Search results for: washing machine
2722 Developed Text-Independent Speaker Verification System
Authors: Mohammed Arif, Abdessalam Kifouche
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Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based.Keywords: speaker verification, text-independent, support vector machine, Gaussian mixture model, cepstral analysis
Procedia PDF Downloads 462721 Uplink Throughput Prediction in Cellular Mobile Networks
Authors: Engin Eyceyurt, Josko Zec
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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.Keywords: drive test, LTE, machine learning, uplink throughput prediction
Procedia PDF Downloads 1512720 Movie Genre Preference Prediction Using Machine Learning for Customer-Based Information
Authors: Haifeng Wang, Haili Zhang
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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.Keywords: computational social science, movie preference, machine learning, SVM
Procedia PDF Downloads 2512719 Sensitivity of the Estimated Output Energy of the Induction Motor to both the Asymmetry Supply Voltage and the Machine Parameters
Authors: Eyhab El-Kharashi, Maher El-Dessouki
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The paper is dedicated to precise assessment of the induction motor output energy during the unbalanced operation. Since many years ago and until now the voltage complex unbalance factor (CVUF) is used only to assess the output energy of the induction motor while this output energy for asymmetry supply voltage does not depend on the value of unbalanced voltage only but also on the machine parameters. The paper illustrates the variation of the two unbalance factors, complex voltage unbalance factor (CVUF) and impedance unbalance factor (IUF), with positive sequence voltage component, reveals that degree and manner of unbalance in supply voltage. From this point of view the paper delineates the current unbalance factor (CUF) to exactly reflect the output energy during unbalanced operation. The paper proceeds to illustrate the importance of using this factor in the multi-machine system to precise prediction of the output energy during the unbalanced operation. The use of the proposed unbalance factor (CUF) avoids the accumulation of the error due to more than one machine in the system which is expected if only the complex voltage unbalance factor (CVUF) is used.Keywords: induction motor, electromagnetic torque, voltage unbalance, energy conversion
Procedia PDF Downloads 5532718 Design and Performance Evaluation of Synchronous Reluctance Machine (SynRM)
Authors: Hadi Aghazadeh, Mohammadreza Naeimi, Seyed Ebrahim Afjei, Alireza Siadatan
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Torque ripple, maximum torque and high efficiency are important issues in synchronous reluctance machine (SynRM). This paper presents a view on design of a high efficiency, low torque ripple and high torque density SynRM. To achieve this goal SynRM parameters is calculated (such as insulation ratios in the d-and q-axes and the rotor slot pitch), while the torque ripple can be minimized by determining the best rotor slot pitch in the d-axis. The presented analytical-finite element method (FEM) approach gives the optimum distribution of air gap and iron portion for the maximizing torque density with minimum torque ripple.Keywords: torque ripple, efficiency, insulation ratio, FEM, synchronous reluctance machine (SynRM), induction motor (IM)
Procedia PDF Downloads 2162717 Developing a Hybrid Method to Diagnose and Predict Sports Related Concussions with Machine Learning
Authors: Melody Yin
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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time
Procedia PDF Downloads 1612716 Supervised Machine Learning Approach for Studying the Effect of Different Joint Sets on Stability of Mine Pit Slopes Under the Presence of Different External Factors
Authors: Sudhir Kumar Singh, Debashish Chakravarty
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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.Keywords: finite element method, geotechnical engineering, machine learning, slope stability
Procedia PDF Downloads 952715 Practical Model of Regenerative Braking Using DC Machine and Boost Converter
Authors: Shah Krupa Rajendra, Amit Kumar
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Increasing use of traditional vehicles driven by internal combustion engine is responsible for the environmental pollution. Further, it leads to depletion of limited energy resources. Therefore, it is required to explore alternative energy sources for the transportation. The promising solution is to use electric vehicle. However, it suffers from limited driving range. Regenerative braking increases the range of the electric vehicle to a certain extent. In this paper, a novel methodology utilizing regenerative braking is described. The model comprising of DC machine, feedback based boost converter and micro-controller is proposed. The suggested method is very simple and reliable. The proposed model successfully shows the energy being saved into during regenerative braking process.Keywords: boost converter, DC machine, electric vehicle, micro-controller, regenerative braking
Procedia PDF Downloads 2682714 Characteristics of Double-Stator Inner-Rotor Axial Flux Permanent Magnet Machine with Rotor Eccentricity
Authors: Dawoon Choi, Jian Li, Yunhyun Cho
