Search results for: coarse classification

Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

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Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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Authors: Q. Toufeeq, R. Kauser, U. R. Jamil, N. Sohaib

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Foundation design of a structure needs soil investigation to avoid failures due to settlements. This soil investigation is expensive and time-consuming. Developments of new residential societies involve huge leveling of large sites that is accompanied by heavy land filling. Poor practices of land fill for deep depths cause differential settlements and consolidations of underneath soil that sometimes result in the collapse of structures. The extent of filling remains unknown to the individual developer unless soil investigation is carried out. Soil investigation cannot be performed on each available site due to involved costs. However, fair estimate of bearing capacity can be made if such tests are already done in the surrounding areas. The geotechnical guidance maps can provide a fair assessment of soil properties. Previously, GIS-based approaches have been used to develop maps using extrapolation and interpolations techniques for bearing capacities, underground recharge, soil classification, geological hazards, landslide hazards, socio-economic, and soil liquefaction mapping. Standard penetration test (SPT) data of surrounding sites were already available. Google Earth is used for digitization of collected data. Few points were considered for data calibration and validation. Resultant Geographic information system (GIS)-based guidance maps are helpful to anticipate the bearing capacity in the real estate industry.

Keywords: bearing capacity, soil classification, geographical information system, inverse distance weighted, radial basis function

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

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This paper presents an approach for the classification of an unstructured format description for identification of file formats. The main contribution of this work is the employment of data mining techniques to support file format selection with just the unstructured text description that comprises the most important format features for a particular organisation. Subsequently, the file format indentification method employs file format classifier and associated configurations to support digital preservation experts with an estimation of required file format. Our goal is to make use of a format specification knowledge base aggregated from a different Web sources in order to select file format for a particular institution. Using the naive Bayes method, the decision support system recommends to an expert, the file format for his institution. The proposed methods facilitate the selection of file format and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and specifications of file formats. To facilitate decision-making, the aggregated information about the file formats is presented as a file format vocabulary that comprises most common terms that are characteristic for all researched formats. The goal is to suggest a particular file format based on this vocabulary for analysis by an expert. The sample file format calculation and the calculation results including probabilities are presented in the evaluation section.

Keywords: data mining, digital libraries, digital preservation, file format

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Authors: Randa Ismail Sharafeddine

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The Islamic economic system is distinctive in that it implicitly recognizes money as a separate, independent component of production capable of assuming risk and so entitled to the same reward as other Entrepreneurial Factors of Production (EFP). Conventional theory does not identify money capital explicitly as a component of production; rather, interest is recognized as a reward for capital, the interest rate is the cost of money capital, and it is also seen as a cost of physical capital. The conventional theory of production examines how diverse non-entrepreneurial resources (Land, Labor, and Capital) are selected; however, the economic theory community is largely unaware of the reasons why these resources choose to remain as non-entrepreneurial resources as opposed to becoming entrepreneurial resources. Should land, labor, and financial asset owners choose to work for others in return for rent, income, or interest, or should they engage in entrepreneurial risk-taking in order to profit. This is a decision made often in the actual world, but it has never been effectively treated in economic theory. This article will conduct a critical analysis of the conventional classification of factors of production and propose a classification for resource allocation and income distribution (Rent, Wages, Interest, and Profits) that is more rational, even within the conventional theoretical framework for evaluating and developing production and distribution theories. Money is an essential component of production in an Islamic economy, and it must be used to sustain economic activity.

Keywords: financial capital, production theory, distribution theory, economic activity, riba avoidance, institution of participation

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Authors: Mst Ilme Faridatul, Bo Wu

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Multi-temporal urban land cover mapping is of paramount importance for monitoring urban sprawl and managing the ecological environment. For diversified urban activities, it is challenging to map land covers in a complex urban environment. Spectral indices have proved to be effective for mapping urban land covers. To improve multi-temporal urban land cover classification and mapping, we evaluate the performance of three spectral indices, e.g. modified normalized difference bare-land index (MNDBI), tasseled cap water and vegetation index (TCWVI) and shadow index (ShDI). The MNDBI is developed to evaluate its performance of enhancing urban impervious areas by separating bare lands. A tasseled cap index, TCWVI is developed to evaluate its competence to detect vegetation and water simultaneously. The ShDI is developed to maximize the spectral difference between shadows of skyscrapers and water and enhance water detection. First, this paper presents a comparative analysis of three spectral indices using Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM) and Operational Land Imager (OLI) data. Second, optimized thresholds of the spectral indices are imputed to classify land covers, and finally, their performance of enhancing multi-temporal urban land cover mapping is assessed. The results indicate that the spectral indices are competent to enhance multi-temporal urban land cover mapping and achieves an overall classification accuracy of 93-96%.

