Search results for: brain machine interface (BMI)

Authors: Fahad Qureshi

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This paper provides detailed study on the diagnosis of intermittent high vibration spikes at exhaust bearing (Non-Drive End) of Mitsubishi H-25 gas turbine installed in a petrochemical plant in Pakistan. The diagnosis is followed by successful root cause analysis of the issue and recommendations for improving the reliability of machine. Engro Polymer and Chemicals (EPCL), a Chlor Vinyl complex, has a captive power plant consisting of one combined cycle power plant (CCPP), having two gas turbines each having 25 MW capacity (make: Hitachi) and one extraction condensing steam turbine having 15 MW capacity (make: HTC). Besides, one 6.75 MW SGT-200 1S gas turbine (make: Alstom) is also available. In 2018, the organization faced an issue of intermittent high vibration at exhaust bearing of one of H-25 units having tag GT-2101 A, which eventually led to tripping of machine at configured securities. Since the machine had surpassed 64,000 running hours and major inspection was also due, so bearings inspection was performed. Inspection revealed excessive coke deposition at labyrinth where evidence of rotor rub was also present. Bearing clearance was also at upper limit, and slight babbitt (soft metal) chip off was observed at one of its pads so it was preventively replaced. The unit was restated successfully and exhibited no abnormality until October 2020, when these spikes reoccurred, leading to machine trip. Recurrence of the issue within two years indicated that root cause was not properly addressed, so this paper furthers the discussion on in-depth analysis of findings and establishes successful root cause analysis, which captured significant learnings both in terms of machine design deficiencies and gaps in operation & maintenance (O & M) regime. Lastly, revised O& M regime along with set of recommendations are proposed to avoid recurrence.

Keywords: exhaust side bearing, Gas turbine, rubbing, vibration

Procedia PDF Downloads 167

Authors: Mandlenkosi George Robert Mahlobo, Tumelo Seadira, Major Melusi Mabuza, Peter Apata Olubambi

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Cathodic protection (CP) has been widely considered a suitable technique for mitigating corrosion of buried metal structures. Plenty of efforts have been made in developing techniques, in particular non-destructive techniques, for monitoring and quantifying the effectiveness of CP to ensure the sustainability and performance of buried steel structures. The aim of this study was to investigate the evolution of the electrochemical processes at the steel/soil interface during the application of CP on steel in simulated soil. Carbon steel was subjected to electrochemical tests with NS4 solution used as simulated soil conditions for 4 days before applying CP for a further 11 days. A previously modified non-destructive voltammetry technique was applied before and after the application of CP to measure the corrosion rate. Electrochemical impedance spectroscopy (EIS), in combination with mathematical modeling through equivalent electric circuits, was applied to determine the electrochemical behavior at the steel/soil interface. The measured corrosion rate was found to have decreased from 410 µm/yr to 8 µm/yr between days 5 and 14 because of the applied CP. Equivalent electrical circuits were successfully constructed and used to adequately model the EIS results. The modeling of the obtained EIS results revealed the formation of corrosion products via a mixed activation-diffusion mechanism during the first 4 days, while the activation mechanism prevailed in the presence of CP, resulting in a protective film. The x-ray diffraction analysis confirmed the presence of corrosion products and the predominant protective film corresponding to the calcareous deposit.

Keywords: carbon steel, cathodic protection, NS4 solution, voltammetry, EIS

Procedia PDF Downloads 42

Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Saroj Bijarnia, Preety Singh

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We propose a simple and effective biometrics system based on face verification across aging using a new variant of texture feature, Pyramid Binary Pattern. This employs Local Binary Pattern along with its hierarchical information. Dimension reduction of generated texture feature vector is done using Principal Component Analysis. Support Vector Machine is used for classification. Our proposed method achieves an accuracy of 92:24% and can be used in an automated age-invariant face verification system.

