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检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

共 108425 条记录，以下是4151-4160 订阅

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learning from Time-Tracking Data: Gaussian Process Regression for Efficient Time Management Systems

Learning from Time-Tracking Data: Gaussian Process Regressio...

引用

2024 IEEE international conference on Communication, computing and Signal Processing, IICCCS 2024

作者： Thakur, Bandna Allirani, P. Chourasiya, Prashant Srinu, K. Al-Farouni, Mohammed Santhosh, S. Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Aditya Engineering College Department of Artificial Intelligence & Machine Learning Surampalem India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Department of Ise Nitte India

ISBN: (纸本)9798350390759

This paper aims at analyzing the use of Gaussian Process Regression (GPR) in working and creating effective time management system. It incorporates the GPR model that helps in predicting the future time usage by dissecting the time-study data this assists the users in scheduling. The approach adopted entails harvesting data from automated tools and manual logs, pre-processing the data collected, using k-fold cross-validation in training and validating the model. The forecasting accuracy features a low Mean Absolute Error and a high R-squared value, which signifies that the models perform well in terms of predictive capability. This research proves that GPR can actively help with data on time management and benefit time management systems as a whole. Some of the challenges that faced the research include quality of the data and the computation required to process the data was enhanced by effective preprocessing and refined algorithms respectively. © 2024 IEEE.

关键词： Data accuracy

来源：评论

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Utilizing Artificial Immune System to Improve Account Takeover Fraud Detection 2

Utilizing Artificial Immune System to Improve Account Takeov...

引用

2nd IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2024

作者： Gupta, Rohan Ghanghas, Peshal Kumar, Ridil Gupta, Arshiya Uie Chandigarh University Dept. of Ece Mohali India Uie Chandigarh University Dept. of Cse Mohali India

ISBN: (纸本)9798350306415

The rise of e-banking and plastic money has increased the risk of account takeover fraud, resulting in unauthorized transactions and identity theft. To counter this, the paper suggests using artificial immune systems that mimic the human immune system's adaptive and memory mechanisms, predator-prey response and pattern recognition. By incorporating concepts such as immune learning along with memory and adaptive immune response into machine learning algorithms, fraud detection accuracy can be significantly improved. This approach holds great promise in effectively combating fraud in our increasingly digital world. © 2024 IEEE.

关键词： AIR: Adaptive Immune Response AIS: Artificial Immune system ATO: Account takeover FD: Fraud detection IE: Immune engineering PPR: Predator Prey Response

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Machine learning Insights into Predicting Crude Oil Prices 2

Machine Learning Insights into Predicting Crude Oil Prices

引用

2nd IEEE international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Toppo, Akriti Mahajan, Jayant Singh, Vikas Pratap Paswan, Anand Shankar Saxena, Anshul Christ University Bengaluru India Ewec United Arab Emirates

ISBN: (纸本)9798350384277

This research paper delves into the complexities of crude oil, highlighting its extraction, composition, and transformation into valuable derivatives. Examining the pricing dynamics, it explores the intricate interplay of social and economic factors that shape crude oil's value, emphasizing its critical role in global energy and industrial sectors. A forecasting model is introduced, focusing on key factors - heating oil, SPX, GPNY, and EU DOL EX - utilizing five machine learning models. Historical data reveals the efficacy of conventional models, particularly Random Forest, in predicting crude oil prices, enhanced by feature engineering techniques. The paper concludes by suggesting avenues for further exploration, offering valuable insights for readers in this dynamic research domain. © 2024 IEEE.

关键词： Crude oil price

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Utilizing AI and IoT Communication for SMART Environment System in Intrusion and Fire Incident Scenarios 4

Utilizing AI and IoT Communication for SMART Environment Sys...