Keywords: land cover, mapping, multi-temporal, spectral indices

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Authors: Usama Shahid, Muhammad Zunnurain Hussain

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This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

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Authors: Ugochukwu Ejike Akpudo, Jank-Wook Hur

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The increasing demand for improved productivity, maintainability, and reliability has prompted rapidly increasing research studies on the emerging condition-based maintenance concept- Prognostics and health management (PHM). Varieties of fuel pumps serve critical functions in several hydraulic systems; hence, their failure can have daunting effects on productivity, safety, etc. The need for condition monitoring and assessment of these pumps cannot be overemphasized, and this has led to the uproar in research studies on standard feature extraction techniques for optimized condition assessment of fuel pumps. By extracting time-based, frequency-based and the more robust time-frequency based features from these vibrational signals, a more comprehensive feature assessment (and selection) can be achieved for a more accurate and reliable condition assessment of these pumps. With the aid of emerging deep classification and regression algorithms like the locally linear embedding (LLE), we propose a method for comprehensive condition assessment of electromagnetic fuel pumps (EMFPs). Results show that the LLE as a comprehensive feature extraction technique yields better feature fusion/dimensionality reduction results for condition assessment of EMFPs against the use of single features. Also, unlike other feature fusion techniques, its capabilities as a fault classification technique were explored, and the results show an acceptable accuracy level using standard performance metrics for evaluation.

Keywords: electromagnetic fuel pumps, comprehensive feature extraction, condition assessment, locally linear embedding, feature fusion

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Authors: H. Arzani, Z. Arzani

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Every piece of rangeland has a different set of physical and biological characteristics. This requires the manager to synthesis various information for regular monitoring to define changes trend to get wright decision for sustainable management. So range managers need to use computerized technologies to monitor rangeland, and select. The best management practices. There are four examples of computerized technologies that can benefit sustainable management: (1) Photographic method for cover measurement: The method was tested in different vegetation communities in semi humid and arid regions. Interpretation of pictures of quadrats was done using Arc View software. Data analysis was done by SPSS software using paired t test. Based on the results, generally, photographic method can be used to measure ground cover in most vegetation communities. (2) GPS application for corresponding ground samples and satellite pixels: In two provinces of Tehran and Markazi, six reference points were selected and in each point, eight GPS models were tested. Significant relation among GPS model, time and location with accuracy of estimated coordinates was found. After selection of suitable method, in Markazi province coordinates of plots along four transects in each 6 sites of rangelands was recorded. The best time of GPS application was in the morning hours, Etrex Vista had less error than other models, and a significant relation among GPS model, time and location with accuracy of estimated coordinates was found. (3) Application of satellite data for rangeland monitoring: Focusing on the long term variation of vegetation parameters such as vegetation cover and production is essential. Our study in grass and shrub lands showed that there were significant correlations between quantitative vegetation characteristics and satellite data. So it is possible to monitor rangeland vegetation using digital data for sustainable utilization. (4) Rangeland suitability classification with GIS: Range suitability assessment can facilitate sustainable management planning. Three sub-models of sensitivity to erosion, water suitability and forage production out puts were entered to final range suitability classification model. GIS was facilitate classification of range suitability and produced suitability maps for sheep grazing. Generally digital computers assist range managers to interpret, modify, calibrate or integrating information for correct management.

