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Application of Internet Fusion Document Recognition in the Realization of Legal Wisdom Guiding Platform with R

Application of Internet Fusion Document Recognition in the R...

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6th international conference on Intelligent computing and Control Systems, ICICCS 2022

作者： Wu, Jing Shanghai University of Engineering Science School of Air Transportation Shanghai 201620 China

ISBN: (纸本)9781665410366

In the construction of smart legal teaching based on the R platform, in terms of teaching concept, it is necessary to pay attention to the cultivation of morality;in terms of education mode, it is necessary to emphasize individualized cultivation;in terms of teaching methods, it is necessary to implement smart education, realize the sharing of educational resources, and fully Encourage the use of the "technology + education"approach to realize the intelligence of specific educational means. After the integration of document subject information, the evaluation performance has been further improved in some systems. Experiments in the lexical cohesion part also show that the proposed sum is superior in document evaluation, and they give the best fusion evaluation results in most cases. © 2022 IEEE.

关键词： Visualization Law Education learning (artificial intelligence) Control systems Internet Knowledge transfer

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Java-Based Simulation Model Design for Cloud computing Physical Education

Java-Based Simulation Model Design for Cloud Computing Physi...

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2022 IEEE international conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Sheng, Ziying Physical Education School Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9781665456371

The emergence of cloud computing(CC), the three service levels of IaaS, PaaS and SaaS, and the cloud service delivery model of 'cloud' + 'end' have changed the development thinking of the entire IT industry. The education industry has also been influenced by new technologies and concepts, especially the core technologies of CC-'virtualization' and 'distribution', which make the education industry face the challenges in teaching and learning resources management and laboratory management. In particular, the core technologies of CC-'virtualization' and 'distributed'-have given the education industry a revolutionary solution to the challenges faced in the management of teaching resources and laboratory management. In this paper, we design a simulation model of physical education(PE) teaching in CC based on Java. The results show that the Java-based CC PE teaching is more conducive to improving students' performance and thus physical and motor skills;stimulating students' learning needs and thus forming expectations for course learning;and being able to continuously improve students' performance. The results show that the Java-based cloud-based PE teaching is more conducive to improving students' performance and thus physical and motor skills;stimulating students' learning needs and thus forming expectations for the learning of the course;and continuously stimulating students' learning initiatives to achieve the purpose of improving academic interest indicators. © 2022 IEEE.

关键词： Curricula

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6th international conference on Emerging Technologies in computing, iCETiC 2023

6th International Conference on Emerging Technologies in Com...

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6th international conference on Emerging Technologies in computing, iCETiC 2023

ISBN: (纸本)9783031502149

The proceedings contain 15 papers. The special focus in this conference is on Emerging Technologies in computing. The topics include: On the Design and Performance Evaluation of Android Based Alarming Applications;investigation of Air Effluence Using IoT and Machine learning;exploring the Emerging Technologies Within the Blockchain Landscape;socialEcho: A Social Networking Platform with Community Guidelines Violation Pre-check;green-IoT Based Automated Field Maintenance System;chaotic Chimp Based African Vulture Optimization Algorithm with Stability Tests for Feature Selection Algorithms;event-Based Data Pipelines in Recommender Systems: The Data engineering Perspective;pre-planning for Plastic Surgery Using Machine learning: A Proof of Concept;academic Integrity in the Face of Generative Language Models;docBot: A System for Disease Detection and Specialized Doctor Recommendation Using Patient’s Speech of Symptoms;Digitalisation Transformation in High Schools: Analysis of the COVID-19 Pandemic’s Accelerating Impact;Cube Attacks on Round-Reduced Grain-128AEAD;a Literature Review of Various Analysis Methods and Classification techniques of Malware.

