Search results for: detect and avoid

Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining

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Authors: Francesco Morgan Bono, Simone Cinquemani

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This research aims to enhance bridge monitoring by employing innovative techniques that incorporate exogenous factors into the modeling of sensor signals, thereby improving long-term predictability beyond traditional static methods. Using real datasets from two different bridges equipped with Linear Variable Displacement Transducer (LVDT) sensors, the study investigates the fundamental principles governing sensor behavior for more precise long-term forecasts. Additionally, the research evaluates performance on noisy and synthetically damaged data, proposing a residual-based alarm system to detect anomalies in the bridge. In summary, this novel approach combines advanced modeling, exogenous factors, and anomaly detection to extend prediction horizons and improve preemptive damage recognition, significantly advancing structural health monitoring practices.

Keywords: structural health monitoring, dynamic models, sindy, railway bridges

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Authors: Saad Alkentar, Abdulkareem Assalem

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Recognizing small targets has always posed a significant challenge in image analysis. Over long distances, the image signal-to-noise ratio tends to be low, limiting the amount of useful information available to detection systems. Consequently, visual target recognition becomes an intricate task to tackle. In this study, we introduce a Track Before Detect (TBD) approach that leverages target trajectory information (coordinates) to effectively distinguish between noise and potential targets. By reframing the problem as a multivariate time series classification, we have achieved remarkable results. Specifically, our TBD method achieves an impressive 97% accuracy in separating target signals from noise within a mere half-second time span (consisting of 10 data points). Furthermore, when classifying the identified targets into our predefined categories—airplane, drone, and bird—we achieve an outstanding classification accuracy of 96% over a more extended period of 1.5 seconds (comprising 30 data points).

Keywords: small targets, drones, trajectory information, TBD, multivariate time series

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Authors: Huda Alazmi

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Social studies educational research has, so far, devoted very little attention towards spatial thinking in classroom teaching. To help address such paucity, this study explores the spatial thinking instructional experiences of middle school social studies teachers in Kuwait. The goal is to learn their teaching practices and assess teacher understanding for the spatial thinking concept to enable future improvements. Using a qualitative study approach, the researcher conducted semi-structured interviews to examine the relevant experiences of 14 social studies teachers. The findings revealed three major themes: (1) concepts of space, (2) tools of representation, and (3) spatial reasoning. These themes illustrated how social studies teachers focus predominantly upon simple concepts of space, using multiple tools of representation, but avoid addressing critical spatial reasoning. The findings help explain the current situation while identifying weaker areas for further analysis and improvement.

Keywords: spatial thinking, concepts of space, spatial representation, spatial reasoning

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Authors: Sumayah Mohammed Asiri A., Aviva Levina B., Elizabeth New C., Peter Lay D.

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Pt compounds have been among the most successful anticancer drugs in the last 40 years, but the development of resistance to them is an increasing problem. Cellular homeostasis of an essential metal, Cu, is known to be involved in Pt resistance, but mechanisms of this process are poorly understood. We used a novel ratiometric Cu(I)-sensitive fluorescent probeInCCu1 dye to detect Cu(I) in the mitochondria. Total Cu and labile Cu pool measured using AAS and InCCu1 dye in A2780 cells and their corresponding resistant cells A2780-cis.R cells treated with Cu and cisplatin. The main difference between both cell lines in the presence and absence of Cu(II) is that resistant cells have lower total Cu content but higher labile Cu levels than cisplatin-sensitive cells. This means that resistant cells can metabolize and export excess Cu more efficiently. Furthermore, InCCu1 has emerged not only as an indicator of labile cellular Cu levels in the mitochondria but as a potentially versatile multi-organelle probe.

Keywords: AAS and ICPMS, A2780 and its resistant cells, ratiometric fluorescent sensors, inCCu1, and total and labile Cu

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Authors: A. Mohammadpour, Ebrahim Najafi Kajabad, Ghazale Ipakchi

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Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms.

