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AI-Driven Mentor Service Advising application for Timely Course Completion using RNN/LSTM 4

AI-Driven Mentor Service Advising Application for Timely Cou...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Sumathy, K. Surendran, R. Sathiamurthy, Maha Sweta Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Puducherry Technological University Department of Electronics and Communication Engineering Puducherry India

ISBN: (纸本)9798331540364

To be precise, time is a very sensitive factor that concerns course completion in colleges. MOOCs and other such sources of online learning have begun to make education accessible to everyone. One of the significant problems that exist in the course of studying is the fact that so many students flunk. the study recommends an AI-based mentor service thus integrating an advising app that will ensure that students complete their courses on time. the program applies the AI and ML to provide the academic planning of performance and direction, immediate assistance and constant feedback to students, resulting in higher scores and enhanced satisfaction levels. Recurrent Neural Networks (RNN s) and in particular the implementation of Long Short-Term Memory (LSTM) are applied to an innovative virtual mentor service which allows the generation of individual learning pathways and enhancement of completion time for online students. the LSTM network determines and categories how the students are transitioning throughout the course content and distinction between the zones of strength and areas that might require the students to be guided. Possible Challenges With the help of the technology, a student might indicate such problems as being lost at a topic or lagging behind Growth of learning patterns can be estimated. Proposing specific actions As regards barrier-related knowledge deficits, the system provides specific recommendations in terms of special approaches. Such interventions may comprise of suggesting new resources that include products, or skillful practice sessions, or even different learning resources. Accelerate Completion the technology to complete courses in time based on students' performance as well as recommendations. It was used for Comparing therapies and suggestion using experiments hence used as AlB Tool. the rationale of this approach is to enhance timely completion of courses and consequently the results of learning processes. © 2024 IEEE.

关键词： Long short-term memory

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Green litchi automatic learning based on YOLOX-S, Faster-RCNN, SSD deep learning algorithm 4

Green litchi automatic learning based on YOLOX-S, Faster-RCN...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Yang, Maoling Wang, Chenglin College of Intelligent Manufacturing Engineering Chongqing University of Arts and Sciences Chongqing China Faculty of Modern Agricultural Engineering Kunming University of Science and Technology Kunming China

ISBN: (纸本)9781510672505

In the past 10 years, many models of target detection based on convolution networks have been proposed, which are mainly divided into single-stage target detection models and two-stage target detection models. the main feature of single-stage is fast detection speed, and the main feature of two-stage is high detection accuracy. the purpose of this experiment is to select the most suitable target detection model for Qinglitchi by comparing the performance of the single-stage target detection model and the two-stage target detection model. After many and sufficient model training, the experimental results show that the YOLOX-S model has the fastest detection speed, and FPS is 37.965;the average accuracy of detection is the highest, with an average accuracy of 89.98%. therefore, the YOLOX-S model is selected as the target detection model of green litchi. © 2023 SPIE.

关键词： Deep learning

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Evaluation of teaching effect of online education courses based on random forest 4

Evaluation of teaching effect of online education courses ba...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Li, Ru Anhui Open University Hefei230022 China

At present, online education platforms are very popular. However, these platforms also have some drawbacks, including that they cannot be completely based on the needs of students, many students ignore the needs of students in order to adapt to the platform, do not adopt a more personalized learning style, and do not fully realize the individualized teaching. there are many factors at play, such as shifts in learning and teaching patterns, online platforms, hardware limitations of the network, and more. However, whether this model can finally satisfy teachers and students, and how the teaching effect is, has become a problem of concern. We propose a random forest-based method to evaluate college online courses. this method not only focuses on a single influencing factor, but also takes into account the special needs of students and teachers, and formulates different evaluation criteria, so that the system has not only universal applicability. the experimental results show that this method can improve the proficiency and level of teachers' online teaching, and can effectively combine several courses taught by students to form a personalized teaching method. © 2024 the Authors.

关键词： Contrastive learning

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4th international conference on Big Data and Security, ICBDS 2022

4th International Conference on Big Data and Security, ICBDS...