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Early Diagnosis of Schizophrenia in Patients Using Deep learning Techniques 10

Early Diagnosis of Schizophrenia in Patients Using Deep Lear...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Priyanka, G. Nivaashini, M. Pavithra, S. Srimaha, T.S. Kanishgaa, P.R. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

While Brain, emotion, and behaviours abnormalities are hallmarks of schizophrenia, a complex and long-lasting mental illness. Its symptoms are divided into three categories: positive, negative, and cognitive. Positive symptoms include delusions, disordered thinking, abnormal gait, and hallucinations. Diminished ability to express emotions, alogia, anhedonia, avolition, and social disengagement are detrimental symptoms. Deficiencies in working memory, problems paying attention, and trouble making decisions are examples of cognitive symptoms. Environmental variables, genetic predisposition, and neurochemical imbalances interact intricately in an etiology of schizophrenia. Typically, antipsychotic drugs, psychotherapy, and social support are used in conjunction for treatment. The management of symptoms and enhancement of the general health of persons diagnosed with schizophrenia need early intervention and customized treatment strategies. The CNN, BiLSTM, GRU, and Transformer deep learning models are implemented in this study, and their respective performances are assessed. The transformer and GRU models outperform the other models when hyperparameter adjustment is carried out to increase the models' efficacy. © 2024 IEEE.

关键词： Diseases

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Deep learning for Precise Brain Hemorrhage Detection from CT scans: A Web - Based Approach 15

Deep Learning for Precise Brain Hemorrhage Detection from CT...

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

作者： Rooshita, Karamala Vyshnavi, N.V. Chowdary, Nara R Sanjana Singh, Tripty Patra, Payel Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India University of L'Aquila DISIM Department L'Aquila Italy

ISBN: (纸本)9798350370249

Advancements in medical imaging has greatly improved the diagnosis and treatment of neurological disorders. Prompt identification of brain hemorrhage is essential for enhancing patient outcomes. This research introduces an innovative deep learning approach to automate the detection of brain hemorrhage in CT scans. It involves thorough data preprocessing, including synthetic data generation, and utilizes advanced convolutional neural networks (CNNs) with recurrent layers and ResNet models. The models are trained with dynamic augmentation techniques using an image generator to improve generalization. Evaluation methods include traditional metrics and a web application that loads a pre-trained model, providing heatmaps of Region of Interest (ROI) indicating hemorrhage or normal conditions. This tool aids in interpreting the model's predictions, potentially improving the accuracy of brain hemorrhage detection in medical practice. By integrating deep learning with visualization techniques, this methodology aims to streamline diagnosis, enabling radiologists to make more informed decisions efficiently. Ultimately, it could lead to better patient care through early detection and intervention for brain hemorrhages. © 2024 IEEE.

关键词： Convolutional neural networks

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A Critical Study on Hybrid computing and Its Current Influences and Future Challenges 3

A Critical Study on Hybrid Computing and Its Current Influen...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Praveen, K.V. Joe Prathap, P.M. Beevi, L. Sherin Dani, W. Vinil St. Peter's College of Engg. and Tech. Department of Information Technology Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331515911

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computers

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Disease Prediction System in Human Beings using Machine learning Approaches 4

Disease Prediction System in Human Beings using Machine Lear...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Joshi, Kireet Gupta, Vishan Kumar Jain, Paras Shukla, Anurag Bharti, Monika Jindal, Himanshu Graphic Era Deemed to Be University Department of Cse Uttarakhand Dehradun India Amity University Department of Cse Punjab Mohali India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh Sehore India School of Management Sir Padampat Singhania University Rajasthan Udaipur India Amity University Department of Computer Science Punjab Mohali India

ISBN: (纸本)9798350343670

The disease prediction system predicts the disease by taking symptoms from the user and predict using machine learning algorithms that whether the user has disease or not. The proposed model supports the user-friendly interface and is easy to handle and performs operations accordingly. It is built to help the people at early stage to check the presence of disease, producing the results with an accuracy of almost 86% for Parkinson's disease, 97% for Gestational disease and 85% for cardiovascular disease. Our methodology is performing better in comparison of existing methods, where we have developed one algorithm for the same. The dataset of various patients related to this disease is taken from Kaggle websites. We represented our results with various diagrams and charts as well. © 2024 IEEE.