Keywords: intrusion detection systems, clustering, k-means, k-medoids, SV clustering, ensemble

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Authors: K. A.Veselova, N. V.Gromova, I. N.Antonova, I. N. Kalakutskii

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There are many diseases and incidents that may result facial defects and deformities: cancer, trauma, burns, congenital anomalies, and autoimmune diseases. In some cases, patient may acquire atypically extensive facial defect, including more than one anatomical region or, by contrast, atypically small defect (e.g. partial auricular defect). The anaplastology gives us opportunity to help patient with facial disfigurement in cases when plastic surgery is contraindicated. Using of implant retention for facial prosthesis is strongly recommended because improves both aesthetic and functional results and makes using of the prosthesis more comfortable. Prostheticly oriented fixture position is extremely important for aesthetic and functional long-term result; however, the optimal site for fixture placement is not clear in cases with atypical configuration of facial defect. The objective of this report is to demonstrate challenges in fixture position determination we have faced with and offer the solution. In this report, four cases of implant-supported facial prosthesis are described. Extra-oral implants with four millimeter length were used in all cases. The decision regarding the quantity of surgical stages was based on anamnesis of disease. Facial prostheses were manufactured according to conventional technique. Clinical and technological difficulties and mistakes are described, and prostheticly oriented approach for determination of fixture position is demonstrated. The case with atypically large combined orbital and nasal defect resulting after arteriovenous malformation is described: the correct positioning of artificial eye was impossible due to wrong position of the fixture (with suprastructure) located in medial aspect of supraorbital rim. The suprastructure was unfixed and this fixture wasn`t used for retention in order to achieve appropriate artificial eye placement and better aesthetic result. In other case with small partial auricular defect (only helix and antihelix were absent) caused by squamoized cell carcinoma T1N0M0 surgical template was used to avoid the difficulties. To achieve the prostheticly oriented fixture position in case of extremely small defect the template was made on preliminary cast using vacuum thermoforming method. Two radiopaque markers were incorporated into template in preferable for fixture placement positions taking into account future prosthesis configuration. The template was put on remaining ear and cone-beam CT was performed to insure, that the amount of bone is enough for implant insertion in preferable position. Before the surgery radiopaque markers were extracted and template was holed for guide drill. Fabrication of implant-retained facial prostheses gives us opportunity to improve aesthetics, retention and patients’ quality of life. But every inaccuracy in planning leads to challenges on surgery and prosthetic stages. Moreover, in cases with atypically small or extended facial defects prostheticly oriented approach for determination of fixture position is strongly required. The approach including surgical template fabrication is effective, easy and cheap way to avoid mistakes and unpredictable result.

Keywords: anaplastology, facial prosthesis, implant-retained facial prosthesis., maxillofacil prosthese

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Authors: Rohaya Abdul Wahab, Raja Mohd Fuad Tengku Aziz, Nazaliza Othman, Sharifah Saleh, Nabihah Razali, Muhammad Al Baqir Zinal Abidin, Md Hanif Md Nasir

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This paper will discuss how we optimize our physical verification flow in our IC Design Department having various rule decks from multiple foundries. Our ultimate goal is to achieve faster time to tape-out and avoid schedule delay. Currently the physical verification runtimes and memory usage have drastically increased with the increasing number of design rules, design complexity and the size of the chips to be verified. To manage design violations, we use a number of solutions to reduce the amount of violations needed to be checked by physical verification engineers. The most important functions in physical verifications are DRC (design rule check), LVS (layout vs. schematic) and XRC (extraction). Since we have a multiple number of foundries for our design tape-outs, we need a flow that improve the overall turnaround time and ease of use of the physical verification process. The demand for fast turnaround time is even more critical since the physical design is the last stage before sending the layout to the foundries.

Keywords: physical verification, DRC, LVS, XRC, flow, foundry, runset

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Authors: Tanmay Bisen, Aastha Shayla, Susham Biswas

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Oil spills from tankers can cause significant harm to the environment and local communities, as well as have economic consequences. Early predictions of oil spills can help to minimize these impacts. Our proposed system uses machine learning and neural networks to predict potential oil spills by monitoring data from ship Automatic Identification Systems (AIS). The model analyzes ship movements, speeds, and changes in direction to identify patterns that deviate from the norm and could indicate a potential spill. Our approach not only identifies anomalies but also predicts spills before they occur, providing early detection and mitigation measures. This can prevent or minimize damage to the reputation of the company responsible and the country where the spill takes place. The model's performance on the MV Wakashio oil spill provides insight into its ability to detect and respond to real-world oil spills, highlighting areas for improvement and further research.

