and is computationally expensive. To decrease the dimensions of the feature vector and the computing time, PCA technique is considered. In the last stage, according to different extracted features, the brain tumor is classified into either benign or malignant tumor using Support Vector Machine (SVM) algorithm. A CAD tool for brain tumor detection and classification, including all above-mentioned stages, is designed and developed using MATLAB guide user interface.

Keywords: MRI, brain tumor, CAD, feature extraction, DWT, PCA, classification, SVM

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Authors: Sokkhey Phauk, Takeo Okazaki

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The challenging task in educational institutions is to maximize the high performance of students and minimize the failure rate of poor-performing students. An effective method to leverage this task is to know student learning patterns with highly influencing factors and get an early prediction of student learning outcomes at the timely stage for setting up policies for improvement. Educational data mining (EDM) is an emerging disciplinary field of data mining, statistics, and machine learning concerned with extracting useful knowledge and information for the sake of improvement and development in the education environment. The study is of this work is to propose techniques in EDM and integrate it into a web-based system for predicting poor-performing students. A comparative study of prediction models is conducted. Subsequently, high performing models are developed to get higher performance. The hybrid random forest (Hybrid RF) produces the most successful classification. For the context of intervention and improving the learning outcomes, a feature selection method MICHI, which is the combination of mutual information (MI) and chi-square (CHI) algorithms based on the ranked feature scores, is introduced to select a dominant feature set that improves the performance of prediction and uses the obtained dominant set as information for intervention. By using the proposed techniques of EDM, an academic performance prediction system (APPS) is subsequently developed for educational stockholders to get an early prediction of student learning outcomes for timely intervention. Experimental outcomes and evaluation surveys report the effectiveness and usefulness of the developed system. The system is used to help educational stakeholders and related individuals for intervening and improving student performance.

Keywords: academic performance prediction system, educational data mining, dominant factors, feature selection method, prediction model, student performance

Procedia PDF Downloads 106

Authors: Youngjae Jin, Daeshik Kim

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This paper describes a cycle accurate simulation results of weight values learned by an auto-encoder behavior model in terms of pre-route simulation. Given the results we visualized the first layer representations with natural images. Many common deep learning threads have focused on learning high-level abstraction of unlabeled raw data by unsupervised feature learning. However, in the process of handling such a huge amount of data, the learning method’s computation complexity and time limited advanced research. These limitations came from the fact these algorithms were computed by using only single core CPUs. For this reason, parallel-based hardware, FPGAs, was seen as a possible solution to overcome these limitations. We adopted and simulated the ready-made auto-encoder to design a behavior model in Verilog HDL before designing hardware. With the auto-encoder behavior model pre-route simulation, we obtained the cycle accurate results of the parameter of each hidden layer by using MODELSIM. The cycle accurate results are very important factor in designing a parallel-based digital hardware. Finally this paper shows an appropriate operation of behavior model based pre-route simulation. Moreover, we visualized learning latent representations of the first hidden layer with Kyoto natural image dataset.

Keywords: auto-encoder, behavior model simulation, digital hardware design, pre-route simulation, Unsupervised feature learning

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Authors: Abdul-Hakeem Olatunji Abiola, A. E. Adeniran, A. O. Ajimo, A. B. Lamilisa

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Introduction: Conventional wastewater treatment technology has been found to fail in developing countries because they are expensive to construct, operate and maintain. Constructed wetlands are nowadays considered as a low-cost alternative for effective wastewater treatment, especially where suitable land can be available. This study aims to evaluate the performance of the constructed wetland vegetated with water hyacinth (Eichhornia crassipes) plant for the treatment of wastewater. Methodology: The sub-surface flow wetland used for this study was an experimental scale constructed wetland consisting of four beds A, B, C, and D. Beds A, B, and D were vegetated while bed C which was used as a control was non-vegetated. This present study presents the results from bed B vegetated with water hyacinth (Eichhornia crassipes) and control bed C which was non-vegetated. The influent of the experimental scale wetland has been pre-treated with sedimentation, screening and anaerobic chamber before feeding into the experimental scale wetland. Results: pH and conductivity level were more reduced, colour of effluent was more improved, nitrate, iron, phosphate, and chromium were more removed, and dissolved oxygen was more improved in the water hyacinth bed than the control bed. While manganese, nickel, cyanuric acid, and copper were more removed from the control bed than the water hyacinth bed. Conclusion: The performance of the experimental scale constructed wetland bed planted with water hyacinth (Eichhornia crassipes) is better than that of the control bed. It is therefore recommended that plain bed without any plant should not be encouraged.

