Search results for: cross-validation support vector machine

Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian automotive sector, stock market decisions, equity portfolio analysis, decision tree classifiers, statistical data analysis

Procedia PDF Downloads 488

Authors: Sunil Verma

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In today’s world non-renewable sources are depleting quickly, so there is a need to produce efficient and unconventional engines to minimize the use of fuel. Also, there are many fatal accidents happening every year during extraction, distillation, transportation and storage of fuel. Reason for explosions of gaseous fuel is unwanted autoignition. Autoignition characterstics of fuel are mandatory to study to build efficient engines and to avoid accidents. This report is concerned with study of autoignition delay characteristics of iso-octane by using rapid compression machine. The paper clearly explains the dependence of ignition delay characteristics on variation of equivalence ratios from lean to rich mixtures. The equivalence ratio is varied from 0.3 to 1.2.

Keywords: autoignition, iso-octane, combustion, rapid compression machine, equivalence ratio, ignition delay

Procedia PDF Downloads 450

Authors: Vlado Ostovic

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An electric machine topology characterized by single tooth winding in both stator and rotor is presented. The proposed machine is capable of operating as a direct drive double fed wind generator (DDDF, D3F) because it requires no gearbox and only a reduced-size converter. A wind turbine drive built around a D3F generator is cheaper to manufacture, requires less maintenance, and has a higher energy yield than its conventional counterparts. The single tooth wound generator of a D3F turbine has superb volume utilization and lower stator I2R losses due to its extremely short-end windings. Both stator and rotor of a D3F generator can be manufactured in segments, which simplifies its assembly and transportation to the site, and makes production cheaper.

Keywords: direct drive, double fed generator, gearbox, permanent magnet generators, single tooth winding, wind power

Procedia PDF Downloads 193

Authors: Revoti Prasad Bora, Nikita Katyal

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Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, Cannibalization, promotion, Baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression

Procedia PDF Downloads 182

Authors: Maha Jazouli

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Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition

Procedia PDF Downloads 191

Authors: Arpit Patil, Atharav Bhagwat, Rajas Bhope, Pramod Bide

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This research integrates IoT and ML to predict heart failure risks, utilizing the Framingham dataset. IoT devices gather real-time physiological data, focusing on heart rate dynamics, while ML, specifically Random Forest, predicts heart failure. Rigorous feature selection enhances accuracy, achieving over 90% prediction rate. This amalgamation marks a transformative step in proactive healthcare, highlighting early detection's critical role in cardiovascular risk mitigation. Challenges persist, necessitating continual refinement for improved predictive capabilities.

Keywords: cardiovascular diseases, internet of things, machine learning, cardiac risk assessment, heart failure prediction, early detection, cardio data analysis

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Authors: Georgios Charizanos, Haydar Demirhan, Duygu Icen

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Two of the most impactful issues in classical logistic regression are class imbalance and complete separation. These can result in model predictions heavily leaning towards the imbalanced class on the binary response variable or over-fitting issues. Fuzzy methodology offers key solutions for handling these problems. However, most studies propose the transformation of the binary responses into a continuous format limited within [0,1]. This is called the possibilistic approach within fuzzy logistic regression. Following this approach is more aligned with straightforward regression since a logit-link function is not utilized, and fuzzy probabilities are not generated. In contrast, we propose a method of fuzzifying binary response variables that allows for the use of the logit-link function; hence, a probabilistic fuzzy logistic regression model with the Monte Carlo method. The fuzzy probabilities are then classified by selecting a fuzzy threshold. Different combinations of fuzzy and crisp input, output, and coefficients are explored, aiming to understand which of these perform better under different conditions of imbalance and separation. We conduct numerical experiments using both synthetic and real datasets to demonstrate the performance of the fuzzy logistic regression framework against seven crisp machine learning methods. The proposed framework shows better performance irrespective of the degree of imbalance and presence of separation in the data, while the considered machine learning methods are significantly impacted.