Keywords: computer, GPS, GIS, remote sensing, photographic method, monitoring, rangeland ecosystem, management, suitability, sheep grazing

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Authors: Johnson Rotimi Oluremi, A. Adedayo Adegbola, A. Samson Adediran, O. Solomon Oladapo

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Spent engine oil contains heavy metals and polycyclic aromatic hydrocarbons which contribute to chronic health hazards, poor soil aeration, immobilisation of nutrients and lowering of pH in soil. It affects geotechnical properties of lateritic soil thereby constituting geotechnical and foundation problems. This study is therefore based on the stabilization of spent engine oil (SEO) contaminated lateritic soil using cement kiln dust (CKD) as a mean of restoring it to its pristine state. Geotechnical tests which include sieve analysis, atterberg limit, compaction, California bearing ratio and unconfined compressive strength tests were carried out on the natural, SEO contaminated and CKD stabilized SEO contaminated lateritic soil samples. The natural soil classified as A-2-7 (2) by AASHTO classification and GC according to the Unified Soil Classification System changed to A-4 non-plastic soil due to SEO contaminated even under the influence of CKD it remained unchanged. However, the maximum dry density (MDD) of the SEO contaminated soil increased while the optimum moisture content (OMC) behaved vice versa with the increase in the percentages of CKD. Similarly, the bearing strength of the stabilized SEO contaminated soil measured by California Bearing Ratio (CBR) increased with percentage increment in CKD. In conclusion, spent engine oil has a detrimental effect on the geotechnical properties of the lateritic soil sample but which can be remediated using 10% CKD as a stand alone admixture in stabilizing spent engine oil contaminated soil.

Keywords: spent engine oil, lateritic soil, cement kiln dust, stabilization, compaction, unconfined compressive strength

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Authors: Rohit Gupta, Ravinder Agarwal

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Surface electromyographic (sEMG) signal has the potential to identify the human activities and intention. This potential is further exploited to control the artificial limbs using the sEMG signal from residual limbs of amputees. The paper deals with the development of multichannel cost efficient sEMG signal interface for research application, along with evaluation of proposed class dependent statistical approach of the feature selection method. The sEMG signal acquisition interface was developed using ADS1298 of Texas Instruments, which is a front-end interface integrated circuit for ECG application. Further, the sEMG signal is recorded from two lower limb muscles for three locomotions namely: Plane Walk (PW), Stair Ascending (SA), Stair Descending (SD). A class dependent statistical approach is proposed for feature selection and also its performance is compared with 12 preexisting feature vectors. To make the study more extensive, performance of five different types of classifiers are compared. The outcome of the current piece of work proves the suitability of the proposed feature selection algorithm for locomotion recognition, as compared to other existing feature vectors. The SVM Classifier is found as the outperformed classifier among compared classifiers with an average recognition accuracy of 97.40%. Feature vector selection emerges as the most dominant factor affecting the classification performance as it holds 51.51% of the total variance in classification accuracy. The results demonstrate the potentials of the developed sEMG signal acquisition interface along with the proposed feature selection algorithm.

Keywords: classifiers, feature selection, locomotion, sEMG

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Authors: Mofizul Islam Awwal

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Due to globalization, industries are rapidly growing throughout the world which leads to many manufacturing organizations. But recently, service industries are beginning to emerge in large numbers almost in all parts of the world including some developing countries. In this context, organizations need to have strong competitive advantage over their rivals to achieve their strategic business goals. Manufacturing industries are adopting many methods and techniques in order to achieve such competitive edge. Over the last decades, manufacturing industries have been successfully practicing lean concept to optimize their production lines. Due to its huge success in manufacturing context, lean has made its way into the service industry. Very little importance has been addressed to service in the area of operations management. Service industries are far behind than manufacturing industries in terms of operations improvement. It will be a hectic job to transfer the lean concept from production floor to service back/front office which will obviously yield possible improvement. Service processes are not as visible as production processes and can be very complex. Lack of research in this area made it quite difficult for service industries as there are no standardized frameworks for successfully implementing lean concept in service organization. The purpose of this research paper is to capture the present scenario of service industry in terms of lean implementation. Thorough analysis of past literature will be done on the applicability and understanding of lean in service structure. Classification of research papers will be done and critical factors will be unveiled for implementing lean in service industry to achieve operational excellence.