Keywords: constructed experimental scale wetland, domestic sewage, treatment, water hyacinth

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Authors: Asad Abbas, Muhammad Fezan Afzal, Muhammad Latif Anjum, Muhammad Azmat

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The Internet of Things (IoT) is used to link up real objects that use the internet to interact. IoT applications allow handling and operating the equipment in accordance with environmental needs, such as transportation and healthcare. IoT devices are linked together via a number of agents that act as a middleman for communications. The operation of a heat sensor differs indoors and outside because agent applications work with environmental variables. In this article, we suggest using Software Product Line (SPL) to model IoT agents and applications' features on an XML-based basis. The contextual diversity within the same domain of application can be handled, and the reusability of features is increased by XML-based feature modelling. For the purpose of managing contextual variability, we have embraced XML for modelling IoT applications, agents, and internet-connected devices.

Keywords: IoT agents, IoT applications, software product line, feature model, XML

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Authors: Cuong V Nguyen, Ralph Horne, John Fien, France Cheong

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Social vulnerability to climate change is increasingly being acknowledged, and proposals to measure and manage it are emerging. Building upon this work, this paper proposes an approach to social vulnerability assessment using a new mechanism to aggregate and account for causal relationships among components of a Social Vulnerability Index (SVI). To operationalize this index, the authors propose a means to develop an appropriate primary dataset, through application of a specifically-designed household survey questionnaire. The data collection and analysis, including calibration and calculation of the SVI is demonstrated through application in case study city in central coastal Vietnam. The calculation of SVI at the fine-grained local neighbourhood scale provides high resolution in vulnerability assessment, and also obviates the need for secondary data, which may be unavailable or problematic, particularly at the local scale in developing countries. The SVI household survey is underpinned by the results of a Delphi survey, an in-depth interview and focus group discussions with local environmental professionals and community members. The research reveals inherent limitations of existing SVIs but also indicates the potential for their use in assessing social vulnerability and making decisions associated with responding to climate change at the local scale.

Keywords: climate change, local scale, social vulnerability, social vulnerability index

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Authors: Vimuckthi Charika Wickramaratne, Prasansha Kumari

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Sport tourism refers to travel which involves either observing or participating in a sporting event apart from their usual environment. Sport tourism in a fast growing sector of the Sri Lankan travelling industry since Cricket are more popular sport game in the country. This study intends to analyze the impact of these popular sport events for creating and developing small scale business in the country. Primary data gathered from 100 small entrepreneurs around Keththarama Cricket Ground in Sri Lanka. Collected data analyzed using descriptive research methods. The study revealed that local and international visitors for cricket games had impacted on small scale business activities such as retail, handicraft, transport, vehicle parking, small restaurant, hotels, foods and beverage industry. In addition, it was identified that these type of small business are sessional income generating activities for the short period.

Keywords: sport tourism, small scale business, cricket, entrepreneurs

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Authors: M. Ramesh Kanthan, S. Naga Nandini Sujatha

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The goal of haze removal algorithms is to enhance and recover details of scene from foggy image. In enhancement the proposed method focus into two main categories: (i) image enhancement based on Adaptive contrast Histogram equalization, and (ii) image edge strengthened Gradient model. Many circumstances accurate haze removal algorithms are needed. The de-fog feature works through a complex algorithm which first determines the fog destiny of the scene, then analyses the obscured image before applying contrast and sharpness adjustments to the video in real-time to produce image the fusion strategy is driven by the intrinsic properties of the original image and is highly dependent on the choice of the inputs and the weights. Then the output haze free image has reconstructed using fusion methodology. In order to increase the accuracy, interpolation method has used in the output reconstruction. A promising retrieval performance is achieved especially in particular examples.

Keywords: single image, fusion, dehazing, multi-scale fusion, per-pixel, weight map

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Authors: Sarah Triller, Ferdi Schüth

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The challenges encountered in upscaling the mechanochemical synthesis of high surface area α-alumina are investigated in this study. After lab-scale experiments in shaker mills and planetary ball mills, the optimization of reaction parameters of the conversion in the smallest vessel of a scalable mill, named Simoloyer, was developed. Furthermore, the future perspectives by scaling up the conversion in several steps are described. Since abrasion from the steel equipment can be problematic, the process was transferred to a ceramically lined mill, which solved the contamination problem. The recovered alpha-alumina shows a high specific surface area in all investigated scales.

