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Research on Multi-task Load Dynamic Planning of Online Education Platform Based on Edge computing 3

Research on Multi-task Load Dynamic Planning of Online Educa...

引用

3rd international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2022

作者： Jiang, Jingjing Li, Nan Li, Yao Zhang, Haibo Dalian University of Science and Technology Dalian China

ISBN: (纸本)9781510661301

The online education platform is a typical Internet scenario. As an online education platform, the platform faces many users, including students, teachers, and adult users. The system positioning determines that the platform needs to deal with high concurrency issues and consider the scalability of the system. When the number of users is too large, a single development framework cannot solve the problem well. Therefore, considering this type of typical Internet scenario system and taking system load dynamic planning as the research content, the MTBBO algorithm is proposed in the paper to deal with this problem. Through the comparison between MTBBO and single-task single-objective models GA, DE and BBO, the superiority of the algorithm in this paper is verified. © 2023 SPIE.

关键词： Edge computing

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Minimizing Data Retrieval Delay in Edge computing 20th

Minimizing Data Retrieval Delay in Edge Computing

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20th EAI international conference on Mobile and Ubiquitous Systems - computing Networking and Services (EAI MobiQuitous)

作者： Rajashekar, Kolichala Paul, Souradyuti Karmakar, Sushanta Sidhanta, Subhajit Indian Inst Technol Bhilai Bhilai India Indian Inst Technol Guwahati Gauhati India

ISBN: (纸本)9783031639913;9783031639920

For real-time mission-critical applications such as forest fire detection, oil refinery monitoring, etc., the edge computing paradigm is heavily used to process data fetched from IoT devices spread over a considerably large geographical region. For such real-time edge computing applications working under stringent deadlines, the overall retrieval delay, i.e., the delay in fetching the data from the IoT devices to the edge servers, needs to be minimized;Otherwise, the retrieval delay in fetching the data from IoT devices distributed over such a large geographical region can be prohibitively large. To achieve the above goal, each IoT device must be assigned to a particular edge server while considering the relative positioning as per the topology of the edge cluster. We prove that the above assignment of IoT devices to an edge cluster, which we denote as the Edge Assignment Problem (EAP), is NP-Hard. Therefore, obtaining a polynomial time solution is infeasible. For the above EAP problem, instead of performing both exploration and exploitation on the search space, state-of-the-art heuristic algorithms will only exploit the search space. As a result, these algorithms are unable to achieve an appreciably large reduction in the overall retrieval delay. To that end, we propose a Deep Reinforcement learning-based algorithm that is able to produce a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge servers is overloaded. We motivate and demonstrate our proposed algorithm with the use case of federated learning (FL) - a popular distributed machine learning paradigm that is based on the principle of edge computing such that the clients, i.e., edge servers, train local models from the data obtained from local IoT devices. These local models are further aggregated into a global model at an aggregator (the cloud/fog) by exchanging the model parameters instead of raw data. In that case, an optimal assignment of the IoT devices to each

关键词： IoT edge computing reinforcement learning

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A Hybrid Quantum -classical Fusion Neural Network to Improve Protein-ligand Binding Affinity Predictions for Drug Discovery 5

A Hybrid Quantum -classical Fusion Neural Network to Improve...

引用

2024 international conference on Quantum computing and engineering

作者： Domingo, L. Chehimi, M. Banerjee, S. Yuxun, S. He Konakanchi, S. Ogunfowora, L. Roy, S. Selvarajan, S. Djukic, M. Johnson, C. Ingenii Inc New York NY 11580 USA Purdue Univ Data Mine W Lafayette IN 47907 USA

ISBN: (纸本)9798331541378

Drug discovery hinges on the accurate prediction of binding affinity between prospective drug molecules and target proteins that influence disease progression, which is financially and computationally demanding. Although classical and hybrid quantum machine learning models have been employed in previous studies to aid in binding affinity prediction, they encounter several issues related to convergence stability and prediction accuracy. In this regard, this paper introduces a novel hybrid quantum -classical deep learning model tailored for binding affinity prediction in drug discovery. Specifically, the proposed model synergistically integrates 3D and spatial graph convolutional neural networks within an optimized quantum circuit architecture. Simulation results demonstrate a 6% improvement in prediction accuracy relative to existing classical models, as well as a significantly more stable convergence performance compared to previous classical approaches. Moreover, to scalably deploy the proposed framework over today's noisy intermediate-scale quantum (NISQ) devices, a novel quantum error mitigation algorithm is proposed. This algorithm outperforms existing techniques and is capable of mitigating errors with gate noise probabilities, p < 0.05, while resulting in no additional overhead during the training and testing phases.