Keywords: lean service, lean literature classification, lean implementation, service industry, service excellence

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Authors: T. D. Gunneswara Rao, Mudimby Andal

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Research on the utilization of fly ash will no longer refer the fly ash as a waste material of thermal power plants. Use of fly ash in concrete making, makes the concrete economical as well as durable. The fly ash is being added to the concrete in three ways namely, as partial replacement to cement, partial replacement to fine aggregates and admixture. Addition of fly ash to the concrete in each one of the form mentioned above, makes the concrete more workable and durable than the conventional concrete. Studies on fly ash as partial replacement to cement gained momentum as such replacement makes the concrete economical. In the present study, an attempt has been made to understand the effects of fly ash on the workability characteristics and strength aspects of fly ash concretes. In India, major number of thermal power plants are producing low calcium fly ash. Hence, in the present investigation, low calcium fly ash has been used. Fly ash in concrete was considered for the partial replacement of cement. The percentage replacement of cement by fly ash varied from 0% to 40% at regular intervals of 10%. Moreover the fine aggregate to coarse aggregate ratio also has been varied as 1:1, 1:2, and 1:3. The workability tests revealed that up to 30% replacement of cement by fly ash in concrete mixes water demand for reduces and beyond 30% replacement of cement by fly ash demanded more water content for constant workability.

Keywords: cementing efficiency, compressive strength, low calcium fly ash, workability

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Authors: A. Sivakumar, K. Srinivasan

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The main objective of the study is focused in producing slag based geopolymer concrete obtained with the addition of alkali activator. Test results indicated that the reaction of silicates in slag is based on the reaction potential of sodium hydroxide and the formation of alumino-silicates. The study also comprises on the evaluation of the efficiency of polymer reaction in terms of the strength gain properties for different geopolymer mixtures. Geopolymer mixture proportions were designed for different binder to total aggregate ratio (0.3 & 0.45) and fine to coarse aggregate ratio (0.4 & 0.8). Geopolymer concrete specimens casted with normal curing conditions reported a maximum 28 days compressive strength of 54.75 MPa. The addition of glued steel fibres at 1.0% Vf in geopolymer concrete showed reasonable improvements on the compressive strength, split tensile strength and flexural properties of different geopolymer mixtures. Further, comparative assessment was made for different geopolymer mixtures and the reinforcing effects of steel fibres were investigated in different concrete matrix.

Keywords: accelerators, alkali activators, geopolymer, hot air oven curing, polypropylene fibres, slag, steam curing, steel fibres

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Authors: Sandeep Thakur, Ismail Mondal, Phani Bhusan Ghosh, Papita Das, Tarun Kumar De

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Sundarbans Reserve forest of India has been undergoing major transformations in the recent past owing to population pressure and related changes. This has brought about major changes in the spatial landscape of the region especially in the western parts. This study attempts to assess the impacts of the Landcover changes on the mangrove habitats. Time series imageries of Landsat were used to analyze the Normalized Differential Vegetation Index (NDVI) patterns over the western parts of Indian Sundarbans forest in order to assess the heath of the mangroves in the region. The images were subjected to Land use Land cover (LULC) classification using sub-pixel classification techniques in ERDAS Imagine software and the changes were mapped. The spatial proliferation of aquaculture farms during the study period was also mapped. A multivariate regression analysis was carried out between the obtained NDVI values and the LULC classes. Similarly, the observed meteorological data sets (time series rainfall and minimum and maximum temperature) were also statistically correlated for regression. The study demonstrated the application of NDVI in assessing the environmental status of mangroves as the relationship between the changes in the environmental variables and the remote sensing based indices felicitate an efficient evaluation of environmental variables, which can be used in the coastal zone monitoring and development processes.

Keywords: aquaculture farms, LULC, Mangrove, NDVI

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Authors: Niharika Verma, Sandeep Santosh

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DOA technique is a localization technique used in the communication field. Various algorithms have been developed for direction of arrival estimation like MUSIC, ROOT MUSIC, etc. These algorithms depend on various parameters like antenna array elements, number of snapshots and various others. Basically the MUSIC spectrum is evaluated and peaks obtained are considered as the angle of arrivals. The angles evaluated using this process depends on the scanning interval chosen. The accuracy of the results obtained depends on the coarseness of the interval chosen. In this paper, golden section search is applied to the MUSIC algorithm and therefore, more accurate results are achieved. Initially the coarse DOA estimations is done using the MUSIC algorithm in the range -90 to 90 degree at the interval of 10 degree. After the peaks obtained then fine DOA estimation is done using golden section search. Also, the partitioning method is applied to estimate the number of signals incident on the antenna array. Dependency of the algorithm on the number of snapshots is also being explained. Hence, the accurate results are being determined using this algorithm.