Keywords: fuzzy logistic regression, fuzzy, logistic, machine learning

Procedia PDF Downloads 77

Authors: Pachawo Bisani, Goodall Nyirenda

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The Malawi National Antiretroviral Therapy (NART) Electronic Medical Record (EMR) system was designed and developed with guidance from the Ministry of Health through the Department of HIV and AIDS (DHA) with the aim of supporting the management of HIV patient data and reporting in high prevalence ART clinics. As of 2021, the system has been scaled up to over 206 facilities across the country. The system is integrated with the clinical decision support system (CDSS) to assist healthcare providers in making a decision about an individual patient at a particular point in time. Despite NART EMR undergoing several evaluations and assessments, little has been done to evaluate the clinical decision support system in the NART EMR system. Hence, the study aimed to evaluate the use of CDSS in the NART EMR system in Malawi. The study adopted a mixed-method approach, and data was collected through interviews, observations, and questionnaires. The study has revealed that the CDSS tools were integrated into the ART clinic workflow, making it easy for the user to use it. The study has also revealed challenges in system reliability and information accuracy. Despite the challenges, the study further revealed that the system is effective and efficient, and overall, users are satisfied with the system. The study recommends that the implementers focus more on the logic behind the clinical decision-support intervention in order to address some of the concerns and enhance the accuracy of the information supplied. The study further suggests consulting the system's actual users throughout implementation.

Keywords: clinical decision support system, electronic medical record system, usability, antiretroviral therapy

Procedia PDF Downloads 108

Authors: Md. Qamruzzaman, Wei Jianguo

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Over the decade, it is observed that financial development, through financial innovation, not only accelerated development of efficient and effective financial system but also act as a catalyst in the economic development process. In this study, we try to explore insight about how financial innovation causes economic growth in Bangladesh by using Vector Error Correction Model (VECM) for the period of 1990-2014. Test of Cointegration confirms the existence of a long-run association between financial innovation and economic growth. For investigating directional causality, we apply Granger causality test and estimation explore that long-run growth will be affected by capital flow from non-bank financial institutions and inflation in the economy but changes of growth rate do not have any impact on Capital flow in the economy and level of inflation in long-run. Whereas, growth and Market capitalization, as well as market capitalization and capital flow, confirm feedback hypothesis. Variance decomposition suggests that any innovation in the financial sector can cause GDP variation fluctuation in both long run and short run. Financial innovation promotes efficiency and cost in financial transactions in the financial system, can boost economic development process. The study proposed two policy recommendations for further development. First, innovation friendly financial policy should formulate to encourage adaption and diffusion of financial innovation in the financial system. Second, operation of financial market and capital market should be regulated with implementation of rules and regulation to create conducive environment.

Keywords: financial innovation, economic growth, GDP, financial institution, VECM

Procedia PDF Downloads 274

Authors: R. Nandhini, Gaurab Mudbhari

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Ranging from the field of health care to self-driving cars, machine learning and deep learning algorithms have revolutionized the field with the proper utilization of images and visual-oriented data. Segmentation, regression, classification, clustering, dimensionality reduction, etc., are some of the Machine Learning tasks that helped Machine Learning and Deep Learning models to become state-of-the-art models for the field where images are key datasets. Among these tasks, classification and clustering are essential but difficult because of the intricate and high-dimensional characteristics of image data. This finding examines and assesses advanced techniques in supervised classification and unsupervised clustering for image datasets, emphasizing the relative efficiency of Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Deep Embedded Clustering (DEC), and self-supervised learning approaches. Due to the distinctive structural attributes present in images, conventional methods often fail to effectively capture spatial patterns, resulting in the development of models that utilize more advanced architectures and attention mechanisms. In image classification, we investigated both CNNs and ViTs. One of the most promising models, which is very much known for its ability to detect spatial hierarchies, is CNN, and it serves as a core model in our study. On the other hand, ViT is another model that also serves as a core model, reflecting a modern classification method that uses a self-attention mechanism which makes them more robust as this self-attention mechanism allows them to lean global dependencies in images without relying on convolutional layers. This paper evaluates the performance of these two architectures based on accuracy, precision, recall, and F1-score across different image datasets, analyzing their appropriateness for various categories of images. In the domain of clustering, we assess DEC, Variational Autoencoders (VAEs), and conventional clustering techniques like k-means, which are used on embeddings derived from CNN models. DEC, a prominent model in the field of clustering, has gained the attention of many ML engineers because of its ability to combine feature learning and clustering into a single framework and its main goal is to improve clustering quality through better feature representation. VAEs, on the other hand, are pretty well known for using latent embeddings for grouping similar images without requiring for prior label by utilizing the probabilistic clustering method.