引用

4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bisht, Rajendra Singh Al-Farouni, Mohammed Dhabi, Mr.Dharmesh Abdullah, Falah Hassan Abdtawfeeq, Tareq Hafdhi Jaafar, Abdul Hassan Majli Mezaal, Ali Abdulkareem Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Bhimtal India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Vishwakarma Institute of Information Technology Department of Information Technology Pune India National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Medical Instrument Department Basra Iraq Kut University College Business Administration Department wasit Al kut52001 Iraq

ISBN: (纸本)9798350360165

One of the most common and damaging natural catastrophes, wildfires seriously harm the environment, society, and economy. It is possible to significantly improve early forest fire prediction, detection, and suppression by utilising transfer learning methods and contemporary machine learning tools. The proposed method uses a fleet of drones equipped with advanced decision-making tools and capable of processing high-definition photos to detect forest fires early on. Particle swarm optimisation techniques are used to quickly contain the fire at its beginning thanks to the system's creation of firefighting plans and fast notification of the fire and rescue departments. In addition, the drone network actively tracks forest fires in real time and helps put them out. Utilising deep learning models like ResNet, VGGNet, MobileNet, AlexNet, and GoogLeNet, the system demonstrates an impressive level of accuracy in identifying probable fire threats. The suggested GoogLeNet-TL approach outperforms cutting-edge deep learning models in terms of accuracy and F1 score, as evidenced by experimental findings. © 2024 IEEE.

关键词： Drones

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Bottle Cleanliness Detection Device with Electronic Nose using PCA-KNN Algorithm 15

Bottle Cleanliness Detection Device with Electronic Nose usi...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mendoza, Sean Dominic O. Novela, Jann Ryan J. Latina, Mary Ann E. Mapúa University School of Electronics Electrical and Computer Engineering City of Manila Philippines

ISBN: (纸本)9798350370249

This paper proposes a compact Bottle Cleanliness Detection Device that uses the PCA-KNN algorithm and an Electronic Nose system to expedite bottle inspection procedures. This is especially important in areas like the Philippines, where bottle reuse is prevalent. Using machine learning-processed sensor data, the device uses smell analysis to distinguish between 'clean' and 'unclean' bottles. The investigation confirms the device's reliability with a high accuracy rate of 94.67% and an F1 score of 94.52%. This research provides small and medium-sized businesses (SMEs) with a portable, affordable quality assurance solution supporting consumer safety. © 2024 IEEE.

关键词： Bottles Contamination Electronic Nose Gas Sensor Inspection KNN Machine learning PCA Quality Raspberry Pi Safety

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Student performance prediction in e-learning system and evaluating effectiveness of online courses

Student performance prediction in e-learning system and eval...

引用

2023 international conference on Advances in Intelligent computing and Applications, AICAPS 2023

作者： Blessy Paul, P. Kurian, Cini Mg University School of Computer Sciences Kerala Kottayam India Al-Ameen College Kerala Edathala India

ISBN: (纸本)9798350333817

In recent years, the number of online courses in India has skyrocketed especially due to the Covid pandemic. The most significant increments have happened in degree colleges, where 85% concur that internet based courses are important for their drawn-out procedure when contrasted with 60% in 2015. The distribution of online courses has evolved dramatically as technology has advanced. Web-based platform provides new challenges for both teachers and students. Teachers should be clear about the effectiveness of online learning in teaching students. For that, the possibilities of online learning should be compared with traditional learning. Students are evaluated based on their focus on online learning. This study aims to determine the efficacy of online courses by predicting student performance in an e-learning system. These research findings evaluate modern learning methods, highlight students' potential and help teachers understand how to assess and lead students on online platforms. © 2023 IEEE.

关键词： Decision trees

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Research on Scheduling Algorithms for AI Data-Intensive Tasks in Edge Heterogeneous Environments

Research on Scheduling Algorithms for AI Data-Intensive Task...

引用

2023 IEEE international conference on Electrical, Automation and Computer engineering, ICEACE 2023

作者： Liu, Luanqi Xu, Zixian Wang, Yingxun Wang, Xiang School of Computer and Information Engineering Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350309614

Edge computing has become a new computing service model by virtue of its low latency and high performance characteristics. It deploys cloud server resources at the edge of the network to help mobile devices to complete data-intensive, large-scale and complex computing tasks. The heterogeneity of resources and the mobility of devices in the edge environment make the edge heterogeneous environment often suffer from unbalanced distribution of resources and loads, which leads to the reduction of system computing performance and resource utilisation. The CPU-GPU heterogeneous computing system in the edge environment has higher performance than the isomorphic system, and the existing task scheduling strategy still limits the performance of the heterogeneous system. Aiming at the above problems, a task scheduling model for CPU-GPU heterogeneous systems is designed, and a genetic algorithm based on reinforcement learning is proposed to achieve efficient allocation of AI data-intensive tasks. The Q-learning algorithm is used to achieve adaptive adjustment of parameters to solve the complex and high-dimensional task scheduling problem. Simulation experiments are carried out by Edge-cloudsim platform, compared with other methods, the results prove that the QL-GA task scheduling method proposed in this paper can provide an efficient solution to the task scheduling problem in edge heterogeneous computing systems. © 2023 IEEE.