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Axial Flux Permanent Magnet (AFPM) machines have been widely used in various applications due to their important merits, such as compact structure, high efficiency and high torque density. This paper presents one of the most important characteristics in the design process of the AFPM device, which is a recent issue. To design AFPM machine, the predicting electromagnetic forces between the permanent magnets and stator is important. Because of the magnitude of electromagnetic force affects many characteristics such as machine size, noise, vibration, and quality of output power. Theoretically, this force is canceled by the equilibrium of force when it is in the middle of the gap, but it is inevitable to deviate due to manufacturing problems in actual machine. Such as large scale wind generator, because of the huge attractive force between rotor and stator disks, this is more serious in getting large power applications such as large. This paper represents the characteristics of Double-Stator Inner –Rotor AFPM machines when it has rotor eccentricity. And, unbalanced air-gap and inclined air-gap condition which is caused by rotor offset and tilt in a double-stator single inner-rotor AFPM machine are each studied in electromagnetic and mechanical aspects. The output voltage and cogging torque under un-normal air-gap condition of AF machines are firstly calculated using a combined analytical and numerical methods, followed by a structure analysis to study the effect to mechanical stress, deformation and bending forces on bearings. Results and conclusions given in this paper are instructive for the successful development of AFPM machines.Keywords: axial flux permanent magnet machine, inclined air gap, unbalanced air gap, rotor eccentricity
Procedia PDF Downloads 2092713 Machine Learning Data Architecture
Authors: Neerav Kumar, Naumaan Nayyar, Sharath Kashyap
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Most companies see an increase in the adoption of machine learning (ML) applications across internal and external-facing use cases. ML applications vend output either in batch or real-time patterns. A complete batch ML pipeline architecture comprises data sourcing, feature engineering, model training, model deployment, model output vending into a data store for downstream application. Due to unclear role expectations, we have observed that scientists specializing in building and optimizing models are investing significant efforts into building the other components of the architecture, which we do not believe is the best use of scientists’ bandwidth. We propose a system architecture created using AWS services that bring industry best practices to managing the workflow and simplifies the process of model deployment and end-to-end data integration for an ML application. This narrows down the scope of scientists’ work to model building and refinement while specialized data engineers take over the deployment, pipeline orchestration, data quality, data permission system, etc. The pipeline infrastructure is built and deployed as code (using terraform, cdk, cloudformation, etc.) which makes it easy to replicate and/or extend the architecture to other models that are used in an organization.Keywords: data pipeline, machine learning, AWS, architecture, batch machine learning
Procedia PDF Downloads 582712 Machine Learning for Classifying Risks of Death and Length of Stay of Patients in Intensive Unit Care Beds
Authors: Itamir de Morais Barroca Filho, Cephas A. S. Barreto, Ramon Malaquias, Cezar Miranda Paula de Souza, Arthur Costa Gorgônio, João C. Xavier-Júnior, Mateus Firmino, Fellipe Matheus Costa Barbosa
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Information and Communication Technologies (ICT) in healthcare are crucial for efficiently delivering medical healthcare services to patients. These ICTs are also known as e-health and comprise technologies such as electronic record systems, telemedicine systems, and personalized devices for diagnosis. The focus of e-health is to improve the quality of health information, strengthen national health systems, and ensure accessible, high-quality health care for all. All the data gathered by these technologies make it possible to help clinical staff with automated decisions using machine learning. In this context, we collected patient data, such as heart rate, oxygen saturation (SpO2), blood pressure, respiration, and others. With this data, we were able to develop machine learning models for patients’ risk of death and estimate the length of stay in ICU beds. Thus, this paper presents the methodology for applying machine learning techniques to develop these models. As a result, although we implemented these models on an IoT healthcare platform, helping clinical staff in healthcare in an ICU, it is essential to create a robust clinical validation process and monitoring of the proposed models.Keywords: ICT, e-health, machine learning, ICU, healthcare
Procedia PDF Downloads 972711 How Is a Machine-Translated Literary Text Organized in Coherence? An Analysis Based upon Theme-Rheme Structure
