Search results for: classification of filters

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 Boukhris, Alima Damak, Dorra Sellami

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Fingervein biometric authentication is one of the most popular and accurate technologies. However, low cost embedded solution is still an open problem. In this paper, a real-time implementation of fingervein recognition process embedded in Raspberry-Pi has been proposed. The use of Raspberry-Pi reduces overall system cost and size while allowing an easy user interface. Implementation of a target technology has guided to opt some specific parallel and simple processing algorithms. In the proposed system, we use four structural directional kernel elements for filtering finger vein images. Then, a Top-Hat and Bottom-Hat kernel filters are used to enhance the visibility and the appearance of venous images. For feature extraction step, a simple Local Directional Code (LDC) descriptor is applied. The proposed system presents an Error Equal Rate (EER) and Identification Rate (IR), respectively, equal to 0.02 and 98%. Furthermore, experimental results show that real-time operations have good performance.

Keywords: biometric, Bottom-Hat, Fingervein, LDC, Rasberry-Pi, ROI, Top-Hat

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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: Yaacoub Tina, Youssef Roua, Radoi Emanuel, Burel Gilles

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Recently, low-complexity sub-Nyquist sampling schemes based on the Finite Rate of Innovation (FRI) theory have been introduced to sample parametric signals at minimum rates. The multichannel modulating waveforms (MCMW) is such an efficient scheme, where the received signal is mixed with an appropriate set of arbitrary waveforms, integrated and sampled at rates far below the Nyquist rate. In this paper, the MCMW scheme is adapted to the special case of ultra wideband (UWB) channel estimation, characterized by dense multipaths. First, an appropriate structure, which accounts for the bandpass spectrum feature of UWB signals, is defined. Then, a novel approach to decrease the number of processing channels and reduce the complexity of this sampling scheme is presented. Finally, the proposed concepts are validated by simulation results, obtained with real filters, in the framework of a coherent Rake receiver.

Keywords: coherent rake receiver, finite rate of innovation, sub-nyquist sampling, ultra wideband

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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: R. Ananthanarayanan, S. B. Nakula, D. R. Seenivasagam, J. Naua, B. Sharma

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Product packaging is a critical component of production, trade, and marketing, playing numerous vital roles that often go unnoticed by consumers. Packaging is essential for maintaining the shelf life, quality assurance, and safety of both manufactured and agricultural products. For example, harvested produce or processed foods can quickly lose quality and freshness, making secure packaging crucial for preservation and safety throughout the food supply chain. In Fiji, agricultural packaging has primarily been managed by local companies for international trade, with gradual advancements in these practices. To further enhance the industry’s performance, this study examines the challenges and constraints hindering the optimization of agricultural packaging practices in Fiji. The study utilizes Multi-Criteria Decision Making (MCDM) tools, specifically Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). ISM analyzes the hierarchical structure of barriers, categorizing them from the least to the most influential, while MICMAC classifies barriers based on their driving and dependence power. This approach helps identify the interrelationships between barriers, providing valuable insights for policymakers and decision-makers to propose innovative solutions for sustainable development in the agricultural packaging sector, ultimately shaping the future of packaging practices in Fiji.

Keywords: agricultural packaging, barriers, ISM, MICMAC

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Authors: M. Brahim, I. Bahri, Y. Bernard

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Traveling Wave Ultrasonic Motor (TWUM) is a compact, precise, and silent actuator generating high torque at low speed without gears. Moreover, the TWUM has a high holding torque without supply, which makes this motor as an attractive solution for holding position of robotic arms. However, their nonlinear dynamics, and the presence of load-dependent dead zones often limit their use. Those issues can be overcome in closed loop with effective and precise controllers. In this paper, robust H-infinity (H∞) and discrete time RST position controllers are presented. The H∞ controller is designed in continuous time with additional weighting filters to ensure the robustness in the case of uncertain motor model and external disturbances. Robust RST controller based on the pole placement method is also designed and compared to the H∞. Simulink model of TWUM is used to validate the stability and the robustness of the two proposed controllers.

