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检索条件"任意字段=3rd International Conference on Intelligent Data Engineering and Automated Learning"

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Evaluating The Usability of Massive Open Online Course User Interface Based on Eye-Tracker data 3

Evaluating The Usability of Massive Open Online Course User ...

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3rd international conference on intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Herlina, Herlina Nugraha, Kristian Adi Universitas Atma Jaya Yogyakarta Faculty of Industrial Technolgy Yogyakarta Indonesia Universitas Kristen Duta Wacana Faculty of Information Technology Yogyakarta Indonesia

ISBN: (纸本)9798350394559

Massive Open Online Course (MOOC) is a learning platform widely used today, especially since the Covid-19 pandemic. MOOC allows someone to gain knowledge from various universities around the world quickly. MOOCs have become a popular source for seeking knowledge because the costs are relatively cheap. The number of users is quite large worldwide, so a universal user interface (UI) is essential for a MOOC because different backgrounds and cultures can cause people to have different ways of interacting with interfaces. MOOCs should have good UX when used by people from various regions. To measure the UX of an UI, we can use several metrics, one of which is behavioral metrics. This research examines user behavior towards several types of MOOC layouts with the help of an eye tracker to measure user behavior when interacting with MOOC. Test results show that users only pay attention to the first appeared content when they did not have any goals yet. However, if the user understands their goals, UI appearance variations only slightly impact user behavior. Therefore, in MOOCs, important content should be located in the center of the screen, both vertically and horizontally. Apart from that, content considered the most important must appear first compared to other content. © 2023 IEEE.

关键词： Eye tracking

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An Extensive Machine learning Framework Employing Ensemble Classifiers for Heart Disease Detection 3

An Extensive Machine Learning Framework Employing Ensemble C...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandralekha, E. Vinodhini, S. Kandasamy, V. Rama, P. Ravikumar, S. Saravanan, T.R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept.of CSE Chennai India Velammal Engineering College Dept. of Information Technology Chennai India Panimalar Engineering College Dept. of Information Technology Chennai India College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Chennai India SRM Institute of Science and Technology Dept. of Computational Intelligence Chennai India

ISBN: (纸本)9798350343632

Heart disease is a significant global health challenge, impacting many lives and often being a leading cause of mortality and illness. Detecting heart disease accurately and promptly is essential for effective medical care. Due to the complex interplay of risk factors and the intricate nature of diagnosis, advanced technologies like machine learning are being explored. This study introduces a comprehensive framework that employs machine learning techniques for heart disease detection. The framework involves critical stages such as data preprocessing, feature selection, and training ensemble classifiers. Experimental results highlight the importance of specific attributes in each classifier, offering insights into predictions. Notably, attributes like oldpeak, thalach, ca, cp, and thal are key features in the Random Forest classifier, while Gradient Boosting underscores ca, cp, thal, oldpeak, and age. AdaBoost and the Extra Trees Classifier reveal similar significant attributes. Evaluation metrics emphasize the models' robust performance, with the Extra Trees Classifier achieving the highest accuracy. This research contributes to a practical heart disease detection framework with potential for improving medical decision-making. © 2023 IEEE.

关键词： AdaBoost Extra Trees Classifier Gradient Boosting Machine learning Random Forest

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Identification of Consumer Buying Patterns using KNN in E-Commerce Applications 3

Identification of Consumer Buying Patterns using KNN in E-Co...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Yasin, Wasim Harimoorthy, Karthikeyan Vetriveeran, Divya Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350322842

In recent days, with the advancement of technologies, people use electronic medium to carry out their businesses. E-commerce is a process of allowing people to buy and sell products online using electronic medium. E-commerce has a wide range of customer base as well. The data generated through transaction helps the enterprises to develop the marketing strategy. The growth of this e-commerce application depends on several factors. Some of the factors are follows 1) Customer demand, 2) Analyzing buying pattern of the users, 3) Customer retention, 4) dynamic pricing etc. It is very difficult to analyze the buying pattern of customers as there is a wide range of customer base in the online platform. To overcome this problem, this research study discusses about the challenges and issues in e-commerce applications, also identifies and analyses the buying patterns of customer using various machine learning techniques. From the implementation it is identified that, KNN algorithm performed well while comparing it with various other machine learning algorithms. Performances of these algorithms have been analyzed using various matrices. For analyzing, the model is tested using e-commerce dataset (Amazon dataset downloaded from ***). From the analysis it found that KNN algorithm computes and predicts better compared to other machine learning algorithms either Naïve Bayes, or Random Forest, or Logistic Regression etc. © 2023 IEEE.