Keywords: machine learning, deep learning, image classification, image clustering

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Authors: Rui Liu, Pengyu Cui, Nan Jiang

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At present, there is much research on the application of centralized management and cross-integration application of basic geographic information. However, the idea of information integration and sharing between land, sea, and air navigation targets is not deeply applied into the research of navigation information service, especially in the information expression. Targeting at this problem, the paper carries out works about the expression pattern of navigation electronic map for ground vehicles under air and land united navigation mechanism. At first, with the support from multi-source information fusion of GIS vector data, RS data, GPS data, etc., an air and land united information expression pattern is designed aiming at specific navigation task of emergency rescue in the earthquake. And then, the characteristics and specifications of the united expression of air and land navigation information under the constraints of map load are summarized and transferred into expression rules in the rule bank. At last, the related navigation experiment is implemented to evaluate the effect of the expression pattern. The experiment selects evaluation factors of the navigation task accomplishment time and the navigation error rate as the main index, and make comparisons with the traditional single information expression pattern. To sum up, the research improved the theory of navigation electronic map and laid a certain foundation for the design and realization of united navigation system in the aspect of real-time navigation information delivery.

Keywords: navigation electronic map, united navigation, multi-element expression pattern, multi-source information fusion

Procedia PDF Downloads 203

Authors: Stela Doncheva

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Postpartum distress, encompassing depressive symptoms, obsessions, and anxiety, remains a subject of significant scientific interest due to its prevalence among individuals giving birth. This critical and transformative period presents a multitude of factors that impact women's health. On the one hand, variables such as social support, satisfaction in romantic relationships, shared newborn care, and birth satisfaction directly affect the mental well-being of new mothers. On the other hand, the interplay of hormonal changes, personality characteristics, emotional difficulties, and the profound life adjustments experienced by mothers can profoundly influence their self-esteem and overall physical and emotional well-being. This paper extensively explores the factors of alexithymia, social support, partners' support, and birth satisfaction to gain deeper insights into their impact on postpartum distress. Utilizing a qualitative survey consisting of six self-reflective questionnaires, this study collects valuable data regarding the individual postpartum experiences of Bulgarian mothers. The primary objective is to enrich our understanding of the complex factors involved in the development of postpartum distress during this crucial period. The results shed light on the intricate nature of the problem and highlight the significant influence of bio-psycho-social elements. By contributing to the existing knowledge in the field, this research provides valuable implications for the development of interventions and support systems tailored to the unique needs of mothers in the postpartum period. Ultimately, this study aims to improve the overall well-being of new mothers and promote optimal maternal health during the postpartum journey.

Keywords: maternal mental health, postpartum distress, postpartum depression, postnatal mothers

Procedia PDF Downloads 69

Authors: Fenni Sim

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Background: The application of systemic family therapy approaches to community health cases have not gathered traction. In National Kidney Foundation, Singapore, the belief is that community support has great potential in helping End Stage Renal Failure (ESRF) patients manage the demands of their treatment regime, whether Hemodialysis (HD) or Peritoneal Dialysis(PD) and sustain them on the treatment. However, the current community support does not include family interventions and is largely nursing based. Although nursing support is well provided to patients, and their family members in issues related to treatment and compliance, complex family issues and dynamics arising from caregiver strain or pre-dialysis relationship strain might deter efforts in managing the challenges of the treatment. Objective: The objective of the study is to understand the potential scope of work provided by a social worker who is trained in systemic family therapy and the effects of these interventions. Methodology: 3 families on HD and 3 families on PD who have been receiving family intervention for the past 6 months would be chosen for the study. A qualitative interview would be conducted to review the effectiveness for the family. Scales such as SCORE-15, PHQ-9, and Zarit Burden were used to measure family functioning, depression, and caregiver’s burden for the families. Results: The research is still in preliminary phase. Conclusion: The study highlights the importance of family intervention for families with multiple stressors on different treatment modalities who might have different needs and challenges. Nursing support needs to be complemented with family-based support to manage complex family issues in order to achieve better health outcomes and improved family coping.