Keywords: Direction of Arrival (DOA), golden section search, MUSIC, number of snapshots

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Authors: Shams Ul Khaliq, Mushtaq Zeb, Fawad Bilal, Faizan Akbar, Syed Aamir Abbas

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Self Compacting Concrete as the name implies--is the concrete requiring a very little or no vibration to fill the form homogeneously. Self Compacting Concrete (SCC) is defined by two primary properties: Ability to flow or deform under its own weight (with or without obstructions) and the ability to remain homogeneous while doing so. Flow ability is achieved by utilizing high range water reducing admixtures and segregation resistance is ensured by introducing a chemical viscosity modifying admixture (VMA) or increasing the amount of fines in the concrete. The study explores the use waste marble dust (WMD) to increase the amount of fines and hence achieve self-compatibility in an economical way, suitable for Pakistani construction industry. The study focuses on comparison of fresh properties of SCC containing varying amounts of waste marble dust (WMD) with that containing commercially available viscosity modifying admixture. The comparison is done at different dosages of super plasticizer keeping cement, water, coarse aggregate, and fine aggregate contents constant.

Keywords: self compacting concrete, waste marble dust (WMD), flow ability, segregation resistance

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Authors: Salahaldein Alsadey, Issa Amaish

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Recycled aggregates (RA) have the potential to compromise concrete performance, contributing to issues such as reduced strength and increased susceptibility to cracking. This study investigates the impact of sisal fiber (SF) on the mechanical properties of concrete, with the objective of utilizing sisal fibers as a reinforcing element in concrete compositions containing natural aggregate and varying percentages (25%, 50%, and 75%) of coarse recycled aggregate replacement. The investigation aims to discern the positive and negative effects on compressive and flexural strength, thereby assessing the viability of sisal fiber-reinforced recycled concrete in comparison to conventional concrete composed of natural aggregate without sisal fiber. Test results revealed that concrete samples incorporating sisal fiber exhibited elevated compressive and flexural strength. Comparative analysis of these strength values was conducted with reference to samples devoid of sisal fiber.

Keywords: sustainable construction, construction materials, recycled aggregate, sisal fibers, compressive strength, flexural strength, eco-friendly concrete, natural fiber composites, recycled materials, construction waste management

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Authors: Shidvash Vakilipour, Mehdi Habibnia, Rouzbeh Riazi, Masoud Mohammadi, Mohammad H. Sabour

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The flow field passing through a highly loaded low pressure (LP) turbine cascade is numerically investigated at design and off-design conditions. The Field Operation And Manipulation (OpenFOAM) platform is used as the computational Fluid Dynamics (CFD) tool. Firstly, the influences of grid resolution on the results of k-ε, k-ω, and LES turbulence models are investigated and compared with those of experimental measurements. A numerical pressure under-shoot is appeared near the end of blade pressure surface which is sensitive to grid resolution and flow turbulence modeling. The LES model is able to resolve separation on a coarse and fine grid resolutions. Secondly, the off-design flow condition is modeled by negative and positive inflow incidence angles. The numerical experiments show that a separation bubble generated on blade pressure side is predicted by LES. The total pressure drop is also been calculated at incidence angle between -20◦ and +8◦. The minimum total pressure drop is obtained by k-ω and LES at the design point.