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With the ultimate goal to automatically generate translated texts with high quality, machine translation has made tremendous improvements. However, its translations of literary works are still plagued with problems in coherence, esp. the translation between distant language pairs. One of the causes of the problems is probably the lack of linguistic knowledge to be incorporated into the training of machine translation systems. In order to enable readers to better understand the problems of machine translation in coherence, to seek out the potential knowledge to be incorporated, and thus to improve the quality of machine translation products, this study applies Theme-Rheme structure to examine how a machine-translated literary text is organized and developed in terms of coherence. Theme-Rheme structure in Systemic Functional Linguistics is a useful tool for analysis of textual coherence. Theme is the departure point of a clause and Rheme is the rest of the clause. In a text, as Themes and Rhemes may be connected with each other in meaning, they form thematic and rhematic progressions throughout the text. Based on this structure, we can look into how a text is organized and developed in terms of coherence. Methodologically, we chose Chinese and English as the language pair to be studied. Specifically, we built a comparable corpus with two modes of English translations, viz. machine translation (MT) and human translation (HT) of one Chinese literary source text. The translated texts were annotated with Themes, Rhemes and their progressions throughout the texts. The annotated texts were analyzed from two respects, the different types of Themes functioning differently in achieving coherence, and the different types of thematic and rhematic progressions functioning differently in constructing texts. By analyzing and contrasting the two modes of translations, it is found that compared with the HT, 1) the MT features “pseudo-coherence”, with lots of ill-connected fragments of information using “and”; 2) the MT system produces a static and less interconnected text that reads like a list; these two points, in turn, lead to the less coherent organization and development of the MT than that of the HT; 3) novel to traditional and previous studies, Rhemes do contribute to textual connection and coherence though less than Themes do and thus are worthy of notice in further studies. Hence, the findings suggest that Theme-Rheme structure be applied to measuring and assessing the coherence of machine translation, to being incorporated into the training of the machine translation system, and Rheme be taken into account when studying the textual coherence of both MT and HT.Keywords: coherence, corpus-based, literary translation, machine translation, Theme-Rheme structure
Procedia PDF Downloads 1952710 Seroprevalence Study of Cystic Echinococcosis and Its Associated Risk Factors in Fars Province, Southern Iran
Authors: Mahmoud Reza Tahamtan, Mohammad Saleh Bahreini
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Background and Purpose: Cystic echinococcosis, caused by the larval stage of Echinococcus granulosus, is a common parasitic infection of humans and is endemic in many parts of the world, including Iran. So that, one percent of those admitted to surgery departments are hydatid cyst patients, and using the ELISA method, the infection rate has been reported in different regions of Iran from 1.2% to 21.4%. Therefore, the aim of this study was to investigate the seroepidemiology of human hydatid cysts in Fars province, southern Iran, by ELISA method. Methods: In this cross-sectional study, 600 serum samples of persons who were referred to the laboratory of Nemazi Hospital in Shiraz for normal tests were examined for the presence of specific Anti-IgG antibodies to hydatid cysts by ELISA method. During the sampling, a structured questionnaire was used to obtain social data of individuals with determinants of risk factors for Cystic echinococcosis. Finally, the results of the ELISA test, along with demographic information completed by individuals, were analyzed using SPSS software. Results: The average age of the subjects in this study was 40.01 ± 9.166. The prevalence of hydatidosis was reported as 5.66% (34/600). The disease was more in the age group of 21-30, people living in villages, working in rural areas, and people with a history of other parasitic diseases. Statistically, a significant difference was observed between the prevalence of the disease and two risk factors, contact with dogs (OR= 0.042; 95%CI: 0.014-0.12; P= 0.001) and washing vegetables with water (OR= 0.08; 95%CI: 0.011-0.56; P= 0.012). Conclusion: The present study showed that hydatid cyst disease has a significant prevalence in this area. Also, based on the results, contact with dogs and not properly washing vegetables are two important factors of disease transmission.Keywords: Echinococcus granulosus, Cystic echinococcosis, hydatid cyst, Fars province
Procedia PDF Downloads 912709 Water Resources and Sanitation in Public Schools of Datu Odin Sinsuat, Maguindanao
Authors: Lahaina U. Dilangalen
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Using descriptive-experimental research methods, this study aimed to identify the main resources of water, assessed the water quality, sanitation and hygiene practices, and extent of implementation. Complete enumeration was done in 28 elementary public schools of Datu Odin Sinsuat Municipality. Questionnaires were given to the school advisers. Water samples were obtained from the same schools and were submitted to the Department of Science and Technology (DOST) Region XII for microbial analysis, specifically the presence of fecal coliform bacteria. Four water resources such as hand pump, faucet, deep well and spring were found being used in the 28 schools. Of water resources, the only treated was from the faucet. Most of the schools used the water for drinking and washing. Two schools strongly agreed, nine schools agreed and seventeen schools disagreed that they implemented DepEd Order no.56 s. 2009. In addition, two schools strongly agreed and twenty six agreed that they implemented DepEd Order No. 65 s. 2009. Five schools had water supply that were safe to drink while sixteen schools had water supply that were not safe to drink due to high fecal coliform count and did not undergo chemical treatment. The only safe for drinking were water resources that came from faucet because they were