Keywords: piezoelectric motors, position control, H∞, RST, stability criteria, robustness

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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: Syazwani Idrus, S. Charles J. Banks, Sonia Heaven

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The study compared the performance of upflow anaerobic sludge blanket (UASB) reactors and anaerobic filters (AF) for the treatment of wheat straw washwater (WSW) which has a high concentration of Potassium ions. The trial was conducted at mesophilic temperatures (37 °C). The digesters were started up over a 48-day period using a synthetic wastewater feed and reached an organic loading rate (OLR) of 6 g COD L^-1 day^-1 with a specific methane production (SMP) of 0.333 L CH4 g^-1 COD. When the feed was switched to WSW it was not possible to maintain the same loading rate as the SMP in all reactors fell sharply to less than 0.1 L CH4 g^-1 COD, with the AF affected more than the UASB. On reducing the OLR to 3 g COD L^-1 day^-1 the reactors recovered to produce 0.21 L CH4 g^-1 CODadded and gave 82% COD removal. A discrepancy between the COD consumed and the methane produced could be accounted for through increased maintenance energy requirement of the microbial community for osmo-regulation as K+ was found to accumulate in the sludge and in the UASB reached a concentration of 4.5 mg K g^-1 wet weight of granules.

Keywords: anaerobic digestion, osmotic stress, chemical oxygen demand, specific methane production

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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

Procedia PDF Downloads 367

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

Procedia PDF Downloads 293

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

Procedia PDF Downloads 375

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: Mohammad Shams Esfand Abadi, Mehrdad Zalaghi, Reza ebrahimpour

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In this paper, we propose a sign adaptive filter algorithm with the ability of dynamic selection of subband filters which leads to low computational complexity compared with conventional sign subband adaptive filter (SSAF) algorithm. Dynamic selection criterion is based on largest reduction of the mean square deviation at each adaption. We demonstrate that this simple proposed algorithm has the same performance of the conventional SSAF and somewhat faster than it. In the presence of impulsive interferences robustness of the simple proposed algorithm as well as the conventional SSAF and outperform the conventional normalized subband adaptive filter (NSAF) algorithm. Therefore, it is preferred for environments under impulsive interferences. Simulation results are presented to verify these above considerations very well have been achieved.

Keywords: acoustic echo cancellation (AEC), normalized subband adaptive filter (NSAF), dynamic selection subband adaptive filter (DS-NSAF), sign subband adaptive filter (SSAF), impulsive noise, robust filtering

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Authors: Nadeen Felemban, Hamsa Banjer, Rabaah Jaafari

