Search results for: extreme learning machine
2849 Blended Learning through Google Classroom
Authors: Lee Bih Ni
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This paper discusses that good learning involves all academic groups in the school. Blended learning is learning outside the classroom. Google Classroom is a free service learning app for schools, non-profit organizations and anyone with a personal Google account. Facilities accessed through computers and mobile phones are very useful for school teachers and students. Blended learning classrooms using both traditional and technology-based methods for teaching have become the norm for many educators. Using Google Classroom gives students access to online learning. Even if the teacher is not in the classroom, the teacher can provide learning. This is the supervision of the form of the teacher when the student is outside the school.
Keywords: Blended learning, learning app, Google classroom, schools.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24642848 Design and Implementation of a Software Platform Based on Artificial Intelligence for Product Recommendation
Authors: G. Settanni, A. Panarese, R. Vaira, A. Galiano
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Nowadays, artificial intelligence is used successfully in the field of e-commerce for its ability to learn from a large amount of data. In this research study, a prototype software platform was designed and implemented in order to suggest to users the most suitable products for their needs. The platform includes a recommender system based on artificial intelligence algorithms that provide suggestions and decision support to the customer. Specifically, support vector machine algorithms have been implemented combined with natural language processing techniques that allow the user to interact with the system, express their requests and receive suggestions. The interested user can access the web platform on the internet using a computer, tablet or mobile phone, register, provide the necessary information and view the products that the system deems them the most appropriate. The platform also integrates a dashboard that allows the use of the various functions, which the platform is equipped with, in an intuitive and simple way. Also, Long Short-Term Memory algorithms have been implemented and trained on historical data in order to predict customer scores of the different items. Items with the highest scores are recommended to customers.
Keywords: Deep Learning, Long Short-Term Memory, Machine Learning, Recommender Systems, Support Vector Machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3262847 The Future of Blended Learning
Authors: Reem A. Alebaikan
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The emergence of blended learning has been influenced by the rapid changes in Higher Education within the last few years. However, there is a lack of studies that look into the future of blended learning in the Saudi context. The most likely explanation is that blended learning is relatively new and, with respect to learning in general, under-researched. This study addresses this gap and explores the views of lecturers and students towards the future of blended learning in Saudi Arabia. This study was informed by the interpretive paradigm that appears to be most appropriate to understand and interpret the perceptions of students and instructors towards a new learning environment. While globally there has been considerable research on the perceptions of e-learning and blended learning with its different models, there is plenty of space for further research specifically in the Arab region, and in Saudi Arabia where blended learning is now being introduced.Keywords: blended learning, higher education.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24172846 Hybrid Anomaly Detection Using Decision Tree and Support Vector Machine
Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour
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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.
Keywords: Intrusion detection system, decision tree, support vector machine, feature selection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 12402845 Novel Direct Flux and Torque Control of Optimally Designed 6 Phase Reluctance Machine with Special Current Waveform
Authors: E T. Rakgati, E. Matlotse
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In this paper the principle, basic torque theory and design optimisation of a six-phase reluctance dc machine are considered. A trapezoidal phase current waveform for the machine drive is proposed and evaluated to minimise ripple torque. Low cost normal laminated salient-pole rotors with and without slits and chamfered poles are investigated. The six-phase machine is optimised in multi-dimensions by linking the finite-element analysis method directly with an optimisation algorithm; the objective function is to maximise the torque per copper losses of the machine. The armature reaction effect is investigated in detail and found to be severe. The measured and calculated torque performances of a 35 kW optimum designed six-phase reluctance dc machine drive are presented.
Keywords: Reluctance dc machine, current waveform, design optimisation, finite element analysis, armature reaction effect.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17282844 Efficient Web-Learning Collision Detection Tool on Five-Axis Machine
Authors: Chia-Jung Chen, Rong-Shine Lin, Rong-Guey Chang
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As networking has become popular, Web-learning tends to be a trend while designing a tool. Moreover, five-axis machining has been widely used in industry recently; however, it has potential axial table colliding problems. Thus this paper aims at proposing an efficient web-learning collision detection tool on five-axis machining. However, collision detection consumes heavy resource that few devices can support, thus this research uses a systematic approach based on web knowledge to detect collision. The methodologies include the kinematics analyses for five-axis motions, separating axis method for collision detection, and computer simulation for verification. The machine structure is modeled as STL format in CAD software. The input to the detection system is the g-code part program, which describes the tool motions to produce the part surface. This research produced a simulation program with C programming language and demonstrated a five-axis machining example with collision detection on web site. The system simulates the five-axis CNC motion for tool trajectory and detects for any collisions according to the input g-codes and also supports high-performance web service benefiting from C. The result shows that our method improves 4.5 time of computational efficiency, comparing to the conventional detection method.