Keywords: low pressure turbine, off-design performance, openFOAM, turbulence modeling, flow separation

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Authors: H. G. C. P. Dinesh, G. Tharshini, M. P. B. Ekanayake, G. M. R. I. Godaliyadda

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This paper presents a new spatial feature extraction method based on principle component analysis (PCA) and Fisher Discernment Analysis (FDA) for facial expression recognition. It not only extracts reliable features for classification, but also reduces the feature space dimensions of pattern samples. In this method, first each gray scale image is considered in its entirety as the measurement matrix. Then, principle components (PCs) of row vectors of this matrix and variance of these row vectors along PCs are estimated. Therefore, this method would ensure the preservation of spatial information of the facial image. Afterwards, by incorporating the spectral information of the eigen-filters derived from the PCs, a feature vector was constructed, for a given image. Finally, FDA was used to define a set of basis in a reduced dimension subspace such that the optimal clustering is achieved. The method of FDA defines an inter-class scatter matrix and intra-class scatter matrix to enhance the compactness of each cluster while maximizing the distance between cluster marginal points. In order to matching the test image with the training set, a cosine similarity based Bayesian classification was used. The proposed method was tested on the Cohn-Kanade database and JAFFE database. It was observed that the proposed method which incorporates spatial information to construct an optimal feature space outperforms the standard PCA and FDA based methods.

Keywords: facial expression recognition, principle component analysis (PCA), fisher discernment analysis (FDA), eigen-filter, cosine similarity, bayesian classifier, f-measure

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Authors: Shazi Farooqui, Anupam Sharma

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The Mahi River (~600km long) is an important west flowing the river of Central India. It originates in Madhya Pradesh and starts flowing in NW direction and enters into the state of Rajasthan. It flows across southern Rajasthan and then enters into Gujarat and finally debouches in the Gulf of Cambay. In Gujarat state, it flows through all four geomorphic zones i.e. eastern upland zone, shallow buried piedmont zone, alluvial zone and coastal zone. In lower reaches and particularly when it is flowing under the coastal regime, it provides an opportunity to study – 1. Land–Sea interaction and role of relative sea level changes, 2. Coastal/estuarine geological process, 3. Landscape evolution in marginal areas and so on. The Late Quaternary deposits of Mainland Gujarat is appreciably studied by Chamyal and his group of MS University of Baroda, and they have established that the 30-35m thick sediment package of the Mainland Gujarat is comprised of marine, fluvial and aeolian sediments. It is also established that in the estuarine zone, the upper few meter thick sediments package is of marine nature. However, its thickness, characters and the depositional environment including the role of climate and tectonics is still not clearly defined. To understand few aspects of the above mentioned, in the present study, a 17m subsurface sediment core has been retrieved from the estuarine zone of Mahi river basin. The Multiproxy studies which include the textural analysis (grain size), Loss on ignition (LOI), Bulk and clay mineralogy and geochemical studies have been carried out. In the entire sedimentary sequence, the grain size largely varies from coarse sand to clay; however, a solitary gravel bed is also noticed. The lower part (depth 9-17m), is mainly comprised of sub equal proportion of sand and silt. The sediments mainly have bimodal and leptokurtic distribution and deposited in alternate sand-silt package, probably indicating flood deposits. Relatively low moisture (1.8%) and organic carbon (2.4%) with increased carbonate values (12%) indicate that conditions must have to remain oxidizing. The middle part (depth 9–6m) has a 1m thick gravel bed at the bottom and overlain by coarse sand to very fine sand showing fining upward sequence. The presence of gravel bed suggests some kind of tectonic activity resulting into change in base level or enhanced precipitation in the catchment region. The upper part (depth 6–0m; top part of sequence) mainly comprised of fine sand to silt size grains (with appreciable clay content). The sediment of this part is Unimodal and very leptokurtic in nature suggesting wave and winnowing process and deposited in low energy suspension environment. This part has relatively high moisture (2.1%) and organic carbon (2.7%) with decreased carbonate content (4.2%) indicating change in the depositional environment probably under estuarine conditions. The presence of chlorite along with smectite clay mineral further supports the significant marine contribution in the formation of upper part of the sequence.