chemically treated. Seven out of 28 schools did not have water supply due to their location in mountainous areas. More than half of the schools did not comply with the DepEd Order No. 56 s. 2009 due to the lack of funds and support from the PTA and LGU. It is recommended that the Department of Education must have an urgent assessment of implementing both DepEd Orders No.56 and 65, to assure that the schoolchildren be protected from water and sanitation related ailments. Also, all water resources that are not treated must be used for washing only. Ideally, all the water resources must be treated to assure the safety of all school constituents. Moreover, the school administrators and teachers in the municipality must be provided copies of the results of this study for reference in implementing the said programs.Keywords: assessment, drinking water, fecal coliform, groundwater
Procedia PDF Downloads 2482708 Intelligent Tooling Embedded Sensors for Monitoring the Wear of Cutting Tools in Turning Applications
Authors: Hatim Laalej, Jon Stammers
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In machining, monitoring of tool wear is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Currently, the task of monitoring the wear on the cutting tool is carried out by the operator who performs manual inspections of the cutting tool, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from loss of productivity. The cutting tool consumable costs may also be higher than necessary when tools are changed before the end of their useful life. Furthermore, damage can be caused to the workpiece when tools are not changed soon enough leading to a significant increase in the costs of manufacturing. The present study is concerned with the development of break sensor printed on the flank surface of poly-crystalline diamond (PCD) cutting to perform on-line condition monitoring of the cutting tool used to machine Titanium Ti-6al-4v bar. The results clearly show that there is a strong correlation between the break sensor measurements and the amount of wear in the cutting tool. These findings are significant in that they help the user/operator of the machine tool to determine the condition of the cutting tool without the need of performing manual inspection, thereby reducing the manufacturing costs such as the machine down time.Keywords: machining, manufacturing, tool wear, signal processing
Procedia PDF Downloads 2342707 Alternative Approach to the Machine Vision System Operating for Solving Industrial Control Issue
Authors: M. S. Nikitenko, S. A. Kizilov, D. Y. Khudonogov
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The paper considers an approach to a machine vision operating system combined with using a grid of light markers. This approach is used to solve several scientific and technical problems, such as measuring the capability of an apron feeder delivering coal from a lining return port to a conveyor in the technology of mining high coal releasing to a conveyor and prototyping an autonomous vehicle obstacle detection system. Primary verification of a method of calculating bulk material volume using three-dimensional modeling and validation in laboratory conditions with relative errors calculation were carried out. A method of calculating the capability of an apron feeder based on a machine vision system and a simplifying technology of a three-dimensional modelled examined measuring area with machine vision was offered. The proposed method allows measuring the volume of rock mass moved by an apron feeder using machine vision. This approach solves the volume control issue of coal produced by a feeder while working off high coal by lava complexes with release to a conveyor with accuracy applied for practical application. The developed mathematical apparatus for measuring feeder productivity in kg/s uses only basic mathematical functions such as addition, subtraction, multiplication, and division. Thus, this fact simplifies software development, and this fact expands the variety of microcontrollers and microcomputers suitable for performing tasks of calculating feeder capability. A feature of an obstacle detection issue is to correct distortions of the laser grid, which simplifies their detection. The paper presents algorithms for video camera image processing and autonomous vehicle model control based on obstacle detection machine vision systems. A sample fragment of obstacle detection at the moment of distortion with the laser grid is demonstrated.Keywords: machine vision, machine vision operating system, light markers, measuring capability, obstacle detection system, autonomous transport
Procedia PDF Downloads 1052706 Glucose Monitoring System Using Machine Learning Algorithms
Authors: Sangeeta Palekar, Neeraj Rangwani, Akash Poddar, Jayu Kalambe
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The bio-medical analysis is an indispensable procedure for identifying health-related diseases like diabetes. Monitoring the glucose level in our body regularly helps us identify hyperglycemia and hypoglycemia, which can cause severe medical problems like nerve damage or kidney diseases. This paper presents a method for predicting the glucose concentration in blood samples using image processing and machine learning algorithms. The glucose solution is prepared by the glucose oxidase (GOD) and peroxidase (POD) method. An experimental database is generated based on the colorimetric technique. The image of the glucose solution is captured by the raspberry pi camera and analyzed using image processing by extracting the RGB, HSV, LUX color space values. Regression algorithms like multiple linear regression, decision tree, RandomForest, and XGBoost were used to predict the unknown glucose concentration. The multiple linear regression algorithm predicts the results with 97% accuracy. The image processing and machine learning-based approach reduce the hardware complexities of existing platforms.Keywords: artificial intelligence glucose detection, glucose oxidase, peroxidase, image processing, machine learning