Keywords: complementing nursing support, end stage renal failure, healthcare, systemic approaches

Procedia PDF Downloads 206

Authors: Taisiia Gorshkova

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Since the 1980s, spatial correlation models have been used more often to model regional indicators. An increasingly popular method for studying regional indicators is modeling taking into account spatial relationships between objects that are part of the same economic zone. In 2000s the new class of model – spatial vector autoregressions was developed. The main difference between standard and spatial vector autoregressions is that in the spatial VAR (SpVAR), the values of indicators at time t may depend on the values of explanatory variables at the same time t in neighboring regions and on the values of explanatory variables at time t-k in neighboring regions. Thus, VAR is a special case of SpVAR in the absence of spatial lags, and the spatial panel data model is a special case of spatial VAR in the absence of time lags. Two specifications of SpVAR were applied to Russian regional data for 2000-2017. The values of GRP and regional CPI are used as endogenous variables. The lags of GRP, CPI and the unemployment rate were used as explanatory variables. For comparison purposes, the standard VAR without spatial correlation was used as “naïve” model. In the first specification of SpVAR the unemployment rate and the values of depending variables, GRP and CPI, in neighboring regions at the same moment of time t were included in equations for GRP and CPI respectively. To account for the values of indicators in neighboring regions, the adjacency weight matrix is used, in which regions with a common sea or land border are assigned a value of 1, and the rest - 0. In the second specification the values of depending variables in neighboring regions at the moment of time t were replaced by these values in the previous time moment t-1. According to the results obtained, when inflation and GRP of neighbors are added into the model both inflation and GRP are significantly affected by their previous values, and inflation is also positively affected by an increase in unemployment in the previous period and negatively affected by an increase in GRP in the previous period, which corresponds to economic theory. GRP is not affected by either the inflation lag or the unemployment lag. When the model takes into account lagged values of GRP and inflation in neighboring regions, the results of inflation modeling are practically unchanged: all indicators except the unemployment lag are significant at a 5% significance level. For GRP, in turn, GRP lags in neighboring regions also become significant at a 5% significance level. For both spatial and “naïve” VARs the RMSE were calculated. The minimum RMSE are obtained via SpVAR with lagged explanatory variables. Thus, according to the results of the study, it can be concluded that SpVARs can accurately model both the actual values of macro indicators (particularly CPI and GRP) and the general situation in the regions

Keywords: forecasting, regional data, spatial econometrics, vector autoregression

Procedia PDF Downloads 143

Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

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Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

Procedia PDF Downloads 301

Authors: Katejarinporn Chaiya, Jarumon Nookong, Nutthapat Kaewrattanapat

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The purposes of this research were to develop and find users’ satisfaction after using the Decision Support System for Examination Selection. This research presents the design of information systems. In order to find the necessary examination of the statistics. Based on the examination of the candidate and then taking the easy difficulty setting statistics applied to the test. In addition, research has also made performance appraisals from experts and user satisfaction. By results of analysis showed that the performance appraisals from experts on the system as a whole and at a good level. mean was 3.44 and S.D. was 0.55 and user satisfaction per system as a whole and the good level mean was 3.37 and S.D. was 0.42 can conclude that effective systems are in a good level. Work has been completed in accordance with the scope of work. The website used developing this project is PHP, MySQL.5.0.45 for database.

Keywords: secision support system, examination, PHP, information systems

Procedia PDF Downloads 455

Authors: Muhammad Ajmair

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This empirical study followed structural vector auto regression (SVAR) approach proposed by the so-called AB-model of Amisano and Giannini (1997) to check the impact of relevant macroeconomic determinants on economic growth in Pakistan. Before that auto regressive distributive lag (ARDL) bound testing technique and time varying parametric approach along with general to specific approach was employed to find out relevant significant determinants of economic growth. To our best knowledge, no author made such a study that employed auto regressive distributive lag (ARDL) bound testing and time varying parametric approach with general to specific approach in empirical literature, but current study will bridge this gap. Annual data was taken from World Development Indicators (2014) during period 1976-2014. The widely-used Schwarz information criterion and Akaike information criterion were considered for the lag length in each estimated equation. Main findings of the study are that remittances received, gross national expenditures and inflation are found to be the best relevant positive and significant determinants of economic growth. Based on these empirical findings, we conclude that government should focus on overall economic growth augmenting factors while formulating any policy relevant to the concerned sector.

Keywords: economic growth, gross national expenditures, inflation, remittances

Procedia PDF Downloads 203

Authors: Mohammad Hadi Jalali, Mostafa Ghayour, Saeed Ziaei-Rad, Behrooz Shahriari

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The vibration problems that can be occurred in the operational conditions of rotating machines may cause damage to the machine or even failure of the machine completely. Therefore, dynamic analysis of rotors is vital in the design and development stages of the rotating machines. In this study, the uncoupled torsional and lateral vibration analysis of a rotor-bearing system is carried out using transfer matrix method. The Campbell diagram, critical speed and the mode shape corresponding to the critical speed are obtained in order to evaluate the dynamic behavior of the rotor.