Keywords: Collision detection, Five-axis machining, Separating axis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21802843 The Design of the Blended Learning System via E-Media and Online Learning for the Asynchronous Learning: Case Study of Process Management Subject
Authors: Pimploi Tirastittam, Suppara Charoenpoom
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Nowadays the asynchronous learning has granted the permission to the anywhere and anything learning via the technology and E-media which give the learner more convenient. This research is about the design of the blended and online learning for the asynchronous learning of the process management subject in order to create the prototype of this subject asynchronous learning which will create the easiness and increase capability in the learning. The pattern of learning is the integration between the in-class learning and online learning via the internet. This research is mainly focused on the online learning and the online learning can be divided into 5 parts which are virtual classroom, online content, collaboration, assessment and reference material. After the system design was finished, it was evaluated and tested by 5 experts in blended learning design and 10 students which the user’s satisfaction level is good. The result is as good as the assumption so the system can be used in the process management subject for a real usage.
Keywords: Blended Learning, Asynchronous Learning, Design, Process Management.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15542842 E-Learning Management Systems General Framework
Authors: Hamed Fawareh
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The recent development in learning technologies leads to emerge many learning management systems (LMS). In this study, we concentrate on the specifications and characteristics of LMSs. Furthermore, this paper emphasizes on the feature of e-learning management systems. The features take on the account main indicators to assist and evaluate the quality of e-learning systems. The proposed indicators based of ten dimensions.
Keywords: E-Learning, System Requirement, Social Requirement, Learning Management System.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25222841 The Defects Reduction in Injection Molding by Fuzzy Logic based Machine Selection System
Authors: S. Suwannasri, R. Sirovetnukul
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The effective machine-job assignment of injection molding machines is very important for industry because it is not only directly affects the quality of the product but also the performance and lifetime of the machine as well. The phase of machine selection was mostly done by professionals or experienced planners, so the possibility of matching a job with an inappropriate machine might occur when it was conducted by an inexperienced person. It could lead to an uneconomical plan and defects. This research aimed to develop a machine selection system for plastic injection machines as a tool to help in decision making of the user. This proposed system could be used both in normal times and in times of emergency. Fuzzy logic principle is applied to deal with uncertainty and mechanical factors in the selection of both quantity and quality criteria. The six criteria were obtained from a plastic manufacturer's case study to construct a system based on fuzzy logic theory using MATLAB. The results showed that the system was able to reduce the defects of Short Shot and Sink Mark to 24.0% and 8.0% and the total defects was reduced around 8.7% per month.Keywords: Injection molding machine, machine selection, fuzzy logic, defects in injection molding, matlab.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27552840 Design of a Permanent Magnet Synchronous Machine for the Hybrid Electric Vehicle
Authors: Arash Hassanpour Isfahani, Siavash Sadeghi
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Permanent magnet synchronous machines are known as a good candidate for hybrid electric vehicles due to their unique merits. However they have two major drawbacks i.e. high cost and small speed range. In this paper an optimal design of a permanent magnet machine is presented. A reduction of permanent magnet material for a constant torque and an extension in speed and torque ranges are chosen as the optimization aims. For this purpose the analytical model of the permanent magnet synchronous machine is derived and the appropriate design algorithm is devised. The genetic algorithm is then employed to optimize some machine specifications. Finally the finite element method is used to validate the designed machine.Keywords: Design, Finite Element, Hybrid electric vehicle, Optimization, Permanent magnet synchronous machine.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 41152839 Automatic Classification of the Stand-to-Sit Phase in the TUG Test Using Machine Learning
Authors: Y. A. Adla, R. Soubra, M. Kasab, M. O. Diab, A. Chkeir
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Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals out of which 11 were chosen based on their Intraclass Correlation Coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, five features were introduced to the Linear Discriminant Analysis classifier and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90% respectively.
Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 4542838 Designing a Framework for Network Security Protection
Authors: Eric P. Jiang
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As the Internet continues to grow at a rapid pace as the primary medium for communications and commerce and as telecommunication networks and systems continue to expand their global reach, digital information has become the most popular and important information resource and our dependence upon the underlying cyber infrastructure has been increasing significantly. Unfortunately, as our dependency has grown, so has the threat to the cyber infrastructure from spammers, attackers and criminal enterprises. In this paper, we propose a new machine learning based network intrusion detection framework for cyber security. The detection process of the framework consists of two stages: model construction and intrusion detection. In the model construction stage, a semi-supervised machine learning algorithm is applied to a collected set of network audit data to generate a profile of normal network behavior and in the intrusion detection stage, input network events are analyzed and compared with the patterns gathered in the profile, and some of them are then flagged as anomalies should these events are sufficiently far from the expected normal behavior. The proposed framework is particularly applicable to the situations where there is only a small amount of labeled network training data available, which is very typical in real world network environments.Keywords: classification, data analysis and mining, network intrusion detection, semi-supervised learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17952837 Statistical Reliability Based Modeling of Series and Parallel Operating Systems using Extreme Value Theory
Authors: Mohamad Mahdavi, Mojtaba Mahdavi
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This paper tries to represent a new method for computing the reliability of a system which is arranged in series or parallel model. In this method we estimate life distribution function of whole structure using the asymptotic Extreme Value (EV) distribution of Type I, or Gumbel theory. We use EV distribution in minimal mode, for estimate the life distribution function of series structure and maximal mode for parallel system. All parameters also are estimated by Moments method. Reliability function and failure (hazard) rate and p-th percentile point of each function are determined. Other important indexes such as Mean Time to Failure (MTTF), Mean Time to repair (MTTR), for non-repairable and renewal systems in both of series and parallel structure will be computed.Keywords: Reliability, extreme value, parallel, series, lifedistribution
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20892836 Harnessing the Power of AI: Transforming DevSecOps for Enhanced Cloud Security
Authors: Ashly Joseph, Jithu Paulose
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The increased usage of cloud computing has revolutionized the IT landscape, but it has also raised new security concerns. DevSecOps emerged as a way for tackling these difficulties by integrating security into the software development process. However, the rising complexity and sophistication of cyber threats need more advanced solutions. This paper looks into the usage of artificial intelligence (AI) techniques in the DevSecOps framework to increase cloud security. This study uses quantitative and qualitative techniques to assess the usefulness of AI approaches such as machine learning, natural language processing, and deep learning in reducing security issues. This paper thoroughly examines the symbiotic relationship between AI and DevSecOps, concentrating on how AI may be seamlessly integrated into the continuous integration and continuous delivery (CI/CD) pipeline, automated security testing, and real-time monitoring methods. The findings emphasize AI's huge potential to improve threat detection, risk assessment, and incident response skills. Furthermore, the paper examines the implications and challenges of using AI in DevSecOps workflows, considering factors like as scalability, interpretability, and adaptability. This paper adds to a better understanding of AI's revolutionary role in cloud security and provides valuable insights for practitioners and scholars in the field.
Keywords: Cloud Security, DevSecOps, Artificial Intelligence, AI, Machine Learning, Natural Language Processing, NLP, cybersecurity, AI-driven Security.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1332835 Development of Multimedia Learning Application for Mastery Learning Style: A Graduated Difficulty Strategy
Authors: Nur Azlina Mohamed Mokmin, Mona Masood
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Guided by the theory of learning styles, this study is based on the development of a multimedia learning application for students with mastery learning style. The learning material was developed by applying a graduated difficulty learning strategy. Algebra was chosen as the learning topic for this application. The effectiveness of this application in helping students learn is measured by giving a pre- and post-test. The result shows that students who learn using the learning material that matches their preferred learning style perform better than the students with a non-personalized learning material.