Keywords: biometrics, age invariant, verification, support vector machine

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Authors: Lakhoua Najeh

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Aims of the work: The aim of this paper is to improve the reliability and the availability of a Packer production line of cigarettes based on two methods: The SADT method (Structured Analysis Design Technique) and the FMECA approach (Failure Mode Effects and Critically Analysis). The first method enables us to describe the functionality of the Packer production line of cigarettes and the second method enables us to establish an FMECA analysis. Methods: The methodology adopted in order to contribute to the improvement of the reliability and the availability of a Packer production line of cigarettes has been proposed in this paper, and it is based on the use of Structured Analysis Design Technique (SADT) and Failure mode, effects, and criticality analysis (FMECA) methods. This methodology consists of using a diagnosis of the existing of all of the equipment of a production line of a factory in order to determine the most critical machine. In fact, we use, on the one hand, a functional analysis based on the SADT method of the production line and on the other hand, a diagnosis and classification of mechanical and electrical failures of the line production by their criticality analysis based on the FMECA approach. Results: Based on the methodology adopted in this paper, the results are the creation and the launch of a preventive maintenance plan. They contain the different elements of a Packer production line of cigarettes; the list of the intervention preventive activities and their period of realization. Conclusion: The diagnosis of the existing state helped us to found that the machine of cigarettes used in the Packer production line of cigarettes is the most critical machine in the factory. Then this enables us in the one hand, to describe the functionality of the production line of cigarettes by SADT method and on the other hand, to study the FMECA machine in order to improve the availability and the performance of this machine.

Keywords: production system, diagnosis, SADT method, FMECA method

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Authors: Lagnajeet Mohanty, Mohnish Mishra, Pratham Tapdiya, Himanshu Sekhar Nayak, Swetapadma Singh

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In the era of global interconnectedness, effective risk assessment and management are critical for organizational resilience. This review explores the integration of machine learning (ML) into risk processes, examining its transformative potential and the challenges it presents. The literature reveals ML's success in sectors like consumer credit, demonstrating enhanced predictive accuracy, adaptability, and potential cost savings. However, ethical considerations, interpretability issues, and the demand for skilled practitioners pose limitations. Looking forward, the study identifies future research scopes, including refining ethical frameworks, advancing interpretability techniques, and fostering interdisciplinary collaborations. The synthesis of limitations and future directions highlights the dynamic landscape of ML in risk management, urging stakeholders to navigate challenges innovatively. This abstract encapsulates the evolving discourse on ML's role in shaping proactive and effective risk management strategies in our interconnected and unpredictable global landscape.

Keywords: machine learning, risk assessment, ethical considerations, financial inclusion

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Authors: Matheus Nunes, Rafael Mendes, Taygoara Felamingo Oliveira, Antonio Brasil Junior

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Wind tunnel experiments of horizontal axis propeller hydrokinetic turbines model were carried out, in order to determine the performance behavior for different configurations and operational range. The present experiments introduce the use of two different geometries of rear diffusers to enhance the performance of the free flow machine. The present paper reports an increase of the power coefficient about 50%-80%. It represents an important feature that has to be taken into account in the design of this kind of machine.

Keywords: diffuser-enhanced turbines, hydrokinetic turbine, wind tunnel experiments, micro hydro

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Authors: O. Sunantha, S. Zhenfeng, S. Phattraporn, A. Zeeshan

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The decline of soil organic carbon (SOC) in global agriculture is a critical issue requiring rapid and accurate estimation for informed policymaking. Previous studies have demonstrated the variability of SOC predictors derived from remote sensing data and environmental variables. However, the specific parameters most suitable for accurately estimating SOC in agricultural areas remain unclear. This study utilizes remote sensing data to precisely estimate SOC and identify influential factors in diverse agricultural areas, such as paddy, corn, sugarcane, cassava, and perennial crops. Extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR) models are employed to analyze these factors impact on SOC estimation. The results show key factors influencing SOC estimation include slope, vegetation indices (EVI), spectral reflectance indices (red index, red edge2), temperature, land use, and surface soil moisture, as indicated by their averaged importance scores across XGBoost, RF, and SVR models. Therefore, using different machine learning algorithms for SOC estimation reveals varying influential factors from remote sensing data and environmental variables. This approach emphasizes feature selection, as different machine learning algorithms identify various key factors from remote sensing data and environmental variables for accurate SOC estimation.