关键词： cardiovascular gestational K- means logistic regression machine learning Parkinson's prediction support vector machine (SVM)

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Software Product Effort Estimation with Sequence Diagrams and Regression Analysis

Software Product Effort Estimation with Sequence Diagrams an...

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2024 international conference on Intelligent computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Sahoo, Pulak Paul, Nayan Ranjan Das, Asit Kumar Sahoo, Prateek Mohanty, J.R. Silicon University Computer Science and Engineering Bhubaneswar India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

Software development effort prediction is a critical aspect of project management. In order to successfully complete a product within all the constraints, the forecasted effort must be fairly reliable. In this work, we have come up with a reasonably viable work-effort estimation for projects developed through Object Oriented Analysis and Design (OOAD) Approach. The approach proposed in this work uses a selected set of features present in the Unified Modeling Language (UML) Sequence diagrams and customized 'Machine-learning Regression-analysis (ML-RA)' programs to come up with a viably accurate prediction. The ML techniques include: Extreme Gradient Boosting (EGB), Extreme learning Machines (ELM), Decision Tree (DT), Bayesian Ridge Regression (BRR) and Gaussian Processes with Neural Network Kernels methods (GPNN). Based on the out-comes of our experiments, it was clearly evident that the GPNN approach provided the highest accuracy with respect to other ML models. © 2024 IEEE.

关键词： Unified Modeling Language

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AI-Driven Eye Disease Detection: A VGG19-based Approach to Retinal Image Classification 6

AI-Driven Eye Disease Detection: A VGG19-based Approach to R...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Sharma, Ankita Gupta, Sheifali Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331522667

Eye disease is also a very severe condition that affects the human eye it has several disorders, including cataracts, glaucoma, and diabetic retinopathy. These disorders can affect the eye in different ways, some of them can cause permanent blindness to the eye. This research addresses the Artificial Intelligence and Deep learning approaches, focusing on Convolutional Neural Networks, to automate the classification of eye illnesses. Utilizing the VGG19 model, a well-established Deep Neural Network the study tries to classify retinal images into two basic categories, Normal and Cataract. The VGG19 model is a pre-trained model trained on a large data set that works well on the image data set. Deep learning methods work well in the field of healthcare, and medical sciences. Due to their pre-training, these models are also called transfer learning models. The VGG19 model displayed a remarkable accuracy of 95% with 0.055 loss, demonstrating its usefulness in eye illness classification. This research stresses the critical significance of CNNs in aiding the timely detection and treatment of eye disease, ultimately leading to techniques for reducing vision loss. © 2025 IEEE.

关键词： Convolutional neural networks

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Maximizing the Promptness of Metaverse Systems Using Edge computing by Deep Reinforcement learning

Maximizing the Promptness of Metaverse Systems Using Edge Co...

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2024 international conference on Advanced Technologies for Communications, ATC 2024

作者： Ninh Thi-Thanh, Tam Van Chien, Trinh Tran, Hung Hoai Son, Nguyen Nhan Vo, Van National Academy of Education Management Faculty of Information and Communication Technology Hanoi Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam Phenikaa University Faculty of Information Technology Hanoi Viet Nam University of Engineering and Technology Vnu Faculty of Information Technology Hanoi Viet Nam Duy Tan University Faculty of Information Technology Da Nang Viet Nam

ISBN: (纸本)9798350353983

Metaverse and Digital Twin (DT) have attracted much academic and industrial attraction to approach the future digital world. This paper introduces the advantages of deep reinforcement learning (DRL) in assisting Metaverse system-based Digital Twin. In this system, we assume that it includes several Metaverse User devices collecting data from the real world to transfer it into the virtual world, a Metaverse Virtual Access Point (MVAP) undertaking the processing of data, and an edge computing server that receives the offloading data from the MVAP. The proposed model works under a dynamic environment with various parameters changing over time. The experiment results show that our proposed DRL algorithm is suitable for offloading tasks to ensure the promptness of DT in a dynamic environment. © 2024 IEEE.

关键词： 6G deep reinforcement learning digital twin edge computing Metaverse system offloading task

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