Keywords: Algebraic Fractions, Graduated Difficulty, Mastery Learning Style, Multimedia.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 26002834 Kinematic Parameter-Independent Modeling and Measuring of Three-Axis Machine Tools
Authors: Yung-Yuan Hsu
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The primary objective of this paper was to construct a “kinematic parameter-independent modeling of three-axis machine tools for geometric error measurement" technique. Improving the accuracy of the geometric error for three-axis machine tools is one of the machine tools- core techniques. This paper first applied the traditional method of HTM to deduce the geometric error model for three-axis machine tools. This geometric error model was related to the three-axis kinematic parameters where the overall errors was relative to the machine reference coordinate system. Given that the measurement of the linear axis in this model should be on the ideal motion axis, there were practical difficulties. Through a measurement method consolidating translational errors and rotational errors in the geometric error model, we simplified the three-axis geometric error model to a kinematic parameter-independent model. Finally, based on the new measurement method corresponding to this error model, we established a truly practical and more accurate error measuring technique for three-axis machine tools.Keywords: Three-axis machine tool, Geometric error, HTM, Error measuring
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21222833 Feature Selection and Predictive Modeling of Housing Data Using Random Forest
Authors: Bharatendra Rai
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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).
Keywords: Housing data, feature selection, random forest, Boruta algorithm, root mean square error.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17162832 Evaluation of Best-Fit Probability Distribution for Prediction of Extreme Hydrologic Phenomena
Authors: Karim Hamidi Machekposhti, Hossein Sedghi
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The probability distributions are the best method for forecasting of extreme hydrologic phenomena such as rainfall and flood flows. In this research, in order to determine suitable probability distribution for estimating of annual extreme rainfall and flood flows (discharge) series with different return periods, precipitation with 40 and discharge with 58 years time period had been collected from Karkheh River at Iran. After homogeneity and adequacy tests, data have been analyzed by Stormwater Management and Design Aid (SMADA) software and residual sum of squares (R.S.S). The best probability distribution was Log Pearson Type III with R.S.S value (145.91) and value (13.67) for peak discharge and Log Pearson Type III with R.S.S values (141.08) and (8.95) for maximum discharge in Jelogir Majin and Pole Zal stations, respectively. The best distribution for maximum precipitation in Jelogir Majin and Pole Zal stations was Log Pearson Type III distribution with R.S.S values (1.74&1.90) and then Pearson Type III distribution with R.S.S values (1.53&1.69). Overall, the Log Pearson Type III distributions are acceptable distribution types for representing statistics of extreme hydrologic phenomena in Karkheh River at Iran with the Pearson Type III distribution as a potential alternative.
Keywords: Karkheh river, log pearson type III, probability distribution, residual sum of squares.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8822831 Machine Learning Techniques for Short-Term Rain Forecasting System in the Northeastern Part of Thailand
Authors: Lily Ingsrisawang, Supawadee Ingsriswang, Saisuda Somchit, Prasert Aungsuratana, Warawut Khantiyanan
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This paper presents the methodology from machine learning approaches for short-term rain forecasting system. Decision Tree, Artificial Neural Network (ANN), and Support Vector Machine (SVM) were applied to develop classification and prediction models for rainfall forecasts. The goals of this presentation are to demonstrate (1) how feature selection can be used to identify the relationships between rainfall occurrences and other weather conditions and (2) what models can be developed and deployed for predicting the accurate rainfall estimates to support the decisions to launch the cloud seeding operations in the northeastern part of Thailand. Datasets collected during 2004-2006 from the Chalermprakiat Royal Rain Making Research Center at Hua Hin, Prachuap Khiri khan, the Chalermprakiat Royal Rain Making Research Center at Pimai, Nakhon Ratchasima and Thai Meteorological Department (TMD). A total of 179 records with 57 features was merged and matched by unique date. There are three main parts in this work. Firstly, a decision tree induction algorithm (C4.5) was used to classify the rain status into either rain or no-rain. The overall accuracy of classification tree achieves 94.41% with the five-fold cross validation. The C4.5 algorithm was also used to classify the rain amount into three classes as no-rain (0-0.1 mm.), few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall accuracy of classification tree achieves 62.57%. Secondly, an ANN was applied to predict the rainfall amount and the root mean square error (RMSE) were used to measure the training and testing errors of the ANN. It is found that the ANN yields a lower RMSE at 0.171 for daily rainfall estimates, when compared to next-day and next-2-day estimation. Thirdly, the ANN and SVM techniques were also used to classify the rain amount into three classes as no-rain, few-rain, and moderate-rain as above. The results achieved in 68.15% and 69.10% of overall accuracy of same-day prediction for the ANN and SVM models, respectively. The obtained results illustrated the comparison of the predictive power of different methods for rainfall estimation.Keywords: Machine learning, decision tree, artificial neural network, support vector machine, root mean square error.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 32302830 Use of Bayesian Network in Information Extraction from Unstructured Data Sources
Authors: Quratulain N. Rajput, Sajjad Haider
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This paper applies Bayesian Networks to support information extraction from unstructured, ungrammatical, and incoherent data sources for semantic annotation. A tool has been developed that combines ontologies, machine learning, and information extraction and probabilistic reasoning techniques to support the extraction process. Data acquisition is performed with the aid of knowledge specified in the form of ontology. Due to the variable size of information available on different data sources, it is often the case that the extracted data contains missing values for certain variables of interest. It is desirable in such situations to predict the missing values. The methodology, presented in this paper, first learns a Bayesian network from the training data and then uses it to predict missing data and to resolve conflicts. Experiments have been conducted to analyze the performance of the presented methodology. The results look promising as the methodology achieves high degree of precision and recall for information extraction and reasonably good accuracy for predicting missing values.Keywords: Information Extraction, Bayesian Network, ontology, Machine Learning
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 22322829 Determination of Extreme Shear Stresses in Teaching Mechanics Using Freely Available Computer Tools
Authors: Rado Flajs
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In the present paper the extreme shear stresses with the corresponding planes are established using the freely available computer tools like the Gnuplot, Sage, R, Python and Octave. In order to support these freely available computer tools, their strong symbolical and graphical abilities are illustrated. The nature of the stationary points obtained by the Method of Lagrangian Multipliers can be determined using freely available computer symbolical tools like Sage. The characters of the stationary points can be explained in the easiest way using freely available computer graphical tools like Gnuplot, Sage, R, Python and Octave. The presented figures improve the understanding of the problem and the obtained solutions for the majority of students of civil or mechanical engineering.
Keywords: engineering, continuum mechanics, extreme shear stresses, Gnuplot, Sage, R, Python, Octave
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13892828 Does Material Choice Drive Sustainability of 3D Printing?
Authors: Jeremy Faludi, Zhongyin Hu, Shahd Alrashed, Christopher Braunholz, Suneesh Kaul, Leulekal Kassaye
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Environmental impacts of six 3D printers using various materials were compared to determine if material choice drove sustainability, or if other factors such as machine type, machine size, or machine utilization dominate. Cradle-to-grave life-cycle assessments were performed, comparing a commercial-scale FDM machine printing in ABS plastic, a desktop FDM machine printing in ABS, a desktop FDM machine printing in PET and PLA plastics, a polyjet machine printing in its proprietary polymer, an SLA machine printing in its polymer, and an inkjet machine hacked to print in salt and dextrose. All scenarios were scored using ReCiPe Endpoint H methodology to combine multiple impact categories, comparing environmental impacts per part made for several scenarios per machine. Results showed that most printers’ ecological impacts were dominated by electricity use, not materials, and the changes in electricity use due to different plastics was not significant compared to variation from one machine to another. Variation in machine idle time determined impacts per part most strongly. However, material impacts were quite important for the inkjet printer hacked to print in salt: In its optimal scenario, it had up to 1/38th the impacts coreper part as the worst-performing machine in the same scenario. If salt parts were infused with epoxy to make them more physically robust, then much of this advantage disappeared, and material impacts actually dominated or equaled electricity use. Future studies should also measure DMLS and SLS processes / materials.