Keywords: factors influencing SOC estimation, remote sensing data, environmental variables, machine learning

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Authors: Mahesh S. Harne, Sunil V. Deshmukh

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A continuous passive motion machine improves and helps the patient to restore range of motion in various physiotherapy activities. The paper presents a concept for portable CPM. The device is used for various joint for upper and lower body extremities. The device is designed so that the active and passive motion is incorporated. During development, the physiotherapist and patient need is integrated with designer aspects. Various tools such as Analytical Higher Hierarchy process (AHP) and Quality Function Deployment (QFD) is used to integrate the need at the design stage. With market survey of various commercial CPM the gaps are identified, and efforts are made to fill the gaps with ergonomic need. Indian anthropomorphic dimension is referred. The device is modular to best suit for all the anthropomorphic need of different human. Experimentation is carried under the observation of physiotherapist and doctor on volunteer patient. We reported better results are compare to conventional CPM with comfort and less pain. We concluded that the concept will be helpful to reduces therapy cost and wide utility of device for various joint and physiotherapy exercise.

Keywords: continuous passive motion machine, ergonomics, physiotherapy, quality function deployment

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Authors: Abdelghani Chahmi

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This article examines the detection and localization of faults in electrical systems, particularly those using asynchronous machines. First, the process of failure will be characterized, relevant symptoms will be defined and based on those processes and symptoms, a model of those malfunctions will be obtained. Second, the development of the diagnosis of the machine will be shown. As studies of malfunctions in electrical systems could only rely on a small amount of experimental data, it has been essential to provide ourselves with simulation tools which allowed us to characterize the faulty behavior. Fault detection uses signal processing techniques in known operating phases.

Keywords: induction motor, modeling, bearing damage, airgap eccentricity, torque variation

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Authors: S. Nandagopalan, N. Pradeep

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The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

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Authors: Naduni Ranasinghe

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E-learning environments are getting more popular than any other due to the impact of COVID19. Even though e-learning is one of the best solutions for the teaching-learning process in the academic process, it’s not without major challenges. Nowadays, machine learning approaches are utilized in the analysis of how behavioral factors lead to better adoption and how they related to better performance of the students in eLearning environments. During the pandemic, we realized the academic process in the eLearning approach had a major issue, especially for the performance of the students. Therefore, an approach that investigates student behaviors in eLearning environments using a data-intensive machine learning approach is appreciated. A hybrid approach was used to understand how each previously told variables are related to the other. A more quantitative approach was used referred to literature to understand the weights of each factor for adoption and in terms of performance. The data set was collected from previously done research to help the training and testing process in ML. Special attention was made to incorporating different dimensionality of the data to understand the dependency levels of each. Five independent variables out of twelve variables were chosen based on their impact on the dependent variable, and by considering the descriptive statistics, out of three models developed (Random Forest classifier, SVM, and Decision tree classifier), random forest Classifier (Accuracy – 0.8542) gave the highest value for accuracy. Overall, this work met its goals of improving student performance by identifying students who are at-risk and dropout, emphasizing the necessity of using both static and dynamic data.