关键词： Consumer behavior

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Investigating the Big data Challenges of Deep learning for data Science 3

Investigating the Big Data Challenges of Deep Learning for D...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Aggarwal, Prateek Singh, Shweta Batri, Krishnan Velusudha, N.T. Kale, Sunil D. Shrimal, Gajendra Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350319125

Deep-gaining knowledge is a subset of synthetic intelligence (AI) that employs superior technology, data mining, and system-gaining knowledge to use massive datasets to find insights and styles. It is an area of advanced computing technology that enables computer systems to get the right of entry to analyze and analyze from many statistics. Even as the ability of deep learning is enormous, where its impact remains uncovered, extensive statistics demanding situations lie at the crossroads of its software and effectiveness. Massive records datasets' high quality and shape are essential for successfully implementing deep studying fashions. There may be an increasing need for highly acceptable statistics, including correctly categorized information, for training deep learning fashions. Moreover, deep studying often requires big datasets to provide correct output. It, therefore, calls for records to be amassed from numerous assets, posing the need for more homogeneous, regular, and standardized information. Adequate statistics garage, processing, and analytic solutions must be followed to analyze massive facts to create reachable and usable datasets for successful deep mastering packages. It includes infrastructure capable of securely keeping a large amount of information, in addition to the cloud-based totally systems to efficiently method extensive quantities of statistics. There has been a shift in the direction of using 'large records' technologies like Apache Hadoop, Apache Spark, and different associated libraries. Even as deep mastering holds massive capability for uncovering new insights and turning in gigantic value to fields which include healthcare, finance, enterprise control, and more incredible, the associated massive data demanding situations ought to be addressed for those programs to reach their full capability. Despite the present-day challenges, advances in extensive records solutions like Apache Hadoop and Apache Spark, coupled with advanced analytic

关键词： challenges efficiently homogeneous management overcoming

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A Comparative Analysis of Machine learning Using Classification Algorithms for the Detection of Breast 1

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3rd international conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Rai, Prerna Sarraf, Jay School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (数字)9789819705733

ISBN: (纸本)9789819705726

Cancer deaths are one of the most important problems faced by people in developing countries. There are many ways to prevent cancer from growing in the first place, but some cancers are still incurable. Breast cancer is a serious disease that is one of the most common health problems among women and causes death worldwide. Accurate diagnosis and classification of breast cancer are important. There are many studies in the literature to predict the type of breast cancer. AI-based automation can save time and reduce errors. In this study, machine learning techniques such as logistic regression, K-nearest neighbor, decision trees, and random forests are used together with data visualization. Python was chosen as the programming language for machine learning models and visualization. The purpose of this study is to compare machine learning and data visualization tools for breast cancer detection and diagnosis. The diagnosis of these applications can be compared to the diagnosis of cancer. data visualization and machine learning techniques can have an impact on cancer diagnosis and decision-making. While the all-featured KNN classifier model achieves the best classification (0.96%), the proposed method shows improvement in accuracy results. These results suggest that new methods can be investigated for cancer diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data visualization

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Adaptive Modeling for Real-Time data Analysis with Machine learning 3

Adaptive Modeling for Real-Time Data Analysis with Machine L...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mahesh, T.R. Singh, Shweta Srivastav, Vaibhav Kalra, Hitesh Surulivel Rajan, T. Yadav, Gitanjali B. Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Adaptive Modeling for actual-Time information evaluation with system studying is a critical studies area in the modern-day technology of information-in depth evaluation. Conventional gadget studying strategies are static, meaning that the models do no longer evolve as information scientist interactions occur or because the data changes over time. As a result, models can grow to be obsolete and further downstream machine overall performance will become constrained. Consequently, the want for adaptive modeling algorithms has arisen with the intention to permit dynamic, system-stage getting to know from statistics. Such strategies look to provide affordable predictions for real-time inputs, while also looking forward to enough destiny reaction behavior to make more accurate predictions for destiny, unseen facts. The purpose of this approach is to create a solution which optimally balances accuracy and flexibility for an ever converting environment. One of these system could be carried out for predictive upkeep, diagnostic analysis, cyber security detection or self-sustaining vehicle decision making. © 2023 IEEE.

关键词： Forecasting

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Concrete Fault Detection Using Deep learning: Towards Waste Reduction in Bridge Inspection 33rd

Concrete Fault Detection Using Deep Learning: Towards Waste ...

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33rd international conference on Flexible Automation and intelligent Manufacturing

作者： Shahin, Mohammad Chen, F. Frank Maghanaki, Mazdak Hosseinzadeh, Ali Firouzranjbar, Shadi Koodiani, Hamid Khodadadi Univ Texas San Antonio Dept Mech Engn San Antonio TX 78249 USA Univ Texas San Antonio Dept Civil Engn San Antonio TX 78249 USA

ISBN: (纸本)9783031744815;9783031744822

Bridge inspections across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there's an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision, integrating it with the principles of waste reduction from Lean manufacturing, to streamline the inspection process. We examined the efficacy of 15 distinct deep learning Convolutional Neural Network (CNN) models, including well-known architectures like Inception, VGG16, Xception, LeNet, and a tailor-made CNN. These models were applied to a data set of 40,000 high-quality images from the Concrete Images for Classification dataset. With their heavy reliance on time and resources, traditional fault detection methods often fall short. In contrast, the deployed models, with accuracy rates reaching up to 99%, showcase a significant potential in promptly identifying concrete anomalies, enhancing safety, conserving resources, and potentially saving lives. VggNet proved the fastest (3.5 h), and LeNet the slowest (100.10 h. Considering their similar performance in other metrics, VggNet stands out as the preferred model for concrete inspection. The custom CNN model also performed well in comparison.