Keywords: 3D printing, Additive Manufacturing, Sustainability, Life-cycle assessment, Design for Environment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 36092827 Machine Scoring Model Using Data Mining Techniques
Authors: Wimalin S. Laosiritaworn, Pongsak Holimchayachotikul
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this article proposed a methodology for computer numerical control (CNC) machine scoring. The case study company is a manufacturer of hard disk drive parts in Thailand. In this company, sample of parts manufactured from CNC machine are usually taken randomly for quality inspection. These inspection data were used to make a decision to shut down the machine if it has tendency to produce parts that are out of specification. Large amount of data are produced in this process and data mining could be very useful technique in analyzing them. In this research, data mining techniques were used to construct a machine scoring model called 'machine priority assessment model (MPAM)'. This model helps to ensure that the machine with higher risk of producing defective parts be inspected before those with lower risk. If the defective prone machine is identified sooner, defective part and rework could be reduced hence improving the overall productivity. The results showed that the proposed method can be successfully implemented and approximately 351,000 baht of opportunity cost could have saved in the case study company.Keywords: Computer Numerical Control, Data Mining, HardDisk Drive.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13952826 Improvement on a CNC Gantry Machine Structure Design for Higher Machining Speed Capability
Authors: Ahmed A. D. Sarhan, S. R. Besharaty, Javad Akbaria, M. Hamdi
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The capability of CNC gantry milling machines in manufacturing long components has caused the expanded use of such machines. On the other hand, the machines’ gantry rigidity can reduce under severe loads or vibration during operation. Indeed, the quality of machining is dependent on the machine’s dynamic behavior throughout the operating process. For this reason, these types of machines have always been used widely and are not efficient. Therefore, they can usually be employed for rough machining and may not produce adequate surface finishing. In this paper, a CNC gantry milling machine with the potential to produce good surface finish has been designed and analyzed. The lowest natural frequency of this machine is 202 Hz corresponding to 12000 rpm at all motion amplitudes with a full range of suitable frequency responses. Meanwhile, the maximum deformation under dead loads for the gantry machine is 0.565*m, indicating that this machine tool is capable of producing higher product quality.
Keywords: Finite element, frequency response, gantry design, gantry machine, static and dynamic analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 60342825 MIMCA: A Modelling and Simulation Approach in Support of the Design and Construction of Manufacturing Control Systems Using Modular Petri net
Authors: S. Ariffin, K. Hasnan, R.H. Weston
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A new generation of manufacturing machines so-called MIMCA (modular and integrated machine control architecture) capable of handling much increased complexity in manufacturing control-systems is presented. Requirement for more flexible and effective control systems for manufacturing machine systems is investigated and dimensioned-which highlights a need for improved means of coordinating and monitoring production machinery and equipment used to- transport material. The MIMCA supports simulation based on machine modeling, was conceived by the authors to address the issues. Essentially MIMCA comprises an organized unification of selected architectural frameworks and modeling methods, which include: NISTRCS, UMC and Colored Timed Petri nets (CTPN). The unification has been achieved; to support the design and construction of hierarchical and distributed machine control which realized the concurrent operation of reusable and distributed machine control components; ability to handle growing complexity; and support requirements for real- time control systems. Thus MIMCA enables mapping between 'what a machine should do' and 'how the machine does it' in a well-defined but flexible way designed to facilitate reconfiguration of machine systems.Keywords: Machine control, architectures, Petri nets, modularity, modeling, simulation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15872824 Design and Implementation of an AI-Enabled Task Assistance and Management System
Authors: Arun Prasad Jaganathan
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In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper presents an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.
Keywords: Artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 762823 On Speeding Up Support Vector Machines: Proximity Graphs Versus Random Sampling for Pre-Selection Condensation
Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint
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Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19192822 Climate Change in Albania and Its Effect on Cereal Yield
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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine learning methods, such as Random Forest (RF), are used to predict cereal yield responses to climacteric and other variables. RF showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the RF method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods: multiple linear regression and lasso regression method.
Keywords: Cereal yield, climate change, machine learning, multiple regression model, random forest.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2492821 Machine Vision for the Inspection of Surgical Tasks: Applications to Robotic Surgery Systems
Authors: M. Ovinis, D. Kerr, K. Bouazza-Marouf, M. Vloeberghs
Abstract:
The use of machine vision to inspect the outcome of surgical tasks is investigated, with the aim of incorporating this approach in robotic surgery systems. Machine vision is a non-contact form of inspection i.e. no part of the vision system is in direct contact with the patient, and is therefore well suited for surgery where sterility is an important consideration,. As a proof-of-concept, three primary surgical tasks for a common neurosurgical procedure were inspected using machine vision. Experiments were performed on cadaveric pig heads to simulate the two possible outcomes i.e. satisfactory or unsatisfactory, for tasks involved in making a burr hole, namely incision, retraction, and drilling. We identify low level image features to distinguish the two outcomes, as well as report on results that validate our proposed approach. The potential of using machine vision in a surgical environment, and the challenges that must be addressed, are identified and discussed.Keywords: Visual inspection, machine vision, robotic surgery.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18002820 A Survey of Sentiment Analysis Based on Deep Learning
Authors: Pingping Lin, Xudong Luo, Yifan Fan
Abstract:
Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.Keywords: Natural language processing, sentiment analysis, document analysis, multimodal sentiment analysis, deep learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2004