Keywords: academic performance prediction, e learning, learning analytics, machine learning, predictive model

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Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

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Authors: Pawan Kumar Gupta, Sumana Ghosh

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Parkinson's Disease (PD) is a long-term neurodegenerative movement disorder of the central nervous system with vast symptoms related to the motor system. The common symptoms of PD are tremor, rigidity, bradykinesia/akinesia, and postural instability, but the clinical symptom includes other motor and non‐motor issues. The motor symptoms of the disease are consequence of death of the neurons in a region of the midbrain known as substantia nigra pars compacta, leading to decreased level of a neurotransmitter known as dopamine. The cause of this neuron death is not clearly known but involves formation of Lewy bodies, an abnormal aggregation or clumping of the protein alpha-synuclein in the neurons. Unfortunately, there is no cure for PD, and the management of this disease is challenging. Therefore, it is critical for a patient to be diagnosed at early stages. A limited choice of drugs is available to improve the symptoms, but those become less and less effective over time. Apart from that, with rapid growth in the field of science and technology, other methods such as multi-area brain stimulation are used to treat patients. In order to develop advanced techniques and to support drug development for treating PD patients, an accurate mathematical model is needed to explain the underlying relationship of dopamine secretion in the brain with the hand tremors. There has been a lot of effort in the past few decades on modeling PD tremors and treatment effects from a computational point of view. These models can effectively save time as well as the cost of drug development for the pharmaceutical industry and be helpful for selecting appropriate treatment mechanisms among all possible options. In this review paper, an effort is made to investigate studies on PD modeling and analysis and to highlight some of the key advances in the field over the past centuries with discussion on the current challenges.

Keywords: Parkinson's disease, deep brain stimulation, tremor, modeling

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Joseph Salim

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This paper proposes the development of a supervised machine learning system to predict the consequences of untreated bruxism (teeth grinding) on gingival (gum) recession and jaw pain (most often bilateral jaw pain with possible headaches and limited ability to open the mouth). As a general dentist in a multi-specialty practice, the author has encountered many patients suffering from these issues due to uncontrolled bruxism (teeth grinding) at night. The most effective treatment for managing this problem involves wearing a nightguard during sleep and receiving therapeutic Botox injections to relax the muscles (the masseter muscle) responsible for grinding. However, some patients choose to postpone these treatments, leading to potentially irreversible and costlier consequences in the future. The proposed machine learning model aims to track patients who forgo the recommended treatments and assess the percentage of individuals who will experience worsening jaw pain, gingival (gum) recession, or both within a 3-to-5-year timeframe. By accurately predicting these outcomes, the model seeks to motivate patients to address the root cause proactively, ultimately saving time and pain while improving quality of life and avoiding much costlier treatments such as full-mouth rehabilitation to help recover the loss of vertical dimension of occlusion due to shortened clinical crowns because of bruxism, gingival grafts, etc.

Keywords: artificial intelligence, machine learning, predictive insights, bruxism, teeth grinding, therapeutic botox, nightguard, gingival recession, gum recession, jaw pain

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Authors: Andreia Guimaraes Farnese, Mateus Fernandes Reu Urban, Leandro Procopio, Renato Zangaro, Regiane Albertini

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Senescence changes include postural balance, inferring the risk of falls, and can lead to fractures, bedridden, and the risk of death. Physical activity, e.g., cardiovascular exercises, is notable for improving balance due to brain cell stimulations, but fine coordination exercises also elevate cell brain metabolism. This study aimed to verify whether the elderly person who performs fine motor activity has a balance similar to that of those who practice physical activity. The subjects were divided into three groups according to the activity practice: control group (CG) with seven participants for the sedentary individuals, motor coordination group (MCG) with six participants, and activity practitioner group (PAG) with eight participants. Data comparisons were from the Berg balance scale, Time up and Go test, and stabilometric analysis. Descriptive statistical and ANOVA analyses were performed for data analysis. The results reveal that including fine motor activities can improve the balance of the elderly and indirectly decrease the risk of falls.