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4th international conference on Big Data and Security, ICBDS 2022

ISBN: (纸本)9789819932993

the proceedings contain 54 papers. the special focus in this conference is on Big Data and Security. the topics include: Research on the Evolution of New Energy Industry Financing Ecosystem Under the Background of Big Data;a Survey of the State-of-the-Art and Some Extensions of Recommender System Based on Big Data;an Innovative AdaBoost Process Using Flexible Soft Labels on Imbalanced Big Data;Feature Fusion Based IPSO-LSSVM Fault Diagnosis of On-Load Tap Changers;graphlet-Based Measure to Assess Institutional Research Teams;Logical Relationship Extraction of Multimodal South China Sea Big Data Using BERT and Knowledge Graph;research on application of Knowledge Graph in War Archive Based on Big Data;Semi-supervised learning Enabled Fault Analysis Method for Power Distribution Network Based on LSTM Autoencoder and Attention Mechanism;fault Detection Method for Power Distribution Network Based on Ensemble learning;Research on Data-Driven AGC Instruction Execution Effect Recognition Method;factors Influencing Chinese Users’ Willingness to Pay for O2O Knowledge Products Based on Information Adoption Model;a Survey of Integrating Federated learning with Smart Grids: application Prospect, Privacy Preserving and Challenges Analysis;application of the Fusion Access Technology of Carrier and 5G in Power Communication;Muscle Fatigue Classification Based on GA Optimization of BP Neural Network;research on Typical Scenario Generation Based on Distribution Network Data Mining and Improved Policy Clustering;a Data-Driven and Deep learning-Based Economic Evaluation Method for New Power System Distribution Grid;study on Random Generation of Virtual Avatars Based on Big Data;an Explainable Optimization Method for Assembly Process Parameter.

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4th international conference on Intelligent Systems in Production Engineering and Maintenance , ISPEM 2023

4th International Conference on Intelligent Systems in Produ...

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4th international conference on Intelligent Systems in Production Engineering and Maintenance , ISPEM 2023

ISBN: (纸本)9783031450204

the proceedings contain 44 papers. the special focus in this conference is on Intelligent Systems in Production Engineering and Maintenance. the topics include: Information Design Management of Machining Parts on Metal Cutting machines;Virtual Training at ELLA Platform – the Case Study;web application for the learning of Emotions in Children with Autism Spectrum Disorder;lean Manufacturing Knowledge Correlation Model;assessment of the Effectiveness of Six Sigma Methodology Implementation - A Literature Review;analysis of Basic Characteristics of Textile Yarn Using Image Processing Techniques;modeling the Impact of Leadership on Change Management Decisions Using System Dynamics Simulation;Using an ISO 9001 Based Framework as a Benchmark to Identify Best Practices Used by Sri Lankan Practitioners when Selecting Suppliers;requirements for Flow Control Systems for Discrete Production of Mechanical Products;NIRF Imaging with Indocyanine Green (ICG) in a Veterinary Minimally Invasive Surgery;an Augmented System Based on machine learning for Boccia Assisted Gameplay;Ultrasound Tissue Differentiation (UTC) an Objective Diagnostic Tool in Prevention and Healing Monitoring Tendon Problems in Race Horses;location of Obstacles on Transport Routes in a Centralized Multi-agent System;converging Data Mesh and Microservice Principles into a Unified Logical Architecture;Development and Comparative Analysis of IT Tools Supporting Data Processing in Custom Manufacturing of Orthoses;design of Experiments: An Overview and Future Paths;SOC Evaluation of Lithium-ion Battery Based on MFC Sensors;indicators of Hierarchical Structure Model of Supporting the Production Management Process – A Framework;gamification-Based Crowdsourcing as a Tool for New Product Development in Manufacturing Companies.

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A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

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10th IEEE international conference on Cyber Security and Cloud Computing and 9th IEEE international conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. the advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

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Badminton Action Recognition and Detection using Artificial Intelligence and Neural Network Technology 4

Badminton Action Recognition and Detection using Artificial ...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Yu, Ling Shanghai Lixin University of Accounting and Finance Shanghai China

ISBN: (纸本)9798350308082

With the progress of society, people pay more and more attention to physical health, in which badminton is popular because of its simplicity and variety. In order to help you to play badminton safely, this paper is based on artificial intelligence and neural network technology, To study the recognition of badminton movements, Using artificial intelligence to collect data on badminton racket swing and other movements, and use neural network technology to strategy these data, To perform the feature extraction, Extract the high-quality badminton characteristic movements, While prompting the exercisers to do physical exercise, Better understand your physical state and competitive ability, By comparison with the deep learning algorithms, Found that the technical algorithm studied in this paper can improve the accuracy of badminton action recognition, the accuracy of the technical algorithm based on this paper is more than 95%;At the same time, the swing movement is studied, It can be found that the accuracy of the technical algorithm studied in this paper is above 96%. therefore, based on artificial intelligence and neural network technology, it is meaningful to identify and detect badminton movements. © 2023 IEEE.