关键词： edge computing heterogeneous computing systems Reinforcement learning task scheduling

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Research on teaching Mode and Platform Construction of Intelligent Manufacturing Practical Training Based on Digital Twins Under the Background of New engineering 13

Research on Teaching Mode and Platform Construction of Intel...

引用

13th international conference on Educational and Information Technology, ICEIT 2024

作者： Cao, Jianhua Jiang, Zhigang Hu, Wei Xia, Xuhui Liu, Xiang Liu, Xiaoqin Bao, Xiong Wuhan University of Science and Technology Engineering Training Center Wuhan China Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Wuhan China

ISBN: (纸本)9798350372663

Aiming at the issues of high risks, high costs, and low efficiency in the training and teaching process of intelligent manufacturing, which seriously affect the quality of talent cultivation in intelligent manufacturing. By adopting digital twin technology, the intelligent manufacturing production line is mapped to virtual space, and a two-stage practical training teaching model that integrates virtual space simulation operations and physical space actual operations is constructed. And an intelligent manufacturing digital twin teaching platform was established to support the teaching model. The safe, efficient and economical operation of intelligent manufacturing practical training and teaching has been achieved. © 2024 IEEE.

关键词： E-learning

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Comparative Analysis of Hybrid CNN+RNN and BiLSTM for Fake News Detection in Healthcare 1

Comparative Analysis of Hybrid CNN+RNN and BiLSTM for Fake N...

引用

1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Saraswat, Laxmi Singh, Simar Preet Quamara, Sidhartha Bennett University Greater Noida ABES Engineering College Ghaziabad Uttar Pradesh 201310 India Bennett University Uttar Pradesh Greater Noida201310 India

ISBN: (纸本)9798350353648

Distinguishing counterfeit news is an essential undertaking in the contemporary era of digital technology. Fabricated news refers to information that has been deliberately fabricated or manipulated and is then published as legitimate news to mislead readers or viewers. To maintain the reliability of information sources and promote well-informed decision-making, It is imperative to combat the spread of inaccurate information. This paper tries to provide a brief insight into the deep learning/machine learning based approaches which have been provided recent times to counter fake news detection Specialists have explored various approaches to detect and prevent the dissemination of false information. This work is the analysis of some methods for detecting false information in the healthcare domain by utilizing Hybrid CNN+RNN and BiLSTM. © 2024 IEEE.

关键词： Long short-term memory

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A Comparative Study on Emotion Analysis Using Transfer learning 6

A Comparative Study on Emotion Analysis Using Transfer Learn...

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6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Saranyaraj, D. Arcot, Pavitra Amrita School of Engineering Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350306613

Emotional analysis plays an important role in improving AI's ability to learn and respond to human emotions. Face emotion recognition is expeditiously expanding its field of research with important applications across various domains such as medical image analysis, monitoring, personal identification, and security and theft alerts in current society This technology enhances human-computer interactions, mental health and well-being, market research, education and training, security, and social robotics by enabling machines to recognize, interpret, and respond to human emotions based on facial expressions. Transfer learning has come up with a promising address to leverage pre-Trained models for emotion analysis tasks. In this paper, a comprehensive comparative analysis of various transfer learning approaches employed in AI emotion analysis, unveiling valuable insights into their performance. The main objective is to assess the effectiveness and performance of the transfer learning algorithms, namely Resnet-50, Vgg-16, Vgg-19. etc. To carry on our analysis, we have used the FER2013 emotion dataset. The model's performance is assessed through evaluation metrics which include precision, recall, F1 score, and AUC. Our experimental results demonstrate that transfer learning algorithms significantly enhance the performance of emotion analysis AI systems. Upon stringent experiment and evaluation on FER2013 dataset, our findings reveal that VGG16 demonstrates the highest accuracy. © 2024 IEEE.

关键词： AI in cognitive research emotion recognition Face recognition

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