Keywords: grain size, statistical analysis, clay minerals, late quaternary, LOI

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Rahul Thakur, Onkar Chandel

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Red Hat is the world leader in providing open source software and solutions. A global enterprise, like Red Hat, has unique issues of connecting employees with content because of distributed offices, multiple teams spread across geographies, multiple languages, and different cultures. Employees, of a global company, create content that is distributed across departments, teams, regions, and countries. This makes finding the best content difficult since owners keep iterating on the existing content. When employees are unable to find the content, they end up creating it once again and in the process duplicating existing material and effort. Also, employees may not find the relevant content and spend time reviewing obsolete duplicate, or irrelevant content. On an average, a person spends 15 minutes/day in failed searches that might result in missed business opportunities, employee frustration, and substandard deliverables. Red Hat Knowledge Management Office (KMO) applied 'open source strategy' to solve the above problems. Under the Open Source Strategy, decisions are taken collectively. The strategy aims at accomplishing common goals with the help of communities. The objectives of this initiative were to save employees' time, get them authentic content, improve their content search experience, avoid duplicate content creation, provide context based search, improve analytics, improve content management workflows, automate content classification, and automate content upload. This session will describe open source strategy, its applicability in content management, challenges, recommended solutions, and outcome.

Keywords: content classification, content management, knowledge management, open source

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Authors: Donggun Lee, Q-Han Park

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Due to its coarse grid, the Pseudo-Spectral Time Domain (PSTD) method has advantages against the Finite Difference Time Domain (FDTD) method in terms of memory requirement and operation time. However, since the efficiency of parallelization is much lower than that of FDTD, PSTD is not a useful method for a large-scale electromagnetic simulation in a parallel platform. In this paper, we propose the parallelization technique of the hybrid PSTD-FDTD (HPF) method which simultaneously possesses the efficient parallelizability of FDTD and the quick speed and low memory requirement of PSTD. Parallelization cost of the HPF method is exactly the same as the parallel FDTD, but still, it occupies much less memory space and has faster operation speed than the parallel FDTD. Experiments in distributed memory systems have shown that the parallel HPF method saves up to 96% of the operation time and reduces 84% of the memory requirement. Also, by combining the OpenMP library to the MPI library, we further reduced the operation time of the parallel HPF method by 50%.

Keywords: FDTD, hybrid, MPI, OpenMP, PSTD, parallelization

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

Abstract:

Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Yoshinori Wakatake

Abstract:

Many defects after the release of software packages are caused due to omissions of sufficient test items in test specifications. Poor test specifications are detected by manual review, which imposes a high human load. The prevention of omissions depends on the end-user awareness of test specification writers. If test specifications were written while envisioning the behavior of end-users, the number of omissions in test items would be greatly reduced. The paper pays attention to the point that writers who can achieve it differ from those who cannot in not only the description richness but also their gaze information. It proposes a method to estimate the degree of user-awareness of writers through the analysis of their gaze information when writing test specifications. We conduct an experiment to obtain the gaze information of a writer of the test specifications. Test specifications are automatically classified using gaze information. In this method, a Random Forest model is constructed for the classification. The classification is highly accurate. By looking at the explanatory variables which turn out to be important variables, we know behavioral features to distinguish test specifications of high quality from others. It is confirmed they are pupil diameter size and the number and the duration of blinks. The paper also investigates test specifications automatically classified with gaze information to discuss features in their writing ways in each quality level. The proposed method enables us to automatically classify test specifications. It also prevents test item omissions, because it reveals writing features that test specifications of high quality should satisfy.

Keywords: blink, eye tracking, gaze information, pupil diameter, quality improvement, specification document, user-awareness

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Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

Abstract:

Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

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Authors: S. Boudali, A. M. Soliman, B. Abdulsalam, K. Ayed, D. E. Kerdal, S. Poncet

Abstract:

This study investigates the potential of using crushed concrete as aggregates to produce green and sustainable concrete. Crushed concrete was sieved to powder fine recycled aggregate (PFRA) less than 80 µm and coarse recycled aggregates (CRA). Physical, mechanical, and microstructural properties for PFRA and CRA were evaluated. The effect of the additional rates of PFRA and CRA on strength development of recycled aggregate concrete (RAC) was investigated. Additionally, the characteristics of interfacial transition zone (ITZ) between cement paste and recycled aggregate were also examined. Results show that concrete mixtures made with 100% of CRA and 40% PFRA exhibited similar performance to that of the control mixture prepared with 100% natural aggregate (NA) and 40% natural pozzolan (NP). Moreover, concrete mixture incorporating recycled aggregate exhibited a slightly higher later compressive strength than that of the concrete with NA. This was confirmed by the very dense microstructure for concrete mixture incorporating recycled concrete aggregates compared to that of conventional concrete mixture.