Keywords: Anomaly Detection, Oil Spill Prediction, Machine Learning, Image Processing, Graph Neural Network (GNN)

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Authors: Naima Ikken, Abdelhadi Bouknadel, Nour-eddine Tariba Ahmed Haddou, Hafsa El Omari

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The operation of grid-connected inverters necessity a single-phase phase locked loop (PLL) is proposed in this article to accurately and quickly estimate and detect the grid phase angle. This article presents the improvement of a method of phase-locked loop. The novelty is to generate a method (PLL) of synchronizing the grid with a Notch filter based on adaptive fuzzy logic for inverter systems connected to the grid. The performance of the proposed method was tested under normal and abnormal operating conditions (amplitude, frequency and phase shift variations). In addition, simulation results with ISPM software are developed to verify the effectiveness of the proposed method strategy. Finally, the experimental test will be used to extract the result and discuss the validity of the proposed algorithm.

Keywords: phase locked loop, PLL, notch filter, fuzzy logic control, grid connected inverters

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Authors: Vimi Jham

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The banking sector has undergone tremendous change in all forms of service it provides to its customers. The efforts of the banks is to avoid customer defection and lead to customer satisfaction. The purpose of the study was to examine the linkages among the constructs such as customer perceived service quality, perceived service recovery quality and customer satisfaction in the banking industry. The moderating effect of negative brand perception due to service failure on recovery satisfaction were investigated. Random sampling methods are used to draw the sample from the population. Data was collected from 262 banking customers and were analyzed with the help of structural equation modelling approach using Smart PLS to understand the relationship among variables being studied. The results of the study contribute to the research by proving that customer service recovery satisfaction is dependent on customer perceived service quality and the moderating effect of negative brand perception due to service failure was insignificant.

Keywords: service recovery satisfaction, perceived service recovery quality, perceived service quality, structural equation modelling

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Authors: Valerie A. Conrade

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Introduction: A large body of literature suggests patients who complete fecal immunochemical testing (FIT) kits are likely to identify colorectal cancer sooner than those who do not complete FIT kits. Background: Patients who do not participate in preventative measures such as the FIT kit are at a higher risk of colorectal cancer growing unnoticed. The objective was to see if the method the principal investigator (PI) uses to educate clinical staff on the importance of FIT kit administration provides an increased amount of FIT kit dissemination to patients post clinical education. Methodologies: Data collection via manual tallies took place before and after the clinical staff was educated on the importance of FIT kits. Results: The results showed an increase in FIT kit dissemination post clinical staff education. Through enhanced instruction to the clinical staff regarding the importance of FIT kits, expanding their knowledge on preventative measures to detect colorectal cancer positively impacted nurses and, in turn, their patients.

Keywords: colon cancer, education, fecal immunochemical testing, nursing

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Authors: T. Penkova, A. Korobko, V. Nicheporchuk, L. Nozhenkova, A. Metus

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This paper presents an approach of on-line control of the state of technosphere and environment objects based on the integration of Data Warehouse, OLAP and Expert systems technologies. It looks at the structure and content of data warehouse that provides consolidation and storage of monitoring data. There is a description of OLAP-models that provide a multidimensional analysis of monitoring data and dynamic analysis of principal parameters of controlled objects. The authors suggest some criteria of emergency risk assessment using expert knowledge about danger levels. It is demonstrated now some of the proposed solutions could be adopted in territorial decision making support systems. Operational control allows authorities to detect threat, prevent natural and anthropogenic emergencies and ensure a comprehensive safety of territory.

Keywords: decision making support systems, emergency risk assessment, natural and anthropogenic safety, on-line control, territory

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Authors: Igor Shardakov, Anton Bykov, Alexey Shestakov, Irina Glot

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This article focuses on the cycle of experimental studies of the formation of cracks and debondings in the concrete reinforced with carbon fiber. This research was carried out in Perm National Research Polytechnic University. A series of CFRP-strengthened RC beams was tested to investigate the influence of preload and crack repairing factors on CFRP debonding. IRT was applied to detect the early stage of IC debonding during the laboratory bending tests. It was found that for the beams strengthened under load after crack injecting, СFRP debonding strain is 4-65% lower than for the preliminary strengthened beams. The beams strengthened under the load had a relative area of debonding of 2 times higher than preliminary strengthened beams. The СFRP debonding strain is weakly dependent on the strength of the concrete substrate. For beams with a transverse wrapping anchorage in support sections FRP debonding is not a failure mode.