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One of the most common hazards in the pharmaceutical industry is the chemical hazard, which can cause harm or develop occupational health diseases/illnesses due to chronic exposures to hazardous substances. Therefore, a chemical agent management system is required, including hazard identification, risk assessment, controls for specific hazards and inspections, to keep your workplace healthy and safe. However, routine management monitoring is also required to verify the effectiveness of the control measures. Moreover, Betamethasone Valerate and Clobetasol Propionate are some of the APIs (Active Pharmaceutical Ingredients) with highly hazardous classification-Occupational Hazard Category (OHC 4), which requires a full containment (ECA-D) during handling to avoid chemical exposure. According to Safety Data Sheet, those chemicals are reproductive toxicants (reprotoxicant H360D), which may affect female workers’ health and cause fatal damage to an unborn child, or impair fertility. In this study, qualitative (chemical Risk assessment-qCRA) was conducted to assess the chemical exposure during handling of Betamethasone Valerate and Clobetasol Propionate in pharmaceutical laboratories. The outcomes of qCRA identified that there is a risk of potential chemical exposure (risk rating 8 Amber risk). Therefore, immediate actions were taken to ensure interim controls (according to the Hierarchy of controls) are in place and in use to minimize the risk of chemical exposure. No open handlings should be done out of the Steroid Glove Box Isolator (SGB) with the required Personal Protective Equipment (PPEs). The PPEs include coverall, nitrile hand gloves, safety shoes and powered air-purifying respirators (PAPR). Furthermore, a quantitative assessment (personal air sampling) was conducted to verify the effectiveness of the engineering controls (SGB Isolator) and to confirm if there is chemical exposure, as indicated earlier by qCRA. Three personal air samples were collected using an air sampling pump and filter (IOM2 filters, 25mm glass fiber media). The collected samples were analyzed by HPLC in the BV lab, and the measured concentrations were reported in (ug/m3) with reference to Occupation Exposure Limits, 8hr OELs (8hr TWA) for each analytic. The analytical results are needed in 8hr TWA (8hr Time-weighted Average) to be analyzed using Bayesian statistics (IHDataAnalyst). The results of the Bayesian Likelihood Graph indicate (category 0), which means Exposures are de "minimus," trivial, or non-existent Employees have little to no exposure. Also, these results indicate that the 3 samplings are representative samplings with very low variations (SD=0.0014). In conclusion, the engineering controls were effective in protecting the operators from such exposure. However, routine chemical monitoring is required every 3 years unless there is a change in the processor type of chemicals. Also, frequent management monitoring (daily, weekly, and monthly) is required to ensure the control measures are in place and in use. Furthermore, a Similar Exposure Group (SEG) was identified in this activity and included in the annual health surveillance for health monitoring.

Keywords: occupational health and safety, risk assessment, chemical exposure, hierarchy of control, reproductive

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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: S. Cheragh Dar, M. Saljooghi, A. Babrgir

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In the last couple of decades researchers' attitudes were focused on developing and enhancing heat transfer processes by using new components or cellular solids that divide into stochastic structures and periodic structures. Open-cell metal foams are part of stochastic structures families that they can be considered as an avant-garde technology and they have unique properties, this porous media can have tremendous achievements in thermal processes. This paper argues and surveys postulating possible in industrial thermal issues which include: compact electronic cooling, heat exchanger, aerospace, fines, turbo machinery, automobiles, crygen tanks, biomechanics, high temperature filters and etc. Recently, by surveying exponential rate of publications in thermal open-cell metal foams, all can be demonstrated in a holistic view which can lead researchers to a new level of understanding in different industrial thermal sections.

Keywords: heat transfer, industrial thermal, cellular solids, open cell metal foam

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

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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

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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

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

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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

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Authors: Mohsen Maraoui

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In the aim of natural language processing applications improvement, such as information retrieval, machine translation, lexical disambiguation, we focus on statistical approach to semantic indexing for multilingual text documents based on conceptual network formalism. We propose to use this formalism as an indexing language to represent the descriptive concepts and their weighting. These concepts represent the content of the document. Our contribution is based on two steps. In the first step, we propose the extraction of index terms using the multilingual lexical resource Euro WordNet (EWN). In the second step, we pass from the representation of index terms to the representation of index concepts through conceptual network formalism. This network is generated using the EWN resource and pass by a classification step based on association rules model (in attempt to discover the non-taxonomic relations or contextual relations between the concepts of a document). These relations are latent relations buried in the text and carried by the semantic context of the co-occurrence of concepts in the document. Our proposed indexing approach can be applied to text documents in various languages because it is based on a linguistic method adapted to the language through a multilingual thesaurus. Next, we apply the same statistical process regardless of the language in order to extract the significant concepts and their associated weights. We prove that the proposed indexing approach provides encouraging results.