Procedia PDF Downloads 1932705 Comprehensive Study of Data Science
Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly
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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.Keywords: data science, machine learning, data analytics, artificial intelligence
Procedia PDF Downloads 752704 A Text Classification Approach Based on Natural Language Processing and Machine Learning Techniques
Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart
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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.Keywords: machine learning, text classification, NLP techniques, semantic representation
Procedia PDF Downloads 902703 Machine Learning in Momentum Strategies
Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu
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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.Keywords: information coefficient, machine learning, momentum, portfolio, return prediction
Procedia PDF Downloads 502702 Machine Learning Approach to Project Control Threshold Reliability Evaluation
Authors: Y. Kim, H. Lee, M. Park, B. Lee
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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.Keywords: machine learning, project control, project progress monitoring, schedule
Procedia PDF Downloads 2392701 Using AI for Analysing Political Leaders
Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu
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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence
Procedia PDF Downloads 812700 Robustness of the Fuzzy Adaptive Speed Control of a Multi-Phase Asynchronous Machine
Authors: Bessaad Taieb, Benbouali Abderrahmen
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Fuzzy controllers are a powerful tool for controlling complex processes. However, its robustness capacity remains moderately limited because it loses its property for large ranges of parametric variations. In this paper, the proposed control method is designed, based on a fuzzy adaptive controller used as a remedy for this problem. For increase the robustness of the vector control and to maintain the performance of the five-phase asynchronous machine despite the presence of disturbances (variation of rotor resistance, rotor inertia variations, sudden variations in the load etc.), by applying the method of behaviour model control (BMC). The results of simulation show that the fuzzy adaptive control provides best performance and has a more robustness as the fuzzy (FLC) and as a conventional (PI) controller.Keywords: fuzzy adaptive control, behaviour model control, vector control, five-phase asynchronous machine
Procedia PDF Downloads 892699 Using Machine Learning to Enhance Win Ratio for College Ice Hockey Teams
Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew
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Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions
Procedia PDF Downloads 1102698 Optimal Location of the I/O Point in the Parking System
Authors: Jing Zhang, Jie Chen
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In this paper, we deal with the optimal I/O point location in an automated parking system. In this system, the S/R machine (storage and retrieve machine) travels independently in vertical and horizontal directions. Based on the characteristics of the parking system and the basic principle of AS/RS system (Automated Storage and Retrieval System), we obtain the continuous model in units of time. For the single command cycle using the randomized storage policy, we calculate the probability density function for the system travel time and thus we develop the travel time model. And we confirm that the travel time model shows a good performance by comparing with discrete case. Finally in this part, we establish the optimal model by minimizing the expected travel time model and it is shown that the optimal location of the I/O point is located at the middle of the left-hand above corner.Keywords: parking system, optimal location, response time, S/R machine
Procedia PDF Downloads 4032697 DAG Design and Tradeoff for Full Live Virtual Machine Migration over XIA Network
Authors: Dalu Zhang, Xiang Jin, Dejiang Zhou, Jianpeng Wang, Haiying Jiang
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Traditional TCP/IP network is showing lots of shortages and research for future networks is becoming a hotspot. FIA (Future Internet Architecture) and FIA-NP (Next Phase) are supported by US NSF for future Internet designing. Moreover, virtual machine migration is a significant technique in cloud computing. As a network application, it should also be supported in XIA (expressive Internet Architecture), which is in both FIA and FIA-NP projects. This paper is an experimental study aims at verifying the feasibility of VM migration over XIA. We present three ways to maintain VM connectivity and communication states concerning DAG design and routing table modification. VM migration experiments are conducted intra-AD and inter-AD with KVM instances. The procedure is achieved by a migration control protocol which is suitable for the characters of XIA. Evaluation results show that our solutions can well supports full live VM migration over XIA network respectively, keeping services seamless.Keywords: DAG, downtime, virtual machine migration, XIA