Keywords: IC debonding, infrared thermography, non-destructive testing methods, quality control, strengthening

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Authors: Syed Mahbubuz Zaman, A. B. M. Abrar Haque, Mehedi Hassan Nayeem, Misbah Uddin Sagor

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Everyday e-mails are being used by millions of people as an effective form of communication over the Internet. Although e-mails allow high-speed communication, there is a constant threat known as spam. Spam e-mail is often called junk e-mails which are unsolicited and sent in bulk. These unsolicited emails cause security concerns among internet users because they are being exposed to inappropriate content. There is no guaranteed way to stop spammers who use static filters as they are bypassed very easily. In this paper, a smart system is proposed that will be using neural networks to approach spam in a different way, and meanwhile, this will also detect the most relevant features that will help to design the spam filter. Also, a comparison of different parameters for different neural network models has been shown to determine which model works best within suitable parameters.

Keywords: long short-term memory, bidirectional long short-term memory, gated recurrent unit, natural language processing, natural language processing

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Authors: F. Kantaş, D. Boyacı, C. Dinç

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Exhaust systems plays an important role in thermal heat management. Exhaust manifold picks burned gas from engine and exhaust pipes transmit exhaust gas to muffler, exhaust gas is reacted chemically to avoid noxious gas and sound is reduced in muffler then gas is threw out with tail pipe from muffler. Exhaust gas flows out from tail pipe and this hot gas flows to many parts that available around tail pipe and muffler, like spare tire, transmission, pipes etc. These parts are heated by hot exhaust gas. Also vehicle on ride, external flow effects exhaust gas flow and exhaust gas behavior is changed. It's impossible to understand which parts are heated by hot exhaust gas in tests. To understand this phenomena, exhaust gas flow is solved in CFD also external flow due to vehicle movement must be solved with exhaust gas flow. Because external flow effects exhaust gas flow behavior with many parameters. This paper investigates external flow effects exhaust gas flow behavior and other critical parameters effect exhaust gas flow behavior, like different tail pipe design, exhaust gas mass flow in critic vehicle driving situations.

Keywords: exhaust, gas flow, vehicle, external flow

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Authors: Gomes C. C. Izabel, Lanzotti B. Rafaela, Orlandi S. Fabiana

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Chronic Kidney Disease is considered a serious public health problem. The exploitation of resilience has been guided by studies conducted in various contexts, especially in hemodialysis, since the impact of diagnosis and restrictions produced during the treatment process because, despite advances in treatment, remains the stigma of the disease and the feeling of pain, hopelessness, low self-esteem and disability. The objective was to evaluate the level of resilience of patients in chronic renal dialysis. This is a descriptive, correlational, cross and quantitative research. The sample consisted of 100 patients from a Renal Replacement Therapy Unit in the countryside of São Paulo. For data collection were used the characterization instrument of Participants and the Resilience Scale. There was a predominance of males (70.0%) were Caucasian (45.0%) and had completed elementary education (34.0%). The average score obtained through the Resilience Scale was 131.3 (± 20.06) points. The resiliency level submitted may be considered satisfactory. It is expected that this study will assist in the preparation of programs and actions in order to avoid possible situations of crises faced by chronic renal patients.

Keywords: hemodialysis units, renal dialysis, renal insufficiency chronic, resilience psychological

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Authors: Sanghoon Bae, Hanju Cha

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Failed fuel detector (FFD) in research reactor is a very crucial instrument to detect the anomaly from failed fuels in the early stage around primary cooling system (PCS) outlet prior to the decay tank. FFD is considered as a mandatory sensor to ensure the integrity of fuel assemblies and mitigate the consequence from a failed fuel accident. For the effective function of FFD, the location of them should be determined by contemplating the effect from coolant flow around two outlets. For this, the analysis on computational flow dynamics (CFD) should be first performed how the coolant outlet flow including radioactive materials from failed fuels are mixed and discharged through the outlet plenum within certain seconds. The analysis result shows that the outlet flow is well mixed regardless of the position of failed fuel and ultimately illustrates the effect of detector location.