关键词： learning algorithms

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Research on the prediction of cross-border e-commerce sales based on stacking integrated learning 4

Research on the prediction of cross-border e-commerce sales ...

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4th international conference on computer Science and Communication Technology, ICCSCT 2023

作者： Fu, Tongyan Du, Huiying School of Information Management Beijing Information Science & Technology University Beijing100192 China

ISBN: (纸本)9781510671232

In the context of the new era of rapid development of big data and artificial intelligence technologies, the use of data mining and algorithmic techniques to build sales prediction models with high accuracy rates can quickly help cross-border e-commerce (CBEC) enterprises to cope with the challenges related to data processing, feature extraction, and value information acquisition. through the analysis and mining of raw data of CBEC transactions, a two-layer fusion model is built by using the stacking integrated learning method, with the Random Forest, XGBoost, and LightGBM algorithms as base learners and the support vector regression algorithm as a meta-learner, to predict CBEC sales and compare the prediction properties with the single model constructed by the base learner. the results show that the integrated learner trained by the stacking integrated learning method can improve the prediction performance of the single learner and performs best among all learners. the model can objectively reflect the status of CBEC sales and play an essential role in the company’s execution of strategic decisions such as merchandise sourcing and sales. © 2023 SPIE.

关键词： Sales

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Code Smell Detection using Hybrid machine learning Algorithms 5

Code Smell Detection using Hybrid Machine Learning Algorithm...

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5th international conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Mahalakshmi, D. Kasinathan, Prabakaran Elangovan, D. Rohith Bhat, C. Balamurugan, M. Sivakumar, S. Panimalar Engineering College Dept. of Information Technology India School of Computing Veltech Rangarajan Dr. Sagunthula R & D Institute of Science and Technology Dept. of Cse India Panimalar Engineering College Dept. of Cse India Chennai India Sri Sairam Engineering College Affiliated to Anna University Dept. of Cse India Lakshmi Bangaru Arts and Science College Dept. of Computer Applications melmaruvathur India

ISBN: (纸本)9798350321425

Code smells frequently leads to the discovery of decreased code quality, drains on application resources, or even critical security vulnerabilities embedded within the application's code. While code smells may not always indicate a particularly serious problem, it do often lead to the discovery of these issues. Software's structural characteristics lead to a design issue that makes it challenging to manage and maintain code refactoring. the goal of the current research is to create methods for identifying code smells. the machine learning algorithm is a reliable method for individualized smell detection, but there aren't many studies on how well it works for different developers. In this proposed work used two different deep learning algorithms and five different machine learning ensembles to detect suspicious code. Investigation of the Data class, God class, Feature-envy, and Long-method datasets revealed that each contained various levels of code smells. Although there is room for improvement, the outcomes of prior publications' applications of machine learning and stacking ensemble learning methods to this dataset were satisfactory. A class balancing method (SMOTE) was implemented to address the problem of class imbalance within the datasets. While the Feature- envy dataset with the selected dozen metrics produced the lowest accuracy (91.45%) for the Max voting method, the Long-method dataset with the various chosen metrics produced the highest accuracy (100%) for all five methods. © 2023 IEEE.

关键词： learning systems

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Deep Q-learning Network-based Optimal Deployment Strategy for Distribution IoT 4

Deep Q-Learning Network-based Optimal Deployment Strategy fo...

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4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Zhang, Peiyao Liu, Di Gao, Liyuan Yang, Xuxin Ma, Hongyue Mei, Xinsu State Grid Information and Telecommunication Group Co. Ltd China

ISBN: (纸本)9781510666412

Edge computing node has large data flow and large resource occupation, which is not conducive to data transmission and processing, and is not conducive to the application of edge computing in the distribution of IoT. thi s paper proposes an optimization algorithm for edge computing nodes based on the deep Q-learning network theory. the node deployment problem is modeled according to the goal of minimum energy consumption and minimum traffic of the network to solve the optimal strategy. the performance of the algorithm is verified by setting different network sizes and different requests. the test shows that the method used can solve the problem of excessive resource consumption on the edge side. © 2023 SPIE.

关键词： Edge computing

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