Keywords: transfer matrix method, rotor-bearing system, campbell diagram, critical speed

Procedia PDF Downloads 492

Authors: Robbie C. Murchison, Ibrahim Küçükdemiral, Andrew Cowell

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Thermal effects are the largest source of dimensional error in precision machining, and a major proportion is caused by ambient temperature variation. The use of coolant is a primary means of mitigating these effects, but there has been limited work on coolant temperature control. This research critically explored whether CNC-machine coolant refrigeration systems adapted to actively compensate for ambient temperature variation could increase machining accuracy. Accuracy data were collected from operators’ checklists for a CNC 5-axis mill and statistically reduced to bias and precision metrics for observations of one day over a sample period of 27 days. Temperature data were collected using three USB dataloggers in ambient air, the chiller inflow, and the chiller outflow. The accuracy and temperature data were analysed using Pearson correlation, then the thermodynamics of the system were described using system identification with MATLAB. It was found that 75% of thermal error is reflected in the hot coolant temperature but that this is negligibly dependent on ambient temperature. The effect of the coolant refrigeration process on hot coolant outflow temperature was also found to be negligible. Therefore, the evidence indicated that it would not be beneficial to adapt coolant chillers to compensate for ambient temperature variation. However, it is concluded that hot coolant outflow temperature is a robust and accessible source of thermal error data which could be used for prevention strategy evaluation or as the basis of other thermal error strategies.

Keywords: CNC manufacturing, machine-tool, precision machining, thermal error

Procedia PDF Downloads 91

Authors: Yeliz Yazıcı, Alev Erenler

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School is an institution designed to provide learning, teaching places and environments under guidance of selected teachers. School is not just a place or institution but it is a place where complex and living structures are alive and always changing. It is also an undeniable fact that schools have shaped the ideas, future, society as well as the students and their lives. While this is the situation, schools having academic excellence is considered as successful ones. Academic excellence is a composition of excellence in teachers, management and physical environment, also. This is the general perception of the authorities and parents when the excellence is the point but the school is a developing and supporting organism. In this concept, the main aim of this study is to compare student and teacher perceptions of school as a ‘positive support factor’. The study is designed as a quantitative and qualitative design and a questionnaire is applied to both teachers and students via online and face to face meetings. It is aimed to define the perceptions of the participants related to the school as a positive support factor. It means the role of school in establishing self-efficacy, shaping and acquiring the behavior etc. Gathered data is analyzed via SPSS program and the detailed discussion is carried in the frame of the related literature.

Keywords: positive support factor, education, school, student teacher perception

Procedia PDF Downloads 176

Authors: Lou J. Pino, Mark Campbell, Matthew J. Kennedy, Ashleigh C. Kennedy

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A concussion is complex and nuanced, with cognitive rest being a key component of recovery. Cognitive overexertion during rehabilitation from a concussion is associated with delayed recovery. However, daily living imposes cognitive demands that may be unavoidable and difficult to quantify. Therefore, a portable tool capable of alerting patients before cognitive overexertion occurs could allow patients to maintain their quality of life while preventing symptoms and recovery setbacks. EEG allows for a sensitive measure of cognitive exertion. Clinical 32-lead EEG headsets are not practical for day-to-day concussion rehabilitation management. However, there are now commercially available and affordable portable EEG headsets. Thus, these headsets can potentially be used to continuously monitor cognitive exertion during mental tasks to alert the wearer of overexertion, with the aim of preventing the occurrence of symptoms to speed recovery times. The objective of this study was to test an algorithm for predicting cognitive exertion from EEG data collected from a portable headset. EEG data were acquired from 10 participants (5 males, 5 females). Each participant wore a portable 4 channel EEG headband while completing 10 tasks: rest (eyes closed), rest (eyes open), three levels of the increasing difficulty of logic puzzles, three levels of increasing difficulty in multiplication questions, rest (eyes open), and rest (eyes closed). After each task, the participant was asked to report their perceived level of cognitive exertion using the NASA Task Load Index (TLX). Each participant then completed a second session on a different day. A customized machine learning model was created using data from the first session. The performance of each model was then tested using data from the second session. The mean correlation coefficient between TLX scores and predicted cognitive exertion was 0.75 ± 0.16. The results support the efficacy of the algorithm for predicting cognitive exertion. This demonstrates that the algorithms developed in this study used with portable EEG devices have the potential to aid in the concussion recovery process by monitoring and warning patients of cognitive overexertion. Preventing cognitive overexertion during recovery may reduce the number of symptoms a patient experiences and may help speed the recovery process.