Keywords: computational flow dynamics (CFD), failed fuel detector (FFD), fresh fuel assembly (FFA), spent fuel assembly (SFA)

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Authors: Hafsa Chergui

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Malnutrition is a very common problem for hospitalized patients in general but it happens most to those who have a chronic disease such as cancer. Learned food aversions are defined as aversions which form toward foods after their ingestion has been temporally paired with illness (nausea or emesis). Learned food aversion may exert a negative impact on nutritional status and quality of life. The present review evaluates the literature derived both from laboratory animals and humans. Also, a questionnaire has been filled by patients under chemotherapy to assess the level of food aversions. This study evaluated the current research for avoiding the formation of aversions to dietary items in 200 cancer patients treated with chemotherapy. A scapegoat food or beverage can be used just before treatment to reduce the incidence of treatment-related aversions to foods in the individual s usual diet. The goal of this work is to inform the nurses and dieticians because they play a vital role in the daily assessment of the patients' nutritional status. Being aware of all the causes of malnutrition may help to suggest solutions to improve the health condition of the patient and avoid severe malnutrition.

Keywords: chemotherapy, oncology, food aversion, taste aversion

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Authors: Amarnath M., Ramachandra C. G., H. Chelladurai, P..Sateesh Kumar, K. Santhosh Kumar

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Journal bearings are extensively used sliding contact machine elements to support radial/axial loaded rotors used in various applications viz. automobile crankshaft, turbine propeller shaft, rope conveyer, heavy duty electric motors. The primary reasons for the failures of these bearings include unstable lubricant film, oil degradation, misalignment, etc. This paper describes the results of experimental investigations carried out to detect surface fatigue wear developed on load bearing the contact surfaces of journal bearing. The test bearing was subjected to fatigue load cycles over a period of 600 hours. The vibration signals were acquired from the journal bearing at regular intervals of 100 hrs. These signals were post-processed by using the vibration analysis technique to obtain diagnostic information of wear propagated in the journal-bearing system.

Keywords: fatigue, journal bearing, sound signals, vibration signals, wear

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Authors: J. Waldon

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The civil aviation industry plays a crucial role in global transportation, connecting people and goods across the world. Ensuring the safety and security of passengers, crew, and aircraft is of paramount importance. This paper aims to address the aspect of training and human factors, amongst others, necessary for enhancing civil aviation safety and security. In this context, we are focusing on the level of attention exhibited in the checking of luggage and travel credentials, with the aim to identify areas of improvement and avoid compromising security and safety at the Nsimalen Airport Yaoundé, Cameroon. We found that there is a lack of proper awareness among both travelers and some staff on the safety and security of goods and passengers. We suggest that improved training and handling, and sensitization in the form of legible billboards are important. Thus, we recommend refresher courses like this one for staff to keep abreast with the fast-changing security landscape in air transport as well as proper sensitization, including health-related issues. In conclusion, we established that the human factors, as well as the frequency of training and refresher courses, have a positive outlook on safety and security in air transport.

Keywords: safety, security, passengers, cargo

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Authors: Abhigyan Hedau, Priya Shelke, Riddhi Mirajkar, Shreyash Chaple, Mrunali Gadekar, Himanshu Akula

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The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifiers (KNN), Decision Tree Classifiers, Random Forest classifiers and Gradient Boosting classifiers. These algorithms are applied to patient data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.

Keywords: logistic regression, support vector classifier, k-nearest neighbour, decision tree, random forest and gradient boosting

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Authors: M. H. Aissa, Amar Bouzid Dj

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As known, many countries are now planning to build new wind farms with high capacity up to 5MW. Consequently, the size of the foundation increase. These kinds of structures are subject to fatigue damage from environmental loading mainly due to wind and waves as well as from cyclic loading imposed through the rotational frequency (1P) through mass and aerodynamic imbalances and from the blade passing frequency (3P) of the wind turbine which make them behavior dynamically very sensitive. That is why natural frequency must be determined with accuracy from the existing data of the soil and the foundation stiffness sources of uncertainties, to avoid the resonance of the system. This paper presents analytical expressions of stiffness foundation with large diameter in linear soil behavior in different soil stiffness profile. To check the accuracy of the proposed formulas, a mathematical model approach based on non-dimensional parameters is used to calculate the natural frequency taking into account the soil structure interaction (SSI) compared with the p-y method and measured frequency in the North Sea Wind farms.