Keywords: balance, barapodometer, coordination, elderly

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Authors: Kenneth Fon Mbah, Arash Habibi Lashkari, Ali A. Ghorbani

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Phishing e-mails are a security issue that not only annoys online users, but has also resulted in significant financial losses for businesses. Phishing advertisements and pornographic e-mails are difficult to detect as attackers have been becoming increasingly intelligent and professional. Attackers track users and adjust their attacks based on users’ attractions and hot topics that can be extracted from community news and journals. This research focuses on deceptive Phishing attacks and their variants such as attacks through advertisements and pornographic e-mails. We propose a framework called Phishing Alerting System (PHAS) to accurately classify e-mails as Phishing, advertisements or as pornographic. PHAS has the ability to detect and alert users for all types of deceptive e-mails to help users in decision making. A well-known email dataset has been used for these experiments and based on previously extracted features, 93.11% detection accuracy is obtainable by using J48 and KNN machine learning techniques. Our proposed framework achieved approximately the same accuracy as the benchmark while using this dataset.

Keywords: phishing e-mail, phishing detection, anti phishing, alarm system, machine learning

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Authors: John M. Ikome

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In the process of manufacturing, a machine breakdown usually forces a modified flow shop out of the prescribed state, this strategy reschedules part of the initial schedule to match up with the pre-schedule at some point with the objective to create a schedule that is reliable with the other production planning decisions like material flow, production and suppliers by utilizing a critical decision-making concept. We propose a rescheduling strategy and a match-up point that will have a determination procedure through an advanced feedback control mechanism to increase both the schedule quality and stability. These approaches are compared with alternative re-scheduling methods under different experimental settings.

Keywords: scheduling, heuristics, branch, integrated

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Kemal Polat

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In this paper, three feature weighting methods have been used to improve the classification performance of diabetic retinopathy (DR). To classify the diabetic retinopathy, features extracted from the output of several retinal image processing algorithms, such as image-level, lesion-specific and anatomical components, have been used and fed them into the classifier algorithms. The dataset used in this study has been taken from University of California, Irvine (UCI) machine learning repository. Feature weighting methods including the fuzzy c-means clustering based feature weighting, subtractive clustering based feature weighting, and Gaussian mixture clustering based feature weighting, have been used and compered with each other in the classification of DR. After feature weighting, five different classifier algorithms comprising multi-layer perceptron (MLP), k- nearest neighbor (k-NN), decision tree, support vector machine (SVM), and Naïve Bayes have been used. The hybrid method based on combination of subtractive clustering based feature weighting and decision tree classifier has been obtained the classification accuracy of 100% in the screening of DR. These results have demonstrated that the proposed hybrid scheme is very promising in the medical data set classification.

Keywords: machine learning, data weighting, classification, data mining

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Authors: Sun Zhe, Ruggero Micheletto

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The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: M. Khaled Abduesslam, Mohammed Ali, Basher H. Alsdai, Muhammad Nizam Inayati

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This paper presents the voltage problem location classification using performance of Least Squares Support Vector Machine (LS-SVM) and Learning Vector Quantization (LVQ) in electrical power system for proper voltage problem location implemented by IEEE 39 bus New-England. The data was collected from the time domain simulation by using Power System Analysis Toolbox (PSAT). Outputs from simulation data such as voltage, phase angle, real power and reactive power were taken as input to estimate voltage stability at particular buses based on Power Transfer Stability Index (PTSI).The simulation data was carried out on the IEEE 39 bus test system by considering load bus increased on the system. To verify of the proposed LS-SVM its performance was compared to Learning Vector Quantization (LVQ). The results showed that LS-SVM is faster and better as compared to LVQ. The results also demonstrated that the LS-SVM was estimated by 0% misclassification whereas LVQ had 7.69% misclassification.