Procedia PDF Downloads 8492696 The Impact of Experiential Learning on the Success of Upper Division Mechanical Engineering Students
Authors: Seyedali Seyedkavoosi, Mohammad Obadat, Seantorrion Boyle
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The purpose of this study is to assess the effectiveness of a nontraditional experiential learning strategy in improving the success and interest of mechanical engineering students, using the Kinematics/Dynamics of Machine course as a case study. This upper-division technical course covers a wide range of topics, including mechanism and machine system analysis and synthesis, yet the complexities of ideas like acceleration, motion, and machine component relationships are hard to explain using standard teaching techniques. To solve this problem, a thorough design project was created that gave students hands-on experience developing, manufacturing, and testing their inventions. The main goals of the project were to improve students' grasp of machine design and kinematics, to develop problem-solving and presenting abilities, and to familiarize them with professional software. A questionnaire survey was done to evaluate the effect of this technique on students' performance and interest in mechanical engineering. The outcomes of the study shed light on the usefulness of nontraditional experiential learning approaches in engineering education.Keywords: experiential learning, nontraditional teaching, hands-on design project, engineering education
Procedia PDF Downloads 892695 Short-Term Load Forecasting Based on Variational Mode Decomposition and Least Square Support Vector Machine
Authors: Jiangyong Liu, Xiangxiang Xu, Bote Luo, Xiaoxue Luo, Jiang Zhu, Lingzhi Yi
Abstract:
To address the problems of non-linearity and high randomness of the original power load sequence causing the degradation of power load forecasting accuracy, a short-term load forecasting method is proposed. The method is based on the Least Square Support Vector Machine optimized by an Improved Sparrow Search Algorithm combined with the Variational Mode Decomposition proposed in this paper. The application of the variational mode decomposition technique decomposes the raw power load data into a series of Intrinsic Mode Functions components, which can reduce the complexity and instability of the raw data while overcoming modal confounding; the proposed improved sparrow search algorithm can solve the problem of difficult selection of learning parameters in the least Square Support Vector Machine. Finally, through comparison experiments, the results show that the method can effectively improve prediction accuracy.Keywords: load forecasting, variational mode decomposition, improved sparrow search algorithm, least square support vector machine
Procedia PDF Downloads 972694 Cyber Attacks Management in IoT Networks Using Deep Learning and Edge Computing
Authors: Asmaa El Harat, Toumi Hicham, Youssef Baddi
Abstract:
This survey delves into the complex realm of Internet of Things (IoT) security, highlighting the urgent need for effective cybersecurity measures as IoT devices become increasingly common. It explores a wide array of cyber threats targeting IoT devices and focuses on mitigating these attacks through the combined use of deep learning and machine learning algorithms, as well as edge and cloud computing paradigms. The survey starts with an overview of the IoT landscape and the various types of attacks that IoT devices face. It then reviews key machine learning and deep learning algorithms employed in IoT cybersecurity, providing a detailed comparison to assist in selecting the most suitable algorithms. Finally, the survey provides valuable insights for cybersecurity professionals and researchers aiming to enhance security in the intricate world of IoT.Keywords: internet of things (IoT), cybersecurity, machine learning, deep learning
Procedia PDF Downloads 212693 The Influence of Different Flux Patterns on Magnetic Losses in Electric Machine Cores
Authors: Natheer Alatawneh
Abstract:
The finite element analysis of magnetic fields in electromagnetic devices shows that the machine cores experience different flux patterns including alternating and rotating fields. The rotating fields are generated in different configurations range between circular and elliptical with different ratios between the major and minor axis of the flux locus. Experimental measurements on electrical steel exposed to different flux patterns disclose different magnetic losses in the samples under test. Consequently, electric machines require special attention during the cores loss calculation process to consider the flux patterns. In this study, a circular rotational single sheet tester is employed to measure the core losses in electric steel sample of M36G29. The sample was exposed to alternating field, circular field, and elliptical fields with axis ratios of 0.2, 0.4, 0.6 and 0.8. The measured data was implemented on 6-4 switched reluctance motor at three different frequencies of interest to the industry as 60 Hz, 400 Hz, and 1 kHz. The results disclose a high margin of error that may occur during the loss calculations if the flux patterns issue is neglected. The error in different parts of the machine associated with considering the flux patterns can be around 50%, 10%, and 2% at 60Hz, 400Hz, and 1 kHz, respectively. The future work will focus on the optimization of machine geometrical shape which has a primary effect on the flux pattern in order to minimize the magnetic losses in machine cores.Keywords: alternating core losses, electric machines, finite element analysis, rotational core losses
Procedia PDF Downloads 244