关键词： Deep learning bridge inspection Convolutional Neural Network Lean manufacturing waste reduction

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Exploring the Impact of Machine learning on Real-Time data Analysis 3

Exploring the Impact of Machine Learning on Real-Time Data A...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Swati Nanda, R.T. Sathya, S. Banchhor, Chitrakant O. Pandey, Pratik Rampal, Sourav Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Basic Science Karnataka Ramnagar India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The emergence of system mastering has enabled actual-time information analysis to come to be extra accurate, efficient, and comfy. ML can rapidly examine and classify excessive-volume streams of raw records, allowing companies to reply to quickly changing environments. This technology has had a considerable impact on the way groups and developers manner complicated datasets and make decisions in realtime. It has revolutionized how agencies use and combine information to gain new insights and understand trends. ML performs a vital position in helping businesses efficiently technique large quantities of data while reducing human errors. At the same time, it provides security and integrity of the data, lowering the capacity for change and loss. In this paper, we discover the effect of ML on actual-time information evaluation and talk about its diverse applications. We additionally offer an overview of different methods of ML and their advantages and disadvantages. ultimately, we speak of future traits for mixing ML with actual-time records analytics and the consequences of this integration. © 2023 IEEE.

关键词： disadvantages environments organizations revolutionized understand

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Emotion Detection Based on De-expression Residue learning and Its intelligent System Design 3

Emotion Detection Based on De-expression Residue Learning an...

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3rd international conference on intelligent Design, ICID 2022

作者： Li, JiuHui Wen, ChaoGuang Zhang, JianXian Gan, YuJian Rui, Gao Yao, JiaMin School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Public Finance & Taxation of Gdufe Guangdong University of Finance & Economics Guangzhou China College of Automation Zhongkai University of Agriculture and Engineering Guangzhou China The First School of Clinical Medicine Southern Medical University Guangzhou China School of Physics and Optoelectronic Engineering Foshan University Foshan China

ISBN: (数字)9781665476775

ISBN: (纸本)9781665476775

Due to the difference of individual facial expression, it will have a significant impact on the detection of the emotion. Therefore, in order to mitigate this impact, and to solve the problems of high complexity and low accuracy of other modern emotion detection algorithms, this algorithm designed in this paper is based on an expression residue learning framework, called De-expression residue learning. Its core idea is to divide Emotion into Expression and Neutral, and then orderly carry out model training, emotion recognition and classification from the extracted Expression. It can effectively reduce the interference caused by human differences and improve the accuracy of emotion classification. It is tested on the public datasets Emotionet dataset and Fer2013 dataset. The experimental results show that the algorithm in this paper has high detection accuracy and effectively reduces the false detection rate. Finally, based on the algorithm in this paper, an intelligent recognition system is designed to increase the practical value of the research. © 2022 IEEE.

关键词： Emotion Recognition

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Comparative Analysis of Short-Term Load Forecasting Using Machine learning Techniques 3rd

Comparative Analysis of Short-Term Load Forecasting Using Ma...

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3rd international conference on Advanced Network Technologies and intelligent Computing (ANTIC)

作者： Shifare, Hagos L. Doshi, Ronak Ved, Amit Marwadi Univ Dept Comp Engn DS Rajkot India Marwadi Univ Dept Elect Engn Rajkot India

ISBN: (纸本)9783031640698;9783031640704

Short-Term Load Forecasting is essential in estimating future energy demand in power systems, energy utilities, and industrial settings. For energy suppliers and other players in the markets for electric energy generation, transmission, and distribution, load forecasting is a crucial instrument. Additionally, the prediction of load is essential for effectively planning and overseeing power system operations. Load forecasting has significant effects on a variety of power system applications, such as energy production, load shedding, contract analysis, and infrastructure construction. In this study, the authors compare and contrast three forecasting methods for short-term load forecasting namely, Gradient Boosting (referred to as GB), Random Forest (referred to as RF), and K-Nearest Neighbors (abbreviated as KNN). The historical load (annual) and New York calendar data are input parameters into the forecasting models for each of these strategies. The research assesses how well these methods perform in terms of Root Mean Square Error (RMSE), Mean Absolute Error, R2-score, and computing time. The comparative analysis finds that KNN is the most effective option, with an amazing R2-score of 98.43%, followed by RF at 97.12% and GB at 95.52%. Furthermore, KNN offers tremendous computing efficiency, emphasizing its suitability for real-time applications. This work lays the door for improved load forecasting in dynamic energy systems by using the capabilities of these models.

关键词： Short-term load forecasting Machine learning algorithms K-nearest neighbor(KNN) Electricity demand estimation

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