Keywords: compressive strength, recycled concrete aggregates, microstructure, interfacial transition zone, powder fine recycled aggregate

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Authors: M. Veeresham

Abstract:

The microstructure, texture, phase stability, and tensile properties of annealed Ta_0.5Nb_0.5Hf_0.5ZrTi_1.5 alloy have been investigated in the present research. The alloy was severely hybrid-rolled up to 93.5% thickness reduction, subsequently rolled samples subjected to an annealing treatment at 800 °C and 1000 °C temperatures for 1 h. Consequently, the rolled condition and both annealed temperatures have a body-centered cubic (BCC) structure. Furthermore, quantitative texture measurements (orientation distribution function (ODF) analysis) and microstructural examinations (analytical electron backscatter diffraction (EBSD) maps) permitted to establish a good relationship between annealing texture and microstructure and universal testing machine (UTM) utilized for obtaining the mechanical properties. Impressive room temperature tensile properties combination with the tensile strength (1380 MPa) and (24.7%) elongation is achieved for the 800 °C heat-treated condition. The evolution of the coarse microstructure featured in the case of 1000 °C annealed temperature ascribed to the influence of high thermal energy.

Keywords: refractory high entropy alloys, hybrid-rolling, recrystallization, microstructure, tensile properties

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Authors: Adrian W. Egger, Savvas P. Triantafyllou, Eleni N. Chatzi

Abstract:

The scaled boundary finite element method (SBFEM) is a semi-analytical numerical method, which introduces a scaling center in each element’s domain, thus transitioning from a Cartesian reference frame to one resembling polar coordinates. Consequently, an analytical solution is achieved in radial direction, implying that only the boundary need be discretized. The only limitation imposed on the resulting polygonal elements is that they remain star-convex. Further arbitrary p- or h-refinement may be applied locally in a mesh. The polygonal nature of SBFEM elements has been exploited in quadtree meshes to alleviate all issues conventionally associated with hanging nodes. Furthermore, since in 2D this results in only 16 possible cell configurations, these are precomputed in order to accelerate the forward analysis significantly. Any cells, which are clipped to accommodate the domain geometry, must be computed conventionally. However, since SBFEM permits polygonal elements, significantly coarser meshes at comparable accuracy levels are obtained when compared with conventional quadtree analysis, further increasing the computational efficiency of this scheme. The generalized stress intensity factors (gSIFs) are computed by exploiting the semi-analytical solution in radial direction. This is initiated by placing the scaling center of the element containing the crack at the crack tip. Taking an analytical limit of this element’s stress field as it approaches the crack tip, delivers an expression for the singular stress field. By applying the problem specific boundary conditions, the geometry correction factor is obtained, and the gSIFs are then evaluated based on their formal definition. Since the SBFEM solution is constructed as a power series, not unlike mode superposition in FEM, the two modes contributing to the singular response of the element can be easily identified in post-processing. Compared to the extended finite element method (XFEM) this approach is highly convenient, since neither enrichment terms nor a priori knowledge of the singularity is required. Computation of the gSIFs by SBFEM permits exceptional accuracy, however, when combined with hybrid quadtrees employing linear elements, this does not always hold. Nevertheless, it has been shown that crack propagation schemes are highly effective even given very coarse discretization since they only rely on the ratio of mode one to mode two gSIFs. The absolute values of the gSIFs may still be subject to large errors. Hence, we propose a post-processing scheme, which minimizes the error resulting from the approximation space of the cracked element, thus limiting the error in the gSIFs to the discretization error of the quadtree mesh. This is achieved by h- and/or p-refinement of the cracked element, which elevates the amount of modes present in the solution. The resulting numerical description of the element is highly accurate, with the main error source now stemming from its boundary displacement solution. Numerical examples show that this post-processing procedure can significantly improve the accuracy of the computed gSIFs with negligible computational cost even on coarse meshes resulting from hybrid quadtrees.

Keywords: linear elastic fracture mechanics, generalized stress intensity factors, scaled finite element method, hybrid quadtrees

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Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

Abstract:

Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

Procedia PDF Downloads 136