Keywords: offshore wind turbines, semi analytical FE analysis, p-y curves, piles foundations

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Isaura Esther Solano Núñez, David Suarez

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The main purpose of this research is to discover what causes death in children of the Wayuu community, and deeply analyze those results in order to take corrective measures to properly control infant mortality. We consider important to determine the reasons that are producing early death in this specific type of population, since they are the most vulnerable to high risk environmental conditions. In this way, the government, through competent authorities, may develop prevention policies and the right measures to avoid an increase of this tragic fact. The methodology used to develop this investigation is data mining, which consists in gaining and examining large amounts of data to produce new and valuable information. Through this technique it has been possible to determine that the child population is dying mostly from malnutrition. In short, this technique has been very useful to develop this study; it has allowed us to transform large amounts of information into a conclusive and important statement, which has made it easier to take appropriate steps to resolve a particular situation.

Keywords: malnutrition, data mining, analytical, descriptive, population, Wayuu, indigenous

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Authors: Aswin Kumar Krishnan, Yat Choy Wong, Reiza Mukhlis, Zipeng Zhang, Arul Arulrajah

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The continuous increase in vehicle uptake escalates the number of rubber tyre waste which need to be managed to avoid landfilling and stockpiling. The present research focused on the sustainable use of rubber crumbs in clay roof tiles. The properties of roof tiles composed of clay, rubber crumbs, NaOH, and Na₂SiO₃ with a 10% alkaline activator were studied. Tile samples were fabricated by heating the compacted mixtures at 50°C for 72 hours, followed by a higher heating temperature of 200°C for 24 hours. The effect of rubber crumbs aggregates as a substitution for the raw clay materials was investigated by varying their concentration from 0% to 2.5%. X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses have been conducted to study the phases and microstructures of the samples. It was found that the optimum rubber crumbs concentration was at 0.5% and 1%, while cracks and larger porosity were found at higher crumbs concentrations. Water absorption and compressive strength test results demonstrated that rubber crumbs and clay satisfied the standard requirement for the roof tiles.

Keywords: rubber crumbs, clay, roof tiles, alkaline activators

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Authors: A. Biran, P. Sobhe Bidari, A. Almazroe, V. Lakshminarayanan, K. Raahemifar

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Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Also, Support Vector Machine (SVM) Classifier is used to classify retinal images to normal or abnormal cases including non-proliferative or proliferative DR. The proposed method has been tested on images selected from Structured Analysis of the Retinal (STARE) database using MATLAB code. The method is perfectly able to detect DR. The sensitivity specificity and accuracy of this approach are 90%, 87.5%, and 91.4% respectively.

Keywords: diabetic retinopathy, fundus images, STARE, Gabor filter, support vector machine

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Authors: Dua Hişam, Serhat İkizoğlu

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Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

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Authors: P. Agnivesh, P. V. Ponambala Moorthi

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Evolution of information modelling leads to the visualisation of well-organized built environment. Building Information Modelling (BIM) is considered as evolution in the off-site construction which essentially enhances and controls the present scenario of on-site construction paradigms. Promptness, sustainability and security are considered as the important characteristics of the building information modelling. Projects that uses BIM are tied firmly by technology but distributed organizationally. This allows different team members in the project to associate and integrate the works and work flows. This will in turn improve the efficiency of work breakdown structure. Internationally BIM had been accepted as modern computer aided way of information sharing by construction industry for efficient way of manipulation in order to avoid the on-site misperceptions. Even though, in developing countries like India BIM is in the phase of start and requires lot of mandates and policies to be brought about by the government for its widespread implementations. This paper reviews the current scenario of BIM worldwide and in India and suggests for the improved implementation of building modelling for Indian policy condition.

Keywords: building information modelling, Indian polity, information modelling, information sharing, mandates and policies, sustainability.

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Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

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Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

Procedia PDF Downloads 276