Keywords: concept extraction, conceptual network formalism, fuzzy association rules, multilingual thesaurus, semantic indexing

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Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

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Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Khalid Al Mutairi, Mashhor Mansor, Magdy El-Bana, Asyraf Mansor, Saud AL-Rowaily

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Urban expansion and the associated increase in anthropogenic pressures have led to a great loss of the Red Sea’s biodiversity. Floristic composition, diversity, and environmental controls were investigated for 210 relive's on twenty coral islands of Farasan in the Red Sea, Saudi Arabia. Multivariate statistical analyses for classification (Cluster Analysis), ordination (Detrended Correspondence Analysis (DCA), and Redundancy Analysis (RDA) were employed to identify vegetation types and their relevance to the underlying environmental gradients. A total of 191 flowering plants belonging to 53 families and 129 genera were recorded. Geophytes and chamaephytes were the main life forms in the saline habitats, whereas therophytes and hemicryptophytes dominated the sandy formations and coral rocks. The cluster analysis and DCA ordination identified twelve vegetation groups that linked to five main habitats with definite floristic composition and environmental characteristics. The constrained RDA with Monte Carlo permutation tests revealed that elevation and soil salinity were the main environmental factors explaining the vegetation distributions. These results indicate that the flora of the study archipelago represents a phytogeographical linkage between Africa and Saharo-Arabian landscape functional elements. These findings should guide conservation and management efforts to maintain species diversity, which is threatened by anthropogenic activities and invasion by the exotic invasive tree Prosopis juliflora (Sw.) DC.

Keywords: biodiversity, classification, conservation, ordination, Red Sea

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Authors: Meenal Surawar, Rajashree Kotharkar

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Urban Heat Island (UHI) is found more pronounced as a prominent urban environmental concern in developing cities. To study the UHI effect in the Indian context, the Nagpur urban area has been explored in this paper using Landsat 7 ETM+ satellite images through Remote Sensing and GIS techniques. This paper intends to study the effect of LU/LC pattern on daytime Land Surface Temperature (LST) variation, contributing UHI formation within the Nagpur Urban area. Supervised LU/LC area classification was carried to study urban Change detection using ENVI 5. Change detection has been studied by carrying Normalized Difference Vegetation Index (NDVI) to understand the proportion of vegetative cover with respect to built-up ratio. Detection of spectral radiance from the thermal band of satellite images was processed to calibrate LST. Specific representative areas on the basis of urban built-up and vegetation classification were selected for observation of point LST. The entire Nagpur urban area shows that, as building density increases with decrease in vegetation cover, LST increases, thereby causing the UHI effect. UHI intensity has gradually increased by 0.7°C from 2000 to 2006; however, a drastic increase has been observed with difference of 1.8°C during the period 2006 to 2013. Within the Nagpur urban area, the UHI effect was formed due to increase in building density and decrease in vegetative cover.

Keywords: land use/land cover, land surface temperature, remote sensing, urban heat island

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Authors: Carolyn Whyte, Tina Halai, Sonita Koshal

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Aim: There are many methods available to assess the potential difficulty of third molar surgery. This study investigated various factors to assess whether they had a bearing on the difficulties encountered. Study design: A retrospective study was completed of 62 single mandibular third molar teeth removed under day case general anaesthesia between May 2016 and August 2016 by 3 consultant oral surgeons. Method: Data collection was by examining the OPG radiographs of each tooth and recording the necessary data. This was depth of impaction, angulation, bony impaction, point of application in relation to second molar, root morphology, Pell and Gregory classification and Winters Lines. This was completed by one assessor and verified by another. Information on medical history, anxiety, ethnicity and age were recorded. Case notes and surgical entries were examined for any difficulties encountered. Results: There were 5 cases which encountered surgical difficulties which included fracture of root apices (3) which were left in situ, prolonged bleeding (1) and post-operative numbness >6 months(1). Four of the 5 cases had Pell and Gregory classification as (B) where the occlusal plane of the impacted tooth is between the occlusal plane and the cervical line of the adjacent tooth. 80% of cases had the point of application as either coronal or apical one third (1/3) in relation to the second molar. However, there was variability in all other aspects of assessment in predicting difficulty of removal. Conclusions: Of the cases which encountered difficulties they all had at least one predictor of potential complexity but these varied case by case.

Keywords: impaction, mandibular third molar, radiographic assessment, surgical removal

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