Keywords: IEEE 39 bus, least squares support vector machine, learning vector quantization, voltage collapse

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Authors: Xiangfang Xu, Bintao Wu, Yugang Miao, Duanfeng Han

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Experiments were conducted on 5A06 aluminum alloy and Q235 steel using the laser-TIG hybrid heat source welding-brazing method to realize the reliable connection of Al/Fe dissimilar metals and the welding characteristics were analyzed. It was found that the joints with uniform seam and high tensile strength could be obtained using such a method, while the welding process demanded special welding parameters. Spectrum measurements showed that the Al and Fe atoms diffused more thoroughly at the brazing interface and formed a 3μm-thick intermetallic compound layer at the Al/Fe joints brazed connection interface. Shearing tests indicated that the shearing strength of the Al/Fe welding-brazed joint was 165MPa. The fracture occurred near the melting zone of aluminum alloy, which belonged to the mixed mode with the ductile fracture as the base and the brittle fracture as the supplement.

Keywords: Al/Fe dissimilar metals, laser-TIG hybrid heat source, shearing strength, welding-brazing method

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Authors: Valencia Fernandes, Dharmendra Kumar Khatri, Shashi Bala Singh

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Background and Aim: Epigenetics are the inaudible signatures of several pathological processes in the brain. This study understands the influence of DNA methylation, a major epigenetic modification, in the prefrontal cortex and hippocampus of the diabetic brain and its notable effect on the cellular chaperones and synaptic proteins. Method: Chronic high fat diet and STZ-induced diabetic mice were studied for cognitive dysfunction, and global DNA methylation, as well as DNA methyltransferase (DNMT) activity, were assessed. Further, the cellular chaperones and synaptic proteins were examined using DNMT inhibitor, 5-aza-2′-deoxycytidine (5-aza-dC)-via intracerebroventricular injection. Moreover, % methylation of these synaptic proteins were also studied so as to correlate its epigenetic involvement. Computationally, its interaction with the DNMT enzyme were also studied using bioinformatic tools. Histological studies for morphological alterations and neuronal degeneration were also studied. Neurogenesis, a characteristic marker for new learning and memory formation, was also assessed via the BrdU staining. Finally, the most important behavioral studies, including the Morris water maze, Y maze, passive avoidance, and Novel object recognition test, were performed to study its cognitive functions. Results: Altered global DNA methylation and increased levels of DNMTs within the nucleus were confirmed in the cortex and hippocampus of the diseased mice, suggesting hypermethylation at a genetic level. Treatment with AzadC, a global DNA demethylating agent, ameliorated the protein and gene expression of the cellular chaperones and synaptic fidelity. Furthermore, the methylation analysis profile showed hypermethylation of the hsf1 protein, a master regulator for chaperones and thus, confirmed the epigenetic involvement in the diseased brain. Morphological improvements and decreased neurodegeneration, along with enhanced neurogenesis in the treatment group, suggest that epigenetic modulations do participate in learning and memory. This is supported by the improved behavioral test battery seen in the treatment group. Conclusion: DNA methylation could possibly accord in dysregulating the memory-associated proteins at chronic stages in type 2 diabetes. This could suggest a substantial contribution to the underlying pathophysiology of several metabolic syndromes like insulin resistance, obesity and also participate in transitioning this damage centrally, such as cognitive dysfunction.

Keywords: epigenetics, cognition, chaperones, DNA methylation

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Authors: Can Huang, Xiaoliang Wang, Qingquan Liu

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Soil‒water coupling is an important process in landslide-generated impulse waves (LGIW) problems, accompanied by large deformation of soil, strong interface coupling and three-dimensional effect. A meshless particle method, smooth particle hydrodynamics (SPH) has great advantages in dealing with complex interface and multiphase coupling problems. This study presents an improved soil‒water coupled model to simulate LGIW problems based on an open source code DualSPHysics (v4.0). Aiming to solve the low efficiency problem in modeling real large-scale LGIW problems, graphics processing unit (GPU) acceleration technology is implemented into this code. An experimental example, subaerial landslide-generated water waves, is simulated to demonstrate the accuracy of this model. Then, the Huangtian LGIW, a real large-scale LGIW problem is modeled to reproduce the entire disaster chain, including landslide dynamics, fluid‒solid interaction, and surge wave generation. The convergence analysis shows that a particle distance of 5.0 m can provide a converged landslide deposit and surge wave for this example. Numerical simulation results are in good agreement with the limited field survey data. The application example of the Huangtian LGIW provides a typical reference for large-scale LGIW assessments, which can provide reliable information on landslide dynamics, interface coupling behavior, and surge wave characteristics.