Keywords: mechanochemistry, scale-up, ball milling, ceramic lining

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Yaeji Lim, Hee-Seok Oh

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The coherence analysis measures the linear time-invariant relationship between two data sets and has been studied various fields such as signal processing, engineering, and medical science. However classical coherence analysis tends to be sensitive to outliers and focuses only on mean relationship. In this paper, we generalized cross periodogram to quantile cross periodogram and provide richer inter-relationship between two data sets. This is a general version of Laplace cross periodogram. We prove its asymptotic distribution under the long range process and compare them with ordinary coherence through numerical examples. We also present real data example to confirm the usefulness of quantile coherence analysis.

Keywords: coherence, cross periodogram, spectrum, quantile

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Authors: Mansoureh Karimollahi, Mehriar Adrmohammadi, Mohsen Mohammadi

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Introduction: Patient satisfaction with nursing care is considered as an important indicator of the quality and effectiveness of the health care system, and improving the quality of care is not possible without paying attention to the opinions and expectations of patients. Considering that the scales for assessing satisfaction with nursing care in our country are not comprehensive and measure very few areas, therefore, in this study, psychometrically, the Persian version of the Newcastle Nursing Care Satisfaction Scale was used in patients hospitalized in the wards. Internal medicine and general surgery were discussed. Methods: This cross-sectional study was conducted on 200 patients admitted to the surgery and internal departments of hospitals affiliated to Ardabil University of Medical Sciences. The Newcastle nursing care satisfaction scale was used for the first time in Iran in comparison with the good nursing care scale from the patients' point of view to evaluate the criterion validity. The Newcastle nursing care satisfaction scale was used after translation, validity, and reliability. Results: The level of satisfaction of patients and the experience of patients with nursing care was at a favorable level, respectively, with an average of 111.8 ± 14.2 and 69.07 ± 14.8. Total CVI was estimated at 0.96 for the experience section, 0.95 for the satisfaction section, and 0.96 for the whole scale. The index (CVR) was also 0.95 for the experience section, 0.95 for the satisfaction section, and 0.95 for the whole scale. Criterion validity was also estimated using 0.725 correlation. The validity of the construct was also confirmed using the goodness of fit index (X2=1932/05, p=0.013, KMO=0.913). Convergent validity was estimated at 0.99 in the experience subscale and 0.98 in the satisfaction subscale. . The overall reliability in the experience subscale and satisfaction subscale was 94%, 92%, and 98%, respectively, which indicated the acceptable reliability of the questionnaire. Conclusion: The Persian version of the Newcastle nursing care satisfaction scale as a comprehensive tool that can be easily completed by patients and is easy to interpret, has good validity and reliability and can be used in patient care centers, in departments Surgery, and internal medicine are recommended.

Keywords: psychometrics, Newcastle nursing care satisfaction scale, nursing care satisfaction, general surgery department

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Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: A. Kulandai Therese

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The eccentric connectivity index based on degree and eccentricity of the vertices of a graph is a widely used graph invariant in mathematics.In this paper, we present the explicit eccentric connectivity index, first and second Zagreb indices for a Corona graph and sub division-related corona graphs.

Keywords: corona graph, degree, eccentricity, eccentric connectivity index, first zagreb index, second zagreb index, subdivision graphs

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Authors: Saad M. Darwish, Mohamed A. El-Iskandarani, Guitar M. Shawkat

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Nowadays, the amount of available multimedia data is continuously on the rise. The need to find a required image for an ordinary user is a challenging task. Content based image retrieval (CBIR) computes relevance based on the visual similarity of low-level image features such as color, textures, etc. However, there is a gap between low-level visual features and semantic meanings required by applications. The typical method of bridging the semantic gap is through the automatic image annotation (AIA) that extracts semantic features using machine learning techniques. In this paper, a multi-label image annotation system guided by Firefly and Bayesian method is proposed. Firstly, images are segmented using the maximum variance intra cluster and Firefly algorithm, which is a swarm-based approach with high convergence speed, less computation rate and search for the optimal multiple threshold. Feature extraction techniques based on color features and region properties are applied to obtain the representative features. After that, the images are annotated using translation model based on the Net Bayes system, which is efficient for multi-label learning with high precision and less complexity. Experiments are performed using Corel Database. The results show that the proposed system is better than traditional ones for automatic image annotation and retrieval.