Keywords: cognitive activity, EEG, machine learning, personalized recovery

Procedia PDF Downloads 222

Authors: Ji Sun Kim, Jae Ho Baek, Kyeong Ho Kim, Ji Hae Ha, Seong Soo Hong, Jung-Wook Park, Man Sig Lee

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Carbon supported palladium catalysts have been used in many industrial reactions, especially for hydrogenation in the fine chemical industry. Porous carbons had been widely used as catalyst supports due to its higher surface area and larger pore volume. The specific surface area, pore structure and surface chemical functional groups of porous carbon affects metal dispersion and particle size. In this paper, we confirm the effect of support texture on the dispersion of Pd. Pd catalyst supported on activated carbon having various specific surface area were characterized by BET, XRD and FE-TEM. Catalyst activity and dispersion of prepared catalyst were evaluated on the basis of the CO adsorption capacity by CO-chemisorption. As concluding remark to this part of our study, let us note that specific area of carbon play important role on the synthesis of Pd/C catalyst/.

Keywords: carbon, dispersion, Pd/C, specific are, support

Procedia PDF Downloads 355

Authors: King Kumire, Amos Kubeka

Abstract:

The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: machine-learning, habitability, exoplanets, supercomputing

Procedia PDF Downloads 91

Authors: King Kumire, Amos Kubeka

Abstract:

The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far, has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: exoplanets, habitability, machine-learning, supercomputing

Procedia PDF Downloads 119

Authors: Wenjuan Du

Abstract:

The phase compensation method was proposed based on the concept of the damping torque analysis (DTA). It is a method for the design of a PSS (power system stabilizer) to suppress local-mode power oscillations in a single-machine infinite-bus power system. This paper presents the application of the phase compensation method for the design of a PSS in a multi-machine power system. The application is achieved by examining the direct damping contribution of the stabilizer to the power oscillations. By using linearized equal area criterion, a theoretical proof to the application for the PSS design is presented. Hence PSS design in the paper is an example of stable tending control by localized method.

Keywords: phase compensation method, power system small-signal stability, power system stabilizer

Procedia PDF Downloads 644

Authors: Yin Ma, Xun Wang, Kelsey Austin

Abstract:

Purpose – Building upon career construction theory and the conservation of resources theory, we developed a moderated mediation model to examine how the perceived university support impact students’ career adapting responses, namely, crystallization, exploration, decision and preparation, via the mediator career adaptability and moderator perceived parental support. Design/methodology/approach – The multi-stage sampling strategy was employed and survey data were collected. Structural equation modeling was used to perform the analysis. Findings – Perceived university support could directly promote students’ career adaptability, and promote three career adapting responses, namely, exploration, decision and preparation. It could also impact four career adapting responses via mediation effect of career adaptability. Its impact on students’ career adaptability can greatly increase when students’ receive parental related career support. Research limitations/implications – The cross-sectional design limits causal inference. Conducted in China, our findings should be cautiously interpreted in other countries due to cultural differences. Practical implications – University support is vital to students’ career adaptability and supports from parents can enhance this process. University-home collaboration is necessary to promote students’ career adapting responses. For students, seeking and utilizing as much supporting resources as possible is vital for their human resources development. On an organizational level, universities could benefit from our findings by introducing the practices which ask students to rate the career-related courses and encourage them to chat with parents regularly. Originality/ value – Using recently developed scale, current work contributes to the literature by investigating the impact of multiple contextual factors on students’ career adapting response. It also provide the empirical support for the role of human intervention in fostering career adapting responses.