Keywords: soil‒water coupling, landslide-generated impulse wave, large-scale, SPH

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Authors: Temidayo O. Omotehinwa, David O. Oyewola, Friday J. Agbo

Abstract:

This study aims to gather a proper perspective of the development landscape of Computer Science research in Nigeria. Therefore, a bibliometric analysis of 4,333 bibliographic records of Computer Science research in Nigeria in the last 31 years (1991-2021) was carried out. The bibliographic data were extracted from the Scopus database and analyzed using VOSviewer and the bibliometrix R package through the biblioshiny web interface. The findings of this study revealed that Computer Science research in Nigeria has a growth rate of 24.19%. The most developed and well-studied research areas in the Computer Science field in Nigeria are machine learning, data mining, and deep learning. The social structure analysis result revealed that there is a need for improved international collaborations. Sparsely established collaborations are largely influenced by geographic proximity. The funding analysis result showed that Computer Science research in Nigeria is under-funded. The findings of this study will be useful for researchers conducting Computer Science related research. Experts can gain insights into how to develop a strategic framework that will advance the field in a more impactful manner. Government agencies and policymakers can also utilize the outcome of this research to develop strategies for improved funding for Computer Science research.

Keywords: bibliometric analysis, biblioshiny, computer science, Nigeria, science mapping

Procedia PDF Downloads 91

Authors: Wei-Han Wang, Hsiang-Yu Yu, Mau-Sun Hua

Abstract:

Theory of Mind (ToM) refers to the ability to infer another’s mental state. With appropriate ToM, one can behave well in social interactions. A growing body of evidence has demonstrated that patients with temporal lobe epilepsy (TLE) may have damaged ToM due to impact on regions of the underlying neural network of ToM. However, the question of whether there is cerebral laterality for ToM functions remains open. This study aimed to examine whether there is cerebral lateralization for ToM abilities in TLE patients. Sixty-seven adult TLE patients and 30 matched healthy controls (HC) were recruited. Patients were classified into right (RTLE), left (LTLE), and bilateral (BTLE) TLE groups on the basis of a consensus panel review of their seizure semiology, EEG findings, and brain imaging results. All participants completed an intellectual test and four tasks measuring basic and advanced ToM. The results showed that, on all ToM tasks; (1)each patient group performed worse than HC; (2)there were no significant differences between LTLE and RTLE groups; (3)the BTLE group performed the worst. It appears that the neural network responsible for ToM is distributed evenly between the cerebral hemispheres.

Keywords: cerebral lateralization, social cognition, temporal lobe epilepsy, theory of mind

Procedia PDF Downloads 407

Authors: Jean Marie Tshimula, Atsushi Togashi

Abstract:

We develop a data-science approach for providing an interactive visualization and predictive models to find insights into the projects' historical data in order for stakeholders understand some unseen opportunities in the African market that might escape them behind the online project portfolio of the African Development Bank. This machine learning-based web application identifies the market trend of the fastest growing economies across the continent as well skyrocketing sectors which have a significant impact on the future of business in Africa. Owing to this, the approach is tailored to predict where the investment needs are the most required. Moreover, we create a corpus that includes the descriptions of over more than 1,200 projects that approximately cover 14 sectors designed for some of 53 African countries. Then, we sift out this large amount of semi-structured data for extracting tiny details susceptible to contain some directions to follow. In the light of the foregoing, we have applied the combination of Latent Dirichlet Allocation and Random Forests at the level of the analysis module of our methodology to highlight the most relevant topics that investors may focus on for investing in Africa.

Keywords: machine learning, topic modeling, natural language processing, big data

Procedia PDF Downloads 158