Keywords: feature extraction, feature selection, image annotation, classification

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Authors: Nishtha Batra

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This study was conducted to identify the effect of perceived stress on emotional regulation, aggression and psychological well-being. Analysis was conducted using correlational and regression models to examine the relationships between perceived stress (independent variable) and psychological factors containing emotional intelligence, psychological well-being and aggression. Subjects N=100, Male students 50 and Female students 50. The data was collected using Cohen's Perceived Stress Scale, Gross’s Emotional Regulation Questionnaire (ERQ), Ryff’s Psychological Well-being scale and Orispina’s aggression scale. Correlation and regression (SPSS version 22) Emotional regulation and psychological well-being had a significant relationship with Perceived stress.

Keywords: perceived stress, psychological well-being, aggression, emotional regulation, students

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Authors: Shane D. Inder, Mehrdad Khamooshi

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Efficient energy storage is a crucial factor in facilitating the uptake of renewable energy resources. Among the many options available for energy storage systems required to balance imbalanced supply and demand cycles, compressed air energy storage (CAES) is a proven technology in grid-scale applications. This paper reviews the current state of micro scale CAES technology and describes a micro-scale advanced adiabatic CAES (A-CAES) system, where heat generated during compression is stored for use in the discharge phase. It will also describe a thermodynamic model, developed in EES (Engineering Equation Solver) to evaluate the performance and critical parameters of the discharge phase of the proposed system. Three configurations are explained including: single turbine without preheater, two turbines with preheaters, and three turbines with preheaters. It is shown that the micro-scale A-CAES is highly dependent upon key parameters including; regulator pressure, air pressure and volume, thermal energy storage temperature and flow rate and the number of turbines. It was found that a micro-scale AA-CAES, when optimized with an appropriate configuration, could deliver energy input to output efficiency of up to 70%.

Keywords: CAES, adiabatic compressed air energy storage, expansion phase, micro generation, thermodynamic

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Authors: R. Mohan, V. Jadhav, A. Ahmed, J. Rivas, A. Kelkar

Abstract:

Cementitious materials are an excellent example of a composite material with complex hierarchical features and random features that range from nanometer (nm) to millimeter (mm) scale. Multi-scale modeling of complex material systems requires starting from fundamental building blocks to capture the scale relevant features through associated computational models. In this paper, molecular dynamics (MD) modeling is employed to predict the effect of plasticizer additive on the mechanical properties of key hydrated cement constituent calcium-silicate-hydrate (CSH) at the molecular, nanometer scale level. Due to complexity, still unknown molecular configuration of CSH, a representative configuration widely accepted in the field of mineral Jennite is employed. The effectiveness of the Molecular Dynamics modeling to understand the predictive influence of material chemistry changes based on molecular/nanoscale models is demonstrated.

Keywords: cement composite, mechanical properties, molecular dynamics, plasticizer additives

Procedia PDF Downloads 454

Authors: Salim H. Al Salmani, Kevin Tansey, Mohammed S. Ozigis

Abstract:

The study of the growth of built-up areas and settlement expansion is a major exercise that city managers seek to undertake to establish previous and current developmental trends. This is to ensure that there is an equal match of settlement expansion needs to the appropriate levels of services and infrastructure required. This research aims at demonstrating the potential of satellite image processing technique, harnessing the utility of single feature probability-object based image analysis technique in assessing the urban growth dynamics of the Muscat Governorate in Oman for the period 1990, 2002 and 2013. This need is fueled by the continuous expansion of the Muscat Governorate beyond predicted levels of infrastructural provision. Landsat Images of the years 1990, 2002 and 2013 were downloaded and preprocessed to forestall appropriate radiometric and geometric standards. A novel approach of probability filtering of the target feature segment was implemented to derive the spatial extent of the final Built-Up Area of the Muscat governorate for the three years period. This however proved to be a useful technique as high accuracy assessment results of 55%, 70%, and 71% were recorded for the Urban Landcover of 1990, 2002 and 2013 respectively. Furthermore, the Normalized Differential Built – Up Index for the various images were derived and used to consolidate the results of the SFP-OBIA through a linear regression model and visual comparison. The result obtained showed various hotspots where urbanization have sporadically taken place. Specifically, settlement in the districts (Wilayat) of AL-Amarat, Muscat, and Qurayyat experienced tremendous change between 1990 and 2002, while the districts (Wilayat) of AL-Seeb, Bawshar, and Muttrah experienced more sporadic changes between 2002 and 2013.

Keywords: urban growth, single feature probability, object based image analysis, landcover change

Procedia PDF Downloads 274