关键词： Quantum machine learning drug discovery binding affinity quantum fusion model

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Enhancing Disease Prediction through Random Forests in Healthcare Analytics 7

Enhancing Disease Prediction through Random Forests in Healt...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Attabi, Kassem Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Boob, Nandini Shirish Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Gla University Department of Electrical Engineering Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

This research investigates the application of Random Forest algorithms to enhance disease prediction within healthcare analytics. Using large healthcare datasets, the research compares Random Forests to other machine learning algorithms, such as Neural Networks and Support Vector Machines, and assesses their ability to forecast different illnesses. According to the findings, Random Forests have the ability to reliably forecast diseases since they provide greater accuracy, precision, recall, and F1-score. In addition, healthcare providers may benefit greatly from the model's feature significance metrics, which help them understand which aspects have the most impact on illness outcomes. This study emphasises the revolutionary power of Random Forests to improve healthcare diagnoses, personalised treatment regimens, and patient care overall. © 2024 IEEE.

关键词： Disease Prediction Healthcare Analytics Machine learning Predictive Performance Random Forests

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Utilizing Deep Reinforcement learning for Advanced Pattern Extraction in Big Data Analytics and Ontology Systems 2

Utilizing Deep Reinforcement Learning for Advanced Pattern E...

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

作者： Karthik, A. Asha, V. Bhardwaj, Garima Nijhawan, Ginni Khan, Irfan Bader, Nabaa M. Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Amity University Amity Business School Uttar Pradesh Greater Noida India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The development of big data has provided unparalleled prospects for uncovering novel patterns and insights in several domains. However, the complex structure and volume of data need the use of advanced methods to successfully extract significant information. This research presents a new framework that utilizes Deep Reinforcement learning (DRL) to improve pattern extraction in big data analytics and ontology systems. DRL, which combines deep learning with reinforcement learning, excels at dealing with data spaces that have a large number of dimensions. This makes it a good option for effectively exploring and analyzing complex datasets. The suggested framework effectively utilizes sophisticated ontological structures to integrate DRL, enabling it to accurately identify and extract complex patterns that may be overlooked by standard approaches. The study showcases the effectiveness of the framework by conducting extensive tests on diverse big datasets, demonstrating that the DRL-enhanced system surpasses previous methods in terms of both accuracy and speed. The study also addresses architectural design, the choice of reinforcement learning methods, and the found implementation issues. Moreover, it delves into the consequences of these discoveries for subsequent investigations and real-world implementations, emphasizing the capacity of DRL to transform the identification of patterns in large-scale data settings. This work enhances the area of big data analytics by offering a strong technique for extracting complex patterns, which in turn enables better informed decision-making and discovery in scientific and commercial sectors. © 2024 IEEE.

关键词： Deep reinforcement learning

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Extraction Method of Disturbance Elements of Urban Emergencies Based on Large Language Model

Extraction Method of Disturbance Elements of Urban Emergenci...

引用

2024 international conference on Intelligent computing and Data Mining, ICDM 2024

作者： Xu, Jian Dang, Anrong Chen, Yang Zhao, Mingyu Sun, Bijiao Wang, Hua Xie, Zhuokun Tsinghua University School of Architecture Beijing China State Key Laboratory of Geo-Information Engineering Xian China

ISBN: (纸本)9798331506261

The general knowledge extraction model lacks the knowledge learning of such as urban emergencies, and the precision of knowledge extraction is insufficient. In this paper, a large language model (LLM) based method for urban disturbance elements extraction (UDEE-LLM) is proposed. The method deploys the local large language model through LangChain and integrates the domain knowledge of urban disturbance, fine-tuning the large language model based on the prompting learning, designing the heterogeneous entity alignment strategy, and optimizing the entity extraction results. The results show that UDEE-LLM can extract and classify the information of urban disturbance elements. The coupling relationship of urban disturbance elements is analyzed, and the potential relationship between mode, object and influence of urban disturbance is dug out, which has important reference for improving urban resilience. © 2024 IEEE.

关键词： Digital elevation model

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Gamification of Business Process Modeling Notation education: an experience report 23

Gamification of Business Process Modeling Notation education...