Keywords: career adapability, university and parental support, China studies, sociology of education

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Authors: Maria Pratt, Lynn Martin

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Context: In nursing education, clinical instructors are crucial in assessing and evaluating students' performance in clinical courses. However, instructors often struggle when assigning failing grades to students at risk of failing. Research Aim: This qualitative study aims to understand clinical instructors' experiences evaluating students with unsatisfactory performance, including how reflection and collegial support impact this evaluation process. Methodology, Data Collection, and Analysis Procedures: This study employs Gadamer's Hermeneutic Inquiry as the research methodology. A purposive maximum variation sampling technique was used to recruit eight clinical instructors from a collaborative undergraduate nursing program in Southwestern Ontario. Semi-structured, open-ended, and audio-taped interviews were conducted with the participants. The hermeneutic analysis was applied to interpret the interview data to allow for a thorough exploration and interpretation of the instructors' experiences evaluating failing students. Findings: The main findings of this qualitative research indicate that evaluating failing students was emotionally draining for the clinical instructors who experienced multiple challenges, uncertainties, and negative feelings associated with assigning failing grades. However, the analysis revealed that ongoing reflection and collegial support played a crucial role in mitigating the challenges they experienced. Conclusion: This study contributes to the theoretical understanding of nursing education by shedding light on clinical instructors' challenges in evaluating failing students. It emphasizes the emotional toll associated with this process and the role that reflection and collegial support play in alleviating those challenges. The findings underscore the need for ongoing professional development and support for instructors in nursing education. By understanding and addressing clinical instructors' experiences, nursing education programs can better equip them to effectively evaluate struggling students and provide the necessary support for their professional growth.

Keywords: clinical instructor, student evaluation, nursing, reflection, support

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Authors: C. Angel, S. Asha

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Despite the advances made in various new technologies, application of these technologies for agriculture still remains a formidable task, as it involves integration of diverse domains for monitoring the different process involved in agricultural management. Advances in ambient intelligence technology represents one of the most powerful technology for increasing the yield of agricultural crops and to mitigate the impact of water scarcity, climatic change and methods for managing pests, weeds, and diseases. This paper proposes a GPS-assisted, machine to machine solutions that combine information collected by multiple sensors for the automated management of paddy crops. To maintain the economic viability of paddy cultivation, the various techniques used in agriculture are discussed and a novel system which uses ambient intelligence technique is proposed in this paper. The ambient intelligence based agricultural system gives a great scope.

Keywords: ambient intelligence, agricultural technology, smart agriculture, precise farming

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Authors: C. Manjula, Lilly Florence

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Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Lorenzo Ciletti, Ed Baines, Matt Somerville

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Internationally, supporting staff with limited training (Teaching Assistants (TA)) has played a critical role in the education of children with special educational needs and/or disabilities (SEND). Researchers have notably illustrated that TAs support the children’s classroom tasks while teachers manage the whole class. Rarely have researchers investigated the TAs’ support for children’s participation in whole-class or peer-group talk, despite this type of “social support” playing a significant role in children’s whole-class integration and engagement with the classroom curriculum and learning. Social support seems particularly crucial for a large proportion of children with SEND, namely those with communication and interaction difficulties (e.g., autism spectrum conditions and speech impairments). This study explored TA practice and, particularly, TA social support in a rarely examined context (Italy). The Italian case was also selected as it provides TAs, known nationally as “support teachers,” with the most comprehensive training worldwide, thus potentially echoing (effective) nuanced practice internationally. Twelve hours of video recordings of a single TA and a child with communication and interaction difficulties (CID) were made. Video data was converted into frequencies of TA multidimensional support strategies, including TA social support and pedagogical assistance. TA-pupil talk oriented to children’s participation in classroom talk was also analysed into thematic patterns. These multi-method analyses were informed by social scaffolding principles: in particular, the extent to which the TA designs instruction contingently to the child’s communication and interaction difficulties and how their social support fosters the child’s highest responsibility in dealing with whole-class or peer-group talk by supplying the least help. The findings showed that the TA rarely supported the group or whole class participation of the child with CID. When doing so, the TA seemed to highly control the content and the timing of the child’s contributions to the classroom talk by a) interrupting the teacher’s whole class or group conversation to start an interaction between themselves and the child and b) reassuring the child about the correctness of their talk in private conversations and prompting them to raise their hand and intervene in the whole-class talk or c) stopping the child from contributing to the whole-class or peer-group talk when incorrect. The findings are interpreted in terms of their theoretical relation to scaffolding. They have significant implications for promoting social scaffolding in TA training in Italy and elsewhere.

Keywords: children with communication and interaction difficulties, children with special educational needs and/or disabilities, social scaffolding, teaching assistants, teaching practice, whole-class talk participation

Procedia PDF Downloads 100