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27th international conference on Evaluation and Assessment in Software engineering (EASE)

作者： Garaccione, Giacomo Coppola, Riccardo Ardito, Luca Torchiano, Marco Politecn Torino Turin Italy

ISBN: (纸本)9798400700446

Business Process Modeling (BPM) is a skill considered fundamental for computer engineers, with Business Process Modeling Notation (BPMN) being one of the most commonly used notations for this discipline. BPMN modeling is present in different curricula in specific Master's Degree courses related to software engineering, but, in practice, students often underperform on BPMN modeling exercises due to difficulties in learning good modeling practices. In recent years, more and more fields of computer science have employed gamification (the usage of game elements in non-recreational contexts to gain benefits in terms of interest, participation, motivation, and enjoyment) with positive results during both development and teaching processes. Thus, we have developed a platform for BPMN modeling that employs gamification mechanics to facilitate learning good modeling practices with mechanisms such as rewarding good modeling solutions and penalizing less correct ones, with a dedicated feedback mechanism that maps correctly modeled elements to the corresponding concept. A preliminary laboratory experiment has been conducted with students of an Information Systems course to evaluate how students receive the mechanics and if there may be benefits in using a gamified environment for teaching process modeling throughout an entire course.

关键词： Education BPMN Modeling Gamification Process Modeling Computer engineering

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Cloud-Based Design of Student Experiment System for University Laboratories 3

Cloud-Based Design of Student Experiment System for Universi...

引用

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

作者： Ma, Jian Ji, Yongcheng Wang, Shuai He, Yuan Li, Xin College of Electronic Science & Engineering Jilin University Jilin Changchun130012 China

ISBN: (纸本)9798350319125

With the rapid development of cloud computing technology, more and more applications are migrating to the cloud. In the field of education, it has become a trend to use cloud computing technology to improve teaching efficiency and resource utilization. Cloud-based experimental system for college laboratory students can not only provide students with a convenient experimental environment, but also help teachers better manage experimental resources and improve teaching quality. This paper will introduce in detail the design of college laboratory student experiment system based on cloud © 2023 IEEE.

关键词： Experiment particle swarm arithmetic student experiment system University laboratory teaching

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Advanced Eye Disease Classification using the EfficientNetB3 Deep learning Model 3

Advanced Eye Disease Classification using the EfficientNetB3...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Soni, Tanishq Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331532420

This research study introduces a novel approach for the classification of ocular diseases that employs the EfficientNetB3 architecture. The backbone of our model is EfficientNetB3, which has a total of 42 layers and a well-optimized layer structure. The model's feature extraction capabilities are optimized for medical image data by utilizing pre-trained weights from ImageNet. The model effectively reduces overfitting and enhances generalization across the dataset by incorporating supplementary layers, including Batch Normalization, Dense, and Dropout. The softmax activation function is employed by the final output layer to forecast class probabilities, which is customized to the precise number of eye disease classes. The proposed model's potential for reliable eye disease classification in clinical contexts was demonstrated by its impressive accuracy of 93.8% on the test dataset. © 2024 IEEE.

关键词： Deep learning

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Chatbots in Education: A Systematic Literature Review 10

Chatbots in Education: A Systematic Literature Review

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10th international conference on Smart computing and Communication, ICSCC 2024

作者： Bekkar, Hibat-Allah Chtouki, Yousra School of Science and Engineering Al Akhawayn University in Ifrane Ifrane53000 Morocco

ISBN: (纸本)9798350363104

Artificial intelligence (AI) has rapidly transitioned from theoretical concepts to practical applications across various sectors, including education. This paper presents a systematic literature review focusing on the use of chatbots, particularly those based on large language models (LLMs), in educational settings. The review examines existing literature, surveys, and studies on the topic, exploring the capabilities of LLM-based chatbots in personalizing tutoring, automating assessments, and providing immediate feedback. Additionally, the paper discusses the challenges of integrating these technologies. Key findings highlight the significant potential of chatbots to enhance educational experiences by offering personalized learning, accessible tools, and interactive tutoring systems. We also gathered and visualized existing studies related to user acceptance and satisfaction using chatbots. The paper concludes with recommendations for future research to address existing challenges and optimize the use of LLM-based chatbots in education. © 2024 IEEE.

关键词： Adversarial machine learning

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